Dinámica de regenerados naturales postincendio. de Pinus halepensis Mill. sometidos a. tratamientos selvícolas tempranos.

Transcripción

1 Dinámica de regenerados naturales postincendio de Pinus halepensis Mill. sometidos a tratamientos selvícolas tempranos. Evolución del secuestro de carbono TESIS DOCTORAL Raquel Alfaro Sánchez Albacete, 2014

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3 UNIVERSIDAD DE CASTILLA LA MANCHA ESCUELA TÉCNICA SUPERIOR DE INGENIEROS AGRÓNOMOS DEPARTAMENTO DE PRODUCTIÓN VEGETAL Y TECNOLOGÍA AGRARIA Dinámica de regenerados naturales post-incendio de Pinus halepensis Mill. sometidos a tratamientos selvícolas tempranos. Evolución del secuestro de carbono TESIS DOCTORAL RAQUEL ALFARO SÁNCHEZ ALBACETE, 2014

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5 UNIVERSIDAD DE CASTILLA LA MANCHA Dinámica de regenerados naturales post-incendio de Pinus halepensis Mill. sometidos a tratamientos selvícolas tempranos. Evolución del secuestro de carbono Memoria que la Licenciada Raquel Alfaro presenta para aspirar al Grado de Doctor por la Universidad de Castilla La Mancha Esta memoria ha sido realizada bajo la dirección de: Dr. Jorge de Las Heras Ibáñez, Dr. Francisco Ramón López Serrano y Dr. Daniel Moya Lda. Raquel Alfaro Sánchez Aspirante al Grado de Doctor Albacete, Marzo de 2014

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7 UNIVERSIDAD DE CASTILLA LA MANCHA Este trabajo estuvo financiado por los proyectos del MICINN CYCIT-AGL /FOR; AGL /FOR y CONSOLIDER- INGENIO 2010: MONTES (CSD ). La investigación presentada en esta Tesis Doctoral se ha realizado conjuntamente en el departamento de Producción Vegetal y Tecnología agraria y el departamento de Ciencia y Tecnología Agroforestal y Genética de la Universidad de Castilla-La Mancha

8 "Levantémonos y demos gracias, porque si hoy no hemos aprendido mucho, al menos habremos aprendido un poco, y si no hemos aprendido un poco, al menos no hemos enfermado, y si hemos enfermado, al menos no hemos muerto; por lo tanto, demos gracias" GAUTAMA BUDA (CIRCA 563 a.c a.c) Fundador del Budismo

9 INDICE GENERAL 1. AGRADECIMIENTOS RESUMEN INTRODUCCIÓN GENERAL Incendios forestales y Pinus halepensis Regeneración post-incendio Secuestro de carbono en regenerados de P. halepensis Enclave del estudio e hipótesis de trabajo Objetivos y estructura en capítulos Referencias CAPÍTULOS CAPÍTULO 1: Vegetation dynamics of managed Mediterranean forests 16 years after large fires in southeastern Spain Abstract Introduction Methods Results Discussion Conclusions Acknowledgements References Appendix CAPÍTULO 2: Growth and reproduction are positively coupled but this coupling depends on site conditions in young Aleppo pines Abstract Introduction Material and methods Results Discussion Conclusions

10 Acknowledgments References Supporting Information CAPÍTULO 3: Competence and site quality- What determines biomass allocation in young Pinus halepensis? Abstract Introduction Material and methods Results Discussion Conclusions Acknowledgments References CAPÍTULO 4: Biomass storage in low timber productivity Mediterranean forests managed after natural post-fire regeneration in southeastern Spain Abstract Introduction Material and methods Results Discussion Acknowledgments References Appendix DISCUSIÓN GENERAL Secuestro de carbono y diversidad de especies Caracteres reproductivos: inversión en recursos Referencias CONCLUSIONES

11 INDICE DE TABLAS 3. INTRODUCCIÓN GENERAL Tabla 1. Especies arbóreas más afectadas por los incendios forestales. Total nacional para el periodo Fuente: Ministerio de Agricultura, Alimentación y Medio Ambiente Figura 1. Mapas de almacenaje de Carbono a nivel nacional en Mg C ha -1 (resolución de pixeles), incluyendo el estrato arbóreo y el arbustivo, a partir de los datos del tercer inventario forestal nacional. Adaptado de Vayreda et al. (2012) Tabla 2. Codificación de los tratamientos selvícolas utilizada en la tesis. Cada tratamiento se llevó a cabo en 3 parcelas de m 2 por sitio. Celdas en blanco indican que el tratamiento se llevó a cabo en ambas zonas de estudio; celdas en gris claro indican que el tratamiento solo se realizó en Yeste; celdas en gris oscuro indican que el tratamiento solo se realizó en Calasparra Tabla 3. Cronograma de tareas de muestreo y monitorizacion para la consecución del estudio diacrónico. Celdas en gris claro indican las tareas realizadas en las tesis anteriores (desde 1999 hasta 2008). Celdas en gris oscuro indican las tareas específicas de esta tesis doctoral (desde 2009 hasta 2013) CAPÍTULO 1 Table 1. Goodness of fit (D 2 : explained deviance (%) and P, significance level) obtained in the GLM analyses for the Species richness and Shannon indices on the effects of Site, Age and Treatment (T) (n=72) Table 2. Absolute increments of Species richness for the life forms during the study period. Gaps in grey show a negative increment and empty gaps represent no change in Species richness over time Table 3. Goodness of fit (D 2 : explained deviance (%) and P, significance level) obtained in the GLM analyses for Total cover and life forms cover trees, shrubs, dwarf shrubs, perennial and annual herbs on the effects of Site, Age and Treatment (T) (n=72) Table 4. Life form covers at 5 and 16 years per site and treatment. Values are average cover (%) and SE. See the Treatment codes in Table Table 5. Values of the factors (Treatment and Site) and vectors (Pine cover and Understory cover) fitted on the first two NMDS ordination axes. The squared correlation coefficients (R 2 ) and empirical p-values (P) are shown for each variable at a) post-fire year 5 and b) post-fire year 16. See the Treatment codes in Table CAPÍTULO 2 Table 1. Percentage of different cone types produced per tree and serotiny (standard errors are given between parentheses) for different tree densities in the study years 2005, 2007, 2009 and Lowercase letters show significant (P<0.05) differences for each site and date between density levels Table 2. Coefficients obtained for the best fitted generalized linear mixed models for either the presence or production of cones (a) or the production of green cones (b) by 3

12 juvenile Aleppo pines in the two study sites (Yeste, dry site; Calasparra, semiarid site) as a function of fixed (tree height, tree density, competition intensity) and random (σ 2 plot, σ 2 tree) effects. Selected models showed the lowest values of the Akaike Information Criterion (AIC) CAPÍTULO 3 Table 1. Main dendrometric characteristics (min-max range) of (a) Aleppo pine trees samples and (b) cones sample from the Dry and Semiarid sites Tabla 2. Simplified models of dry biomass allometries (above- (b A, b CW, b N, b C, b ST g) and below-ground (b R, g)) for each Site and Treatment (a), allometric relationships to estimate the biomass of cones (b CO, g) for each site and goodness of fit estimators (adjusted regression coefficient (R 2 adj), standard error of estimation (SEE) and mean absolute error (MAE) Tabla 3. ANOVA results for the biomass components b ST, b CW, b N, b Cones (on grams) and for the ratios R ST, R CW, R N, R CO (on a per unit basis) at 5- post-fire years at the plot level. Asterisks noted significant differences at P<0.05 in between sites Tabla 4. Coefficients (estimates) and statistical parameters obtained in the GLMMs for the log transformed biomass components b ST, b CW, b N, b CO (on grams) R ST, R CW, R N, R CO (on a per unit basis) (n=1556) at plot level on the effects of Site, Age and Treatment and b R and R R (n=707) on the effects of Treatment. See Table 1 for Treatment codes CAPÍTULO 4 Table 1. Initial tree density (N initial, trees ha -1 ) found at post-fire year 5 of two P. halepensis stands (dry and semiarid sites) and forest stand parameters after the different treatments were carried out in 1999 (T 5), 2004 (T 10) and 2010 (at post-fire year 16). Uppercase letters indicate significant differences (P<0.05) among Sites on N initial. Lowercase letters show significant differences (P<0.05) among Treatments for each Site and Age. Values are the means and standard errors shown in brackets. See Appendix for definitions Table 2. Average crown coverage (CC, cm), height (h U, cm) and ratio estimators (H A and H R, g m -2 ) for the 15 most representative understory species and average ratio estimators (g m -2 ) for the plant functional groups. Values are the means and standard errors shown in brackets. See Appendix for definitions Table 3. Simplified models of dry biomass allometries (above- (b A, g) and below-ground (b R, g)) for each Site and Treatment and goodness of fit estimators (adjusted regression coefficient (R 2 adj), standard error of estimation (SEE) and mean absolute error (MAE). See Appendix for definitions Table 4. Coefficients and R 2 obtained in the GLMMs for variables B A; B A-U and B A-Pine+U. See Appendix for definitions Table 5. Comparison made of the results found in this and previously published studies for dry biomasses (Mg ha -1 ) and carbon pools (Mg C ha -1 ) in unmanaged regenerated P. halepensis stands. See Appendix for definitions

13 INDICE DE FIGURAS 3. INTRODUCCIÓN GENERAL Figura 1. Mapas de almacenaje de Carbono a nivel nacional en Mg C ha -1 (resolución de pixeles), incluyendo el estrato arbóreo y el arbustivo, a partir de los datos del tercer inventario forestal nacional. Adaptado de Vayreda et al. (2012) Figura 2. Esquema conceptual de la estructura en capítulos de la tesis Figura 3. Fotografía de la zona de estudio situada en Yeste Figura 4. Fotografía de la zona de estudio situada en Calaparra Figura 5. Localización de las parcelas experimentales en Yeste (mapa superior) y Calasparra (mapa inferior) Figura 6. Esquema de los tratamientos selvícolas llevados a cabo en Yeste y Calasparra a los 5 (1999) y a los 10 (2004) años de edad. Ver codificación de los tratamientos en Tabla Figura 7. Fotografías de los trabajos en campo. Muestreos de biodiversidad (a); Conteo y medición de piñas (b); extracción de cores (c); muestreos destructivos de especies del matorral (d) y de pinos (e) CAPÍTULO Figure 1. P. halepensis distribution in the Circum-Mediterranean area (upper centre graph) and the corresponding climatic diagrams based on the climatic data from nearby local meteorological stations at the two study sites on the wildfire surface (lower map), Yeste (upper Dry type, Dry site) and Calasparra (lower Semiarid type, Semiarid site) in the provinces of Albacete and Murcia, SE Spain Figure 2. Species richness index (S) and Shannon diversity index (H ) at post-fire year 5 and 16 per site and treatment. Lower case letters show the significant differences obtained from the GLM analysis (P < 0.01) for the Site Treatment interaction at 16 years Figure 3. Total cover rates divided into Pine and Understory cover at 5 and 16 years per site and treatment Figure 4. Non-metric multidimensional scaling (NMDS) ordination of the plant cover in 36 plots at (a) post-fire year 5, (b) post-fire year 16, where circles size are proportional to the Shannon diversity index (H ), and (c) at post-fire year 16, including numbers that represent the different treatments applied for both study sites: 1: Control, 2: T , 3: T , 4: T 5-800, 5: T , 6: T , 7: T Vectors (Pine and Understory cover) were fitted in the ordination diagrams CAPÍTULO Figure 1. Trends in area increment of earlywood (EW) and latewood (LW) in both study sites and considering the three density classes. Box plots show: the median (horizontal 5

14 line within the box), the 25 th and 75 th percentiles (lower and upper box boundaries), the 10 th and 90 th percentiles (error bars), and the 5 th and 95 th percentiles (outliers). The vertical dashed line indicates the thinning treatment. Note the different scales in EW area increment of the two sites Figure 2. Relationships (Pearson correlation coefficients) observed between earlywood EW, white bars or latewood LW, striped bars area increments and monthly climatic variables data (T, mean temperature; P-PET, water balance) in both study sites and considering the three density classes (control or very high tree density, high and moderate tree densities). Growth is related with climatic data from the previous (months abbreviated by lowercase letters) and current (months abbreviated by uppercase letters) years, where the current year is the year of tree-ring formation. The bars surpassing dashed lines indicate significance at P< Figure 3. Number of trees showing significant (P<0.05) Spearman correlation coefficients between the number of produced green cones and monthly climatic data (precipitation and water balance). Correlations were calculated for the years of cone formation (t), and also for one (t-1) and two years before (t-2). Black and grey bars indicate positive and negative correlations, respectively Figure 4. Percentage of reproductive trees as a function of tree age (a), differences in height between the reproductive trees and non-reproductive trees divided by the height of the reproductive trees (see eq. 2) (b), the ratio (mean ± SE) between the height of reproductive trees divided by the maximun dominant height of the plot (H SQ, see eq. 3) (c) tree height at which the probability to have reached sexual maturity was 50% (J 50) for each age (different lines) and site (d) Figure 5. Number of female cones produced by each tree as a function of its basal area. Results are presented for the two study sites and the three density levels (unthinned control very high density, high and moderate tree densities) CAPÍTULO Figura 1. Average biomass components (b ST, b CW, b N, b CO, b R, g) at plot level for the 16 years old data for each Treatment, indicated by the application year and their respective tree density. T C: Unthinned plots (Control); T 5: Thinning in 1999; T 10: Thinning in 2004; T 5+10: Thinning in 1999 to a final density of 1,600 trees ha -1 and thinning in 2004 to a final density of 800 trees ha Figura 2. The allocation ratios, from the destructive sampling data, of R ST and R N versus d (cm) at the dry and semiarid sites, displaying the tree ages of destructive samples Figura 3. The crown biomass (b C, g) regressed out on the stem biomass (b ST, g), from the destructive sampling data, for Moderate, High and Very high tree density groups at the dry and semiarid sites. See Table 1 for Treatment codes.moderate: Treatments T 5:800, T 10:800 and T 5+10; High: Treatments T 5:9500, T 5:1600 and T 10:1600; Very High: Unthinned plots (Control, T C) Figura 4. Average biomass components (R ST, R CW, R N, R CO, R R, on a per unit basis) at plot level for the 16 years old data for each Treatment, indicated by the application year and their respective tree density. T C: Unthinned plots (Control); T 5: Thinning in 1999; T 10: 6

15 Thinning in 2004; T 5+10: Thinning in 1999 to a final density of 1,600 trees ha -1 and thinning in 2004 to a final density of 800 trees ha Figura 5. Curves of the stem biomass (b ST, g) versus the diameter at 30 cm above-ground (d, cm) generated from the allometric relationships displaying the tree ages of destructive samples CAPÍTULO Figure 1. The above-ground biomasses (Mg ha -1 ) of pine (B A) and understory (B A-U) from 5 to 16 years-old and at both sites; dry (a) and semiarid (b). The vertical hatched bars indicate the thinning interventions over time. At the same Age, two B A values appear (before and after treatments were carried out). See Appendix for definitions Figure 2. The relationship between the above-ground understory biomass and the aboveground pine biomass (γ ratios) for the 5- and 16-year-old stands. Values are displayed for each Site and Treatment. Different letters indicate significant differences (P<0.05) among Treatments at the dry site (a, b) and the semiarid site (c, d, e). See Appendix for definitions Figure 3. Carbon storage of the 16-year-old stands (Mg C ha -1 ). The above- and belowground carbon pine biomasses (CB A, CB R) and the above- and below-ground carbon understory biomasses (CB A-U, CB R-U). Lowercase letters indicate significant differences (P<0.05) between treatments. See Appendix for definitions DISCUSIÓN GENERAL Figura 1. Esquema de las relaciones Clima-Crecimiento, Clima-Piñas verdes y Crecimiento-Piñas verdes para Yeste (a) y para Calasparra (b). t = año de referencia para la completa formación de piñas verdes; EW= earlywood (madera temprana); LW= latewood (madera tardía); T= temperatura; P-PET = balance hídrico; P= Precipitación. Flechas rojas indican condiciones secas y cálidas y las flechas azules indican condiciones húmedas y frías. Las flechas rojas y azules más finas corresponden a las relaciones Clima-Crecimiento (parte superior del esquema) mientras que las fechas rojas y azules más gruesas corresponden a las relaciones Clima-Piñas verdes (parte inferior del esquema) Figura 2. Biomasa aérea media por árbol (g) para las dos localidades de estudio a los 5 y a los 16 años. Los asteriscos muestran diferencias significativas entre pies reproductivos y no reproductivos obtenidos mediante ANOVAs (P<0.05) realizados por separado para cada localidad y edad

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17 1. AGRADECIMIENTOS 9

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19 1. AGRADECIMIENTOS En primer lugar dar las gracias a mis directores de tesis, Jorge de las Heras, Francisco Ramón López Serrano y Daniel Moya por darme esta oportunidad y por la confianza que han tenido en mí. A Beatriz Serantes de la Delegación Provincial de Medio Ambiente en Albacete de la Junta de Comunidades de Castilla-La Mancha y a Ana Atienza de la Dirección General del Medio Natural de la Región de Murcia y por el interés y apoyo mostrado. A los agentes medioambientales de ambas administraciones que colaboraron siempre que fue necesario. Mención especial en estos agradecimientos merece Raúl por devolverme la ilusión por la ciencia cuando las fuerzas flaqueaban, y confiar en mí desde el primer día Gracias! Como no también a Eva que ha dedicado muchas horas y esfuerzo a ayudarme y de la que he aprendido muchísimo. A mis compañeros Javi y Quique con los que he compartido todos estos años y sin los cuales este trabajo no existiría. También agradecer la colaboración y ayuda de José Luis, Tarek, Eduardo, David y a Mª Cruz, Javier Soria, Manuel Canós, Belén, Rocío y María por su apoyo en las mediciones en campo. A Virgilio y Mª José por introducirle en el maravilloso mundo de R y solucionarme todas las dudas estadísticas que han surgido. A los profesores José María Herranz, Pablo Ferrandis y a Jose María Sahuquillo por su colaboración en el laboratorio. A J. Julio Camarero por su ayuda con mis amigas las piñas. Cómo no, a las Dendrogirls mis compañeras y amigas Andrea, Virginia, Ángela y Clara y a toda la gente que conocí durante cursos y congresos que me aportaron buenas ideas. 11

20 Agradecer a toda mi familia en especial a mis padres y mis hermanas. También agradecer a mis amigos y amigas María J, Belén, María, Marta, Lucía, Patri, Sonia, Raquel, Jaime, Cuesta, Marca y Noel por su comprensión. Y por último a todos los que en algún momento me han apoyado a lo largo de estos años de tesis. La realización de este estudio se llevó a cabo gracias al apoyo de los proyectos CYCIT-AGL /FOR; AGL /FOR y CONSOLIDER-INGENIO 2010: MONTES (CSD ). 12

21 2. Resumen 13

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23 2. RESUMEN En la presente tesis doctoral se estudia la dinámica post-incendio en dos masas forestales de Pinus halepensis regeneradas tras los incendios acaecidos en verano de 1994 en el sureste español. Para llevarlo se ha realizado un seguimiento de parcelas permanentes en ambos sitios, los cuales presentan diferente calidad de estación. Una de las estaciones de muestreo se encuentra cercana a la población de Yeste (al SE de la provincia de Albacete) perteneciente a un ombroclima seco. La otra estación de seguimiento se encuentra en las inmediaciones de la población de Calasparra (al NO de la provincia de Murcia) a la que le corresponde un ombroclima semiárido. El periodo de estudio abarcó desde los 5 hasta los 19 años después del incendio. El objetivo principal del presente estudio es el desarrollo de herramientas para mejorar la estimación del secuestro de carbono atmosférico y su inmovilización en la biomasa viva de la vegetación de estos ecosistemas, que puedan implementarse como herramientas de manejo forestal adaptativo en áreas propensas a incendios, incluyendo su restauración y la asistencia a zonas en regeneración. En el Capítulo 1, se analizó el efecto de la aplicación de tratamientos selvícolas tempranos (desbroce y diversas intensidades de clareo aplicado a distintas edades) sobre patrones de diversidad vegetal en los regenerados objetos de estudio. Al comparar los resultados obtenidos para los sitios de estudio considerados, se obtuvieron diferencias en los índices de riqueza de especies, diversidad de Shannon y bajo solape entre las especies presentes en ambos sitios. Para los índices riqueza de especies y de diversidad de Shannon, calculados dieciséis años tras incendio en los regenerados de Yeste, no se encontraron diferencias entre las masas tratadas y las no tratadas. Por el contrario, en los regenerados de Calasparra detectamos valores mayores en las masas tratadas a los 10 años de edad. A esta misma edad (16 años después de los incendios) en ambos sitios, las especies acompañantes más abundantes eran semilladoras obligadas o herbáceas rebrotadoras, revelando el efecto positivo de los tratamientos aplicados para reducir el combustible disponible en la masa 15

24 forestal regenerada, reduciendo así el riesgo de incendios de alta severidad. Sin embargo, la resiliencia del ecosistema no mostró una mejoría al no detectarse una representación de especies propias de fases sucesionales más avanzadas, tales como especies leñosas rebrotadoras. En el Capítulo 2, se estudia la relación y repartición de recursos entre varios mecanismos de la resiliencia del ecosistema. Para ello se estudió la producción de piñas, el crecimiento individual de los pinos y su interacción con el clima en los regenerados de ambas zonas. Los resultados muestran que las condiciones que resultan ser favorables para la producción de piñas, a nivel de árbol, también lo son para el crecimiento radial, por lo que no se puede afirmar que existan costes de compensación entre ambos procesos. Comparando resultados entre ambos sitios, se observó que la distinta calidad de estación propició diferentes tasas de individuos reproductivos, mostrando mayor precocidad los regenerados de Calasparra (la zona de clima semiárido), aunque a lo largo de todo el estudio se alcanzaron en total un mayor porcentaje de pies reproductivos en Yeste (la zona de clima seco). El Capítulo 3 se presentan nuevas relaciones alométricas desarrolladas para el cálculo de componentes de biomasa, es decir, tronco, madera de ramas, acículas, piñas y biomasa radicular, creadas específicamente para individuos de pino carrasco de diámetros inferiores a los comerciales. Las ecuaciones se calcularon a partir de individuos cortados a lo largo de una secuencia de años que abarcó desde los 5 hasta los 16 años de edad en ambas zonas de estudio. En estas ecuaciones, la variable predictiva fue el diámetro a 30 cm sobre el suelo, si bien, también se obtuvieron diferencias para determinadas interacciones de densidades finales y sitio, que quedaron incorporadas en las ecuaciones mediante variables categóricas. El estudio detallado de la repartición de los componentes de biomasa mostró diferencias relacionadas con la edad, la calidad de estación y la intensidad y momento de los tratamientos selvícolas. En general, encontramos que la biomasa de tronco y raíces aumenta con la intensidad de clareo, siendo el porcentaje de biomasa acumulada en estas estructuras mayores en individuos creciendo en zonas de baja competencia intraespecifca que aquellos que deben 16

25 competir por los recursos debido a la alta densidad de arbolado. Para la mejor calidad de estación (Yeste), se obtuvieron mayores porcentajes de biomasa acumulada en tronco y raíces, además de observar una variación temporal positiva del porcentaje de biomasa en tronco y negativa del porcentaje de biomasa contenido en acículas. Para individuos localizados en la zona de estudio Calasparra, los resultados mostraron que la alta densidad final de las parcelas control reducía significativamente el porcentaje de biomasa contenida bajo el suelo. Después de analizar la diversidad de plantas en el Capítulo 1 y de desarrollar ecuaciones específicas para el cálculo de los componentes de biomasa a nivel de individuo en el Capítulo 3, en el Capítulo 4, se estudió la evolución en el tiempo de la biomasa almacenada por ambos regenerados a nivel de masa. Conscientes de la importante infravaloración que supondría incluir solamente el estrato arbóreo a la hora de calcular la biomasa total acumulada en la vegetación, se obtuvieron estimadores de razón para determinar la biomasa almacenada por las 15 principales especies acompañantes identificadas en el Capítulo 1, siendo la variable predictiva la fracción de cabida cubierta. También se calcularon estimadores de razón medios para estimar valores de biomasa al resto de especies del sotobosque, según el grupo de forma de vida al que pertenecen, considerando tres: arbustos, matorrales o herbáceas perennes. A nivel de masa, la biomasa del estrato arbóreo y arbustivo de los regenerados mostraron similar acumulación a la edad de 5 años, justo antes de ejecutarse los primeros tratamientos selvícolas. Tras los clareos, se observó una reducción generalizada de la biomasa almacenada en el corto plazo, aunque los tratamientos de clareo más tempranos (a los 5 años de edad) promovieron alta productividad concentrada en pocos individuos, propiciando la recuperación del carbono almacenado y llegando a superar a las parcelas no tratadas en el periodo de estudio, aunque solo en la mejor calidad de estación, Yeste. En conjunto, los resultados obtenidos muestran un acortamiento del periodo para alcanza parámetros de diversidad y madurez reproductiva tras aplicar tratamientos de clareo tempranos en masas en regeneración, siendo estos 17

26 aspectos necesarios para aumentar la resiliencia y disminuir su vulnerabilidad ante nuevos incendios en estos ecosistemas mediterráneos. 18

27 3. Introducción general 19

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29 3. INTRODUCCIÓN GENERAL Incendios forestales y Pinus halepensis El cambio climático y en especial, el aumento en la variabilidad climática, pueden modificar el régimen de incendios forestales. En gran parte de los ecosistemas terrestres mediterráneos, el fuego constituye una perturbación natural que forma parte de la dinámica ecológica (Naveh 1975, Trabaud 1987). En estos ecosistemas los incendios son, además, uno de los principales factores que modifican el balance de carbono, pasando de ser sumidero de carbono a fuente de emisión (Mkhabela et al. 2009), al menos hasta que la regeneración post-incendio se desarrolla y vuelve a tener lugar la fijación de carbono (Wiedinmyer y Neff 2007). En la Región Mediterránea, se ha detectado un incremento tanto en la recurrencia y severidad de incendios, como en el área quemada en las últimas décadas (Flannigan et al. 2009). Las causas de este incremento han sido los cambios en el uso del suelo, principalmente el abandono de tierras de cultivo debido al éxodo rural, o las reforestaciones masivas (con ausencia de manejo) junto con el cambio climático (IPCC 2007) que han propiciado el aumento de la acumulación del combustible disponible (Piñol et al. 1998, Pausas 2004, Moreira et al. 2012). Considerando que no todas las especies mediterráneas son igual de sensibles al cambio climático, resulta indispensable conocer las interacciones entre las distintas especies de los ecosistemas para comprender la respuesta global de la comunidad, ya que se espera que los efectos negativos sobre la comunidad o ecosistema sea menor que en cada una de las especies individuales que la conforman (Valladares 2008). Además de las estrategias de resistencia y resiliencia a las perturbaciones, todas las especies arbóreas se caracterizan por una contradictoria combinación de cambios lentos en macroevolución pero rápidos en microevolución, lo que les permite adaptarse rápidamente a condiciones locales (Petit y Hampe 2006). Sin embargo, la alta tasa de cambio ambiental actual está imponiendo severas 21

30 limitaciones en la capacidad de los árboles para adecuarse a las nuevas condiciones climáticas (Alcamo et al. 2007). En el caso de numerosas especies vegetales es común observar cómo el momento de floración es variable y está relacionado con presiones selectivas naturales. Esta relación puede deberse a causas plásticas o genéticas, por lo que conocer la plasticidad fenotípica de una especie puede ayudar a prever el futuro de la especie ante cambios climáticos estocásticos, siendo necesario establecer estudios locales de larga duración. Dentro de las masas arbóreas mediterráneas, destacan las compuestas por Pinus halepensis Mill. (pino carrasco), principalmente en las zonas costeras y de baja elevación (Blanco et al. 1997). La superficie total ocupada por esta especie en la Región Mediterránea se estima en 3.5 millones de hectáreas (Fady et al. 2003) 1.5 millones de hectáreas en España (datos del IFN2). Debido a su abundancia, P. halepensis es una de las especies que más contribuye a la fijación de carbono en el sureste español pero también es, junto con Pinus pinaster Ait, la conífera más afectada por los incendios forestales en el último decenio (Tabla 1). El pino carrasco ha sido considerado como una especie favorecida por los incendios forestales, sin embargo, frente a perturbaciones recurrentes, esta especie se muestra incapaz de recuperarse debido a la imposibilidad de producir cantidad suficiente de semilla viable en el intervalo entre un incendio y otro (Díaz-Delgado 2003). Tabla 1. Especies arbóreas más afectadas por los incendios forestales. Total nacional para el periodo Fuente: Ministerio de Agricultura, Alimentación y Medio Ambiente Año Especie 1 Sup. (ha) Especie 2 Sup. (ha) 2001 Pinus pinaster 4.021,84 Pinus halepensis 3.520, Pinus pinaster 7.127,89 Pinus halepensis 2.614, Pinus pinaster ,41 Pinus halepensis 6.743, Pinus pinea 7.429,20 Pinus pinaster 6.785, Pinus pinaster ,64 Pinus halepensis 5.052, Pinus pinaster ,24 Pinus halepensis 3.030, Pinus canariensis ,53 Pinus halepensis 2.397, Pinus halepensis 2.279,19 Pinus pinaster 1.056, Pinus halepensis ,80 Pinus pinaster 6.374, Pinus halepensis 3.326,32 Pinus pinaster 1.884,88 22

31 Las principales estrategias del pino carrasco frente al fuego son la presencia de piñas serótinas (Daskalanous y Thanos 1996) y una floración precoz (Tapias et al. 2001). La serotinia es una adaptación al fuego que permite retrasar la dispersión de sus semillas, constituyendo bancos aéreos de piñas que permanecen cerradas en la copa de los árboles durante uno o varios años. La liberación de las semillas almacenadas en las piñas serótinas depende de las condiciones ambientales, viéndose favorecida por altas temperaturas y baja humedad relativa, condiciones que se reúnen en los incendios forestales, aunque también se pueden abrir en ausencia de fuego (estrategia dual), fenómeno conocido como xeriscencia (Nathan et al. 1999). En la Península Ibérica, P. halepensis presenta baja variación genética debido a la reciente historia de migración de la especie (Grivet et al. 2009) pero una alta variación fenotípica que propicia diferentes respuestas reproductivas propiciadas por el estrés ambiental (Santos del Blanco et al. 2013). Estas ideas concuerdan con numerosos estudios donde el inicio reproductivo se ligó a diferencias de sitio, en concreto mayor precocidad en sitios más áridos (Gónzalez- Ochoa et al. 2004, Climent et al. 2008) o debido a altas recurrencias de incendios (Thanos y Daskalakou 2000), que a su vez modula el porcentaje de serotinia (Tapias et al. 2004). El pino carrasco es una especie que resulta muy adecuada para el estudio de caracteres reproductivos, debido a su rápido crecimiento, su precocidad en la producción de piñas femeninas y a que no presenta vecería, es decir mantiene una producción de piñas continuada desde su inicio reproductivo. Además, esta especie conserva varias cohortes de piñas en la copa que pueden competir por recursos y presenta variabilidad en el grado de serotinia (Ne eman et al. 2004; Goubitz et al. 2004). Otro carácter interesante del pino carrasco es su respuesta positiva en la producción de piñas ante tratamientos selvícolas, tales como clareos o podas (Verkaik y Espelta 2006, De Las Heras et al. 2007, González-Ochoa et al. 2004) o fertilización (Ortiz et al. 2012). 23

32 Regeneración post-incendio Tras un incendio, debe realizarse un manejo forestal justificado a través de una serie de objetivos concretos, los cuales han de ir dirigidos a asegurar la regeneración del bosque, si bien este fin último puede verse influido por otros objetivos concretos dependiendo de cada situación. Algunos objetivos podrían incluir la prevención de procesos erosivos, conservación de especies, comunidades y/o hábitats, reducción del riesgo de nuevos incendios (resistencia) (Frinkal y Evans 2008) u otros más recientes como el almacenaje de carbono (Bravo et al. 2008). Las comunidades de plantas mediterráneas presentan una alta resiliencia al fuego debido a la presencia de plantas con estrategias rebrotadoras o plantas que tras un incendio presentan altos valores de reclutamiento, en definitiva rebrotadoras obligadas o germinadoras obligadas (Keely 1986) respectivamente, o especies facultativas que presentan ambas estrategias (Pausas et al. 2004). Sin embargo, no todas las especies de plantas Mediterráneas presentan estrategias eficientes para sobrevivir a los incendios, pudiendo desaparecer ante incendios recurrentes o grandes incendios (Retana et al. 2002). La regeneración postincendio de un ecosistema depende de la capacidad de respuesta de las especies que lo componen, por lo que anticiparse y determinar qué regeneración potencial tendría un determinado ecosistema es posible (Moreira et al. 2012). Tradicionalmente, la estrategia de manejo tras un incendio consiste en implementar actuaciones de emergencia para protección de suelo (mulching, aterrazados, barreras vegetales ) y posteriormente, en áreas donde la cobertura vegetal es baja se planean restauraciones hidrológico-forestales basadas en la construcción de diques y reforestación, siendo el proceso más común iniciar con coníferas (en una primera etapa), para posteriormente introducir especies sucesionales más avanzadas, como las quercíneas (Pausas et al. 2004). Sin embargo, en zonas con alta regeneración natural se deben implementar medidas de restauración indirecta o pasiva (asistencia al regenerado asistida) lo que está relegando a la realización de restauraciones activas (reforestación y reintroducción de especies) a zonas de especial conservación o simplemente 24

33 como intervenciones de emergencia (Moreira et al. 2012). Estas acciones indirectas, se basan en definitiva, en esperar una regeneración natural de especies germinadoras (Pausas et al. 2004) y/o rebrotadoras (Espelta et al. 2003). La restauración asistida incluye tratamientos selvícolas para zonas con densidades de regeneración elevadas que disminuyen la competencia intraespecífica y adelantan la llegada de la madurez (Cañellas et al. 2004, Madrigal et al. 2006; Jiménez et al. 2011) En el caso del pino carrasco, numerosas investigaciones han demostrado que los tratamientos selvícolas, ejecutados a edades tempranas, son beneficiosos para el regenerado natural, ya que mejoran las parámetros reproductivos de la masa, el crecimiento individual de los pinos (González-Ochoa et al. 2004; Moya et al. 2007; De las Heras et al. 2007; Verkaik y Espelta 2006), además de la reducción de la carga de combustible, rompiendo así la continuidad horizontal y vertical de la vegetación, y disminuyendo por tanto la propagación en un nuevo incendio (Vélez 2000). Secuestro de carbono en regenerados de P. halepensis La capacidad de sumidero de los bosques de la Península Ibérica, podría verse alterada por el calentamiento global. Por un lado, mayores tasas de CO 2 y un aumento del periodo vegetativo, derivado del aumento de la temperatura, tendrían efectos fertilizantes en las plantas, por lo que en principio el almacén de C podría aumentar. Sin embargo, el aumento de las temperaturas también podría llevar asociado una reducción de la disponibilidad hídrica, lo que provocaría un efecto contrario al esperado (Vayreda 2013). El incremento de los gases de invernadero, como el ozono troposférico también provoca efectos sobre la vegetación, la biodiversidad y el funcionamiento de los ecosistemas, afectando a especies representativas de los bosques mediterráneos como la encina, el pino carrasco o el madroño, así como a los 25

34 estratos arbustivo y herbáceo entre otros (Bermejo et al. 2013). El ozono puede modificar la magnitud del efecto sumidero de C que tienen los bosques, debido a los cambios que genera en el intercambio gaseoso y en la distribución de asimilados en la planta (Harmens y Mills 2012). También puede alterar la fisiología y disminuir el crecimiento de las raíces (Díaz-de-Quijano et al. 2012). La concentración de este contaminante en el SE de España es de especial relevancia por lo que debería tenerse en cuenta, como un factor más de estrés para el desarrollo de una especie o comunidad (Alonso et al. 2013). El carbono almacenado en los ecosistemas terrestres se reparte entre la biomasa de las plantas vivas (tronco, ramas, hojas, frutos y raíces), el detritus de las plantas (ramas caídas y frutos, hojarasca, tocones etc.) y el suelo (Bravo et al. 2008b). Los suelos son la mayor fuente de carbono terrestre, y supone tres veces el carbono almacenado por la biomasa viva (The Royal Society 2001). Sin embargo, buena parte de la investigación centra sus estudios hacia la biomasa acumulada por las plantas vivas. El carbono almacenado depende de la especie, del manejo y de la calidad de estación (Bravo et al. 2008), por ejemplo, las coníferas secuestran más carbono que las especies de hoja caduca (Ibáñez et al. 2002). A nivel individual, la repartición de biomasa entre los diferentes componentes de las plantas podría verse alterada ante condiciones climáticas erráticas, especialmente en los regenerados, debido a la alta competencia inter e intra-específica. Resulta de gran importancia conocer la variación en el reparto de biomasa a nivel individual para cumplir los objetivos planteados (productivos, bioenergía etc.), por ejemplo con el aumento de la edad se aumenta la acumulación de biomasa en troncos y disminuye en ramas (Bravo et al. 2008b). La estimación de la biomasa aérea se calcula con ecuaciones de biomasa o factores de expansión, mientras que la biomasa subterránea se suele obtener indirectamente a partir de la biomasa aérea (Río et al. 2008) debido a los elevados costos que suponen ejecutar correctamente muestreos destructivos de raíces. En general, se ha prestado poca atención a la estimación del carbono almacenado en 26

35 bosques de regiones semiáridas o Mediterráneas (Vayreda et al. 2012), aunque si existen trabajos previos en España que presentan ecuaciones de biomasa aérea y radicular para individuos adultos de las especies arbóreas forestales más representativas (Montero et al. 2005, Ruíz-Peinado et al. 2011). Además, existen mapas de almacenaje de C a nivel nacional realizados a partir de los datos del tercer inventario forestal nacional, que muestran un claro patrón espacial del secuestro de C, siguiendo un gradiente norte-sur de mayores a menores concentraciones de C (Figura 1). En España, el pino carrasco es la especie, de entre todas las coníferas, que menos C fija. En cambio, si se tiene en cuenta el C fijado por el sotobosque, los bosques de pino carrasco en su conjunto, son los que más proporción fijan con respecto al carbono total (Vayreda et al. 2012) Vayreda et al. (2012) Stocks de C Superficie forestal Figura 1. Mapas de almacenaje de Carbono a nivel nacional en Mg C ha -1 (resolución de pixeles), incluyendo el estrato arbóreo y el arbustivo, a partir de los datos del tercer inventario forestal nacional. Adaptado de Vayreda et al. (2012). La influencia de los tratamiento selvícolas sobre el carbono fijado en regenerados de pino carrasco, ha sido estudiada localmente con anterioridad (De Las Heras et al. 2013), sin embargo son escasos los trabajos que estudian la evolución en el largo plazo del almacenamiento de carbono o comparando diferentes calidades de estación. También hay una falta de conocimiento científico sobre el balance de fijación de carbono del matorral acompañante a la especie 27

36 principal (Kaye et al. 2010), lo que conlleva a una importante infravaloración del almacenamiento real de carbono. Enclave del estudio e hipótesis de trabajo Gracias al proyecto Manejo y restauración de masas forestales incendiadas en castilla-la Mancha. Técnicas de Mejora en Vivero de especies a reintroducir (FEDER-CICYT 1FD97-411), en el año 1999 se inicia el estudio de una serie de parcelas permanentes en regenerados naturales de P. halepensis, sobre el que esta tesis se sustenta. En esta primera fase del estudio, los objetivos más relevantes que se plantearon fueron: estudiar el estado general de dos regenerados post-incendio en distinta calidad de estación, 5 años después del fuego, y su respuesta a distintos tratamientos selvícolas (clareo, desbroce y poda) dos años después de su ejecución. En el año 2004, el estudio continúa a través del proyecto Reproducción de Pinus halepensis Mill. tras grandes incendios. Influencia de distintos tratamientos selvícolas en su dinámica reproductiva y en la biodiversidad del sistema forestal (AGL /FOR). El objetivo general de este nuevo estudio fue el de aportar y desarrollar conocimiento científico para apoyar los criterios de decisión a la hora de realizar un manejo adecuado en regenerado naturales en base a su efecto a corto plazo sobre crecimiento, diversidad y mejora del banco de semillas aéreo de la especie arbórea principal. Las conclusiones más relevantes obtenidas en ambos trabajos fueron las siguientes: de los distintos tratamientos selvícolas ensayados (desbroce, poda y clareo a distintas intensidades) se recomienda el clareo con densidad final de pies ha -1. Con este tratamiento se observó un incremento en el crecimiento en diámetro y altura, así como la probabilidad de producción de piñas en las dos calidades de estación. El desbroce y la poda no produjeron una mejora substancial como para recomendar su ejecución en regenerados de 5 años de edad (González-Ochoa 2003). Además, tratamientos de alta intensidad de clareo realizados a los 10 años mostraron una mejora en el crecimiento y la capacidad reproductiva de los regenerados en la mejor calidad de estación, sin embargo se 28

37 desaconsejaba su implementación en la zona semiárida siendo más apropiados clareos de menor intensidad y a una mayor edad de la masa (Moya 2008). Tras estos dos estudios, muchas de las preguntas planteadas sobre la efectividad de los tratamientos tempranos en el crecimiento y la capacidad reproductiva de regenerados post-incendio de pino carrasco encontraron respuesta. Sin embargo, nuevas preguntas surgieron: La estimulación observada en el crecimiento y la producción de piñas en los primeros años después de la aplicación de los tratamientos selvícolas, fue puntual o continuó afectando positivamente a los regenerados en el medio-largo plazo? Cómo influyen las sequías en el crecimiento y la producción de piñas en las primeras dos décadas de vida de los regenerados? Cómo distribuyen los individuos los recursos entre la inversión reproductiva y la vegetativa? Cómo ha evolucionado el matorral acompañante en los regenerados naturales tras los tratamientos tempranos en el medio-largo plazo? Cuál es el almacenaje de carbono de los regenerados naturales? y Cuánto varía dependiendo de la calidad de sitio para la misma especie arbórea principal? Cómo es el reparto de biomasa en árboles de menos de 20 años? Qué factores modifican este reparto en cada parte del árbol y cómo interactúan entre ellos? Cuál es la importancia del matorral en la cuantificación del carbono almacenado en regenerados naturales? Bajo qué calidades de estación la proporción matorral/pinar tiene mayor peso en el secuestro de carbono? Con el fin de dar respuesta a estas preguntas, en el año 2009 se abre una nueva oportunidad para continuar investigando la dinámica post-incendio de regenerados naturales, en la que se enmarca esta tesis doctoral y que ha sido apoyado por los proyectos CYCIT-AGL /FOR; AGL /FOR y CONSOLIDER-INGENIO 2010: MONTES (CSD ). Se trata por tanto de un estudio a largo plazo de parcelas permanentes, lo cual, además de ser poco frecuente resulta enormemente valioso en cuanto a resultados se refiere, ya que permite obtener conclusiones bajo un punto de vista diacrónico, más de 15 años, 29

38 sobre la regeneración post incendio de pinares de pino carrasco en el SE de España. Objetivos y estructura en capítulos Los objetivos generales de la tesis son: Conocer la dinámica de masas naturales post-incendio de pino carrasco sometidos a tratamientos tempranos (Objetivo 1) y desarrollar herramientas para la estimación del secuestro de carbono de estos ecosistemas (Objetivo 2) bajo climas contrastados y en un intervalo temporal que discurre desde los 5 hasta los 16 años tras el inicio de la regeneración natural, y las hasta los 19 en el caso de las características reproductivas. La memoria de la tesis se compone de cuatro capítulos basados enartículos publicados o en proceso de publicación (Fig. 2), que recogen los objetivos generales expuestos, comprendiendo cada uno de los capítulos uno o varios objetivos parciales: Objetivo 1.1. Analizar la dinámica de la vegetación post-incendio mediante un estudio diacrónico a través de los parámetros de alfa y beta diversidad, así como mediciones de la cobertura vegetal. Objetivo 1.2. Determinar si existen patrones de compensación (trade-offs) entre el crecimiento radial y la producción femenina de conos femeninos a nivel árbol. Objetivo 2.1. Desarrollar relaciones alométricas para la estimación de los componentes de la biomasa, tronco, ramas, madera de ramas, acículas, piñas y raíces. Objetivo 2.2. Analizar el almacenaje de biomasa a nivel árbol y determinar los factores que modulan el reparto de los componentes de la biomasa. Objetivo 2.3. Desarrollar estimadores de razón para la estimación de la biomasa aérea y radicular del matorral acompañante. 30

39 Objetivo 2.4. Secuestro de carbono del estrato arbóreo y arbustivo incluyendo la parte aérea y radicular de ambos estratos. La memoria de la tesis se compone de cuatro capítulos-artículos (Fig. 2), que recogen uno o varios de estos objetivos parciales. De esta forma, en el Capítulo 1 se trata el Objetivo 1.1, en el Capítulo 2 se recoge el Objetivo 1.2, en el Capítulo 3 se tratan los objetivos 2.1 y 2.2 y por último en el Capítulo 4 se abordan los objetivos 2.3 y 2.4. Figura 2. Esquema conceptual de la estructura en capítulos de la tesis. Las conclusiones extraídas en esta tesis permitirán ofrecer herramientas de gestión para el manejo de masas de pino carrasco, abarcando el período de edad desde los 5 hasta casi los 20 años de edad, coincidiendo con la edad de partida tomada por la mayoría de las tablas de producción (Montero et al. 2001). 31

40 Área de estudio y diseño experimental Los cuatro capítulos de esta tesis se desarrollan en dos localidades situadas en el SE español; Yeste en la provincia de Albacete y Calasparra en la provincia de Murcia. Ambas zonas se vieron afectadas por sendos incendios forestales durante el verano de Los incendios quemaron superficies totales cercanas a las ha en Yeste y ha en Moratalla y Calasparra. La vegetación previa a los incendios era masas naturales de P. halepensis en mezcla con Quercus coccifera L. y Quercus ilex L. En Calasparra la vegetación dominante previa al incendio, era una masa compuesta principalmente por P. halepensis procedente de una repoblación de los años 60 y Macrochloa tenacissima Kunth. (esparto) también de origen antrópico y cuyo origen muy anterior a la repoblación de pino carrasco (Ana Atienza, Consejería de Murcia; Comunicación personal). La regeneración tras el incendio fue muy prolífera en ambas localidades, llegando a ser excesiva en algunas áreas. Yeste y Calasparra tienen contrastada calidad de estación. La localidad de Yeste es representativa de un ombroclima seco (dry site en los capítulos correspondientes), con un valores de precipitación y temperatura medios de 595 mm y 13,6ºC, respectivamente (periodo , AEMET), mientras que la localidad de Calasparra representa un ombroclima semiárido (semiarid site en los capítulos correspondientes) con un valores de precipitación y temperatura medios de 340 mm y 16,5ºC, respectivamente (periodo , AEMET). En el año 1999, en Yeste se establecieron 27 parcelas de m 2, mientras que en Calasparra fueron 21. En estas parcelas se testaron clareos, podas y desbroces a distintas edades de aplicación, tomando distintas densidades finales según la intensidad de clareo. Cada uno de estos tratamientos tenía asociada al menos 3 réplicas por sitio. Dentro de cada parcela un número representativo de mínimo 13 pies fue monitorizado. La poda no se ha incluido como factor de tratamiento ya que resultados previos demostraron que no afectaba a las principales características dendrométricas y/o reproductivas de los árboles monitorizados. 32

41 Figura 3. Fotografía de la zona de estudio situada en Yeste Figura 4. Fotografía de la zona de estudio situada en Calaparra Con respecto a los clareos, el diseño experimental inicialmente planteado en 1999 (González-Ochoa 2003), pretendía testar en ambas zonas de estudio dos únicas densidades finales; una intensidad de pies ha -1 y otra de 800 pies ha

42 Yeste Calasparra Figura 5. Localización de las parcelas experimentales en Yeste (mapa superior) y Calasparra (mapa inferior). Sin embargo, cuando se ejecutaron los tratamientos de densidad final pies ha -1 (inicialmente T ), debido al pequeño tamaño de los brinzales junto con la abundante cobertura de matorral, se infravaloraron las densidades 34

43 finales en un alto porcentaje de las parcelas de estudio. Tras contabilizar nuevamente el número de pies de las parcelas en 2009, se decidió incluir las parcelas con mayores densidades finales por el valor añadido que proporcionarían en la consecución de los objetivos de los Capítulos 2 y 4 (Tabla 1, Figura 3). Sin embargo, para los Capítulos 1 y 3, se optó por descartar las parcelas de pies ha -1 en Yeste (T ) y las parcelas de pies ha -1 en Calasparra (T ), con el único fin de obtener conclusiones más sencillas y aplicables al manejo forestal. De esta forma para los Capítulos 2 y 4, se han considerado 21 parcelas por sitio, mientras que para los Capítulos 1 y 3 se han tomado 18 parcelas por sitio. Tabla 2. Codificación de los tratamientos selvícolas utilizada en la tesis. Cada tratamiento se llevó a cabo en 3 parcelas de m 2 por sitio. Celdas en blanco indican que el tratamiento se llevó a cabo en ambas zonas de estudio; celdas en gris claro indican que el tratamiento solo se realizó en Yeste; celdas en gris oscuro indican que el tratamiento solo se realizó en Calasparra. T 5+10 Tratamientos Código Sin tratar (Control) C / T C Clareo a los 5 años (1999) con densidad final de 800 pies ha -1 T Clareo a los 5 años (1999) con densidad final de pies ha -1 T Clareo a los 5 años (1999) con densidad final de pies ha -1 T Clareo a los 5 años (1999) con densidad final de pies ha -1 T Clareo a los 5 años (1999) con densidad final de pies ha -1 T Clareo a los 10 años (2004) con densidad final de 800 pies ha -1 T Clareo a los 10 años (2004) con densidad final de pies ha -1 T Tratamiento secuencial: Clareo a los 5 años (1999) con densidad final de pies ha -1 más clareo a los 10 años (2004) con densidad final de 800 pies ha -1 Para la consecución de este estudio diacrónico se han utilizado datos del grupo de investigación correspondientes a la secuencia de años desde los 5 (1999) hasta los 14 (2008) años tras la regeneración natural, y por otro lado datos específicos para la consecución de este estudio, correspondientes a la secuencia de años desde los 15 (2009) hasta los 19 (2013) años. 35

44 Figura 6. Esquema de los tratamientos selvícolas llevados a cabo en Yeste y Calasparra a los 5 (1999) y a los 10 (2004) años de edad. Ver codificación de los tratamientos en Tabla 1. En la Tabla 3 se muestran las tareas realizadas a lo largo de todo el estudio, incluyendo el o los capítulos en los que se utilizaron los datos obtenidos de cada tarea. Los principales trabajos fueron: 36

45 - Inventarios dendrométricos, en los que se procedió a la medición de altura, diámetro a 30 cm sobre el suelo y diámetro de copa de todos los árboles marcados en las parcelas de seguimiento. - Inventarios de biodiversidad (Figura 7a). - Conteo de todas las piñas presentes en la copa de los árboles monitorizados y medición de diámetro y longitud de las mismas en copa. A partir del año 2005, además de la contabilización y medición de las piñas también se clasificaron en cohortes a partir del color y del tamaño que presentaban en cinco grupos: estróbilos femeninos, piñas verdes, piñas marrones, piñas serótinas y piñas abiertas (Figura 7b). - Extracción de cores o testigos de madera en 2013 mediante una barrena Pressler (Figura 7c). La muestra se extrajo de una selección de 90 árboles monitorizados por sitio, distribuidos aleatoriamente entre los tres grandes grupos de densidades: densidades muy altas (parcelas control), densidades altas ( pies ha -1 ) y densidades moderadas (800 pies ha -1 ). Cuando los árboles eran demasiado pequeños para ser barrenados (diámetros inferiores a 5 cm), se cortaron a ras de suelo y se tomaron rodajas basales. - Inventarios destructivos de biomasa, en los que se apearon una muestra representativa de pinos y especies de matorrales más representativos para una posterior estimación de los componentes la biomasa seca aérea y radicular (Figura 7d, 7e). 37

46 a Figura 7. Fotografías de los trabajos en campo. Muestreos de biodiversidad (a); Conteo y medición de piñas (b); extracción de cores (c); muestreos destructivos de especies del matorral (d) y de pinos (e). b c d e 38

47 Tabla 3. Cronograma de tareas de muestreo y monitorizacion para la consecución del estudio diacrónico. Celdas en gris claro indican las tareas realizadas en las tesis anteriores (desde 1999 hasta 2008). Celdas en gris oscuro indican las tareas específicas de esta tesis doctoral (desde 2009 hasta 2013). Tareas Capítulos Inventarios dendrométricos 1,2,3,4 Transectos biodiversidad 1,4 Conteo y medición piñas 2,3 Extracción cores 2 Extracción biomasa 3,4 39

48 Referencias Alcamo J, Flörke M, Märker M (2007) Future long-term changes in global water resources driven by socio-economic and climatic changes. Hydrolog Sci J 52 (2), Alonso R, Elvira S, Bermejo V, Calvete H, González I (2013) Análisis del riesgo de daños provocados por ozono en la vegetación a nivel de Paisaje: la sierra de Guadarrama. Cómo integrar el cambio global en la gestión de los montes españoles. (Enrique Doblas Miranda, (Ed.)). CREAF pp Bermejo V, Penuelas J, Avila A, Gonzalez I, Estiarte M, Sanz J, Sardans J, Garcia H, Diazde-Quijano M, Rabago I, Cano F, Alonso R (2013) Conservar Aprovechando. Cómo integrar el cambio global en la gestión de los montes españoles. (Enrique Doblas Miranda, (Ed.)). CREAF pp Blanco E, Casado MA, Costa M, Escribano R, García M, Génova M, Gómez A, Gómez F, Moreno JC, Morla C, Regato P, Sainz H, Sainz Ollero H (1997). Los Bosques Ibericos: Una Interpretacion Geobotanica. Planeta Bravo F, Bravo-Oviedo A, Diaz-Balteiro L (2008) Carbon sequestration in Spanish Mediterranean forests under two management alternatives: a modelling approach. Eur J Forest Res 127, Bravo F, Rio M, Bravo-Oviedo A, Peso C Del, Montero G (2008b) Forest management strategies and carbon sequestration. En: Bravo F. (Ed.) Managing Forest Ecosystems: The Challenge of Climate Change. Springer Cañellas I, Del Río M, Roig S, Montero G (2004) Growth response to thinning in Quercus pyrenaica Willd. coppice stands in Spanish central mountain. Ann Forest Sci 61, Climent J, Prada MA, Calama R, Chambel MR, De Ron DS, Alía R (2008) To grow or to seed: ecotypic variation in reproductive allocation and cone production by young female Aleppo pine (Pinus halepensis, Pinaceae). Am J Bot 95, Daskalakou EN, Thanos CA (1996) Aleppo pine (Pinus halepensis) postfire regeneration: The role of canopy and soil seed banks. Int J Wildland Fire 6, De Las Heras J, Moya D, López-Serrano FR, Condés S (2007) Reproduction of postfire Pinus halepensis Mill. stands six years after silvicultural treatments. Ann Forest Sci 64: De las Heras J, Moya D, López-Serrano FR, Rubio E (2013) Carbon sequestration and early thinning in Aleppo pine stands regenerated after fire in South-eastern Spain. New Forest 44:

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51 Naveh Z (1975) The evolutionary significance of fire in the Mediterranean region. Vegetatio 29, Ne eman G, Gidi Goubitz S, Shirrinka Nathan R (2004) Reproductive traits of Pinus halepensis in the light of fire a critical review. Plant Ecology 171, Ortiz O, Ojeda G, Espelta JM, Alcaniz JM (2012) Improving substrate fertility to enhance growth and reproductive ability of a Pinus halepensis Mill. afforestation in a restored limestone quarry. New Forest 43, Pausas JG (2004) Changes in fire and climate in the eastern Iberian peninsula (Mediterranean Basin). Clim Ch 63, Pausas JG, Bradstock RA, Keith DA, Keeley JE, GCTE Fire Network (2004) Plant Functional traits in relation to fire in crown-fire ecosystems. Ecology 85, Petit RJ, Hampe A (2006) Some evolutionary consequences of being a tree. Annual Review of Ecology Evolution and Systematics 37, Piñol J, Terradas J, Lloret F (1998) Climate warming, wildfire hazard, and wildfire occurrence in coastal eastern Spain. Clim Ch 38, Retana J, Espelta JM, Habrouk A, Ordonez JL, de Sola-Morales F (2002) Regeneration patterns of three Mediterranean pines and forest changes after a large wildfire in northeastern Spain. EcoScience 9, Rio M, Barbeito I, Bravo-Oviedo A, Calama R, Cañellas I, Herrero C, Bravo F (2008) Carbon sequestration in Mediterranean pine forests. En: Bravo F. (Ed.) Managing Forest Ecosystems: The Challenge of Climate Change. Springer Ruíz-Peinado R, del Rio M, Montero G (2011) New models for estimating the carbon sink capacity of Spanish softwood species. Forest Systems 20(1), Santos del Blanco L, Bonser SP, Valladares F, Chambel MR, Climent J (2013) Plasticity in reproduction and growth among 52 range-wide populations of a Mediterranean conifer: Adaptive responses to environmental stress. J Evol Biol 26, Tapias R, Climent J, Pardos JA, Gil L (2004) Life histories of Mediterranean pines. Plant Ecol 171, Tapias R, Gil L, Fuentes-Utrilla P, Pardos JA (2001) Canopy seed banks in Mediterranean pines of south-eastern Spain: a comparison between Pinus halepensis Mill., P. pinaster Ait., P. nigra Arn. and P. pinea L. J Ecol 89, The Royal Society (2001) The role of land carbon links in mitigating global climate change. Policy document 10/01 35 pp. Trabaud L (1987) Dynamics after fire of sclerophyllous plant communities in the Mediterranean basin. Ecología Mediterránea 13,

52 Valladares F (2008) A mechanistic view of the capacity of forests to cope with climate change. In: Bravo F, May VL, Jandl R, von Gadow K, editors. Managing forest ecosystems: the challenge of climate change. Berlin: Springer-Verlag Vayreda (2013) Una herramienta para la estimación del balance de C a escala nacional. Cómo integrar el cambio global en la gestión de los montes españoles. (Enrique Doblas Miranda, (Ed.)). CREAF pp Vayreda J, Gracia M, Canadell JG, Retana J (2012) Spatial Patterns and Predictors of Forest Carbon Stocks in Western Mediterranean. Ecosystems 15, Vélez (2000) Los Incendios Forestales en la Cuenca Mediterránea, La Defensa Contra Incendios Forestales: Fundamentos y ExperienciasMcGraw-Hill, Madrid pp Verkaik I, Espelta JM (2006) Post-fire regeneration thinning, cone production, serotiny and regeneration age in Pinus halepensis. Forest Ecol Manag 231, Wiedinmyer C, Neff JC (2007) Estimates of CO 2 from fires in the United States: implications for carbon management. Carbon Balance Manag 1,

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55 4.1 CAPÍTULO 1: Vegetation dynamics of managed Mediterranean forests 16 years after large fires in southeastern Spain Alfaro-Sánchez R, De las Heras J, Hernández-Tecles E, Moya D, López- Serrano FR, Sánchez-Salguero R Submitted to: Applied Vegetation Science Under review 47

56 Abstract Question: How can forest managers maintain resilience in Mediterranean ecosystems with changes in fire regimes in a forecasted climate scenario? Adaptive forest management requires scientific knowledge about post-fire vegetation dynamics, mainly on initial and transitional stages. We analysed the interaction of applying silvicultural treatments at early post-fire Mediterranean vegetation succession stages. Location: Two post-fire regenerated Pinus halepensis stands burned in 1994 under contrasting climatic conditions (Dry and Semiarid sites) in southeastern Spain. Methods: We set experimental plots at both sites to test early treatments, including scrubbing (at post-fire year 5) and thinning (at post-fire years 5 and 10). Alpha and beta diversity parameters, including Species richness index (S), Shannon diversity index (H ), Jaccard index (J) and plant cover, as a measure of vegetation abundance, were diachronically analysed at post-fire years 5 (pre-thinning) and 16 (post-thinning). Results: Significant differences in the diversity parameters (S and H ) were detected between sites. Despite the silvicultural treatments, the S and H values at the Dry site were similar in the treated and control plots at 16 years. Conversely at the Semiarid site, the S and H values were enhanced by the thinning treatments carried out 10 years after the fire. A low species overlap was also found between sites on both dates, but the number of shared species increased by the end of the study period. Plant cover increased despite scrubbing on both the pine and understory layers in every treated plot. At both sites, the most abundant species were obligate seeders or grass resprouters. Conclusion: Our forest management trials enhanced the diversity indices when applied to very high tree density post-fire regenerated stands but they did not stimulate successional processes as expected. Thus, this combination of treatments has proved to be a suitable option to reduce fire hazards, but not to promote ecosystem resilience if it is not accompanied by other restoration actions, such as reintroduction of late successional species like resprouting woody shrubs. 48

57 Keywords: Pinus halepensis; fire-prone Mediterranean ecosystems; post-fire regeneration; Dry and Semiarid Mediterranean climates; scrubbing; thinning; diachronic study; vegetation succession; plant diversity. 49

58 Introduction The role of species diversity in the functioning of ecosystems, where wildfires affect the dynamics, has become one of the most challenging topics in recent ecological research of many Mediterranean forests (Naveh 1975; Lavorel et al. 1998). In the last few decades, climate change has increased forest fires, e.g., number, severity and burned surface, as well as extreme drought periods, in many fire-prone ecosystems in the Mediterranean Basin (Pausas et al. 2004). Life history traits, such as the growth rate and longevity of the main species in ecosystems, are thus major drivers of post-fire dynamics (Pausas 2003; Baeza et al. 2007). Besides, both the structure and composition of Mediterranean communities are characterised by quick post-fire recovery (Malanson & Trabaud 1987), although not all Mediterranean plant species have adopted efficient strategies to survive after fire (Lloret & Vilà 2003). Indeed, post-fire communities dominated by obligate seeders are generally less resilient than communities where there woody resprouters predominate (Agee 1998). Pinus halepensis (Aleppo pine) forests are among the most affected by fire and drought events across the Mediterranean basin (Pasho et al. 2011; Sarris et al. 2011). This species is an obligate seeder, well-adapted to fire-prone habitats showing in the canopy serotinous and non-serotinous cones (bradychory) (Thanos 2004). In areas with adverse conditions (poor soils, severe droughts, etc.), Aleppo pine can occur as the only natural tree species in the plant community (Blanco et al. 1997). In the Mediterranean Basin, post-fire succession has been analysed in a wide range of Aleppo pine forests (Kazanis & Arianoutsou 1996, 2004; Verroios & Georgiadis 2002; Capitanio & Carcaillet 2008). However, the response of biodiversity to silvicultural treatments has not been well studied in fire-prone Mediterranean regions (Osem et al. 2012). Sustainable forest management should be adapted to cope with new forest planning challenges, such as climate change, droughts or the increasing risk of forest fires (Palahí et al. 2008). In this sense, previous attempts have been made to evaluate the short-term effects of forest management on inter- and intra-specific 50

59 species competition in young post-fire regenerated Aleppo pine stands (Leone & Lovreglio 2004; González-Ochoa et al. 2004; Verkaik & Espelta 2006). However, we found that diachronic studies based on mid- to long-term experiments are lacking. Furthermore, the compatibility of applying silvicultural strategies to prevent forest fire hazards and biodiversity conservation is a critical challenge, which is increased by the fact that the general effects of forest management on plant diversity and flora are complex and difficult to generalise (Tárrega et al. 2006). In this context, our study aim was to analyse the post-fire vegetation dynamics of two areas burned in summer 1994 with contrasting climatic conditions and subjected to early treatments, including scrubbing (at post-fire year 5) and thinning (at post-fire years 5 and 10). To achieve this, we monitored experimental plots in a diachronic study by analysing alpha and beta diversity parameters, such as Species richness index, Shannon diversity index, Jaccard index and plant cover, as a measure of vegetation abundance, at two ages: post-fire years 5 (prethinning) and 16 (post-thinning). Methods Study area The study area included two P. halepensis forests located in southeastern Spain close to the villages of Yeste (1010 m.a.s.l.) and Calasparra (325 m.a.s.l.) (Fig. 1). These areas showed similar stand age, soil properties and topographic characteristics (slope <5%), but contrasting climatic conditions and potential natural vegetation. In summer 1994, two wildfires burned almost 14,000 and 30,000 ha in Yeste and Calasparra, respectively. After these forest fires, both stands regenerated naturally and generally reached high density of Aleppo pine saplings (González-Ochoa et al. 2004), with a maximum of 178,000 saplings ha -1 recorded in Calasparra (De las Heras et al. 2011). 51

60 Figure 1. P. halepensis distribution in the Circum-Mediterranean area (upper centre graph) and the corresponding climatic diagrams based on the climatic data from nearby local meteorological stations at the two study sites on the wildfire surface (lower map), Yeste (upper Dry type, Dry site) and Calasparra (lower Semiarid type, Semiarid site) in the provinces of Albacete and Murcia, SE Spain. Using the data from the period, provided by the Spanish National Meteorological Agency (AEMET), average annual precipitation and temperature were obtained. At Calasparra, the values were 340 mm and 16.5ºC, while they were 595 mm and 13.6ºC at Yeste, respectively. Following Rivas- Martinez et al. (1999), the Ombrothermic Index (Io= (Pp/Tp)*10) was calculated, with Pp (mm) being the Yearly Positive Precipitation, i.e., the total average precipitation of those months whose average temperatures were higher than 0ºC; and Tp (ºC) the Yearly Positive Temperature, as the sum of the monthly average temperature of those months whose average temperatures were higher than 0ºC. The values for the referred period were averaged to find a lower Semiarid type at 52

61 Calasparra (Io=1.41) (hereafter referred to as the Semiarid site) and an upper Dry type at Yeste (Io=3.01) (hereafter referred to as the Dry site) (Rivas-Martinez et al. 1999) (Fig. 1). The Semiarid site was located in the low Mesomediterranean belt (Rivas- Martínez 1987) and the potential natural vegetation was a forest of Quercus coccifera (Rhamno-Querceto cocciferae sigmetum (Rivas-Martínez 1987). However, the Dry site was located in the upper Mesomediterranean bioclimatic belt (Rivas-Martínez 1987) and its potential natural vegetation was a forest of Quercus ilex ssp. ballota (Bupleuro rigidi-querceto rotundifoliae sigmetum (Rivas-Martínez 1987). Before the fires, the forests were mature stands of mainly P. halepensis as the tree species of natural and planted origins in a mixture with P. pinaster and Q. ilex (the latter was found only at the Dry site). The understory included mainly Quercus coccifera, Juniperus oxycedrus, Rosmarinus officinalis, Cistus albidus, Cistus clusii, Macrochloa tenacissima (only at the Semiarid site), Dorycnium penthaphyllum, Thymus vulgaris, Arbutus unedo (only on northern slopes at the Dry site), Clematis vitalba, Pistacia lentiscus, Phyllirea angustifolia, Lonicera implexa, Daphne gnidium, Rubia peregrina, Rhamnus lycioides, Brachypodium retusum and Retama sphaerocarpa, among other companion species (González- Ochoa 2003). Field thinning experiment and plant cover estimation Eighteen experimental plots (10 15 m) were set in Calasparra (Semiarid site) and Yeste (Dry site), respectively. A 6-metre wide strip was maintained between plots to prevent the border effect. At each site, silvicultural treatments were carried out in these plots at two ages; five years after the forest fires of 1994 (in 1999, hereafter T 5 ); ten years after these fires (in 2004, T 10 ), or on both dates; i.e., a sequential treatment (in 1999 and 2004, T 5-10 ), and including different thinning intensities to finally obtain two stand densities: high-density plots (final density ranged from 1,600 to 9,500 trees ha -1 ; i.e., treatments T , T and T ) and moderate-density plots (800 trees ha -1 ; i.e., treatments T 5-800, T and T 5-10 ). The silvicultural treatments were randomly assigned to the experimental plots. In addition, all the thinned plots were submitted to scrubbing at post-fire year 53

62 5. Thus, we finally obtained three sampling plots per site and treatment. Besides, we preserved three plots per site from the silvicultural treatments; i.e., control plots (C, non-thinned). All the plots were diachronically studied at two ages: post-fire years 5 (prethinning) and 16 (post-thinning). All the trees inside the experimental plots were monitored, except for T 9500, and the C plots where a representative number of trees were monitored (González-Ochoa et al. 2004; Moya et al. 2008). At both locations, we carried out temporal samplings of the understory cover species by using the line intercept method (Canfield 1941). In each plot, three 10-metre linear transects were set in spring and autumn of 1999 and 2010, corresponding to the 5- and 16-year-old regenerated P. halepensis stands, respectively. Understory species were recorded to either grow along the transects or with their canopy intercepted by them (Kazanis & Arianoutsou 2004). For the overstory cover species, the pine cover in the C plots was estimated by the line intercept method (similarly to the understory species). However for the high- and moderate-density plots, treetops were assumed to not overlap. Thus in these plots, the pine cover per tree was estimated by measuring two diameters of the crown projection along two perpendicular directions. Then the surface of a circle was applied and the canopy surface of all these individuals per plot were added by dividing by the total surface (0.015 ha). Plant diversity Alpha diversity was diachronically studied by using ecological parameters such as Species richness (S), as the number of species recorded on the sampling lines in each plot, and Shannon diversity index (H ). To obtain life-forms composition, plant species were classified according to life forms categories (Tutin et al ; Castroviejo et al. 1986). Five categories were considered: trees, shrubs, dwarf shrubs, perennial herbs and annual herbs. Plant cover was also diachronically recorded in each plot, as a measure of species abundance, for the pine and understory strata and for each life form category. 54

63 In order to infer from the binary differences between both sites, beta diversity indices were used such as the Jaccard similarity index, but also ordination methods; i.e., Non-Metric Multidimensional Scaling (NMDS) (Oksanen et al. 2011). With the Jaccard index (J ) the presence/absence of species between sites was compared by measuring the species overlap of both sites for the two stand ages: 5 and 16 years (Jaccard 1912). In addition, species composition was processed by NMDS using the data obtained on each date: (1) to explore the species composition patterns related to site and Pine and Understory cover; (2) the effect of the silvicultural treatments by post-fire year 16. The plant nomenclature was based on Flora Ibérica (Castroviejo et al. 1986) and Flora Europaea (Tutin et al ) for the families not included in the former. A list of all the species is shown with the authors in Appendix 1. Statistical analysis Generalized Linear Models (GLM) analyses were run to evaluate the effects of factors Age, Site, Treatment and the interactions, Site Age and Site Treatment on variables S, H, Total plant cover (as the sum of the Pine and Understory cover), Understory cover and for the life form s plant cover. The Species richness variable was fitted with a Poisson family GLM. The remaining variables were fitted with Gaussian family GLMs. However for those variables which did not meet the assumptions of normality and homogeneity of variance, logtransformed or rank-transformed data were used (Conover & Iman 1981; Hibsher et al. 2013). For the NMDS analysis, we considered the cover values from all the sampled species, including the main tree species P. halepensis. The values of Site, Pine cover, Understory cover and Treatment (for the 16-year-old data) were fitted onto the first two axes of the NMDS. Squared correlation coefficients (R 2 ) and empirical p-values (P) were calculated for these linear fittings. The significance of the predictor was assessed using the p-value resulting from a chi-square approach (P <0.01). The Tukey-Kramer HSD test was used for the post hoc comparisons. The S, H and the NMDS ordination were performed 55

64 with the vegan package (Oksanen et al. 2011) in the R environment (R Development Core Team 2013). Results Species richness During the diachronic study, 126 plant species were recognised in the two study areas. The pre-thinning general floristic composition revealed a larger number of species at the Semiarid site than at the Dry site (67 and 52 species, respectively). Furthermore, the number of species at the Dry site increased during the study period (53 to 70), while the opposite pattern was noted for the Semiarid site (67 to 63) (Appendix 1). The GLM analysis corroborated these results, given the significance of the Site factor and the interactions Site Age on the S variable (Table 1). When considering the effect of the silvicultural treatments, the Treatment factor and the interaction Site Treatment were found significant (Table 1). Particularly at the Dry site, Species richness showed no significant differences between treatments, and varied between 15 and 19 species (Fig. 2a). At the Semiarid site, the average S values were significantly higher in the treatments carried out later (at 10 years); i.e., T and T (24 species) and the lowest S values were obtained in C plots (10 species) (Fig. 2a). Table 1. Goodness of fit (D 2 : explained deviance (%) and P, significance level) obtained in the GLM analyses for the Species richness and Shannon indices on the effects of Site, Age and Treatment (T) (n=72). Richness Shannon Effects D 2 P D 2 P Site < Age <0.01 T < <0.01 Site Age < Site T 7.43 < <0.01 Total D

65 When analysing Species richness for each life form group (Appendix 1), we found that perennial herbs were the group with the largest number of species on both dates and at both sites, but showed a different behaviour over time; i.e., at the Dry site, an increase was observed, while the opposite was true at the Semiarid site. The second group in the number of species was dwarf shrubs, which showed a similar behaviour over time to perennial herbs, followed by annual herbs, which showed an increase over time at both sites. The shrubs and trees groups were the least represented in terms of S, and they exhibited no major change over time. However in some plots, the disappearance of C. albidus at the Semiarid site and the recruitment of R. sphaerocarpa at the Dry site and Genista scorpius at the Semiarid site (Appendix 1) were recorded. In addition, differences in the S increments of the life forms for each treatment and site were found (Table 2). Therefore in the C plots at the Semiarid site, a decrease in Species richness was noted for all the life form groups, except for the trees group, where only one species co-existed, P. halepensis. In addition at the Semiarid site, the treatments carried out early, i.e., T and T , revealed a general decrease in the shrubs, dwarf shrubs, perennial and annual herbs Species richness, but the opposite trend was noted (except for the dwarf shrubs group) with the treatments carried out later, i.e., T , T and T At the Dry site, changes in the S increments of life forms for each treatment revealed that the resprouter J. oxycedrus disappeared from the trees group for those treatments carried out earlier; i.e., T 5. A general decrease for all the life form groups, except for the annual herbs group, with the T treatment was also noteworthy. Shannon diversity The Shannon diversity index differed significantly for the Age and Treatment factors and the interaction Site Treatment (Table 1). On average, the H values were similar between sites at the beginning of the study (Fig. 2b). Conversely, at the end of the study, a contrasting effect was found for the silvicultural treatments. At the Dry site, no relevant effect of the treatments was detected, but the opposite occurred at the Semiarid site. Thus, the highest H values were attained in T (2.6 ± 0.01) at the Semiarid site, whereas the lowest 57

66 significant values were found in the C plots (1.02 ± 0.12) also at the Semiarid site (Fig. 2b). Table 2. Absolute increments of Species richness for the life forms during the study period. Gaps in grey show a negative increment and empty gaps represent no change in Species richness over time. Site Treatment Δ Total ΔT ΔS ΔDS ΔPH ΔAH Dry site C T T T T T Semiarid site C T T T T T SS: Semiarid site, DS: Dry site, C: Non-thinned plots (Control), T : Thinning at 5 years to a final density of 9,500 trees ha -1, T : Thinning at 5 years to a final density of 1,600 trees ha -1, T 5-800: Thinning at 5 years to a final density of 800 trees ha -1, T : Thinning at 10 years to a final density of 1,600 trees ha -1, T :Thinning at 10 years to a final density of 800 trees ha -1, T 5+10: Thinning at 5 years to a final density of 1,600 trees ha -1 and thinning at 10 years to a final density of 800 trees ha -1, T: trees, S: Shrubs, DS: Dwarf shrubs, PH: Perennial herbs, AH: Annual herbs, Total: Total of life forms. Plant cover The GLM analysis revealed the significance of the Age factor and its interaction on the Total plant cover variable, with the Age factor being the main explanatory effect (44% of explained deviance) (Table 3). For the Understory cover variable, the Age factor was significant and explained 62% of variability (Table 3). An overall increased understory cover was observed in the C plots at both sites as well as in all the treated plots despite scrubbing, with the highest understory cover attained in T at both sites (115±11% at the Dry site and 118±23% at the Semiarid site). Nevertheless at the Semiarid site, this was due mainly to the high cover of perennial grass M. tenacissima. In contrast, the lowest understory cover was found in T (81±11%) at the Dry site and in C plots (51±15%) at the Semiarid site (Fig. 3). 58

67 a) Dry Site Semiarid Site 40 Species Richness (S) b) yr ab ab ab ab ab C T T5-800 T T T5+10 ab 5 yr a ab ab C T T T T T5+10 b b ab 3.5 Diversity (H') yr b bc bc C T T5-800 T T T5+10 b bc bc 5 yr a b bd cd C T T T T T5+10 d bd Figure 2. Species richness index (S) and Shannon diversity index (H ) at post-fire year 5 and 16 per site and treatment. Lower case letters show the significant differences obtained from the GLM analysis (P < 0.01) for the Site Treatment interaction at 16 years. The pre-thinning species composition of the understory layer was dominated by obligate seeder R. officinalis and perennial grass resprouter B. retusum at the Dry site, with no significant differences in cover observed between them (11% and 10%, respectively). By the end of the diachronic study, these two species were also the most abundant ones at the Dry site, but showed a positive increment in cover by attaining more than half the total understory coverage (i.e., R. officinalis 31.9% and B. retusum 26.8%, data not shown). 59

68 At the Semiarid site, no clear dominance of species in the understory layer was observed, neither before nor after carrying out the treatments. Thus at post-fire year 5, the most abundant species were some resprouters, such as M. tenacissima (grass resprouter), Teucrium capitatum and Thymus zygis, as well as obligate seeder Ruta angustifolia, but without any significant differences in Total plant cover (ca. 5% for each one). By the end of the chronosequence, up to six species accounted for 50% of crown coverage: perennial grass resprouters M. tenacissima (20%), B. retusum (6.9%) and P. albicans (5.5%), and obligate seeders R. officinalis (7.7%); T. vulgaris (6.6%) and F. thymifolia (5.7%) (data not shown). The plant cover analysis done according to the different life form groups revealed the following results. The trees group showed significant differences for the Treatment factor and the interaction Site Age, but the former explained more variability (27%) (Table 3). From Table 4 we can see that the pre-thinning tree cover was greater at the Semiarid site than at the Dry site. In addition, the trees group at post-fire year 16 obtained the greatest cover in the C plots at both sites, followed by treatments that left higher tree densities, i.e., T , T and T , and vice versa for the treatments that left moderate tree densities, i.e., T , T and T Shrubs cover was significant in the GLM for factors Site, Age and their interaction, while for dwarf shrubs cover all the factors considered were significant. The most explanatory factor for both groups was factor Site (42% and 52% for the shrubs and dwarf shrubs groups, respectively) (Table 3). Therefore at both ages, shrubs gave higher cover values at the Dry site, while higher cover values were attained at the Semiarid site for the dwarf shrubs group (Table 4). Both the perennial and annual herbs groups were significant for factor Age (Table 3), and the annual herbs group was the least represented group in cover terms at both sites (Table 4). 60

69 Table 3. Goodness of fit (D 2 : explained deviance (%) and P, significance level) obtained in the GLM analyses for Total cover and life forms cover trees, shrubs, dwarf shrubs, perennial and annual herbs on the effects of Site, Age and Treatment (T) (n=72). Total Understory Trees Shrubs Dwarf shrubs Perennial herbs Annual herbs Effects D 2 P D 2 P D 2 P D 2 P D 2 P D 2 P D 2 P Site < < Age 43.7 < < < < < <0.001 T < < Site Age 9.8 < < < < Site T < Total D

70 Total cover (%) yr Dry Site C T T5-800 T T T5+10 Figure 3. Total cover rates divided into Pine and Understory cover at 5 and 16 years per site and treatment. 5 yr Semiarid Site Pine Understory C T T T T T5+10 Table 4. Life form covers at 5 and 16 years per site and treatment. Values are average cover (%) and SE. See the Treatment codes in Table 3. Site Age Treatment T S DS PH AH Dry 5 yr C 28 (8) 23 (4) 1.2 (0.8) 11 (4) 0.06 (0.03) 16 yr C 68 (14) 55 (5) 11.2 (2) 24 (12) 1.7 (1.2) T (11) 61 (3) 13 (8) 36 (13) 5 (3) T (4) 44 (5) 9 (4) 42 (9) 6 (3) T (2) 43.4 (2.3) 3.1 (1) 26 (4) 4.1 (1.8) T (9) 48 (8) 6.0 (1.5) 33 (9) 1.9 (1) T (8) 46 (9) 10.0 (1.7) 29 (6) 1.6 (1.6) Semiarid 5 yr C 60 (16) 6.5 (1.9) 19 (4) 16 (5) 0.17 (0.12) 16 yr C 79 (8) 5.5 (1.7) 6.5 (0.3) 38 (13) 0.6 (0.4) T (13) 25 (5) 17 (1.7) 20 (8) 11 (5) T (4) 29 (15) 39.2 (1.4) 43 (12) 9 (7) T (4) 13.6 (1.1) 19 (5) 60 (6) 2.1 (1.4) T (0.4) 10.3 (2) 39.4 (1.1) 55 (10) 2.4 (2.1) T (1) 15 (7) 20 (5) 44 (22) 5.8 (0.3) T: trees, S: Shrubs, DS: Dwarf shrubs, PH: Perennial herbs, AH: Annual herbs.

71 Beta diversity parameters The Jaccard index revealed a poor species overlap between sites on both dates. However, a large number of shared species was found by the end of the diachronic study (the Jaccard index was 0.24 and 0.34 at post-fire years 5 and 16, respectively). The NMDS results revealed two main groups of plots that were clearly separated on the first NMDS axis on each date, and which explained the distribution and abundance of the best represented species for each study site (Fig 4a, b). Thirty-six species showed a significant relationship with the NMDS ordination axes at post-fire year 5, but diminished to 15 species at post-fire year 16 (P<0.01) (Appendix 1). Factor Site showed the best correlation on both dates (Table 5). Thus the Dry site was found on the left-hand side of axis 1, whereas the Semiarid site was located on the right-hand side of axis 1 (Fig. 4). In addition, the Pine cover vector had a significant linear effect on the two axes of the species ordination at post-fire year 5 (Table 5). The Understory cover vector was not significant on both dates, nor was factor Treatment at post-fire year 16 (Table 5; Fig. 4). Furthermore at postfire year 5, we found that all the plots overlapped in the diagram and greater variability was found at the Semiarid site for H (Figure 4a). a) b) 63

72 c) Figure 4. Non-metric multidimensional scaling (NMDS) ordination of the plant cover in 36 plots at (a) post-fire year 5, (b) post-fire year 16, where circles size are proportional to the Shannon diversity index (H ), and (c) at post-fire year 16, including numbers that represent the different treatments applied for both study sites: 1: Control, 2: T , 3: T , 4: T 5-800, 5: T , 6: T , 7: T Vectors (Pine and Understory cover) were fitted in the ordination diagrams. Figure 4b; 4c depicts the differences observed per site on the effect of silvicultural treatments on plot ordination at post-fire year 16. At the Dry site, all the plots overlapped in the diagram. In addition, the distribution of the Semiarid site plots was more scattered than that of the Dry site plots, which reflects the effect that factor Treatment had on this site, although the statistical analysis showed that it had no significant effect (Table 5). Thus, a gradient following the NMDS axis 2 for the Semiarid site was found where the C plots were located in the upper part, followed by treatment T , with the remaining treatments in the lower part (Fig. 4c). A slight Diversity index tendency was also detected at post-fire year 16; i.e., H followed the second ordination axis with higher values in the lower part of the diagram, which corresponded to high thinning intensity (Fig. 4b). This is in agreement with our previous results; i.e., diminished pine cover enhances H diversity (Table 3, Fig.4). 64

73 Table 5. Values of the factors (Treatment and Site) and vectors (Pine cover and Understory cover) fitted on the first two NMDS ordination axes. The squared correlation coefficients (R 2 ) and empirical p-values (P) are shown for each variable at a) post-fire year 5 and b) post-fire year 16. See the Treatment codes in Table 3. a) Factors NMDS 1 NMDS 2 R 2 P Site 0.85 <0.001 Dry site Semiarid site Vectors Pine <0.01 Understory b) NMDS 1 NMDS 2 R 2 P Factors Site 0.65 <0.001 Dry site Semiarid site Treatment C T T T T T T Vectors PINE UNDERSTORY

74 Discussion Post-fire regeneration is highly dependent on the original burned forest (Keeley 1986; Ne eman 1997). Therefore given the differences in the pre-fire species composition, our study sites appeared to be clearly separated in the NMDS analysis along the axis 1 (Fig. 4) and a low degree of species overlap occurred between sites. Besides, the most abundant species found at the studied sites were obligate seeders or grass resprouters, which may be related to a typical response of Mediterranean ecosystems affected by the intermediate time intervals of fire recurrence (Arnan et al. 2007). This further evidenced that woody resprouters, which generally recover more quickly than obligate seeder species (Agee 1998), did not lead post-fire regeneration. Species richness and diversity In general, the early post-fire succession observations in Mediterranean ecosystems showed greater Species richness at early post-fire stages (the first 3 post-fire years), proceeded by a successive species elimination to attain pre-fire stages following Eagler s initial floristic composition model (1956), which involves an increasingly complex structure (Trabaud 1994; Bond & van Wilgen 1996; Verroios & Georgiadis 2002; Capitanio & Carcalliet 2008). However, post-fire successional patterns in the Mediterranean region are frequently influenced by recurrent disturbances. Here, our study period covered a pseudo-stabilisation stage, which started when the successive species elimination was already ongoing (at post-fire year 5) and which ended before reaching a mature stage (at post-fire year 16). In accordance with Pausas (1999), which showed that Species richness in semiarid Mediterranean areas was usually higher than in wetter areas (Fig. 3), we found significantly higher Species richness at the Semiarid site at post-fire year 5. Then at post-fire year 16 we noted a minor decrease and a slight increase in Species richness at the Dry site and the Semiarid site, respectively, but this was 66

75 due mainly to the establishment of generalist species and to lacking spontaneous vegetation recovery. Besides, the studied communities have been submitted to forest management trials (i.e., thinning and scrubbing), which interfered with natural postfire regeneration in an attempt to enhance diversity and to structure patterns. Accordingly, early treatments applied to post-fire regenerated stands influence midto long-term plant diversity patterns, such species abundance (plant cover), species richness and diversity, but the effects over these patterns differed with thinning intensity and climatic conditions (Pausas 1999; González-Alday et al. 2009). These results are in agreement with previous studies which have revealed how overstory alterations condition the early post-fire response of understory vegetation (González-Ochoa 2003; De las Heras et al. 2004; Moya et al. 2009). Yet unlike these short-term studies, thinning seems to have no significant effect on mid-term Species richness, at least at the Dry site, as reported by Baeza & Vallejo (2008) in managed Ulex parviflorus shrublands. Hence our results evidence that the effects of thinning on the Species richness indices at the Dry site dissipate with time (Fig. 2a). In contrast, we corroborate that thinning significantly raised the Species richness values at the Semiarid site in all the managed plots, and that the greatest increase was associated with later thinning (T 10 ). This outcome confirms that early management (T 5 ) at the Semiarid site does not affect Species richness in the short term (De las Heras et al. 2004; Moya et al. 2009) or in the mid term. When comparing the control plots between sites, the Shannon diversity index lowered at the Semiarid site throughout the diachronic study (Table 2, Fig. 2b), while the opposite was noted when comparing the treated plots between sites. This finding is in line with the work of Lloret et al. (2005), who found that the treatments had a stronger positive effect on the diversity parameters in drier localities than at wetter sites. Therefore, the decreasing tendency of the Shannon diversity index at the Semiarid site followed the thinning intensity gradient, Thus the higher Shannon diversity values were attained with final tree densities of 800 trees ha -1 and the low Shannon diversity values were obtained in the controls plots, which depicts the dominance of only a few species. This result highlights the 67

76 importance of applying adequate silvicultural treatments when very high P. halepensis recruitment takes place. Regarding the mid-term effects of sequential thinning, our results agree with the intermediate disturbance hypothesis put forward by Connell (1978) and corroborated by Torras & Saura (2008) in Mediterranean ecosystems. This hypothesis predicted that maximum richness and diversity occurred at intermediate disturbance levels, in the present study plots which were treated once (including thinning and scrubbing), as compared to sequential thinning where plots were thinned twice. Thus, the plots submitted to several treatments fade the positive effects noted on the diversity patterns when applying only one treatment (Fig. 2b). The pre-thinning composition (at post-fire year 5) was dominated by dwarf shrubs and perennial herbs, while perennial and annual herbs predominated the post-thinning composition (at post-fire year 16). Yet despite annual herbs showing greatest Species richness at the end of the study period, it was the least representative group in cover terms. Besides, most of the species incorporated into the annual herbs group were heliophylous or ruderal species (e.g., Erodium ciconium, Filago pyramidata, Leontodon saxatilis or Papaver rhoeas), all of which are habitat generalists that are highly dependent on yearly weather conditions. We also supported the M. tenacissima hypothesis as a key influential species at the Semiarid site, as it was positively and negatively related to the diversity of vascular plants reported by different authors (Maestre & Cortina 2004; Alados et al. 2006, respectively). Indeed given the capacity of M. tenacissima to concentrate resources and favouring the formation of resource islands (Cortina et al. 2009), the increase in annual herbs Species richness and cover at the Semiarid site may be triggered by the greater cover of M. tenacissima (Sánchez 1995). Notwithstanding, the abundant presence of this species, along with the high postfire recruitment of P. halepensis species, hampered the establishment of late successional species, such as key-stone resprouting shrubs (Maestre & Cortina 2004), as a result of direct competition for soil resources (Alados et al. 2006). 68

77 Plant cover At post-fire year 5, the trees group dominated the cover at both sites, but the average cover values obtained at the Semiarid site doubled those attained at the Dry site. Regarding the understory layer, the understory covers at both sites were similar at post-year 5, but in the mid to long-term (16 years after the fire), clear differences were found as a result of the effect of the silvicultural treatments. We verified that thinning and scrubbing reduced intra- and inter-pecific competition, which increased the potential growing space in the understory (Pausas 1999; González-Alday et al. 2009) and available resources, such as light (De las Heras et al. 2004) and water (Lloret et al. 2005). In Mediterranean ecosystems, a simultaneous increase in both Pine and Understory cover is not possible due to several limiting factors such as water supplies, nutrients or light disposal. This fact is represented by not only the opposite directions of the Pine and Understory cover vectors along NMDS axis 2 (Fig.4b), but also by the intensity of thinning at the Semiarid site (Fig. 4c) (González-Alday et al. 2009). Hence, greater pine cover negatively influences the Shannon diversity index, mainly at the Semiarid site, as intense seedling density occurred after the fire (Fig. 4, Table 5). We detected a general shift in the life forms composition between sites. Accordingly, we found higher shrubs cover values at the Dry site and higher dwarf shrubs cover values at the Semiarid site. These findings are in accordance with Osem et al. (2012), who reported that shrubs better contributed to the understory cover with increasing rainfall (the Dry site in this study), and stated the same of dwarf shrubs, but with decreasing rainfall (the Semiarid site in this study). In addition, the increase in the annual herbs cover reported in the treated plots at both sites evidenced the increased growing space potential of the treatments, as we previously mention, which particularly facilitates the colonisation of annual herb species. 69

78 Conclusions Our study demonstrates the importance of assessing fire-prone Mediterranean vegetation dynamics in a series that lasts longer than a few years (Kazanis & Arianoutsou 2004; Capitanio & Carcaillet 2008). Furthermore, this study helps to better understand the role of forest management in Mediterranean ecosystems undergoing naturally regenerated post-fire succession. We report inter-site differences in plant diversity patterns. Yet despite the perturbation caused by the silvicultural treatments on the pine and understory layers at the two study sites, only at the Semiarid site did the Species richness and Shannon diversity indices significantly vary in the thinned plots as compared to the control plots, and these indices peaked with the thinning that was later tested (at 10 years). At the Semiarid site, the control plots were completely influenced by the large pine tree density presented, and showed the lowest plant diversity and understory cover (Kutiel 2000), unlike the Dry site where plant diversity patterns were not affected by mid-term forest management. In the Mediterranean Basin, shrublands dominated by obligate seeders exhibit a high fire risk and low resilience to fire (Vallejo & Alloza 1998). By taking into account that scrubbing treatments are considered low-impact fuel control techniques if compared to, for instance, prescribed fires (Baeza & Vallejo 2008), selective clearings will cut the fire hazard by reducing high, continuous fuel accumulation. However if native woody resprouters are not introduced, ecosystem resilience diversity or structure will not significantly improve (Cortina et al. 2009; Valdecantos et al. 2009). This would be particularly the case at the Semiarid site, where the current understory vegetation resembles pre-fire vegetation, mainly degraded M. tenaccissima steppes with late successional species lacking. Therefore at both P. halepensis stands, the technique of thinning carried out once at early stages has been shown to be an adequate tool to prevent shortterm fire hazards and to enhance diversity patterns at the Semiarid site, but not to ameliorate resilience given the lack of recruiting late successional species, which are better adapted to fire-prone habitats as woody resprouters. 70

79 Acknowledgements We thank José María Herranz and Ángel Fernández-Cancio for their valuable help with the plant inventories, and Javier Hedo and María Cruz Cano for field assistance. We thank Helen Warburton for reviewing the English. The study has been co-funded by Projects CYCIT-AGL /FOR, AGL /FOR and CONSOLIDER-INGENIO 2010: MONTES (CSD ) of the Spanish Ministry of Science and Innovation and FEDER funds. R. Sánchez- Salguero has been supported by the ceia3-universidad de Córdoba, CEXTREME (FP7-ENV ), DIVERBOS (CGL C02-02) and INIA-RTA (RTA ). References Agee, J.K Fire and pine ecosystems, in: Richardson D.M. (ed.) Ecology and biogeography of Pinus, pp Cambridge University Press,Cambridge. Alados, C.L., Gotor, P., Ballester, P., Navas, D., Escos, J.M., Navarro, T. & Cabezudo, B Association between competition and facilitation processes and vegetation spatial patterns in alpha steppes. Biological Journal of the Linnean Society 87: Arnan, X., Rodrigo, A. & Retana, J Post-fire regeneration of Mediterranean plant communities at a regional scale is dependent on vegetation type and dryness. Journal of Vegetation Science 18: Baeza, M. J. & Vallejo, V. R Vegetation recovery after fuel management in Mediterranean shrublands. Applied Vegetation Science 11: Baeza, M.J., Valdecantos, A., Aloloza, J.A., & Vallejo, V.R., Human disturbance and environmental factors as drivers of long-term post-fire regeneration patterns in Mediterranean forests. Journal of Vegetation Science 18: Blanco, E., Casado, M.A., Costa, M., Escribano, R., García, M., Génova, M., Gómez, Á., Moreno, J.C., Morla, C., Regato, P. & Sanz, H Los bosques ibéricos. Editorial Planeta. Barcelona. Bond, W.J. & van Wilgen, B.W Plants and fire. Chapman and Hall, London. Canfield, R.H Application of the line interception method in sampling range vegetation. Journal of Forestry 39:

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81 Kazanis, D. & Arianoutsou, M Vegetation composition in a post-fire successional gradient of Pinus halepensis forests in Attica Greece. International Journal of Wildland Fire 6: Kazanis, D. & Arianoutsou, M Long-term post-fire vegetation dynamics in Pinus halepensis forests of Central Greece: a functional group approach. Plant Ecology 171: Keeley, J.E Resilience of Mediterranean shrub communities to fires. In: Dell, B., Hopkins, A.J.M. & Lamont, B.B. (eds.) Resilience in Mediterranean-type Ecosystems, pp Junk Publishers, Dordrecht, NL. Kutiel, P Plant composition and plant species diversity in east Mediterranean Pinus halepensis forests. In: Ne eman G & Trabaud L (eds.) Ecology, biogeography and management of Pinus halepensis and P. brutia forest ecosystems in the Mediterranean Basin, pp Backhuys, Leiden, NL. Lavorel, S., Canadell, J., Rambal, S., Terradas, J., Mediterranean terrestrial ecosystems: research priorities on global change effects. Global Ecology and Biogeography Letters 7: Leone, V. & Lovreglio, R Conservation of Mediterranean pine woodlands: scenarios and legislative tools. Plant Ecology 171(1 2): Lloret, F. & Vilá, M Diversity patterns of plant functional types in relation to fire regime and previous land use in Mediterranean woodlands. Journal of Vegetation Science 14: Lloret, F., Estevan, H., Vayreda, J. & Terradas, J Fire regenerative syndromes of forest woody species across fire and climatic gradients. Oecologia 146: Maestre, F.T. & Cortina, J Are Pinus halepensis plantations useful as a restoration tool in semiarid Mediterranean areas? Forest Ecology and Management 198: Malanson, G.P. & Trabaud, L Ordination analysis of components of resilience of Quercus coccifera garrigue. Ecology 68: Moya, D., De las Heras, J., López-Serrano, F.R. & Leone, V Optimal intensity and age of management in young Aleppo pine stands for post-fire resilience. Forest Ecology and Management 255: Moya, D., De las Heras, J., López-Serrano, F.R., Condes, S. & Alberdi, I Structural patterns and biodiversity in burned and managed Aleppo pine stands. Plant Ecology 200(2): Naveh, Z The evolutionary significance of fire in the Mediterranean region. Vegetatio 21:

82 Ne eman, G Regeneration of natural pine forest review of work done after the 1989 fire in Mount Carmel, Israel. International Journal of Wildland Fire 7: Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minghin, P.R., O Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H. & Wagner, H Vegan: Community Ecology Package. R Package Version /r2332. Osem, Y., Zangy, E., Bney-Moshe, E. & Moshe, Y Understory woody vegetation in manmade Mediterranean pine forests: variation in community structure along a rainfall gradient. European Journal of Forest Research 131: Palahi, M., Mavsar, R., Gracia, C. & Birot, Y Mediterranean forests under focus. International Forestry Review 10: Pausas, J.G Response of plant functional types to changes in the fire regime in Mediterranean ecosystems: a simulation approach. Journal of Vegetation Science 10: Pausas, J.G The effect of landscape pattern on Mediterranean vegetation dynamics: a modelling approach using functional type. Journal of Vegetation Science 14: Pausas, J.G., Bradstock, R.A., Keith, D.A., Keeley, J.E. & GCTE Fire Network Plant Functional traits in relation to fire in crown-fire ecosystems. Ecology 85: Pasho, E., Camarero, J.J., de Luis, M. & Vicente-Serrano, S.M Impacts of drought at different time scales on forest growth across a wide climatic gradient in northeastern Spain. Agricultural and Forest Meteorology 151: R Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.< Rivas-Martínez, S Memoria del Mapa de Series de Vegetación de España. ICONA, Madrid. Rivas-Martínez, S., Sánchez-Mata, D. & Costa, M North American boreal and western temperate forest vegetation. Itinera Geobotanica 12: Sánchez, G Arquitectura y dinámica de las matas de esparto (Stipa tenacissima L.), efectos en el medio e interacciones con la erosión. Ph.D. thesis, Universidad Autónoma de Madrid, Madrid. Sarris, D., Christodoulakis, D. & Körner, Ch Impact of recent climatic change on growth of low elevation eastern Mediterranean forest trees. Climatic Change 106 (2): Tárrega, R., Calvo, L., Marcos, E. & Taboada, A Forest structure and understory diversity in Quercus pyrenaica communities with different human uses and disturbances. Forest Ecology and Management 227:

83 Torras, O. & Saura, S Effects of silvicultural treatments on forest biodiversity indicators in the Mediterranean. Forest Ecology and Management 255: Thanos, C.A Bradychory-The coining of a new term. In: Arianoutsou, M., Papanastis (eds.) Proceedings 10th medecos conference, MillPress, Rotterdam. Trabaud, L Posfire plant community dynamics in the Mediterranean basin. In: Moreno, J.M. & Oechel, W.C. (eds.) The role of fire in Mediterranean-type ecosystems. Ecological Studies. Vol. 107, pp Springer Verlag, New York. Tutin, T.G., Heywood, V.H., Burges, N.A., Valentine, D.H., Walters, S.M. & Moore, D.M Flora Europaea, Vol. I-V. Cambridge University Press. Valdecantos, A., Baeza, M.J. & Vallejo, V.R Vegetation Management for Promoting Ecosystem Resilience in Fire-Prone Mediterranean Shrublands. Restoration Ecology 17: Vallejo, R. &. Alloza, J.A The restoration of burned lands: the case of Eastern Spain. In: Moreno, J. M. (ed.) Large forest fires, pp Backhuys Publishers, Leiden, The Netherlands. Verkaik, I. & Espelta, J.M Post-fire regeneration thinning, cone production, serotiny and regeneration age in Pinus halepensis. Forest Ecology and Management 231: Verroios, G. & Georgiadis, T Post-fire vegetation succession: the case of Aleppo Pine (Pinus halepensis Miller) forests of Northern Achaia (Greece). Fresenius Environmental Bulletin 11(4):

84 Appendix. List of the plant species found in the plant inventories of 1999 (5 yr) and 2010 (16 yr) at Yeste (upper Dry type, Dry site) and Calasparra (lower Semiarid type, Semiarid site). Each species has an assigned life form group, family and site of presence (0-ausence, 1-presence). Total Species richness per site, age and life form group are shown at the bottom of the table. We note the significant species (P<0.01) and their correlation (R 2 ) with the NMDS ordination axes. Species Family Life form 5 yr R 2 16 yr DS SS DS SS R 2 Achillea ageratum L. Compositae PH Allium paniculatum L. Liliaceae PH Anagallis arvensis L. Primulaceae AH Andryala integrifolia L. Compositae PH ** 0 0 Anthyllis cytisoides L. Leguminosae S * 0 1 Anthyllis lagascana Benedí Leguminosae DS Antirrhinum controversum Pau Scrophulariaceae PH Argyrolobium zanonii subsp. zanonii (Turra) P.W. Ball Leguminosae DS Artemisia herba-alba Asso Compositae DS * 0 1 Asparagus stipularis Forssk. Liliaceae DS ** 0 1 Asphodelus fistulosus L. Liliaceae PH Asterolinon linum-stellatum (L.) Duby in DC. Primulaceae AH Astragalus incanus L. Leguminosae PH Astragalus sesameus L. Leguminosae PH Atractylis cancellata L. Compositae AH ** 1 1 Atractylis humilis L. Compositae AH * 76

85 Avena barbata Pott ex Link Gramineae AH ** * Avenula bromoides (Gouan) H. Scholz Gramineae PH Ballota hirsuta Benth. Labiatae DS ** 0 0 Bituminaria bituminosa (L.) C.H. Stirt. Leguminosae PH * 1 1 Bombycilaena erecta (L.) Smoljan Compositae AH Brachypodium distachyon (L.) P. Beauv. Gramineae AH Brachypodium retusum (Pers.) P. Beauv. Gramineae PH ** ** Brassica fruticulosa Cirillo Cruciferae AH, PH * 0 0 Bromus hordeaceus L. Gramineae AH Bromus rubens L. Gramineae AH Carlina corymbosa L. Compositae PH Carrichtera annua (L.) DC. Cruciferae AH Centaurea antennata Dufour Compositae PH * Centaurea melitensis L. Compositae AH ** 1 1 Centranthus calcitrapae (L.) Dufr. Valerianaceae AH Cirsium sp. Compositae PH Cistus albidus L. Cistaceae S Cistus clusii Dunal in DC. Cistaceae S ** 1 1 Convolvulus althaeoides L. Compositae PH * Convolvulus lanuginosus Desr. in Lam. Convolvulaceae PH Convolvulus lineatus L. Compositae PH Coronilla juncea L. Leguminosae S Coronilla scorpioides (L.) W.D.J. Koch Leguminosae AH Crepis vesicaria L. Compositae PH ** 1 0 Dactylis glomerata L. Gramineae PH * 77

86 Daphne gnidium L. Timeleaceae S Delphinium gracile DC. Ranunculaceae AH * 1 1 Diplotaxis harra subsp. lagascana (DC.) O. Bolòs & Vigo Cruciferae PH Dorycnium pentaphyllum Scop. Leguminosae S Echium humile Desf. Boraginaceae AH Echium vulgare L. Boraginaceae AH Erodium ciconium (L.) L Her. Geraniaceae AH Eruca vesicaria (L.) Cav. Cruciferae AH Eryngium campestre L. Umbeliferae PH ** ** Euphorbia exigua L. Euphorbiaceae AH Euphorbia nicaeensis All Euphorbiaceae PH Euphorbia serrata L. Euphorbiaceae PH ** Filago pyramidata L. Compositae AH Fritillaria lusitanica Wikstr. Liliaceae PH ** 0 0 Fumana hispidula Loscos & J. Pardo in Willk. (ed.) Cistaceae DS Fumana laevipes (L.) Spach Cistaceae DS * 0 1 Fumana thymifolia (L.) Spach ex Webb Cistaceae DS * 1 1 Genista scorpius (L.) DC. in Lam. & DC. Leguminosae S * 1 1 Genista umbellata (L'Hér.) Dum. Cours. Leguminosae S Helianthemum hirtum (L.) Mill. Cistaceae DS Helichrysum italicum (Roth) G. Don in Loudon Compositae DS Helichrysum stoechas (L.) Moench Compositae DS Hippocrepis ciliata Willd. Leguminosae AH Hyparrhenia hirta (L.) Stapf Gramineae PH ** 0 1 Juniperus oxycedrus subsp. oxycedrus L. Cupresaceae T ** ** Jurinea humilis (Desf.) DC. Compositae PH

87 Koeleria vallesiana (Honck.) Gaudin Gramineae PH Launaea fragilis (Asso) Pau Compositae PH Lavandula latifolia Medik. Labiateae DS Leontodon saxatilis subsp. rothii Maire in Jahand. & Maire Compositae AH Linaria aeruginea (Gouan) Cav. Escrophulariaceae PH Linum strictum L. Linaceae AH ** 1 1 Linum suffruticosum L. Linaceae DS Lomelosia stellata (L.) Raf. Dipsacaceae AH Lotus corniculatus L. Leguminosae PH Macrochloa tenacissima (L.) Kunth Gramineae PH ** ** Marrubium vulgare L. Labiatae PH Medicago littoralis Rohde ex Loisel. Leguminosae AH Medicago minima (L.) L. Leguminosae AH Medicago turbinata (L.) All. Leguminosae AH Mercurialis tomentosa L. Euphorbiaceae DS Moricandia arvensis (L.) DC. Cruciferae AH, PH ** 0 0 Neatostema apulum (L.) I.M. Johnst. Boraginaceae AH ** 0 0 Ononis minutissima L. Leguminosae DS Ononis reclinata L. Leguminosae AH * 0 0 Ornithogalum narbonense L. Liliaceae PH Pallenis spinosa (L.) Cass. in F. Cuvier Compositae PH Papaver rhoeas L. Papaveraceae AH Paronychia capitata (L.) Lam. Cariophyllaceae DS Paronychia suffruticosa (L.) DC. in Lam. Cariophyllaceae DS Petrorhagia nanteuilii (Burnat) P.W. Ball & Heywood Compositae AH Phagnalon rupestre (L.) DC. Compositae DS *

88 Phagnalon saxatile (L.) Cass. Compositae DS Phlomis lychnitis L. Labiatae DS Picnomon acarna (L.) Cass. in F. Cuvier Compositae PH Pinus halepensis Mill. Pinaceae T * 1 1 Piptatherum miliaceum (L.) Coss. Gramineae PH ** ** Pistacia lentiscus L. Anacardiaceae T Plantago albicans L. Plantaginaceae PH * ** Polygala rupestris Pourr. Poligalaceae DS Quercus coccifera L. Fagaceae T Reichardia tingitana (L.) Roth Compositae AH Reseda luteola L. Resedaceae AH Reseda phyteuma L. Resedaceae PH Retama sphaerocarpa (L.) Boiss. Leguminosae S ** * Rhaponticum coniferum(l.) Greuter Compositae PH Rosmarinus officinalis L. Labiatae S ** ** Ruta angustifolia Pers. Rutaceae PH * 0 1 Salvia verbenaca L. Labiatae PH Sanguisorba minor Scop. rosaceae PH Satureja intricata Lange Labiatae DS Scorzonera angustifolia L. Compositae PH Sideritis leucantha Cav. Labiatae DS Sisymbrium orientale L. Cruciferae AH Staehelina dubia L. Compositae DS Stipa capensis Thunb. Gramineae AH Stipa offneri Breistr. Gramineae PH Stipa parviflora Desf. Gramineae PH

89 Teucrium capitatum L. Labiatae DS ** ** Teucrium pseudochamaepitys L. Labiatae DS Thesium humifusum DC. in Lam. & DC. Santalaceae PH Thymus vulgaris L. Labiatae DS * 1 1 Thymus zygis Loefl. ex L. Labiatae DS ** 1 0 Tripodion tetraphyllum (L.) Fourr. Leguminosae AH SpeciesRichness T S DS PH AH Total T: Trees, S: Shrubs, DS: Dwarf shrubs, PH: Perennial herbs, AH: Annual herbs *P <0.01; ** P <0.01 : Total Species richness is not the total sum of the different life form groups due to two species being categorised within two life form groups 81

90 82

91 4.2 CAPÍTULO 2: Growth and reproduction are positively coupled but this coupling depends on site conditions in young Aleppo pines Raquel Alfaro-Sánchez, J. Julio Camarero, Francisco R. López-Serrano, Raúl Sánchez-Salguero, Daniel Moya, Jorge De Las Heras Submitted to: Forest Ecology and Management Under review 83

92 Abstract In Mediterranean Aleppo pine forests frequent wildfires and recurrent summer droughts condition their reproduction and growth patterns. Nevertheless, post-fire juvenile Aleppo pines show very high growth rates and start reproducing very early (precocious behaviour) which opens the question if both processes show negative relationships or trade-offs. Here we aim to evaluate if radial growth and femalecone production are linked at the tree level in juvenile Aleppo pines recruited after fires in south-eastern Spain. We evaluate how growth and reproduction respond to climate and competition by comparing a dry vs. a semiarid sites and by considering stands with three different tree densities (very high density control, high density and moderate density thinned plots). We found no support for any trade-off between growth and female cone production in juvenile Aleppo pines. Growth and cone production were mainly enhanced by lagged wet winter-to-spring conditions up to two years before tree-ring formation or cone maturation. Trees with higher basal areas produced more female cones and such positive association intensified as water balance improved. Aleppo pines from the semiarid site were more precocious in reproductive terms than coetaneous pines from the dry site, i.e. trees produced cones earlier under harsh site conditions. However, the long-term cone production was higher, either on tree or on basal-area bases, at the dry than at the semiarid site. Dry climatic conditions induced by Sirocco-type winds triggered the loss of the canopy serotinous seed bank in the semiarid site. Reproductive precocity and thinning may enhance the resilience of xeric Aleppo pine forests facing recurrent fires and severe droughts. Keywords: female cone production; Mediterranean forests; Pinus halepensis; post-fire reproduction; serotiny. 84

93 Introduction Mediterranean pine forests are characterised by frequent wildfires and recurrent summer droughts which condition their reproduction and growth patterns (Naveh 1974, Trabaud 1987). It is generally assumed that warming-induced aridification could increase fire hazard (Piñol et al. 1998, Pausas 2004). Extreme climatic events such as severe droughts are also expected to increase their frequency in the Mediterranean Basin during this century (Meehl and Tebaldi 2004). But we still lack solid datasets to evaluate how negatively adverse climatic conditions affect growth and how this translates into the reproductive performance of juvenile pines in post-fire stands. For instance, if rising temperatures magnify drought severity and increase the frequency of fires, this would constrain tree growth and reduce the reproductive potential of those young stands cascading through several ecosystem services (Corona et al. 1998, Thanos and Daskalakou 2000). Are growth and reproduction coupled in drought-constrained post-fire juvenile pine stands? Do these juvenile pines reach an asymptotic reproductive capacity through time before reaching maturity? To answer these questions we selected Aleppo pine (Pinus halepensis Mill.), which is the most widely distributed pine throughout the Mediterranean region and one of the tree species best adapted to fire (Barbéro et al. 1998, Ne eman and Trabaud 2000). For instance, P. halepensis is the most widespread species undergoing active regeneration after the big wildfires which occurred in Spain during the past two decades (Pausas 2004). The increasing number and extent (burnt area) of fires across many Mediterranean areas requires knowledge on how to manage the early stages of Aleppo pine post-fire regeneration. Aleppo pine is considered a pioneer short-living conifer species, with almost no recruitment under forest canopy, and showing high post-fire seeding capacity and a precocious reproduction (Richardson 2000). Small trees tend to produce only female cones, that have a higher probability of being pollinated than the probability of male cones pollinating, and this female-first strategy enhances population spread (Ne eman et al. 2011). In P. halepensis, pollination occurs in spring, fertilization happens a year later, and seed dispersal takes place three years after pollination (Panetsos 1981). 85

94 Serotiny (the long-term retention of seeds in the canopy) is also frequent in Aleppo pine (Tapias et al. 2001), being evidence that high crown fire recurrence increases serotiny levels (Ne eman et al. 2004; Hernández-Serrano et al. 2013). However, despite serotiny is generally considered as a synonym for fire-induced seed release (pyriscense) dry and hot climatic conditions also induce seed release (xeriscence, see Nathan et al. 1999). The Aleppo pine tolerates pronounced water deficit during several seasons (Borghetti et al. 1998), maintaining radial growth at a reduced rate even during the dry summer (de Luis et al. 2007, Camarero et al. 2010). Furthermore, longitudinal and radial growth and cone production of this species are very sensitive to the negative effects of drought (Girard et al. 2012, Pasho et al. 2012). Nevertheless, to the best of our knowledge, there are no studies analysing jointly the effects of climate on radial growth and fruiting in Aleppo pine. Post-fire juvenile Aleppo pines show very high growth rates and start reproducing very early which opens the question if both processes show either positive or negative relationships and, if inverse associations imply trade-offs between growth and reproduction (Knops et al. 2007). Furthermore, the high density of post-fire Aleppo pine stands could intensify conspecific competition among neighbouring trees leading to negative growth-reproductive associations at the stand level. Both tree size and interactions with neighbours must be taken into account to understand how cone production varies among individuals within the same population (Haymes and Fox 2012). Aleppo pine is a suitable tree species to tackle these issues because of its ability to fruit at early ontogenetic stages and because the crop amount can be enhanced through thinning (González-Ochoa et al. 2004, De las Heras et al. 2004, 2007, Moya et al. 2008, Ruano et al. 2013). In addition, there is evidence about a wide geographical variability of Aleppo pine reproductive traits (Climent et al. 2008, Santos del Blanco et al. 2013) and growthrelated wood-anatomical features (Esteban et al. 2010). This variability suggest that this species presents multiple adaptive responses in reproductive and growth terms in response to stressful environments. Consequently, growth-reproduction associations must be assessed in stands with different densities and subjected to 86

95 contrasting climatic conditions (e.g., different water balances). Here we aim to evaluate if radial growth and female-cone production are linked at the tree level in juvenile Aleppo pine recruited after fires in south-eastern Spain. We evaluate how growth and reproduction respond to climate and to conspecific competition by comparing two climatically contrasting sites (dry vs. semiarid sites) and by considering stands with three different tree densities (very high density control, high density and moderate density thinned plots), respectively. Material and methods Study sites and experimental design Twenty-one rectangular plots (10 15 m) per site were set up in 1999 in two post-fire P. halepensis stands naturally regenerating in south-eastern Spain, close to Yeste and Calasparra villages. Prior to the wildfires that burned both locations in summer 1994, the study areas were covered by mature stands of P. halepensis mixed with Pinus pinaster Ait. and Quercus ilex L. subsp. ballota (Desf.) Samp. (the latter species was only found at Yeste). In Yeste, the average annual rainfall and temperature were 595 mm and 13.6ºC, respectively, whereas in Calasparra the average rainfall and temperature values were 340 mm and 16.5ºC, respectively (Supporting Information, Fig. S1). Therefore, Yeste and Calasparra are hereafter referred as the dry and semiarid sites, respectively, based on their contrasting climatological conditions. Study plots presented high initial tree densities and were subjected to different thinning intensities in 1999 and 2004 up to reaching two final densities: (i) 9 high-density plots per site which had densities ranging from 1,600 to 2,400 at the dry site and from 1,600 to 9,500 trees ha -1 at the semiarid site; (ii) 9 moderatedensity plots per site which had densities of 800 trees ha -1. At both sites three plots remained unthinned (termed control plots), presenting very high densities (~7,000 and ~ 130,000 trees ha -1 at the dry and semiarid sites, respectively) (see Alfaro- Sánchez et al for additional details). 87

96 Dendrochonological analysis Dendrochronology was used to quantify the temporal patterns of radial growth expressed as earlywood (hereafter abbreviated as EW) and latewood (hereafter abbreviated as LW) production. We randomly selected 90 trees per site (30 trees per each tree density group with at least 3 trees from different plots) and they were cored near the tree base (height ca. 0.3 m) using a Pressler increment borer. Two cores per tree were sampled with at least one of them reaching the pith. When trees were too small to be cored (diameter < 5 cm), a wood disk was obtained by sawing them. Cores and disks were air dried and sanded until EW and LW boundaries were clearly visible under a stereomicroscope. All samples were visually cross-dated. EW and LW widths series were then measured to the nearest mm with a LINTAB measuring device (Rinntech, Heidelberg, Germany). We distinguished visually EW and LW based on the cross-sectional area of tracheid lumens and the thickness of their cell walls (de Luis et al. 2007, Camarero et al. 2010). Despite it is assumed that short series reduce the likeliness of cross-dating errors in P. halepensis (Eugenio et al. 2006), we further checked cross-dating quality using the software Cofecha (Holmes 1983). The trend due to the geometrical constraint of adding a volume of wood to a stem of increasing size was corrected by converting EW and LW widths into area increments. Structure variables and cone production The plots were sampled in years 1999, 2000, 2001, 2005, 2007, 2009 and 2011, which correspond to pine ages ranging from 5 (1999) to 17 (2011) years. A representative number of trees per plot were tagged and monitored during the study period, accounting for a total number of ca. 1,000 trees monitored per year. The structural variables measured in those trees were: stem diameter at 0.3 m above-ground (d), total height (h), dominant height (H d, defined as the total height of the highest tree located in each plot) and the number of cones visible in the crown. We calculated a distance-independent competition index k for each plot and study year as follows: 88

97 k = (d/dg) -1 (1) where dg is the mean square diameter of the trees located within each plot. Trees were considered as reproductive when they bore at least one conelet. In the samplings performed in 2005, 2007, 2009 and 2011 we also classified the cones according to its age, differentiated them by size and colour in: red conelets or female strobili (cone age < 1 year), green or immature cones (cone age < 2 years), brown or mature (cone age = 2-3 years), grey or serotinous and open cones (cone age > 3 years). Furthermore, in 2013 we also counted and classified the cones presented in the crowns of the 90 trees selected for the dendrochronological analyses. Serotiny level for each tree was estimated from 2005 onwards as the number of serotinous cones with respect to the total (open and serotinous) number of cones observed in each tree (Tapias et al. 2001). To determine the relationships between the onset of reproduction and the tree height, we defined the following ratios obtained at the plot level: H R-NR = 100 (HR i HNR i ) / HR i (2) H SQ = 100 (HR i / Hd Max ) (3) where HR i and HNR i are the average tree heights measured in sampling year i (1999, 2000, 2001, 2005, 2009, 2011) for reproductive and non reproductive trees, respectively, whereas Hd Max is the maximum dominant height measured in each plot during all the age-sequence studied. Climatic effects on growth and reproduction We obtained monthly climatic variables (mean temperature, total precipitation) for the study period ( ) from nearby meteorological stations. We used water balance instead of precipitation because the former represents more accurately the available water to growth and reproduce as a function of rainfall and thermal conditions. The water balance (P-PET) was calculated as the difference between monthly precipitation (P) and potential evapotranspiration 89

98 (PET), where the last variable was estimated using the method proposed by Hargreaves and Samani (1985). To quantify how these climatic variables affect radial growth (EW and LW area increments) or cone production we calculated Pearson and Spearman correlation coefficients between climate and the corresponding variable, respectively. Since growth and reproduction show lagged responses to climate we calculated those correlations up to one (from previous September up to current October) or two years (from previous January to current August) prior to the formation of the tree-ring or the green cones, respectively. In the case of climatecone associations we excluded from the analyses non-reproductive trees considering 262 and 233 trees in the dry and semiarid sites, respectively. To obtain consecutive series of green cone production, we assumed that green cones of the year t corresponded to brown cones of year t+1 in those years when green cones were not directly counted. Significance levels for the Pearson and Spearman correlation coefficients were set at P<0.05 and corrected for the presence of temporal autocorrelation following Mudelsee (2003). Statistical analyses ANOVAs and post-hoc comparisons (Tukey-Kramer HSD test) were used to evaluate if there were differences between sites in several variables (H SQ, number of produced cones). Generalized linear mixed models (GLMMs) were calculated to model cone production in each site as a function of tree height, plot tree density and competition intensity. GLMMs were fitted to: (i) the presence or absence of cones or cone types (conelets, green, brown and serotinous cones) and (ii) the total production of cones or green cones. The probability of presence versus absence was modelled using a binomial error distribution (logit link). To model the production of all or green cones, a negative binomial error distribution (log link) was used to account for overdispersion in the count data. The size of a tree at which the probability to have reached sexual maturity was 50% (hereafter abbreviated as J 50 ) was also obtained from the GLMMs models. 90

99 We introduced facto plot and factor tree as random effects in the GLMMs, to account spatial and temporal autocorrelation in cone production, respectively. GLMMs were fitted by using maximum likelihood methods. The best fitted models were considered those showing the lowest Akaike Information Criterion (AIC) (Burnham and Anderson 2002). Since the difference between the best model and alternative ones were >2AIC units, we reported only the best-fitted models. GLMMs were fitted using lme4 package (Bates et al. 2013) in R package version 3.1 (R Core Development Team 2014). Results Radial growth, thinning and climate During the study period, EW and LW area increments were higher at the dry than at the semiarid site (Fig. 1). For instance, EW (LW) reached maximum median values up to 3.5 (0.3) and 1.2 (0.1) cm 2 yr -1 at the dry and semiarid sites, respectively. Those maximum growth values were observed in plots with moderate densities in both sites and after the thinning treatment. The lowest minimum growth rates were observed in the highest-density unthinned plots (control) at the semiarid site. The growth response to thinning as EW increment peaked in the moderatedensity plots but such response was modulated by climate. For instance, EW area increment was low in 2005 (particularly at the dry site) but high in , two periods characterized by dry and wet winter-spring conditions, respectively (Supporting Information, Fig. S1). However, at the semiarid site thinning enhanced EW growth and the drought-induced growth decline of 2005 was delayed and mainly observed in

100 EW area increment (cm 2 ) LW area increment (cm 2 ) EW area increment (cm 2 ) LW area increment (cm 2 ) Yeste (dry site) Control High tree density Moderate tree density Calasparra (semiarid site) Year Figure 1. Trends in area increment of earlywood (EW) and latewood (LW) in both study sites and considering the three density classes. Box plots show: the median (horizontal line within the box), the 25 th and 75 th percentiles (lower and upper box boundaries), the 10 th and 90 th percentiles (error bars), and the 5 th and 95 th percentiles (outliers). The vertical dashed line indicates the thinning treatment. Note the different scales in EW area increment of the two sites. 92

101 Climate-growth relationships Wet and cool conditions in the previous autumn (September, November) and the current spring (May, June) were associated to improved EW growth in trees located in the dry site, mainly in plots with high or very high (control) densities (Fig. 2). Enhanced EW formation in juvenile Aleppo pines from the semiarid site was related to a higher water balance and cool conditions in early-spring (March, April) and late summer to early autumn (August, September) of the year of treering formation. The former climatic effects were more important in the control plots while the latter effects prevailed in the high- and moderate-density plots, respectively. LW formation mainly responded positively to warm summer to autumn conditions in the moderate-density plots located at the dry site. 93

102 Yeste (dry site) Calasparra (semiarid site) Correlation Correlation 0.6 Control T 0.6 T P-PET EW LW High tree density P-PET Correlation Moderate tree density s o n d J F M A M J J A S O s o n d J F M A M J J A S O Month s o n d J F M A M J J A S O s o n d J F M A M J J A S O Month 94

103 Figure 2. Relationships (Pearson correlation coefficients) observed between earlywood EW, white bars or latewood LW, striped bars area increments and monthly climatic variables data (T, mean temperature; P-PET, water balance) in both study sites and considering the three density classes (control or very high tree density, high and moderate tree densities). Growth is related with climatic data from the previous (months abbreviated by lowercase letters) and current (months abbreviated by uppercase letters) years, where the current year is the year of tree-ring formation. The bars surpassing dashed lines indicate significance at P<0.05. Climate-cone production relationships Green cone production was mainly related to wet and cool conditions in spring and summer of the year of formation of the green cones, i.e. year t-2 (Fig. 3). Usually, more trees with such significant relationships were observed in the dry than in the semiarid site, excepting in the case of September precipitation of year t- 2. In particular, at the dry site correlations revealed positive responses of green cone production to summer temperatures of years t-1 and t, but negative responses to spring (May, June) and early autumn (September) of years t-2 and t- 1, respectively. In the case of precipitation, wet winters in years t-2 and t-1 were associated to higher cone production in both study sites. Finally, warm and dry conditions in spring and summer of year t-1 and cold March conditions of year t were related to higher cone production at the semiarid site. Reproductive patterns In general, contrasting frequencies of production of different types of cones were observed in the two study sites along time. For instance, the most elevated percentages of conelets production were observed in 2005 and 2009 at the dry and semiarid sites, respectively (Table 1). Furthermore, higher percentages of brown (mature) cones were attained in thinned plots, i.e, under moderate or high tree densities, than in control plots. These differences were highly significant (P<0.005) in 2005 at the dry site and in 2007 and 2011 at the semiarid site. We also detected significantly (P<0.05) higher productions of serotinous cones during years 2007, 2009 and 2011 in control plots than in the other thinned plots at the semiarid site. Serotiny was very high (70-100%) for every year and study site excepting during the dry year 2005 in the semiarid site. Excepting that year, serotiny remained very 95

104 high in the semiarid site (>96%) while it slightly decreased through time at the dry site. Yeste (dry site) % Trees showing significant correlations Inmature cones Temperature Water balance J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A year t-2 year t-1 year t Calasparra (semiarid site) %Trees showing significant correlations Inmature cones Temperature Water balance J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A year t-2 year t-1 year t Figure 3. Number of trees showing significant (P<0.05) Spearman correlation coefficients between the number of produced green cones and monthly climatic data (precipitation and water balance). Correlations were calculated for the years of cone formation (t), and also for one (t-1) and two years before (t-2). Black and grey bars indicate positive and negative correlations, respectively. 96

105 Table 1. Percentage of different cone types produced per tree and serotiny (standard errors are given between parentheses) for different tree densities in the study years 2005, 2007, 2009 and Lowercase letters show significant (P<0.05) differences for each site and date between density levels. Yeste (dry site) Year Density Conelets Green cones Brown cones Serotinous cones Open cones Serotiny Moderate 73.5 (2.5) a 5.0 (1.0) 7.5 (1.1) b 12.6 (1.9) ab 1.4 (0.4) 88 (3) High 80.8 (1.5) b 3.2 (0.6) 5.9 (0.8) b 8.8 (1.0) a 1.3 (0.5) 88 (3) Control 80.1 (5.0) ab 2.0 (1.0) 1.0 (0.8) a 17.0 (5.0) b 1.0 (0.2) 91 (7) Moderate 23.1 (2.5) a 4.3 (0.7) b 47.0 (3.0) 22.5 (2.3) b 2.8 (0.7) 90 (2) High 31.8 (2.1) b 2.8 (0.5) ab 47.5 (2.0) 15.7 (1.3) a 2.1 (0.5) 87 (2) Control 23.0 (6.0) ab 0.4 (0.3) a 57.0 (6.0) 18.0 (4.0) ab 1.9 (1.1) 93 (4) 11.0 Moderate 7.3 (1.3)ab 25.5 (1.9) b 9.7 (1.2) 46.5 (2.5) (1.9) b 82 (3) High 5.3 (0.7) a 29.5 (1.5) b 12.9 (1.2) 45.8 (1.8) 6.5 (0.9) a 88 (2) Control 12.0 (4.1) b 15.0 (3) a 10.0 (3.3) 54.0 (6.0) 9.0 (3.0) ab 81 (5) Moderate 25.0 (3.0) 12.5 (1.5)a 17.9 (1.5) 32.0 (2.4) (1.4) b 71 (3) a High 23.5 (1.6) 14.4 (1.0) ab 16.5 (1.0) 37.5 (1.7) 8.1 (0.9) a 83 (2) b Control 21.0 (4.9) 21.0 (4) b 13.0 (4.0) 38.0 (6.0) 7.0 (3.0) ab 84 (7) ab Calasparra (semiarid site) Year Density Conelets Green cones Brown cones Serotinous cones Open cones Serotiny Moderate 5.6 (2.0) 5.8 (1.6) 11 (3) ab 0.1 (0.1) 78.0 (3.1) b 0 (0) High 4.5 (1.0) 5.0 (1.0) 9.0 (1.4) a 0.3 (0.2) 81.3 (1.7) b 1 (1) Control 8.0 (4.0) 8.0 (3.2) 21.0 (5.0) b 0.0 (0.0) 63.0 (4.0) a 0 (0) Moderate 27.0 (3.0) ab 12.0 (5.1) 22.0 (3.0) b 39.0 (6.1) a 0.4 (0.4) 99 (1) High 27.8 (2.0) b 7.8 (2.2) 22.4 (2.2) b 42.0 (4.0) a 0.3 (0.3) 98 (2) Control 15.1 (5.0) a 1.7 (1.7) 3.3 (2.3) a 80.0 (7.0) b 0.1 (0.1) 100 (0) Moderate 60.0 (5.0) 2.5 (0.7) 9.0 (3.0) 28.0 (5.0) ab 0.1 (0.1) 99 (1) High 67.0 (3.0) 3.7 (0.9) 4.7 (1.1) 24.8 (2.4) a 0.3 (0.3) 99 (1) Control 49.1 (8.0) 1.9 (1.1) 2.5 (1.5) 47.0 (9.2) b 0.1 (0.1) 100 (0) Moderate 32.1 (5.0) 9.4 (1.9) a 33.2 (4.0) b 25.3 (4.1) a 0.1 (0.1) 97 (3) High 25.8 (2.3) 15.7 (1.5) b 35.3 (2.5) b 23.0 (2.3) a 0.2 (0.1) 99 (1) Control 25.0 (1.3) 0.1 (0.1) 7.1 (5.0) a 68.1 (4.1) b 0.0 (0.0) 100 (0) 97

106 Five years after the fires, the percentage of reproductive trees was greater at the semiarid site than at the dry site (15 vs. 7%, respectively) (Fig. 4a). However, such pattern reversed later, i.e. from 7 to 17 years after a fire, when the percentage of reproductive trees at the dry site overtook those of the semiarid site (Fig. 4a). For example, 17 years after the fires 83% and 60% of all trees were reproductive at the dry and semiarid sites, respectively. Such reversal was more evident when we calculated the height ratios considering separately reproductive and nonreproductive trees. The H R-NR ratio continuously increased through time more at the dry than at the semiarid site (Fig. 4b). For instance, at age 5 reproductive trees from the dry and semiarid sites had mean (± SE) heights of 147 ± 4 and 90 ± 2 cm, respectively. When considering the ratio between that height and the maximum dominant height in each plot (H SQ ) we found no significant differences between sites (P=0.50) (Fig. 4c). Therefore, the height of reproductive trees seems to be proportional to the maximum dominant height of the plot, suggesting that microsite effects mainly drive the height at which reproduction starts. Consequently, the tree height at which the probability to have reached sexual maturity was 50% differed between sites when pines were young but later converged at older ages (Fig 4d). Thus, from post-fire years 5 to 7, J 50 was higher at the dry than at the semiarid site, while this trend reversed later. Effects of tree height, plot density and competition intensity on cone production Tree height was the main factor positively driving cone production and the presence or absence of different cone types (Table 2), while tree density and competition played secondary roles despite showing significant effects. Usually, taller trees had higher probabilities to be reproductive trees than smaller trees. Conversely, cone abundance was negatively related to plot tree density (Table 2). The positive coefficients of years in the models of cone production or presence indicated an ontonegenetic effect in the reproductive trends of trees from the dry site. On the contrary, at the semiarid site, models revealed a deceleration on the number of new reproductive trees since Lastly, the best explanatory variables of green cone production were similar as in the case of all cones but also including the presence of other types of cones. 98

107 Figure 4. Percentage of reproductive trees as a function of tree age (a), differences in height between the reproductive trees and non-reproductive trees divided by the height of the reproductive trees (see eq. 2) (b), the ratio (mean ± SE) between the height of reproductive trees divided by the maximun dominant height of the plot (H SQ, see eq. 3) (c) tree height at which the probability to have reached sexual maturity was 50% (J 50) for each age (different lines) and site (d). (a) (b) % Reproductive trees Dry site Semiarid site H R-NR 50 Dry site Semiarid site Age Age (c) (d) Dry site Semiarid site J50 (cm) Dry site Semiarid site H SQ Age 99

108 Table 2. Coefficients obtained for the best fitted generalized linear mixed models for either the presence or production of cones (a) or the production of green cones (b) by juvenile Aleppo pines in the two study sites (Yeste, dry site; Calasparra, semiarid site) as a function of fixed (tree height, tree density, competition intensity) and random (σ 2 plot, σ 2 tree) effects. Selected models showed the lowest values of the Akaike Information Criterion (AIC). (a) Presence of cones Cone production Dry site (Yeste) Semiarid site (Calasparra) Dry site (Yeste) Semiarid site (Calasparra) Fixed effects Intercept -48 (5) -38(4) -2.3 (0.2) -0.8 (0.2) 2000* 9.5 (2.2) 6.5 (1.8) 1.5 (0.2) 0.2 (0.1) (2.3) 6.6 (1.8) 1.8 (0.2) 0.2 (0.1) (3.0) -3.1 (1.3) 2.7 (0.2) -0.8 (0.2) (2.9) 1.0 (0.8) 2.8 (0.2) 0.1 (0.2) (2.7) -1.2 (0.3) 2.9 (0.2) 0.9 (0.2) (2.8) 0.4 (2.4) 3.0 (0.2) 0.8 (0.2) Tree height 0.17 (0.02) 0.23 (0.03) (0.0006) (0.0011) Tree density not included not included ( ) ( ) Competition intensity not included not included 0.92 (0.10) 1.22(0.14) Random effects σ 2 plot σ 2 tree AIC *Year 1999 was considered the reference level. 100

109 (b) Dry site (Yeste) Green cone production Semiarid site (Calasparra) Fixed effects Intercept -3.7 (0.3) -4.0 (0.4) (0.1) -0.4 (0.2) (0.1) -0.1 (0.1) (0.1) 0.8 (0.3) Tree height (0.0011) (0.0024) Tree density ( ) ( ) Competition intensity 0.89 (0.19) 1.4 (0.3) Presence of brown, serotinous or open cones 1.19 (0.15) 1.48 (0.21) Random effects σ 2 plot σ 2 tree AIC Year 2005 was considered the reference level. 101

110 Associations between radial growth and green cone production We found positive relationships between green cone production and EW or LW area increments. Regarding lagged growth-cone production associations at the tree level, green cone production was significantly (P<0.05) and positively related to EW area increment of the year t-1 in 12% of trees from the dry site. However, cone production was significantly and positively correlated with EW formation growth of the year t-2 in 11% of the semiarid site trees Control Yeste (dry site) Calasparra (semiarid site) No. female cones High tree density 0 80 Moderate tree density Basal area (cm 2 ) 102

111 Figure 5. Number of female cones produced by each tree as a function of its basal area. Results are presented for the two study sites and the three density levels (unthinned control very high density, high and moderate tree densities). As Aleppo trees aged and their stems thickened they produced more cones, although there was a large variation in this basal area-cone numbers association between sites, density levels and even among trees growing in the same site and plot and showing similar basal areas (Fig. 5). First, the lowest cone production was observed at the semiarid site irrespective of tree basal area. Second, stand attributes modulated this association since the highest cone production was observed in thinned plots with moderate to high tree densities. Discussion We found no evidence of negative associations between reproduction and growth at the tree level in juvenile Aleppo pines as was also observed at the branch level by comparing reproductive (either male or female) vs. nonreproductive branches (Ne eman et al. 2011). Those years characterized by large area increments corresponded to years of abundant cone formation and wet conditions just before or during the early growing season, thus rejecting a trade-off between growth and reproduction. Consequently, the larger the basal area as a result of improved growth conditions (e.g., higher water availability as a result of a more positive water balance or because of thinning) the higher the cone production. The response of cone production to climatic variability was higher at the dry than at the semiarid site. The interannual variability of cone production in Aleppo pine has been related to climate and tree vigour or size (Girard et al. 2012). Cone development depends mainly of resource availability during the two-years after the pollination since the largest carbon investment during cone ripening occurs from the conelet inititation up to the complete formation of green cones (Ne eman et al. 2011). Accordingly, cone production responded to lagged climatic effects related to the phenological stages of the fruiting processes in P. halepensis, such as conelet induction (spring to early autumn in year t-2) and conelet formation 103

112 (winter in year t-1), confirming that enough water availability determines reproductive success at young stages (Girard et al. 2012). The climatic control on fruit production has mainly been identified in less productive areas (Calama et al. 2011), yet we found similar cone-productions responses to water availability at the dry and semiarid sites suggesting either that reproduction was similarly constrained by the water balance in both sites or that additional environmental factors not accounted for (light, air or soil temperature, etc.) also determined reproductive success (Ne eman et al. 2011). In Mediterranean Aleppo pine forests, site conditions, mostly related to winter-spring water availability, greatly drive growth and reproductive performance in agreement with previous findings (Ne eman et al. 2011). First, we observed that radial growth was lower at the semiarid (Calasparra) than at the dry site (Yeste) by comparing coetaneous trees and unthinned stands. Second, pines from the semiarid site showed a shorter non-reproductive juvenile period (they were more precocious) than pines from the dry site, i.e., trees produced cones earlier under worse site conditions as has been already reported by González-Ochoa et al. (2004). This means that trees experiencing more stressful conditions may accelerate their ontogenetic phases proportionally to its life expectations, which also implies becoming reproductive at smaller size and also at younger stages. Despite that precocity at the semiarid site, the long-term cone production was higher, either on tree or on basal-area bases, at the dry than at the semiarid site. Third, thinning of post-fire regenerated P. halepensis forests enhanced radial growth, and the amount of cones produced per tree (see also Verkaik and Espelta 2006, De Las Heras et al. 2007). This effect was noticeable at the dry site because semiarid sites cannot support stands of sufficiently high density which may obscure the advantages of thinning for growth and reproduction (Misson et al. 2003). Regarding radial growth, the formation of earlywood was mainly driven by winter-spring water availability, with slight differences between sites, as noted before in mature Aleppo pines Pasho et al. (2011). The negative impact of winterspring droughts on earlywood growth was also detected during the dry year 2005 in the unthinned plots. However, a lagged growth response to drought was also 104

113 observed in the thinned plots where warmer summer conditions enhanced latewood formation. This suggests that the improvement of radial growth due to thinning could increase the sensitivity of juvenile Aleppo pines to climate conditions in terms of stemwood formation (Olivar et al. 2012). Stressful environments elicit phenotypic reproductive responses in Aleppo pine (Santos del Blanco et al. 2013). This ideas agrees with our finding that the onset of cone production was related to site differences, i.e. more xeric sites could be reproductively more precocious because slow growth induces an earlier reproduction or due to the high recurrence of fires, being both ideas consistent with geographical gradients in Aleppo pine showing enhanced reproduction in stressful environments (Climent et al. 2008). Accordingly, higher threshold height for reproduction (J 50 ) were also observed at the dry than at the semiarid. At site level, stressful water-deficit condition induced an earlier reproduction, which does not agree with observation at the tree level showing that most vigorous trees begin their reproduction earlier in even-aged populations (Thanos and Daskalakou 2000, Ne eman et al. 2004). Our results revealed that height at the onset of reproduction was largely modulated by climate even for even-aged stands. In this study, proportional size of reproductive trees to their life expectations have been reported for two different locations with notable differences in terms of water availability. Similarly, Tapias et al. (2001) reported lower height at first reproduction in stands with the lowest growth rates of the non-burned trees. Therefore, reproductive precocity and investment, sometimes tightly linked (Santos del Blanco 2013), are highly dependent on site conditions others than the fire recurrence (Keely and Zedler 1998). In other words, stressful conditions could induce an earlier onset of reproduction by constraining growth. The presence and abundance of cones were mainly dependent on tree height as has been reported in other long-lived tree species, particularly in stands younger than 20 years (Thornley and Johnson, 1990). Indeed, the juvenile phase of the study stands promoted a progressive income of new reproductive trees or an improvement of cone production in already reproductive trees. Thus, trees bearing 105

114 mature, serotinous or open cones in the crown showed a higher probability of bearing new cones and consequently once the tree has left the juvenile phase, there are higher probabilities of having abundant annual crops as trees enlarged. However, the abundance of cones was also largely influenced by conspecific competition (Calama et al. 2011), which confirms that thinning does not only improves growth but also cone production through an alleviation of water deficit at the tree basis. Contrastingly, we detected a significant effect of tree density on the serotiny levels at the dry site, showing the highest levels in the control plots with very high tree densities which indicated that other factors than climate determine serotiny. In Aleppo pine high population levels of serotiny are associated to a high recurrence of fires, favouring the tree establishment in areas with long and intense historical fire regimes (Keeley and Zedler 1998, Ne eman et al. 2004, Hernández- Serrano et al. 2013). The negative correlation of serotiny with tree height observed at the semiarid site agrees with the tendency of smaller pines to produce more serotinous cones in general, and especially in post-fire stands (Goubitz et al. 2004). This provides further evidence for a specific selection of serotiny by fire rather than by other disturbances (Ne eman et al. 2004). Climatic harshness in terms of water availably played a minor role as driver of serotiny since we found very high levels of serotiny at both the dry and the semiarid sites. Lastly, extreme climate events as severe droughts alter the reproductive patterns of juveniles Aleppo pone stands. For example, the xeriscense phenomena (sensu Nathan et al. 1999, Espelta et al. 2011) detected in 2005 at the semiarid site, when hot and dry conditions generated by Sirocco-type winds (called Leveche winds and blowing across south-eastern Spain) promoted the seed release of all the serotinous cones born in the crowns. Such xeriscence episode represented a negative contribution to post-fire regeneration in that site (see also Ne eman et al. 2004). In particular, this massive seed release did not result in new cohort recruitment due to unfavourable dry conditions for tree recruitment (R. Alfaro- 106

115 Sánchez, pers. observ.), which also constrained radial growth at both sites. Successful establishment in similar xeric areas does not only depend climatic (water availability) and post-fire soil conditions (ash, nitrogen supply, litter cover, etc; Naveh 1974), but it is also conditioned by seed viability, which was moderate (64%, see Moya et al. 2008), and by seed dispersal from mother trees (Nathan et al. 2000). For instance, Espelta et al. (2011) found in a 19 years-old P. halepensis forest of NE Spain the first seedlings established after the drought of 2005, which opened a large amount of serotinous cones. Conclusions Our results rejected the existence of any trade-off between growth (stemwood formation) and reproduction (cone production) in juvenile Aleppo pines regenerated after fires. Growth and cone production were affected by climatic conditions, mainly enhanced by wet winter-to-spring conditions, up to two years before tree-ring formation or cone maturation. Trees with higher basal areas produced more female cones and such positive association intensified as water balance improved either under less stressful site conditions (dry vs. semiarid site) or through thinning. Thinning also enhances more cone production in the semiarid than in the dry site at early ontogenetic stages. Aleppo pines from the semiarid site were more precocious in reproductive terms than coetaneous pines from the dry site, i.e., trees produced cones earlier under worse site conditions which, in combination with thinning, may enhance the resilience of these xeric forests facing recurrent fires and droughts. Dry climatic conditions induced by Sirocco-type winds also trigger the loss of the serotinous seed banks stored in the canopy. Despite trees were more precocious in the semiarid site, the long-term cone production was higher at the dry than at the semiarid site. 107

116 Acknowledgments We thank the Spanish Ministry of Science and Innovation for its funding and support to the Forest Ecology Research Group (UCLM, Albacete) in Projects CYCIT-AGL /FOR; AGL /FOR and CONSOLIDER- INGENIO 2010: MONTES (CSD ) and FEDER funds. R. Sánchez- Salguero thanks the financial support from University of Córdoba-Campus de Excelencia ceia3. We also thank Javier Hedo and Enrique Hernández for their field assistance and Helen Warburton for the language review. J.J. Camarero thanks the support of ARAID. References Bates, D.M., Maechler, M. & Bolker, B Package lme4: linear mixed-effects models using S4 classes. R package version Borghetti M, Cinnirella S, Magnani F, Saracino A Impact of long-term drought on xylem embolism and growth in Pinus halepensis Mill. Trees 12: Burnham, K. P., and D. R. Anderson Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York. Calama, R., Mutke, S., Tomé, J., Gordo, J., Montero, G., Tomé, M., Modelling spatial and temporal variability in a zero-inflated variable: The case of Stone pine (Pinus pinea L.) cone production. Ecological Modeling 222, Camarero JJ, Olano JM, Parras A Plastic bimodal xylogenesis in conifers from continental Mediterranean climates. New Phytologist 185: Climent, J., Prada, M.A., Calama, R., Chambel, M.R., De Ron, D.S. and Alía, R To grow or to seed: ecotypic variation in reproductive allocation and cone production by young female Aleppo pine (Pinus halepensis, Pinaceae). American Journal of Botany 95: Corona,P., Leone,V., and Saracino, A Plot size and shape for the early assessment of post-fire regeneration in Aleppo pine stands. New Forest 16: De las Heras, J., Moya, D., Lopez-Serrano, FR, Condes, S Reproduction of postfire Pinus halepensis Mill. stands six years after silvicultural treatments. Annals of Forest Science 64:

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118 Meehl G.A., Tebaldi, C More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305: Misson, L., Vincke, C. and Devillez, F Frequency responses of radial growth series after different thinning intensities in Norway spruce (Picea abies (L.) Karst.) stands. Forest Ecology and Management 177: Moya, D., De las Heras, J., López-Serrano, F.R. and Leone, V Optimal intensity and age of management in young Aleppo pine stands for post-fire resilience. Forest Ecology and Management 255: Mudelsee M Estimating Pearson s correlation coefficient with bootstrap confidence interval from serial dependent time series. Mathematical Geology 35: Nathan, R., Safriel, U.N., Noy-Meir, I., Schiller, G Seed release without fire in Pinus halepensis, a Mediterranean serotinuous wind-dispersed tree. Journal of Ecology 87: Nathan R., Safriel U. N., Noy-Meir I. and Schiller G Spatio temporal variation in seed dispersal and recruitment near and far from Pinus halepensis trees. Ecology 81: Naveh Z Effects of fire in the Mediterranean region. In: Kozlowski T.T. and Ahlgren C.E. (eds), Fire and Ecosystems. Academic Press, New York, pp Ne eman G, Goubitz S, Nathan R Reproductive traits of Pinus halepensis in the light of fire a critical review. Plant Ecology 171: Ne eman G, Goubitz S, Werger MJA, Shmida A Relationships between tree size, crown shape, gender segregation and sex allocation in Pinus halepensis, a Mediterranean pine tree. Annals of Botany 108: Olivar J, Bogino S, Spiecker H, Bravo F Climate impact on growth dynamic and intraannual density fluctuations in Aleppo pine (Pinus halepensis) trees of different crown classes. Dendrochronologia 30: Panetsos KP Monograph of Pinus halepensis Mill. and P. brutia Ten. Ann For (Zagreb) 9: Pasho E, Camarero JJ, Vicente-Serrano SM Climatic impacts and drought control of radial growth and seasonal wood formation in Pinus halepensis. Trees 26: Pausas J.G Changes in fire and climate in the eastern Iberian peninsula (Mediterranean Basin). Climatic Change 63: Piñol J, Terradas J, Lloret F Climate warming, wildfire hazard, and wildfire occurrence in coastal eastern Spain. Climatic Change 38:

119 R Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN , URL Richardson D.M Mediterranean pines as invaders in the Southern Hemisphere. In: Ne'eman G. and Trabaud L. (eds), Ecology, Biogeography, and Management of Pinus halepensis and P. brutia Forest Ecosystems in the Mediterranean basin. Backhuys Publishers, Leiden, pp Ruano, I., Rodríguez-García, E., Bravo, F Effects of pre-commercial thinning on growth and reproduction in post-fire regeneration of Pinus halepensis Mill. Annals of Forest Science 70: Santos del Blanco L, Bonser SP, Valladares F, Chambel MR, Climent J Plasticity in reproduction and growth among 52 range-wide populations of a Mediterranean conifer: Adaptive responses to environmental stress. Journal of Evolutionary Biology 26: Tapias R., Gil L., Fuentes-Utrilla, Pardos J.A Canopy seed banks in Mediterranean pines of south-eastern Spain: a comparison between Pinus halepensis Mill., P. pinaster Ait., P. nigra Arn. and P. pinea L. Journal of Ecology 89: Thanos, A.T. and Daskalakou, E.N Reproduction in Pinus halepensis and Pinus brutia. In: Ecology, Biogeography and Management of Pinus halepensis and Pinus brutia Forest Ecosystems in the Mediterranean Basin, Eds. Ne eman G., Trabaud L., pp Backhuys Publishers, Leiden. Thornley, J.H.M., Johnson, I.R., Plant and Crop Modelling. Blackburn, Caldwell. Trabaud L Fire and survival traits in plants. In: Trabaud L. (ed.), The Role of Fire in Ecological Systems. SPB Academic Publishers The Hague, pp Verkaik, I. and Espelta, J.M Post-fire regeneration thinning, cone production, serotiny and regeneration age in Pinus halepensis. Forest Ecology and Management 231:

120 Supporting Information Figure S1. (a) Annual and seasonal precipitacion and temperatures measured during the study period at Yeste (dry site) and Calasparra (semiarid site) locations. (b) Climatic diagrams of both study sites showing the pronounced summer drought. Yeste (Dry site) Calasparra (Semiarid site) 800 Annual Annual Spring Spring Precipitation (mm) Summer Autumn Temperature (ºC) Precipitation (mm) Summer Autumn Temperature(ºC) Winter Winter Years Years Temperature (ºC) J F M A M J J A S O N D Precipitation (mm) Temperature (ºC) J F M A M J J A S O N D Precipitation (mm) 112

121 4.3 CAPÍTULO 3: Competence and site quality- What determines biomass allocation in young Pinus halepensis? Raquel Alfaro-Sánchez, Francisco R. López-Serrano, Eva Rubio, Raúl Sánchez-Salguero, Daniel Moya, Enrique Hernández-Técles, Jorge De Las Heras 113

122 Abstract The increase of burned surface by wildfires in the Mediterranean Basin, particularly in the last decades, triggered the proliferation of large forested areas of Aleppo pine trees undergoing regeneration. These young naturally regenerated stands required specific biomass models which allow accurate stocks quantification to a better implementation of adaptive forest management facing global change effects in fire regimes. We aimed to develope biomass equations for young Aleppo pine trees considering the effects of thinning carried out at younger stages and site quality. To accomplish it we destructively sampled 251 individual Aleppo pine trees across an age sequence from 5- to 16-years-old, considering three different tree densities (Very High control plots, High and Moderate final tree densities thinned plots) and two contrasting climates (dry and semiarid). The biomass equations obtained allowed the estimation of stem, crown branches and twigs wood and needles-, cones and roots biomass components. We applied our equations to two plot inventories (at 5 and 16-post fire years) and found, first that independently of the site, the crown was the largest biomass component, followed by the root component and the stem component. Second, site quality was the first limiting factor constraining biomass accumulation and biomass allocation, thus higher biomass accumulation per tree was found at the dry site, due to favourable ecological conditions. Finally, the allocation of biomass to the stem and needles increased with tree age while the allocation of biomass to branches decreased. On the whole, our results revealed the high intraspecific variability of this species when different ecological factors are considered in terms of biomass accumulation and providing new information about the biomass allocation to the tree partitions that will facilitate forest management in order to ensure the permanence of these fireprone stands. Keyworks: Pinus halepensis, thinning, allometric relationships, biomass allocation, reproductive effort, adaptative forest management 114

123 Introduction Over the last decades, recurrence and severity of forest fires have experienced a significant increase at the Mediterranean Region (Pausas 2004; Pausas and Fernandez-Muñoz 2012), and it is expected to raise more in the near future (Mouillot et al. 2002). Changes in fire regime are due to the impact of climate and land use change which trigger hazardous fuel accumulations (Lloret 2004). A clear consequence of more recurrent forest fires is the enlargement of areas that are under post-fire regeneration. According to Spanish Ministry of Agriculture, Food and Environment (MAGRAMA 2012), in Spain over 280,000 ha of Aleppo pine (Pinus halepensis Mill) forest have been burned since 1993, which are currently undergoing natural regeneration. Generally, young post-fire pine stands require silvicultural management and assistance to reduce inter-tree competition for growth-limiting soil resources, i.e., water and nutrients (De las Heras et al. 2012) and to shorten the immaturity risk (Zedler 1995). Thus, the main objective of forest management in low-productivity Mediterranean terrestrial ecosystems should be conservation by promoting resilience and resistance to periods of drought or new fires, but also climate change mitigation by enhancing the carbon storage capacity. Nevertheless, predicting the effect of post-fire treatments on the resilience of forest stands in the face of climate change becomes a challenge. In addition, there is a lack of longterm researches related to post-fire rehabilitation (Lavorel et al 1998) focused into implement management in growth models (Vicente-Serrano et al 2010; Ruiz-Benito et al. 2013). Foliage and stem wood production are major components of tree growth, yet the effects of tree age, size, competitive status and site quality on growth allocation between these components remains the subject of both empirical and theoretical controversy (Vanninen and Mäkelä 2000). Previous studies carried out on young regenerated Aleppo pines stands reported between 1 to 8.5 Mg C ha -1 stored in living pine biomass, being dependent on site quality and silvicultural treatments (Heras et al. 2013; Alfaro-Sánchez et al. 2014). Accordingly, this proved intraspecific variability of Aleppo pine urges to consider interactions among site quality and silvicultural treatments in the specific young P.halepensis biomass 115

124 models to improve carbon sequestration estimations (López-Serrano et al. 2005; Heras et al. 2013). Regarding the allocation patterns, concepts and mechanisms involved in the ecophysiological processes underlying allocation to the different biomass components are not well understood and therefore not included in practical growth forest models (Landsberg and Sands 2011). However, a better understanding of biomass accumulation and allocation patterns will foster appropriate silvicultural decisions for forest managers (Rubilar et al. 2013). Changes in the allocation of biomass components have been previously related to biotic factors as tree age, size and competitive status supported by the pipe model theory and the functional carbon balance (Vanninen et al 2004). Following the functional carbon balance (Brouwer 1983), we expected an increase to root partitions under limited nutrient or water availability, i.e, more fine roots and less foliage (Guo and Gifford 2002). Nevertheless, the influence of site conditions together with the interaction with silvicultural treatments on allocation to the different biomass partitions is poorly understood, particularly allocation of biomass to cones also called reproductive effort (Prairie and Bird 1989; Ne eman et al. 2011). P.halepensis is a post-fire obligate seeder with an early production of cones as one of the main adaptations to fire-prone areas (Thanos and Daskalakou 2000). Therefore, this species allocates many resources to produce cones with viable seeds at very early ages to reduce the immaturity risk of facing a recurrent fire (Ne eman et al. 2004). It is plausible that Aleppo pine has inherited advantageous traits for post-fire regeneration (Keeley et al. 2012), but it is difficult to distinguish between adaptations to fire from adaptations to other disturbances or dual life history strategy (Ne eman et al. 2004). Considering that allocation patterns differ within tree species (Vanninen et al 2004), we hypothesized that inherited adaptive traits to fire regimes in P. halepensis, as precocious and copious cone production, could be also shaping biomass allocation patterns, particularly at younger stages. The main objective of this study was to develop allometric relationships for Aleppo pine biomass of the stem, crown wood of branches, needles and cones- in 116

125 an age sequence from 5 to 16 years considering two quality sites and different thinning intensities in naturally post-fire regenerated forest stands. In addition, we analyzed the biomass accumulation partitions per tree at post-fire years 5 and 16, for the stem, wood of the branches, needles, cones and roots; and also the biomass allocation of these partitions related to the factors age, forest management and site. Material and methods Study site and silvicultural treatments The study areas were located in southeastern Spain, close to the villages of Yeste and Calasparra, in two post-fire P. halepensis stands naturally regenerated after wildfires occurring in summer Before the wildfires, the study areas were mature stands of mainly P. halepensis, from natural and planted origins, in mixture with Pinus pinaster Aiton and Quercus ilex L. subsp. ballota (the latter was only found at Yeste). At Yeste, the average annual rainfall and temperature were 595 mm and 13.6ºC, respectively, whereas at Calasparra, the average rainfall and temperature values were 340 mm and 16.5ºC, respectively (data provided by the Spanish National Meteorological Agency (AEMET)). The Ombrothermic Index (Rivas- Martinez et al. 1999) revealed an upper dry type at Yeste (hereafter dry site) and a lower semiarid type at Calasparra (hereafter semiarid site). In 1999, eighteen permanent plots (each of one m) were established in low-slope locations (<5%) of the two natural regenerated sites, being the primary densities of Aleppo pine saplings 8200 saplings ha -1 at the dry site and 79,000 saplings ha -1 at the semiarid site. These plots allowed for applying different silvicultural treatments which included two factors: age and final tree density (González-Ochoa et al. 2004; Moya et al. 2008). The treatments are indicated by the application year (same meaning that age) (T 5, T 10, and T 5+10 ) and their respective tree density (x-y, where x is the application year, and y is the tree density). The first factor had three levels: thinning applied at post-fire year 5 (in 1999, hereafter T 5 ); at post-fire year 10 (in 2004, hereafter T 10 ); and at both dates 117

126 (in 1999 and 2004, hereafter T 5+10 ). The second factor, final tree density, can be grouped as follows: High tree density (1,600 and 9,500 trees ha -1 ; i.e., treatments T , T and T ); and Moderate tree density (800 trees ha -1 ; i.e., treatments T 5-800, T and T 5+10 ). In addition, a Very high tree density group was considered, comprising the unthinned or control plots, hereafter T C. Site quality differences determined contrasting natural post-fire recruitments which conditioned the design of the silvicultural management. Consequently, at the semiarid site (with a very high recruitment of saplings ha -1 ) and in order to skirt the risk of stand decay, we omitted the application of the most drastic treatment (T ), and instead, we carried out a higher density treatment (T ). At each site, the permanent plots were randomly assigned to these seven groups, i.e., six thinning treatments and the control group, obtaining three replicates per group. Plot inventories The permanent plots were inventoried at the beginning and the end of the study at both sites, in 1999 and in 2010; to cover the age sequence from 5- to 16- years-old. For plots with a tree density lower than 9,500 trees ha-1, all trees were tagged and monitored. For plots with that or higher density only a sample of 24 trees was selected, with a diameter distribution alike to the diameter distribution of the whole plot. The measured parameters for each tagged tree were: the stem diameter at 30 cm above-ground, d, and the individual length, L, and width, D, of all the females cones bore in the crown. These parameters were required subsequently to generate the component biomasses of each monitored tree, b i, where subscript i stands for the tree component (i.e., stem, crown branches, needles, roots and cones), and superscript indicates that the biomass is an estimated value by using the appropriate allometric relationship. See following sections. Biomass estimation at tree level Pine biomass Above-ground biomasses of individual pine trees were obtained from destructive samplings carried out in different years: 1999, 2001, 2008 and 2010, 118

127 which corresponds to the age sequence of 5-, 7-, 14-, and 16-years-old, respectively. Below-ground biomasses were obtained from two destructive samplings at the dry site, carried out in 2008 and 2010 (at 14-, and 16-years-old, respectively) and from one survey at the semiarid carried out in 2010 (at 16-yearsold). We used a mini-digger to extract the roots of sampled trees extracting at least all the primary and secondary roots. From each silvicultural treatment, we sampled at least nine pine trees distributed along all the diameter classes (Table 1a). See Alfaro-Sánchez et al. (2014) for further details. From each individual pine tree we measured the stem diameter at 30 cm above-ground, d. The total fresh weight of the felled trees components (roots, b R-f, stems, b ST-f, and crown, b C-f ) were recorded using a 60-kg field scale (sensitivity 20 g) and a 6-kg table scale (sensitivity 0.2 g) for light material. To estimate the dry biomass of woody material, we sampled three subsamples (slices) of roots, b R, and stem, b ST, from each tree, which were taken to the laboratory and dried in an oven (105ºC, 48 h, Pardé and Bouchon 1994) to obtain humidity (%) in a dry basis. A double random sampling with ratio estimators, Z i, and K i, was conducted to calculate the total dry biomass components of the crown, b C (De Vries 1986). The first group of ratio estimators, Z i, was used to calculate the fresh biomass of twigs, b TW-f, and dry wood of branches, b BR, at crown level. To accomplish it, eight branches from each tree were randomly sampled and the total fresh weight, W C-f, from each one was recorded. Subsequently, all the twigs on the sampled branches were removed and weighed, W TW-f, similarly it was the wood of the branches, W BR-f. The dry biomass of wood branches, W BR, were obtained in a similar way to that from stems and roots; i.e., subtracting humidity on a dry basis (obtained from three slices per branch wood) from W BR-f. To estimate the total fresh biomass of twigs and total dry biomass of the wood of branches, the ratio estimators (Z i ) were calculated as: W f TW 1 WC f Z 119

128 Z 2 W BR W C f Thus, the total fresh biomass of twigs was calculate as b TW f Z 1 b C f and the total dry biomass of wood of branches was obtained as b BR Z 2 b C f.the second group of ratio estimators, K i, was defined to obtain separately the dry biomass of twigs, i.e., wood of twigs and needles. Thus, from all the twigs removed from the eight branches per tree, we selected six random sets composed of 1, 2, 4, 6, 8 and 10 twigs, respectively (31 sampled twigs in all). We recorded the fresh weight of each twig set, w TW-f. Then we separated woody twigs and needles, w WTW and w N, respectively, which were dried (85ºC for 24 h) and weighed (Pardé and Bouchon, 1994). The ratio estimators were calculated as: K 1 w w WTW TW-f K 2 w w N TW-f In order to assess the dry biomass of twigs at crown level, b TW, needles, b N, and woody twigs, b WTW, total fresh biomass of twigs was multiply by the K i ratio estimators: b N K 1 b TW f b WTW K 2 b TW f Finally, total dry crown wood biomass, b CW, was obtained as the sum of wood of twigs, b TW, and wood of branches, b BR, (i.e. b CW = b TW + b BR ), and total dry crown biomass, b C, was obtained as the sum of crown wood biomass and needles biomass (i.e. b C = b CW + b N ). Next, total dry aboveground biomass, b A, was obtained as the sum of the crown and stem biomasses (i.e. b A = b ST + b C ). 120

129 Allometric relationships for each dry biomass component (stem b ST, crown b C, wood biomass of the crown (branches and twigs) b CW and needles b N ) were developed by multiple regression analysis, considering both categorical (Treatment at each site) and continuous (d) variables following Alfaro-Sánchez et al. (2014) methods. The models were simplified using a forward stepwise regression method, following the general linear statistic test (F-test; Neter et al. 1996). For the selection of the best models, we took into account the highest adjusted regression coefficients (R 2 adj), the lowest sum of squares error (SEE) and the lack of colineality in the predictor variables. Additivity of allometric relationships, regarding the biomass of the tree components was not forced because the bias was minimal (Parresol 1999) (Table 2a). In addition we included the allometric relationships for the aboveground b A and roots b R components previously developed by Alfaro-Sánchez et al. (2014). Cone biomass In order to obtain allometries for the estimation of individual biomass cones, b Co, we selected and cut mature cones at random from each site in 2007, 2009, 2011 and 2013, which corresponds to 5-, 7-, 14-, and 16-years-old trees, respectively. The total fresh weight of the cones was recorded using a high precision laboratory balance (0.001g to 500g). Individual dry cone biomasses were estimated similarly to dry biomasses of woody material. Allometric relationships for each dry cone biomass were developed by multiple regression analysis, considering both categorical (site) and continuous (maximum length, L, and width, D of the cone) variables. The highest R 2 adj, the lowest SEE and the lack of colineality in the predictor variables were considered to select the best model (Table 2b). Biomass allocation ratios Changes in allocation patterns of the different pine components were studied from ratios between component biomasses. First, the ratios of the 121

130 component biomasses from the destructive sampling were analyzed. However, only the ratios that provided new insight into the biomass allocation patterns were considered, i.e., R N =b N /b A ; R ST =b ST /b A and R CW-ST =b C /b ST. Second, the analysis of the ratios was extended to the whole plots by considering the component biomasses, estimated with allometric relationships, for each monitored tree in the two inventories, being: R ST =b ST /b A ; R CW =b CW / b A ; R N =b N / b A ; R R =b R / b A and the reproductive effort R Co =b Co / b A (Prairie and Bird 1989). Statistical analysis We run one-way ANOVA to analyze the effect of the interaction Site Age, at post-fire year 5, on the dry biomass components per tree, b ST, b CW, b N, b R, b CO and the variables regarding biomass allocation, R ST, R CW, R N and R CO. All response variables were examined for a normal distribution of errors and for homogeneity of variance. To accomplish both, the normality and homoscedasticity assumptions of the variables, we applied log transformations. The Tukey-Kramer HSD test was used for post-hoc comparisons. Generalized linear mixed models (GLMM) analyses were performed to evaluate the effects of factors Age and Treatment, at the plot level, on the dry biomass components per tree, b ST, b CW, b N, b CO and the variables regarding biomass allocation, R ST, R CW, R N and R Co. Independent GLMMs were fitted for each site. We included as random effect factor plot to account for spatial autocorrelation among trees within plots at each Age. We selected the models with the smallest values of Akaike s information criterion (AIC). We also reported the equivalent models in terms of statistical explanatory power (those within 2 AIC units of the best model). The R 2 of the model fit of observed versus predicted was used as a measure of goodness-of-fit. Generalized linear models (GLM) analyses were performed to evaluate the effects of factors Treatment, at the plot level, for the b R and R R. Independent GLMs were fitted for each site. For the models selection we followed the same procedure than that exposed for GLMMs. The models were fitted using lme4 package (Bates et al. 2014) and MuMln package (Barton 2013) in R package version 3.1 (R Core Development Team 2013). 122

131 Results Allometric relationships The complete model for each dry biomass components was simplified into four homogeneous groups which differed significantly in intercepts and slopes (Table 2a). By proceeding in this way, we obtained for each one the above- and below-ground dry biomass components, four robust allometric relationships of the form Y= a 0 + a 1 d 2 based on the data for all ages including d and particular interactions of the factors Site and Treatment as significant predictive variables (P<0.05, 95%> R 2 adj > 87%). These allometric relationships are valid for the whole range of d values recorded at each site, i.e., the range [0.7, 13.2] cm at the dry site, and the range [0.4, 7.6] cm at the semiarid site (Table 1, Table 2a). All the values of the F statistic, testing the null hypothesis versus the complete model were small, verifying that: F 0.66 < F (0.95; 16, 227) for the aboveground biomass components and F = 0.02 < F (0.95; 16, 142) for the belowground biomass fraction (Alfaro-Sánchez et al. 2014) (Table 2a). Table 1. Main dendrometric characteristics (min-max range) of (a) Aleppo pine trees samples and (b) cones sample from the Dry and Semiarid sites. (a) Above-ground Below-ground Site Treatment Age n d Age n d Dry Semiarid T C 5,7, 14, , T , 14, , T , 14, , T , , T , , T , , T C 5, 7, T , T , T T T

132 (b) Site Age CO Age n D L Dry 12, 15, 17, Semiarid 12, 15, 17, n: number of sampled trees/cones;); d: average diameter at 30 cm above-ground (cm); CO Age: Age of the cones; D: individual width of female cones; L: individual length of female cones; T C: Unthinned plots (Control); T 5:9500: Thinning in 1999 to a final density of 9,500 trees ha -1 ; T 5:1600: Thinning in 1999 to a final density of 1,600 trees ha -1 ; T 5:800: Thinning in 1999 to a final density of 800 trees ha -1 ; T 10:1600: Thinning in 2004 to a final density of 1,600 trees ha -1 ; T 10:800:Thinning in 2004 to a final density of 800 trees ha -1 ; T 5+10:800: Thinning in 1999 to a final density of 1,600 trees ha -1 and thinning in 2004 to a final density of 800 trees ha -1. Effect of Age, Treatments and Site on biomass per tree ANOVA analysis revealed higher b A for the reproductive trees at each date and site. Comparison of alternative GLMMs and GLMs revealed those that best fit the above and belowground biomass components at the plot level, i.e., b ST, b CW, b N, b CO and b R (Table 4). At the dry site, the best fit models included factors Age and Treatment as explanatory variables for b ST, b CW, b N and b CO and factor Treatment for b R. The significance of the models was higher for all components (R 2 40%; Table 4a). At the semiarid site, the best fit models included factors Age and Treatment as explanatory variables for b ST, b CW and b N and factor Treatment for b R (R 2 63%; Table 4b). For the variable b CO only factor Age resulted significant (R 2 = 4%; Table 4b). Between sites, ANOVA analysis revealed significantly higher values of b ST, b CW and b N at 5 post-fire years at the dry site. However, b CO was similar at both sites at this age (Table 3). Regarding the effect of Treatment, at the dry site the biomass components of the pine trees occurring in plots with the same Tree density, but with different cutting age (i.e., T vs T 10:800 or T 5+10 and T vs. T ) were compared (Fig. 1). The results showed that, at 16 post-fire years, b ST, b CW, b N and b R for T was almost twice the value for T , and 1.7 folds the value for T No differences were found between T and T Besides, the value of b ST, b CW, b N and b R for T tripled that for T

133 Tabla 2. Simplified models of dry biomass allometries (above- (b A, b CW, b N, b C, b ST g) and below-ground (b R, g)) for each Site and Treatment (a), allometric relationships to estimate the biomass of cones (b CO, g) for each site and goodness of fit estimators (adjusted regression coefficient (R 2 adj), standard error of estimation (SEE) and mean absolute error (MAE). (a) Site Treatment Above-ground Below-ground Dry Semiarid T C T 5:9500; T 5:3000; T 5:1600; T 10:1600; T 10:800; T 5+10 b CW= d 2 b N= d 2 b C= d 2 b ST= d 2 b A= d 2 b R= d 2 Dry T ; T ; T ; T ; T 5+10 b CW= d 2 b N= d 2 b C= d 2 b ST= d 2 b A= d 2 b R= d 2 Dry T 5:800 b CW= d 2 b N= d 2 b C= d 2 b ST= d 2 b A= d 2 b R= d 2 Semiarid T C b CW= d 2 b N= d 2 b C= d 2 b ST= d 2 b A= d 2 b R= d 2 R 2 adj MAE SEE (b) Dry site b CO = *L*D 2 Semiarid site b CO = *L*D 2 R 2 adj 88 SEE MAE b CW: dry crown wood biomass; b N: dry needles biomass; b C; dry crown biomass; b ST: dry stem wood biomass; b A: above-ground dry biomass; b R: dry roots biomass; b CO: dry cone biomass; d: average diameter at 30 cm above-ground (cm); D: individual width of female cones; L: individual length of female cones; Alfaro-Sánchez et al. (2014) 125

134 Tabla 3. ANOVA results for the biomass components b ST, b CW, b N, b Cones (on grams) and for the ratios R ST, R CW, R N, R CO (on a per unit basis) at 5- post-fire years at the plot level. Asterisks noted significant differences at P<0.05 in between sites. Site b ST b CW b N b CO R ST R CW R N R CO Dry 169 (8)* 224 (10)* 216 (8)* 2.3 (0.4) (0.001) (0.0003)* (0.001) (0.0005) Semiarid 22.0 (1.1) 26.3 (1.4) 27.7 (1.2) 2.1 (0.3) (0.0003)* (0.001) (0.001)* (0.004)* Tabla 4. Coefficients (estimates) and statistical parameters obtained in the GLMMs for the log transformed biomass components b ST, b CW, b N, b CO (on grams) R ST, R CW, R N, R CO (on a per unit basis) (n=1556) at plot level on the effects of Site, Age and Treatment and b R and R R (n=707) on the effects of Treatment. See Table 1 for Treatment codes. (a) Dry site Fixed effect b ST b CW b N b CO b R R ST R CW R N R CO R CO Alternative model (Intercept) 4.91(0.09) 5.23(0.09) 5.24(0.08) 2.07(0.15) 6.41(0.19) (0.008) (0.002) (0.008) -4.22(0.14) -4.14(0.13) (0.020) Age (0.10) 1.63(0.09) 1.46(0.08) 1.8(0.3) n.i 0.132(0.010) 0.032(0.003) (0.009) 0.52(0.13) n.i n.i T (0.15) 0.28(0.13) 0.32(0.12) 0.9(0.3) 0.8(0.3) 0.115(0.015) (0.004) (0.013) n.i 0.65(0.21) 0.19(0.03) T (0.15) 0.47(0.13) 0.47(0.12) 0.8(0.3) 0.7(0.3) 0.125(0.015) (0.004) (0.013) n.i 0.44(0.24) 0.18(0.03) T (0.16) 0.75(0.15) 0.76(0.13) 1.1(0.3) 1.3(0.3) 0.174(0.016) (0.005) (0.014) n.i 0.23(0.25) 0.20(0.03) T (0.17) 0.68(0.16) 0.79(0.14) 1.5(0.3) 1.7(0.3) 0.188(0.017) (0.005) (0.015) n.i 0.21(0.25) 0.16(0.03) T (0.16) 0.53(0.15) 0.56(0.13) 1.5(0.3) 1.2(0.3) 0.150(0.016) (0.005) (0.014) n.i 0.95(0.22) 0.20(0.03) Random effect σ 2 Plot n.i n.i AIC R R R 126

135 (b) Semiarid site Fixed effect b ST b CW b N b CO b R R ST R CW R N R CO R R (Intercept) 2.89(0.05) 3.06(0.05) 3.16(0.05) 2.26(0.14) 4.08(0.11) (0.002) (0.003) (0.004) -2.06(0.11) -1.63(0.03) Age (0.09) 1.74(0.09) 1.56(0.08) 0.37(0.13) n.i 0.044(0.004) (0.0017) (0.003) -1.77(0.23) n.i T (0.12) 0.86(0.12) 0.89(0.11) n.i 1.64(0.16) (0.006) n.i n.i -0.2(0.3) 0.66(0.04) T (0.12) 1.29(0.12) 1.28(0.11) n.i 1.90(0.16) (0.006) n.i n.i -0.9(0.3) 0.59(0.04) T (0.14) 1.07(0.14) 1.08(0.12) n.i 1.82(0.17) (0.007) n.i n.i -0.4(0.3) 0.61(0.04) T (0.16) 0.98(0.16) 1.01(0.14) n.i 1.87(0.19) (0.008) n.i n.i -0.2(0.3) 0.60(0.05) T (0.12) 0.92(0.12) 0.94(0.11) n.i 1.60(0.16) (0.006) n.i n.i -0.7(0.3) 0.68(0.04) Random effect σ 2 Plot n.i n.i n.i AIC R b ST: average dry stem wood biomass (g); b CW: average dry crown wood (g); b N: average dry needles biomass (g); b CO: average dry roots biomass (g); b R: average dry roots biomass (g); R ST: average stem biomass divided by the average above-ground biomass ratio; R CW: average crown wood biomass divided by the average above-ground biomass artio ; R N: average needles biomass divided by the average above-ground biomass ratio; R CO: the reproductive effort as the average cones biomass divided by the average above-ground biomass ratio; R R: average root biomass divided by the average above-ground biomass ratio;*age 5 and T C were considered the reference level; D 2 : explained deviance (%); n.i: not included 127

136 At the semiarid site, b ST, b CW, b N and b R of the pine trees occurring in plots with the same Tree density (i.e., T vs. T 5+10, and T vs. T ) were compared to find that there were no differences between T and T 5+10, but T was 1.5 times T Besides, no differences were noted between the value of b ST, b CW, b N and b R for T or T 5+10 and that for T The outcomes for b ST, b CW, b N and b R differed for the b CO. Therefore, the highest b CO were attained at both sites with the treatments T or T and the lowest b CO were found in the unthinned plots at both sites (Fig. 1). At the dry site, no differences were found for b CO between T and T and between T and T , but T and T doubled the value of T , T and T At the semiarid site, T was 1.5 times T 5+10 and T was 1.7 times T Besides, T was 2.4 times T Effects of Age, Site and Treatment on biomass allocation Our results revealed a significant effect of factor Age in the biomass allocation. Firstly, from the destructively sampled trees, we noted that individuals within the lowest diameter class (identified as the youngest trees) exhibited very different values for ratios R N (ranging from ~ 0.20 to 0.55) and R ST (from ~ 0.15 to 0.60), whereas those individuals within the highest diameter class (> 4-6 cm) (and identified as the oldest trees) presented a limit value for these ratios. In particular, ratio R N came close to 0.25 at the dry site and to 0.30 at the semiarid site, whereas ratio R ST came close to 0.40 at the dry site and to 0.30 at the semiarid site (Fig. 2). Fig. 3 depicts the relationships between the b C with b ST for the Moderate, High and Very high tree densities groups at both sites (dry site and semiarid site), obtained from the destructively sampled trees data. The results showed a high correlation between b C with b ST. 128

137 b' ST Dry site TC T5 T10 T5+10 Semiarid site TC T5 T10 T5+10 b' CW trees ha trees ha b' N 3000 b' CO trees ha trees ha b' R trees ha Figura 1. Average biomass components (b ST, b CW, b N, b CO, b R, g) at plot level for the 16 years old data for each Treatment, indicated by the application year and their respective tree density. T C: Unthinned plots (Control); T 5: Thinning in 1999; T 10: Thinning in 2004; T 5+10: Thinning in 1999 to a final density of 1,600 trees ha -1 and thinning in 2004 to a final density of 800 trees ha

138 R ST Dry site Age Semiarid site Age d d R N d d Figura 2. The allocation ratios, from the destructive sampling data, of R ST and R N versus d (cm) at the dry and semiarid sites, displaying the tree ages of destructive samples. Hence, a less pronounced slope was found at the dry site (1.42) than at the semiarid site (2.56), which indicates that the individuals at the dry site allocated more biomass in stem and less in crown. Secondly, non-linear regressions were established at both sites and for the High and Very high tree densities, in which the slopes decreased with b ST. Thus at younger stages, tree density had no clear effect on the slope values. In general, these initial slope values were higher or equal than at the end of the juvenile phase. Conversely, at the end of the juvenile phase, the regressions showed a limit on the slope values; i.e., 0.33 at the dry site and 0.26 for the High and Very high tree densities, respectively; 1.96 at the semiarid site and 0.23 for the High and Very high tree densities, respectively (Fig. 3). 130

139 Dry site Semiarid site R 2 = Moderate High Very high R ST-CW R ST-CW R ST-CW R 2 = R 2 =0.98 b C R 2 = R 2 = R 2 = b ST b ST Figura 3. The crown biomass (b C, g) regressed out on the stem biomass (b ST, g), from the destructive sampling data, for Moderate, High and Very high tree density groups at the dry and semiarid sites. See Table 1 for Treatment codes.moderate: Treatments T 5:800, T 10:800 and T 5+10; High: Treatments T 5:9500, T 5:1600 and T 10:1600; Very High: Unthinned plots (Control, T C). 131

140 Secondly, at the plot level a comparison of alternative GLMMs revealed those that best fit the biomass allocation ratios, i.e., R ST, R CW, R N, R CO and R R (Table 4). At the dry site, for R ST, R CW, R N the best fit models included factors Age and Treatment as explanatory variables. The significance of these models was higher for all the ratios (R 2 68%; Table 4a). For R R the best fit model included factor Treatment as explanatory variable (R 2 = 43%; Table 4a). For R CO two models showed similar explanatory power two best models were obtained (AIC 2; Table 4a), one including factor Age as explanatory variable and the alternative one including factor Treatment as explanatory variable. Both models showed low significance (R 2 10%; Table 4a). At the semiarid site, for R ST and R CO the best fit models included factors Age and Treatment as explanatory variables (R 2 =25% and R 2 = 58%, respectively; Table 4b). For R CW and R N the best fit models included factor Age as explanatory variable (R 2 =73% and R 2 = 47%, respectively; Table 4b) whereas for R R the best fit model included factor Treatment (D 2 = 84%; Table 4b). Between sites and at 5 post-fire years, ANOVA analysis revealed significantly higher values of R ST at the dry site and significantly higher values of R CW, R N and R CO at the semiarid site (Table 3). The effect of treatments was analysed at the plot level in Fig.4 for the 16 years-old data. The results showed that factor Treatment determined contrasting responses to the silvicultural treatments between sites. In particular, thinning at the dry site prompted a significant increase in the R ST and R CO ratios of the individual pine trees, and a significant decrease in R CW and R N. However, thinning at the semiarid site prompted a significant decrease in R CO ratio and a non-significant effect over R CW and R N ratios. In general a significant increase of R ST over time was found at the dry site (average values from 0.27 to ~0.34), whereas it remained constant at the semiarid site (~0.29) (Table 3; Fig.4). Ratio R CW marginally decreased over time at the dry site (from 0.36 to ~0.35) while it increased over time at the semiarid site (from 0.34 to ~0.37). At both sites and dates R N was higher at the semiarid site than at the dry site and it showed a decreased over time (from 132

141 0.37 to ~0.31 at the dry site and from 0.38 to ~0.35 at the semiarid site). Besides, R CO increased over time at the dry site (average values from to 0.03), whereas it decreased at the Semiarid site (from 0.03 to 0.01) (Table 3; Fig. 4). The results for R R revealed greater R R at the dry site than at the semiarid site. Besides, at both sites the lowest percentages were found for the individuals from the control stands (0.33 at the dry site and 0.20 at the semiarid site) (Table 4; Fig. 4). Discussion From an operational and useful point of view for managers, we developed allometric relationships for biomass components based on an easily measurable variable, the diameter. Besides, no common agreement has been reached on the biomass mathematical function to be used, although one of the most widespread models is the logarithmic model (Pretzsch 2010). In this study, we chose the quadratic model as opposed to the logarithmic model because it not only blocks the possibility of negative values (particularly important for lower size classes individuals), but also because of its better performance. The dependence of the biomass equations according to particular interactions of factors Site and Treatment was clearly shown when comparing the biomass models calculated, represented in Fig. 5 for the stem biomass component. Effects of competition on allometry have been previously depicted for young trees (Nilsson 1993; Landsberg and Sands 2011, Heras et al. 2013). This result has practical implications for the experimental design of the destructive sample. Thus, the sample to characterize a given stand should be homogeneous in terms of tree density and the cutting age of potential previous thinning. Consequently, specific allometric relationships should be applied only for the corresponding homogeneous areas (Lehtonen et al. 2004; Tobin and Nieuwenhuis 2007; Correia et al. 2010). 133

142 R' ST R' CW Dry site Semiarid site TC TC T5 T5 T10 T5+10 T10 T trees ha trees ha R' N R' CO trees ha trees ha R' R trees ha Figura 4. Average biomass components (R ST, R CW, R N, R CO, R R, on a per unit basis) at plot level for the 16 years old data for each Treatment, indicated by the application year and their respective tree density. T C: Unthinned plots (Control); T 5: Thinning in 1999; T 10: Thinning in 2004; T 5+10: Thinning in 1999 to a final density of 1,600 trees ha -1 and thinning in 2004 to a final density of 800 trees ha

143 b ST Age Eq d b ST Age Eq d b ST Age Eq d b ST Age Eq d Figura 5. Curves of the stem biomass (b ST, g) versus the diameter at 30 cm above-ground (d, cm) generated from the allometric relationships displaying the tree ages of destructive samples. It is noteworthy that each biomass equation was obtained for an age sequence including a wide range of size classes. There was some overlapping between the range of diameters from different ages, although, for a given equation, large diameter classes generally correspond to older individuals (Fig. 5). In fact, we expected more linear and less overlapping in specific allometric relationships for a particular age. Furthermore, the allometric coefficients for the biomass partitions showed lower intercepts and higher slopes for the individual pine trees occurring in control stands, mainly at the semiarid site, which emphasises the effect of site quality (Fig. 5; Table 2). Thus, our results revealed an advanced ontogenic development at the 135

144 dry site (presenting higher individual pine biomass), due to favourable ecological conditions than at the semiarid site. We found a positive effect of tree density reduction in all the biomass components at both sites, reaching a maximum when applying treatment T at the dry site and treatment T at the semiarid site (Fig. 2). i.e., the earliest treatment that left the lowest tree density tested per site. Biomass accumulation and biomass allocation were primarily constrained by soil nutrients and water availability. Thus, when comparing individuals from different site qualities but with similar tree density, biomass and ratios at the most limiting site (semiarid) differ from those of the dry site independently of the silvicultural treatment applied. In addition, the effect of treatment at the semiarid site showed an attenuation due to the hard conditions of this location, which was more notable for biomass accumulation than for biomass allocation (Fig. 2; 5). Biomass allocation When obtaining the allometric relationships of the dry biomass components, factor Age was not significant and factor Treatment was considered for four particular interactions of factors Site and Treatment. Thus, these four resulting equations were very appropriate for forest managers due to their simplicity. However, sometimes the equations do not allow us to identify certain dependences between the biomass components. Therefore, changes in allocation patterns of young Aleppo pine stands were analysed as a function of different factors, i.e., Age, Treatment and Site, with the ratios obtained after applying our biomass equations to the two plot inventories, but also with the ratios obtained from the destructive samplings trees data, whenever the plot level do not allow inferring adequate conclusions. Our findings revealed that, although biomass can be modelled in terms of d with good performance, the same was not true when modelling biomass allocation ratios (Fig. 2; López-Serrano et al. 2005). Furthermore, the destructive sampling 136

145 results revealed very high correlations between the measured biomass components, i.e., crown and stem (Fig. 3). These dependences evidenced the nonlocal condition of the processes involved in the growth of a given component (Landsberg and Sands 2011). The proportion of crown biomass vs. stem biomass was lower at the dry site as a response to bioclimatic restrictions to plant growth; i.e., at the dry site, less biomass was invested in the crown to favour the stem while at the semiarid site, the individual pine trees allocate more biomass to the crown in order to face a higher photosynthetic demand (Vanninen and Mäkelä 2000, López- Serrano et al. 2005). Furthermore, although both sites are representative of fireprone communities, the tendency to allocate more biomass to the crown at the semiarid site, could point to an amplification of a typical trait from crown fire communities, which is flammability, including a large loading of dead branches and needles which promotes more flammable canopies (Keeley and Zedler 1998; Schwilk and Ackerly 2001; Keely et al. 2012). Focusing on the effect of forest management (factor Treatment) on the biomass allocation, we identified a concomitant growth of the crown and stem biomasses (linearly related) for individual pine trees under Moderate tree densities. However, for High but particularly, for Very high tree densities we observed that for a progressive increase in the biomass allocated to the stem, the biomass allocated to the crown concurrently decreased due to strong intraspecific competition until the canopy completely closes (Fig. 3). This tendency has been identified in previous studies (Nilsson 1993; Vanninen and Mäkelä 2000; Zianis et al. 2011) as an adaptive mechanism to cope with strong competition in the juvenile phase. In addition, according to the pipe model theory (Vanninen and Mäkelä 2000) we found more biomass allocated to the stem in suppressed trees than at individuals in lower tree density stands. At the plot level, our results revealed an increase of R ST with age at the dry site and that this effect was enlarged in trees under thinned plots. However, at the semiarid site, R ST remained constant during the age-sequence studied in both, thinned and unthinned plots (Table 3, Fig. 4), whereas R N lowered at both sites. This agrees with the fact that the variables which accumulate during a tree s life 137

146 span, such as stem and coarse roots, increasingly contribute to the above-ground biomass with age. However, the opposite is true for those variables that are not totally cumulative, such as needles and fine roots (Vanninen et al 1996; López- Serrano et al. 2005; Landsberg and Sands 2011; Xie et al. 2012). The diminution of R N as the stand became older has been described to result from the formation of new wood structures and from the maintenance of older ones (Ryan and Yoder 1997; Montès et al. 2004). We found similar needles ratio than Montès et al. (2004) for a 10-year-old P. halepensis stand, and also the same tendency to lower with age. Accordingly, the obtained needles ratios for younger stands were higher than those obtained by Lopez-Serrano et al. (2005) for adult Aleppo pines (i.e., between 0.04 and 0.12). On average, young Aleppo pines allocated more resources to the crown (R C ~0.68) than to the stem (R ST ~0.32) at post-fire year 16, in opposition to mature Aleppo pines, where similar estimations of R ST and R C were roughly 50% each one (Ruiz-Peinado et al. 2011). Besides, we found higher root:shoot ratios (R R ~0.37) compared to mature Aleppo pines that was 0.23 (Ruiz-Peinado et al. 2011). Yearly changes in root:shoot ratios or even a declined with age are possible (Albaugh et al. 2006), although similar techniques for roots extractions should be considered, due to the possibility of underestimation increases when tackling with large trees Adegbidi et al. (2002), that could attain ca. of the 40% (Robinson 2004). Our values for the pine layer were in keeping with root:shoot ratios found on a per hectare basis (0.37, Alfaro-Sánchez et al. 2014) and for open adult P. halepensis stands (0.35, unpublished results) by using the same extraction technique for roots, which further supports the notion that differences in site quality and tree density are related to differences in the root:shoot ratios per tree and on a per hectare basis and that differences in root:shoot ratios regarding age should be taking with caution (Retzlaff et al. 2001, Rubilar et al. 2013). There is evidence that suppressed trees suffer greater drought stress as a result of a greater competition of roots for the soil (Olivar et al. 2012). Accordingly, we reported lower root:shoot ratios in suppressed trees, mainly at the individuals occurring in control plots at the semiarid site, but also occurred at control individuals at the dry site (Table 4) in agreement with Shelton et al. (1984), 138

147 Albaugh et al. (2006) and Rubilar et al. (2012). This finding was in opposition to the functional carbon balance model (Brouwer 1983), which suggested that plants growing with limited soil resources (nutrients or water) should allocate high biomass to roots. After thinning application, pines in low density plots are consistently less water stressed than those in the unthinned plots, and consequently an increase in coarse roots is expected. Conversely, pines in the unthinned plots are forced to increase allocation to fine roots, at expenses of coarse roots, in order to improve their ability to capture scarce resources under stress conditions (Albaugh et al. 1998; Rubilar et al 2012). Taking into account that our approach for the estimation of root:shoot ratios included both coarse and fine roots, this could explain our low root:shoot ratios found in supressed trees. Cone biomass To our best knowledge this is the first attempt to calculate the cone biomass of young P. halepensis individuals by using specific biomass equations extrapolated to complete and accurate cone inventories (but see Ne eman et al. 2011). We reported a very early production of cones at our study sites (González- Ochoa et al 2004), beginning about 3 years-old in accordance with previous studies (Thanos and Daskalakou 2000; Tapias et al. 2001). Our results revealed similar reproductive development between sites, attained at post-fire year 5 but with very different biomass components and tree sizes at the onset of reproduction (Table 3). As a consequence, strategies of resilience at early stages, such as abundant cone production, may be triggered by an adaptive trait of Aleppo pine developed to high fire recurrences (Naveh 1990; Agee 1998; Tapias et al 2001; Ne eman 2004). However, larger reproductive investment (i.e., R CO ) must be moderated by other different factors, such as competition, individual variation or 139

148 resource availability (Obeso 2002), as revealed the low deviance explained of our models for this ratio, particularly at the dry site. In this study we can distinguish different reproductive patterns for two coetaneous stands located at different sites. Particularly notable was the higher ratio of biomass of cones respect to the aboveground biomass at 5 post-fire years at the semiarid site in comparison to the dry site (Table 3). Conversely, at the end of the age-sequence, the opposite trend was observed, and higher ratio of biomass of cones respect to the aboveground biomass was attained at the dry site (0.061) vs. the semiarid site (0.009) (Fig. 4). This outcome denoted the high reproductive effort carried out by these individuals at early stages at the semiarid site (Haymes and Fox 2012) which could have affected the growth of the subsequent years, influencing the allocation of other biomass fractions, mainly less biomass could be allocated to the stem in favour to the crown. Furthermore, at post-fire years 16, only one third of the individuals at the semiarid site were reproductive trees, which implied a high immaturity risk of the stand to cope with a new post-fire recruitment (Keely et al. 2012). In fact, high reproductive cost at the expense of growth at early stages could be an strategy developed to face a short-term survival (Stearns 1976) hence, mortality risk and its predictability drive the onset of reproduction among the same species (Kozlowski 1992) as a result of local selective pressures, confirming the hypothesis of Harper and White (1974) developed for polycarpic perennials, that plants with a brief juvenile period have a short life span. On the whole, Aleppo pine has demonstrated a strong genetic control of reproductive traits to face selective pressures (Santos-del Blanco et al. 2013), as we found at the semiarid site, where the pine was forced to invest the available resources primarily in the reproductive growth. On the other hand, at the dry site we found the most usual tendency reported, i.e., to invest primarily in growth and secondarily in reproduction (Goubitz et al. 2002; Moya et al 2008). Conclusions Our results presented a set of biomass equations which allow estimating above and belowground biomass components for young P. halepensis trees at two different site qualities (dry and semiarid sites) and considering different silvicultural 140

149 treatments (thinning at two ages) in a range of diameters not previously defined in former biomass models fitted in Spain (López-Serrano et al. 2005; Montero et al. 2005; Ruíz-Peinado et al. 2011). Site quality seems to be the first limiting factor constraining biomass accumulation and biomass allocation. When silvicultural treatments were applied, the enhancement observed of the above-ground biomass components per tree was maximum with the earliest treatment tested. In addition, comparatively with the individuals occurring at the control plots, the effect of the best treatment found per site was larger at the dry site than at the semiarid site (~4.1 and ~2.7 times for the dry and semiarid sites, respectively). In general, the biomass allocation analysis revealed that the crown was the largest biomass fraction, followed by the root fraction and the stem fraction, although Age, forest management (Tree density) and site quality factors modified those proportions. Besides, the allocation of biomass to the stem increase with lower final tree densities, but that to the crown decreased, mainly at the end of the juvenile phase, which coincided with canopy closure. Furthermore, less biomass was allocated to the roots in high density stands whereas, between sites, we found larger biomass allocated to both stem and roots (mainly coarse roots) at the best quality site, related to higher water availability. These findings, together with the contrasting reproductive effort found at early stages, which was larger at the semiarid site, support our initial hypothesis that, variances in biomass allocation at different quality sites could be triggered, at this most xeric site, by different inherited adaptive traits to fire regimes as a strategy developed to face a short-term survival. In conclusion, our results highlight the importance to apply adequate forest management in order to facilitate water and nutrients availability (Albaugh et al. 2004) that will help to ensure the permanence of the stand in time. Acknowledgments We thank the Spanish Ministry of Science and Innovation for its funding and support to the Forest Ecology Researching Group in Projects CYCIT-AGL /FOR; AGL /FOR and CONSOLIDER-INGENIO 2010: 141

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155 4.4 CAPÍTULO 4: Biomass storage in low timber productivity Mediterranean forests managed after natural post-fire regeneration in southeastern Spain Raquel Alfaro-Sánchez, Francisco R. López-Serrano, Eva Rubio, Daniel Moya, Raúl Sánchez-Salguero, Jorge De Las Heras Published in: European Journal of Forest Research DOI: /s

156 Abstract Despite the low timber productivity of Mediterranean Pinus halepensis Mill. forests in southeastern Spain, they are a valuable carbon sequestration source which could be extended if young stands and understories were considered. We monitored changes in biomass storage of young Aleppo pine stands naturally regenerated after wildfires, with a diachronic approach from 5- to 16-years-old, including pine and understory strata, at two different quality sites (dry and semiarid climates). At each site, we set 21 permanent plots and carried out different thinning intensities at two ages, 5 and 10 years after fires. We found similar post-fire regeneration capacity at both sites in terms of total above-ground biomass storage ~6 Mg ha -1 (3 Mg ha -1 of the above-ground pine biomass plus 3 Mg ha -1 of the above-ground understory biomass), but with a contrasting pine layer structure. Generally, across the diachronic study, the earlier thinning reduced biomass stocks at both sites, except for the best quality site (the dry site), where the earliest thinning (applied at post-fire year 5) enlarged carbon storage by 11% as compared to non-thinned plots. We found root:shoot ratios of an average 0.37 for the pine layer and 0.45 for the understory layer. These results provided new information which not only furthers our understanding of carbon sequestration in low timber productivity Mediterranean forests, but will also help to develop new guidelines for sustainable management adapted to the high-risk terrestrial carbon losses of fireprone areas. Keyworks: Pinus halepensis; wildfire; thinning; diachronic study; biomass; carbon storage 148

157 Introduction Wildfires are increasing the risk of terrestrial carbon losses, but these are partly counterbalanced by the increase in CO 2 storage from the vegetation that recovers after fire (Wiedinmyer and Neff 2007). In the Iberian Peninsula, wildfires are a major, frequent disturbance, mainly in the Mediterranean area, where the fire regime is varying due to land use and climate changes (Flannigan et al. 2005). Depending on forest type and changes in the fire regime, the post-fire restored area may not be able to recover the pre-fire carbon stock, which might be delayed for decades (Moreira et al. 2012). Forest management is an important factor that influences both, timber productivity and carbon sequestration (Bravo et al. 2008). In addition, an adequate silvicultural management may not only diminish the risk of catastrophic wildfires, but can also restore forest structural complexity (Finkral and Evans 2008). In post-fire regenerated stands, assistance to natural regeneration, by implementing sustainable forest management has proven to be an efficient tool for global change mitigation, but also for fire prevention, restoration and resilience (Penman et al. 2003). It can also accelerate processes to convert, in the short term, the carbon balance of the ecosystem from a source into a CO 2 sink (Jiménez et al. 2011). Pinus halepensis Mill. (Aleppo pine) is one of the most representative species in the lower altitudinal rank of the Mediterranean Basin, whose total forest area in the world is about 3.5 million hectares (Fady et al. 2003). The high degree of serotiny in this species (seeds enclosed and protected in cones) confers high resilience to forest fires. After fire, seed dispersion could induce excessive post-fire recovery, which led to strong intra-specific competition, and therefore to increased fuel loads (De las Heras et al. 2012). Furthermore, with higher fire recurrence, young Aleppo pine stands can be burnt before they reach the maturity stage (De las Heras et al. 2012; Verkaik and Espelta 2006; Moya et al. 2008). In lowproductivity forests, where profitable timber production is not expected, precommercial thinning interventions are rarely accomplished because they are 149

158 economically unviable. However, early thinning carried out in young post-fire P.halepensis stands has been seen to increase nutrient availability, individual growth, cone production and the number of reproductive pine trees (González- Ochoa et al. 2004; Verkaik and Espelta 2006; López-Serrano et al. 2006; Moya et al. 2008). Nevertheless, determining optimal thinning intensity is no easy task and natural regeneration models are not well developed in Spain (Bravo et al. 2012). For adult Aleppo pines stands, several authors have developed above- and below-ground biomass estimations, which allow the assessment of carbon pools (López-Serrano et al. 2005; Montero et al. 2005; Grunzweig et al. 2007). Comparatively, young Aleppo stands have not received due attention as revealed the scarcity of studies found in the literature (Montès et al. 2004; Kaye et al. 2010; De las Heras et al. 2013). Thus, young tree stands and understories have been ignored when general carbon sequestration approaches have been carried out. For instance, Montero et al. (2005) predicted that the carbon uptake by Aleppo pines stands in Spain would amount to about 40.3 million tons of carbon for 2012, but this disregards young naturally regenerated or reforested Aleppo stands. Hence, the addition of carbon storage by these young tree stands could substantially increase this amount (Peichl and Arain 2006). In addition, and according to several authors (e.g., Navarro et al. 2010; Jiménez et al. 2011), a realistic biomass approach is only attainable if an assessment of the understory total biomass is contemplated. In this sense, fire risk models required the inclusion of biomass estimations from young stands and understories to improve their predictions of fire behaviour (Kazanis et al. 2012). However, rapid recovery and high specific richness in young Mediterranean communities after fire (Capitanio and Carcaillet 2008) imply difficulties in understory biomass estimations and time-consuming samplings to prepare species-specific understory biomass estimators. In this study, we used a long-term carbon storage monitoring in burned and naturally recovered Aleppo pine stands. We analysed the influence of forest management by applying different thinning intensities at two early ages. We also 150

159 included the study of site quality effects by replicating the experimental design into two different climates. To include the biomass stored in the understory, we used a mixed methodology that involves the specific determination of biomass estimators for the most characteristic understory species and the assessment of average estimators for plant functional groups. Our main objective was to determine how different thinning interventions (two cutting ages and different thinning intensities) influenced above- and belowground biomass storage on pine and understory strata, at the stand level. Material and methods Study site and thinning interventions The study was carried out in two young post-fire Aleppo pine stands in southeastern Spain, close to the villages of Yeste (38 20 N, 2 20 W; 1010 m a.s.l.) and Calasparra (38 16 N, 1 38 W; 325 m a.s.l.). Yeste is representative of a dry ombroclimate (referred to from now onwards as, dry site DS-) located in the upper Mesomediterranean bioclimatic belt (Rivas-Martínez 1987). Potential natural vegetation was a sclerophyllous oak forest of Quercus ilex L. (Bupleuro rigidi- Querceto rotundifoliae sigmetum) (Rivas-Martínez 1987). Average annual rainfall and temperature were 595 mm and 13.6ºC, respectively. Calasparra is characterised as a semiarid ombroclimate (referred to from now onwards as, semiarid site SS-) located in the low Mesomediterranean belt (Rivas-Martínez 1987). Potential natural vegetation was a kermes oak forest of Quercus coccifera L. (Rhamno-Querceto cocciferae sigmetum) (Rivas-Martínez 1987). At the SS, the average rainfall and temperature values were 340 mm and 16.5ºC, respectively. Both areas presented similar topographic and soil characteristics (carbonate substratum, ph value of about 8.5, low slope (<5%)). Large forest fires occurred at both stands (summer 1994) and 14,000 ha and 30,000 ha were burned in Yeste and Calasparra, respectively. In both areas, the ecosystem response was natural post-fire recruitment with high-density Aleppo pine saplings covering more than 9,000 ha in Yeste and almost 19,000 ha in Calasparra. In post-fire year 5 (1999), we set 21 rectangular experimental plots at 151

160 each site (10 15 m) to carry out pre-commercial thinning (referred to from now onwards as thinning) at early ages, including different final intensities. We maintained a strip (6 m wide) between plots in order to prevent interactions and border effects. The experimental plots were randomly treated. The cutting ages selected to carry out the thinning interventions were postfire years 5 and 10 (summer 1999 and winter 2004, referred to from now onwards as T 5 and T 10, respectively). A sequential treatment (referred to from now onwards as T 5+10 ) was also applied, which comprised two phases: a first phase when plots were thinned at post-fire year 5 and a second phase when thinning was redone on the same thinned plots (Table 1). Table 1. Initial tree density (N initial, trees ha -1 ) found at post-fire year 5 of two P. halepensis stands (dry and semiarid sites) and forest stand parameters after the different treatments were carried out in 1999 (T 5), 2004 (T 10) and 2010 (at post-fire year 16). Uppercase letters indicate significant differences (P<0.05) among Sites on N initial. Lowercase letters show significant differences (P<0.05) among Treatments for each Site and Age. Values are the means and standard errors shown in brackets. See Appendix for definitions. Dry site Site quality 8 Age 5 n 21 N initial 7600 (700) A T 5 C T T T n N (900) d 2400 (240) c 1600 (0) b 800 (0) a d 1.40 (0.03) a 1.30 (0.04) a 1.70 (0.04) b 2.50 (0.20) c h 97.0 (1.4) a 93.0 (1.6) a (1.8) b 132 (5) c H d 188 (8) 211 (7) 190 (13) 175 (11) I 5 0 a 55 (5) bc 61 (6) c 48 (5) b T 10 C T T T T T 5+10 T n N (3000) b 1600 (0) a 800 (0) a 2400 (240) a 1600 (0) a 800 (0) a 800 (0) a d 2.30 (0.06) a 4.40 (0.11) b 4.80 (0.22) bc 4.70 (0.13) b 5.50 (0.22) d 5.9 (0.3) d 7.6 (0.4) e h 134 (2) a 185 (3) c 203 (7) d 168 (3) b 199 (6) d 208 (6) d 229 (9) e H d 232 (15) a 249 (13) a 277 (13) ab 237 (12) a 280 (50) ab 279 (10) ab 312 (6) b I 10 0 a 69 (3) c 79 (8) d 0 a 0 a 50 b 0 a Age 16 C T T T T T 5+10 T d 4.8 (0.5) a 6.60 (0.16) b 7.4 (0.8) bc 6.50 (0.15) c 8 (1) b 8.1 (0.2) b 9.9 (0.9) b h 191 (3) a 236 (5) ab 260 (22) cd 210 (13) d 250 (30) bc 253 (7) bd 282 (15) bd H d 280 (30) a 323 (9) ac 344 (17) bc 297 (18) ab 340 (30) ac 341 (21) ac 371 (3) c 152

161 Semiarid site Site quality 6 Age 5 n 21 N initial (5000) B T 5 C T T T n N (15000) d 9500 (800) c 3000 (570) b 1600 (0) a d 0.67 (0.004) a (0.004) c (0.007) c (0.005) b h 38 (0.3) a 54 (0.3) c 53 (0.5) c 49 (0.4) b H d 107 (5) 108 (7) 105 (4) 111 (7) I 5 0 a 82 (2) b 86 (3) b 95 (1) c T 10 C T T T T T T 5+10 n N (30000) b 1600 (0) a 800 (0) a 9500 (800) a 3000 (600) a 1600 (0) a 800 (0) a d 0.7 (0.01) a 1.8 (0.04) c 2 (0.15) d 1.2 (0.02) b 1.7 (0.07) c 2.2 (0.15) e 2.5 (0.11) f h 76 (0.2) a 104 (1.7) c 118 (5) d 92 (0.5) b 100 (1.6) b 97 (5) ac 119 (4) d H d 154 (13) 134 (8) 151 (25) 159 (21) 155 (11) 155 (19) 149 (15) I 10 0 a 96 (1.0) c 99 (0.2) d 0 a 0 a 0 a 50 b Age 16 d 1.1 (0.07) a 3.4 (0.18) b 3.7 (0.5) bc 3.1 (0.09) bc 4.1 (0.3) c (0.17) bc (0.6) bc h 99 (6) a 146 (7) ab 161 (19) b 152 (6) b 160 (10) b 138 (9) b 180 (30) b H d 210 (40) 198 (4) 213 (15) 234 (12) 230 (17) 230 (30) 200 (30) The final tree densities selected followed the recommendations found for mature P. halepensis forests thinning, roughly ~1,600 trees ha -1 (Franz and Forster 1979). Thus for this study, we selected tree densities above and below this reference density; i.e., 800; 1,600; 2,400 and 3,000 trees ha -1. However, the experimental design was largely conditioned by the contrasting natural post-fire recruitments found at both sites. For example at the semiarid site, due to high recruitment (107,000 ± 5,000 saplings ha -1 ), we avoided the application of the most drastic treatment, that which left 800 trees ha -1 at post-fire year 5, given the risk of stand decay. Instead, we included a treatment that left 9,500 trees ha -1. In addition, three plots were preserved from the thinning interventions at both sites, referred to from now onwards as the control or non-thinned plots C- (see the Appendix for definitions) (Table 1). 153

162 Plot sampling The experimental plots were diachronically sampled in 1999, 2001, 2004 and 2010, which correspond to the age sequence of 5-, 7-, 10-, and 16-years-old, respectively. Twenty-four trees per plot were tagged and monitored during the study, except for the plots thinned to 800 trees ha -1 where only 12 living trees remained in those plots. The recorded variables were: stem diameter at 30 cm above-ground (d, cm) and total height (h, cm) (Table 1). We defined dominant height (H d, cm) as the total height of the highest tree per plot (Pardé and Bouchon 1994). It is widely accepted that the differences in H d between the sites, indicated dissimilarities relating to site quality. Therefore, by following the site quality curves for Aleppo pine in Spain, as developed by Erviti (1991), and using variables Age and H d, we classified our study sites into two different site quality classes; quality 8 and quality 6, which corresponded to the two lowest timber productivity qualities defined for Aleppo pine in Spain. These site qualities indicate that at age 40, top heights will reach 8 m and 6 m, respectively. The understory vegetation coverage was diachronically recorded in the spring and autumn of 1999, 2001 and 2010 (5-, 7-, and 16-years old, respectively) following the line intercept sampling method (Candfield 1941). In each plot, we set three parallel linear transects (10 m long) with a 2.5 m separation from the adjacent transects and plot edge using field measuring tapes (10 m in length, accuracy of 1 cm). Vegetation cover of each understory species was recorded to either grow along the transects or with their canopy intercepted by them (Kazanis and Arianoutsou 2004). In addition, the recorded understory species were classified according to their growth form into four plant functional groups: shrubs, dwarf shrubs, perennial herbs and annual herbs (Tutin et al ; Castroviejo et al ). Biomass estimation Pine biomass We carried out destructive samplings in 1999, 2001, 2008 and 2010 to obtain both the above- and below-ground biomasses of individual pine trees, which 154

163 correspond to an age range from 5- to 16-years-old. In particular, the aboveground biomasses were estimated at the semiarid site from individual 5-7-, (Gonzalez-Ochoa, 2003) and 16-year-old trees, harvesting a total of 103 pine trees. At the dry site and due to its higher growth potential, an additional survey at post-fire year 14 was performed (De las Heras et al. 2013), harvesting a total of 148 pine trees. The below-ground biomasses were obtained at the semiarid site from the survey performed at post-fire year 16, including 60 pine trees; and at the dry site from the surveys performed at post-fire years 14 (De las Heras et al. 2013) and 16, including 106 pine trees. The sampled trees were distributed along all treatments and diameter classes for each date and site. Allometric relationships for each dry biomass component (above-ground b A, and below-ground (roots) b R ) were developed by multiple regression analysis, considering both categorical (TS, Treatment at each site) and continuous (d) variables according to: y = a + a d + a TS + a TS d + ε where y is the response variable (b A ; b R ); i and j are the TS and the tree indicators, respectively; ɑ 0, ɑ 1, ɑ 2i and ɑ 3i are the regression coefficients to be estimated and Ɛ are the residuals (it is assumed that the Ɛ are independent and identically distributed with an N (0, σ 2 ) distribution, where σ 2 represents the residual variance). The models were simplified using a forward stepwise regression method, following the general linear statistic test (F-test; Neter et al. 1996). The highest adjusted regression coefficients (R 2 adj), the lowest sum of squares error (SEE) and the lack of colineality in the predictor variables were considered to select the best models. Additivity of allometric relationships, regarding the biomass of the tree components was not forced because the bias was minimal (Parresol 1999). We applied the specific individual allometric relationships developed to obtain individual pine biomass. Next, we extrapolated to the stand level by summing up the biomasses from all individuals within a plot and divided by plot area (Clark et al. 2001). 155

164 In order to study biomass allocation to roots at the stand level, we estimated the root:shoot ratio, i.e., the biomass of roots (B R ) divided by the aboveground biomass (B A ). Understory biomass At both sites, we estimated the above-ground understory biomasses at post-fire years 5, 7, and 16. Below-ground understory biomass was determined just after 16 years. To accomplish this, we selected the 15 most representative understory species of the study sites based on the coverage values (86.3% of the total understory coverage at the dry site and 78.5% at the semiarid site). For each of the 15 representative species, we harvested a minimum of 3-4 individuals (depending on size range). We recorded the crown coverage by measuring two radii of the crown diameter along two perpendicular directions (CC, cm) and the height (h U, cm) of each harvested species (Table 2). We calculated the total fresh weight of both components: i.e., the fresh above-ground weight and the fresh root weight. The corresponding dry biomass components (dry above- and below-ground biomass) were also obtained by drying the samples in an oven (85ºC, 24 h). In order to assess the understory dry biomass components at the stand level based on the understory species crown coverage, we calculated and validated two average ratio estimators: H Ai for the above-ground biomass per square metre and H Ri for the below-ground biomass per square metre, where i refers to species. Previously we checked no significant differences for factors Age and Site. In addition, we averaged values of both ratio estimators according to plant functional groups (H Aj and H Rj ), where j refers to the growth-form category. Annual herbs were not included due to the high inter-annual variability and the low contribution to overall understory biomass storage (less than 6% of the understory coverage). Thus for the non-destructive samplings of the different shrubs, dwarf shrubs or perennial herbs, we assigned the averaged ratio estimator of their corresponding plant functional group only with their coverage estimation (Table 2). 156

165 Table 2. Average crown coverage (CC, cm), height (h U, cm) and ratio estimators (H A and H R, g m -2 ) for the 15 most representative understory species and average ratio estimators (g m - 2 ) for the plant functional groups. Values are the means and standard errors shown in brackets. See Appendix for definitions. Plant functional group Species CC h U H A H R Shrubs 1120 (130) 510 (110) Anthyllis citisoides 52 (11) 42 (10) 1200 (300) 260 (150) Cistus clussii Dunal subsp. multiflorus 43 (7) 53 (12) 1100 (200) 290 (60) Genista scorpius 52 (17) 63 (21) 1200 (600) 510 (150) Juniperus oxycedrus subsp. oxycedrus Retama sphaerocarpa Rosmarinus officinalis 60 (15) 92 (21) 840 (190) 510 (170) 115 (8) 230 (35) 1500 (500) 1000 (700) 67 (12) 69 (5) 980 (230) 500 (140) Dwarf shrubs 500 (70) 160 (60) Dorycnium pentaphyllum 17 (5) 23 (4) 520 (170) 380 (180) Fumana thymifolia 5.83 (0.08) 9.2 (0.7) 200 (30) 26 (5) Helichrysum stoechas 98 (9) 74 (8) 480 (160) 100 (70) Teucrium capitatum 16 (4) 15.9 (2.1) 570 (190) 80 (70) Thymus vulgaris 26 (3) 26 (4) 630 (80) 220 (30) Perennial herbs 420 (110) 210 (50) Brachypodium retusum 12 (4) 18.7 (1.9) 380 (90) 210 (60) Eryngium campestre 10 (2) 11(3) 92 (13) 500 (300) Macrochloa tenacissima 69 (17) 65 (10) 700 (240) 140 (30) Plantago albicans 9 (2) 12.5 (1.4) 181 (18) 84 (12) At the stand level, understory biomass allocation was studied through the root:shoot ratio (B R-U / B A-U ), calculated as the dry mass relationship between the root understory biomass (B R-U ) and the above-ground understory biomass (B A-U ). Furthermore, we assessed the γ ratio at the stand level, defined as the proportion of the above-ground understory biomass (BA-U) to the above-ground 157

166 pine biomass (B A ), for each Site and Treatment at each Age (5-, 7-, and 16-yearsold). We calculated the equivalent carbon fixed by multiplying dry biomasses by a conversion factor, assuming that carbon content was 0.5 tonne per tonne of dry biomass (De Vries et al. 2003; Montero et al. 2005). Therefore, component carbon storages, CB i, were estimated by multiplying the corresponding dry biomasses, B i, by 0.5; where i = A, R, A-U and R-U refers to the above-ground, roots, the aboveground of the understory and roots of the understory, respectively. The total carbon storage by the pine layer (CB T ) and the understory layer (CB T-U ) was estimated as the sum of the above- and below-ground carbon storages. Statistical analysis We run one-way ANOVA to check differences on the initial tree density (N initial, trees ha -1 ) for each Site at post-fire year 5. In addition, a set of One-way ANOVAs was used to check significant differences among Treatments for each Site and Age on forest stand variables: tree density at 5 and 10 years (N 5 and N 10, respectively); stem diameter, mean height and dominant height (d, h and H d ) and thinning intensity as percentage of thinned trees at 5 and 10 years (I 5 and I 10, respectively). One-way ANOVA was also used to test a significant effect among Treatments for each Site and Age on the γ ratios and on total carbon biomass (CB T-Pine+U ) (see Appendix for more details). The response variables were examined to check normal distribution of errors and homogeneity of variance. For variables that did not meet the assumptions of normality and homogeneity of variance, log-transformed or rank-transformed data were used (Conover & Iman 1981). The Tukey-Kramer HSD test was used for post-hoc comparisons. Generalized Linear mixed Models (GLMM) were run to evaluate the effect of factors Age and Treatment on the above-ground pine biomass (B A ), the aboveground understory biomass (B A-U ) and the pine plus the understory above-ground biomass (B A-Pine+U ). Independent GLMMs were fitted for each site. A Gaussian error 158

167 distribution was used and plot was introduced as random effect to account for temporal autocorrelation according to: y = a + a Age + a Treatment + a + ε where y is the response variable (B A ; B A-U and B A-Pine+U ); j is the plot; i and k are the Age and Treatments indicators, respectively; ɑ 0, ɑ 1i and ɑ 2k are the coefficients of the fixed effects to be estimated; ɑ j is the random effect (it is assumed that the ɑ j are independent and identically distributed with an N (0, σ 2 a) distribution, where σ 2 a represents the covariance matrix for the random effects) and Ɛ are the residuals (it is assumed that the Ɛ are independent and identically distributed with an N (0, σ 2 ) distribution, where σ 2 represents the residual variance, and they are independent from the ɑ j ). The best models were selected by using the lowest Akaike Information Criterion (AIC). Since the difference between the best model and alternative ones were >2AIC units, we reported only the best-fitting models. The R 2 of the model fit of observed versus predicted was used as a measure of goodness-of-fit. GLMMs were fitted using lme4 package (Bates et al. 2014) and MuMln package (Barton 2013) in R package version 3.1 (R Core Development Team 2013). Results The recruitment of Aleppo pine showed significant differences between Sites at post-fire year 5, with an average of 7,600 ± 700 trees ha -1 at the dry site and of 107,000 ± 5,000 trees ha -1 at the semiarid site (Table 1). The contrasting sapling density resulted in different intensities of thinning between sites, varying from 48 to 79% at the DS, and from 82 to 99% at the SS. Immediately after treatments T 5 and T 10 were carried out, both sites presented significant differences among Treatments for dendrometric parameters d and h, but not for H d, with the exception of T at the DS at 10 post-fire years. In the last stage of the study, the averaged H d value of 326 ± 8 cm was found at the DS, which substantially changed depending on factor Treatment from 280 in the non-thinned plots to 371 cm in T 5-159

168 800. In contrast, at the SS, no significant differences between treatments were observed, with an average H d value of 216 ± 8 cm (Table 1). The development of the dry biomass allometric relationships was preceded by a statistical analysis to identify predictive variables (i.e., stem diameter at 30 cm above-ground, d, and tree Age) and factors (Site and Treatment). The results revealed d as the single significant quantitative predictive variable. Besides, particular interactions of the factors Site and Treatment, included in the model by dummy variables, were statistically related to dry biomass. Thus, it was possible to simplify the complete model (at the beginning, we hypothesised that all intercepts and slopes were different for each Site and Treatment) into four homogeneous groups (Table 3). The four allometric relationships were based on the data for all ages (P<0.05, R 2 adj = 93% for the aboveground and R 2 adj = 92% for the belowground) and differed significantly in intercepts and slopes. All the values of the F statistic, testing the null hypothesis versus the complete model were small, verifying that: F = 1.26 < F (0.95; 16, 227) for the above-ground biomass fraction and F = 0.02 < F (0.95; 16, 142) for the below-ground biomass fraction. Thus, it is proved that the proposed simplification of the complete model does not lose signification and, in addition, permits to understand, in an easy way, the behaviour of the allometric equations in terms of Site and Treatment (Table 3). Above-ground biomass stocks Figure 1 shows the change in the stand level biomass in the diachronic study (5-, 7-, 10- and 16-year-old stands) as a function of factor Treatment and Site. The GLMM analysis proved the significance of factor Age at the dry site and factors Age and Treatment at the semiarid site on B A, B A-U, and B A-Pine+U (Table 4). 160

169 Table 3. Simplified models of dry biomass allometries (above- (b A, g) and below-ground (b R, g)) for each Site and Treatment and goodness of fit estimators (adjusted regression coefficient (R 2 adj), standard error of estimation (SEE) and mean absolute error (MAE). See Appendix for definitions. Site Treatment Above-ground Below-ground Eq.1 Dry site Semiarid site T C T 5:9500; T 5:3000; T 5:1600; T 10:1600; T 10:800; T 5+10 b A= d 2 b R= d 2 Eq.2 Dry site T ; T ; T ; T ; T 5+10 b A= d 2 b R= d 2 Eq.3 Dry site T 5:800 b A= d 2 b R= d 2 Eq.4 Semiarid site T C b A= d 2 b R= d 2 R 2 adj MAE SEE Regarding the above-ground pine biomass at the stand level, the GLMM analysis provided the following results. There was a significant positive correlation between B A and factor Age at both sites. Furthermore, the tendency of the B A values during the study differed between both sites, with a lower growth rate at the SS than at the DS. In addition, a positive correlation, but only significant at the semiarid site, was found between B A and Treatment; i.e., the greater the tree density, the larger the B A stock (Figure 1, Table 4). Both sites exhibited a similar natural post-fire regeneration capacity regarding B A, although they presented very different structures in the pine layer. At post-fire year 5, the non-thinned plots attained ~3 Mg ha -1 at both sites, but with 8,200 trees ha -1 at the DS versus 79,000 trees ha -1 at the SS. Over the following years, B A increased in the non-thinned plots without generating any statistical difference between sites. During the same period, B A continued to be higher in the non-thinned plots than in the thinned plots, with one exception at the DS; here the B A of the C plots was overtaken by the B A of the earlier thinned plots (i.e., T 5-800, T , and T ) 5 years after these treatments were carried out (i.e., since 2004). The B A stocks of the different treatments done at the DS were compared, and a relevant effect of cutting age was found. Thus, at 161

170 post-fire year 16, the B A for T and T doubled the value for T and T , respectively, which depicted the greater response of T 5 as compared to T 10. Furthermore, no significant differences were found between T and T Table 4. Coefficients and R 2 obtained in the GLMMs for variables B A; B A-U and B A-Pine+U. See Appendix for definitions. a) Dry site log(b A) B A-U log(b A-Pine+U) Fixed effect Estimate (SE) Estimate (SE) Estimate (SE) (Intercept)* 0.56 (0.08) 2.93 (0.23) 1.55 (0.04) Age (0.08) 1.9 (0.3) 0.42 (0.05) Age (0.08) n.i n.i Age (0.08) 3.9 (0.3) 1.21 (0.05) Random effect σ 2 Plot R n.i: not included; *Age 5 was considered the reference level b) Semiarid site log(b A) B A-U B A-Pine+U Fixed effect Estimate (SE) Estimate (SE) Estimate (SE) (Intercept) 1.48 (0.19) 1.7 (0.5) 7.7 (0.6) Age (0.18) 0.3 (0.3) 0.3 (0.4) Age (0.18) n.i n.i Age (0.18) 2.0 (0.3) 4.3 (0.4) T (0.32) 2.8 (0.9) -3.2 (1.1) T (0.23) 1.0 (0.6) -4.5 (0.8) T (0.21) 1.2 (0.6) -3.8 (0.7) T (0.25) 1.3 (0.7) -2.9 (0.9) T (0.21) 0.6 (0.6) -3.8 (0.7) T (0.23) 1.7 (0.6) -4.3 (0.8) Random effect σ 2 Plot R Non-thinned plots (C) and Age 5 were considered the reference level 162

171 In contrast, the comparison made of the different treatments at the SS in terms of B A stocks, revealed no significant differences due to cutting age or the different final tree densities for the same cutting age. In reference to the above-ground understory biomass at the stand level, the analysis of the diachronic biodiversity inventories of 1999, 2001, 2010 revealed a change over time in the proportion of crown coverage of each species at each site (results are not shown). Despite this change, at the DS more than half the understory coverage was accounted for by only two species at the end of the study: Rosmarinus officinalis L. and the perennial grass Brachypodium retusum (Pers.) Beauv. By the end of the study, at the SS up to six species were required to account for 50% of crown coverage, these being: Macrochloa tenacissima (L.) Kunth.; R. officinalis; B. retusum; Thymus vulgaris L.; Fumana thymifolia (L.) Spach ex Webb. and Plantago albicans L. The development of the average ratio estimators for the growth form groups facilitated the estimation of a higher proportion of understory biomass (a further 7.7% and 15.1% of the total understory coverage at the dry and semiarid sites, respectively). The GLMM analysis revealed a positive correlation of factor Age at dry and semiarid sites on B A-U ; i.e., B A-U increased from nearly ~3 Mg ha -1 at both sites to 6.8 Mg ha -1 at the DS and to 4.8 Mg ha -1 at the SS. The non-thinned plots at the SS showed constant B A-U stocks over time due to the high tree density existing. Unlike the trend shown for B A, no correlation was observed between B A-U and Treatment at the DS. However, a negative correlation was found at the SS; i.e., the lower the final tree density, the larger the B A-U stocks (Figure 1, Table 4). The results revealed statistical differences in the γ ratios not only over time, but also between Sites and Treatments (Figure 2). Therefore, although no significant differences were found in γ for the 5-7-year-old stands related to Treatments, γ lowered between the 5-7-year-old stands and the 16-year-old stands at both sites. In particular, the γ ratios at the DS showed no significant differences related to Treatments for the 5-7-year-old stands (average ratio of ~2). 163

172 a) Dry site Mg ha C BA-U BA T5-800 T T BA-U B A T T B A-U B A T 5+10 BA-U B A b) Semiarid site Mg ha BA-U C BA T T T BA-U B A T T BA-U BA T 5+10 BA-U B A Figure 1. The above-ground biomasses (Mg ha -1 ) of pine (B A) and understory (B A-U) from 5 to 16 years-old and at both sites; dry (a) and semiarid (b). The vertical hatched bars indicate the thinning interventions over time. At the same Age, two B A values appear (before and after treatments were carried out). See Appendix for definitions. 164

173 Dry site Semiarid site e de Age 5 d 2 0 C c T5-800 T T T T Age cd ab c cd b ab ab c ab a C T5-800 d T T T T T T T5+10 Figure 2. The relationship between the above-ground understory biomass and the aboveground pine biomass (γ ratios) for the 5- and 16-year-old stands. Values are displayed for each Site and Treatment. Different letters indicate significant differences (P<0.05) among Treatments at the dry site (a, b) and the semiarid site (c, d, e). See Appendix for definitions. Significant and higher γ ratio was attained for Treatment T for the 16-year-old stands (1.5). At the SS, γ correlated highly with the final tree density; thus after T 5, the removal of ~90% of the initial tree density led to an increase in γ to ~6.9, whereas at the C plots, the γ ratio remained at 1.1. d d ab Finally, total above-ground biomass was considered and a significant positive correlation was found between B A-Pine+U and factor Age at both sites. B A- Pine+U increased from almost 6 Mg ha -1 at both sites to 16.5 Mg ha -1 at the DS and to 165

174 8.8 Mg ha -1 at the SS. Besides, no correlation was found between B A-Pine+U and Treatment at the DS, but a negative one was noted between B A-Pine+U and Treatment at the SS, except for T 5+10 (Figure 1, Table 4). Carbon storage in 16-year-old stands The results indicated that CB T varied with Treatment at each Site, ranging from 4.3 to 8.5 Mg C ha -1 at the dry site, and from 1.01 to 6.9 Mg C ha -1 at the semiarid site. In addition, CB T-U also varied with Treatment, and became higher at the DS (from 4.2 to 5.5 Mg C ha -1 ) than at the SS (from 2.1 to 5.4 Mg C ha -1 ) (Figure 3). Figure 3. Carbon storage of the 16-year-old stands (Mg C ha -1 ). The above- and belowground carbon pine biomasses (CB A, CB R) and the above- and below-ground carbon understory biomasses (CB A-U, CB R-U). Lowercase letters indicate significant differences (P<0.05) between treatments. See Appendix for definitions. On the whole, at both sites, the 16-year-old Aleppo pine stands achieved higher total carbon storage values CB T-Pine+U for T 5 and non-thinned plots (significant only for the semiarid site). In particular, the maximum values were attained for treatment T (13.8 ± 1.1 Mg C ha -1 ) at the DS, and for C (9 ± 1 Mg C ha -1 ) at the SS (Figure 3). In addition, carbon storage at the DS increased on average by 11% with T 5 in comparison to C, whereas it is diminished -21% with T 10. Conversely, at the SS, carbon storage lowered with all the tested treatments in 166

175 comparison to C, and the reduction was stronger with T 10 (-44%) than with T 5 (- 34%). Biomass allocation to roots We studied the effects of factors Site and Treatment on the root:shoot ratios of both, pine and understory strata (i.e., B R /B A and B R-U /B A-U ). In the pine layer, the root:shoot ratio (B R /B A ) varied between treatments (from 0.36 to 0.44 at the DS and from 0.21 to 0.49 at the SS) and was slightly higher, on average, at the DS (0.38) than at the SS (0.34) (Figure 3). Additionally, the root:shoot ratio was lower in the non-thinned plots than in the thinned ones; i.e., at the DS we found on average 0.36 for the non-thinned plots and 0.39 for the thinned plots, whereas we found 0.21 for the non-thinned plots and 0.35 for the thinned plots at the SS. In the understory layer, the root:shoot ratio (B R-U /B A-U ) did not significantly change with treatments, but it became higher at the DS (0.49) than at the SS (0.41) (Figure 3). Discussion Biomass stocks P. halepensis is one of the low timber productivity species of all the native pine trees in Spain, and our study sites located in southeastern Spain corresponded to the lowest site quality of this species (8 and 6, respectively). As Montero et al. (2001) suggested for low site quality stands, thinning should be carried out for other objectives rather than timber, like conservation, biodiversity, restoration, fire prevention or carbon sequestration. The inclusion of the biomass storage of young stands can substantially modify the total carbon pool stored in forest carbon estimations; i.e., young Aleppo pine stands (only pine layer) stored ~10% of the adult Aleppo pine stands (pine layer of about 440 trees ha -1 ) (unpublished results). Auto-succession post-fire regeneration strategies prompted the quick recovery of the affected areas (Trabaud 1994). Thus at the stand level, it is notable that in the short term both stands presented very similar post-fire regeneration 167

176 capacities, accounting a total above-ground biomasses, B A-Pine+U, of ~6 Mg ha -1 (i.e., 3 Mg ha -1 of B A + 3 Mg ha -1 of B A-U ), but with a contrasting pine layer structure. Similar results are reported in the literature for Pinus brutia stands (Zianis et al. 2011). Montès et al. (2004) estimated B A-Pine+U in unmanaged10-year-old post-fire P. halepensis stands, whose understory layer was dominated by Q. coccifera and C. albidus. This author reported B A-Pine+U values of 78.7 Mg ha -1, (29.7 Mg ha -1 for the above-ground biomass of P. halepensis). These results were higher than those reported herein for the 16-year-old non-thinned plots at the dry site (with similar site quality) due to higher tree density (26,400 trees ha -1 ), but also to the enlargement promoted by kermes oaks, whose presence was poor at our dry site (Table 5). The significant and positive effect of factor Age on B A and B A-U showed a higher tendency for B A than for B A-U. Hence at the end of the study, mean annual productivities (López-Serrano et al. 2010) were positive for the pine layer, but negative for the understory layer as the understory was more competitive at younger succession stages (Montès et al. 2004). Obviously, the thinning application reduced B A stocks due to the removal of pine trees. However, at the dry site, the earlier thinning (T 5 ) enlarged the B A storage in the short term (two years after the application) compared to the nonthinned plots. During the diachronic study, the non-thinned plots showed comparable B A stocks at both sites. Furthermore, we found a similar tendency at both sites between treatments, but significant only for the dry site; i.e., the larger the tree density, the larger B A stocks. However, for the aboveground understory biomass we found dissimilar trends between sites regarding factor Treatment; i.e., at the dry site factor Treatment did not significantly affect B A-U stocks, but the lower tree densities at the semiarid site led to larger B A-U stocks. This was due to the gaps opened in the stands after thinning, which positively affected understory coverage (De las Heras et al. 2004; Lloret et al. 2005). 168

177 Table 5. Comparison made of the results found in this and previously published studies for dry biomasses (Mg ha -1 ) and carbon pools (Mg C ha -1 ) in unmanaged regenerated P. halepensis stands. See Appendix for definitions. Location Site P N Understory Age B A B A-Pine+U CB A-Pine+U Southeastern Spain Northeastern Spain Kaye et al. (2010) Southeastern France Montès et al. (2004) Albacete (Yeste) Murcia (Calasparra) Barcelona (Garraf Massif) Marsella (Bouches-du Rhône) P: Precipitation (mm); n.d: no data 595 >7, >100,000 ~650 n.d ,400 Rosmarinus officinalis, Brachypodium retusum Macrochloa tenacissima, Rosmarinus officinalis, Brachypodium retusum, Thymus vulgaris, Fumana thymifolia, Plantago albicans Quercus coccifera, Pistacia lentiscus, Rosmarinus officinalis, Erica multiflora, Cistus albidus, Juniperus oxycedrus, Ulex parviflorus, Ampelodesmos mauritanica Quercus coccifera, Cistus albidus Diachronic 5-16 Diachronic 5-16 Chronosequence/ diachronic ~16 Mg ha -1 with poor pine regeneration ~120 Mg ha -1 with successful pine regeneration ~8 Mg C ha -1 with poor pine regeneration ~60 Mg C ha -1 with successful pine regeneration 3 years-old years-old

178 Furthermore, comparing thinned stands between sites, higher values of γ ratio were attained at the semiarid site (Figure 2). This result depicted the greater contribution of the understory layer to the total biomass storage at the lowest site quality (the semiarid site), mainly in the short term after the earliest thinning (T 5 ) when the understory layer stored seven times more biomass than the pine layer. Consequently, our approach confirmed the negative impact produced after early thinning on carbon storage; but at the semiarid site, this effect was attenuated in the short term due to the quickly recovery of the understory (Navarro et al. 2010). Total understory carbon stocks varied with final tree density and ranged between sites from 4.2 to 5.5 Mg C ha -1, at the dry site, and from 2.1 to 5.4 Mg C ha -1, at the semiarid site. Our results were consistent with those found for Cistus ladanifer and Retama sphaerocarpa communities of Iberian dehesas (6.77 and 4.47 Mg C ha -1, respectively, for each species) (Ruíz-Peinado et al. 2013) and for several Spanish and Italian shrublands (Beier et al. 2009). For the pine layer, we found that the medium thinning intensity (48-61%) carried out 5 years after the establishment did not incur a decay risk for the stands in the areas included as optimal areas, such as the Aleppo pine in dry climates (De las Heras et al. 2013) or the matitime pine (Pinus pinaster) in subhumid climates (Madrigal et al. 2006). In fact, our results revealed that carbon pools enlarged under these final tree densities. However, at the semiarid site, we verified that performing thinning at early stages decreased the dry biomass stocks and carbon storage, mainly for the high thinning intensity (82-95%, 5-year-old stands). The fact that, in our most xeric study area (the semiarid site), the total carbon stocks of forest ecosystems decreased with increasing thinning intensity is in accordance with previous studies on other tree species, such as Skovsgaard et al. (2006) on spruce stands and Jiménez et al. (2011) on young P. pinaster stands. In general, we found several differences between our post-fire regeneration sites and those described by Mòntes et al. (2004) and by Kaye et al. (2010) (Table 5). Taking into account that the post-fire natural regeneration of P. halepensis was highly variable (Trabaud et al. 1985; Lloret and Vilá 2003; Pausas et al. 2004), we 170

179 asserted that the main aspect to consider in order to achieve the rapid recovery of plant coverage and carbon pools in burned ecosystems, lied mainly in the presence or absence of resprouting shrubs. The dominance of Q. coccifera in the understory layer (or other common woody Mediterranean resprouters) guaranteed rapid short-term carbon pool recovery (at post-fire year 15). Nevertheless, the ecosystem s long-term carbon sequestration depended mainly on the tree carbon accumulation rates (Kaye et al. 2010). In this study, the most abundant perennial herbs were resprouters (B. retusum, M. tenacissima and P. albicans), but the biomass storage found in herb species was negligible compared to woody resprouters. The above-ground carbon storage at the dry climate (8 Mg C ha -1 ) was similar to the lowest values reported by Kaye et al. (2010) for young Aleppo pine stands in northeastern Spain (8 to 60 Mg C ha -1, depending on natural pine regeneration success). We found lower values for the above-ground carbon storage at the semiarid site (4.4 Mg C ha -1 ). When we added the below-ground biomass, we obtained ~7 Mg C ha -1 in non-thinned plots at both sites, although with different tree density (ca. 7,000 and 130,000 trees ha -1, at the dry and semiarid climates, respectively); hence the interactions between Site and tree density overlapped. These outcomes were lower than those found in naturally regenerated P. pinaster stands in Central Spain, i.e., 16 Mg C ha -1, with a tree density of 10,400 trees ha -1 (Madrigal et al. 2006). Biomass allocation to roots In this study, the root:shoot ratio for the pine layer accounted for an average 0.37 for both the study sites in the 16-year-old stands. Higher root:shoot ratios were attained for the understory layer, with average values of 0.45 at both sites. However, these results were comparatively lower than the root:shoot ratios found in steppes dominated by M. tenacissima (~0.6) (Sánchez 1995) or in garrigue ecosystems dominated by Q. coccifera (~3.5) (Cañellas and San Miguel 2000). Our values for the pine layer were in keeping with the root:shoot ratios found in the open adult P. halepensis stands (0.35) in Mediterranean dry climates (unpublished results), but were lower than stands of adult P. halepensis trees (0.23) occurring in southeastern Spain (Ruíz-Peinado et al. 2011). In addition, 171

180 other authors recorded lower root:shoot ratios for other pine species at younger stages. For instance, Xiao et al. (2004) obtained average ratios of 0.26 for 10-yearold Pinus sylvestris, and Peichl and Arain (2006) reported a value of 0.24 in 15- year-old Pinus strobus. Differences in the root:shoot ratios can considerably change owing to tree size and stand characteristics (Albaugh et al. 2006; Ruíz- Peinado et al. 2011), but also due to inaccuracies occurring during the root extraction process. In this study, differences in the pine root:shoot ratios were triggered by factors Site and Treatment, and showed high variability in accordance with final tree density; i.e., higher ratios were attained at the best site quality (the dry site), but lowered with higher tree density at both sites. Conclusions In total above-ground biomass storage terms, similar post-fire regeneration capacities were observed at both site qualities, but with a contrasting pine layer structure. At the stand level, thinning brought about a general decrease in the biomass stocks at both sites, except for the best site quality (the dry site), where earliest thinning (carried out at 5 post-fire) enlarged carbon storage compared to the non-thinned plots. Thus, for Aleppo pine forests occurring at semiarid sites with a substantial post-fire P. halepensis sapling establishment, carbon storage was maximized with no forest management. However, we suggest applying early thinning to avoid high fuel load accumulation, to prevent fire hazards, to enhance structural patterns and to improve resilience. The root:shoot ratios obtained for pine and understory strata (on average 0.37 and 0.45 for each layer, respectively), highlight the importance of including roots to accurately estimate carbon pools (Ruíz-Peinado et al. 2011). In the global change context, where increases in temperature, low and irregular rainfall, plus a greater fire risk are predicted, an assessment of the long-term effects of thinning interventions is mandatory in order to design optimal forest management for different biomass storage priorities (Millar et al. 2007). In southeastern Spain, low productivity means losing commercial opportunities for timber, but a new window related to carbon storage and climate 172

181 change mitigation can be opened (Montero et al. 2005; Bravo et al. 2008; Doblas- Miranda 2013). Acknowledgments We thank the Spanish Ministry of Science and Innovation for its funding and support to the Forest Ecology Researching Group in Projects CYCIT-AGL /FOR; AGL /FOR and CONSOLIDER-INGENIO 2010: MONTES (CSD ). R. Sánchez-Salguero thanks the financial support from University of Córdoba-Campus de Excelencia ceia3. We also wish to thank the Regional Forestry Services of Castilla-La Mancha and Murcia for providing the research sites and Helen Warburton for the language review. References Albaugh TJ, Allen HL, Kress LW (2006) Root and stem partitioning of Pinus taeda. Trees 20, Barton K (2013) MuMIn: Multi-model inference. R package version Bates DM, Maechler M, Bolker B (2014) lme4: linear mixed-effects models using S4 classes. R package version Beier C, Emmett BA, Tietema A, Schmidt IK, Peñuelas J, Lang EK, Duce P, De Angelis P, Gorissen A, Estiarte M, de Pato GD, Sowerby A, Kröel-Dulay G, Lellei-Kovacs E, Kull O, Mand P, Petersen H, Gjelstrup P, Spano D (2009) Carbon and nitrogen balances for six shrublands across Europe.Global Biogeochem Cycles 23, GB4008 Bravo F, Bravo-Oviedo A, Diaz-Balteiro L (2008) Carbon sequestration in Spanish Mediterranean forests under two management alternatives: a modelling approach. Eur J Forest Res 127, Bravo F, Álvarez-González JG, del Río M, Barrio-Anta M, Bonet JA, Bravo-Oviedo A, Calama R, Castedo-Dorado F, Crecente-Campo F, Condés S, Diéguez-Aranda U, González-Martínez SC, Lizarralde I, Nanos N, Madrigal A, Martínez-Millán FJ, Montero G, Ordóñez C, Palahí M, Piqué M, Rodríguez F, Rodríguez-Soalleiro R, Rojo, A, Ruiz-Peinado R, Sánchez-González M, Trasobares A, Vázquez-Piqué J (2012) Growth and yield models in Spain: historical overview, contemporary examples and perspectives. < 173

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187 Appendix. Symbols, definitions and units used Symbol or Definition Unit acronym DS Site representative of a dry ombroclimate SS Site representative of a semiarid ombroclimate C Non-thinned plots (control) T 5 Thinning treatments carried out at five years-old (1999) T 10 Thinning treatments carried out at ten years-old (2004) T Thinning at five years-old (1999) to a final density of 800 trees ha -1 T Thinning at five years-old (1999) to a final density of 1,600 trees ha -1 T Thinning at five years-old (1999) to a final density of 2,400 trees ha -1 T Thinning at five years-old (1999) to a final density of 3,000 trees ha -1 T Thinning at five years-old (1999) to a final density of 9,500 trees ha -1 T Thinning at ten years-old (2004) to a final density of 800 trees ha -1 T Thinning at ten years-old (2004) to a final density of 1,600 trees ha -1 T 5+10 Thinning at five years-old (1999) to a final density of 1,600 trees ha -1 and thinning at ten years-old (2004) to a final density of 800 trees ha -1 n Number of plots N Tree density trees ha -1 N initial Tree density before treatments trees ha -1 N 5 Tree density after T 5 trees ha -1 N 10 Tree density after T 10 trees ha -1 d Average diameter at 30 cm above-ground cm h Average height of pines after treatments cm H d Dominant height cm I 5 Thinning intensity (percentage of thinned trees) = 100 (N initial- N 5)/N initial) % I 10 Thinning intensity (percentage of thinned trees) = 100 (N 5- N 10)/N 5) % b A Above-ground pine biomass of a tree g b R Below-ground pine biomass of a tree g h Ui Average height for the i understory species cm CC i Crown coverage for the i understory species cm H Ai Ratio estimator of the above-ground understory biomass for the species i g m -2 H Ri Ratio estimator of the below-ground understory biomass for the species i g m -2 B A-U Above-ground understory biomass at the stand level Mg ha -1 B R-U Below-ground understory biomass at the stand level Mg ha -1 Understory root:shoot ratio at the stand level B R-U/B A- U B A Above-ground pine biomass at the stand level Mg ha -1 γ The relationships between the above-ground understory biomass and the above-ground pine biomass = B A-U / B A B R Below-ground pine biomass at the stand level Mg ha -1 B R/B A Pine root:shoot ratio at the stand level B A-Pine+U Total above-ground biomass at the stand level (pine plus understory) Mg ha -1 CB A Above-ground carbon pine biomass at the stand level Mg C ha -1 CB R Below-ground carbon pine biomass at the stand level Mg C ha

188 CB T Total carbon pine biomass at the stand level = CB A + CB R Mg C ha -1 CB A-U Above-ground carbon understory biomass at the stand level Mg C ha -1 CB R-U Below-ground carbon understory biomass at the stand level Mg C ha -1 CB T-U Total carbon understory biomass at the stand level = CB A-U + CB R-U Mg C ha -1 Total carbon pine plus understory biomass at the stand level Mg C ha -1 CB T- Pine+U 180

189 5. Discusión general 181

190 182

191 5. DISCUSIÓN GENERAL Secuestro de carbono y diversidad de especies La planificación forestal de masas poco productivas del SE español debería incluir entre sus objetivos el mantenimiento y mejora de la capacidad de secuestro de carbono a nivel de paisaje (Bravo et al. 2008), pero sin aumentar por ello el riesgo de incendios forestales u otros efectos adversos Para ello se deben implementar herramientas de manejo que sean eficaces para la consecución de dicho objetivo, como la aplicación de tratamientos selvícolas. El éxito por tanto de un adecuado manejo del monte en estas zonas pasaría por encontrar el equilibrio adecuado entre capacidad de secuestro de carbono y grado de vulnerabilidad ante perturbaciones, considerando como tal, la combinación entre riesgo de afectación y aumento de la resiliencia. Los resultados del Capítulo 3 mostraron que la aplicación de tratamientos a edades tempranas aumentaban la biomasa acumulada (que multiplicada por un factor de conversión (0.5) se convierte a C) a nivel de árbol en ambas zonas de estudio, corroborando los resultados de otros trabajos que estudiaban implementar herramientas similares (De las Heras et al. 2013, Jiménez et al. 2011). Sin embargo, a nivel de paisaje los resultados del Capítulo 4 evidenciaron una alta variabilidad de la biomasa acumulada en función de las densidades finales del arbolado, constatando también diferencias entre sitios así como en las estructuras de almacenaje de este C fijado. Por un lado, regenerados con densidades finales entre pies ha -1 generan elevadas concentraciones de biomasa en un menor número de individuos. Estos valores de biomasa fijadas no solo son mayores, sino que se concentran en el fuste de troncos, a la vez que se reduce la masa acumulada en copa, que es la parte más inflamable del individuo. Por otro lado, las masas que no fueron clareadas y presentaban alta densidad de individuos llegaron a almacenar más biomasa (C) que aquellas tratadas, pero repartiendo mayor porcentaje de recursos a la copa (Capítulo 4). 183

192 Al tratarse de masas jóvenes, el reparto entre los componentes de la biomasa a nivel de árbol presentó de media un mayor porcentaje en la copa (68%) que en el tronco (32%) en comparación con individuos adultos de pino carrasco que mostraron igual reparto entre copa y tronco, es decir 50% cada parte (Ruíz- Peinado et al. 2011). Sin embargo, se encontraron diferencias significativas comparando entre sitios al estudiar árboles procedentes de parcelas con las mismas densidades. Por ejemplo, se hallaron menores porcentajes de copa (presentado altos valores de biomasa localizada en el tronco) en Yeste (ombroclima seco) y viceversa para Calasparra (semiárido), debido principalmente a la mayor disponibilidad de nutrientes y agua de la mejor calidad de sitio (Yeste) (López-Serrano et al. 2005). Además, los individuos mostraron una tendencia a reducir la inflamabilidad con la edad, disminuyendo el porcentaje de acículas (parte fina muy inflamable) y aumentando el porcentaje de biomasa localizada en tronco (Capítulo 3). En este sentido, los clareos, las podas y los desbroces, se muestran como herramientas selvícolas útiles para implementar un manejo forestal adaptativo en áreas propensas a incendios y sometidos a gran influencia de efectos negativos derivados del cambio global (p.ej. variación del régimen de incendios), como el caso que ocupa este estudio localizado en zonas xericas de la Cuenca Mediterranea. Con la implementación de estas herramientas se busca reducir la continuidad vertical y horizontal del combustible, especialmente en masas coetáneas y por tanto la probabilidad o el riesgo de sufrir un incendio de alta intensidad (Vayreda et al. 2012). Además, los bosques menos densos serian menos vulnerables a ataques bióticos y más resilientes ante la incidencia de episodios cada vez más frecuentes de sequía extrema (Poyatos et al. 2013). Los estudios de secuestro de C a gran escala (global o nacional), suponen una fuente de información muy interesante a la hora de cuantificar y entender los patrones de reservorio y de sumidero C a esas escalas (Vayreda 2013). Sin embargo, los resultados de esta tesis demuestran la infravaloración que conlleva extrapolar resultados obtenidos en un estudio a escala nacional, o aplicar metodologías generales, a escalas más reducidas. Se pone de manifiesto por 184

193 tanto, la importancia de llevar a cabo estudios locales, al menos para los bosques de pino carrasco, a la hora de recomendar estrategias de gestión que aseguren un manejo adaptativo preciso. Un ejemplo de lo anteriormente dicho puede encontrarse en los resultados obtenidos en el presente estudio, en relación a la fijación de carbono por parte de las raíces. Así, los cálculos obtenidos a escala de paisaje en el Capítulo 4 indican que la parte subterránea supone el 37% de la parte aérea del estrato arbóreo y llega a representar el 45% para el sotobosque. Estos valores resultaron superiores a los obtenidos en estudios a escala nacional para el pino carrasco donde se estimó un 25% para estrato arbóreo y 29% para el sotobosque (Vayreda et al. 2012). Por otro lado, la fracción representada por la vegetación leñosa alcanza el 23% del C total acumulado en los bosques de pino carrasco españoles (Vayreda et al. 2012). El presente estudio, desarrollado a escala local, muestra cómo para masas jóvenes esta proporción resulta considerablemente más alta y por otro lado, que la proporción de sotobosque disminuye conforme aumenta la edad de la masa en favor del estrato arbóreo. De esta manera, para las parcelas control la proporción de sotobosque se reduce en Yeste desde un 244% a los 5 años de edad al 77% a los 16 años y del 630% al 30% en Calasparra, debido a la mayor competencia del matorral en las etapas sucesionales iniciales (Montès et al. 2004). Del mismo modo, estas proporciones se ven muy afectadas por el manejo selvícola, debido a que la puesta en luz del suelo tras los tratamientos, favorece la colonización de especies del sotobosque (De las Heras et al. 2004; Lloret et al. 2005). Los resultados reflejaron este efecto especialmente en la zona semiárida, donde a los 16 años la proporción de sotobosque llegó a un 300% en las zonas tratadas, mientras que en Yeste esta proporción fue justo la mitad (Capítulo 4). Estudios previos a escala nacional han demostrado la importancia de la riqueza de especies a la hora de predecir el C acumulado (Vayreda et al. 2012), si bien determinadas especies clave pueden resultan mejores predictoras para el almacenaje de C que la riqueza total de especies (Kahmen et al. 2005). Por ejemplo, en las localidades de estudio, la presencia o ausencia de especies 185

194 leñosas rebrotadoras como la coscoja (Quercus coccifera) podría aumentar considerablemente la fijación de C. La diferente respuesta de los ecosistemas mediterráneos ante un incendio forestal está condicionada por numerosos factores, siendo determinante las características de la vegetación previa (Arnan et al. 2007) y el historial de uso (Puerta-Pinero et al. 2012). Los resultados obtenidos en este estudio matizan esta hipótesis especialmente para la zona de Calasparra, ya que la regeneración postincendio estuvo dominada en el sotobosque por el esparto, debido a su naturaleza rebrotadora (Rodrigo et al. 2004). El hecho de que la masa quemada en esta zona fuera una repoblación donde el origen de la semilla se desconoce, puede haber influido en la respuesta observada de la masa en cuanto a la riqueza de especies detectada y en consecuencia al almacenamiento de biomasa (Hernández-Técles 2014). Conforme aumenta la aridez, la abundancia de especies rebrotadoras disminuye a la par que aumentan las germinadoras (Pausas et al. 2004, Lloret et al. 2005) (Lloret y Vilà 1997), debido a la mayor tolerancia a la sequía de las últimas en comparación con las rebrotadoras (Keely 1986). En este sentido, el aumento de la superficie cubierta por especies arbustivas leñosas y/ o especies de hoja caduca, sería fundamental para mejorar el almacenaje de C en zonas tan poco productivas como las del SE español. La biodiversidad, distribución de especies y tasas de crecimiento se esperan que cambien con los cambios locales de temperatura y disponibilidad de agua (De Luis et al. 2013) aunque también se ven afectados por su distribución espacial, competencia y variación temporal, según el estado sucesional se acerque o aleje de la situación climax. Sin embargo, la implementación de una gestión que favorezca la biodiversidad de los ecosistemas forestales, tanto interespecifica como intra-especifica incrementa la heterogeneidad del paisaje también aumentará la resiliencia de los mismos. (Puerta-Piñero et al. 2013). Además, bosques con mayor diversidad de especies promueven un uso más eficiente de 186

195 los recursos y mayores tasas de carbono almacenado que áreas con baja diversidad de especies (Vilà et al. 2007). Caracteres reproductivos: inversión en recursos La resiliencia de los ecosistemas mediterráneos frente a los incendios forestales puede no ser suficiente ante el nuevo régimen de incendios modificado por los efectos del cambio global, lo que puede comprometer la recuperación del ecosistema (De las Heras et al. 2013b). Ante este escenario, las comunidades vegetales deberían manejarse mediante tratamientos selvícolas para adelantar la madurez reproductiva, disminuyendo así el riesgo de desaparición local y aumentando su capacidad de regeneración tras una nueva perturbación. El ciclo reproductivo del pino carrasco requiere cuatro estaciones de crecimiento, o al menos tres años de calendario (Panetsos 1981). La formación de los estróbilos femeninos comienza al final de la estación de crecimiento, el proceso de polinización ocurre en primavera mientras que la fertilización tiene lugar a la primavera siguiente. Entre las dos localidades de estudio encontramos diferencias en la fenología reproductiva, adelantándose ligeramente en Calasparra debido a las condiciones más templadas de esta localidad situada más cerca de la costa Mediterránea. En la Figura 1 se muestran las relaciones entre el crecimiento radial del individuo, repartido en madera temprana (EW) y madera tardía (LW), la producción de piñas verdes y su interacción con el clima. Para incluir todo el periodo de tiempo que pudiera estar afectando a ambas variables, se analizaron las condiciones climáticas desde la formación total de las piñas verdes hasta dos años antes de su aparición. Tras analizar dichas interacciones, se encontró una relación positiva entre el crecimiento radial y la formación de piñas verdes, es decir condiciones favorables para el crecimiento radial de los individuos también estimularon una mayor producción de piñas nuevas (Figura 1). Además, la producción de piñas mostró una correlación positiva con condiciones climáticas 187

196 favorables previas a la inducción de la formación de los estróbilos femeninos y antes de la fertilización confirmando la importancia de la disponibilidad hídrica adecuada para favorecer la formación de las piñas y también para el crecimiento radial (Figura 1). 188

197 (a) Yeste (sitio seco) t-2 t-1 t Clima-Piñas verdes Clima-Crecimiento EW t-2 LW t-2 EW t-1 LW t-1 EW t T- P-PET+ T+ M A My J Jl A S O N D E F M A My J Jl A S O N D E F M A My J Jl A T- T- P+ P- T+ T- T+ P+ Inducción estróbilos fem. T- P-PET+ Polinización T+ Estróbilos fem. T- P-PET+ Fertilización Piñas verdes LW t T+ Crecimiento-Piñas verdes 189

198 (b) Calasparra (sitio semiárido) t-2 t-1 t Clima-Piñas verdes Clima-Crecimiento EW t-2 LW t-2 EW t-1 LW t-1 EW t T- P-PET+ M A My J Jl A S O N D E F M A My J Jl A S O N D E F M A My J Jl A T- P+ P- T+ T- Inducción estróbilos fem T- P-PET+ Polinización Estróbilos fem. T- P-PET+ Fertilization Piñas verdes LW t Crecimiento-Piñas verdes 190

199 Figura 1. Esquema de las relaciones Clima-Crecimiento, Clima-Piñas verdes y Crecimiento- Piñas verdes para Yeste (a) y para Calasparra (b). t = año de referencia para la completa formación de piñas verdes; EW= earlywood (madera temprana); LW= latewood (madera tardía); T= temperatura; P-PET = balance hídrico; P= Precipitación. Flechas rojas indican condiciones secas y cálidas y las flechas azules indican condiciones húmedas y frías. Las flechas rojas y azules más finas corresponden a las relaciones Clima-Crecimiento (parte superior del esquema) mientras que las fechas rojas y azules más gruesas corresponden a las relaciones Clima-Piñas verdes (parte inferior del esquema). En este estudio se detectaron diferentes dinámicas reproductivas entre las masas de pino carrasco situadas en las dos localidades de estudio. Al comparar la regeneración natural en las distintas poblaciones seleccionadas para este estudio, se pueden confundir las causas de variación genética y ambiental debido a la interacción de los caracteres reproductivos y climáticos. Sin embargo, aunque el origen genético de las poblaciones resulta fundamental para entender las respuestas reproductivas de los individuos y su interacción con el ambiente, estudios previos han revelado una elevada plasticidad fenotípica en el pino carrasco (Santos-del-Blanco et al. 2013), además de una baja diferenciación genética en la Península Ibérica (Grivet et al. 2009). Cinco años después del incendio, y antes de que se ejecutaran los tratamientos propuestos, los regenerados de pino carrasco localizados en Calasparra presentaban un mayor porcentaje de pies reproductivos que los de Yeste (15 vs. 7%, respectivamente). Solo dos años después de que se ejecutaran los tratamientos, los individuos de Yeste superaban en porcentaje de pies reproductivos a los individuos de Calasparra. En el periodo que abarca el presente estudio, de 17 años de edad, obtuvimos porcentajes finales de pies reproductivos del 83% en Yeste frente al 60% de Calasparra (Capítulo 2). Por otro lado, los resultados también mostraron que en masas coetáneas, los pies reproductivos presentaban mayores valores significativos de biomasa tanto a los 5 como a los 16 años tras la regeneración post-incendio frente a individuos no reproductivos (Figura 2), demostrando que los individuos con mayores crecimientos (más vigorosos) inician la producción de conos femeninos antes (Thanos y Daskalakou 2000, Ne eman et al. 2004, Daskalakou et al. 2014),

200 presentando además una mayor producción de piñas (Moya et al. 2008, Ortiz et al. 2011) (Capítulo 2). Biomasa aérea (g) Figura 2. Biomasa aérea media por árbol (g) para las dos localidades de estudio a los 5 y a los 16 años. Los asteriscos muestran diferencias significativas entre pies reproductivos y no reproductivos obtenidos mediante ANOVAs (P<0.05) realizados por separado para cada localidad y edad. En el Capítulo 3 se observó como la precocidad en la producción de conos femeninos en Calasparra está ligada con una elevada inversión reproductiva por unidad de biomasa (Haymes y Fox 2012). Sin embargo, esta mayor inversión de recursos hacia las piñas no se mantuvo en el tiempo en estos individuos. Al final del periodo de estudio encontramos una mayor asignación de recursos a las piñas en los individuos de Yeste, debido en parte a las mayores producciones de piñas por árbol registradas * No reproductivos Reproductivos DS -5 yr SS -5 yr DS -16 yr SS -16 yr Una mayor inversión de recursos a los 5 años de edad protagonizado por individuos localizados en zonas de baja calidad de estación, podría atender a adaptaciones de la especie como resultado de una alta recurrencia de incendios (Naveh 1990; Agee 1998; Tapias et al 2001; Ne eman 2004). * Una alta producción de piñas a edades muy tempranas se produce como respuesta de los individuos a condiciones ambientales adversas, puesto que el * Yeste Calasparra Yeste Calasparra 5 años 16 años * 192

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205 6. Conclusiones 197