Climate change and exotic pathogens shift carbon allocation in Mediterranean mixed forests

Journal of Ecology. 2024;112:2843–2860. | 2843wileyonlinelibrary.com/journal/jec Received: 25 March 2024 | Accepted: 30 August 2024 DOI: 10.1111/1365-2745.14426 RESEARCH ARTICLE Climate change and exotic pathogens shift carbon allocation in Mediterranean mixed forests Álvaro Gaytán1 | Luis Matías2 | Óscar Godoy3 | Ignacio M. Pérez- Ramos1 | Pablo Homet4 | Xoaquín Moreira5 | Lorena Gómez- Aparicio1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. 1Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), Consejo Superior de Investigaciones Científicas (CSIC), Sevilla, Spain 2Departamento de Biología Vegetal y Ecología, Universidad de Sevilla, Sevilla, Spain 3Departamento de Biología, Instituto Universitario de Investigación Marina (INMAR), Universidad de Cádiz, Puerto Real, Spain 4Integrative Ecology Group, Estación Biológica de Doñana, Sevilla, Spain 5Misión Biológica de Galicia (MBG), Consejo Superior de Investigaciones Científicas (CSIC), Pontevedra, Galicia, Spain Correspondence Álvaro Gaytán Email: [email protected] Funding information Ministerio de Ciencia e Innovación, Grant/ Award Number: CGL2014- 56739- R, JDC2022- 048313- I, RTI2018- 094394- B- I00 and RYC2017- 23666; Universidad de Sevilla, Grant/Award Number: VIPPIT; Xunta de Galicia, Grant/Award Number: IN607A 2021/03; Consejo Superior de Investigaciones Científicas, Grant/ Award Number: 2021AEP082; Ministerio de Economía y Competitividad, Grant/ Award Number: PID2019- 108288RA- I00; European Regional Development Fund, Grant/Award Number: LIFEWATCH- 2019- 0 9 - C S I C - 1 3 Handling Editor: Anne Kempel Abstract 1. Forecasting the effects of global change drivers on ecosystems is one of the most pressing challenges for scientists worldwide. Particularly, climate change and exotic pathogens might have a large impact on plant community dynamics and ecosystem functioning through changes in carbon uptake and sinks. Nevertheless, we still have a poor understanding of the combined effects of these two drivers on plant communities. 2. Here, we explored the impact of rainfall reduction and exotic pathogens on the carbon balance of Mediterranean tree species. For this, we performed a 3- year field experiment taking advantage of rainfall exclusion infrastructures (30% exclusion) installed in the southernmost European oak forests invaded by the aggressive exotic pathogen Phytophthora cinnamomi. We measured a set of 10 variables representative of tree carbon sources (photosynthetic rates) and sinks (primary production, reproduction, defence, and reserves) in adult trees of three species in two forest types: closed forests of Quercus suber and Q. canariensis, and open woodlands of Q. suber and Olea europaea. 3. We found a large variability in the sensitivity of the different carbon sources and sinks to the effects of drought and pathogens, from variables highly sensitive to both factors (carbon fixation and reproduction, root chemistry) to variables only responsive to drought (litter production) or totally unresponsive (tree trunk, leaf chemistry). Although negative effects predominated, positive effects of rainfall exclusion were also detected in wet years, likely due to a reduction of pathogen abundance in drier soil. Tradeoffs between carbon sinks appeared in all tree species, but rainfall exclusion only modified tradeoffs in Q. suber, the species most susceptible to P. cinnamomi. 4. Synthesis. We provide evidence on the complexity of the combined effects of abiotic (drought) and biotic (pathogens) global change drivers on carbon source and sinks of adult trees, including both negative direct effects and positive indirect effects. Our results showed that these effects varied among coexisting species, particularly for carbon sinks directly related to tree demography (reproduction). 2844 | GAYTÁN et al. 1 | INTRODUCTION Abiotic and biotic drivers of global change are having large impacts on plant community dynamics and ecosystem function (Asner et al., 2004; Peñuelas et al., 2017; Stocker et al., 2013). In large parts of the planet, rainfall reduction has been shown to strongly modify tree performance, reducing growth (Camarero et al., 2018; Jin, Li, Liu, Liu, Zhang, Sha, et al., 2018; Moreno et al., 2021), reproduction success (Gavinet et al., 2019; Ogaya & Peñuelas, 2007; Pérez- Ramos et al., 2010), and altering nutrient balance and resource investment in chemical defences (Guo et al., 2022; Lin et al., 2018; Rosas et al., 2013). Among biotic drivers, exotic pathogens are behind many serious emerging diseases responsible for increasing tree mortality rates, constituting a major threat to global biodiversity and the functioning of terrestrial ecosystems (Essl et al., 2020; Hansen, 2008; Lowe et al., 2000). Both abiotic and biotic drivers of global change can act together to affect plant species in both additive and nonadditive ways (Gomez- Gallego et al., 2022; Gutbrodt et al., 2011; Homet et al., 2021, 2024; Huang et al., 2020; Serrano et al., 2021, 2022). Despite the recent studies in this line, we still have a poor understanding of the combined effects of abiotic and biotic global change drivers on key aspects of plant functioning, including carbon (C) uptake and its allocation to different C- sinks such as growth, reproduction, defence, or reserves. Drought and damage by pathogens frequently decrease plant C- uptake (Dietze et al., 2014; Gessler & Grossiord, 2019; Weber et al., 2019). Such abiotic and biotic stresses can translate into a lower leaf production, which implies lower C assimilation due to reduced photosynthetically active tissue (Galiano et al., 2011; Gomez- Gallego et al., 2022; Salmon et al., 2015). Trees could also respond to climate change and biotic damage by shifting allocation between the different C- sinks, resulting in tradeoffs with direct relevance for individual fitness and population dynamics (Dietze et al., 2014; Huang et al., 2020; Huot et al., 2014; Salguero- Gómez et al., 2012). A classical tradeoff in ecological theory is the one involving defence and growth, where investment in defensive compounds to minimize biotic damage is conducted at the expense of lower growth (Coley et al., 1985; Glynn et al., 2007; Loomis, 1932). Similarly, trees with C limitations due to abiotic drivers of global change could prioritize resource investment in growth at the expense of defences, facilitating damage by antagonistic organisms (Gomez- Gallego et al., 2022; Gutbrodt et al., 2011; Lemmermeyer et al., 2015). Studies of tradeoffs between defence and reproduction are much scarcer, but their existence has been proved at least in some contexts (Bogdziewicz et al., 2020; Obeso, 2002; Redmond et al., 2019; Tuller et al., 2018). Tree species also vary strongly in their allocation of resources between growth and reproduction, with fastgrowing species tending to maintain their fruit production at the expense of decreasing growth and slowgrowing species usually prioritizing primary production over reproduction (Dietze et al., 2014; Vergotti et al., 2019). Moreover, longlived organisms like trees can allocate C to reserves at the expense of other functions as a strategy to survive under stressful periods (Hartmann et al., 2020). Understanding the combined effects of abiotic and biotic global change drivers on C tradeoffs is of upmost importance, since they might have direct consequences on tree demographic rates (regeneration, growth, and mortality) that will determine the ability of each species to persist in future forests (Salguero- Gómez et al., 2016). Waterlimited ecosystems as those of the Mediterranean region are particularly susceptible to the impact of different global change factors (Ali et al., 2022; Stocker et al., 2013). The Mediterranean region is warming 20% faster than the global average, which will exert additional pressure on already stressed ecosystems due to water scarcity (Newbold et al., 2020; Peñuelas et al., 2017). Furthermore, the Mediterranean region is a hotspot for exotic species, where the number of exotic pathogens has increased exponentially in the last four decades due to a high international trade, an enormous heterogeneity of environments and a high diversity of potential hosts (Garbelotto & Pautasso, 2012; Santini et al., 2013). Water limitation together with many exotic species make Mediterranean forests a key system to study the combined effects of abiotic and biotic drivers of global change on ecosystems. In addition, Mediterranean forests are frequently composed of tree species that vary in their response to global change drivers, including species with contrasting drought tolerance and susceptibility to exotic pathogens (Bashir et al., 2021; Hardham & Blackman, 2018; Pagán & García- Arenal, 2018). These speciesspecific responses might imply alterations of forest composition in the longterm, promoting the dominance of those species with the highest resistance to the negative effects of multiple global change drivers on their C balance. In this study, we aim to investigate the individual and combined effects of abiotic (increasing drought) and biotic (exotic pathogens) drivers of global change on the C allocation of adult trees in Mediterranean mixed forests. For this, we took advantage of rainfall exclusion infrastructures installed in the southernmost European oak forests, which currently suffer an important oak decline due Therefore, longterm changes in the structure of Mediterranean mixed forests might be expected towards the dominance of species highly resistant to both drought and pathogens. KEYWORDS litterfall, nonstructural carbohydrates, phenols, photosynthesis, rainfall reduction, reproduction, soilborne pathogens 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 2845 GAYTÁN et al. to increasing aridity and infection by the aggressive exotic pathogen Phytophthora cinnamomi (Avila et al., 2017; Gómez- Aparicio et al., 2012). Rainfall exclusion infrastructures were installed in spring 2016 in two types of mixed oak forests in southern Spain: a closed forest of Quercus suber and Q. canariensis, and an open woodland of Q. suber and Olea europaea. These infrastructures excluded 30% of the annual rainfall, simulating predictions of climate change models for southern Europe (Ali et al., 2022). During the first 2 years after the installation of the infrastructure (2016–2018), we measured a set of variables related to C- uptake and investment in primary production, reproduction, defence, and reserves in adult trees of the three species. We addressed three specific questions: (1) How rainfall exclusion and the abundance of P. cinnamomi affect the main C- source (photosynthesis) and C- sinks (primary production, reproduction, defences, and reserves) of adult trees? We hypothesized that both rainfall exclusion and the abundance of exotic pathogens would negatively affect C- uptake and C- sinks of adult trees (Adams et al., 2015; Chambi- Legoas et al., 2020; Guo et al., 2022; Limousin et al., 2009). However, recent studies have demonstrated that rainfall exclusion can significantly reduce P. cinnamomi abundance in the soil (Serrano et al., 2022, 2024). Therefore, we also predicted that rainfall exclusion could have not only direct negative effects on adult performance due to lower soil water availability but also positive (indirect) effects mediated by lower pathogen abundance, as has been shown for tree seedlings (Homet et al., 2024); (2) How does rainfall exclusion affect tradeoffs between different C- sinks of adult trees? We expected tree species to follow a conservative strategy, prioritizing defence and storage over primary production or reproduction to mitigate the C- limitation imposed by rainfall exclusion and P. cinnamomi attack (Dietze et al., 2014; Kuang et al., 2017; Signori- Müller et al., 2021); (3) Do coexisting tree species differ in their response to rainfall exclusion and P. cinnamomi in terms of C balance? We hypothesized that rainfall reduction and exotic pathogens would have asymmetric impacts among the studied species, since they differ in their susceptibility to drought (Q. canariensis > Q. suber > O. europaea; Gómez- Aparicio et al., 2008; Ibáñez et al., 2017) and P. cinnamomi attack (Q. suber > Q. canariensis > O. europaea; Gómez et al., 2020). The answers to these questions will contribute to advance our understanding of the combined impacts of both abiotic and biotic stressors on the C balance of tree species coexisting in ecosystems particularly sensitive to global change, as the Mediterranean forests. 2 | METHODS 2.1 | Study site and species The study was conducted in Los Alcornocales Natural Park (Cádiz, Spain, 36.568749, −5.535640) from 2016 to 2018, where we obtained the consent of the local landowners to proceed with our experiment. Los Alcornocales is the largest and bestpreserved Quercus suber (L.) forest in Europe (Figure S1a,b; Urbieta et al., 2008). Quercus suber is an ecologically and economically emblematic tree for Mediterranean landscapes that provides valuable natural products such as cork and acorns (Moreno & Pulido, 2009). The climate is subhumid Mediterranean and 95% of rainfall is concentrated from October to May, with an annual mean precipitation of 748 mm (ranging from 620 to 2100 mm; Junta de Andalucía, 2017). The broad variability in total rainfall described for this area was represented during the study period: 2016 was a dry year (670 mm), 2017 was an average year (980 mm), and 2018 was an unusual wet year (1384 mm). Soils are sandy, acidic and nutrientpoor, derived from bedrock dominated by Oligo- Miocene sandstones. We selected two sites separated by c. 4 km. The first site was a closed forest where the evergreen Q. suber coexists with the semideciduous shadetolerant Quercus canariensis (Willd.). Within the natural park, closed forests appear in wet areas on sandy soils and have a sparse understory dominated by tall shrubs, particularly Erica arborea (Figure S1c). The second site was an open woodland where Q. suber coexists with the evergreen, shadeintolerant Olea europaea var. sylvestris (L.). Open woodlands generally appear in the drier lowlands of the area on clayish soils and have a dense and rich understorey composed by shrub species such as Pistacia lentiscus, Genista spp., Ulex spp., Erica scoparia and Cistus spp. (Figure S1e). Both study sites are invaded by the oomycete soilborne pathogen Phytophthora cinnamomi (Serrano et al., 2022, 2024), considered one of the 100 most devastating exotic species in the world (Camilo- Alves et al., 2013; Lowe et al., 2000). P. cinnamomi is a generalist pathogen with thousands of hosts that strongly differ in susceptibility (Hardham & Blackman, 2018). Among the three study species, Q. suber is with difference the most susceptible species, followed by Q. canariensis and O. europaea (Gómez et al., 2020). Although P. cinnamomi is widely spread in the study area (Serrano et al., 2024), aboveground symptoms of the disease at the start of the experiment were still very limited and restricted to some Q. suber trees, likely reflecting the initial stage of the decline process. In fact, P. cinnamomi has been shown to cause a large destruction of fine roots before aboveground symptoms are detected (Homet et al., 2019). 2.2 | Experimental setup of the rainfall exclusion infrastructure In March 2016, rainfall exclusion infrastructures were installed to evaluate the combined effect of rainfall reduction and the exotic soilborne pathogen P. cinnamomi on the dynamics of Mediterranean mixed forests. At each of the two study sites, we established six plots of 20 × 15 m distributed in three blocks (i.e. 2 plots per block), with an average distance of 30 m among blocks. In each block, one plot was assigned to a 30% rainfall exclusion treatment, matching future climate predictions for southern Spain (model CMIP5 for the scenario RCP 8.5 for the period 2081–2100; Stocker et al., 2013), and the other served as control. Rainfall was excluded using polyvinyl chloride (PVC) gutters that covered 30% of the plot area excluding a similar amount of rainfall (Figure S1d,f). Gutters were placed at least 1 m height over the forest floor. In 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2846 | GAYTÁN et al. controls, the same gutters were placed upside down to simulate potential secondary effects of the infrastructure on the forest microenvironment while maintaining the natural precipitation regime (Asbjornsen et al., 2018). All adult trees within the plots were individually marked (3–4 trees per plot and species), ending up with 21 Q. suber and 22 Q. canariensis trees in the closed forest, and 21 Q. suber and 22 O. europaea individuals in the open woodland (86 trees in total). Basal area, tree density, and light levels were similar in both treatments at the two sites before the start of the experiment (cf. Homet et al., 2021). The effect of the rainfall exclusion treatment on soil moisture was quantified at four depths (10, 20, 30 and 40 cm) with a PR2 Profile Probe, Delta- T Devices (UK). Measurements were conducted every 3–4 weeks at six points per plot where access tubes were permanently installed for easy probe insertion and removal. Access tubes were installed in November 2015, to have a record of soil moisture in the different plots before the start of the rainfall treatments in March 2016. Soil texture (0–20 cm) was measured next to each access tube because previous studies in the area have shown a large variability in the sand content at small spatial scales, which might affect soil moisture measurements and therefore the quantification of the effect of the rainfall exclusion treatment (Gómez- Aparicio et al., 2012). 2.3 | Quantification of P. cinnamomi abundance in the soil To quantify P. cinnamomi, we took soil samples (5–20 cm in depth) in early spring (April) of each study year (2016, 207 and 2018). Soils were taken using a soil auger within 1 metre from each tree trunk and further airdried and sieved through a 2 mm mesh. Aliquots of 10 g from each soil sample were processed (c.f. Serrano et al., 2022), preparing soil suspensions in 100 mL Water–Agar 0.2%. Aliquots of 1 mL taken from the soil suspensions were plated on 20 NARPH Petri dishes per sample (Romero et al., 2007). P. cinnamomi colonies growing on the plates were morphologically identified under the inverted microscope (rounded hyphal swellings in clusters and smooth cellwalled chlamydospores; Erwin & Ribeiro, 1996) and counted. As shown in Serrano et al. (2024), P. cinnamomi abundance in the soil was influenced by soil texture and tree species composition, being lower in sandy soils and under Olea europaea compared with Quercus species. More importantly, the rainfall exclusion treatment had a negative effect on pathogen abundance across soils and species, particularly in wet years such as 2018 (Serrano et al., 2022, 2024). 2.4 | Quantification of tree photosynthetic rates To quantify C- uptake, we measured net photosynthetic rates (Amax) in a subset of the experimental trees (2 trees per species and plot, 48 in total). Measurements were taken twice during the spring of the first year of the experiment: once right at the start of the exclusion treatment (April 2016) as representative of pretreatment levels in spring and once in early summer (June 2016) before the start of the dry season. In the following years (2017–2018), these two measurements were complemented with a third measurement in late summer (September) to explore differences under stressful conditions at the end of the dry season. Due to the height of the trees, measurements were taken in leaves from shoots previously cut from the outer part of the crowns using an extendable 6- m pole. Shoots were rapidly introduced into a recipient with water, and the stem was further cut within the recipient to avoid leaf turgor loss (cf. Koike, 1986). Measurements were taken in one fully expanded and no damaged leaf per tree, using a LI- 6400xt infrared gasexchange analyser system (Li- Cor Inc., Lincoln, NE, USA). The measurements took place between 10:00 and 14:00 h using the 2 × 3 LED with lightsaturating photosynthetic photon flux density (1500 μmol m−2 s−1), 400 ppm reference CO2 concentration, block temperature at 25°C, and relative humidity on full scrub. 2.5 | Quantification of primary production Primary production of experimental trees was measured in terms of trunk growth and leaf litter production. The trunk diameter at breast height (DBH = 1.3 m) of each tree was measured at the start of the experiment to capture the initial variability in tree size. Then, a band dendrometer was installed at the breast height (DB20, EMS Brno, Czech Republic) of each tree and read monthly to calculate the annual increment in trunk diameter per tree and year. Because dendrometers were installed in April 2016, annual growth rates for this year are not comparable in absolute terms with those of the following years (2017 and 2018). However, they are useful to capture rainfall exclusion effects during the first months of the experiment. To quantify leaf litter production, we placed 0.5 m diameter litter traps (nylon baskets) hanging from the PVC gutters under the canopy of each experimental tree. We placed 2–3 traps per tree (depending on its size) in late summer (September 2016) and emptied them once per year in spring to calculate the annual litter production. By emptying the traps in spring, we captured the main period of litter production of both semideciduous (fall) and evergreen (summer) tree species. Leaf litter was taken to the lab, dried for 48 h at 70°C, and weighed by species to calculate the litter biomass produced annually by each species in g per m2. 2.6 | Quantification of reproduction To quantify tree investment in reproduction, we used two different methodologies, one for Quercus spp. and another for O. europaea trees. For Quercus spp., we used the litter traps to collect acorns during each dispersal season (September–May 2016, 2017 and 2018). Acorns were taken to the lab, counted, and separated into three categories: aborted acorns (incompletely or undeveloped fruits with length < 13 mm or diameter < 7 mm), predated 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 2847 GAYTÁN et al. acorns (with signs of predation by birds, rodents, or insects) and mature, healthy acorns (c.f. Pérez- Ramos et al., 2015). Each acorn category was separately dried for 48 h at 60°C and weighed by species. Data were used to calculate the acorn production and abortion rate of each experimental oak tree in each of the three study years. Unfortunately, due to problems during storage, acorn production from the open woodland could not be analysed in 2017 and was not included in the study. Regarding O. europaea, since its fruits are generally dispersed by frugivorous birds, we used an alternative methodology based on the direct counting of fruits in branches. We marked three representative branches per tree at the beginning of the experiment, and the annual fruit production was estimated as the number of mature fruits (i.e. 1–3 cm length and darkpurple colour) at the end of the maturation period and before the start of the dispersal period (i.e. November–December). Data from the three branches were combined to calculate one average value per tree and year. 2.7 | Quantification of chemical defences and C reserves Analyses of chemical defences (total phenols) and C reserves (nonstructural carbohydrates) were conducted twice a year in leaf and root tissues of each experimental tree during the 3 years of study: one during the peak of maximum plant activity in spring (May) and another at the end of the drought season in late summer (September). Leaf chemical analyses were conducted in a representative sample of 30 green leaves collected from three branches per tree. For root chemical analyses, root samples were collected in the first 0–20 cm of the soil by following large roots from the base of each tree trunk in, at least, two different cardinal directions. A representative sample of the fine roots (<0.2 mm ø) attached to the larger roots were collected for each individual tree. Soil and root samples were kept moist in sealed plastic bags and transported inside an icebox to the laboratory, where they were stored at 4°C until processing. In the laboratory, roots were gently washed to remove soil particles. Leaves and roots were ovendried at 45°C for 48 h and grounded with liquid nitrogen. Total phenols were colorimetrically determined by the Folin–Ciocalteu assay, whereas nonstructural carbohydrates (starch and sugar) were quantified using the anthrone method (cf. Sampedro et al., 2011). 2.8 | Statistical analyses We used generalized linear models (GLMs) to examine the impact of rainfall exclusion and P. cinnamomi abundance on the different variables measured to characterize C sources (photosynthetic rates) and sinks (primary production, reproduction, defence, and reserves). A total of 10 variables were analysed: photosynthetic rate, trunk growth, litter production, acorn production and abortion rate (only for Quercus spp.), fruit production (only for O. europaea), total phenols in leaves and roots, and nonstructural carbohydrates (NSC) in leaves and roots. Models were run separately for each combination of forest type (closed forest and open woodland) and year (2016, 2017 and 2018) to explore differential impacts across sites with contrasting characteristics (soil texture, species composition) and years with different precipitation. Models included rainfall exclusion (RE), P. cinnamomi abundance (PC), tree species (SP), and their two- and threeway interactions as fixed factors. To control for the nested structure of the experimental design, block (BL) was included as an additional fixed factor. Block was not included as a random factor because it did not have enough levels (i.e. >4; Ieno & Zuur, 2015). Tree size (DBH) and soil texture (% sand content) were included as covariates in the models to control for their potential confounding effects on tree performance. For variables measured multiple times per year (i.e. photosynthetic rates, defences and NSC), analyses were repeated separately for each date. We used a gamma distribution of errors for all variables except for the abortion rate, which followed a binomial distribution. The full model took the following form: Starting from the full model, we followed a process of model comparison using AICc (Akaike information criterion corrected for small sample sizes) to find the best model (lowest AIC) or group of best models (ΔAICc < 2) for each variable. To evaluate the existence of tradeoffs between the four C- sinks evaluated (primary production, reproduction, defence, and reserves), and whether rainfall exclusion modified these tradeoffs, we fit GLM models for pairs of variables representing different C- sinks, where one of them (C- sinkA) was included as a dependent variable and the other (C- sinkB) as a fixed factor. Rainfall exclusion was included as a second fixed factor, as well as its interaction with C- sinkB. Block, DBH, and texture (Sand) were included as covariates to control for spatial autocorrelation and potential confounding effects of tree size and soil texture. Models were run separately for each combination of forest type, year, and tree species. The full model took the following form: A tradeoff between C- sinks was considered to exist when the best model retained the C- sinkB factor, and the sign of parameter β5 was negative (i.e. higher C investment in C- sinkB implied lower investment in C- sinkA). Rainfall exclusion was considered to modify tradeoffs between C- sinks when the C- sinkB × RE interaction was retained in the best model. GLMs were performed using R- package lme4 (Bates et al., 2015) and compared with the MuMIn R- package (Barton, 2009). The MuMIn R- package was also used to calculate the variance explained by the GLM models (Barton, 2009; Nakagawa & Schielzeth, 2013). Model fit was assessed using the R- packages sjPlot and DHARMa (Hartig, 2020; Lüdecke, 2020). All statistical analyses were conducted with R software version 4.2.1 (R Development Core Team, 2024). (1) Y∼ 𝛽1+𝛽2 BL +𝛽3 DBH +𝛽4 Sand +𝛽5 RE +𝛽6 PC +𝛽7 SP +𝛽8 RE ×PC+ 𝛽9 RE×SP + 𝛽10 PC×SP + 𝛽10 RE ×PC×SP + 𝜀 . (2) C − sinkA∼𝛽 1+ 𝛽 2 BL + 𝛽 3 DBH + 𝛽 4 Sand + 𝛽 5 C − sinkB + 𝛽 6 RE + 𝛽7 C−sinkB×RE + 𝜀 . 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2848 | GAYTÁN et al. 3 | RESULTS 3.1 | Effect of rainfall exclusion on soil moisture The rainfall exclusion treatment clearly magnified the initial differences between drought and control plots (Table S1) across the different soil depths both in the closed forest (Table S2; Figure S2) and the open woodland (Table S2; Figure S3). The largest differences occurred after large rainfall events, as those occurred during the spring of 2018, where soil moisture reached values 33% higher in control than in drought plots (Figures S2 and S3). 3.2 | Impact of rainfall exclusion and P. cinnamomi abundance on photosynthetic rates Photosynthetic rates followed a clear seasonal trend, decreasing from maximum values in early spring to minimum values in late summer (Figure 1). This pattern followed the variation in soil water content, which reached annual minimums in late summer (Figures S2 and S3). Rainfall exclusion had negative effects on the maximum photosynthetic rates of the three species, mostly in spring and early summer (Tables 1 and 2; Table S3), although its magnitude varied among forest types, years, and species (Figure 1). The most important exception to this pattern was the positive response of Q. suber to rainfall exclusion during the wet 2018 in the open woodland (RE × SP interaction included in the best model; Table 2), with trees in rainfall exclusion plots having photosynthetic rates 47% higher than in control plots (Figure 1). Negative effects of P. cinnamomi on photosynthetic rates were detected in the closed forest, but not in the open woodland (Tables 1 and 2; Table S3). The only exception to this pattern was Q. suber during 2017 (PC × SP interaction; Table 1), which showed a positive response to P. cinnamomi abundance (Figure 2). No interactions between rainfall exclusion and P. cinnamomi effects on photosynthesis were found for any site and date combination (Table S3). FIGURE 1 Effect of rainfall exclusion on photosynthetic rates along the study period. Values represent means ± SE. Shaded areas represent measurements prior to the experiment (year 2016). Asterisks denote dates for which the rainfall exclusion treatment was included in the best model, either as the main effect or in interaction with the tree species (Table S3). The inset boxplots show differential responses of Q. suber and O. europaea trees to rainfall exclusion in the open woodland. 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 2849 GAYTÁN et al. 3.3 | Impact of rainfall exclusion and P. cinnamomi abundance on primary production Primary production expressed in terms of trunk growth was not affected either by rainfall exclusion or P. cinnamomi abundance, as shown by the fact that these factors were very rarely retained in the best growth models (Tables 1 and 2). On the contrary, litter production showed a clear negative response to rainfall exclusion in all species. The decrease of litter production was particularly large in Q. suber the first year after the start of the experiment in the open woodland (RE × SP interaction, Table 2), where trees in rainfall exclusion plots had 27% lower litter production than control trees (Figure 3). P. cinnamomi in the soil was not retained in the best model of litter production for any site and species combination (Tables 1 and 2). 3.4 | Impact of rainfall exclusion and P. cinnamomi abundance on reproduction One year after the start of the experiment, reproduction of all species experienced important changes associated with both rainfall exclusion and P. cinnamomi abundance (Tables 1 and 2). Thus, in 2017, acorn production was lower and abortion rates higher in trees under rainfall exclusion than in control plots, particularly for Q. suber (RE × SP interaction; Table 1; Figure 3). However, the sign TABLE 1 Best linear models analysing the impact of rainfall exclusion (RE), P. cinnamomi abundance (PC), and tree species (SP) on C sources and sinks in the closed forest during the 3 years of the experiment. Closed forest Year Response variable Predictors Par ∆AICc R2 2016 Phytosynthetic rate Null 20.00 Trunk growth DBH + SP 426.32 0.50 Leaf litter DBH + SP[PC] 428.31 0.57 Acorn production Null 20.00 Acorn abortion Null 20.00 Leaf phenols SP 36.29 0.20 Leaf NSC SP[RE] 34.19 0.16 Root phenols B + RE + PC 55.02 0.13 Root NSC T + RE + PC + RE × PC 62.07 0.27 2017 Photosynthetic rate T + PC + SP + PC × SP 66.65 0.47 Trunk growth DBH + SP 44.31 0.40 Leaf litter T 3 1.32 0.09 Acorn production B + RE + SP 54.03 0.36 Acorn abortion B + DBH + RE + PC + SP + RE × SP + RE × PC 9 242.02 0.26 Leaf phenols DBH + SP[PC] 41.45 0.04 Leaf NSC Null[PC] 20.00 Root phenols Null[RE,PC] 20.00 Root NSC B + T + RE[PC] 57.47 0.31 2018 Photosynthetic rate B + RE + SP[PC] 50.70 0.49 Trunk growth B + DBH + T + SP [RE,PC] 66.74 0.45 Leaf litter RE 30.61 0.07 Acorn production RE + PC + SP + RE × PC 63.38 0.12 Acorn abortion B + DBH + RE + PC + SP + RE × SP + PC × SP + RE × PC 10 79.80 0.26 Leaf phenols B + T[PC] 425.26 0.58 Leaf NSC B + T + RE + SP 65.49 0.46 Root phenols B[RE] 30.92 0.16 Root NSC RE 31.08 0.08 Note: Full models also included three covariates: Block (B), tree size (diameter at breast height, DBH), and soil texture (T). ∆AICc represents the difference between the AICc (Akaike information criterion corrected for small sample sizes) of the best and null models. The total number of parameters in the best model (‘Par’) and the goodness- of- fit (R2) are also given. [RE] and [PC] indicate when rainfall exclusion and P. cinnamomi were included in the subset of best models (∆AIC < 2 with the best model). For variables measured more than once per year (photosynthetic rates and leaf/ root chemistry), only spring results are shown. 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2850 | GAYTÁN et al. of the rainfall exclusion effect turned positive in the wet 2018, oaks under rainfall exclusion showing larger acorn production and lower abortion rates than control trees (Figure 3). The response of O. europaea to rainfall exclusion followed a similar pattern than oaks, with a decrease in fruit number being detected in 2017 but not in 2018 (Table 2). The abundance of P. cinnamomi in the soil, in Open woodland Year Response variable Predictors Par ∆AICc R2 2016 Phytosynthetic rate DBH 30.85 0.09 Trunk growth DBH + SP 422.83 0.47 Leaf litter DBH + T 418.85 0.43 Acorn production Null 20.00 Acorn abortion B + DBH + T 577.29 0.13 Number of Olea fruits Null[RE] 21.70 Leaf phenols SP[RE] 358.21 0.76 Leaf NSC T + SP 452.36 0.73 Root phenols DBH + RE 424.32 0.39 Root NSC RE + SP + RE × SP 512.21 0.36 2017 Photosynthetic rate SP 329.84 0.70 Trunk growth DBH + SP 42.61 0.58 Leaf litter RE + SP + RE × SP 522.38 0.66 Number of Olea fruits RE + PC 44.43 0.38 Leaf phenols RE 332.09 0.05 Leaf NSC DBH[PC] 31.95 0.09 Root phenols DBH[PC] 339.50 0.62 Root NSC DBH + PC 47.44 0.24 2018 Photosynthetic rate RE + SP + RE × SP 57.38 0.33 Trunk growth DBH + PC + SP + PC × SP 60.65 0.52 Leaf litter DBH + RE 419.30 0.42 Acorn production DBH[PC] 31.51 0.18 Acorn abortion RE + PC 418.47 0.08 Number of Olea fruits Null[PC] 20.00 Leaf phenols B + DBH 43.42 0.64 Leaf NSC DBH 312.68 0.30 Root phenols DBH + PC + SP 530.02 0.60 Root NSC DBH[RE] 31.77 0.12 Note: Full models also included three covariates: Block (B), tree size (diameter at breast height, DBH), and soil texture (T). ∆AICc represents the difference between the AICc (Akaike Information Criterion corrected for small sample sizes) of the best and null models. The total number of parameters in the best model (‘Par’) and the goodness- of- fit (R2) are also given. [RE] and [PC] indicate when rainfall exclusion and P. cinnamomi were included in the subset of best models (∆AIC < 2 with the best model). For variables measured more than once per year (photosynthetic rates and leaf/root chemistry), only spring results are shown. TABLE 2 Best linear models analysing the impact of rainfall exclusion (RE), P. cinnamomi abundance (PC), and tree species (SP) on C sources and sinks in the open woodland during the 3 years of the experiment. FIGURE 2 Relationships between P. cinnamomi abundance in the soil and photosynthetic rates, acorn production and abortion, Olea fruit production, and root NSC at the two forest types 1 year (2017) and 2 years (2018) after the start of the experiment. The lines and shaded areas are the model predictions and associated 95% confidence intervals (respectively). Continuous lines indicate effects included in the best models, whereas discontinuous lines indicate effects included in the subset of best models (∆AIC < 2 with the best model; Tables 1 and 2). Regressions are shown separated by species only when the best models included the interaction of rainfall exclusion with the species. NA indicates the absence of data. Note that the range of the xaxis varied among forest types and years, being larger in the closed forest, particularly in 2018. In panels marked with asterisks (i.e., acorn production in 2018), yvalues should be interpreted as of an order of magnitude smaller (y- a x i s × 10−1). 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 2851 GAYTÁN et al. general, was negatively related to acorn production and positively to abortion rates (Figure 2). Interestingly, P. cinnamomi effects on acorn production were modified by the rainfall exclusion treatment in the closed forest (RE × PC interaction included in the best models, Table 2) with abortion rates increasing with P. cinnamomi abundance only in control plots both in 2017 and 2018 (Figure S4). Negative 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. 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Global Change Biology, 17, 927–942. SUPPORTING INFORMATION Additional supporting information can be found online in the Supporting Information section at the end of this article. Table S1: Results of the linear mixed models that test the differences in soil moisture between the control and rainfall exclusion plots before the installation of the rainfall exclusion infrastructure at the two study sites. Table S2: Results of the linear mixed models testing the effect of the rainfall exclusion treatment on soil moisture at the two study sites during the study period. Table S3: Best linear models analysing the impact of rainfall exclusion (RE), P. cinnamomi abundance (PC) and tree species (SP) on tree photosynthetic rates at different times during the study period in the closed forest and the open woodland. Table S4: Best linear models analysing the impact of rainfall exclusion (RE), P. cinnamomi abundance (PC) and tree species (SP) on leaf and root reserves and defence compounds in the closed forest throughout the study period (2016–2018). Table S5: Best linear models analysing the impact of rainfall exclusion (RE), P. cinnamomi abundance (PC) and tree species (SP) on leaf and root reserves and defence compounds in the open woodland throughout the study period (2016–2018). Table S6: Relationships among C- sinks in Quercus suber trees in the closed forest and the open woodland during 2017 and 2018 (β 6), as well as their interactions with the rainfall exclusion treatment (β7). Table S7: Relationships among C- sinks in Quercus canariensis and Olea europaea trees in the closed forest and the open woodland (respectively) during 2017 and 2018 (β 6). Figure S1: Location of Los Alcornocales Natural Park in Southern Spain (a) and of the study area within the park (b). Figure S2: (A) Monthly precipitation in the study area during the study period; (B) Predicted values of volumetric soil water content (%) corrected by soil texture at different soil depths (10, 20, 30 and 40 cm) in the closed forest. Figure S3: (A) Monthly precipitation in the study area during the study period; (B) Predicted values of volumetric soil water content (%) corrected by soil texture at different soil depths (10, 20, 30 and 40 cm) in the open woodland. Figure S4: Relationships between the abundance of P. cinnamomi in the soil and the acorn abortion rate (2017 and 2018) and production (2018) in the closed forest. Figure S5: Summary of main effects of rainfall exclusion and exotic pathogens on tree C- source and C- sinks in Mediterranean mixed forests. How to cite this article: Gaytán, Á., Matías, L., Godoy, Ó., Pérez- Ramos, I. M., Homet, P., Moreira, X., & Gómez- Aparicio, L. (2024). Climate change and exotic pathogens shift carbon allocation in Mediterranean mixed forests. Journal of Ecology, 112, 2843–2860. https://doi.org/10.1111/1365-2745.14426 13652745, 2024, 12, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14426 by Readcube (Labtiva Inc.), Wiley Online Library on [10/04/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License