Lithology modulates the response of litter decomposition to precipitation in Mediterranean forests

Lithology modulates the response of litter decomposition to precipitation in Mediterranean forests | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lithology modulates the response of litter decomposition to precipitation in Mediterranean forests Daniel James Carlton Fishburn, Andrew R. Smith, Lars Markesteijn, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6333544/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and aims Plant litter decomposition has a major influence on the global carbon cycle. While extensive research has examined the primary environmental drivers of decomposition, the influence of lithology remains poorly understood. Methods We investigated the combined effects of lithology and climate on needle litter decomposition through a field experiment along a decreasing precipitation gradient (1097 to 641 mm yr − 1 ) located in the province of Malaga (Andalucía, Spain) where maritime pine ( Pinus pinaster ) forests occur on three distinct soil types: calcareous, metapelite, and peridotite. Additionally, we conducted a reciprocal transplant experiment at the intermediate precipitation site to test the home-field advantage hypothesis, using litter from Pinus pinaster and Abies pinsapo on calcareous and peridotite soils. Results After 1.5 years of decomposition, under intermediate precipitation, litter mass loss was highest on calcareous soils, exceeding mass loss on metapelite soils by 24% and peridotite soils by 50%. Decreased precipitation reduced decomposition by 35% on calcareous soils but had minimal effects on metapelite and peridotite soils. On peridotite soils, labile carbon decomposition was delayed by one dry season, whereas lignin decomposition began immediately. A home-field advantage pattern was observed on calcareous soils, while an away-field advantage was detected on peridotite soils. Conclusion Lithology modulates litter decomposition by influencing litter quality. Since lithology affects both, decomposition rates and their sensitivity to precipitation, understanding these interactions is critical for predicting climate change impacts on nutrient cycling and carbon dynamics. Plant-soil interactions Mediterranean forests bedrock litter quality litter decomposition dynamics drought tolerance soil elemental composition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Plant litter decomposition is a fundamental biogeochemical process that regulates nutrient cycling, soil carbon storage, and atmospheric CO 2 emissions in forest ecosystems (Andrews & Schlesinger, 2000 ; Cotrufo et al., 2013 ). Globally, 36–54% of photosynthetically fixed carbon returns to the atmosphere through plant litter (hereafter litter), root and mycorrhizal hyphal turnover, and soil organic matter decomposition (Sha et al., 2022 ; Joly et al., 2023 ). Despite the substantial research on the drivers of litter decomposition (e.g. Bradford et al., 2016), the influence of lithology remains largely unexplored (e.g. Malik, 2019, Malik 2023). However, recent studies suggest that soil minerology influences soil structure (Angst et al., 2021; Matus 2021), litter quality (Couteaux et al., 1995 ), microbial community composition and functionality (Doetterl et al., 2018; Schroeter et al., 2022), and soil carbon mineralisation (Sagliker et al., 2018; Rocci et al. 2021; Liao et al., 2022), all of which can directly or indirectly affect litter decomposition rates. Plant decomposition is constrained by abiotic factors, such as water availability, and biotic factors, particularly soil microbial communities, which are both strongly influenced by lithology(Throop & Archer, 2009 ). Classic litter decomposition models have identified climate as a dominant driver, with secondary influences from soil properties, litter quality and soil decomposer communities (Swift et al., 1979 ). More recent studies indicate that litter quality exerts a dominant influence at broad spatial scales, with microbial activity shaped by both climate and litter chemistry (Bradford et al., 2016; Suseela & Tharayil, 2018 ; Prieto et al., 2019 ). Joly et al. ( 2023 ) argue that decomposition studies relying on standardised litter types may overlook the importance of locally adapted litter, which decomposes differently than standardised or non-native litter types. Globally, a combination of macroclimate factors and litter characteristics explain over two-thirds of the variance observed in litter decomposition (Zhang et al., 2008 ; Prieto et al., 2019 ; Joly et al., 2023 ) however the influence of macroclimate reduces when accounting for microclimatic variations (Bradford et al., 2014 , 2016b , 2017 ). These findings highlight the coevolution between local flora and decomposer communities, an interaction further shaped by climatic conditions. Litter quality is determined by its chemical composition, including lignin content, carbon-to-nitrogen (C:N) ratios, and lignin-to-nitrogen (Lig:N) ratios. Historically, the lignocellulose index (LCI) has been used to predict decomposition rates (Berg & McClaugherty, 2008 ). Advances in our understanding of litter decomposition have shown that the accessibility of labile C and N sources are important for decomposer communities (Zhang et al., 2008 ; Abbott et al., 2013 ; Austin et al., 2016). Additionally, both macro- and micro-elements, along with structural and metabolic compounds, have been found to strongly influence litter chemistry and litter decomposition rates (Ball et al., 2022 ). For example, Sun et al. ( 2019 ) showed that Mn fertilisation accelerates late-stage litter decomposition by increasing the activity of manganese peroxidase, an enzyme that degrades the phenolic structure of lignin (Berg & McClaugherty, 2008 ). Furthermore, Zhang et al. ( 2008 ) found that litter N content alone explained 39% of the variation in litter decomposition rates across a global study spanning 110 sites (38˚S to 69˚N). Since most plant nutrients originate from soils, lithology directly influences plant stoichiometry, which in turn affects litter quality and decomposition dynamics. During pedogenesis, lithology influences soil properties through: (i) elemental composition and, subsequently, soil pH (Schaetzl & Thompson, 2007 ; Hahm et al., 2014 ; Campillo-Cora et al., 2022 ); and (ii) structure and texture (Angst et al., 2018 ; Pichler et al., 2021 ), which in turn can affect hydraulic conductivity and water retention (Hillel, 2004 ; Hahm et al., 2014 ; Callahan et al., 2022 ). These factors play a crucial role in shaping the suitability of the soil matrix for plant litter decomposition by influencing microbial activity, nutrient availability, and organic matter stabilisation. Consequently, lithology-driven soil characteristics can also affect plant community structure, productivity and nutrient stoichiometry, further modulating litter decomposition dynamics (Searcy et al., 2003 ; Ribeiro et al., 2007 ; Muñoz et al., 2023 ). Despite the growing recognition of lithology as a key determinant of soil microbiome, its role regulating forest productivity, plant drought vulnerability, and soil carbon storage has only recently been explored (Hahm et al., 2014 ; Reichenbach et al., 2021 ; Callahan et al., 2022 ; 2023). However, its effect on litter decomposition, particularly through changes in plant nutrient balance, litter quality, and soil microbial communities, remains poorly understood. Michalet & Liancourt ( 2024 ) found that bedrock characteristics and aridity have an interactive effect on litter decomposition, with faster rates on acidic, coarse siliceous soils in arid conditions but slower on fine-textured calcareous soils in wet conditions, independent of soil pH. While this supports lithology-driven effects, further research is needed to assess its role in precipitation-driven decomposition across ecosystems and bedrock types. Reciprocal transplant experiments provide a robust framework for detecting local adaptation and disentangling the interactions between litter quality, climate, and soil properties. Two major hypotheses have been proposed to explain local adaptation in litter decomposition rates: (i) the home-field advantage (HFA) hypothesis (Ayres, et al., 2009 ); and (ii) the substrate-quality-matrix quality interaction (SMI) hypothesis (Freschet et al., 2012 ). The HFA hypothesis postulates that litter decomposes faster at its site of origin due to local specialisation of decomposer communities (Henry et al., 2000; Ayres et al., 2009 ). A meta-analysis of 125 reciprocal transplant studies across 35 ecosystems found an average 7.5% faster mass loss at home sites (Veen et al., 2015). However, inconsistencies in HFA patterns suggest that climate, soil properties, and microbial community composition may override HFA effects (Wang et al. 2020 ; Fanin et al. 2021 ). On the other hand, the SMI hypothesis postulates that litter decomposition rates are determined by the match between litter quality and soil characteristics, with greater differences reducing decomposition efficiency (Freschet et al., 2012 ). Some studies argue that decomposition is primarily controlled by initial litter quality, rather than SMI effects (Perez et al., 2013 ; Bachega et al., 2016 ), which would imply an indirect lithological influence through litter chemistry. This aim of this study was to investigate the combined effects of lithology and climate on litter decomposition in Mediterranean forests. Specifically, the study sought to: (a) investigate the relative control of climate and lithology on leaf litter decomposition in Pinus pinaster Ation. forests; (b) analyse the interactive effects of leaf litter quality and the physicochemical properties of soils derived from distinct lithological substrates on leaf litter decomposition in two Mediterranean forest species ( Pinus pinaster and Abies Pinsapo Boiss.); and (3) predict how the lithological substrate influences the response of litter decomposition to decreasing precipitation. To address these objectives, the following hypotheses were tested: (i) litter quality and decomposition rates will decline with decreasing precipitation; (ii) lithology will affect temporal decomposition via soil physicochemical properties; (iii) the effects of lithology on decomposition will diminish under conditions of reduced precipitation; and (iv) decomposition will follow an HFA pattern on calcareous soils and an SMI pattern for peridotite soils. Materials and Methods Site description The sites are located along a precipitation gradient spanning three mountain ranges in Southern Spain: The Natural Reserve Sierra Bermeja-Sierra Crestellina (West), the National Park Sierra de las Nieves (Centre), and the Natural Park Sierra Alhama, Tejeda y Almijara (East). This 100 km gradient exhibits a decline in mean annual precipitation from 1097 mm in the west to 641 mm in the east. Elevation ranges from 534 to 1150 m asl, with mean annual temperatures fluctuating between 15 and 17°C depending on the site’s position along the gradient (Table 1). All sites fall within the Csa Köppen climate classification, characterised by hot, dry summers (warmest month exceeding 22°C) and bimodal precipitation patterns, with distinct dry (June-August) and wet (November-February) seasons (Kottek et al., 2006 ). The region’s complex geomorphology is a result of 600 million years of lithospheric fragmentation producing a diverse array of sedimentary, metamorphic, plutonic and volcanic formations due to the collision of the European and African tectonic plates (Rehault et al., 1984 ; Garfunkel, 1998 ; Hidas et al., 2017 ). Within our study sites, three distinct lithological soil types – calcareous, metapelite, and peridotite – are present across all three mountain ranges. Each soil type exhibits unique soil physiochemical characteristics that are predicted to influence litter decomposition: (i) calcareous soils have high pH and carbonate content, reducing the solubility and availability of essential plant nutrients such as. Fe, Mn, Cu, Zn, and P (Ström et al., 2001 ); (ii) peridotite-derived soils are rich in phytotoxic heavy metals (e.g., Ni, Cr, Co), deficient in essential nutrients (N, K, and P ) and exhibit a high Mg:Ca ratio (Kazakou et al., 2006 ; Bini et al., 2017 ); and (iii) metapelite soils contain high quartz and K-feldspar content contributing to poorly developed soils with low nutrient availability. By examining litter decomposition across this climatic gradient and conducting a reciprocal transplant experiment, we aim to identify the predominant lithological controls on litter decomposition processes. Two tree species were selected based on their distribution patterns and contrasting litter chemistry: (i) Pinus pinaster , due to its broad distribution across all lithological substrates along the precipitation gradient; and (ii) Abies pin s apo , a relic endemic species which has contrasting litter chemistry and is distributed across all lithologies but restricted to the central part of the gradient. Experimental design To investigate the role of lithology on litter decomposition and its response to decreasing precipitation, we conducted two complementary field experiments: needle litter decomposition of maritime pine along the precipitation gradient, where we placed needle litter from P. pinaster in litterbags across all sites spanning the precipitation gradient and lithological soil types (Experiment 1), and the effect of litter quality and its interaction with lithology, where we conducted a reciprocal transplant experiment using litter from P. pinaster and A. pinsapo on calcareous and peridotite soils to evaluate the home filed-advantage and SMI hypotheses (Experiment 2). Experiment 1: Maritime pine litter decomposition along the precipitation gradient Between January and April 2019, we established replicated circular plots (n = 4) within each lithological soil type at each position along the precipitation gradient, totalling 36. Plots were selected to minimise variability in physiographical factors. Each plot was 30 m Ø and was divided into four quadrants with a central tree marking the reference point. This experiment included two fixed factors; (i) POSITION (three levels: West, Centre, and East) as a proxy for climate, and (ii) LITHOLOGY (three levels: calcareous, metapelite, and peridotite). Experiment 2: Effect of litter quality and its interaction with lithology on litter decomposition Since Abies pinsapo has a limited distribution on peridotite soils, monospecific forests stands of each species were selected for this study at the centre of the precipitation gradient (Sierra de las Nieves National Park). Plots in this experiment were larger (50 m Ø) to account for species-specific variation. Within each forest, six trees were selected and litter originating from two lithological soil types (calcareous and peridotite) was placed in forests dominated by either Pinus pinaster or Abies pinsapo . The transplant experiment included three fixed factors: (i) LITHOLOGY (two levels: calcareous and peridotite), (ii) litter SOURCE (origin) (two levels: home and away), and (iii) forest SPECIES (two levels: P. pinaster and A. pinsapo ). Litterbag preparation and sampling Recently senesced needle litter was collected from all quadrants of the 36 experimental plots on 10th October 2020. Intact needles - exhibiting a range of lengths with two or three needles attached to their sheath - were selected after visual inspection. The litter was air-dried to constant mass in a well-ventilated room. Two sizes of polyethylene mesh litterbags were used in this experiment: 15 × 25 cm for P. pinaster , and 7.5 × 25 cm for A. pinsapo . The mesh aperture (0.8 mm 2 ) was selected to exclude macroinvertebrates. Bag sizes were selected to maintain a constant surface-area-to-volume ratio. Each litterbag contained 10 ± 0.09 g of P. pinaster litter (larger bags), or 5 ± 0.01 g of A. pinsapo litter (smaller bags). Four replicate samples per plot were prepared and collected at four post-deployment intervals: 118, 229, 380 and 572 days. Deployment began in October 2020, with the final samples collected in November 2022. In Experiment 1, four subplots were selected within 3 m of a P. pinaster tree. Litterbags were randomly arranged in a 2 × 2 m grid, totalling 576 litterbags. In Experiment 2, six subplots were selected with litterbags randomly arranged in a 3 × 2 m grid with both P. pinaster and A. pinsapo litter, totalling 192 litterbags. In all cases, the litter layer was removed to the O horizon and litterbags were placed at the interface with the L horizon. Litter mass To quantify litter mass, three 30 × 20 cm² plastic trays were randomly placed in each quadrant. All organic material down to the O horizon was collected, homogenised, and weighed (± 0.1 g). A subsample of litter was used to determine litter moisture content, while dry litter mass was assessed after oven-drying at 80ºC for 48 h. Total litter mass per unit area was estimated as kg dry mass per m 2 . Litter chemistry Litter carbon (C) and nitrogen (N) contents were quantified in duplicate by grinding 350 ± 100 mg of dried litter to a fine powder using a ball mill (Retsch MM200 GmbH, Hann, Germany). Elemental analysis was conducted with a TruSpec CHN/S analyser (LECO, St. Joseph, MI, USA). Soluble cell fraction (SCF), cellulose, hemicellulose and acid-insoluble fractions – including lignin, tannins, cutin, suberin, and other phenolic compounds (Corbeels, 2001 ) – were determined in duplicate by sequential Van Soest digestions (Van Soest et al., 1991 ). Briefly, litter subsamples (n = 4, 0.05 ± 0.01 g) were ground and sealed in F57 filter bags (Ankom Technology, Fairport, NY, USA). Crude fiber analysis (neutral and acid detergent) was conducted using an Ankom200 Fiber Analyzer, with acid detergent lignin digestion in a Daisy Incubator, following ANKOM protocols (Ankom Technology, 2016 , 2017a , 2017b ). Ash content was determined by combustion at 500˚C for 5 hours, with ash-corrected litter chemistry calculated using standard methods (Ankom Technology, 2016 , 2017a , 2017b ; Q. Wang et al., 2009 ). The Lignocellulose Index (LCI) was calculated using Eq. 1. \(\:LCI=\frac{lignin}{(lignin+cellulose+hemicellulose)}\) Eq. 1 Elemental composition Duplicate subsamples (250 ± 100 mg; n = 4) of powdered needle litter were digested using reverse aqua regia (1:3 concentrated HCl:HNO 3 ) in a Multiwave 3000 microwave digestor (Anton Paar, Graz, Austria) following Method 3051A (EPA, 2007). Briefly, the procedure involved the following steps: (i) pre-digested of samples in XQ80 reaction tubes by adding 7–10 ml of digestant and allowing effervescence to subside, (ii) sealing of tubes and subjecting them to microwave digestion (175 ºC, Ramp 15, Hold 15, Fan 1; Temp OFF, Hold 20, Fan 3), (iii) filtering digestion samples through pre-leached, acid-washed filter paper and a 400 µm syringe filter, (iv) analysis of elemental composition using ICP-OES IntelliQuant; (v) quantification of elemental composition for both initial samples and those after 572 days of decomposition; and (vi) conversion of raw concentration data (mg l − 1 ) to percentage composition using a density of 1.35 g cm − 3 for reverse aqua regia. Litter mass loss At each sampling period, one litterbag was retrieved from each plot quadrant. Freshly collected samples were weighed immediately, oven-dried at 80°C to a constant mass, and reweighed before subsequent analysis. Mass loss was determined on an ash-free dry mass basis. Litter decomposition was modelled using five alternative models: single exponential, single exponential with asymptote, discrete parallel, discrete series, and continuous quality models. Likelihood-based fitting with 500 permutations was performed using the “Litterfitter” R package (Cornwell & Weedon, 2014 ) (Table 2 ). Based on Akaike Information Criterion (AIC) values and visualisation, the best fit was obtained using a first-order single exponential decay model with an asymptote (Eq. 2) (Howard & Howard, 1974 ) \(\:{M}_{t}=\:{M}_{1}\times\:{e}^{-kt}+c\) Eq. 2. where M t = Mass remaining at time t , M 1 = decomposable litter pool, k = decomposition rate and c = asymptote (proportion of litter where k ≈ 0). The relative decomposition rate (RDR) was calculated according to Eq. 3 (Wang et al., 2009 ) where t i t 0 represents the sampling interval in days, and M t and M 0 denote the mass remaining at times t and 0, respectively. \(\:RDR\:\left(\%\:mass\:loss\:{day}^{-1}\right)=\:-\frac{\text{ln}\left(\frac{{M}_{t}}{{M}_{0}}\right)}{({t}_{i}-{t}_{0})}\times\:100\) Eq. 3. Between 7th and 18th September 2021, a forest fire burned over 10000 ha of woodland across the West/Centre of the precipitation gradient, including two of our study sites. As a result, the transplant experiment with A. pinsapo was terminated after one year. Statistical analyses All statistical analyses were performed in R v3.4.3 (R Core Team, 2022 ). A Multiple Analysis of Covariance (MANCOVA) was conducted to determine whether site characteristics, beyond precipitation gradient position and lithological substrate, varied significantly among plots. Results revealed significant differences in altitude and stand density across the sites (Table 3 ), which were log-transformed and included as covariates in subsequent analysis. Response variables were standardised using estimated marginal means with mean centering to control for these covariates (Russell et al., 2023 ). In Experiment 1, the fixed factors were (i) POSITION (three levels; West, Centre, and East) as a proxy for climate, and (ii) LITHOLOGY (three levels: calcareous, metapelite and peridotite). In Experiment 2, the fixed factors within each tree species ( P. pinaster and A . pinsapo ), were: (i) LITHOLOGY (two levels: calcareous and peridotite), and (ii) SOURCE (two levels: home and away). Both studies included time as a repeated measures factor with five levels: 0, 118, 229, 380 and 572 days. General linear models (GLMs) were used to examine response variables, selecting best-fit model on the Akaike Information Criterion (AIC). Model diagnostics were performed using diagnostic plots to check for normality, heteroscedasticity, and multicollinearity. The Durbin-Watson test was used to assess autocorrelation in the data (Fox & Weisberg, 2019 ). We also attempted to use generalised linear mixed effect models to account for autocorrelation within plots over time but had insufficient replication to produce well-fitting models. For variables affected by the fire, analyses were conducted both including and excluding the West (wet) category to assess the influence of an unbalanced design. The significance of the model terms was determined through Analysis of Variance (ANOVA), employing type III sum of squares when including the West category for interaction terms, and type II sum of squares when excluding it. Effect sizes (η²) were calculated by dividing the sum of squares of each predictor by the total sum of squares, representing the proportion of total variance explained by each fixed effect. Post-hoc analyses for pairwise comparisons were conducted using Tukey’s Honestly Significant Difference (HSD) test. Temporal changes in chemical composition were modelled using General Additive Models (GAMs) defined as y ~ s(x, k = 4), restricting the maximum degrees of freedom to four (n-1 time points) to capture non-linear trends over time (Wood, 2011 ). Results Impact of lithology on pine litter quantity and quality along the precipitation gradient The mass of litter on the forest floor decreased consistently along the precipitation gradient from west to east for all lithologies ( P 0.05; Supplementary Table 2) despite observed differences in tree productivity between metapelite and peridotite soils (data not shown). Notably, litter quantity of forests on peridotite soils were less affected by decreasing precipitation than those on calcareous or metapelite soils ( P < 0.01). Initial litter chemistry and quality varied across positions along the gradient and lithological soil types (Table 3 ). The SCF and hemicellulose exhibited small but significant differences within both position and lithology ( P < 0.05; SCF interquartile range (IQR) = 7.4%; hemicellulose IQR = 11.7%; Supplementary Table 2). Initial lignin content was 12% higher in metapelite-derived litter compared to peridotite-derived litter ( P < 0.01). Litter quality indices (C:N, Lig:N) differed significantly among lithologies ( P < 0.01 in all cases) but were not affected by precipitation (Fig. 2 ). Litter from calcareous soils had lower quality than that from metapelite and peridotite soils. Yet, neither precipitation nor lithology affected LCI (x̄ = 0.38 ± 0.01; P > 0.05), while Lig:N ranged from 31 to 68 across lithologies ( P < 0.01; Table 3 ; Fig. 2 ), with calcareous forest litter exhibiting 27% higher Lig:N than peridotite forest litter ( P < 0.05; Supplementary Table 2). A significant position × lithology interaction was detected for C:N ratios ( P < 0.01; Table 3 ; Fig. 3 ) though lithology alone explained 40% of the variance (η 2 = 0.40; P < 0.05). C:N was 22% higher in calcareous litter ( P < 0.05). Litter mass loss and litter decomposition rates (Experiment 1) During the 572-day decomposition experiment, pine litter mass loss ranged from 15 to 45%, with notable intra-site variation (IQR = 10.9%) (Fig. 3 ). There was a significant interaction between lithology and precipitation ( P < 0.001) since litter mass loss decreased in calcareous soils while did not significantly change in the other two lithological substrates (Fig. 3 ). Most litter decomposition occurred early in the experiment: 67% of mass loss occurred within the first 118 days (wet season) and 87% mass loss occurred after 229 days (second sampling interval; Fig. 4 ). The effects of lithology were most pronounced at intermediate precipitation levels (Centre; P < 0.001) where litter decomposed fastest on calcareous soils, exceeding metapelite soils by 24% ( P < 0.05) and peridotite soils by 50% ( P < 0.001). Litter decomposed 21% more on metapelite soils compared to peridotite soils ( P = 0.076). A general linear model incorporating position, lithology, and covariates (C:N, altitude, stand density) explained 42% of the total variation in litter mass loss, with the position × lithology interaction accounting for 16% ( P < 0.001; Fig. 4 ; Table 6; Supplementary Table 1). Litter mass loss was positively correlated with remaining lignin content (t (28) = 5.64, P < 0.001, Adj. R 2 = 0.52), increasing by 1% for every 5 mg g − 1 AFDM of lignin remaining. Overall, litter mass loss was lowest at the driest (East) sites (mean = -15.4%; P < 0.05). A single exponential decay model with asymptote provided the best fit (R 2 = 0.91–0.99, Fig. 4 ; Table 2 ). Litter decomposition rates were influenced by the labile carbon pool (M 1 ) but were unaffected by position or lithology ( P > 0.05; Tables 3 ; Supplementary Table 1). Interestingly, the lithology × position interaction was significant, M 1 and asymptote values showed little variation across position (3–4%) and lithology (2–3%; Table 4; η 2 = 0.27). Temporal changes in litter chemistry SCF content was surprisingly low during the first 118-days (9%), with hemicellulose (25.8%) and lignin (43.4%) accounting for most decomposition. During the following dry season (2nd sampling interval; 229–380 days), SCF mass loss accelerated for calcareous (81%) and metapelite (57%) soils, whereas peridotite soil-derived litter lost only 6.5% SCF and 15.5% hemicellulose, while lignin and cellulose fractions increased slightly. Litter decomposition was consistently influenced by lithology, irrespective of position along the precipitation gradient ( P < 0.001; Supplementary Table 3). Litter from peridotite soils exhibited a seasonal time-lag in crude fibre fraction loss (Fig. 5 ). Elemental composition shifts were observed over time (Fig. 6 ), with peridotite soils accumulating Co, Cr, Cu, Fe, Ni and Mg ( P < 0.05 in all cases). Eight of thirteen elements were influenced by lithology, though only six showed significant differences after 572 days ( P < 0.05). Major nutrients, C and N were unaffected by lithology, with only C decreasing significantly over time ( P 0.05; Table 4 ; Supplementary Table 5), whereas A. pinsapo litter varied significantly across lithologies ( P < 0.05; Table 5 ; Supplementary Table 6), with a tendency towards higher lability in calcareous substrates, as demonstrated by a 12% increase in SCF ( P < 0.01) and a 43% decrease in lignin contents ( P < 0.001). A forest fire in September 2021 prematurely ended the transplant experiment after 250 days, Missing data for P. pinaster home-field sites was estimated using adjacent site data, while calcareous-away data was inferred using the asymptote from the single exponential decay model. Litter decomposed faster in calcareous soils, regardless of species and source location (Fig. 7 ). Within home-field sites, litter decomposed 27% faster in P. pinaster ( P < 0.01) and 45% faster in A. pinsapo ( P < 0.01) in calcareous soils compared to peridotite soils. Transplanted A. pinsapo litter from calcareous soils decomposed 31% faster at home than away ( P < 0.05). P. pinaster litter from peridotite origin lost 45% more mass when transplanted away, while A. pinsapo litter from peridotite-origin lost 104% more mass away than at home ( P < 0.05; Fig. 7 ). Discussion The primary aim of this study was to assess the role of lithology in plant litter decomposition, particularly whether it modulates decomposition responses to declining precipitation, a key climate change threat in the Mediterranean. We hypothesised that (1) litter quality and decomposition rates would decline with decreasing precipitation, (2) lithology will affect temporal decomposition dynamics via soil physicochemical properties, (3) the effect of lithology on litter decomposition will diminish under conditions of reduced precipitation; and (4) decomposition will follow an HFA pattern on calcareous soils and an SMI pattern on peridotite soils since these soils contain potentially toxic metals. Our findings show that lithology affects plant litter decomposition through two primary pathways: by regulating litter nutrient quality and by shaping microbial substrate accessibility. Effect of lithology on litter decomposition After 1.5 years of decomposition in the field, mass loss of P. pinaster litter ranged from 15 to 45%, depending on local precipitation and lithological substrate. These values align with Kurz et al., ( 2000 ) who reported mass loss after one year in P. pinaster forests, and with Prescott ( 2010 ), who found approximately 40% loss for recalcitrant litter (Lig:N > 30) after three years. As expected, litter decomposition slowed over time, with 65 and 85% of litter mass remaining after 572 days, which is 2–3 times higher than global averages using asymptotic models (Berg et al., 1996 ; Osono & Takeda, 2005 ). This slow mass loss could potentially contribute to long-term soil carbon storage (Berg & Meentemeyer, 2002 ), but it is probably somewhat underestimated due to the exclusion of soil fauna and shredders (Kurz et al. 2000 ). Nonetheless the comparison across lithologies remain valid and comparable with many other reported values in the literature using similar litterbags. Analysis of litter chemistry revealed significant differences in initial litter quality across lithologies, independent of tree species showing a clear indirect impact of lithology on litter quality. Litter from forests on calcareous soils was more recalcitrant with higher C:N ratios. However, litter from peridotite-derived forests decomposed slower than that from calcareous and metapelite soils. SCF and cellulose loss occurred after 229 days on calcareous and metapelite soils but was delayed until after 380 days on peridotite soils, despite immediate lignin degradation during the first wet season. This suggest that substrate accessibility for microbial decomposers was altered, potentially due to unusual elemental stoichiometry of peridotite soils. High Mg content in peridotite-derived litter may have enhanced lignin-degrading phenol oxidase activity (Carine, et al., 2009 ), while elevated Ni levels may have selectively inhibited microbial decomposition of high-Ni litter fractions (Adamidis et al., 2016 ; Nakamura et al., 2019 ; Islam & Sandhi, 2023 ). This disparity in elemental stoichiometry could also explain the delay in labile compound decomposition to the second wet season for peridotite litter. Despite the essential role of Mg as a macronutrient, its role in decomposition remains underexplored. In a synthesis of 68 decomposition studies, Berg et al. (2021) found Mg to be a limiting factor in decomposition, with microbial uptake increasing as decomposition progresses. In our study, litter Mg content remained significantly higher in peridotite-derived litter throughout the decomposition period, suggesting that early-stage lignin degradation may have facilitated Mg leaching, stimulating microbial decomposition of labile and intermediate crude fibre fractions later on. Lignin decomposition dynamics also ensued immediately for both calcareous and metapelite sites, although the decomposition rate was slower relative to peridotite sites. Contrary to conventional decomposition models, lignin breakdown occurred simultaneously with labile compound decomposition on all lithologies, rather than following a sequential pattern. (i.e., labile SCF → holocellulose → lignocellulose) (Van Der Heijden et al., 2008 ; Allison, 2012 ; Palozzi & Lindo, 2018 ). This may be due to differences in microbial substrate accessibility or the loss of a non-lignin compounds within the acid-insoluble fraction, which Van Soest Digestion lacks the resolution to detect (Van Soest er al., 1991). Lithology-driven impact on microbial substrate accessibility The expectation that fungal decomposition dominates in peridotite soils due to their higher fungal-to-bacterial ratios, as found in Nakamura et al., 2023 , was not fully supported. Instead, evidence suggests an important role for bacterial lignin degradation (DeAngelis et al., 2011 ; Woo et al., 2014 ; Pold et al., 2015 ; Wilhelm et al., 2019 ). A parallel study to ours (Rey et al., submitted) found significantly higher microbial and fungal diversity in peridotite soils, particularly at the driest sites. Yi et al. ( 2023 ) found that 19% of samples showed a similar delayed peak in lignin decomposition, linked to low soil-pH, fungal community composition, and high extractable Mn content, as typically found in peridotite soils. Manganese is a cofactor for lignin-degrading peroxidases in both fungi and bacteria (Hatakka, 1994 ; Hofrichter, 2002 ; Osono, 2007 ; Brown & Chang, 2014 ; Datta et al., 2017 ; Qin et al., 2017 ) and is thus likely to explain delayed lignin decomposition. P. pinaster litter derived from peridotite soils also exhibited high Ni, Cr, and Co concentrations after 572 days, in line with previous findings (DeGrood et al., 2005 ; Kazakou et al., 2006 Baumeister et al., 2015 ; Bini et al., 2017 ; Nakamura et al., 2023 ). Heavy metals such as Ni shape microbial community composition in non-serpentine soils(Bååth et al., 1998 ; Pennanen, 2001 ) to a much larger degree than serpentine soils(Héry et al., 2003 ) indicating that peridotite soils may host microbial communities specifically adapted to heavy metal toxicity (Adamidis et al., 2016 ). A concurrent study at these sites supports this finding, showing that high microbial C use efficiency—linked to faster biomass turnover—likely arises as a trade-off to withstand heavy metal toxicity, suggesting that beyond community shifts, microbial function is also impaired by hostile soil conditions. Home-field disadvantage and lithology-driven SMI effects The reciprocal transplant experiment demonstrated a home-field disadvantage for peridotite-derived litter, which decomposed faster when transplanted to calcareous substrates. Calcareous-origin litter decompose fastest at home. This challenges the substrate-quality matrix hypothesis (Freschet et al. 2012 ; Aerts & Cornelissen, 2012) which predicts that high-quality litter decomposes faster in high-quality matrices. Instead, our results suggest that lithology modulates decomposer access to substrates, overriding expected litter-matrix quality interactions. Calcareous and peridotite soils represented extremes, where lithology either enhanced or suppressed decomposer efficiency, rather than acting as a neutral medium. This aligns with the mass ratio hypothesis (Grime, 1998 ; Garnier et al., 2004 ; Grime, 1998 ) suggesting that decomposer community composition, under influence of resource history, reflects the dominant litter quality in a given matrix (Keiser et al., 2011 ). Lithology influenced initial litter quality in a species-dependent, spatially variable manner. In Experiment 1, higher-quality calcareous substrate produced lower-quality P. pinaster litter (22–27% higher C:N and Lig:N) along the precipitation gradient but had no effect in the transplant experiment. This variability may result from other litter chemistry drivers, such as precipitation, topography and altitude (Zhang et al., 2008 ; Gong et al., 2016 ). Notably, despite the small spatial scale of the transplant experiment, A. pinsapo litter quality improved (44% lower LCI) in calcareous substrates. Given little quality’s key role in decomposition (Aerts, 1997 ; Cornwell et al., 2008 ; Zhang et al., 2008 ) the influence of lithology should not be overlooked. Interaction of lithology and climate on litter decomposition Climate, particularly precipitation is a strong global predictor of plant litter decomposition (García- Palacios et al., 2013 ; Parton et al., 2007 ) controlling soil microbial activity via moisture availability and limiting the supply and diffusion of soil nutrients (Noy-Meir, 1973 ; Hueso et al., 2012 ; Manzoni et al., 2012 ; Schimel, 2018 ). Accordingly, we expected precipitation to be the main driver of litter decomposition, but its effect varied by lithology, with decreasing precipitation impacting decomposition differently depending on the substrate. In this litter mass loss in calcareous sites showed the strongest response to decreasing precipitation, with 35% less mass loss in the driest Eastern site. This aligns with the greater moisture sensitivity of forests on calcareous soils, where productivity declines under drought (Rey et al., in prep.). Higher mass loss in the West and Centre, along with uniform increases in microbial biomass N (data not shown), suggests that litter decomposition in calcareous sites is more microbially mediated, making them more vulnerable to drought. Similar patterns occur in forests on nutrient rich, weatherable bedrock, where high productivity in wet years leads to excess water demand and increased drought-induced dieback, driving a boom-bust response (Callahan et al., 2022 ). This finding is further corroborated by Wilhelm et al. ( 2023 ) who found that Ca-treated soils cycle more C through microbial biomass due to increased litter-derived C incorporation. This supports our findings that productive lithologies, like calcareous soils, reinforcing the boom-bust hypothesis and highlighting forest vulnerability to climate change (Callahan et al., 2022 ). Reduced precipitation had unexpected effects on litter mass loss in metapelite and peridotite lithologies. Mass loss remained unchanged in metapelite soils and slightly increased in peridotite soils under dry conditions. The observed increase in litter decomposition may be attributed to higher N availability in East–peridotite sites, which reduced the recalcitrance of litter, as indicated by the lower C:N and Lig:N ratios (Aerts, 1997 ; Talbot & Treseder, 2012 ; Ge et al., 2013 ). Elevated N levels likely result from increased atmospheric NO 3 − deposition (Aber et al., 1989 ; DeForest et al., 2004 ; Waldrop et al., 2004 ), due to the Sierra de Aguas proximity to Málaga (Lozano et al., 2009 ). Some metapelite soils in this study have weathered for over 500 million years forming acidic (pH 4.7–6.8) and nutrient-poor soils dominated by quartz and K-feldspar. Given pH near 5.5, where Al solubilises (Jones et al., 2019 ), microbial secretion of extracellular polymeric substances may counteract Al and Ni toxicity (Hu et al., 2019 ; Chen et al., 2023 ). These substances also help litter microbes retain moisture during drought (Malik et al., 2019 ), potentially mitigating the effects of reduced precipitation. This interactive effect of lithology and climate on decomposition rates has recently been attributed to the physical chemistry of bedrock, where calcareous sights experience higher drought stress and siliceous bedrocks compensate for water-deficit by capturing runoff more effectively (Michalet & Liancourt, 2024 ). Conclusions As climate change intensifies, understanding the role of lithology in regulating litter decomposition and ecosystem-level C cycling is essential for refining global ecosystem models. This study demonstrates that lithology regulates litter decomposition through two primary mechanisms: (i) directly, by determining initial litter chemistry and quality, and (ii) indirectly, by modifying microbial accessibility to substrates through soil physicochemistry and elemental composition. Peridotite-derived litter exhibited an altered decomposition sequence, where lignin degraded before more labile compounds. This is likely due to elevated Mg and Ni concentrations, which impact microbial enzyme activity and decomposition pathways. The transplant experiment revealed a home-field disadvantage for peridotite-derived litter, suggesting that lithology strongly modulates microbial specialisation and decomposition dynamics. Decomposition responses to decreasing precipitation were lithology dependent, supporting the recently documented influence of lithology on drought-sensitivity. In calcareous sites, reduced precipitation resulted in lower mass loss, while peridotite sites showed negligible responses, suggesting that high soil metal content and microbial adaptations buffer decomposition against moisture fluctuations. Given the implications for long-term soil carbon storage, future research should focus on how lithology shapes microbial community composition and function, particularly in mediating responses to climate extremes. Understanding the interactions between lithology, decomposition, and microbial dynamics is crucial for predicting carbon turnover and nutrient cycling under future climate change scenarios. Declarations This work was supported by the Ministry of Science of Spain (RT12018-095345-BC22), the School of Environmental and Natural Sciences at Bangor University, and by the Natural Environment Research Council (NE/L002604/1). The authors have no relevant financial or non-financial interests to disclose. Author Contributions Study design and conceptualisation was undertaken by Ana Rey, José Carreria, and Daniel Fishburn. Material preparation, data collection and analysis were performed by Daniel Fishburn, Ana Rey, Victor Lechuga and Andy Smith. The first draft of the manuscript was written by Daniel Fishburn with critical revisions provided by Ana Rey, Andy Smith, and Lars Markesteijn. All authors reviewed and approved the final manuscript. Acknowledgements We would like to thank the Envision Doctoral Training Partnership for supporting Daniel Fishburn throughout his PhD studies. Envision was funded by the UK’s Natural Environment Research Council (NE/L002604/1), while the LITHOFOR project was funded by the Ministry of Science of Spain (RT12018-095345-BC22). Many thanks to the Spanish field team for technical assistance throughout the LITHOFOR project. Data Availability The datasets generated during and/or analysed during the current study have been submitted as supplementary material. References Abbott, D., Sparks, D., Herzberg, C., Mooney, W., Nikishin, A., Zhang, Y. S., Aber, J., Mcdowell, W., Nadelhoffer, K., Magill, A., Berntson, G., Kamakea, M., Mcnulty, S., Currie, W., Abril, G., Guerin, F., Richard, S., Delmas, R., Galy-Lacaux, C., … Hart, S. C. (2013). Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates. Biogeochemistry . https://doi.org/10.1111/j.1365-2486.2008.01674.x Aber, J. D., Nadelhoffer, K. J., Steudler, P., & Melillo, J. M. (1989). Nitrogen Saturation in Northern Forest Ecosystems. BioScience , 39(6), 378–386. https://doi.org/10.2307/1311067 Adair, E. C., Parton, W. J., Del Grosso, S. J., Silver, W. L., Harmon, M. E., Hall, S. A., Burke, I. C., & Hart, S. C. (2008). Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates. Global Change Biology , 14 (11), 2636–2660. https://doi.org/10.1111/j.1365-2486.2008.01674.x Adamidis, G. C., Kazakou, E., Aloupi, M., & DImitrakopoulos, P. G. (2016). Is it worth hyperaccumulating Ni on non-serpentine soils? Decomposition dynamics of mixed-species litters containing hyperaccumulated Ni across serpentine and non-serpentine environments. Annals of Botany , 117 (7), 1241–1248. https://doi.org/10.1093/aob/mcw050 Aerts, R. (1997). Climate, Leaf Litter Chemistry and Leaf Litter Decomposition in Terrestrial Ecosystems: A Triangular Relationship. Oikos , 79 (3), 439–449. https://doi.org/10.2307/3546886 Allison, S. D. (2012). A trait-based approach for modelling microbial litter decomposition. Ecology Letters , 15 (9), 1058–1070. https://doi.org/10.1111/j.1461-0248.2012.01807.x Almagro, M. M., Maestre, F. T., Martinez-Lopez, J., Valencia, E., Rey, A., Martínez-López, J., Valencia, E., & Rey, A. (2015). Climate change may reduce litter decomposition while enhancing the contribution of photodegradation in dry perennial Mediterranean grasslands. Soil Biology and Biochemistry , 90 , 214–223. https://doi.org/10.1016/j.soilbio.2015.08.006 Andrews, J. A., & Schlesinger, W. H. (2000). Soil Respiration and the Global Carbon Cycle. Biogeochemistry , 48 (1), 7–20. Angst, G., Messinger, J., Greiner, M., Häusler, W., Hertel, D., Kirfel, K., Kögel-Knabner, I., Leuschner, C., Rethemeyer, J., & Mueller, C. W. (2018). Soil organic carbon stocks in topsoil and subsoil controlled by parent material, carbon input in the rhizosphere, and microbial-derived compounds. Soil Biology and Biochemistry , 122 (July 2017), 19–30. https://doi.org/10.1016/j.soilbio.2018.03.026 Ankom Technology. (2016). Determining Acid Detergent Lignin . Ankom Technology. (2017a). Determining Acid Detergent Fiber in Feeds Filter Bag Technique . Ankom Technology. (2017b). Determining Neutral detergent fiber in feeds - filter bag technique (for A2000 and A2000I) . https://www.ankom.com/sites/default/files/document-files/Method_13_NDF_Method_A2000_RevE_4_10_15.pdf Ayres, E., Steltzer, H., Simmons, B. L., Simpson, R. T., Steinweg, J. M., Wallenstein, M. D., Mellor, N., Parton, W. J., Moore, J. C., & Wall, D. H. (2009). Home-field advantage accelerates leaf litter decomposition in forests. Soil Biology and Biochemistry , 41 (3), 606–610. https://doi.org/10.1016/j.soilbio.2008.12.022 Bååth, E., Díaz-Raviña, M., Frostegård, Å., & Campbell, C. D. (1998). Effect of metal-rich sludge amendments on the soil microbial community. Applied and Environmental Microbiology , 64 (1), 238–245. https://doi.org/10.1128/aem.64.1.238-245.1998 Bachega, L. R., Bouillet, J. P., Piccolo, M. de C., Saint-André, L., Bouvet, J.-M., Nouvellon, Y., Moraes, J. L. de, Gonçalves, Robin, A., & C, J.- P. L. (2016). Decomposition of Eucalyptus grandis and Acacia mangium leaves and fine roots in tropical conditions did not meet the Home Field Advantage hypothesis. Forest Ecology and Management 359 , 33–43. https://doi.org/10.1016/j.foreco.2015.09.026 Ball, B. A., Christenson, L. M., & Wickings, K. G. (2022). A Cross-System Analysis of Litter Chemical Dynamics Throughout Decomposition. Ecosystems 25 , 1792–1808. https://doi.org/10.1007/s10021-022-00749-6 Baumeister, J. L., Hausrath, E. M., Olsen, A. A., Tschauner, O., Adcock, C. T., & Metcalf, R. V. (2015). Biogeochemical weathering of serpentinites: An examination of incipient dissolution affecting serpentine soil formation. Applied Geochemistry 54 , 74–84. https://doi.org/10.1016/j.apgeochem.2015.01.002 Berg, B., Ekbohm, G., Johansson, M. B., McClaugherty, C., Rutigliano, F., & De Santo, A. V. (1996). Maximum decomposition limits of forest litter types: A synthesis. Canadian Journal of Botany , 74 (5), 659–672. https://doi.org/10.1139/b96-084 Berg, B., & Meentemeyer, V. (2002). Litter quality in a north European transect versus carbon storage potential. Plant and Soil , 242 (1), 83–92. https://doi.org/10.1023/A:1019637807021 Berg, B. & McClaugherty, C. (2008) Plant litter: Decomposition, Humus Formation, Carbon Sequestration. Berlin: Springer. https://doi.org/10.1007/978-3-540-74923-3. Bini, C., Maleci, L., & Wahsha, M. (2017). Potentially toxic elements in serpentine soils and plants from Tuscany (Central Italy). A proxy for soil remediation. Catena , 148 , 60–66. https://doi.org/10.1016/j.catena.2016.03.014 Bradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R., & Wood, S. A. (2016a). Understanding the dominant controls on litter decomposition. Journal of Ecology , 104 (1), 229–238. https://doi.org/10.1111/1365-2745.12507 Bradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R., & Wood, S. A. (2016b). Understanding the dominant controls on litter decomposition. Journal of Ecology , 104 (1), 229–238. https://doi.org/10.1111/1365-2745.12507 Bradford, M. A., Ciska, G. F., Bonis, A., Bradford, E. M., Classen, A. T., Cornelissen, J. H. C., Crowther, T. W., De Long, J. R., Freschet, G. T., Kardol, P., Manrubia-Freixa, M., Maynard, D. S., Newman, G. S., Logtestijn, R. S. P., Viketoft, M., Wardle, D. A., Wieder, W. R., Wood, S. A., & Van Der Putten, W. H. (2017). A test of the hierarchical model of litter decomposition. Nature Ecology and Evolution , 1 (12), 1836–1845. https://doi.org/10.1038/s41559-017-0367-4 Bradford, M. A., Warren, R. J., Baldrian, P., Crowther, T. W., Maynard, D. S., Oldfield, E. E., Wieder, W. R., Wood, S. A., & King, J. R. (2014). Climate fails to predict wood decomposition at regional scales. Nature Climate Change , 4 (7), 625–630. https://doi.org/10.1038/nclimate2251 Brown, M. E., & Chang, M. C. Y. (2014). Exploring bacterial lignin degradation. Current Opinion in Chemical Biology , 19 (1), 1–7. https://doi.org/10.1016/j.cbpa.2013.11.015 Callahan, R. P., Riebe, C. S., Sklar, L. S., Pasquet, S., Ferrier, K. L., Hahm, W. J., Taylor, N. J., Grana, D., Flinchum, B. A., Hayes, J. L., & Holbrook, W. S. (2022). Forest vulnerability to drought controlled by bedrock composition. Nature Geoscience , 15 (9), 714–719. https://doi.org/10.1038/s41561-022-01012-2 Campillo-Cora, C., Soto-Gómez, D., Arias-Estévez, M., Bååth, E., & Fernández-Calviño, D. (2022). Estimation of baseline levels of bacterial community tolerance to Cr, Ni, Pb, and Zn in unpolluted soils, a background for PICT (pollution-induced community tolerance) determination. Biology and Fertility of Soils , 58 (1), 49–61. https://doi.org/10.1007/s00374-021-01604-x Carine, F., Enrique, A. G., & Stéven, C. (2009). Metal effects on phenol oxidase activities of soils. Ecotoxicology and Environmental Safety , 72 (1), 108–114. https://doi.org/10.1016/j.ecoenv.2008.03.008 Chapin, S., Maston, P. A., & Vitousek, P. M. (2011). Principles of Terrestrial Ecosystem Ecology (Second). Springer Science & Business Media. Chen, G., Shi, H., Ding, H., Zhang, X., Gu, T., Zhu, M., & Tan, W. (2023). Multi-scale analysis of nickel ion tolerance mechanism for thermophilic Sulfobacillus thermosulfidooxidans in bioleaching. Journal of Hazardous Materials , 443 , 130245. https://doi.org/10.1016/j.jhazmat.2022.130245 Corbeels, M. (2001). Plant Litter and Decomposition: General Concepts and Model Approaches. In NEE Workshop Proceedings (pp. 124–133). CSIRO Forestry and Forest Products. Cornwell, W. K., Cornelissen, J. H. C., Amatangelo, K., Dorrepaal, E., Eviner, V. T., Godoy, O., Hobbie, S. E., Hoorens, B., Kurokawa, H., Pérez-Harguindeguy, N., Quested, H. M., Santiago, L. S., Wardle, D. A., Wright, I. J., Aerts, R., Allison, S. D., Van Bodegom, P., Brovkin, V., Chatain, A., … Westoby, M. (2008). Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecology Letters , 11 (10), 1065–1071. https://doi.org/10.1111/j.1461-0248.2008.01219.x Cornwell, W. K., & Weedon, J. T. (2014). Decomposition trajectories of diverse litter types: A model selection analysis. Methods in Ecology and Evolution , 5 (2), 173–182. https://doi.org/10.1111/2041-210X.12138 Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K., & Paul, E. (2013). The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter? Global Change Biology , 19 (4), 988–995. https://doi.org/10.1111/gcb.12113 Couteaux, M. M., Bottner, P., & Berg, B. (1995). Litter decomposition climate and litter quality. Trends in Ecology and Evolution , 10 (2), 63–66. https://doi.org/10.1016/S0169-5347(00)88978-8 Datta, R., Kelkar, A., Baraniya, D., Molaei, A., Moulick, A., Meena, R. S., & Formanek, P. (2017). Enzymatic degradation of lignin in soil: A review. Sustainability (Switzerland) , 9 (7). https://doi.org/10.3390/su9071163 DeAngelis, K. M., Allgaier, M., Chavarria, Y., Fortney, J. L., Hugenholtz, P., Simmons, B., Sublette, K., Silver, W. L., & Hazen, T. C. (2011). Characterization of trapped lignin-degrading microbes in tropical forest soil. PLoS ONE , 6 (4). https://doi.org/10.1371/journal.pone.0019306 DeForest, J. L., Zak, D. R., Pregitzer, K. S., & Burton, A. J. (2004). Atmospheric nitrate deposition and the microbial degradation of cellobiose and vanillin in a northern hardwood forest. Soil Biology and Biochemistry , 36 (6), 965–971. https://doi.org/10.1016/j.soilbio.2004.02.011 DeGrood, S. H., Claassen, V. P., & Scow, K. M. (2005). Microbial community composition on native and drastically disturbed serpentine soils. Soil Biology and Biochemistry , 37 (8), 1427–1435. https://doi.org/10.1016/j.soilbio.2004.12.013 U.S. Environmental Protection Agency (E PA). (2007). Method 3051A: Microwave assisted acid digestion of sediments, sludges, soils, and oils . U.S. Environmental Protection Agency, Office of Solid Waste. Fanin, N., Lin, D., Freschet, G. T., Keiser, A. D., Augusto, L., Wardle, D. A., & Veen, G. F. (2021). Home-field advantage of litter decomposition: from the phyllosphere to the soil. New Phytologist , 231 (4), 1353–1358. https://doi.org/10.1111/nph.17475 Fox, J., & Weisberg, S. (2019). An {R} Companion to Applied Regression (Third). Saghge. https://socialsciences.mcmaster.ca/jfox/Books/Companion/%7D Freschet, T., Aerts, R., & Cornelissen, J. H. C. (2012). Multiple mechanisms for trait effects on litter decomposition: moving beyond home-field advantage with a new hypothesis. Journal of Ecology , 100 (3), 619–630. https://doi.org/10.1111/j.1365-2745.2011.01943.x García- Palacios, P., Maestre, F. T., Kattge, J., & Wall, D. H. (2013). Climate and litter quality differently modulate the effects of soil fauna on litter decomposition across biomes. Ecology Letters , 16 (8), 1045–1053. https://doi.org/10.1111/ele.12137 Garfunkel, Z. (1998). Constrains on the origin and history of the Eastern Mediterranean basin. Tectonophysics , 298 (1–3), 5–35. https://doi.org/https://doi.org/10.1016/S0040-1951(98)00176-0 Garnier E., Cortez J., Billès G., Navas M. L. , Roumet C., Debussche M., Laurent G., Blanchard A., Aubry D., Bellmann A, C. N. and J.- P. T. (2004). Plant Functional Markers Capture Ecosystem Properties during Secondary Succession. Ecology , 85 (9), 2630–2637. Ge, X., Zeng, L., Xiao, W., Huang, Z., Geng, X., & Tan, B. (2013). Effect of litter substrate quality and soil nutrients on forest litter decomposition: A review. Acta Ecologica Sinica , 33 (2), 102–108. https://doi.org/10.1016/j.chnaes.2013.01.006 Gong, X. Y., Giese, M., Dittert, K., Lin, S., & Taube, F. (2016). Topographic influences on shoot litter and root decomposition in semiarid hilly grasslands. GEODERMA , 282 , 112–119. https://doi.org/10.1016/j.geoderma.2016.07.017 Grime, J. P. (1998). Benefits of Plant Diversity to Ecosystems: Immediate , Filter and Founder Effects. British Ecological Society , 86 (6), 902–910. Hahm, W. J., Riebe, C. S., Lukens, C. E., & Araki, S. (2014). Bedrock composition regulates mountain ecosystems and landscape evolution. Proceedings of the National Academy of Sciences of the United States of America , 111 (9), 3338–3343. https://doi.org/10.1073/pnas.1315667111 Hatakka, A. (1994). Lignin-modifying enzymes from selected white-rot fungi: production and role from in lignin degradation. FEMS Microbiology Reviews , 13 (2–3), 125–135. https://doi.org/10.1111/j.1574-6976.1994.tb00039.x Héry, M., Nazaret, S., Jaffré, T., Normand, P., & Navarro, E. (2003). Adaptation to nickel spiking of bacterial communities in neocaledonian soils. Environmental Microbiology , 5 (1), 3–12. https://doi.org/10.1046/j.1462-2920.2003.00380.x Hidas, K., Garrido, C. J., Marchesi, C., Bodinier, J., & Louni-hacini, A. (2017). Geochemical and Textural Constraints on Wehrlite Formation by Melt-rock Reaction in the Shallow Subcontinental Lithospheric Mantle (Oran, Tell Atlas, N-Algeria). EGU General Assembly , 19 , 4–5. https://ui.adsabs.harvard.edu/abs/2017EGUGA..19.7953H/abstract Hillel, D. (2004). Introduction to Environmental Soil Physics (Elsevier, Ed.; Issue 1). http://journal.um-surabaya.ac.id/index.php/JKM/article/view/2203 Hofrichter, M. (2002). Review: Lignin conversion by manganese peroxidase (Mn P). Enzyme and Microbial Technology , 30 (4), 454–466. https://doi.org/10.1016/S0141-0229(01)00528-2 Howard, A. P. J. A., & Howard, D. M. (1974). Microbial Decomposition of Tree and Shrub Leaf Litter. Weight Loss and Chemical Composition of Decomposing Litter. Oikos , 25 (3), 341–352. Hu, X., Yang, L., Lai, X., Yao, Q., & Chen, K. (2019). Influence of Al(III) on biofilm and its extracellular polymeric substances in sequencing batch biofilm reactors. Environmental Technology (United Kingdom) , 40 (1), 53–59. https://doi.org/10.1080/09593330.2017.1378268 Hueso, S., García, C., & Hernández, T. (2012). Severe drought conditions modify the microbial community structure, size and activity in amended and unamended soils. Soil Biology and Biochemistry , 50 , 167–173. https://doi.org/10.1016/j.soilbio.2012.03.026 Islam, M., & Sandhi, A. (2023). Heavy Metal and Drought Stress in Plants: The Role of Microbes—A Review. Gesunde Pflanzen , 75 (4), 695–708. https://doi.org/10.1007/s10343-022-00762-8 Joly, F. X., Scherer-Lorenzen, M., & Hättenschwiler, S. (2023). Resolving the intricate role of climate in litter decomposition. Nature Ecology and Evolution , 7 (2), 214–223. https://doi.org/10.1038/s41559-022-01948-z Jones, D. L., Cooledge, E. C., Hoyle, F. C., Griffiths, R. I., & Murphy, D. V. (2019). pH and exchangeable aluminum are major regulators of microbial energy flow and carbon use efficiency in soil microbial communities. Soil Biology and Biochemistry , 138 (July), 0–4. https://doi.org/10.1016/j.soilbio.2019.107584 Kazakou, E., Vile, D., Shipley, B., Gallet, C., & Garnier, E. (2006). Co-variations in litter decomposition, leaf traits and plant growth in species from a Mediterranean old-field succession. Functional Ecology . https://doi.org/10.1111/j.1365-2435.2006.01080.x Keiser, A. D., Strickland, M. S., Fierer, N., & Bradford, M. A. (2011). The effect of resource history on the functioning of soil microbial communities is maintained across time. Biogeosciences , 8 (6), 1477–1486. https://doi.org/10.5194/bg-8-1477-2011 Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift , 15 (3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130 Kurz, C., Coûteaux, M. M., & Thiéry, J. M. (2000). Residence time and decomposition rate of Pinus pinaster needles in a forest floor from direct field measurements under a Mediterranean climate. Soil Biology and Biochemistry , 32 (8–9), 1197–1206. https://doi.org/10.1016/S0038-0717(00)00036-5 Lozano, A., Usero, J., Vanderlinden, E., Raez, J., Contreras, J., & Navarrete, B. (2009). Air quality monitoring network design to control nitrogen dioxide and ozone, applied in Malaga, Spain. Microchemical Journal , 93 (2), 164–172. https://doi.org/10.1016/j.microc.2009.06.005 Malik, A. A., Swenson, T., Weihe, C., Morrison, E., Martiny, J. B. H., Brodie, E. L., Northen, T. R., & Allison, S. D. (2019). Physiological adaptations of leaf litter microbial communities to long-term drought. BioRxiv , 631077. https://doi.org/https://doi.org/10.1101/631077 Malik, R. J. (2022). Decomposing the novel decomposer-sphere concept: decomposition byproducts can shape surrounding communities through space and time. Biogeochemistry , 162(1), 1–15. https://doi.org/10.1007/s10533-022-01001-y Manzoni, S., Schimel, J. P., & Porporato, A. (2012). Responses of soil microbial communities to water stress: Results from a meta-analysis. Ecology , 93 (4), 930–938. https://doi.org/10.1890/11-0026.1 Michalet, R., & Liancourt, P. (2024). The interplay between climate and bedrock type determines litter decomposition in temperate forest ecosystems. Soil Biology and Biochemistry , 195, 109476. https://doi.org/10.1016/j.soilbio.2024.109476 Mimeau, L., Tramblay, Y., Brocca, L., Massari, C., Camici, S., & Finaud-Guyot, P. (2021). Modeling the response of soil moisture to climate variability in the Mediterranean region. Hydrology and Earth System Sciences , 25 (2), 653–669. https://doi.org/10.5194/hess-25-653-2021 Muñoz, R., Enríquez, M., Bongers, F., López-Mendoza, R. D., Miguel-Talonia, C., & Meave, J. A. (2023). Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics. Frontiers in Forests and Global Change , 6 (March), 1–11. https://doi.org/10.3389/ffgc.2023.1082207 Nakamura, R., Kajino, H., Kawai, K., Nakai, W., Ohnuki, M., & Okada, N. (2019). Diverse recalcitrant substrates slow down decomposition of leaf litter from trees in a serpentine ecosystem. Plant and Soil , 442 (1–2), 247–255. https://doi.org/10.1007/s11104-019-04183-x Nakamura, R., Tatsumi, C., Kajino, H., Fujimoto, Y., Fujii, R., Yokobe, T., Mori, T., & Okada, N. (2023). Plant material decomposition and bacterial and fungal communities in serpentine and karst soils of Japanese cool-temperate forests. Soil Science and Plant Nutrition , 69 (3), 163–171. https://doi.org/10.1080/00380768.2023.2177493 Nepstad, D., Lefebvre, P., Da Silva, U. L., Tomasella, J., Schlesinger, P., Solórzano, L., Moutinho, P., Ray, D., & Benito, J. G. (2004). Amazon drought and its implications for forest flammability and tree growth: A basin-wide analysis. Global Change Biology , 10 (5), 704–717. https://doi.org/10.1111/j.1529-8817.2003.00772.x Noy-Meir, I. (1973). Desert ecosystems: environment and producers. Annual Review of Ecological Systems , 4 (67), 25–52. Osono, T. (2007). Ecology of ligninolytic fungi associated with leaf litter decomposition. Ecological Research , 22 (6), 955–974. https://doi.org/10.1007/s11284-007-0390-z Osono, T., & Takeda, H. (2005). Limit values for decomposition and convergence process of lignocellulose fraction in decomposing leaf litter of 14 tree species in a cool temperate forest. Ecological Research , 20 (1), 51–58. https://doi.org/10.1007/s11284-004-0011-z Palozzi, J. E., & Lindo, Z. (2018). Are leaf litter and microbes team players? Interpreting home-field advantage decomposition dynamics. Soil Biology and Biochemistry , 124 (January), 189–198. https://doi.org/10.1016/j.soilbio.2018.06.018 Parton, W., Silver, W. L., Burke, I. C., Grassens, L., Harmon, M. E., Currie, W. S., King, J. Y., Adair, E. C., Brandt, L. A., Hart, S. C., & Fasth, B. (2007). Global-Scale Similarities in Nitrogen Release Patterns During Long-Term Decomposition. Science Reports , 315 (January), 361–364. https://doi.org/10.1126/science.1134853 Pennanen, T. (2001). Microbial communities in boreal coniferous forest humus exposed to heavy metals and changes in soil pH - A summary of the use of phospholipid fatty acids. Geoderma , 100 (1–2), 91–126. https://doi.org/10.1016/S0016-7061(00)00082-3 Perez, G., Aubert, M., Decaëns, T., Trap, J., & Chauvat, M. (2013). Home-Field Advantage: A matter of interaction between litter biochemistry and decomposer biota. Soil Biology and Biochemistry , 67 , 245–254. https://doi.org/10.1016/j.soilbio.2013.09.004 Pichler, V., Gömöryová, E., Leuschner, C., Homolák, M., Abrudan, I. V., Pichlerová, M., Střelcová, K., Di Filippo, A., & Sitko, R. (2021). Parent material effect on soil organic carbon concentration under primeval european beech forests at a regional scale. Forests , 12 (4), 1–12. https://doi.org/10.3390/f12040405 Pold, G., Melillo, J. M., & DeAngelis, K. M. (2015). Two decades of warming increases diversity of a potentially lignolytic bacterial community. Frontiers in Microbiology , 6 (MAY). https://doi.org/10.3389/fmicb.2015.00480 Prescott, C. E. (2010). Litter decomposition: What controls it and how can we alter it to sequester more carbon in forest soils? Biogeochemistry , 101 , 113–149. https://doi.org/10.1007/s10533-010-9439-0 Prieto, I., Almagro, M., Bastida, F., & Ignacio Querejeta, J. (2019). Altered leaf litter quality exacerbates the negative impact of climate change on decomposition. Journal of Ecology , 107 (5), 2364–2382. https://doi.org/10.1111/1365-2745.13168 Qin, X., Sun, X., Huang, H., Bai, Y., Wang, Y., Luo, H., Yao, B., Zhang, X., & Su, X. (2017). Oxidation of a non-phenolic lignin model compound by two Irpex lacteus manganese peroxidases: Evidence for implication of carboxylate and radicals. Biotechnology for Biofuels , 10 (1), 1–13. https://doi.org/10.1186/s13068-017-0787-z R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org Rehault, J. P., Boillot, G., & Mauffret, A. (1984). The Western Mediterranean Basin geological evolution. Marine Geology , 55 (3–4), 447–477. https://doi.org/10.1016/0025-3227(84)90081-1 Reichenbach, M., Fiener, P., Garland, G., Griepentrog, M., Six, J., & Doetterl, S. (2021). The role of geochemistry in organic carbon stabilization against microbial decomposition in tropical rainforest soils. Soil , 7 (2), 453–475. https://doi.org/10.5194/soil-7-453-2021 Reichenbach, M., Fiener, P., Hoyt, A., Trumbore, S., Six, J., & Doetterl, S. (2023). Soil carbon stocks in stable tropical landforms are dominated by geochemical controls and not by land use. Global Change Biology , 29 (9), 1–17. https://doi.org/10.1111/gcb.16622 Ribeiro, K. T., Medina, B. M. O., & Scarano, F. R. (2007). Species composition and biogeographic relations of the rock outcrop flora on the high plateau of Itatiaia, SE-Brazil. Revista Brasileira de Botanica , 30 (4), 623–639. https://doi.org/10.1590/S0100-84042007000400008 Russell, A., Lenth, V., Bolker, B., Buerkner, P., Giné-vázquez, I., Herve, M., Love, J., Singmann, H., & Lenth, M. R. V. (2023). Emmeans . 34 (4), 216–221. https://doi.org/10.1080/00031305.1980.10483031>.License Sala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L. R., Sykes, M. T., Walker, B. H., Walker, M., & Wall, D. H. (2000). Global biodiversity scenarios for the year 2100. Science , 287 (5459), 1770–1774. https://doi.org/10.1126/science.287.5459.1770 Schaetzl, R. J., & Thompson, M. L. (2007). Soils: Genesis and Geomorphology. In Cambridge University Press (Second, Vol. 6, Issue 2). Cambridge University Press, The Edinburgh Building, Cambridge CB2 2RU United Kingdom. https://doi.org/10.2136/vzj2007.0030br Schimel, J. P. (2018). Life in dry soils: Effects of drought on soil microbial communities and processes. Annual Review of Ecology, Evolution, and Systematics , 49 , 409–432. https://doi.org/10.1146/annurev-ecolsys-110617-062614 Schröter, D., Cramer, W., Leemans, R., Prentice, I. C., Araújo, M. B., Arnell, N. W., Bondeau, A., Bugmann, H., Carter, T. R., Gracia, C. A., De La Vega-Leinert, A. C., Erhard, M., Ewert, F., Glendining, M., House, J. I., Kankaanpää, S., Klein, R. J. T., Lavorel, S., Lindner, M., … Zierl, B. (2005). Ecology: Ecosystem service supply and vulnerability to global change in Europe. Science , 310 (5752), 1333–1337. https://doi.org/10.1126/science.1115233 Searcy, K. B. ., Wilson, B. F. ., & Fownes, J. H. . (2003). Influence of Bedrock and Aspect on Soils and Plant Distribution in the Holyoke Range, Massachusetts. Torrey Botanical Society , 130 (3), 158–169. https://doi.org/https://doi.org/10.2307/3557551 Sha, Z., Bai, Y., Li, R., Lan, H., Zhang, X., Li, J., Liu, X., Chang, S., & Xie, Y. (2022). The global carbon sink potential of terrestrial vegetation can be increased substantially by optimal land management. Communications Earth and Environment , 3 (1), 1–10. https://doi.org/10.1038/s43247-021-00333-1 Spano, D., Snyder, R. L., & Cesaraccio, C. (2013). Mediterranean Phenology. In M. Schwartz (Ed.), Phenology: An Integrative Environmental Science. (pp. 173–196). Springer. https://doi.org/10.1007/978-94-007-6925-0_10 Ström, L., Godbold, D. L., & Jones, D. L. (2001). Procedure for Determining the Biodegradation of Radiolabeled Substrates in a Calcareous Soil. Soil Science Society of America Journal , 65 (2), 347–351. https://doi.org/10.2136/sssaj2001.652347x Sun, T., Cui, Y., Berg, B., Zhang, Q., Dong, L., Wu, Z., & Zhang, L. (2019). A test of manganese effects on decomposition in forest and cropland sites. Soil Biology and Biochemistry , 129 (November 2018), 178–183. https://doi.org/10.1016/j.soilbio.2018.11.018 Suseela, V., & Tharayil, N. (2018). Decoupling the direct and indirect effects of climate on plant litter decomposition: Accounting for stress-induced modifications in plant chemistry. Global Change Biology , 24 (4), 1428–1451. https://doi.org/10.1111/gcb.13923 Swift, M. J., Heal, O. W., & Anderson, J. M. (1979). Decomposition in terrestrial ecosystems. In Review Literature And Arts Of The Americas (Vol. 5, pp. 12–24). https://doi.org/10.1007/s00114-006-0159-1 Talbot, J. M., & Treseder, K. K. (2012). Interactions between lignin, cellulose, and nitrogen drive litter chemistry - decay relationships. Ecology , 93 (2), 345–354. https://doi.org/10.1890/11-0843.1 Throop, H. L., & Archer, S. R. (2009). Resolving the Dryland Decomposition Conundrum: Some New Perspectives on Potential Drivers. Progress in Botany , 70 , 171–194. https://doi.org/10.1007/978-3-540-68421-3, Tuel, A., & Eltahir, E. A. B. (2020). Why Is the Mediterranean a Climate Change Hot Spot? Journal of Climate , 33 (14), 5829–5843. https://doi.org/10.1175/JCLI-D-19-0910.1 Uller, T., & Lala, K. N. (2019). Evolutionary Causation: Biological and Philosophical Reflections. In T. Uller & K. N. Lala (Eds.), The MIT Press (1st ed.). The MIT Press. https://doi.org/10.7551/mitpress/11693..001.0001 Van Der Heijden, M. G. A., Bardgett, R. D., & Van Straalen, N. M. (2008). The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecology Letters , 11 (3), 296–310. https://doi.org/10.1111/j.1461-0248.2007.01139.x Van Soest, P. J., Robertson, J. B., & Lewis, B. A. (1991). Methods for Dietary Fiber, Neutral Detergent Fiber, and Nonstarch Polysaccharides in Relation to Animal Nutrition. Journal of Dairy Science , 74 (10), 3583–3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2 Waldrop, M. P., Zak, D. R., & Sinsabaugh, R. L. (2004). Microbial community response to nitrogen deposition in northern forest ecosystems. Soil Biology and Biochemistry , 36 (9), 1443–1451. https://doi.org/10.1016/j.soilbio.2004.04.023 Wallenstein, M. D., McNulty, S., Fernandez, I. J., Boggs, J., & Schlesinger, W. H. (2006). Nitrogen fertilization decreases forest soil fungal and bacterial biomass in three long-term experiments. Forest Ecology and Management , 222 (1–3), 459–468. https://doi.org/10.1016/j.foreco.2005.11.002 Wang, J., Liu, L., Wang, X., & Chen, Y. (2015). The interaction between abiotic photodegradation and microbial decomposition under ultraviolet radiation. Global Change Biology , 21 (5), 2095–2104. https://doi.org/10.1111/gcb.12812 Wang, Q., Wang, S., & Huang, Y. (2009). Leaf litter decomposition in the pure and mixed plantations of Cunninghamia lanceolata and Michelia macclurei in subtropical China. Biology and Fertility of Soils , 45 (4), 371–377. https://doi.org/10.1007/s00374-008-0338-7 Wang, X., Gossart, M., Guinet, Y., Fau, H., Lavignasse-Scaglia, C. D., Chaieb, G., & Michalet, R. (2020). The consistency of home-field advantage effects with varying climate conditions. Soil Biology and Biochemistry , 149 (November 2019), 107934. https://doi.org/10.1016/j.soilbio.2020.107934 Waring, B. G., Averill, C., & Hawkes, C. V. (2013). Differences in fungal and bacterial physiology alter soil carbon and nitrogen cycling: Insights from meta-analysis and theoretical models. Ecology Letters , 16 (7), 887–894. https://doi.org/10.1111/ele.12125 Wilhelm, R. C., Singh, R., Eltis, L. D., & Mohn, W. W. (2019). Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME Journal , 13 (2), 413–429. https://doi.org/10.1038/s41396-018-0279-6 Wilhelm, R., Hoeschen, C., Buckley, D., & Lehmann, J. (2023). Calcium promotes persistent soil organic matter by altering microbial transformation of plant litter. Research Square , February . https://doi.org/10.21203/rs.3.rs-2606058/v1 Woo, H. L., Hazen, T. C., Simmons, B. A., & DeAngelis, K. M. (2014). Enzyme activities of aerobic lignocellulolytic bacteria isolated from wet tropical forest soils. Systematic and Applied Microbiology , 37 (1), 60–67. https://doi.org/10.1016/j.syapm.2013.10.001 Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society. Series B: Statistical Methodology , 73 (1), 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x Yi, B., Lu, C., Huang, W., Yu, W., Yang, J., Howe, A., Weintraub-Leff, S. R., & Hall, S. J. (2023). Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data. Global Change Biology , 29 (20), 5968–5980. https://doi.org/10.1111/gcb.16875 Zhang, D., Hui, D., Luo, Y., & Zhou, G. (2008). Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors. Journal of Plant Ecology , 1 (2), 85–93. https://doi.org/10.1093/jpe/rtn002 Zheng, H., Heděnec, P., Rousk, J., Schmidt, I. K., Peng, Y., & Vesterdal, L. (2022). Effects of common European tree species on soil microbial resource limitation, microbial communities and soil carbon. Soil Biology and Biochemistry , 172 (February). https://doi.org/10.1016/j.soilbio.2022.108754 Tables Table 1. Mean site characteristics of the mountain ranges along the precipitation gradient in the province of Málaga. Values are mean ± 1SE (n = 4). Abbreviations are as follows: EPT = evapotranspiration, MAP = mean annual precipitation, MAT = mean annual temperature. Letters represent significant differences between categories of lithology, within position based on Tukey’s HSD test at the α = 0.05 level. POSITION LOCATION LITHOLOGY MAT (°C) MAP (mm) EPT (mm) Stand density (ind. ha -1 ) Altitude (m) Orientation (º) West Crestellina/Bermeja (36˚27'57''N. 5˚59'3''W) Calcareous 15.71 ± 0.72 1112.25 ± 1.89 793.15 ± 3.85 a 433.9 ± 137.0 597.25 ± 21.36 a 291.73 ± 10.66 Metapelite 15.36 ± 0.73 1115.25 ± 3.50 781.00 ± 3.70 ab 946.1 ± 257.6 679.75 ± 14.95 a 252.93 ± 68.61 Peridotite 14.48 ± 0.74 1062.75 ± 19.38 750.50 ± 11.51 b 679.1 ± 239.7 847.00 ± 71.70 b 207.43 ± 46.51 Centre Sierra de las Nieves (36˚40'59''N. 4˚59'58''W) Calcareous 13.77 ± 0.81 a 709.50 ± 11.45 763.48 ± 7.26 a 442.1 ± 117.1 1000.5 ± 60.51 a 88.08 ± 25.89 Metapelite 14.77 ± 0.77 b 813.50 ± 53.36 752.73 ± 2.68 ab 986.8 ± 356.2 804.50 ± 67.24 b 127.35 ± 58.52 Peridotite 14.33 ± 0.79 ab 831.25 ± 38.85 778.80 ± 8.91 b 752.5 ± 131.0 779.75 ± 54.44 b 233.53 ± 51.36 East Sierra Alhama/Aguas (36˚49'60'' N. -3˚52'0'' W) (6˚51'36'' N. 4˚46'0'6' W) Calcareous 14.75 ± 0.77 687.75 ± 12.75 733.50 ± 8.46 386.4 ± 55.9 804.00 ± 50.99 294.45 ± 9.42 Metapelite 14.44 ± 0.77 685.75 ± 11.03 764.85 ± 11.75 497.1 ± 88.5 847.25 ± 23.57 216.40 ± 33.01 Peridotite 14.98 ± 0.82 650.50 ± 23.20 750.60 ± 20.33 573.0 ± 80.1 764.25 ± 51.09 174.55 ± 63.22 Table 2. Mean (n = 36) Akaike Information Criterion values of the five fitted decomposition models described in Cornwell & Weedon (2014). Single Exponential Single Exp. Asymptote Discrete Parallel Discrete Series Continuous Quality -10.86 ± 0.82 -24.73 ± 5.16 -24.14 ± 5.08 -20.65 ± 5.11 -10.38 ± 0.92 Table 3 . Mean initial litter quality (±1SE) along the precipitation gradient: West, Centre, and East for the three lithological substrates: calcareous, metapelite and peridotite. Significance was measured by comparing the full model to the null model, * represents significance at the α=0.05 level, ** at the α=0.01 level, *** at the α=0.001 level. Letters indicate significant differences within position, among lithologies based on one-way type II ANOVAs, where letters are not present, no significant differences were observed. SCF represents the soluble cell fraction, LCI the lignocellulose index, C:N the carbon to nitrogen ratio and Lignin:N the lignin to nitrogen ratio. VARIABLE POSITION ON THE GRADIENT West Centre East LITHOLOGY Calcareous Metapelite Peridotite Calcareous Metapelite Peridotite Calcareous Metapelite Peridotite SCF (%) *** 42.64 ± 0.76 a 38.26 ± 1.33 b 43.91 ± 0.85 a 43.88 ± 0.72 ab 43.05 ± 0.58 a 46.4 ± 0.72 b 43.85 ± 0.32 ab 42.13 ± 1.01 a 45.51 ± 0.63 b Cellulose (%) 23.29 ± 0.34 23.19 ± 0.60 23.27 ± 0.46 22.85 ± 0.68 24.24 ± 0.47 23.27 ± 0.18 23.97 ± 0.82 23.44 ± 1.75 22.04 ± 1.01 Hemicellulose (%) ** 12.05 ± 0.19 11.08 ± 0.30 11.28 ± 0.21 12.13 ± 0.24 a 11.54 ± 0.46 ab 10.67 ± 0.14 b 11.43 ± 0.15 a 14.18 ± 0.96 a 12.17 ± 0.74 b Lignin ** 20.78 ± 0.62 22.67 ± 1.06 19.05 ± 0.60 21.17 ± 0.76 22.09 ± 0.79 20.55 ± 0.56 21.96 ± 0.34 ab 24.12 ± 1.36 a 21.88 ± 0.78 b LCI 0.38 ± 0.01 0.39 ± 0.06 0.39 ± 0.01 0.38 ± 0.01 0.38 ± 0.01 0.38 ± 0.01 0.37 ± 0.01 0.40 ± 0.02 0.35 ± 0.01 C:N *** 138.01 ± 3.40 a 105.02 ± 13.21 b 100.17 ± 0.97 b 109.28 ± 10.66 102.48 ± 7.15 111.83 ± 13.80 143.82 ± 6.36 a 114.33 ± 5.34 b 91.89 ± 4.63 c Lignin:N * 59.80 ± 1.56 49.48 ± 4.89 51.48 ± 5.58 48.22 ± 6.03 45.37 ± 4.07 46.53 ± 7.82 60.49 ± 2.57 a 51.48 ± 0.99 b 35.07 ± 2.28 c Table 4. Mean mass loss and parameter estimates of the model used to fit the temporal dynamics of litter decomposition (±1SE; n = 4) used in this study along the precipitation gradient: west, centre, and east for the three lithological substrates: calcareous, metapelite and peridotite. Mean (±1SE). Significance was measured by comparing the full model to the null model, *represents significance at the α=0.05 level, ** at the α=0.01 level, *** at the α=0.001 level. Letters indicate significant differences within position, between lithologies based on one-way type II ANOVAs, where letters are not present, no significant differences were observed. The decomposition rate ( k ) and M1 represents the proportion of decomposable mass as determined by a single exponential model with asymptote. VARIABLE POSITION ON THE GRADIENT West Centre East LITHOLOGY Calcareous Metapelite Peridotite Calcareous Metapelite Peridotite Calcareous Metapelite Peridotite Mass Loss (%) *** 30.83 ± 1.76 26.74 ± 1.72 30.14 ± 1.64 35.41 ± 1.10 a 28.35 ± 1.95 ab 23.37 ± 1.30 b 23.15 ± 1.10 a 24.68 ± 2.37 a 27.24 ± 1.31 b k (% yr -1 ) 3.43 ± 0.73 5.21 ± 0.70 6.08 ± 1.09 4.73 ± 0.76 5.56 ± 1.28 6.84 ± 1.68 6.79 ± 0.56 5.97 ± 0.52 5.64 ± 0.82 Asymptote (%) * 69.40 ± 1.88 76.53 ± 2.24 70.89 ± 2.86 65.23 ± 2.16 a 72.33 ± 3.24 ab 78.72 ± 2.67 b 75.91 ± 1.36 75.07 ± 3.55 73.23 ± 1.94 M1 (%) * 30.33 ± 1.82 23.42 ± 2.21 29.11 ± 2.86 34.74 ± 2.36 a 27.57 ± 3.32 ab 21.28 ± 2.72 b 24.17 ± 1.34 25.03 ± 3.56 26.74 ± 1.95 Table 5. Mean initial litter quality (n = 6, ±1SE) by species, P. pinaster and A. pinsapo for the two lithological substrates: calcareous and peridotite. Letters indicate significant differences within species, between lithologies based on t-tests, where letters are not present, no significant differences were observed. SCF represents the soluble cell fraction, LCI the lignocellulose index and the carbon to nitrogen (C:N) ratio. Variable SPECIES Pinus pinaster Abies pinsapo Calcareous Peridotite Calcareous Peridotite SCF (%) 46.24 ± 1.78 44.42 ± 1.22 62.83 ± 0.23 a 54.92 ± 0.75 b Cellulose (%) 21.89 ± 0.43 22.82 ± 0.43 15.65 ± 0.25 a 12.71 ± 0.90 b Hemicellulose (%) 12.24 ± 0.47 11.63 ± 0.51 10.24 ± 0.12 a 11.64 ± 0.31 b Lignin (%) 19.64 ± 1.26 21.13 ± 1.04 11.28 ± 0.10 a 20.73 ± 1.00 b LCI 0.36 ± 0.01 0.37 ± 0.01 0.30 ± 0.00 a 0.46 ± 0.02 b C:N 85.76 ± 25.77 106.49 ± 4.79 52.27 ± 6.32 40.66 ± 0.36 Supplementary Files litterdatasetcombined.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6333544","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437845062,"identity":"b55946f4-72c8-4538-a153-78345fbb535c","order_by":0,"name":"Daniel James Carlton Fishburn","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDCCA4wPDjA2HACymA8YfKgA0cwNBLQwG0C1sCUUzjgD0sJIWAsDRAuPwWfeNpAQAS18B5gZD/7ccSdx++wew82882qj+duBWn5UbMOpRfIAM8Nh3jPPEufcOVZsOHfb8dwZhxkbGHvO3MapxeAA/4HDjG2HE2dIJG8zeLvtWG4DUAszYxs+LcwMB3+CtSSY/+Cdcyx3PjFaDvCCtaQYGPI21ORuIKRF8jDIL23PjGdIpCUYzjh2IHcjUMtBfH7hO97M/PFn2x1ZoF+AUVlTlzvv/OGDD35U4NbCwIzKPQwmD+BWjwnqSFE8CkbBKBgFIwQAAIS6aR09e5KrAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-7569-4385","institution":"Bangor University","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"James Carlton","lastName":"Fishburn","suffix":""},{"id":437845063,"identity":"d2e4b4ec-6e7d-4104-95cd-248489446db9","order_by":1,"name":"Andrew R. Smith","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"R.","lastName":"Smith","suffix":""},{"id":437845064,"identity":"48f81131-4cbc-42f7-a5ef-cf3d1535265e","order_by":2,"name":"Lars Markesteijn","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lars","middleName":"","lastName":"Markesteijn","suffix":""},{"id":437845065,"identity":"6a12c88d-68ab-4b4c-94f0-b1c0b066466a","order_by":3,"name":"Victor Lechuga","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Lechuga","suffix":""},{"id":437845066,"identity":"d6d9f620-4263-488b-accd-c49f66d6d708","order_by":4,"name":"Ana Rey","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Rey","suffix":""},{"id":437845067,"identity":"be237ebf-718e-4314-a341-0c2f0be7adc4","order_by":5,"name":"José Carreria","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"","lastName":"Carreria","suffix":""}],"badges":[],"createdAt":"2025-03-29 10:35:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6333544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6333544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80038177,"identity":"b9e6d787-ceff-40f7-97b0-60ba95d58ba0","added_by":"auto","created_at":"2025-04-07 08:50:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":285880,"visible":true,"origin":"","legend":"\u003cp\u003eLitter mass (kg m\u003csup\u003e-2\u003c/sup\u003e) in \u003cem\u003ePinus pinaster\u003c/em\u003e forests across three lithological substrates (calcareous, metapelite and peridotite) along the precipitation gradient (West (W), Centre (C), and East (E)). Bars represent estimated marginal means, standardising for altitude and stand density (± 1 SE; n = 4). Statistical analyses were conducted using type III ANOVAs, and pair-ways significance was determined using t-tests with a false-discovery rate (FDR) \u003cem\u003eα\u003c/em\u003e correction. Lowercase letters represent significant differences within lithology, and uppercase letters indicate differences between lithologies, within positions.\u003c/p\u003e","description":"","filename":"fig1col.png","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/854a4a1389ddbee2b3688230.png"},{"id":80038178,"identity":"8c35bfce-217b-4615-b342-a78ae0499740","added_by":"auto","created_at":"2025-04-07 08:50:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":483281,"visible":true,"origin":"","legend":"\u003cp\u003eInitial litter quality indexes, including (\u003cstrong\u003eA\u003c/strong\u003e) carbon to nitrogen (C:N) and (\u003cstrong\u003eB\u003c/strong\u003e) lignin to nitrogen (Lig:N) ratios from the \u003cem\u003ePinus pinaster\u003c/em\u003e forests across three lithological substrates (calcareous, metapelite, peridotite) along the precipitation gradient (West (W), Centre (C), and East (E)). Bars represent estimated marginal means, standardising for altitude and density (± 1 SE; n = 4). Statistical analyses were conducted using Type III ANOVAs, with pairwise significance determined using t-tests with false-discovery rate (FDR) \u003cem\u003eα\u003c/em\u003ecorrection. Lowercase letters indicate significant differences within lithologies and uppercase letters indicate differences between lithologies, within positions.\u003c/p\u003e","description":"","filename":"fig2col.png","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/8c1f41da623f6351bd74636b.png"},{"id":80037308,"identity":"482626f7-a8cb-4738-8b9c-7dd3e2b28c33","added_by":"auto","created_at":"2025-04-07 08:42:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":240464,"visible":true,"origin":"","legend":"\u003cp\u003eLitter mass loss, adjusted for altitude and stand density, in \u003cem\u003ePinus pinaster\u003c/em\u003e forests after 572 days of field decomposition across three lithological substrates (calcareous, metapelite, peridotite) along the precipitation gradient (West (W), Centre (C), and East (E)). The category West-P (indicated with hashed lines) shows the predicted mass based a the single exponential model with asymptote. Bars represent estimated marginal means (± 1 SE; n = 4). Statistical analyses were conducted using type III ANOVAs, and pair-wise significance was determined using t-tests with a false-discovery rate (FDR) \u003cem\u003eα\u003c/em\u003e correction. Lowercase letters indicate significant differences within lithologies and uppercase letters indicate differences between lithologies, within positions.\u003c/p\u003e","description":"","filename":"fig3col.png","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/1ceccc0ad7a8caae2dd7b637.png"},{"id":80037310,"identity":"99e2d373-8886-4217-947f-5b41be544ab5","added_by":"auto","created_at":"2025-04-07 08:42:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2032365,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal changes in litter mass during decomposition. Lines represent predicted values from a single exponential decay asymptotic model. Underlying points represent actual mass loss t (±SE).\u003c/p\u003e","description":"","filename":"fig4col.png","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/0d46329c82cce36811a36c9b.png"},{"id":80037314,"identity":"6c83e179-c06f-4835-8fdf-38f1002c81ad","added_by":"auto","created_at":"2025-04-07 08:42:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":762306,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal changes in relative (\u003cstrong\u003eA\u003c/strong\u003e) Cell solubles, (\u003cstrong\u003eB\u003c/strong\u003e) Hemicellulose, (\u003cstrong\u003eC\u003c/strong\u003e) Cellulose and (\u003cstrong\u003eD\u003c/strong\u003e) Lignin content by lithology, modelled using a General Additive Model (GAM). The GAM was defined as y ~ s(x, k = 4), restricting maximum degrees of freedom to four (n-1 time points) to account for dataset non-linearity. Points represent relative chemistry. Statistics were derived from II ANOVAs, with grey areas representing 95% confidence intervals.\u003c/p\u003e","description":"","filename":"fig5col.png","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/f9e52452dd88034ef1f4a347.png"},{"id":80038181,"identity":"912d8650-e8cb-4c04-a52b-29911f127279","added_by":"auto","created_at":"2025-04-07 08:50:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":578238,"visible":true,"origin":"","legend":"\u003cp\u003eRadar plots of elemental compounds, determined by ICP groups, during a 572-day decomposition experiment. Data were standardised as a proportion of the mean of each chemical parameter to allow for cross-scale comparison, as described in Ball et al., (2022). Raw data are provided in Supplementary Data. Statistics were derived from one-way ANOVAs assessing lithology’s impact on individual molecular groups at each time point, with significant differences are denoted by *. Lithological classes: C = calcareous, M = metapelite, P = peridotite.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/6df9fc23ef060107bd1a2baa.png"},{"id":80039430,"identity":"42fe74d1-15f2-40ee-8ecf-1a58295003a6","added_by":"auto","created_at":"2025-04-07 08:58:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":404607,"visible":true,"origin":"","legend":"\u003cp\u003eMass loss of (\u003cstrong\u003eA\u003c/strong\u003e) \u003cem\u003eP. pinaster\u003c/em\u003e and (\u003cstrong\u003eB\u003c/strong\u003e) \u003cem\u003eA. pinsapo\u003c/em\u003e litter after 572 days of decomposition across two lithological substrates (calcareous and peridotite). The category Away-Calcareous (indicated with hashed lines) shows the predicted mass based on a single exponential decay model with asymptote. Bars represent means (± 1 SE; n = 4-6). Statistical were conducted using Type II and III ANOVAs (type III applied when interaction effects were present), and pairwise significance was determined using t-tests with a false-discovery rate (FRD) \u003cem\u003eα\u003c/em\u003e correction. Lowercase letters indicate significant differences within species, and uppercase letters indicate differences between species within lithology.\u003c/p\u003e","description":"","filename":"fig7col.png","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/8679c9bfa3e0bdbcd000d5d0.png"},{"id":82786204,"identity":"67926ebb-b8a9-4078-a5f2-df5747921fd2","added_by":"auto","created_at":"2025-05-15 09:12:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6178229,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/3059b407-9105-419d-a4f9-4228f9b2e3b4.pdf"},{"id":80037330,"identity":"d0dc41d5-2267-42fc-bdfa-ea0a93bb8f64","added_by":"auto","created_at":"2025-04-07 08:42:04","extension":"xlsx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":247639,"visible":true,"origin":"","legend":"","description":"","filename":"litterdatasetcombined.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6333544/v1/f4cb76b9fa0f5a985e6109a3.xlsx"}],"financialInterests":"","formattedTitle":"Lithology modulates the response of litter decomposition to precipitation in Mediterranean forests","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlant litter decomposition is a fundamental biogeochemical process that regulates nutrient cycling, soil carbon storage, and atmospheric CO\u003csub\u003e2\u003c/sub\u003e emissions in forest ecosystems (Andrews \u0026amp; Schlesinger, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Cotrufo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Globally, 36\u0026ndash;54% of photosynthetically fixed carbon returns to the atmosphere through plant litter (hereafter litter), root and mycorrhizal hyphal turnover, and soil organic matter decomposition (Sha et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Joly et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the substantial research on the drivers of litter decomposition (e.g. Bradford et al., 2016), the influence of lithology remains largely unexplored (e.g. Malik, 2019, Malik 2023). However, recent studies suggest that soil minerology influences soil structure (Angst et al., 2021; Matus 2021), litter quality (Couteaux et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), microbial community composition and functionality (Doetterl et al., 2018; Schroeter et al., 2022), and soil carbon mineralisation (Sagliker et al., 2018; Rocci et al. 2021; Liao et al., 2022), all of which can directly or indirectly affect litter decomposition rates. Plant decomposition is constrained by abiotic factors, such as water availability, and biotic factors, particularly soil microbial communities, which are both strongly influenced by lithology(Throop \u0026amp; Archer, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClassic litter decomposition models have identified climate as a dominant driver, with secondary influences from soil properties, litter quality and soil decomposer communities (Swift et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). More recent studies indicate that litter quality exerts a dominant influence at broad spatial scales, with microbial activity shaped by both climate and litter chemistry (Bradford et al., 2016; Suseela \u0026amp; Tharayil, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Prieto et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Joly et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argue that decomposition studies relying on standardised litter types may overlook the importance of locally adapted litter, which decomposes differently than standardised or non-native litter types. Globally, a combination of macroclimate factors and litter characteristics explain over two-thirds of the variance observed in litter decomposition (Zhang et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Prieto et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Joly et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) however the influence of macroclimate reduces when accounting for microclimatic variations (Bradford et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These findings highlight the coevolution between local flora and decomposer communities, an interaction further shaped by climatic conditions.\u003c/p\u003e \u003cp\u003eLitter quality is determined by its chemical composition, including lignin content, carbon-to-nitrogen (C:N) ratios, and lignin-to-nitrogen (Lig:N) ratios. Historically, the lignocellulose index (LCI) has been used to predict decomposition rates (Berg \u0026amp; McClaugherty, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Advances in our understanding of litter decomposition have shown that the accessibility of labile C and N sources are important for decomposer communities (Zhang et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Abbott et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Austin et al., 2016). Additionally, both macro- and micro-elements, along with structural and metabolic compounds, have been found to strongly influence litter chemistry and litter decomposition rates (Ball et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, Sun et al. (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) showed that Mn fertilisation accelerates late-stage litter decomposition by increasing the activity of manganese peroxidase, an enzyme that degrades the phenolic structure of lignin (Berg \u0026amp; McClaugherty, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Furthermore, Zhang et al. (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found that litter N content alone explained 39% of the variation in litter decomposition rates across a global study spanning 110 sites (38˚S to 69˚N). Since most plant nutrients originate from soils, lithology directly influences plant stoichiometry, which in turn affects litter quality and decomposition dynamics.\u003c/p\u003e \u003cp\u003eDuring pedogenesis, lithology influences soil properties through: (i) elemental composition and, subsequently, soil pH (Schaetzl \u0026amp; Thompson, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hahm et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Campillo-Cora et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); and (ii) structure and texture (Angst et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pichler et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which in turn can affect hydraulic conductivity and water retention (Hillel, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hahm et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Callahan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These factors play a crucial role in shaping the suitability of the soil matrix for plant litter decomposition by influencing microbial activity, nutrient availability, and organic matter stabilisation. Consequently, lithology-driven soil characteristics can also affect plant community structure, productivity and nutrient stoichiometry, further modulating litter decomposition dynamics (Searcy et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ribeiro et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the growing recognition of lithology as a key determinant of soil microbiome, its role regulating forest productivity, plant drought vulnerability, and soil carbon storage has only recently been explored (Hahm et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Reichenbach et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Callahan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; 2023). However, its effect on litter decomposition, particularly through changes in plant nutrient balance, litter quality, and soil microbial communities, remains poorly understood. Michalet \u0026amp; Liancourt (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that bedrock characteristics and aridity have an interactive effect on litter decomposition, with faster rates on acidic, coarse siliceous soils in arid conditions but slower on fine-textured calcareous soils in wet conditions, independent of soil pH. While this supports lithology-driven effects, further research is needed to assess its role in precipitation-driven decomposition across ecosystems and bedrock types. Reciprocal transplant experiments provide a robust framework for detecting local adaptation and disentangling the interactions between litter quality, climate, and soil properties. Two major hypotheses have been proposed to explain local adaptation in litter decomposition rates: (i) the \u003cem\u003ehome-field advantage\u003c/em\u003e (HFA) \u003cem\u003ehypothesis\u003c/em\u003e (Ayres, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); and (ii) \u003cem\u003ethe substrate-quality-matrix quality interaction\u003c/em\u003e (SMI) \u003cem\u003ehypothesis\u003c/em\u003e (Freschet et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The HFA hypothesis postulates that litter decomposes faster at its site of origin due to local specialisation of decomposer communities (Henry et al., 2000; Ayres et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A meta-analysis of 125 reciprocal transplant studies across 35 ecosystems found an average 7.5% faster mass loss at home sites (Veen et al., 2015). However, inconsistencies in HFA patterns suggest that climate, soil properties, and microbial community composition may override HFA effects (Wang et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fanin et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, the SMI hypothesis postulates that litter decomposition rates are determined by the match between litter quality and soil characteristics, with greater differences reducing decomposition efficiency (Freschet et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Some studies argue that decomposition is primarily controlled by initial litter quality, rather than SMI effects (Perez et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bachega et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which would imply an indirect lithological influence through litter chemistry.\u003c/p\u003e \u003cp\u003eThis aim of this study was to investigate the combined effects of lithology and climate on litter decomposition in Mediterranean forests. Specifically, the study sought to: (a) investigate the relative control of climate and lithology on leaf litter decomposition in \u003cem\u003ePinus pinaster\u003c/em\u003e Ation. forests; (b) analyse the interactive effects of leaf litter quality and the physicochemical properties of soils derived from distinct lithological substrates on leaf litter decomposition in two Mediterranean forest species (\u003cem\u003ePinus pinaster\u003c/em\u003e and \u003cem\u003eAbies Pinsapo\u003c/em\u003e Boiss.); and (3) predict how the lithological substrate influences the response of litter decomposition to decreasing precipitation. To address these objectives, the following hypotheses were tested: (i) litter quality and decomposition rates will decline with decreasing precipitation; (ii) lithology will affect temporal decomposition via soil physicochemical properties; (iii) the effects of lithology on decomposition will diminish under conditions of reduced precipitation; and (iv) decomposition will follow an HFA pattern on calcareous soils and an SMI pattern for peridotite soils.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSite description\u003c/h2\u003e\n \u003cp\u003eThe sites are located along a precipitation gradient spanning three mountain ranges in Southern Spain: The Natural Reserve Sierra Bermeja-Sierra Crestellina (West), the National Park Sierra de las Nieves (Centre), and the Natural Park Sierra Alhama, Tejeda y Almijara (East). This 100 km gradient exhibits a decline in mean annual precipitation from 1097 mm in the west to 641 mm in the east. Elevation ranges from 534 to 1150 m asl, with mean annual temperatures fluctuating between 15 and 17\u0026deg;C depending on the site\u0026rsquo;s position along the gradient (Table 1). All sites fall within the Csa K\u0026ouml;ppen climate classification, characterised by hot, dry summers (warmest month exceeding 22\u0026deg;C) and bimodal precipitation patterns, with distinct dry (June-August) and wet (November-February) seasons (Kottek et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe region\u0026rsquo;s complex geomorphology is a result of 600 million years of lithospheric fragmentation producing a diverse array of sedimentary, metamorphic, plutonic and volcanic formations due to the collision of the European and African tectonic plates (Rehault et al., \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e; Garfunkel, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; Hidas et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Within our study sites, three distinct lithological soil types \u0026ndash; calcareous, metapelite, and peridotite \u0026ndash; are present across all three mountain ranges. Each soil type exhibits unique soil physiochemical characteristics that are predicted to influence litter decomposition: (i) calcareous soils have high pH and carbonate content, reducing the solubility and availability of essential plant nutrients such as. Fe, Mn, Cu, Zn, and P (Str\u0026ouml;m et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e); (ii) peridotite-derived soils are rich in phytotoxic heavy metals (e.g., Ni, Cr, Co), deficient in essential nutrients (N, K, and \u003cem\u003eP\u003c/em\u003e) and exhibit a high Mg:Ca ratio (Kazakou et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bini et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); and (iii) metapelite soils contain high quartz and K-feldspar content contributing to poorly developed soils with low nutrient availability.\u003c/p\u003e\n \u003cp\u003eBy examining litter decomposition across this climatic gradient and conducting a reciprocal transplant experiment, we aim to identify the predominant lithological controls on litter decomposition processes.\u003c/p\u003e\n \u003cp\u003eTwo tree species were selected based on their distribution patterns and contrasting litter chemistry: (i) \u003cem\u003ePinus pinaster\u003c/em\u003e, due to its broad distribution across all lithological substrates along the precipitation gradient; and (ii) \u003cem\u003eAbies pin\u003c/em\u003es\u003cem\u003eapo\u003c/em\u003e, a relic endemic species which has contrasting litter chemistry and is distributed across all lithologies but restricted to the central part of the gradient.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eTo investigate the role of lithology on litter decomposition and its response to decreasing precipitation, we conducted two complementary field experiments: needle litter decomposition of maritime pine along the precipitation gradient, where we placed needle litter from \u003cem\u003eP. pinaster\u003c/em\u003e in litterbags across all sites spanning the precipitation gradient and lithological soil types (Experiment 1), and the effect of litter quality and its interaction with lithology, where we conducted a reciprocal transplant experiment using litter from \u003cem\u003eP. pinaster\u003c/em\u003e and \u003cem\u003eA. pinsapo\u003c/em\u003e on calcareous and peridotite soils to evaluate the home filed-advantage and SMI hypotheses (Experiment 2).\u003c/p\u003e\n\u003cp\u003eExperiment 1: \u003cem\u003eMaritime pine litter decomposition along the precipitation gradient\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBetween January and April 2019, we established replicated circular plots (n\u0026thinsp;=\u0026thinsp;4) within each lithological soil type at each position along the precipitation gradient, totalling 36. Plots were selected to minimise variability in physiographical factors. Each plot was 30 m \u0026Oslash; and was divided into four quadrants with a central tree marking the reference point. This experiment included two fixed factors; (i) POSITION (three levels: West, Centre, and East) as a proxy for climate, and (ii) LITHOLOGY (three levels: calcareous, metapelite, and peridotite).\u003c/p\u003e\n\u003cp\u003eExperiment 2: \u003cem\u003eEffect of litter quality and its interaction with lithology on litter decomposition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSince \u003cem\u003eAbies pinsapo\u003c/em\u003e has a limited distribution on peridotite soils, monospecific forests stands of each species were selected for this study at the centre of the precipitation gradient (Sierra de las Nieves National Park). Plots in this experiment were larger (50 m \u0026Oslash;) to account for species-specific variation. Within each forest, six trees were selected and litter originating from two lithological soil types (calcareous and peridotite) was placed in forests dominated by either \u003cem\u003ePinus pinaster\u003c/em\u003e or \u003cem\u003eAbies pinsapo\u003c/em\u003e. The transplant experiment included three fixed factors: (i) LITHOLOGY (two levels: calcareous and peridotite), (ii) litter SOURCE (origin) (two levels: home and away), and (iii) forest SPECIES (two levels: \u003cem\u003eP. pinaster\u003c/em\u003e and \u003cem\u003eA. pinsapo\u003c/em\u003e).\u003c/p\u003e\n\u003ch3\u003eLitterbag preparation and sampling\u003c/h3\u003e\n\u003cp\u003eRecently senesced needle litter was collected from all quadrants of the 36 experimental plots on 10th October 2020. Intact needles - exhibiting a range of lengths with two or three needles attached to their sheath - were selected after visual inspection. The litter was air-dried to constant mass in a well-ventilated room. Two sizes of polyethylene mesh litterbags were used in this experiment: 15 \u0026times; 25 cm for \u003cem\u003eP. pinaster\u003c/em\u003e, and 7.5 \u0026times; 25 cm for \u003cem\u003eA. pinsapo\u003c/em\u003e. The mesh aperture (0.8 mm\u003csup\u003e2\u003c/sup\u003e) was selected to exclude macroinvertebrates. Bag sizes were selected to maintain a constant surface-area-to-volume ratio. Each litterbag contained 10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 g of \u003cem\u003eP. pinaster\u003c/em\u003e litter (larger bags), or 5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 g of \u003cem\u003eA. pinsapo\u003c/em\u003e litter (smaller bags). Four replicate samples per plot were prepared and collected at four post-deployment intervals: 118, 229, 380 and 572 days. Deployment began in October 2020, with the final samples collected in November 2022.\u003c/p\u003e\n\u003cp\u003eIn Experiment 1, four subplots were selected within 3 m of a \u003cem\u003eP. pinaster\u003c/em\u003e tree. Litterbags were randomly arranged in a 2 \u0026times; 2 m grid, totalling 576 litterbags. In Experiment 2, six subplots were selected with litterbags randomly arranged in a 3 \u0026times; 2 m grid with both \u003cem\u003eP. pinaster\u003c/em\u003e and \u003cem\u003eA. pinsapo\u003c/em\u003e litter, totalling 192 litterbags. In all cases, the litter layer was removed to the O horizon and litterbags were placed at the interface with the L horizon.\u003c/p\u003e\n\u003ch3\u003eLitter mass\u003c/h3\u003e\n\u003cp\u003eTo quantify litter mass, three 30 \u0026times; 20 cm\u0026sup2; plastic trays were randomly placed in each quadrant. All organic material down to the O horizon was collected, homogenised, and weighed (\u0026plusmn;\u0026thinsp;0.1 g). A subsample of litter was used to determine litter moisture content, while dry litter mass was assessed after oven-drying at 80\u0026ordm;C for 48 h. Total litter mass per unit area was estimated as kg dry mass per m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eLitter chemistry\u003c/h3\u003e\n\u003cp\u003eLitter carbon (C) and nitrogen (N) contents were quantified in duplicate by grinding 350\u0026thinsp;\u0026plusmn;\u0026thinsp;100 mg of dried litter to a fine powder using a ball mill (Retsch MM200 GmbH, Hann, Germany). Elemental analysis was conducted with a TruSpec CHN/S analyser (LECO, St. Joseph, MI, USA). Soluble cell fraction (SCF), cellulose, hemicellulose and acid-insoluble fractions \u0026ndash; including lignin, tannins, cutin, suberin, and other phenolic compounds (Corbeels, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e) \u0026ndash; were determined in duplicate by sequential Van Soest digestions (Van Soest et al., \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). Briefly, litter subsamples (n\u0026thinsp;=\u0026thinsp;4, 0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 g) were ground and sealed in F57 filter bags (Ankom Technology, Fairport, NY, USA). Crude fiber analysis (neutral and acid detergent) was conducted using an Ankom200 Fiber Analyzer, with acid detergent lignin digestion in a Daisy Incubator, following ANKOM protocols (Ankom Technology, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2017a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2017b\u003c/span\u003e). Ash content was determined by combustion at 500˚C for 5 hours, with ash-corrected litter chemistry calculated using standard methods (Ankom Technology, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2017a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Q. Wang et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The Lignocellulose Index (LCI) was calculated using Eq.\u0026nbsp;1.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:LCI=\\frac{lignin}{(lignin+cellulose+hemicellulose)}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e Eq. 1\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eElemental composition\u003c/h2\u003e\n \u003cp\u003eDuplicate subsamples (250\u0026thinsp;\u0026plusmn;\u0026thinsp;100 mg; n\u0026thinsp;=\u0026thinsp;4) of powdered needle litter were digested using reverse aqua regia (1:3 concentrated HCl:HNO\u003csub\u003e3\u003c/sub\u003e) in a Multiwave 3000 microwave digestor (Anton Paar, Graz, Austria) following Method 3051A (EPA, 2007). Briefly, the procedure involved the following steps: (i) pre-digested of samples in XQ80 reaction tubes by adding 7\u0026ndash;10 ml of digestant and allowing effervescence to subside, (ii) sealing of tubes and subjecting them to microwave digestion (175 \u0026ordm;C, Ramp 15, Hold 15, Fan 1; Temp OFF, Hold 20, Fan 3), (iii) filtering digestion samples through pre-leached, acid-washed filter paper and a 400 \u0026micro;m syringe filter, (iv) analysis of elemental composition using ICP-OES IntelliQuant; (v) quantification of elemental composition for both initial samples and those after 572 days of decomposition; and (vi) conversion of raw concentration data (mg l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) to percentage composition using a density of 1.35 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e for reverse aqua regia.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eLitter mass loss\u003c/h3\u003e\n\u003cp\u003eAt each sampling period, one litterbag was retrieved from each plot quadrant. Freshly collected samples were weighed immediately, oven-dried at 80\u0026deg;C to a constant mass, and reweighed before subsequent analysis. Mass loss was determined on an ash-free dry mass basis.\u003c/p\u003e\n\u003cp\u003eLitter decomposition was modelled using five alternative models: single exponential, single exponential with asymptote, discrete parallel, discrete series, and continuous quality models. Likelihood-based fitting with 500 permutations was performed using the \u0026ldquo;Litterfitter\u0026rdquo; R package (Cornwell \u0026amp; Weedon, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBased on Akaike Information Criterion (AIC) values and visualisation, the best fit was obtained using a first-order single exponential decay model with an asymptote (Eq. 2) (Howard \u0026amp; Howard, \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{t}=\\:{M}_{1}\\times\\:{e}^{-kt}+c\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e Eq. 2.\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eM\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e = Mass remaining at time \u003cem\u003et\u003c/em\u003e, \u003cem\u003eM\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;decomposable litter pool, \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;decomposition rate and \u003cem\u003ec\u003c/em\u003e\u0026thinsp;=\u0026thinsp;asymptote (proportion of litter where \u003cem\u003ek\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0). The relative decomposition rate (RDR) was calculated according to Eq. 3 (Wang et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) where \u003cem\u003et\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e \u003cem\u003et\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e represents the sampling interval in days, and \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e denote the mass remaining at times \u003cem\u003et\u003c/em\u003e and 0, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:RDR\\:\\left(\\%\\:mass\\:loss\\:{day}^{-1}\\right)=\\:-\\frac{\\text{ln}\\left(\\frac{{M}_{t}}{{M}_{0}}\\right)}{({t}_{i}-{t}_{0})}\\times\\:100\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e Eq. 3.\u003c/p\u003e\n\u003cp\u003eBetween 7th and 18th September 2021, a forest fire burned over 10000 ha of woodland across the West/Centre of the precipitation gradient, including two of our study sites. As a result, the transplant experiment with \u003cem\u003eA. pinsapo\u003c/em\u003e was terminated after one year.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed in R v3.4.3 (R Core Team, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). A Multiple Analysis of Covariance (MANCOVA) was conducted to determine whether site characteristics, beyond precipitation gradient position and lithological substrate, varied significantly among plots. Results revealed significant differences in altitude and stand density across the sites (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), which were log-transformed and included as covariates in subsequent analysis. Response variables were standardised using estimated marginal means with mean centering to control for these covariates (Russell et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn Experiment 1, the fixed factors were (i) POSITION (three levels; West, Centre, and East) as a proxy for climate, and (ii) LITHOLOGY (three levels: calcareous, metapelite and peridotite). In Experiment 2, the fixed factors within each tree species (\u003cem\u003eP. pinaster\u003c/em\u003e and \u003cem\u003eA\u003c/em\u003e. \u003cem\u003epinsapo\u003c/em\u003e), were: (i) LITHOLOGY (two levels: calcareous and peridotite), and (ii) SOURCE (two levels: home and away). Both studies included time as a repeated measures factor with five levels: 0, 118, 229, 380 and 572 days.\u003c/p\u003e\n\u003cp\u003eGeneral linear models (GLMs) were used to examine response variables, selecting best-fit model on the Akaike Information Criterion (AIC). Model diagnostics were performed using diagnostic plots to check for normality, heteroscedasticity, and multicollinearity. The Durbin-Watson test was used to assess autocorrelation in the data (Fox \u0026amp; Weisberg, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). We also attempted to use generalised linear mixed effect models to account for autocorrelation within plots over time but had insufficient replication to produce well-fitting models.\u003c/p\u003e\n\u003cp\u003eFor variables affected by the fire, analyses were conducted both including and excluding the West (wet) category to assess the influence of an unbalanced design. The significance of the model terms was determined through Analysis of Variance (ANOVA), employing type III sum of squares when including the West category for interaction terms, and type II sum of squares when excluding it. Effect sizes (\u0026eta;\u0026sup2;) were calculated by dividing the sum of squares of each predictor by the total sum of squares, representing the proportion of total variance explained by each fixed effect.\u003c/p\u003e\n\u003cp\u003ePost-hoc analyses for pairwise comparisons were conducted using Tukey\u0026rsquo;s Honestly Significant Difference (HSD) test. Temporal changes in chemical composition were modelled using General Additive Models (GAMs) defined as y\u0026thinsp;~\u0026thinsp;s(x, k\u0026thinsp;=\u0026thinsp;4), restricting the maximum degrees of freedom to four (n-1 time points) to capture non-linear trends over time (Wood, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eImpact of lithology on pine litter quantity and quality along the precipitation gradient\u003c/h2\u003e\n \u003cp\u003eThe mass of litter on the forest floor decreased consistently along the precipitation gradient from west to east for all lithologies (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). However, lithology did not significantly influence total litter mass (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Supplementary Table\u0026nbsp;2) despite observed differences in tree productivity between metapelite and peridotite soils (data not shown). Notably, litter quantity of forests on peridotite soils were less affected by decreasing precipitation than those on calcareous or metapelite soils (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n \u003cp\u003eInitial litter chemistry and quality varied across positions along the gradient and lithological soil types (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The SCF and hemicellulose exhibited small but significant differences within both position and lithology (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; SCF interquartile range (IQR)\u0026thinsp;=\u0026thinsp;7.4%; hemicellulose IQR\u0026thinsp;=\u0026thinsp;11.7%; Supplementary Table\u0026nbsp;2). Initial lignin content was 12% higher in metapelite-derived litter compared to peridotite-derived litter (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n \u003cp\u003eLitter quality indices (C:N, Lig:N) differed significantly among lithologies (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 in all cases) but were not affected by precipitation (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Litter from calcareous soils had lower quality than that from metapelite and peridotite soils. Yet, neither precipitation nor lithology affected LCI (x̄ = 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while Lig:N ranged from 31 to 68 across lithologies (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), with calcareous forest litter exhibiting 27% higher Lig:N than peridotite forest litter (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Table\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eA significant position \u0026times; lithology interaction was detected for C:N ratios (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) though lithology alone explained 40% of the variance (\u0026eta;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.40; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). C:N was 22% higher in calcareous litter (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eLitter mass loss and litter decomposition rates (Experiment 1)\u003c/h2\u003e\n \u003cp\u003eDuring the 572-day decomposition experiment, pine litter mass loss ranged from 15 to 45%, with notable intra-site variation (IQR\u0026thinsp;=\u0026thinsp;10.9%) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). There was a significant interaction between lithology and precipitation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) since litter mass loss decreased in calcareous soils while did not significantly change in the other two lithological substrates (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMost litter decomposition occurred early in the experiment: 67% of mass loss occurred within the first 118 days (wet season) and 87% mass loss occurred after 229 days (second sampling interval; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The effects of lithology were most pronounced at intermediate precipitation levels (Centre; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) where litter decomposed fastest on calcareous soils, exceeding metapelite soils by 24% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and peridotite soils by 50% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Litter decomposed 21% more on metapelite soils compared to peridotite soils (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.076).\u003c/p\u003e\n \u003cp\u003eA general linear model incorporating position, lithology, and covariates (C:N, altitude, stand density) explained 42% of the total variation in litter mass loss, with the position \u0026times; lithology interaction accounting for 16% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Table\u0026nbsp;6; Supplementary Table\u0026nbsp;1). Litter mass loss was positively correlated with remaining lignin content (t\u003csub\u003e(28)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.52), increasing by 1% for every 5 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e AFDM of lignin remaining. Overall, litter mass loss was lowest at the driest (East) sites (mean = -15.4%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eA single exponential decay model with asymptote provided the best fit (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.91\u0026ndash;0.99, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Litter decomposition rates were influenced by the labile carbon pool (M\u003csub\u003e1\u003c/sub\u003e) but were unaffected by position or lithology (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table\u0026nbsp;1). Interestingly, the lithology \u0026times; position interaction was significant, M\u003csub\u003e1\u003c/sub\u003e and asymptote values showed little variation across position (3\u0026ndash;4%) and lithology (2\u0026ndash;3%; Table 4; \u0026eta;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.27).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eTemporal changes in litter chemistry\u003c/h2\u003e\n \u003cp\u003eSCF content was surprisingly low during the first 118-days (9%), with hemicellulose (25.8%) and lignin (43.4%) accounting for most decomposition. During the following dry season (2nd sampling interval; 229\u0026ndash;380 days), SCF mass loss accelerated for calcareous (81%) and metapelite (57%) soils, whereas peridotite soil-derived litter lost only 6.5% SCF and 15.5% hemicellulose, while lignin and cellulose fractions increased slightly. Litter decomposition was consistently influenced by lithology, irrespective of position along the precipitation gradient (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Table 3). Litter from peridotite soils exhibited a seasonal time-lag in crude fibre fraction loss (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eElemental composition shifts were observed over time (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), with peridotite soils accumulating Co, Cr, Cu, Fe, Ni and Mg (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in all cases). Eight of thirteen elements were influenced by lithology, though only six showed significant differences after 572 days (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Major nutrients, C and N were unaffected by lithology, with only C decreasing significantly over time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Table\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eReciprocal transplant experiment (Experiment 2)\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eP. pinaster\u003c/em\u003e litter showed no significant differences in initial chemistry (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Table 5), whereas \u003cem\u003eA. pinsapo\u003c/em\u003e litter varied significantly across lithologies (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Table\u0026nbsp;6), with a tendency towards higher lability in calcareous substrates, as demonstrated by a 12% increase in SCF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a 43% decrease in lignin contents (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eA forest fire in September 2021 prematurely ended the transplant experiment after 250 days, Missing data for \u003cem\u003eP. pinaster\u003c/em\u003e home-field sites was estimated using adjacent site data, while calcareous-away data was inferred using the asymptote from the single exponential decay model. Litter decomposed faster in calcareous soils, regardless of species and source location (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Within home-field sites, litter decomposed 27% faster in \u003cem\u003eP. pinaster\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 45% faster in \u003cem\u003eA. pinsapo\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in calcareous soils compared to peridotite soils.\u003c/p\u003e\n \u003cp\u003eTransplanted \u003cem\u003eA. pinsapo\u003c/em\u003e litter from calcareous soils decomposed 31% faster at home than away (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eP. pinaster\u003c/em\u003e litter from peridotite origin lost 45% more mass when transplanted away, while \u003cem\u003eA. pinsapo\u003c/em\u003e litter from peridotite-origin lost 104% more mass away than at home (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary aim of this study was to assess the role of lithology in plant litter decomposition, particularly whether it modulates decomposition responses to declining precipitation, a key climate change threat in the Mediterranean. We hypothesised that (1) litter quality and decomposition rates would decline with decreasing precipitation, (2) lithology will affect temporal decomposition dynamics via soil physicochemical properties, (3) the effect of lithology on litter decomposition will diminish under conditions of reduced precipitation; and (4) decomposition will follow an HFA pattern on calcareous soils and an SMI pattern on peridotite soils since these soils contain potentially toxic metals. Our findings show that lithology affects plant litter decomposition through two primary pathways: by regulating litter nutrient quality and by shaping microbial substrate accessibility.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEffect of lithology on litter decomposition\u003c/h2\u003e \u003cp\u003eAfter 1.5 years of decomposition in the field, mass loss of \u003cem\u003eP. pinaster\u003c/em\u003e litter ranged from 15 to 45%, depending on local precipitation and lithological substrate. These values align with Kurz et al., (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) who reported mass loss after one year in \u003cem\u003eP. pinaster\u003c/em\u003e forests, and with Prescott (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), who found approximately 40% loss for recalcitrant litter (Lig:N\u0026thinsp;\u0026gt;\u0026thinsp;30) after three years.\u003c/p\u003e \u003cp\u003eAs expected, litter decomposition slowed over time, with 65 and 85% of litter mass remaining after 572 days, which is 2\u0026ndash;3 times higher than global averages using asymptotic models (Berg et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Osono \u0026amp; Takeda, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This slow mass loss could potentially contribute to long-term soil carbon storage (Berg \u0026amp; Meentemeyer, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), but it is probably somewhat underestimated due to the exclusion of soil fauna and shredders (Kurz et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Nonetheless the comparison across lithologies remain valid and comparable with many other reported values in the literature using similar litterbags.\u003c/p\u003e \u003cp\u003eAnalysis of litter chemistry revealed significant differences in initial litter quality across lithologies, independent of tree species showing a clear indirect impact of lithology on litter quality. Litter from forests on calcareous soils was more recalcitrant with higher C:N ratios. However, litter from peridotite-derived forests decomposed slower than that from calcareous and metapelite soils. SCF and cellulose loss occurred after 229 days on calcareous and metapelite soils but was delayed until after 380 days on peridotite soils, despite immediate lignin degradation during the first wet season. This suggest that substrate accessibility for microbial decomposers was altered, potentially due to unusual elemental stoichiometry of peridotite soils. High Mg content in peridotite-derived litter may have enhanced lignin-degrading phenol oxidase activity (Carine, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), while elevated Ni levels may have selectively inhibited microbial decomposition of high-Ni litter fractions (Adamidis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nakamura et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Islam \u0026amp; Sandhi, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This disparity in elemental stoichiometry could also explain the delay in labile compound decomposition to the second wet season for peridotite litter. Despite the essential role of Mg as a macronutrient, its role in decomposition remains underexplored. In a synthesis of 68 decomposition studies, Berg et al. (2021) found Mg to be a limiting factor in decomposition, with microbial uptake increasing as decomposition progresses. In our study, litter Mg content remained significantly higher in peridotite-derived litter throughout the decomposition period, suggesting that early-stage lignin degradation may have facilitated Mg leaching, stimulating microbial decomposition of labile and intermediate crude fibre fractions later on.\u003c/p\u003e \u003cp\u003eLignin decomposition dynamics also ensued immediately for both calcareous and metapelite sites, although the decomposition rate was slower relative to peridotite sites. Contrary to conventional decomposition models, lignin breakdown occurred simultaneously with labile compound decomposition on all lithologies, rather than following a sequential pattern. (i.e., labile SCF \u0026rarr; holocellulose \u0026rarr; lignocellulose) (Van Der Heijden et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Allison, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Palozzi \u0026amp; Lindo, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This may be due to differences in microbial substrate accessibility or the loss of a non-lignin compounds within the acid-insoluble fraction, which Van Soest Digestion lacks the resolution to detect (Van Soest er al., 1991).\u003c/p\u003e \u003cp\u003e \u003cem\u003eLithology-driven impact on microbial substrate accessibility\u003c/em\u003e The expectation that fungal decomposition dominates in peridotite soils due to their higher fungal-to-bacterial ratios, as found in Nakamura et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, was not fully supported. Instead, evidence suggests an important role for bacterial lignin degradation (DeAngelis et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Woo et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pold et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wilhelm et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A parallel study to ours (Rey et al., submitted) found significantly higher microbial and fungal diversity in peridotite soils, particularly at the driest sites. Yi et al. (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that 19% of samples showed a similar delayed peak in lignin decomposition, linked to low soil-pH, fungal community composition, and high extractable Mn content, as typically found in peridotite soils. Manganese is a cofactor for lignin-degrading peroxidases in both fungi and bacteria (Hatakka, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Hofrichter, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Osono, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Brown \u0026amp; Chang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Datta et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qin et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and is thus likely to explain delayed lignin decomposition.\u003c/p\u003e \u003cp\u003e \u003cem\u003eP. pinaster\u003c/em\u003e litter derived from peridotite soils also exhibited high Ni, Cr, and Co concentrations after 572 days, in line with previous findings (DeGrood et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kazakou et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e Baumeister et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bini et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nakamura et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Heavy metals such as Ni shape microbial community composition in non-serpentine soils(B\u0026aring;\u0026aring;th et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Pennanen, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) to a much larger degree than serpentine soils(H\u0026eacute;ry et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) indicating that peridotite soils may host microbial communities specifically adapted to heavy metal toxicity (Adamidis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A concurrent study at these sites supports this finding, showing that high microbial C use efficiency\u0026mdash;linked to faster biomass turnover\u0026mdash;likely arises as a trade-off to withstand heavy metal toxicity, suggesting that beyond community shifts, microbial function is also impaired by hostile soil conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eHome-field disadvantage and lithology-driven SMI effects\u003c/h2\u003e \u003cp\u003eThe reciprocal transplant experiment demonstrated a home-field disadvantage for peridotite-derived litter, which decomposed faster when transplanted to calcareous substrates. Calcareous-origin litter decompose fastest at home. This challenges the substrate-quality matrix hypothesis (Freschet et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Aerts \u0026amp; Cornelissen, 2012) which predicts that high-quality litter decomposes faster in high-quality matrices.\u003c/p\u003e \u003cp\u003eInstead, our results suggest that lithology modulates decomposer access to substrates, overriding expected litter-matrix quality interactions. Calcareous and peridotite soils represented extremes, where lithology either enhanced or suppressed decomposer efficiency, rather than acting as a neutral medium. This aligns with the mass ratio hypothesis (Grime, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Garnier et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Grime, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) suggesting that decomposer community composition, under influence of resource history, reflects the dominant litter quality in a given matrix (Keiser et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLithology influenced initial litter quality in a species-dependent, spatially variable manner. In Experiment 1, higher-quality calcareous substrate produced lower-quality \u003cem\u003eP. pinaster\u003c/em\u003e litter (22\u0026ndash;27% higher C:N and Lig:N) along the precipitation gradient but had no effect in the transplant experiment. This variability may result from other litter chemistry drivers, such as precipitation, topography and altitude (Zhang et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notably, despite the small spatial scale of the transplant experiment, \u003cem\u003eA. pinsapo\u003c/em\u003e litter quality improved (44% lower LCI) in calcareous substrates. Given little quality\u0026rsquo;s key role in decomposition (Aerts, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Cornwell et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) the influence of lithology should not be overlooked.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eInteraction of lithology and climate on litter decomposition\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eClimate, particularly precipitation is a strong global predictor of plant litter decomposition (Garc\u0026iacute;a- Palacios et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Parton et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) controlling soil microbial activity via moisture availability and limiting the supply and diffusion of soil nutrients (Noy-Meir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Hueso et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Manzoni et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Schimel, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Accordingly, we expected precipitation to be the main driver of litter decomposition, but its effect varied by lithology, with decreasing precipitation impacting decomposition differently depending on the substrate.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn this litter mass loss in calcareous sites showed the strongest response to decreasing precipitation, with 35% less mass loss in the driest Eastern site. This aligns with the greater moisture sensitivity of forests on calcareous soils, where productivity declines under drought (Rey et al., in prep.). Higher mass loss in the West and Centre, along with uniform increases in microbial biomass N (data not shown), suggests that litter decomposition in calcareous sites is more microbially mediated, making them more vulnerable to drought. Similar patterns occur in forests on nutrient rich, weatherable bedrock, where high productivity in wet years leads to excess water demand and increased drought-induced dieback, driving a boom-bust response (Callahan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This finding is further corroborated by Wilhelm et al. (\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who found that Ca-treated soils cycle more C through microbial biomass due to increased litter-derived C incorporation. This supports our findings that productive lithologies, like calcareous soils, reinforcing the boom-bust hypothesis and highlighting forest vulnerability to climate change (Callahan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReduced precipitation had unexpected effects on litter mass loss in metapelite and peridotite lithologies. Mass loss remained unchanged in metapelite soils and slightly increased in peridotite soils under dry conditions. The observed increase in litter decomposition may be attributed to higher N availability in East\u0026ndash;peridotite sites, which reduced the recalcitrance of litter, as indicated by the lower C:N and Lig:N ratios (Aerts, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Talbot \u0026amp; Treseder, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ge et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Elevated N levels likely result from increased atmospheric NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e deposition (Aber et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; DeForest et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Waldrop et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), due to the Sierra de Aguas proximity to M\u0026aacute;laga (Lozano et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome metapelite soils in this study have weathered for over 500\u0026nbsp;million years forming acidic (pH 4.7\u0026ndash;6.8) and nutrient-poor soils dominated by quartz and K-feldspar. Given pH near 5.5, where Al solubilises (Jones et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), microbial secretion of extracellular polymeric substances may counteract Al and Ni toxicity (Hu et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These substances also help litter microbes retain moisture during drought (Malik et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), potentially mitigating the effects of reduced precipitation.\u003c/p\u003e \u003cp\u003eThis interactive effect of lithology and climate on decomposition rates has recently been attributed to the physical chemistry of bedrock, where calcareous sights experience higher drought stress and siliceous bedrocks compensate for water-deficit by capturing runoff more effectively (Michalet \u0026amp; Liancourt, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAs climate change intensifies, understanding the role of lithology in regulating litter decomposition and ecosystem-level C cycling is essential for refining global ecosystem models. This study demonstrates that lithology regulates litter decomposition through two primary mechanisms: (i) directly, by determining initial litter chemistry and quality, and (ii) indirectly, by modifying microbial accessibility to substrates through soil physicochemistry and elemental composition.\u003c/p\u003e \u003cp\u003ePeridotite-derived litter exhibited an altered decomposition sequence, where lignin degraded before more labile compounds. This is likely due to elevated Mg and Ni concentrations, which impact microbial enzyme activity and decomposition pathways. The transplant experiment revealed a home-field disadvantage for peridotite-derived litter, suggesting that lithology strongly modulates microbial specialisation and decomposition dynamics.\u003c/p\u003e \u003cp\u003eDecomposition responses to decreasing precipitation were lithology dependent, supporting the recently documented influence of lithology on drought-sensitivity. In calcareous sites, reduced precipitation resulted in lower mass loss, while peridotite sites showed negligible responses, suggesting that high soil metal content and microbial adaptations buffer decomposition against moisture fluctuations.\u003c/p\u003e \u003cp\u003eGiven the implications for long-term soil carbon storage, future research should focus on how lithology shapes microbial community composition and function, particularly in mediating responses to climate extremes. Understanding the interactions between lithology, decomposition, and microbial dynamics is crucial for predicting carbon turnover and nutrient cycling under future climate change scenarios.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis work was supported by the Ministry of Science of Spain (RT12018-095345-BC22), the School of Environmental and Natural Sciences at Bangor University, and by the Natural Environment Research Council (NE/L002604/1). The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eStudy design and conceptualisation was undertaken by Ana Rey, Jos\u0026eacute; Carreria, and Daniel Fishburn. Material preparation, data collection and analysis were performed by Daniel Fishburn, Ana Rey, Victor Lechuga and Andy Smith. The first draft of the manuscript was written by Daniel Fishburn with critical revisions provided by Ana Rey, Andy Smith, and Lars Markesteijn. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to thank the Envision Doctoral Training Partnership for supporting Daniel Fishburn throughout his PhD studies. Envision was funded by the UK\u0026rsquo;s Natural Environment Research Council (NE/L002604/1), while the LITHOFOR project was funded by the Ministry of Science of Spain (RT12018-095345-BC22). Many thanks to the Spanish field team for technical assistance throughout the LITHOFOR project.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study have been submitted as supplementary material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbott, D., Sparks, D., Herzberg, C., Mooney, W., Nikishin, A., Zhang, Y. S., Aber, J., Mcdowell, W., Nadelhoffer, K., Magill, A., Berntson, G., Kamakea, M., Mcnulty, S., Currie, W., Abril, G., Guerin, F., Richard, S., Delmas, R., Galy-Lacaux, C., \u0026hellip; Hart, S. C. (2013). Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates. \u003cem\u003eBiogeochemistry\u003c/em\u003e. https://doi.org/10.1111/j.1365-2486.2008.01674.x\u003c/li\u003e\n\u003cli\u003eAber, J. D., Nadelhoffer, K. J., Steudler, P., \u0026amp; Melillo, J. M. (1989). Nitrogen Saturation in Northern Forest Ecosystems. \u003cem\u003eBioScience\u003c/em\u003e, 39(6), 378\u0026ndash;386. https://doi.org/10.2307/1311067\u003c/li\u003e\n\u003cli\u003eAdair, E. C., Parton, W. J., Del Grosso, S. J., Silver, W. L., Harmon, M. E., Hall, S. A., Burke, I. C., \u0026amp; Hart, S. C. (2008). Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(11), 2636\u0026ndash;2660. https://doi.org/10.1111/j.1365-2486.2008.01674.x\u003c/li\u003e\n\u003cli\u003eAdamidis, G. C., Kazakou, E., Aloupi, M., \u0026amp; DImitrakopoulos, P. G. (2016). Is it worth hyperaccumulating Ni on non-serpentine soils? Decomposition dynamics of mixed-species litters containing hyperaccumulated Ni across serpentine and non-serpentine environments. \u003cem\u003eAnnals of Botany\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e(7), 1241\u0026ndash;1248. https://doi.org/10.1093/aob/mcw050\u003c/li\u003e\n\u003cli\u003eAerts, R. (1997). Climate, Leaf Litter Chemistry and Leaf Litter Decomposition in Terrestrial Ecosystems: A Triangular Relationship. \u003cem\u003eOikos\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(3), 439\u0026ndash;449. https://doi.org/10.2307/3546886\u003c/li\u003e\n\u003cli\u003eAllison, S. D. (2012). A trait-based approach for modelling microbial litter decomposition. \u003cem\u003eEcology Letters\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(9), 1058\u0026ndash;1070. https://doi.org/10.1111/j.1461-0248.2012.01807.x\u003c/li\u003e\n\u003cli\u003eAlmagro, M. M., Maestre, F. T., Martinez-Lopez, J., Valencia, E., Rey, A., Mart\u0026iacute;nez-L\u0026oacute;pez, J., Valencia, E., \u0026amp; Rey, A. (2015). Climate change may reduce litter decomposition while enhancing the contribution of photodegradation in dry perennial Mediterranean grasslands. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e, 214\u0026ndash;223. https://doi.org/10.1016/j.soilbio.2015.08.006\u003c/li\u003e\n\u003cli\u003eAndrews, J. A., \u0026amp; Schlesinger, W. H. (2000). Soil Respiration and the Global Carbon Cycle. \u003cem\u003eBiogeochemistry\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(1), 7\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eAngst, G., Messinger, J., Greiner, M., H\u0026auml;usler, W., Hertel, D., Kirfel, K., K\u0026ouml;gel-Knabner, I., Leuschner, C., Rethemeyer, J., \u0026amp; Mueller, C. W. (2018). Soil organic carbon stocks in topsoil and subsoil controlled by parent material, carbon input in the rhizosphere, and microbial-derived compounds. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e122\u003c/em\u003e(July 2017), 19\u0026ndash;30. https://doi.org/10.1016/j.soilbio.2018.03.026\u003c/li\u003e\n\u003cli\u003eAnkom Technology. (2016). \u003cem\u003eDetermining Acid Detergent Lignin\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAnkom Technology. (2017a). \u003cem\u003eDetermining Acid Detergent Fiber in Feeds Filter Bag Technique\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAnkom Technology. (2017b). \u003cem\u003eDetermining Neutral detergent fiber in feeds - filter bag technique (for A2000 and A2000I)\u003c/em\u003e. https://www.ankom.com/sites/default/files/document-files/Method_13_NDF_Method_A2000_RevE_4_10_15.pdf\u003c/li\u003e\n\u003cli\u003eAyres, E., Steltzer, H., Simmons, B. L., Simpson, R. T., Steinweg, J. M., Wallenstein, M. D., Mellor, N., Parton, W. J., Moore, J. C., \u0026amp; Wall, D. H. (2009). Home-field advantage accelerates leaf litter decomposition in forests. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(3), 606\u0026ndash;610. https://doi.org/10.1016/j.soilbio.2008.12.022\u003c/li\u003e\n\u003cli\u003eB\u0026aring;\u0026aring;th, E., D\u0026iacute;az-Ravi\u0026ntilde;a, M., Frosteg\u0026aring;rd, \u0026Aring;., \u0026amp; Campbell, C. D. (1998). Effect of metal-rich sludge amendments on the soil microbial community. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(1), 238\u0026ndash;245. https://doi.org/10.1128/aem.64.1.238-245.1998\u003c/li\u003e\n\u003cli\u003eBachega, L. R., Bouillet, J. P., Piccolo, M. de C., Saint-Andr\u0026eacute;, L., Bouvet, J.-M., Nouvellon, Y., Moraes, J. L. de, Gon\u0026ccedil;alves, Robin, A., \u0026amp; C, J.- P. L. (2016). Decomposition of Eucalyptus grandis and Acacia mangium leaves and fine roots in tropical conditions did not meet the Home Field Advantage hypothesis. \u003cem\u003eForest Ecology and Management\u003c/em\u003e \u003cem\u003e359\u003c/em\u003e, 33\u0026ndash;43. https://doi.org/10.1016/j.foreco.2015.09.026\u003c/li\u003e\n\u003cli\u003eBall, B. A., Christenson, L. M., \u0026amp; Wickings, K. G. (2022). A Cross-System Analysis of Litter Chemical Dynamics Throughout Decomposition. \u003cem\u003eEcosystems\u003c/em\u003e \u003cem\u003e25\u003c/em\u003e, 1792\u0026ndash;1808. https://doi.org/10.1007/s10021-022-00749-6\u003c/li\u003e\n\u003cli\u003eBaumeister, J. L., Hausrath, E. M., Olsen, A. A., Tschauner, O., Adcock, C. T., \u0026amp; Metcalf, R. V. (2015). Biogeochemical weathering of serpentinites: An examination of incipient dissolution affecting serpentine soil formation. \u003cem\u003eApplied Geochemistry\u003c/em\u003e \u003cem\u003e54\u003c/em\u003e, 74\u0026ndash;84. https://doi.org/10.1016/j.apgeochem.2015.01.002\u003c/li\u003e\n\u003cli\u003eBerg, B., Ekbohm, G., Johansson, M. B., McClaugherty, C., Rutigliano, F., \u0026amp; De Santo, A. V. (1996). Maximum decomposition limits of forest litter types: A synthesis. \u003cem\u003eCanadian Journal of Botany\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(5), 659\u0026ndash;672. https://doi.org/10.1139/b96-084\u003c/li\u003e\n\u003cli\u003eBerg, B., \u0026amp; Meentemeyer, V. (2002). Litter quality in a north European transect versus carbon storage potential. \u003cem\u003ePlant and Soil\u003c/em\u003e, \u003cem\u003e242\u003c/em\u003e(1), 83\u0026ndash;92. https://doi.org/10.1023/A:1019637807021\u003c/li\u003e\n\u003cli\u003eBerg, B. \u0026amp; McClaugherty, C. (2008) Plant litter: Decomposition, Humus Formation, Carbon Sequestration. Berlin: Springer. https://doi.org/10.1007/978-3-540-74923-3.\u003c/li\u003e\n\u003cli\u003eBini, C., Maleci, L., \u0026amp; Wahsha, M. (2017). Potentially toxic elements in serpentine soils and plants from Tuscany (Central Italy). A proxy for soil remediation. \u003cem\u003eCatena\u003c/em\u003e, \u003cem\u003e148\u003c/em\u003e, 60\u0026ndash;66. https://doi.org/10.1016/j.catena.2016.03.014\u003c/li\u003e\n\u003cli\u003eBradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R., \u0026amp; Wood, S. A. (2016a). Understanding the dominant controls on litter decomposition. \u003cem\u003eJournal of Ecology\u003c/em\u003e, \u003cem\u003e104\u003c/em\u003e(1), 229\u0026ndash;238. https://doi.org/10.1111/1365-2745.12507\u003c/li\u003e\n\u003cli\u003eBradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R., \u0026amp; Wood, S. A. (2016b). Understanding the dominant controls on litter decomposition. \u003cem\u003eJournal of Ecology\u003c/em\u003e, \u003cem\u003e104\u003c/em\u003e(1), 229\u0026ndash;238. https://doi.org/10.1111/1365-2745.12507\u003c/li\u003e\n\u003cli\u003eBradford, M. A., Ciska, G. F., Bonis, A., Bradford, E. M., Classen, A. T., Cornelissen, J. H. C., Crowther, T. W., De Long, J. R., Freschet, G. T., Kardol, P., Manrubia-Freixa, M., Maynard, D. S., Newman, G. S., Logtestijn, R. S. P., Viketoft, M., Wardle, D. A., Wieder, W. R., Wood, S. A., \u0026amp; Van Der Putten, W. H. (2017). A test of the hierarchical model of litter decomposition. \u003cem\u003eNature Ecology and Evolution\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(12), 1836\u0026ndash;1845. https://doi.org/10.1038/s41559-017-0367-4\u003c/li\u003e\n\u003cli\u003eBradford, M. A., Warren, R. J., Baldrian, P., Crowther, T. W., Maynard, D. S., Oldfield, E. E., Wieder, W. R., Wood, S. A., \u0026amp; King, J. R. (2014). Climate fails to predict wood decomposition at regional scales. \u003cem\u003eNature Climate Change\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(7), 625\u0026ndash;630. https://doi.org/10.1038/nclimate2251\u003c/li\u003e\n\u003cli\u003eBrown, M. E., \u0026amp; Chang, M. C. Y. (2014). Exploring bacterial lignin degradation. \u003cem\u003eCurrent Opinion in Chemical Biology\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 1\u0026ndash;7. https://doi.org/10.1016/j.cbpa.2013.11.015\u003c/li\u003e\n\u003cli\u003eCallahan, R. P., Riebe, C. S., Sklar, L. S., Pasquet, S., Ferrier, K. L., Hahm, W. J., Taylor, N. J., Grana, D., Flinchum, B. A., Hayes, J. L., \u0026amp; Holbrook, W. S. (2022). Forest vulnerability to drought controlled by bedrock composition. \u003cem\u003eNature Geoscience\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(9), 714\u0026ndash;719. https://doi.org/10.1038/s41561-022-01012-2\u003c/li\u003e\n\u003cli\u003eCampillo-Cora, C., Soto-G\u0026oacute;mez, D., Arias-Est\u0026eacute;vez, M., B\u0026aring;\u0026aring;th, E., \u0026amp; Fern\u0026aacute;ndez-Calvi\u0026ntilde;o, D. (2022). Estimation of baseline levels of bacterial community tolerance to Cr, Ni, Pb, and Zn in unpolluted soils, a background for PICT (pollution-induced community tolerance) determination. \u003cem\u003eBiology and Fertility of Soils\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(1), 49\u0026ndash;61. https://doi.org/10.1007/s00374-021-01604-x\u003c/li\u003e\n\u003cli\u003eCarine, F., Enrique, A. G., \u0026amp; St\u0026eacute;ven, C. (2009). Metal effects on phenol oxidase activities of soils. \u003cem\u003eEcotoxicology and Environmental Safety\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(1), 108\u0026ndash;114. https://doi.org/10.1016/j.ecoenv.2008.03.008\u003c/li\u003e\n\u003cli\u003eChapin, S., Maston, P. A., \u0026amp; Vitousek, P. M. (2011). \u003cem\u003ePrinciples of Terrestrial Ecosystem Ecology\u003c/em\u003e (Second). Springer Science \u0026amp; Business Media.\u003c/li\u003e\n\u003cli\u003eChen, G., Shi, H., Ding, H., Zhang, X., Gu, T., Zhu, M., \u0026amp; Tan, W. (2023). Multi-scale analysis of nickel ion tolerance mechanism for thermophilic Sulfobacillus thermosulfidooxidans in bioleaching. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, \u003cem\u003e443\u003c/em\u003e, 130245. https://doi.org/10.1016/j.jhazmat.2022.130245\u003c/li\u003e\n\u003cli\u003eCorbeels, M. (2001). Plant Litter and Decomposition: General Concepts and Model Approaches. In NEE Workshop Proceedings (pp. 124\u0026ndash;133). CSIRO Forestry and Forest Products.\u003c/li\u003e\n\u003cli\u003eCornwell, W. K., Cornelissen, J. H. C., Amatangelo, K., Dorrepaal, E., Eviner, V. T., Godoy, O., Hobbie, S. E., Hoorens, B., Kurokawa, H., P\u0026eacute;rez-Harguindeguy, N., Quested, H. M., Santiago, L. S., Wardle, D. A., Wright, I. J., Aerts, R., Allison, S. D., Van Bodegom, P., Brovkin, V., Chatain, A., \u0026hellip; Westoby, M. (2008). Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. \u003cem\u003eEcology Letters\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(10), 1065\u0026ndash;1071. https://doi.org/10.1111/j.1461-0248.2008.01219.x\u003c/li\u003e\n\u003cli\u003eCornwell, W. K., \u0026amp; Weedon, J. T. (2014). Decomposition trajectories of diverse litter types: A model selection analysis. \u003cem\u003eMethods in Ecology and Evolution\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 173\u0026ndash;182. https://doi.org/10.1111/2041-210X.12138\u003c/li\u003e\n\u003cli\u003eCotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K., \u0026amp; Paul, E. (2013). The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter? \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(4), 988\u0026ndash;995. https://doi.org/10.1111/gcb.12113\u003c/li\u003e\n\u003cli\u003eCouteaux, M. M., Bottner, P., \u0026amp; Berg, B. (1995). Litter decomposition climate and litter quality. \u003cem\u003eTrends in Ecology and Evolution\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 63\u0026ndash;66. https://doi.org/10.1016/S0169-5347(00)88978-8\u003c/li\u003e\n\u003cli\u003eDatta, R., Kelkar, A., Baraniya, D., Molaei, A., Moulick, A., Meena, R. S., \u0026amp; Formanek, P. (2017). Enzymatic degradation of lignin in soil: A review. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(7). https://doi.org/10.3390/su9071163\u003c/li\u003e\n\u003cli\u003eDeAngelis, K. M., Allgaier, M., Chavarria, Y., Fortney, J. L., Hugenholtz, P., Simmons, B., Sublette, K., Silver, W. L., \u0026amp; Hazen, T. C. (2011). Characterization of trapped lignin-degrading microbes in tropical forest soil. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4). https://doi.org/10.1371/journal.pone.0019306\u003c/li\u003e\n\u003cli\u003eDeForest, J. L., Zak, D. R., Pregitzer, K. S., \u0026amp; Burton, A. J. (2004). Atmospheric nitrate deposition and the microbial degradation of cellobiose and vanillin in a northern hardwood forest. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(6), 965\u0026ndash;971. https://doi.org/10.1016/j.soilbio.2004.02.011\u003c/li\u003e\n\u003cli\u003eDeGrood, S. H., Claassen, V. P., \u0026amp; Scow, K. M. (2005). Microbial community composition on native and drastically disturbed serpentine soils. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(8), 1427\u0026ndash;1435. https://doi.org/10.1016/j.soilbio.2004.12.013\u003c/li\u003e\n\u003cli\u003eU.S. Environmental Protection Agency (E PA). (2007). \u003cem\u003eMethod 3051A: Microwave assisted acid digestion of sediments, sludges, soils, and oils\u003c/em\u003e. U.S. Environmental Protection Agency, Office of Solid Waste.\u003c/li\u003e\n\u003cli\u003eFanin, N., Lin, D., Freschet, G. T., Keiser, A. D., Augusto, L., Wardle, D. A., \u0026amp; Veen, G. F. (2021). Home-field advantage of litter decomposition: from the phyllosphere to the soil. \u003cem\u003eNew Phytologist\u003c/em\u003e, \u003cem\u003e231\u003c/em\u003e(4), 1353\u0026ndash;1358. https://doi.org/10.1111/nph.17475\u003c/li\u003e\n\u003cli\u003eFox, J., \u0026amp; Weisberg, S. (2019). \u003cem\u003eAn {R} Companion to Applied Regression\u003c/em\u003e (Third). Saghge. https://socialsciences.mcmaster.ca/jfox/Books/Companion/%7D\u003c/li\u003e\n\u003cli\u003eFreschet, T., Aerts, R., \u0026amp; Cornelissen, J. H. C. (2012). Multiple mechanisms for trait effects on litter decomposition: moving beyond home-field advantage with a new hypothesis. \u003cem\u003eJournal of Ecology\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e(3), 619\u0026ndash;630. https://doi.org/10.1111/j.1365-2745.2011.01943.x\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a- Palacios, P., Maestre, F. T., Kattge, J., \u0026amp; Wall, D. H. (2013). Climate and litter quality differently modulate the effects of soil fauna on litter decomposition across biomes. \u003cem\u003eEcology Letters\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(8), 1045\u0026ndash;1053. https://doi.org/10.1111/ele.12137\u003c/li\u003e\n\u003cli\u003eGarfunkel, Z. (1998). Constrains on the origin and history of the Eastern Mediterranean basin. \u003cem\u003eTectonophysics\u003c/em\u003e, \u003cem\u003e298\u003c/em\u003e(1\u0026ndash;3), 5\u0026ndash;35. https://doi.org/https://doi.org/10.1016/S0040-1951(98)00176-0\u003c/li\u003e\n\u003cli\u003eGarnier E., Cortez J., Bill\u0026egrave;s G., Navas M. L. , Roumet C., Debussche M., Laurent G., Blanchard A., Aubry D., Bellmann A, C. N. and J.- P. T. (2004). Plant Functional Markers Capture Ecosystem Properties during Secondary Succession. \u003cem\u003eEcology\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(9), 2630\u0026ndash;2637.\u003c/li\u003e\n\u003cli\u003eGe, X., Zeng, L., Xiao, W., Huang, Z., Geng, X., \u0026amp; Tan, B. (2013). Effect of litter substrate quality and soil nutrients on forest litter decomposition: A review. \u003cem\u003eActa Ecologica Sinica\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 102\u0026ndash;108. https://doi.org/10.1016/j.chnaes.2013.01.006\u003c/li\u003e\n\u003cli\u003eGong, X. Y., Giese, M., Dittert, K., Lin, S., \u0026amp; Taube, F. (2016). Topographic influences on shoot litter and root decomposition in semiarid hilly grasslands. \u003cem\u003eGEODERMA\u003c/em\u003e, \u003cem\u003e282\u003c/em\u003e, 112\u0026ndash;119. https://doi.org/10.1016/j.geoderma.2016.07.017\u003c/li\u003e\n\u003cli\u003eGrime, J. P. (1998). Benefits of Plant Diversity to Ecosystems: Immediate , Filter and Founder Effects. \u003cem\u003eBritish Ecological Society\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(6), 902\u0026ndash;910.\u003c/li\u003e\n\u003cli\u003eHahm, W. J., Riebe, C. S., Lukens, C. E., \u0026amp; Araki, S. (2014). Bedrock composition regulates mountain ecosystems and landscape evolution. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e(9), 3338\u0026ndash;3343. https://doi.org/10.1073/pnas.1315667111\u003c/li\u003e\n\u003cli\u003eHatakka, A. (1994). Lignin-modifying enzymes from selected white-rot fungi: production and role from in lignin degradation. \u003cem\u003eFEMS Microbiology Reviews\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2\u0026ndash;3), 125\u0026ndash;135. https://doi.org/10.1111/j.1574-6976.1994.tb00039.x\u003c/li\u003e\n\u003cli\u003eH\u0026eacute;ry, M., Nazaret, S., Jaffr\u0026eacute;, T., Normand, P., \u0026amp; Navarro, E. (2003). Adaptation to nickel spiking of bacterial communities in neocaledonian soils. \u003cem\u003eEnvironmental Microbiology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 3\u0026ndash;12. https://doi.org/10.1046/j.1462-2920.2003.00380.x\u003c/li\u003e\n\u003cli\u003eHidas, K., Garrido, C. J., Marchesi, C., Bodinier, J., \u0026amp; Louni-hacini, A. (2017). Geochemical and Textural Constraints on Wehrlite Formation by Melt-rock Reaction in the Shallow Subcontinental Lithospheric Mantle (Oran, Tell Atlas, N-Algeria). \u003cem\u003eEGU General Assembly\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 4\u0026ndash;5. https://ui.adsabs.harvard.edu/abs/2017EGUGA..19.7953H/abstract\u003c/li\u003e\n\u003cli\u003eHillel, D. (2004). \u003cem\u003eIntroduction to Environmental Soil Physics\u003c/em\u003e (Elsevier, Ed.; Issue 1). http://journal.um-surabaya.ac.id/index.php/JKM/article/view/2203\u003c/li\u003e\n\u003cli\u003eHofrichter, M. (2002). Review: Lignin conversion by manganese peroxidase (Mn P). \u003cem\u003eEnzyme and Microbial Technology\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 454\u0026ndash;466. https://doi.org/10.1016/S0141-0229(01)00528-2\u003c/li\u003e\n\u003cli\u003eHoward, A. P. J. A., \u0026amp; Howard, D. M. (1974). Microbial Decomposition of Tree and Shrub Leaf Litter. Weight Loss and Chemical Composition of Decomposing Litter. \u003cem\u003eOikos\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 341\u0026ndash;352.\u003c/li\u003e\n\u003cli\u003eHu, X., Yang, L., Lai, X., Yao, Q., \u0026amp; Chen, K. (2019). Influence of Al(III) on biofilm and its extracellular polymeric substances in sequencing batch biofilm reactors. \u003cem\u003eEnvironmental Technology (United Kingdom)\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(1), 53\u0026ndash;59. https://doi.org/10.1080/09593330.2017.1378268\u003c/li\u003e\n\u003cli\u003eHueso, S., Garc\u0026iacute;a, C., \u0026amp; Hern\u0026aacute;ndez, T. (2012). Severe drought conditions modify the microbial community structure, size and activity in amended and unamended soils. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e, 167\u0026ndash;173. https://doi.org/10.1016/j.soilbio.2012.03.026\u003c/li\u003e\n\u003cli\u003eIslam, M., \u0026amp; Sandhi, A. (2023). Heavy Metal and Drought Stress in Plants: The Role of Microbes\u0026mdash;A Review. \u003cem\u003eGesunde Pflanzen\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(4), 695\u0026ndash;708. https://doi.org/10.1007/s10343-022-00762-8\u003c/li\u003e\n\u003cli\u003eJoly, F. X., Scherer-Lorenzen, M., \u0026amp; H\u0026auml;ttenschwiler, S. (2023). Resolving the intricate role of climate in litter decomposition. \u003cem\u003eNature Ecology and Evolution\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 214\u0026ndash;223. https://doi.org/10.1038/s41559-022-01948-z\u003c/li\u003e\n\u003cli\u003eJones, D. L., Cooledge, E. C., Hoyle, F. C., Griffiths, R. I., \u0026amp; Murphy, D. V. (2019). pH and exchangeable aluminum are major regulators of microbial energy flow and carbon use efficiency in soil microbial communities. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e138\u003c/em\u003e(July), 0\u0026ndash;4. https://doi.org/10.1016/j.soilbio.2019.107584\u003c/li\u003e\n\u003cli\u003eKazakou, E., Vile, D., Shipley, B., Gallet, C., \u0026amp; Garnier, E. (2006). Co-variations in litter decomposition, leaf traits and plant growth in species from a Mediterranean old-field succession. \u003cem\u003eFunctional Ecology\u003c/em\u003e. https://doi.org/10.1111/j.1365-2435.2006.01080.x\u003c/li\u003e\n\u003cli\u003eKeiser, A. D., Strickland, M. S., Fierer, N., \u0026amp; Bradford, M. A. (2011). The effect of resource history on the functioning of soil microbial communities is maintained across time. \u003cem\u003eBiogeosciences\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(6), 1477\u0026ndash;1486. https://doi.org/10.5194/bg-8-1477-2011\u003c/li\u003e\n\u003cli\u003eKottek, M., Grieser, J., Beck, C., Rudolf, B., \u0026amp; Rubel, F. (2006). World map of the K\u0026ouml;ppen-Geiger climate classification updated. \u003cem\u003eMeteorologische Zeitschrift\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 259\u0026ndash;263. https://doi.org/10.1127/0941-2948/2006/0130\u003c/li\u003e\n\u003cli\u003eKurz, C., Co\u0026ucirc;teaux, M. M., \u0026amp; Thi\u0026eacute;ry, J. M. (2000). Residence time and decomposition rate of \u003cem\u003ePinus pinaster\u003c/em\u003e needles in a forest floor from direct field measurements under a Mediterranean climate. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(8\u0026ndash;9), 1197\u0026ndash;1206. https://doi.org/10.1016/S0038-0717(00)00036-5\u003c/li\u003e\n\u003cli\u003eLozano, A., Usero, J., Vanderlinden, E., Raez, J., Contreras, J., \u0026amp; Navarrete, B. (2009). Air quality monitoring network design to control nitrogen dioxide and ozone, applied in Malaga, Spain. \u003cem\u003eMicrochemical Journal\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e(2), 164\u0026ndash;172. https://doi.org/10.1016/j.microc.2009.06.005\u003c/li\u003e\n\u003cli\u003eMalik, A. A., Swenson, T., Weihe, C., Morrison, E., Martiny, J. B. H., Brodie, E. L., Northen, T. R., \u0026amp; Allison, S. D. (2019). Physiological adaptations of leaf litter microbial communities to long-term drought. \u003cem\u003eBioRxiv\u003c/em\u003e, 631077. https://doi.org/https://doi.org/10.1101/631077\u003c/li\u003e\n\u003cli\u003eMalik, R. J. (2022). Decomposing the novel decomposer-sphere concept: decomposition byproducts can shape surrounding communities through space and time. \u003cem\u003eBiogeochemistry\u003c/em\u003e, 162(1), 1\u0026ndash;15. https://doi.org/10.1007/s10533-022-01001-y\u003c/li\u003e\n\u003cli\u003eManzoni, S., Schimel, J. P., \u0026amp; Porporato, A. (2012). Responses of soil microbial communities to water stress: Results from a meta-analysis. \u003cem\u003eEcology\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e(4), 930\u0026ndash;938. https://doi.org/10.1890/11-0026.1\u003c/li\u003e\n\u003cli\u003eMichalet, R., \u0026amp; Liancourt, P. (2024). The interplay between climate and bedrock type determines litter decomposition in temperate forest ecosystems. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, 195, 109476. https://doi.org/10.1016/j.soilbio.2024.109476\u003c/li\u003e\n\u003cli\u003eMimeau, L., Tramblay, Y., Brocca, L., Massari, C., Camici, S., \u0026amp; Finaud-Guyot, P. (2021). Modeling the response of soil moisture to climate variability in the Mediterranean region. \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2), 653\u0026ndash;669. https://doi.org/10.5194/hess-25-653-2021\u003c/li\u003e\n\u003cli\u003eMu\u0026ntilde;oz, R., Enr\u0026iacute;quez, M., Bongers, F., L\u0026oacute;pez-Mendoza, R. D., Miguel-Talonia, C., \u0026amp; Meave, J. A. (2023). Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics. \u003cem\u003eFrontiers in Forests and Global Change\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(March), 1\u0026ndash;11. https://doi.org/10.3389/ffgc.2023.1082207\u003c/li\u003e\n\u003cli\u003eNakamura, R., Kajino, H., Kawai, K., Nakai, W., Ohnuki, M., \u0026amp; Okada, N. (2019). Diverse recalcitrant substrates slow down decomposition of leaf litter from trees in a serpentine ecosystem. \u003cem\u003ePlant and Soil\u003c/em\u003e, \u003cem\u003e442\u003c/em\u003e(1\u0026ndash;2), 247\u0026ndash;255. https://doi.org/10.1007/s11104-019-04183-x\u003c/li\u003e\n\u003cli\u003eNakamura, R., Tatsumi, C., Kajino, H., Fujimoto, Y., Fujii, R., Yokobe, T., Mori, T., \u0026amp; Okada, N. (2023). Plant material decomposition and bacterial and fungal communities in serpentine and karst soils of Japanese cool-temperate forests. \u003cem\u003eSoil Science and Plant Nutrition\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(3), 163\u0026ndash;171. https://doi.org/10.1080/00380768.2023.2177493\u003c/li\u003e\n\u003cli\u003eNepstad, D., Lefebvre, P., Da Silva, U. L., Tomasella, J., Schlesinger, P., Sol\u0026oacute;rzano, L., Moutinho, P., Ray, D., \u0026amp; Benito, J. G. (2004). Amazon drought and its implications for forest flammability and tree growth: A basin-wide analysis. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(5), 704\u0026ndash;717. https://doi.org/10.1111/j.1529-8817.2003.00772.x\u003c/li\u003e\n\u003cli\u003eNoy-Meir, I. (1973). Desert ecosystems: environment and producers. \u003cem\u003eAnnual Review of Ecological Systems\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(67), 25\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eOsono, T. (2007). Ecology of ligninolytic fungi associated with leaf litter decomposition. \u003cem\u003eEcological Research\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(6), 955\u0026ndash;974. https://doi.org/10.1007/s11284-007-0390-z\u003c/li\u003e\n\u003cli\u003eOsono, T., \u0026amp; Takeda, H. (2005). Limit values for decomposition and convergence process of lignocellulose fraction in decomposing leaf litter of 14 tree species in a cool temperate forest. \u003cem\u003eEcological Research\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 51\u0026ndash;58. https://doi.org/10.1007/s11284-004-0011-z\u003c/li\u003e\n\u003cli\u003ePalozzi, J. E., \u0026amp; Lindo, Z. (2018). Are leaf litter and microbes team players? Interpreting home-field advantage decomposition dynamics. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e(January), 189\u0026ndash;198. https://doi.org/10.1016/j.soilbio.2018.06.018\u003c/li\u003e\n\u003cli\u003eParton, W., Silver, W. L., Burke, I. C., Grassens, L., Harmon, M. E., Currie, W. S., King, J. Y., Adair, E. C., Brandt, L. A., Hart, S. C., \u0026amp; Fasth, B. (2007). Global-Scale Similarities in Nitrogen Release Patterns During Long-Term Decomposition. \u003cem\u003eScience Reports\u003c/em\u003e, \u003cem\u003e315\u003c/em\u003e(January), 361\u0026ndash;364. https://doi.org/10.1126/science.1134853\u003c/li\u003e\n\u003cli\u003ePennanen, T. (2001). Microbial communities in boreal coniferous forest humus exposed to heavy metals and changes in soil pH - A summary of the use of phospholipid fatty acids. \u003cem\u003eGeoderma\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e(1\u0026ndash;2), 91\u0026ndash;126. https://doi.org/10.1016/S0016-7061(00)00082-3\u003c/li\u003e\n\u003cli\u003ePerez, G., Aubert, M., Deca\u0026euml;ns, T., Trap, J., \u0026amp; Chauvat, M. (2013). Home-Field Advantage: A matter of interaction between litter biochemistry and decomposer biota. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e, 245\u0026ndash;254. https://doi.org/10.1016/j.soilbio.2013.09.004\u003c/li\u003e\n\u003cli\u003ePichler, V., G\u0026ouml;m\u0026ouml;ryov\u0026aacute;, E., Leuschner, C., Homol\u0026aacute;k, M., Abrudan, I. V., Pichlerov\u0026aacute;, M., Střelcov\u0026aacute;, K., Di Filippo, A., \u0026amp; Sitko, R. (2021). Parent material effect on soil organic carbon concentration under primeval european beech forests at a regional scale. \u003cem\u003eForests\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 1\u0026ndash;12. https://doi.org/10.3390/f12040405\u003c/li\u003e\n\u003cli\u003ePold, G., Melillo, J. M., \u0026amp; DeAngelis, K. M. (2015). Two decades of warming increases diversity of a potentially lignolytic bacterial community. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(MAY). https://doi.org/10.3389/fmicb.2015.00480\u003c/li\u003e\n\u003cli\u003ePrescott, C. E. (2010). Litter decomposition: What controls it and how can we alter it to sequester more carbon in forest soils? \u003cem\u003eBiogeochemistry\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e, 113\u0026ndash;149. https://doi.org/10.1007/s10533-010-9439-0\u003c/li\u003e\n\u003cli\u003ePrieto, I., Almagro, M., Bastida, F., \u0026amp; Ignacio Querejeta, J. (2019). Altered leaf litter quality exacerbates the negative impact of climate change on decomposition. \u003cem\u003eJournal of Ecology\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e(5), 2364\u0026ndash;2382. https://doi.org/10.1111/1365-2745.13168\u003c/li\u003e\n\u003cli\u003eQin, X., Sun, X., Huang, H., Bai, Y., Wang, Y., Luo, H., Yao, B., Zhang, X., \u0026amp; Su, X. (2017). Oxidation of a non-phenolic lignin model compound by two Irpex lacteus manganese peroxidases: Evidence for implication of carboxylate and radicals. \u003cem\u003eBiotechnology for Biofuels\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1\u0026ndash;13. https://doi.org/10.1186/s13068-017-0787-z\u003c/li\u003e\n\u003cli\u003eR Core Team. (2022). \u003cem\u003eR: A language and environment for statistical computing.\u003c/em\u003e R Foundation for Statistical Computing. https://www.r-project.org\u003c/li\u003e\n\u003cli\u003eRehault, J. P., Boillot, G., \u0026amp; Mauffret, A. (1984). The Western Mediterranean Basin geological evolution. \u003cem\u003eMarine Geology\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(3\u0026ndash;4), 447\u0026ndash;477. https://doi.org/10.1016/0025-3227(84)90081-1\u003c/li\u003e\n\u003cli\u003eReichenbach, M., Fiener, P., Garland, G., Griepentrog, M., Six, J., \u0026amp; Doetterl, S. (2021). The role of geochemistry in organic carbon stabilization against microbial decomposition in tropical rainforest soils. \u003cem\u003eSoil\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 453\u0026ndash;475. https://doi.org/10.5194/soil-7-453-2021\u003c/li\u003e\n\u003cli\u003eReichenbach, M., Fiener, P., Hoyt, A., Trumbore, S., Six, J., \u0026amp; Doetterl, S. (2023). Soil carbon stocks in stable tropical landforms are dominated by geochemical controls and not by land use. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(9), 1\u0026ndash;17. https://doi.org/10.1111/gcb.16622\u003c/li\u003e\n\u003cli\u003eRibeiro, K. T., Medina, B. M. O., \u0026amp; Scarano, F. R. (2007). Species composition and biogeographic relations of the rock outcrop flora on the high plateau of Itatiaia, SE-Brazil. \u003cem\u003eRevista Brasileira de Botanica\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 623\u0026ndash;639. https://doi.org/10.1590/S0100-84042007000400008\u003c/li\u003e\n\u003cli\u003eRussell, A., Lenth, V., Bolker, B., Buerkner, P., Gin\u0026eacute;-v\u0026aacute;zquez, I., Herve, M., Love, J., Singmann, H., \u0026amp; Lenth, M. R. V. (2023). \u003cem\u003eEmmeans\u003c/em\u003e. \u003cem\u003e34\u003c/em\u003e(4), 216\u0026ndash;221. https://doi.org/10.1080/00031305.1980.10483031\u0026gt;.License\u003c/li\u003e\n\u003cli\u003eSala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L. R., Sykes, M. T., Walker, B. H., Walker, M., \u0026amp; Wall, D. H. (2000). Global biodiversity scenarios for the year 2100. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e287\u003c/em\u003e(5459), 1770\u0026ndash;1774. https://doi.org/10.1126/science.287.5459.1770\u003c/li\u003e\n\u003cli\u003eSchaetzl, R. J., \u0026amp; Thompson, M. L. (2007). Soils: Genesis and Geomorphology. In \u003cem\u003eCambridge University Press\u003c/em\u003e (Second, Vol. 6, Issue 2). Cambridge University Press, The Edinburgh Building, Cambridge CB2 2RU United Kingdom. https://doi.org/10.2136/vzj2007.0030br\u003c/li\u003e\n\u003cli\u003eSchimel, J. P. (2018). Life in dry soils: Effects of drought on soil microbial communities and processes. \u003cem\u003eAnnual Review of Ecology, Evolution, and Systematics\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 409\u0026ndash;432. https://doi.org/10.1146/annurev-ecolsys-110617-062614\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;ter, D., Cramer, W., Leemans, R., Prentice, I. C., Ara\u0026uacute;jo, M. B., Arnell, N. W., Bondeau, A., Bugmann, H., Carter, T. R., Gracia, C. A., De La Vega-Leinert, A. C., Erhard, M., Ewert, F., Glendining, M., House, J. I., Kankaanp\u0026auml;\u0026auml;, S., Klein, R. J. T., Lavorel, S., Lindner, M., \u0026hellip; Zierl, B. (2005). Ecology: Ecosystem service supply and vulnerability to global change in Europe. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e310\u003c/em\u003e(5752), 1333\u0026ndash;1337. https://doi.org/10.1126/science.1115233\u003c/li\u003e\n\u003cli\u003eSearcy, K. B. ., Wilson, B. F. ., \u0026amp; Fownes, J. H. . (2003). Influence of Bedrock and Aspect on Soils and Plant Distribution in the Holyoke Range, Massachusetts. \u003cem\u003eTorrey Botanical Society\u003c/em\u003e, \u003cem\u003e130\u003c/em\u003e(3), 158\u0026ndash;169. https://doi.org/https://doi.org/10.2307/3557551\u003c/li\u003e\n\u003cli\u003eSha, Z., Bai, Y., Li, R., Lan, H., Zhang, X., Li, J., Liu, X., Chang, S., \u0026amp; Xie, Y. (2022). The global carbon sink potential of terrestrial vegetation can be increased substantially by optimal land management. \u003cem\u003eCommunications Earth and Environment\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 1\u0026ndash;10. https://doi.org/10.1038/s43247-021-00333-1\u003c/li\u003e\n\u003cli\u003eSpano, D., Snyder, R. L., \u0026amp; Cesaraccio, C. (2013). Mediterranean Phenology. In M. Schwartz (Ed.), \u003cem\u003ePhenology: An Integrative Environmental Science.\u003c/em\u003e (pp. 173\u0026ndash;196). Springer. https://doi.org/10.1007/978-94-007-6925-0_10\u003c/li\u003e\n\u003cli\u003eStr\u0026ouml;m, L., Godbold, D. L., \u0026amp; Jones, D. L. (2001). Procedure for Determining the Biodegradation of Radiolabeled Substrates in a Calcareous Soil. \u003cem\u003eSoil Science Society of America Journal\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(2), 347\u0026ndash;351. https://doi.org/10.2136/sssaj2001.652347x\u003c/li\u003e\n\u003cli\u003eSun, T., Cui, Y., Berg, B., Zhang, Q., Dong, L., Wu, Z., \u0026amp; Zhang, L. (2019). A test of manganese effects on decomposition in forest and cropland sites. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e129\u003c/em\u003e(November 2018), 178\u0026ndash;183. https://doi.org/10.1016/j.soilbio.2018.11.018\u003c/li\u003e\n\u003cli\u003eSuseela, V., \u0026amp; Tharayil, N. (2018). Decoupling the direct and indirect effects of climate on plant litter decomposition: Accounting for stress-induced modifications in plant chemistry. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(4), 1428\u0026ndash;1451. https://doi.org/10.1111/gcb.13923\u003c/li\u003e\n\u003cli\u003eSwift, M. J., Heal, O. W., \u0026amp; Anderson, J. M. (1979). Decomposition in terrestrial ecosystems. In \u003cem\u003eReview Literature And Arts Of The Americas\u003c/em\u003e (Vol. 5, pp. 12\u0026ndash;24). https://doi.org/10.1007/s00114-006-0159-1\u003c/li\u003e\n\u003cli\u003eTalbot, J. M., \u0026amp; Treseder, K. K. (2012). Interactions between lignin, cellulose, and nitrogen drive litter chemistry - decay relationships. \u003cem\u003eEcology\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e(2), 345\u0026ndash;354. https://doi.org/10.1890/11-0843.1\u003c/li\u003e\n\u003cli\u003eThroop, H. L., \u0026amp; Archer, S. R. (2009). Resolving the Dryland Decomposition Conundrum: Some New Perspectives on Potential Drivers. \u003cem\u003eProgress in Botany\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e, 171\u0026ndash;194. https://doi.org/10.1007/978-3-540-68421-3,\u003c/li\u003e\n\u003cli\u003eTuel, A., \u0026amp; Eltahir, E. A. B. (2020). Why Is the Mediterranean a Climate Change Hot Spot? \u003cem\u003eJournal of Climate\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(14), 5829\u0026ndash;5843. https://doi.org/10.1175/JCLI-D-19-0910.1\u003c/li\u003e\n\u003cli\u003eUller, T., \u0026amp; Lala, K. N. (2019). Evolutionary Causation: Biological and Philosophical Reflections. In T. Uller \u0026amp; K. N. Lala (Eds.), \u003cem\u003eThe MIT Press\u003c/em\u003e (1st ed.). The MIT Press. https://doi.org/10.7551/mitpress/11693..001.0001\u003c/li\u003e\n\u003cli\u003eVan Der Heijden, M. G. A., Bardgett, R. D., \u0026amp; Van Straalen, N. M. (2008). The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. \u003cem\u003eEcology Letters\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 296\u0026ndash;310. https://doi.org/10.1111/j.1461-0248.2007.01139.x\u003c/li\u003e\n\u003cli\u003eVan Soest, P. J., Robertson, J. B., \u0026amp; Lewis, B. A. (1991). Methods for Dietary Fiber, Neutral Detergent Fiber, and Nonstarch Polysaccharides in Relation to Animal Nutrition. \u003cem\u003eJournal of Dairy Science\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(10), 3583\u0026ndash;3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2\u003c/li\u003e\n\u003cli\u003eWaldrop, M. P., Zak, D. R., \u0026amp; Sinsabaugh, R. L. (2004). Microbial community response to nitrogen deposition in northern forest ecosystems. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(9), 1443\u0026ndash;1451. https://doi.org/10.1016/j.soilbio.2004.04.023\u003c/li\u003e\n\u003cli\u003eWallenstein, M. D., McNulty, S., Fernandez, I. J., Boggs, J., \u0026amp; Schlesinger, W. H. (2006). Nitrogen fertilization decreases forest soil fungal and bacterial biomass in three long-term experiments. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e222\u003c/em\u003e(1\u0026ndash;3), 459\u0026ndash;468. https://doi.org/10.1016/j.foreco.2005.11.002\u003c/li\u003e\n\u003cli\u003eWang, J., Liu, L., Wang, X., \u0026amp; Chen, Y. (2015). The interaction between abiotic photodegradation and microbial decomposition under ultraviolet radiation. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(5), 2095\u0026ndash;2104. https://doi.org/10.1111/gcb.12812\u003c/li\u003e\n\u003cli\u003eWang, Q., Wang, S., \u0026amp; Huang, Y. (2009). Leaf litter decomposition in the pure and mixed plantations of Cunninghamia lanceolata and Michelia macclurei in subtropical China. \u003cem\u003eBiology and Fertility of Soils\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(4), 371\u0026ndash;377. https://doi.org/10.1007/s00374-008-0338-7\u003c/li\u003e\n\u003cli\u003eWang, X., Gossart, M., Guinet, Y., Fau, H., Lavignasse-Scaglia, C. D., Chaieb, G., \u0026amp; Michalet, R. (2020). The consistency of home-field advantage effects with varying climate conditions. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e149\u003c/em\u003e(November 2019), 107934. https://doi.org/10.1016/j.soilbio.2020.107934\u003c/li\u003e\n\u003cli\u003eWaring, B. G., Averill, C., \u0026amp; Hawkes, C. V. (2013). Differences in fungal and bacterial physiology alter soil carbon and nitrogen cycling: Insights from meta-analysis and theoretical models. \u003cem\u003eEcology Letters\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(7), 887\u0026ndash;894. https://doi.org/10.1111/ele.12125\u003c/li\u003e\n\u003cli\u003eWilhelm, R. C., Singh, R., Eltis, L. D., \u0026amp; Mohn, W. W. (2019). Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. \u003cem\u003eISME Journal\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 413\u0026ndash;429. https://doi.org/10.1038/s41396-018-0279-6\u003c/li\u003e\n\u003cli\u003eWilhelm, R., Hoeschen, C., Buckley, D., \u0026amp; Lehmann, J. (2023). Calcium promotes persistent soil organic matter by altering microbial transformation of plant litter. \u003cem\u003eResearch Square\u003c/em\u003e, \u003cem\u003eFebruary\u003c/em\u003e. https://doi.org/10.21203/rs.3.rs-2606058/v1\u003c/li\u003e\n\u003cli\u003eWoo, H. L., Hazen, T. C., Simmons, B. A., \u0026amp; DeAngelis, K. M. (2014). Enzyme activities of aerobic lignocellulolytic bacteria isolated from wet tropical forest soils. \u003cem\u003eSystematic and Applied Microbiology\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 60\u0026ndash;67. https://doi.org/10.1016/j.syapm.2013.10.001\u003c/li\u003e\n\u003cli\u003eWood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. \u003cem\u003eJournal of the Royal Statistical Society. Series B: Statistical Methodology\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(1), 3\u0026ndash;36. https://doi.org/10.1111/j.1467-9868.2010.00749.x\u003c/li\u003e\n\u003cli\u003eYi, B., Lu, C., Huang, W., Yu, W., Yang, J., Howe, A., Weintraub-Leff, S. R., \u0026amp; Hall, S. J. (2023). Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(20), 5968\u0026ndash;5980. https://doi.org/10.1111/gcb.16875\u003c/li\u003e\n\u003cli\u003eZhang, D., Hui, D., Luo, Y., \u0026amp; Zhou, G. (2008). Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors. \u003cem\u003eJournal of Plant Ecology\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 85\u0026ndash;93. https://doi.org/10.1093/jpe/rtn002\u003c/li\u003e\n\u003cli\u003eZheng, H., Heděnec, P., Rousk, J., Schmidt, I. K., Peng, Y., \u0026amp; Vesterdal, L. (2022). Effects of common European tree species on soil microbial resource limitation, microbial communities and soil carbon. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e172\u003c/em\u003e(February). https://doi.org/10.1016/j.soilbio.2022.108754\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"936\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 59.645%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Mean site characteristics of the mountain ranges along the precipitation gradient in the province of M\u0026aacute;laga. Values are mean \u0026plusmn; 1SE (n = 4). Abbreviations are as follows: EPT = evapotranspiration, MAP = mean annual precipitation, MAT = mean annual temperature. \u0026nbsp;Letters represent significant differences between categories of lithology, within position based on Tukey\u0026rsquo;s HSD test at the \u0026alpha; = 0.05 level.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.0995%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOSITION\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0075%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOCATION\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5196%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLITHOLOGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAT\u0026nbsp;\u003c/strong\u003e(\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.745%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAP\u0026nbsp;\u003c/strong\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.745%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStand density\u0026nbsp;\u003c/strong\u003e(ind. ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAltitude\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrientation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(\u0026ordm;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 5.0995%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14.0075%;\"\u003e\n \u003cp\u003e\u003cem\u003eCrestellina/Bermeja\u0026nbsp;\u003c/em\u003e(36˚27\u0026apos;57\u0026apos;\u0026apos;N. 5˚59\u0026apos;3\u0026apos;\u0026apos;W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003eCalcareous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e15.71 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e1112.25 \u0026plusmn; 1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e793.15 \u0026plusmn; 3.85 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e433.9 \u0026plusmn; 137.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e597.25 \u0026plusmn; 21.36 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e291.73 \u0026plusmn; 10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003eMetapelite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e15.36 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e1115.25 \u0026plusmn; 3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e781.00 \u0026plusmn; 3.70 \u003cstrong\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e946.1 \u0026plusmn; 257.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e679.75 \u0026plusmn; 14.95 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e252.93 \u0026plusmn; 68.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003ePeridotite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e14.48 \u0026plusmn; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e1062.75 \u0026plusmn; 19.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e750.50 \u0026plusmn; 11.51 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e679.1 \u0026plusmn; 239.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e847.00 \u0026plusmn; 71.70 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e207.43 \u0026plusmn; 46.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 5.0995%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentre\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14.0075%;\"\u003e\n \u003cp\u003e\u003cem\u003eSierra de las Nieves\u0026nbsp;\u003c/em\u003e(36˚40\u0026apos;59\u0026apos;\u0026apos;N. 4˚59\u0026apos;58\u0026apos;\u0026apos;W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003eCalcareous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e13.77 \u0026plusmn; 0.81 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e709.50 \u0026plusmn; 11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e763.48 \u0026plusmn; 7.26 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e442.1 \u0026plusmn; 117.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e1000.5 \u0026plusmn; 60.51 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e88.08 \u0026plusmn; 25.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003eMetapelite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e14.77 \u0026plusmn; 0.77 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e813.50 \u0026plusmn; 53.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e752.73 \u0026plusmn; 2.68 \u003cstrong\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e986.8 \u0026plusmn; 356.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e804.50 \u0026plusmn; 67.24 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e127.35 \u0026plusmn; 58.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003ePeridotite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e14.33 \u0026plusmn; 0.79 \u003cstrong\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e831.25 \u0026plusmn; 38.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e778.80 \u0026plusmn; 8.91 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e752.5 \u0026plusmn; 131.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e779.75 \u0026plusmn; 54.44 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e233.53 \u0026plusmn; 51.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 5.0995%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEast\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14.0075%;\"\u003e\n \u003cp\u003e\u003cem\u003eSierra Alhama/Aguas\u0026nbsp;\u003c/em\u003e(36˚49\u0026apos;60\u0026apos;\u0026apos; N. -3˚52\u0026apos;0\u0026apos;\u0026apos; W)\u003cbr\u003e\u0026nbsp;(6˚51\u0026apos;36\u0026apos;\u0026apos; N. 4˚46\u0026apos;0\u0026apos;6\u0026apos; W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003eCalcareous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e14.75 \u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e687.75 \u0026plusmn; 12.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e733.50 \u0026plusmn; 8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e386.4 \u0026plusmn; 55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e804.00 \u0026plusmn; 50.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e294.45 \u0026plusmn; 9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003eMetapelite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e14.44 \u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e685.75 \u0026plusmn; 11.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e764.85 \u0026plusmn; 11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e497.1 \u0026plusmn; 88.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e847.25 \u0026plusmn; 23.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e216.40 \u0026plusmn; 33.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.5196%;\"\u003e\n \u003cp\u003ePeridotite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e14.98 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e650.50 \u0026plusmn; 23.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.745%;\"\u003e\n \u003cp\u003e750.60 \u0026plusmn; 20.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.4223%;\"\u003e\n \u003cp\u003e573.0 \u0026plusmn; 80.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.4551%;\"\u003e\n \u003cp\u003e764.25 \u0026plusmn; 51.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9387%;\"\u003e\n \u003cp\u003e174.55 \u0026plusmn; 63.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 614px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eMean (n\u003cem\u003e\u0026nbsp;=\u0026nbsp;\u003c/em\u003e36) Akaike Information Criterion values of the five fitted decomposition models described in Cornwell \u0026amp; Weedon (2014).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eSingle Exponential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSingle Exp. Asymptote\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDiscrete Parallel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDiscrete Series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eContinuous Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-10.86 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-24.73 \u0026plusmn; 5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-24.14 \u0026plusmn; 5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-20.65 \u0026plusmn; 5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-10.38 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"932\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 932px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Mean initial litter quality (\u0026plusmn;1SE) along the precipitation gradient: West, Centre, and East for the three lithological substrates: calcareous, metapelite and peridotite. Significance was measured by comparing the full model to the null model, * represents significance at the \u0026alpha;=0.05 level, ** at the \u0026alpha;=0.01 level, *** at the \u0026alpha;=0.001 level. Letters indicate significant differences within position, among lithologies based on one-way type II ANOVAs, where letters are not present, no significant differences were observed. SCF represents the soluble cell fraction, LCI the lignocellulose index, C:N the carbon to nitrogen ratio and Lignin:N the lignin to nitrogen ratio.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVARIABLE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 814px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOSITION ON THE GRADIENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 274px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentre\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEast\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLITHOLOGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcareous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetapelite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeridotite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcareous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetapelite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeridotite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcareous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetapelite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeridotite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCF\u0026nbsp;\u003c/strong\u003e(%) \u003cstrong\u003e***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e42.64 \u0026plusmn; 0.76 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e38.26 \u0026plusmn; 1.33 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e43.91 \u0026plusmn; 0.85 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e43.88 \u0026plusmn; 0.72 \u003cstrong\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e43.05 \u0026plusmn; 0.58 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e46.4 \u0026plusmn; 0.72 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e43.85 \u0026plusmn; 0.32 \u003cstrong\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e42.13 \u0026plusmn; 1.01 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e45.51 \u0026plusmn; 0.63 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCellulose\u003c/strong\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e23.29 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e23.19 \u0026plusmn; 0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e23.27 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e22.85 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e24.24 \u0026plusmn; 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e23.27 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e23.97 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e23.44 \u0026plusmn; 1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e22.04 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemicellulose\u0026nbsp;\u003c/strong\u003e(%) \u003cstrong\u003e**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e12.05 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e11.08 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e11.28 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e12.13 \u0026plusmn; 0.24 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e11.54 \u0026plusmn; 0.46 \u003cstrong\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10.67 \u0026plusmn; 0.14 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e11.43 \u0026plusmn; 0.15 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e14.18 \u0026plusmn; 0.96 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e12.17 \u0026plusmn; 0.74 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLignin **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e20.78 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e22.67 \u0026plusmn; 1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e19.05 \u0026plusmn; 0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e21.17 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e22.09 \u0026plusmn; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e20.55 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e21.96 \u0026plusmn; 0.34 \u003cstrong\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e24.12 \u0026plusmn; 1.36 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e21.88 \u0026plusmn; 0.78 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.38 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.39 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.39 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.38 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.38 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.38 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.37 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.40 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.35 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC:N ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e138.01 \u0026plusmn; 3.40 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e105.02 \u0026plusmn; 13.21 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e100.17 \u0026plusmn; 0.97 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e109.28 \u0026plusmn; 10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e102.48 \u0026plusmn; 7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e111.83 \u0026plusmn; 13.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e143.82 \u0026plusmn; 6.36 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e114.33 \u0026plusmn; 5.34 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e91.89 \u0026plusmn; 4.63 \u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLignin:N *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e59.80 \u0026plusmn; 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e49.48 \u0026plusmn; 4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e51.48 \u0026plusmn; 5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e48.22 \u0026plusmn; 6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e45.37 \u0026plusmn; 4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e46.53 \u0026plusmn; 7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e60.49 \u0026plusmn; 2.57 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e51.48 \u0026plusmn; 0.99 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e35.07 \u0026plusmn; 2.28 \u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"917\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 917px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eMean mass loss and parameter estimates of the model used to fit the temporal dynamics of litter decomposition (\u0026plusmn;1SE; \u003cem\u003en\u003c/em\u003e = 4) used in this study along the precipitation gradient: west, centre, and east for the three lithological substrates: calcareous, metapelite and peridotite. Mean (\u0026plusmn;1SE). Significance was measured by comparing the full model to the null model, *represents significance at the \u0026alpha;=0.05 level, ** at the \u0026alpha;=0.01 level, *** at the \u0026alpha;=0.001 level. Letters indicate significant differences within position, between lithologies based on one-way type II ANOVAs, where letters are not present, no significant differences were observed. The decomposition rate (\u003cem\u003ek\u003c/em\u003e) and M1 represents the proportion of decomposable mass as determined by a single exponential model with asymptote.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVARIABLE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 794px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOSITION ON THE GRADIENT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 274px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentre\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLITHOLOGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcareous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetapelite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeridotite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcareous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetapelite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeridotite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcareous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetapelite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeridotite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMass Loss\u003c/strong\u003e (%)\u003cstrong\u003e\u0026nbsp;***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30.83 \u0026plusmn; 1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26.74 \u0026plusmn; 1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30.14\u0026nbsp;\u0026plusmn; 1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e35.41 \u0026plusmn; 1.10 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e28.35 \u0026plusmn; 1.95 \u003cstrong\u003e\u003csup\u003eab\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e23.37 \u0026plusmn; 1.30\u003csup\u003e\u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23.15 \u0026plusmn; 1.10\u003csup\u003e\u0026nbsp;\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e24.68 \u0026plusmn; 2.37\u003csup\u003e\u0026nbsp;\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e27.24 \u0026plusmn; 1.31\u003csup\u003e\u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ek\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(% yr\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.43 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5.21 \u0026plusmn; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6.08 \u0026plusmn; 1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.73 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e5.56 \u0026plusmn; 1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6.84 \u0026plusmn; 1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.79 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.97 \u0026plusmn; 0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5.64 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsymptote\u0026nbsp;\u003c/strong\u003e(%)\u003cstrong\u003e\u0026nbsp;*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e69.40 \u0026plusmn; 1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e76.53 \u0026plusmn; 2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70.89 \u0026plusmn; 2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e65.23 \u0026plusmn; 2.16 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e72.33 \u0026plusmn; 3.24\u003csup\u003e\u0026nbsp;\u003cstrong\u003eab\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e78.72 \u0026plusmn; 2.67\u003csup\u003e\u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e75.91 \u0026plusmn; 1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e75.07 \u0026plusmn; 3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e73.23 \u0026plusmn; 1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM1\u0026nbsp;\u003c/strong\u003e(%)\u003cstrong\u003e\u0026nbsp;*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30.33 \u0026plusmn; 1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23.42 \u0026plusmn; 2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e29.11 \u0026plusmn; 2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e34.74 \u0026plusmn; 2.36 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e27.57 \u0026plusmn; 3.32\u003csup\u003e\u0026nbsp;\u003cstrong\u003eab\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21.28 \u0026plusmn; 2.72 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24.17 \u0026plusmn; 1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e25.03 \u0026plusmn; 3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e26.74 \u0026plusmn; 1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eMean initial litter quality (n = 6, \u0026plusmn;1SE) by species, \u003cem\u003eP. pinaster\u0026nbsp;\u003c/em\u003eand \u003cem\u003eA. pinsapo\u0026nbsp;\u003c/em\u003efor the two\u003cem\u003e\u0026nbsp;\u003c/em\u003elithological substrates: calcareous and peridotite. Letters indicate significant differences within species, between lithologies based on t-tests, where letters are not present, no significant differences were observed. SCF represents the soluble cell fraction, LCI the lignocellulose index and the carbon to nitrogen (C:N) ratio.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPECIES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePinus\u0026nbsp;pinaster\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAbies\u0026nbsp;pinsapo\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcareous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeridotite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalcareous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeridotite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCF\u0026nbsp;\u003c/strong\u003e(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e46.24 \u0026plusmn; 1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e44.42 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e62.83 \u0026plusmn; 0.23 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e54.92 \u0026plusmn; 0.75 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCellulose\u003c/strong\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e21.89 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e22.82 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e15.65 \u0026plusmn; 0.25 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e12.71 \u0026plusmn; 0.90 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemicellulose\u0026nbsp;\u003c/strong\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e12.24 \u0026plusmn; 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e11.63 \u0026plusmn; 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e10.24 \u0026plusmn; 0.12 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e11.64 \u0026plusmn; 0.31 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLignin\u003c/strong\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e19.64 \u0026plusmn; 1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e21.13 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e11.28 \u0026plusmn; 0.10 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e20.73 \u0026plusmn; 1.00 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.36 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.37 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.30 \u0026plusmn; 0.00 \u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.46 \u0026plusmn; 0.02 \u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC:N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e85.76 \u0026plusmn; 25.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e106.49 \u0026plusmn; 4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e52.27 \u0026plusmn; 6.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e40.66 \u0026plusmn; 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Plant-soil interactions, Mediterranean forests, bedrock, litter quality, litter decomposition dynamics, drought tolerance, soil elemental composition","lastPublishedDoi":"10.21203/rs.3.rs-6333544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6333544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aims\u003c/h2\u003e \u003cp\u003ePlant litter decomposition has a major influence on the global carbon cycle. While extensive research has examined the primary environmental drivers of decomposition, the influence of lithology remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe investigated the combined effects of lithology and climate on needle litter decomposition through a field experiment along a decreasing precipitation gradient (1097 to 641 mm yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) located in the province of Malaga (Andaluc\u0026iacute;a, Spain) where maritime pine (\u003cem\u003ePinus pinaster\u003c/em\u003e) forests occur on three distinct soil types: calcareous, metapelite, and peridotite. Additionally, we conducted a reciprocal transplant experiment at the intermediate precipitation site to test the home-field advantage hypothesis, using litter from \u003cem\u003ePinus pinaster\u003c/em\u003e and \u003cem\u003eAbies pinsapo\u003c/em\u003e on calcareous and peridotite soils.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter 1.5 years of decomposition, under intermediate precipitation, litter mass loss was highest on calcareous soils, exceeding mass loss on metapelite soils by 24% and peridotite soils by 50%. Decreased precipitation reduced decomposition by 35% on calcareous soils but had minimal effects on metapelite and peridotite soils. On peridotite soils, labile carbon decomposition was delayed by one dry season, whereas lignin decomposition began immediately. A \u003cem\u003ehome-field advantage\u003c/em\u003e pattern was observed on calcareous soils, while an \u003cem\u003eaway-field advantage\u003c/em\u003e was detected on peridotite soils.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLithology modulates litter decomposition by influencing litter quality. Since lithology affects both, decomposition rates and their sensitivity to precipitation, understanding these interactions is critical for predicting climate change impacts on nutrient cycling and carbon dynamics.\u003c/p\u003e","manuscriptTitle":"Lithology modulates the response of litter decomposition to precipitation in Mediterranean forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 08:41:59","doi":"10.21203/rs.3.rs-6333544/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"697243e4-ed64-4bd2-96c2-a9e97ea12732","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-15T09:04:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-07 08:41:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6333544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6333544","identity":"rs-6333544","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}