Recent increase in tree damage and mortality and their spatial dependence on drought intensity in Mediterranean forests

Context Land-use and climate change are leading to more frequent and intense tree damage and mortality events. Drought-induced tree mortality is occurring worldwide leading to broad-scale events, but the spatial patterns of tree damage and mortality, their underlying drivers and their variation over time is largely unknown. Objectives We investigated the spatial patterns of tree damage and mortality across Mediterranean forests of the Iberian Peninsula, the underlying effects of competition and climate, and how the spatial patterns and relationships with underlying drivers changed over time. Methods We used the Spanish Forest Inventory to analyse the autocorrelation in tree damage and mortality across forest types, hurdle-gamma models to quantify the effect of competition and climate on tree damage and mortality, and cross-correlograms to assess their spatial dependence and its change over time.


Introduction
Forests represent about 30% of the worldwide land area (FAO 2020), constituting a key nature contribution for human well-being (Gamfeldt et Carnicer et al. 2011).Climate change is characterised by increasing temperatures as well as a higher occurrence and intensity of droughts and heatwaves (Allen et al. 2015).This interacts with the abandonment of forest management leading to increased competition and alterations to forest structure, as well as an increase in vulnerability and exposure to biotic and abiotic disturbances (e.g., Jump et al. 2017;Seidl et al. 2017).These alterations can cause a range of forest responses, such as changes in tree phenology (Yuan et  The "spiral of tree mortality" suggests that tree mortality depends on multiple interactive factors, where several biotic and abiotic drivers lead an individual tree to die (Manion 1991;Franklin 1987).The underlying factors can be classi ed as predisposing factors (i.e., those reducing the tree resistance), inciting factors (i.e., those initiating the tree mortality process) and contributing factors (i.e., those leading to tree mortality, Manion 1991).Forest structure is a key predisposing factor.For example, greater stand biomass increases the degree of competition from neighbours (Gómez-Aparicio et al. 2011), reduces growth (Ruiz-Benito et al. 2017a) and increases mortality likelihood (Jump et al. 2017;Changenet et al. 2021; Moreno-Fernández et al. 2019).Another predisposing factor can be tree size, which is related with tree mortality so that small trees are particularly vulnerable to mortality (Lines et al. 2010; Ruiz-Benito et al. 2013; Andivia et al. 2020).Drought is a key inciting factor due to its interaction with forest structure which strongly reduces tree vigour (McDowell et al. 2015;Choat et al. 2012).Overall, the patterns and processes leading to tree mortality and damage are largely dependent on biotic and abiotic conditions (Montoya et al. 2009; Lehman and Tilman, 1997;Fortin and Dale, 2014).
Tree mortality is a complex and hardly predictable process due to its stochastic nature and the multiple interactive drivers controlling the process (Trugman et al. 2021;Hartmann et al. 2018).On the one hand, background tree mortality can be de ned as the occurrence of tree mortality without extreme disturbances (Franklin et al. 1987).This type of mortality is linked to forest succession and species ontogeny (Bréda and Badeau, 2008).On the other hand, die-off mortality is related to events where a large proportion of the trees die as a consequence of extreme abiotic and biotic disturbances (e. g., res, droughts or pests, Mueller-Dombois 1987; Hammond et al. 2022).Forest die-off events are being increasingly observed worldwide due to global environmental change (Jump et al. 2017;Hammond et al. 2022;Schelhaas et al. 2003), pointing to the need of investigating broad-scale mortality events to understand forest dynamics under changing conditions (Allen et al. 2015;Jump et al. 2017).
Tree mortality events occur at different spatial scales (Allen et al. 2010).Tree mortality affects speci c trees at local scales, yet die-off mortality can affect a larger proportion of trees at landscape scales (Jump et al. 2017;Changenet et al. 2021).When regional die-off events occur, the spatial autocorrelation of tree damage and mortality decreases with distance, informing about the spatial extent of these events (Gazol et al. 2022;Fortin and Dale, 2014;Borcard and Legendre, 2012).Furthermore, declining trees tend to occur close to each other and, therefore, the spatial autocorrelation of tree damage can be used as an early warning signals of die-off events (Camarero et al. 2015).These early-warning signals include extraordinary growth reductions, defoliations and discolorations (Dobbertin 2005) or partial canopy dieback (Jump et al. 2017).However, the spatial patterns of tree damage and mortality at large spatial extents and their variation with forest structure and climate effects are largely unexplored.
Tree vulnerability in terms of tree damage and mortality is strongly linked to the functional characteristics of tree species (Greenwood et al. 2017).In this sense, gymnosperms can be highly sensitive to increased temperature and drought (Carnicer et al. 2013, Anderegg et al. 2015b), but angiosperms are not exempt for suffering drought-induced decline events (e.g., Camarero et al. 2021).Angiosperm species are more competitive and tend to dominate in forests with high species diversity while gymnosperms are more sensitive to competition (Zavala et al. 2000).Despite the potential contrasting responses of angiosperm, gymnosperm and mixed forests, it is not well known if the spatial patterns of tree damage and mortality vary between these different functional groups.Here, we quanti ed the spatial patterns and the underlying drivers of tree damage and mortality in Mediterranean forests and analysed how these spatial patterns and the effects of the drivers are changing over time.For this, we used three consecutive censuses from the Spanish National Forest Inventory since the 80s.Our speci c objectives were to: (i) analyse the spatio-temporal patterns of tree damage and mortality by forest type (angiosperm, gymnosperm and mixed forests) in Mediterranean forests of the Iberian Peninsula; (ii) assess to what extent these patterns are spatially related to structural and climatic drivers; and (iii) investigate the temporal dynamics in the spatial dependence of tree damage and mortality on structural and climatic drivers.We hypothesised that there is spatial aggregation in tree damage and mortality events, being stronger in tree damage than tree mortality and in gymnosperm forests than in angiosperm and mixed forests.In addition, we expected a spatial dependence between tree mortality/damage and their drivers, aggregation increasing with basal area and drought and decreasing with tree size.Finally, we hypothesised that the spatial aggregation of tree damage and mortality increased over time as well as the effect of competition and drought.The assessment of spatiotemporal trends in tree damage and mortality patterns is key for understanding the effects of global change and the identi cation of vulnerable areas required in order to design cost-effective adaptation measures.

Study area
We focused on forests under Mediterranean climate in Spain.In the East of the Iberian Peninsula, a temperate and humid climate with dry and hot summers predominates.The mean temperature is 17-18 ºC and approximate precipitation is 400-450 mm.A more continental Mediterranean climate with dry and warm summers and frequent frosts in winter is the most common in the inner Iberian Peninsula, with mean temperatures around 15 ºC and annual precipitations of 420 mm.In the southern and southeastern area, the climate is semi-arid, with a mean temperature of 18 ºC and annual precipitation of 300 mm (AEMET and IMP, 2011).More than half of the forest area is represented by angiosperm forests, dominated by Quercus ilex L., Quercus suber L., and Quercus pyrenaica Wild.Gymnosperm forests occupy 38% of the forest area with Pinus halepensis Mill.and Pinus pinaster Ait. as the most common species (MITECO 2020).

National Forest Inventory data
To assess the temporal and spatial patterns of tree damage and mortality in Mediterranean forests, we used data from the Spanish National Forest Inventory (SFI hereafter).SFI comprises plots systematically distributed in areas considered as forests (tree coverage greater than 5%) on a 1-km 2 cell grid (Villaescusa and Diaz, 1998;Alberdi et al. 2016).Four censuses of SFI are currently available: 1SFI (1965)(1966)(1967)(1968)(1969)(1970)(1971)(1972)(1973)(1974), 2SFI (19862SFI ( -1996)), 3SFI (19973SFI ( -2007) ) and 4SFI (2008-present).The data between SFI censuses is only comparable since the 2SFI, in which permanent plots were established.In addition, the information of the 4SFI is not yet available for the whole country, but we used the most updated data covering a large environmental gradient (see Fig. 1c).From the SFI plots, we removed the Atlantic, Alpine and Macaronesian forests following Olson et al. (2001).
Each SFI plot follows a nested design of four concentric circular subplots of 5, 10, 15 and 25 m radius.In these subplots, adult trees are sampled according to their diameter at breast height (tree size): ≥ 7.5 cm, ≥ 12.5 cm, ≥ 22.5 cm and ≥ 42.5 cm, respectively (Villaescusa and Díaz, 1998).For each sampled tree, height, size, species identity, the origin and magnitude of tree damage and the status (alive, dead) are recorded.The origin of the damage is classi ed as biotic (e.g., fungi, insects), abiotic (e.g., snows, winds, droughts or res) or anthropogenic (e.g., machinery damage).For this study, we discarded the latter group.The magnitude of the damage is classi ed as low, medium or high, but here we only considered medium or high tree damage to follow a conservative approach.Additionally, plots with signals of forest management (e.g., thinning or cutting) were removed from the analyses.To reduce the bias in the damage and mortality estimation introduced by silvicultural operations, we also removed permanent plots in all censuses with any signal of silviculture in any of the previous censuses.
We quanti ed absolute and relative tree damage and mortality through tree basal area, which is the sum of the cross-sectional area of the trunk from the diameter at breast height of each living adult individual (m 2 ha -1 ).Absolute tree mortality was calculated as the sum of the basal area of dead trees divided by the number of years between consecutive inventories (23SFI and 34SFI; m 2 ha -1 yr -1 ).Absolute tree damage was calculated as the sum of basal area of all trees that experienced medium or high tree damage in the 3SFI (m 2 ha -1 ) and the 4SFI (m 2 ha -1 ).To calculate relative tree damage (%) and mortality (% yr -1 ), we divided absolute values by the total basal area of the plot (the second census in the case of mortality).Initially, we explored absolute and relative tree damage and mortality because they could have different patterns between close and open forests (the later particularly common in SW Spain, see Moreno-Fernández et al. 2019).However, we retained for subsequent analysis absolute tree damage and mortality because the correlation between absolute and relative values was high (r ~ 0.6 for both tree damage and mortality; see Appendix A).
We used all available plots in the 3SFI to evaluate the spatial pattern of tree damage and all permanent plots between the second and third inventories (23SFI) for forest mortality.To investigate the role of forest type on tree damage and mortality, we classi ed each plot into angiosperm, gymnosperm, or mixed forests.Angiosperm and gymnosperm forest types correspond to those plots with dominance equal or greater than 80% in total basal area for angiosperms or gymnosperm species, respectively, while the rest of the plots were classi ed as mixed forests.We analysed 37,128 plots in the 3SFI (i.e., 12,799 plots of angiosperm, 15,847 plots of gymnosperm and 8,482 plots of mixed forests; see Appendix B) and 33,124 permanent plots in the 23SFI (i.e., 11,103 plots of angiosperm, 14,369 plots of gymnosperm and 7,652 plots of mixed forests; see Appendix B).To study the temporal changes in the spatial patterns of tree damage and mortality, we used permanent plots across the three consecutive inventories (i.e., 8,789 permanent plots in the 2-3-4SFI; 4,106 correspond to angiosperm, 3,771 to gymnosperm and 912 to mixed forests).

Underlying drivers of tree damage and mortality
We considered stand features (basal area and tree size), and spatio-temporal variations in climate as key drivers of tree damage and mortality.Stand basal area (m 2 ha -1 ) was included as a proxy of competition, and tree size (mm) as a proxy of tree biomass.The spatial variations in climate were characterised through the water availability, calculated as the difference between annual precipitation and potential evapotranspiration, divided by potential evapotranspiration (%).Thus, high values of water availability correspond to wet regions and low values to arid regions.Water availability was calculated using data ).We used absolute terms since we needed positive values for the statistical analyses.SPEI in absolute term is a dimensionless index with large values related to dry conditions and low values to wet climatic conditions.We calculated the 12-months minimum SPEI per plot for each month for the period between 23SFI and 34SFI, and the mean minimum SPEI per year using the SPEIbase v2.7.database and the SPEI package (Vicente-Serrano et al. 2010; Beguería and Vicente-Serrano 2017).All analyses were performed using R Statistical Software (v4.1.2;R Core Team 2021).

Statistical analyses to assess the spatio-temporal patterns of tree damage and mortality
To describe the spatial patterns of tree damage and mortality by forest type (angiosperm, gymnosperm and mixed forests), we used spline correlograms.Spline correlograms are a generalization of spatial correlograms (Bjørnstad and Falck 2009) which calculate the relationship between two distance matrices and provide a global measure of the spatial autocorrelation of events in space (Fortin and Dale 2014; Borcard and Legendre 2012).Values can indicate, at a given spatial lag, a negative spatial correlation (i.e., -1 indicates high and low values of the target variable tend to occur close in space, that is, repulsion), or a positive spatial correlation (i.e., 1 means that spatially nearby values of a variable tend to be similar, that is aggregation).Values close to 0 represent the absence of spatial correlation (Burian 2012; Fletcher and Fortin 2018).For tree damage and tree mortality in each forest type, we calculated correlograms up to 50 km and 95% con dence intervals were generated by a bootstrapped distribution with 999 resamples.To investigate the temporal dynamics in tree damage and mortality over the three last censuses of the SFI corresponding to a timeframe of approximately 30 years (see Appendix B), we calculated the spatial autocorrelation in tree damage and tree mortality in permanent plots available across the three censuses in the Spanish Forest Inventory.

Spatial dependence between tree damage and mortality with underlying structural and climatic factors, and its variation over time
To test the effect of the underlying drivers on tree damage and mortality as well as the associated spatial patterns between the events and the drivers, we followed a two-step approach.First, we tted hurdlegamma models (Mullahy 1986).We modelled tree damage and mortality in each stand considering stand variables (basal area and tree size) and climate (water availability and drought intensity) as xed factors, a ziGamma conditional error distribution, and a log link function.Afterwards, the importance of the variables was estimated using the Akaike Information Criterion (AIC; Burnham and Anderson 2002).We compared the full model (i.e., including all structural and climatic variables as xed effects) with models in which each explanatory variable from the xed effects was removed.To characterise the goodness of t, we observed the distribution of the residuals of the most parsimonious model and calculated pseudo- Second, we performed cross-correlogram analyses to assess the spatial dependence between tree damage or mortality and the structural and climatic drivers (Bjørnstad 2009).The cross-correlogram uses a modi ed Moran's I statistic to evaluate the relationship between two variables at different spatial scales (Fortin and Dale 2014) and, therefore, inform about the spatial dependence between co-existing events (Rossi et al. 1992).This can be interpreted as segregation (negative values) or aggregation (positive values) between variables (Larson and Franklin 2006).Further than 50 km, the spatial patterns did not vary, so we interpreted the results at distances lower than 50 km.All correlograms and cross-correlograms were computed using the package ncf (Bjørnstad 2022).
Finally, to assess if the underlying factors of tree damage and mortality varied over time, we performed four hurdle-gamma models with the permanent plots available in the three consecutives censuses of the Spanish Forest Inventory, i.e., one model for each response variable and time period (23SFI and 34SFI for tree mortality, 3SFI and 4SFI for tree damage).We modelled tree damage and mortality as a function of basal area, tree size, water availability and drought intensity.We used Gamma conditional error distribution using a log link function.The most parsimonious model for each response variable and time period was chosen based on the ΔAIC.We checked the distribution of the residuals of the most parsimonious model and calculated pseudo-R 2 (Nakagawa and Schiele 2013; see Appendix C).Finally, we calculated cross-correlograms to study the variation over time in the spatial dependence between tree damage and mortality with the relevant structural and climatic drivers (Bjørnstad 2009).

Spatio-temporal patterns of tree damage and mortality in Mediterranean forests
Overall, we found a mean increase of 63% in tree damage between 3SFI and 4SFI; and 23% in tree mortality between 23SFI and 34SFI, with patches of high damage and mortality distributed across the Iberian Peninsula (see Fig. 1a, b and Appendix A).Mixed forests had the highest tree damage and mortality, whereas angiosperm forests had more damage and less mortality than gymnosperm forests in the studied periods (Fig. 1c, d).Tree damage increased between consecutive inventories in all studied forests and mortality increased in gymnosperm forests (Fig. 1c, d).Mixed forests had the highest increase in tree damage (from 1.24 ± 2.20 to 2.32 ± 3.04 m 2 ha -1 ) followed by angiosperm forests (from 1.59 ± 4.57 to 2.53 ± 4.87 m 2 ha -1 , Fig. 1c).
We found a positive spatial autocorrelation in tree damage and mortality, being stronger for tree damage (with values ranging between 0.32 and 0.43 at short distances, i.e., 1-3 km) than tree mortality (0.08-0.21;Fig. 2a, b).Furthermore, while tree damage had a strong spatial aggregation at distances shorter than 20 km, tree mortality had a strong spatial autocorrelation only for gymnosperm forests at distances shorter than 10 km (see autocorrelation > 0.1, Fig. 2a, b).Gymnosperm forests showed the highest spatial aggregation in tree mortality at short distances while angiosperms and mixed forest the lowest (see Fig. 2b and Appendix A).At longer distances there were no differences among the three forest types (Fig. 2a, b).We did not nd important differences in the autocorrelation of tree damage and tree mortality over time (Fig. 2c, d).The only exception was found for tree damage at c. 10 km, with higher autocorrelation in the second period (Fig. 2c).
Underlying drivers of the spatial patterns in tree damage and mortality The selected models for tree damage and mortality included basal area, tree size, water availability and drought intensity (see ΔAIC in Table 1).Basal area, water availability and drought intensity had positive cross-correlation with tree damage and mortality at short distances.Water availability showed the strongest positive cross-correlation with both tree damage and mortality (i.e., correlation between 0.1 and 0.2, decreasing until distances up to 50 km, Fig. 3c, d) followed by drought intensity for tree damage (Fig. 3c), and basal area for tree mortality (Fig. 3d).Mean tree size had a low and negative spatial crosscorrelation with both tree damage and mortality (Fig. 3c, d).The full models include the effects of basal area, tree size, water availability and drought intensity.The 'No' models ignore the effect of each explanatory variable.The predictor variables included in the best model are marked in bold.The AIC comparison is shown for each model: ΔAIC = AIC 1 -AIC min .
The best model is the one with the AIC min = ΔAIC = 0.The AIC for the best models, the pseudo-R 2 and the number of parameters (NP) for the best models are also shown Temporal variations in tree damage and mortality and their underlying drivers The selected model for tree damage included basal area, tree size, water availability and drought intensity for both time periods (Table 2).However, water availability and tree size were not included in the mortality model for the rst and second period, respectively (Table 2).The spatial cross-correlation of tree damage and mortality with the underlying drivers changed over time in some cases (Fig. 4).The spatial crosscorrelation of tree damage with basal area, water availability and drought intensity signi cantly increased from the rst to the second period (Fig. 4a, e, g).We observed no signi cant differences between periods in the cross-correlation of tree mortality with basal area, tree size and water availability (Fig. 4b, d, f).
Nonetheless, the spatial dependence between tree mortality and drought intensity increased over time (Fig. 4h).The full models include the effects of initial basal area for each period, mean tree size, water availability and drought intensity.The 'No' models ignore the effect of the explanatory variable.The predictor variables included in each model are given in bold.The AIC comparison is shown for each model: ΔAIC = AIC 1 -AIC min .The best model is the one with the AIC min = ΔAIC = 0.The AIC for the best models, the pseudo-R 2 and the number of parameters (NP) for the best models are also shown

Discussion
We found positive spatial autocorrelation of tree damage and mortality in Spanish Mediterranean forests.Tree damage showed higher autocorrelation and at longer distances than tree mortality.As hypothesised, our analyses revealed distance-dependent spatial relationships between tree damage and mortality with competition, water availability and drought intensity.However, the spatial autocorrelation of tree damage and mortality did not vary over time, neither in extension nor in magnitude.Despite this unexpected result, the spatial dependence between tree damage and their underlying drivers increased over time, while the spatial dependence of tree mortality on drivers only increased with drought intensity.Altogether, our results provide key information about the spatio-temporal variation of tree damage and mortality patterns in the Iberian Peninsula and the role of competition, water availability and drought in these spatial patterns at landscape scale.

Spatio-temporal patterns of tree damage and mortality in Mediterranean forests
Tree damage was greater in magnitude and showed higher spatial autocorrelation than mortality (see Fig. 2).Despite scarce previous information on the spatial aggregation of tree damage and mortality at large spatial extents, these results agree with studies reporting aggregated patterns of tree decline (Prieto-Recio et al. 2015) and mortality at ner scales (Silver et al. 2013, Baguskas et al. 2014).Moreover, tree mortality due to insect and pathogens have shown greater spatial autocorrelation (e.g., 25 km in Calvão et al. 2019).This might be related to the fact that biotic disturbances can lead to regional events of dieoff and mortality (Kautz et al. 2017).
Forest decline and mortality events are occurring up to regional extents worldwide (Allen et al. 2015).In agreement, we found a greater magnitude in the spatial autocorrelation for tree damage than for tree mortality.This suggests that tree damage can be considered an early-warning indicator in the spiral of tree mortality, because tree damage generally precede tree mortality events (i.e., pitch defence or recovery; Franklin et al. 1987;Jump et al. 2017).Furthermore, the interaction of drought with biotic agents and res can trigger tree mortality and determine its spatial extent (Moreno-Fernández et al. 2021;Camarero et al. 2015), and, therefore, tree damage can be used as early warning signals for potential dieoff events.
Mixed forests had the greatest tree damage, with the greatest increase over time in Mediterranean forests of the Iberian Peninsula (Fig. 1).Higher functional diversity can lead to increases in tree density and basal area, which are important drivers of tree mortality due to competition (Searle et al. 2022).Tree Contrary to our hypothesis, we did not nd support for increases in the spatial autocorrelation of tree damage and mortality over time (see Fig. 2).This result could be due to two plausible explanations.First, widespread die-off and mortality events are relatively rare and constitute small proportions of the landscape where speci c networks of measurement in the clusters of change will be required (Trumbore et al. 2015).Second, a mean period of ~ 10 years is relatively short to study temporal changes in forests composed by long-lived species (see e.g., Ruiz-Benito et al. 2020).The combination of the two potential explanations could be causing the spatial trends in tree damage and mortality, because we observed an increase in the magnitude of tree damage and mortality, but we did not observe a variation in their spatial extent because regional die-off is strongly dependent on drought-induced extreme events (Allen et al. 2010, Broodbid et al. 2020) and a period longer than 10 years continuously measured across the space might be required to further assess the temporal trends of these patterns.

Effects of competition, water availability and drought intensity on the spatial patterns of tree damage and mortality
Water availability had the greatest effect on the spatial patterns of tree damage and mortality, which can be related to the steep spatial gradient of aridity in the Mediterranean region of the Iberian Peninsula (i.e., from 100 to 700 mm of precipitation and 5 to 17. ).Therefore, the increased sensitivity to drought over time on the spatio-temporal patterns of tree damage and mortality suggest the importance of further assessing both response variables and their dependence over time and space when evaluating die-off events.
Mediterranean forests are highly vulnerable to climate change (IPCC 2018) due to hot and dry summers, and future scenarios that project increasing aridity (Pasho et al. 2011; Gazol and Camarero, 2022).Furthermore, Mediterranean forests are subjected to strong legacy effects of land use abandonment or intensi cation (Doblas-Miranda et al. 2017).On the one hand, the abandonment of forest management and forest expansion in old agricultural elds are leading to biomass accumulation, which increases forest vulnerability to pests, pathogens and res (Cruz-Alonso et al. 2019; Bradford and Bell 2017).On the other hand, afforestation practices have led to dense and monospeci c forests (Ruiz-Benito et al. 2012; Vadell et al. 2016), which are also very vulnerable to disturbances (Sánchez-Salguero et al. 2012; Ruiz-Benito et al. 2013).

from
Moreno and Hasenauer (2016) and Rammer et al. (2018) databases with the easyclimate R package (Cruz-Alonso et al. 2023) for the period between 2SFI and 4SFI.Temporal variations in climate were characterised through the absolute terms of the Standardised Precipitation Evapotranspiration Index (SPEI, Vicente-Serrano et al. 2010
damage due to biotic and abiotic factors (i.e., bark beetles, exotic pathogens or res) has increased in European forests since the 60s (Patacca et al. 2022; Adame et al. 2022; Santini et al. 2013).The increases in tree mortality rates over time agree with temporal trends in Spain indicating higher mortality rates in gymnosperm forests (Astigarraga et al. 2020; Gazol et al. 2022), which can be related to their high sensitivity to increasing temperatures and droughts (Ruiz-Benito et al. 2017b).

Table 1
Comparisons of alternate models of tree damage and mortality based on Akaike information criterion (AIC) to test the main effects of structure and climate

Table 2
Comparisons of models of tree damage for 3SFI and 4SFI and mortality for 23SFI and 34SFI, i.e., the consecutive periods of the Spanish Forest Inventory data, based on Akaike information criterion (AIC) to test the main effects of structure and climate (Jump et al. 20172015;Bréda et al. 20062013;Benito-Garzón et al. 2013), but we suggest for the rst time that the spatial aggregation also depends on the aridity gradient.Greater mortality in wet sites has been linked to the speci c biotic and abiotic conditions of productive sites, such as sporadic events of droughts or high temperatures and higher competition (see e.g., Ruiz-Benito et al.2013,Changenet et al. 2021).Furthermore, despite tree mortality being high in drier areas, tree species adaptation to Mediterranean conditions can buffer the effects of adverse climatic conditions at large spatial extents(Settelle et al. 2015;Bréda et al. 2006) and local increases can be less representative than those caused by the steep gradient in water availability.We found support for our hypothesis of increasing droughts and basal area leading to more ample aggregation in tree damage and mortality(Jump et al. 2017).On the one hand, trees under more frequent and intense droughts have greater probabilities of die-off and mortality events(Neumann etal.2017;Allen et al. 2010; Gazol and Camarero 2022).Under dry conditions, the water can abruptly change from liquid to gas creating xylem cavitations and, ultimately, embolism (Choat et al. 2018; Tyree and Zimmermann 2002).This process interrupts water ow and diminishes the transport of water to the canopy, causing hydraulic failure and tree mortality (Nardini et al. 2013).On the other hand, trees in dense forests are more vulnerable to experiencing tree mortality, because of increased competition for light and soil resources (Bradford and Bell 2017; Astigarraga et al. 2020; Jump et al. 2017).Therefore, all this suggests that forests under increased climatic stress and competition have a greater probability of experiencing tree damage and mortality at wide spatial extents (e.g., Allen et al. 2015, Jump et al 2017).The spatial dependency of tree damage with competition, aridity and drought intensity increased over time, but no signi cant increases were found for tree mortality except for drought intensity.Our result agrees with increased negative effects of global environmental change on the spatial patterns of tree damage over time, that could be enhanced by increased sensitivity to climate as forests develops particularly high for tree damage over tree mortality (i.e., increased effect of competition and drought, McIntyre et al. 2015; Astigarraga et al. 2020).However, Mediterranean forests are suffering warmer and dryer summers, with subsequent effects on tree demography and damage (Ruiz-Benito et al. 2013; Anderegg et al. 2015a; Díaz-Martínez et al. 2023).The increase in the spatial dependence of tree damage and mortality with drought over time, which remains strong as distance increases, highlights a strong drought-induced effect with intensi ed sensitivity over time (Jump et al. 2017, Astigarraga et al 2020, Anderegg et al. 2019 5 ºC of average temperature, Costa et al. 2005).Previous studies have observed that wetter areas are experiencing higher levels of tree damage and mortality than drier areas (