Early stage litterfall decomposition dynamics in Pinus halepensis and Pinus brutia stands: Disentangling the effect of climate, species identity and management practices

Early stage litterfall decomposition dynamics in Pinus halepensis and Pinus brutia stands: Disentangling the effect of climate, species identity and management practices | 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 Early stage litterfall decomposition dynamics in Pinus halepensis and Pinus brutia stands: Disentangling the effect of climate, species identity and management practices Efstathia D. Mantzari, Nikoleta Eleftheriadou, Georgios Xanthopoulos, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8310680/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 Understanding the main drivers of decomposition, a fundamental ecosystem process, is crucial for predicting carbon dynamics in Mediterranean pine forests in the context of climate change. Litter decomposition is highly influenced by micro-environmental conditions, litter chemistry and forest management practices. Methods A multi-plot decomposition experiment was conducted in Pinus brutia and Pinus halepensis dominated forests at three regions in Greece. In each plot, different forest management practices have been implemented over the last decades (overstory thinning, understory removal and lack of management). The mass loss of pine needles and standardized material (cellulose papers, wood sticks) was systematically measured along with micro-environmental conditions. Nonlinear mixed-effect models were used to explore the influence of species, stand structure, and micro-climatic and soil conditions on the early decomposition rates. Results Needle litter in P. halepensis dominated stands decomposed approximately twice as fast as litter in P. brutia stands; however, these species-specific differences were probably masked by micro-environmental variation and therefore could not be attributed to differences in litter chemistry. The analysis of the decomposition rates of cellulose papers revealed an across-region positive effect of pH that interacts with variation in microclimate (air temperature and soil water content) and stand structure (total basal area and leaf area index). For wood sticks, pH was the single across-plot predictor of decomposition. Conclusion The process of early-stage decomposition across regions could be primarily controlled by variation in soil conditions and their microbial communities, with microclimate and stand structure operating at a smaller (plot-level) scale. decomposition drivers carbon sequestration litterbag method pine forests Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Forests account for the largest terrestrial carbon (C) pool on the planet, playing a key role in removing anthropogenic CO 2 emissions from the atmosphere and storing them in their soil and biomass. At the global scale, approximately 1500 Pg C are stored at the top one meter of soil (FAO 2022) and ~320 Gt C in the aboveground plant biomass (Bar-On et al. 2018), while soil respiration releases approximately 87-95 Pg C yr -1 (Hashimoto et al. 2015). Shifting rainfall patterns, drought events and increased air and soil temperatures are modifying carbon and nitrogen balance, by affecting microclimatic conditions, litter chemistry and the decomposers’ community. In the context of climate change, where Mediterranean ecosystems are considered highly vulnerable (IPCC 2023), a clear understanding of litter decomposition is thus needed for designing efficient forest adaptation practices. Mediterranean ecosystems, such as low elevation pine forests, are characterized by remarkable dry-to-wet cycles that affect decomposition rates (Gallardo and Merino 1993; Fioretto et al. 2001, 2003; Kazakou et al. 2006; Jarvis et al. 2007; Arslan et al. 2010; Dimitrakopoulos et al. 2010; Incerti et al. 2011, 2018; de la Riva et al. 2019). During the wet season, microbial degradation is substantially enhanced by the incoming precipitation and other non-rainfall water sources such as fog or dew (Gliksman et al. 2018a). In dry season, photodegradation is strongly driven by the high radiation fluxes and the prevailing high temperatures (Grünzweig and Gliksman 2021). In general, the decomposition rates in Mediterranean ecosystems are considered optimal within a temperature range of 20-35 o C and a litter moisture range between 40-90% (Incerti et al. 2011). At the same time, recent studies highlight the role of microbial community biomass in regulating decomposition process, with mixed-species litter effects ranging from synergistic (where higher species richness accelerates decay to antagonistic effects that slow down decomposition rates (Gartner and Cardon 2004; Hättenschwiler et al. 2005; Ball et al. 2008; Dimitrakopoulos 2010; Sheffer et al. 2015; Zhao et al. 2022). Studies that systematically control the effect of all the above abiotic and biotic factors on the process of decomposition are highly relevant in the context of climate change, and its potential response to global change. Forest management practices, such as canopy gap creation and understory removal, influence decomposition through different mechanisms. For example, canopy gaps, occurring either naturally or through stand thinning can accelerate litter decay through photodegradation (Gliksman et al. 2018b; Wang et al. 2021; 2022). Stand thinning can also enhance soil fertility and nutrients circulation (Lado-Monserrat et al. 2016), but reduce litterfall production and thus the forest floor biomass intake (Blanco et al. 2006; Doukalianou et al. 2022), affect water availability (Bueis et al. 2018), rain interception and soil microclimatic conditions (Whitford and Duval 2019; Grünzweig and Gliksman 2021), and shape the composition of understory vegetation (Navarro et al. 2013). Findings from studies of forest management practices’ influence on litter decomposition dynamics are accumulating (Purahong et al. 2014; Jabiol and Chauvet 2015; Santonja et al. 2017; Lagomarsino et al. 2020; Matos et al. 2022), with their results suggesting that specific silvicultural treatments can mitigate fluctuations in regional temperature and humidity conditions (Bravo-Oviedo et al. 2017). In the context of adaptive forest management, the potential effect of different forest management practices on decomposition is highly relevant (Latterini et al. 2023). Around the Mediterranean basin, Pinus brutia and Pinus halepensis forests cover approximately 7 million ha and play a major ecological and economic role (Chambel et al. 2013; Mauri et al. 2016). Decomposition studies in these forests have focused on their post-fire decomposition dynamics (Radea and Arianoutsou 2000; Sazeides et al. 2021a), the effect of different precipitation regimes on litter decomposition rates (Santonja et al. 2022), the nutrient release during decomposition (García-Plé et al. 1995; Viros et al. 2021), as well as the effect of different silviculture practices on nutrient availability (Lado-Monserrat et al. 2015). Most of these studies have used methods that monitor the rate of mass loss of litterbags (Berg 2014) and/or of standardized materials (cellulose papers, fabric clots, wood sticks etc.), aiming to distinguish environmental effects from those regulated by the quality of litter. Some studies have implemented a common decomposition protocol for several climatic regions (Joly et al. 2023) however Mediterranean pine forests were not included or the variety of management practices implemented were not specifically taken into account. Even though recent studies have investigated litterfall composition and carbon accumulation in Mediterranean pine forests under discrete management regimes (Doukalianou et al. 2022; Ganatsas et al. 2024; Bueis et al. 2018), none has focused on the process of decomposition through the implementation of common across-sites decomposition protocol. In the present study we implemented a common decomposition monitoring protocol in P. brutia and P. halepensis dominated forests at three different geographic regions in Greece. Within these regions, different plots are characterized by discrete management practices and thus provide a natural laboratory to disentangle the effect of climate, dominant species identity and thus litter quality, and management on decomposition dynamics. Our aim was to identify the main drivers of decomposition dynamics in Mediterranean pine forests and to rank the magnitude of their effects and their interactions. To the best of our knowledge, this study represents the first multisite and multiregional decomposition experiment in Greece. 2. Materials & methods 2.1 Study sites We used a network of 10 permanent monitoring plots (ca 0.1 ha each) established across three study sites in northern Greece. The four (4) plots on the island of Lesvos and the three (3) plots at the continental area of Xanthi are dominated by P. brutia. The three (3) plots at Sani are dominated by the sympatric P. halepensis pine (Figure 1). At Lesvos there is no understory vegetation, at Sani there is an understory shrub layer (0.5-2.0 m height), consisting mainly of the evergreens Pistacia lentiscus, Phyllirea latifolia and Quercus coccifera , and at Xanthi there is an at least 5 m-high floor below the pines, consisting of deciduous broadleaves trees (mainly Carpinus orientalis, Fraxinus ornus, Quercus coccifera, Q. frainetto ). Sani experiences a Mediterranean climate with typical hot dry summers and mild, cool winters (with temperatures ranging from 2 o C to 10 o C in the winter and 20-32 o C in the summer, with maximum precipitation of ~60mm in November and December). In the area, three P. halepensis plots, all at sea level, are systematically monitored as part of the European LTER network ( Sani Environmental Observatory – LTER-Greece ). Each plot is managed with a different intensity of overstory thinning / understory removal aiming to reduce fire risk (Table 1). At Xanthi, three study plots are established approximately 4 km north of the city, at an elevation of 290m asl. The climate of Xanthi approaches a continental status with Mediterranean features (with mean temperatures ranging from 2 to 8 o C during winter and from 21 to 31 o C in the summer). Summers are short, hot and dry, while winters are cold, with significant rainfall and snowfall, particularly in nearby mountains. Xanthi also faces strong stormy winds, especially during winter months. The forest plots at Xanthi are dominated by P. brutia , with a significant contribution of various deciduous and broadleaves species. These plots have been subjected to three different thinning practices since 2016 (Table 1): no thinning (control), traditional thinning (~20% removal of total basal area) and intensive thinning (~40% removal of the total basal area) (Doukalianou et al. 2022). On Lesvos Island, four P. brutia plots are established within the island ‘s central pine forest. Lesvos experiences typical Mediterranean climate conditions with mild, rainy winters and hot dry summers. Temperatures range from 6-12 o C during winter and 27.5 up to 39 o C during the summer, while most precipitation of 115-140mm occurs from December to February. Throughout the year the climate is relatively humid, except during the summer months, when aridity increases due to high temperatures along with strong northerly winds from the Sea of Marmara. The four monitoring plots represent a post-fire chronosequence ranging from approximately 20 to 90 years after stand-replacing fires. These plots are relatively unmanaged and thus provide an example of natural post-fire Pine forest dynamics (Sazeides et al. 2021b). Variability of stand structure among the plots, as expressed through variation in Basal Area (BA) and Leaf Area Index (LAI), has been shaped by the different management practices, disturbance history and microclimatic conditions. Across all plots, soil, tree and stand characteristics are systemically monitored (Table 1). Table 1. Characteristics of the permanent monitoring plots. Species Site Plot Longitude Latitude LAI * Basal area (m 2 /ha) Age Tree DBH (cm) Tree height (m) Soil texture Soil pH Origin Management P. brutia Lesvos LES1 26.36179 39.17160 1.06 9.69 ± 3.86 20 7.36 3.34 CL 6.7 Natural post-fire regeneration None LES2 26.29545 39.12662 1.66 22.2 ± 5.48 46 7.76 4.16 SCL 6.6 LES3 26.38450 39.15641 2.13 31.8 ± 9.09 78 27.2 10.2 SCL 6.6 LES4 26.37402 39.16189 2.50 31.2 ± 8.54 97 38.6 16.2 SiL 7.1 P. halepensis Sani SAN1 23.31355 40.1085 2.14 (1.88) 23.5 ± 15.7 85 40.5 15.0 S 7.6 Natural forest None (low density) SAN2 23.33419 40.07637 2.29 27.4 ± 7.58 64 28.2 15.7 C 7.2 Understory removal SAN3 23.31331 40.09282 3.06 (4.76) 25.3 ± 7.10 72 26.5 15.2 SCL 7.1 None (high density) P. brutia Xanthi XAN1 24.88772 41.15615 1.29 49.3 ± 16.2 72 36.6 20.7 SL 6.1 Planted forest None XAN2 24.88650 41.15597 1.38 43.3 ± 12.5 72 29.9 15.6 LS 6.2 Moderate thinning XAN3 24.88765 41.15548 0.73 36.3 ± 11.2 72 30.7 16.0 SL 5.6 Intensive thinning (*) In plots SAN1 and SAN3 the LAI of the understory shrub vegetation is given in parentheses 2.2 Plot-level measurements Across the ten study plots tree diameter at breast height (DBH- cm) and (H- m) are systematically recorded (since 2017 at Sani, since 2016 in Xanthi and since 2020 on Lesvos) for all individuals with D > 5cm. Tree diameters were used to calculate the plot-level basal area (BA- m 2 ha -1 ). LAI (m 2 m -2 ) was estimated using a ceptometer (ACCUPAR LP-80; Decagon Devices Inc, WA, USA), by systematically measuring photosynthetic active radiation (PAR – μmol quanta m -2 s -1 ) at 36 points above the forest floor (1.3 m) and comparing each PAR with that measured outside of the canopy under full light conditions. All LAI measurements were made around solar noon, and the leaf distribution parameter was set to X = 1. Key soil parameters (soil texture, soil pH, soil organic carbon (SOC), soil nitrogen (N), total plant available phosphorus (Olsen P) and soil water holding capacity (WHC)) were measured at soil samples collected at a depth ranging from 5 to 20 cm, depending on local conditions in each plot, using ISO lab techniques (Soil and Water Resources Institute (SWRI) of the Hellenic Agricultural Organization Dimitra). 2.3 Experimental design A common experimental design that included two decomposition protocols was implemented simultaneously across all regions and study plots. The litterbag method focuses on the decomposition of pine needles from each site (Berg 2014; Krishna and Mohan 2017), including the effect of litter quality on the decomposition rate. The standard material protocol, using paper sheets and wood sticks (Joly et al. 2017) focuses on the decomposition of common materials across sites, emphasizing the effect of microenvironmental conditions on decomposition dynamics. In the litterbag protocol eighteen (18) litterbags (15x15 cm) containing pine needles (~1g of air-dry mass per litterbag) were placed in a subplot within each study plot. The litterbags were placed below the surface of the forest floor and above the humus layer. They were fixed with fine plastic mesh parallel to the ground. Each litterbag featured a double mesh design, with the upper side made of plastic wire (mesh size 0.15x0.15 cm), to facilitate micro-fauna interaction with litter, and the underside constructed from non-degradable organic material with a finer mesh size of approximately 0.05x0.05 cm. Every 90 days 3 litterbag samples were collected. The same type of litterbags was used for the paper sheets. We used non-recycled, chlorine-free, A3 printing paper, of an indicative size of 297 x 420 mm and weight ~10g, folded in 1/6 of their size to fit the litterbags These represent decomposition materials of high cellulose concentration. For wood sticks (low lignin, recalcitrant compounds of organic matter proxy), we used common tongue depressors (made of birch wood) of indicative size of 149 x 19 x 1 mm and weight of 2.5g. In the standard material protocol, eighteen (18) paper sheets and eighteen (18) wood sticks were placed adjacent to the pine litter subplot within each study plot. Every 90 days, three (3) paper sheets and three (3) wood sticks samples were collected. For this protocol, a supplementary subplot was also installed, to determine the incubation period needed in each plot to reach the same proportion of mass loss across all sites. This threshold was defined at 30-45% for cellulose papers and 7-28% of initial mass for wood sticks (Joly et al. 2017). Samples from the supplementary subplot were collected every 60 days. After retrieval, all samples (both pine needles and standard material) were dried for 48 hours at a temperature of 70 o C and then weighed after carefully removing them from the litterbags and separating them from soil debris. The experiment was initiated in all study sites in July of 2024. 2.4 Monitoring of micro-climate conditions Micro-climate conditions on each plot were measured using a set of HOBO® data loggers. The MX230x soil moisture and temperature sensors were established at a depth of 5cm in every subplot. The MX2031A air temperature and relative humidity sensors were placed on trees adjacent to the decomposition subplots. Soil sensors measure soil temperature ( o C) and soil water content (m 3 m -3 ), while air sensors measure air temperature ( o C), relative humidity (%) and dew point ( o C), at a 30 minute step. Mean, maximum and minimum values of all parameters were calculated both daily and for the 90 days period on each plot. The latter occurred to follow the number of samples collected to ensure their incorporation into the statistical analysis. 2.5 Statistical analysis We initially used non-linear regression to fit the Olson decay model (Olson 1963), for each study plot and type of decomposition material: with M(t) (g) the mass remaining after incubation time t (days), M 0 is the initial incubated mass (g), RM the remaining mass portion ( M(t)/M 0 ) and k the decomposition rate (day -1 ). In this model, incubation time (t) was the single predictor and the estimated k ‘s represents the average (constant) decomposition rate over the study period. Non- linear regression models were fitted using the “nlsList” function in R (R Core Development Team 2025). We then used a non-linear mixed effects model (NLMM) analysis, in which the decomposition rate was allowed to vary with the microenvironmental conditions, and the hierarchical structure of our dataset was specifically considered. Mixed effect analysis was chosen since it is considered suitable for handling ecological data, due to its resilience to spatial dependence (Fletcher et al. 2018), its ability to produce consistent results from clustered data, and its flexibility to incorporate site-specific parameters and their contribution to decomposition dynamics (Oberpriller et al. 2022). For each type of material, we initially fitted alternative NLMMs setting plot, species and plots nested in species as random effects, using the “nlme” package (Pinheiro et al. 2025). By considering the variance associated with each random term and the overall model performance we decided to proceed with our analysis using plot as the only random term. For the fixed part of the model, we initially tested for the multicollinearity of all potential fixed terms i.e. T air RH , T soil SWC , elevation, total stand BA and average LAI, soil pH, soil organic carbon ( SOC ), soil nitrogen (N) and phosphorus (P) concentration as well as soil water holding capacity (WHC), by considering the Variance Inflation Factors (VIF) through the “ car ” package (Fox et al., 2001). We then fitted a maximal nonlinear mixed effect model, including all non-collinear fixed effects, and proceeded to a stepwise elimination by sequentially removing nonsignificant predictors (at p<0.05) and comparatively considering the value of the model’s Akaike Information Criterion (AIC). During the elimination process of collinear variables (from a maximum to minimal structure), we chose to keep at least one from each group (e.g. micro-climatic, soil, stand structure) providing us with ecological meaning. In all cases we retained the simplest model that had a ΔAIC lower than 4 from the previous model. The outcome of our model fits represents the decomposition rate as a linear function of the fixed effect terms ( F i ), i.e. 3. Results 3.1 Microclimatic Conditions During the July 2024 – August 2025 period, the average daily temperature ranged from -0.2 to 32.9 o C at Lesvos, from 3.8 to 32.9 o C at Sani and from -1.2 to 33.1 o C at Xanthi (Fig 2a). Average daily RH ranged from 25.9 to 98.2% at Lesvos, from 37.1 to 96.9 % at Sani and from 27.9 to 96.5 % at Xanthi (Fig 2b). We estimated that at Lesvos there were 94 to 104 days with an average atmospheric vapor pressure deficit (VPD – kPa) higher than 1.5 kPa, thus the forest there seem to be under the driest atmospheric conditions, compared with 50 to 61 days at Xanthi and 15 to 34 days at Sani (Fig 2c), probably due to proximity of the stands at Sani to the coastline. 3.2 Average Decomposition Rates Among the used materials, decomposition rates followed the expected ranking (from fast to slow) across all sites: cellulose papers > pine litter > wood (lignin material). At Lesvos and Xanthi, the needle litter of P. brutia had an almost three times lower decomposition rate compared to the P. halepensis litter at Sani ( k litter ≈0.0017 d -1 vs k litter ≈0.0006 d -1 , Table 2). At Sani and Xanthi, cellulose papers had a similar incubation period of 180 days and presented a faster decomposition compared to Lesvos (Xanthi: k paper ≈0.0026 d -1 , Sani k paper ≈0.0022 d -1 vs Lesvos k paper ≈0.0005 d -1 , Table 3). On the other hand, the decomposition of recalcitrant wood sticks followed a more distinct regional pattern. On Lesvos, the recorded wood sticks’ decomposition rates were the slowest (incubation period of ~240 days), at Sani the fastest (~120 days) with Xanthi in between (~180 days). When data were grouped per species, litter of P. halepensis stands decomposed ~2.83 times faster than litter of P. brutia stands , papers in P. halepensis stands decomposed ~1.8 times faster than papers in P. brutia stands. The wood sticks in P. halepensis stands decomposed ~2.8 times faster than the woods in P. brutia stands. The plot-specific non-linear regression models provided a good fit for the needle litter (Fig.3) and paper sheets (Fig. 4) data. For wood sticks, nonlinear regression did not provide a good fit and thus linear regression was applied (Fig 5). For the pine needles litter and papers, the estimated decomposition rate was highest at SAN1 and lowest at LES2. For wood sticks the fastest rate was also at SAN1, while the slowest at LES1 (Table 3). Table 2: Decomposition rates (day -1 ) for pine litter, papers and wood sticks estimated through nonlinear regression, grouped by region (left columns) and dominant species (right columns). R 2 was traditionally calculated by the ratio of the sum of squares of the difference between observed and predicted values to the sum of squares of the difference between the observed values and the mean observed value per grouping variable. Region k litter R 2 k paper R 2 k wood R 2 Dominant species k litter R 2 k paper R 2 k wood R 2 Lesvos 0.000607 ± 0.000048 66.10% 0.000504 ± 0.000006 55.70% 0.000357 ± 0.000012 86.90% P. brutia 0.000617 ± 0.000037 53.37% 0.001243 ± 0.000079 53.38% 0.000495 ± 0.000026 62.61% Sani 0.001745 ± 0.000079 71.86% 0.002245 ± 0.000118 43.14% 0.001387 ± 0.000073 80.53% P. halepensis 0.001745 ± 0.000079 71.86% 0.002245 ± 0.000168 71.86% 0.001387 ± 0.000073 80.53% Xanthi 0.0006312 ± 0.000058 40.74% 0.002612 ± 0.000124 66.34% 0.000689 ± 0.000054 64.57% Table 3: Decomposition rates (day -1 ) for pine litter, papers and wood sticks estimated through plot-specific nonlinear regression (pine litter and papers) and linear regression (wood sticks). Coefficient of determination R 2 is also provided (and calculated similarly as mentioned in Table 2). Plot k litter R 2 k paper R 2 k wood R 2 LES1 0.000764 ± 0.000095 74.51% 0.000654 ± 0.000092 64.71% 0.000295 ± 0.000024 83.49% LES2 0.000485 ± 0.000089 68.69% 0.000322 ± 0.000085 69.11% 0.000391 ± 0.000027 87.58% LES3 0.000556 ± 0.000089 79.24% 0.000422 ± 0.000086 67.24% 0.000344 ± 0.000024 87.49% LES4 0.000624 ± 0.000092 63.90% 0.000623 ± 0.000093 74.10% 0.000400 ± 0.000022 92.01% SAN1 0.002011 ± 0.000144 72.05% 0.004146 ± 0.000267 69.48% 0.001657 ± 0.000111 88.47% SAN2 0.001633 ± 0.000126 78.44% 0.000924 ± 0.000103 68.19% 0.001128 ± 0.000095 83.35% SAN3 0.001513 ± 0.000122 79.86% 0.002582 ± 0.000162 78.63% 0.001368 ± 0.000152 74.18% XAN1 0.000793 ± 0.000099 44.55% 0.002668 ± 0.000165 78.62% 0.000611 ± 0.000059 78.66% XAN2 0.000593 ± 0.000094 58.10% 0.002908 ± 0.000176 79.14% 0.000713 ± 0.000075 75.77% XAN3 0.000515 ± 0.000092 22.22% 0.002278 ± 0.000149 45.39% 0.000742 ± 0.000136 52.00% 3.3 Environmental Effects on Decomposition Rates A summary of the mixed effect model analysis used to decide for the optimum hierarchical structure of our dataset is presented in Supplementary Material (A). Using plot as the only random term, the NLMMs yielded the best fitting results with similar estimates for the plots’ decomposition rates to the nonlinear simple plot-level analysis (Table S1). By using species as the only random term, thus grouping together all data from Xanthi and Lesvos ( P. brutia ) in one group and all data from Sani ( P. halepensis ) in another one, the NLMM analysis suggested that litter from the P. halepensis stands decomposed 2.79 times faster than the litter from P. brutia stands ( k Phalepensis =0.001734 d -1 , k Pbrutia =0.0006195 d -1 ) (Fig. S1). Similar results were obtained when a random structure of plot- nested within species was used (Table S2). Even though the “plots nested in species” random term probably represents in a more informative way the structure of our dataset, the simpler “plot only” random effect model was used to avoid unnecessary complexity when micro-environmental fixed effects terms were added. The correlation and the VIF analysis revealed intercorrelation between the micro-climatic, edaphic and stand structure variables (Supplementary Material B, Fig. S2). Thus RH , VPD and WHC were excluded from the set of variables used as fixed terms. For all materials, the elimination process was finalized when the most significant factors were identified, multicollinearity among predictors was low VIF <5, and the optimum R 2 and AIC were achieved (Supplementary Material C, Table S3, S4 and S5). The effect of the maintained fixed effect terms on the decomposition rate of each material is summarised in Table 4. pH presented a universal positive effect on the decomposition rate across all materials. T air and SWC presented positive effects on the decomposition rate for pine litter and paper sheets. The effect of stand structure, expressed through total stand BA and LAI , affected significantly only the decomposition rate of paper sheets in a positive and a negative way respectively. On the other hand, the decomposition rate of woods sticks was only significantly (positively) affected by the pH of the soil. To summarize the seasonal variation in the decomposition rate of each material we used the estimated coefficients of the environmental ( T air , SWC, pH ) and stand structure ( BA , LAI ) predictors and inferred the plot specific k ’s for each decomposing material (Fig. 6). The predicted decomposition rates at Sani present the wider variation, in contrast to Lesvos. For example, k paper varies from less than 0.001 d -1 to SAN2 to more than 0.004 d -1 at SAN1, underlying the importance of plot specific conditions, even within the site. The smallest seasonal variation in the decomposition rates was found at Lesvos, with the k ’s for all materials not exceeding in any season the value of 0.001 d -1 . Xanthi on the other hand illustrated systematically low k ’s for needle litter and wood sticks (below of 0.001 d -1 ) but had higher decomposition rates for paper (from ca 0.002 to 0.003 d -1 ). For plots at Lesvos and Sani, the fastest decomposition rates of litter and papers were shown for the plots with the smallest BA (LES1 and SAN1 respectively), a pattern which was not observed at Xanthi ‘s plots. In terms of seasonality, the decomposition rate at Lesvos seemed to be higher during the winter 2025 period, reaching a plateau or even reducing during the spring and summer 2025 periods. At Sani the estimated decomposition rates seem to progressively increase from autumn 2024 to summer 2025. At Xanthi the decomposition rates seem to progressively increase at XAN1 and XAN2 but reach a plateau during spring 2025 at XAN3. Table 4: Optimum fixed effect structure for decomposition of each material. Marginal (R 2 M ) and conditional (R 2 C ) coefficient of determination are also reported. Both R M 2 and conditional R C 2 coefficients use the same total variance (sum of predicted fixed, random and residuals variances) as denominator. R M 2 uses only the predicted fixed effect variance in its numerator, while ( R C 2 ) uses the sum of fixed and random effect variance in its numerator. Intercept T air SWC pH BA LAI R 2 M R 2 C k litter 0.005896 +0.000029 +0.003226 +0.000560 64.46% 81.24% p 0.026 0.019 0.013 k paper 0.020061 +0.000054 +0.005676 +0.002902 +0.000130 -0.002035 65.89% 82.94% p <0.001 <0.001 0.002 <0.001 0.002 k woods 0.003055 +0.000594 28.02% 64.02% p 0.038 4. Discussion In this study we present the results of an early-stage decomposition experiment in Mediterranean pine forests. The common monitoring protocol identified between regions and dominant species differences in the decomposition rate of different materials. Across regions, soil pH emerged as a consistent predictor of the process of early-stage decomposition. Within the relatively narrow range of climatic conditions that our monitoring protocol was implemented, microclimate conditions seem to partially control the rates of early-stage decomposition. The effect of litter quality as expressed through the chemical composition of litter from the two study species could only be indirectly considered as the latter was not measured throughout the implementation period. However, differences in the decomposition rates of standard material, where the effect of litter quality is by-passed, highlighted the effect of soil pH as a common predictor of across region differences in the rates of decomposition. Our results show that litter P. halepensis stands decomposed significantly faster than litter of P. brutia stands. We estimated an average decomposition rate of k NLR = 0.001745417 d -1 for P. halepensis and k NLR =0.000617105 d -1 for P. brutia . The estimated decomposition rate for P. halepensis is close to those reported in other studies. For example, Gliksman et al. (2018b) reported an average decomposition rate ranging from 0.001338 d -1 in large forest gaps, to -0.001057 d -1 in shady areas, for P. halepensis after a one-year incubation period under relatively warmer conditions. In Greece, Arianoutsou and Radea (2000) reported a k around 0.00029 d -1 for P. halepensis litter, estimated through monthly litterfall production and accumulation. Plots at Sani presented decomposition rates ranging from 0.002011 d -1 to 0.001633 d -1 , which is an order of magnitude greater than the decomposition rates of P. halepensis stands of various silvicultural treatments reported in Lado-Monserrat et al. (2016) (from k=0.00078 d -1 of the densest plot to k=0.00066 d -1 on the clear-cut plot). On the other hand, decomposition rates of P. halepensis measured by García-Plé et al. (1995) were significantly faster than the ones reported here (ranging from 0.0170 d -1 to 0.0120 d -1 in their case). Litter mass loss in P. brutia stands yielded an average decomposition rate from k NLR =0.000485 d -1 to k NLR =0.000764 d -1 across our monitoring plots at Lesvos and Xanthi. Our estimates are within the range reported by Tsiafouli et al. (2018) under different drought treatments (from 0.0001999 d -1 to 0.0009001 d -1 ) for similar incubation period, but with their litterbags intercepting micro-fauna. Unfortunately, few decomposition studies on P. brutia were found, with some concerning woods decomposition (De Meo et al. 2019). This variation in the observed decomposition rates at different areas around the Mediterranean basin suggests that microenvironmental conditions could significantly affect the process of early decay, as also highlighted by the results of this study. From the two to three times higher k litter in Sani compared to Lesvos and Xanthi and assuming “all else being equal”, we could hypothesize that P. halepensis litter should be of higher quality than that of P. brutia . P. brutia is encountered in more arid and semi-arid zones in the eastern Mediterranean basin, hence it could be considered to require lower amounts of precipitation and humidity to grow than P. halepensis (Boydak 2004; Ne’eman and Trabaud 2000). Differences in their needle water retention have been reported, with P. brutia being able to retain more water under drought stress (Houminer et al. 2022). Although we did not specifically measure litter quality, needle chemical concentration data from previous studies (Michelaki et al. 2019; Fotelli et al. 2020) suggest that fresh P. brutia needles have a slightly non-significant higher C/N ratio compared to P. halepensis across different sites in Greece. Nevertheless, this difference does not seem rigid enough to explain the observed differences in the decomposition rates between our study species, in the lengths that higher leaf C/N ratios could slow down decomposition rates (McClaugherty and Berg 1987; Aber et al. 1990; Arianoutsou 1993; Berg et al. 1993; Arianoutsou and Radea 2000), even before moisture conditions and their attribution to C/N ratio are considered (Aerts 1997; Petraglia et al. 2019). Furthermore, C/N ratio has been reported as partially insufficient to estimate predicted mass loss of decomposed material (Bonanomi et al. 2013). Additionally, differences in leaf flavonoids concentration that control litter UV sensitivity or microbial activity (Buer et al. 2010), could potentially explain the between species k litter difference, but do not seem to be of an adequate magnitude between these two sympatric species (Kaundun et al. 1997). Unfortunately, since a chemical analysis (including C/N, lignin, cellulose composition etc.) of leaf litter during the implementation period of this set-up was not available, a definitive distinction of species versus microclimatic conditions effects cannot be derived. However, by considering the results from the decomposition of the standardized material, where the effect of litter quality has been excluded, “all else is not equal”. Cellulose papers decomposed significantly faster at Sani and Xanthi with k paper at SAN1 being almost an order of magnitude higher than the average k paper at Lesvos. During the incubation period SAN1 had the least atmospherically dry conditions across all plots, and in general plots at Sani and Xanthi experienced a lower amount of atmospheric dry days. Thus, microclimatic conditions and in particular RH and/or VPD could be considered a candidate for explaining differences across sites. However, in the case of paper sheets, the per-plot ranking of k paper does not follow the same order as the estimated indexes of atmospheric dryness, with k paper at Sani illustrating a wide range that included all the respective decomposition rates at the relatively drier plots at Xanthi. For wood sticks however, a clear regional separation based on atmospheric dryness is achieved, although no climate variable was maintained as significant in the NLMM analysis. In general, the effect of microclimatic conditions on the decomposition process seems to agree with previous studies, identifying air temperature and soil moisture as important predictors (Moore 1986; Laskowski 2012; Sierra et al. 2017; Wallace et al. 2018; Schwieger et al. 2025), through their influence on micro-fauna activity and thus decomposition rates (Cortez 1998; Bonanomi et al. 2023; Villazón-Orozco et al. 2025). Soil pH was the single predictor that was systematically retained in our NLMM analysis and could be used to separate regional level differences in the early decomposition stage. All plots at Sani had a pH > 7 in contrast with the more acidic plots at Lesvos and Xanthi. Thus, the higher atmospheric wetness and the favorable edaphic condition at Sani could explain the regional differences in the observed decomposition rates, suggesting that between these two sympatric species, litter quality could not be a strong determinant. Microbial activity is relatively higher in more alkaline soils rather than acidic ones (Cao et al. 2016). Soil pH is related to local bacterial and fungal diversity (Romanowicz et al. 2016), their phylogenetic structure (Freedman and Zak 2015) and has been suggested as the primary driver for microbial communities’ composition and richness, even stronger than plants’ phylogenetic diversity (Ni et al. 2021). This could be particularly important for wood sticks, with the positive effect of pH likely linked to the composition of recalcitrant compounds at this early- decay stage, with the influence of climatic conditions and nutrients availability expected to emerge over longer incubation periods (Bonanomi et al. 2021), probably explaining the better fit of linear regression at this stage of decay. The common positive effect of pH across all materials could be associated with the synthesis and activity of fungal and soil bacteria communities (Sinsabaugh et al. 2008; Rousk et al. 2010), with Malik et al. (2018) also highlighting the role of pH in explaining two fundamental mechanisms of soil organic carbon accumulation in soils with varying land use intensities. The direct effect of stand structure, through variation in total basal area and leaf area index, was only identified in the paper sheet decomposition rates. A positive effect of BA on the decomposition process has been found in other studies (Hutson and Veitch 1985; Kim 2016; Bueis et al. 2018; Liu et al. 2018; Santos et al. 2019). In our study the stand thinning practices at Xanthi (XAN1- XAN3 from control to heavier thinning) seem to affect the decomposition rate with XAN3 experiencing a slower decomposition for needle litter and paper sheets, but a higher rate for wood sticks. This trend aligns with the findings of Bravo-Oviedo et al. (2017) for mixed oak and pine forests, where heavy thinning led to decrease in decomposition rates. On the contrary, across the post-fire chronosequence gradient on Lesvos, the youngest and least dense plot (LES1) illustrated faster decomposition of paper sheet and needle litter, with wood decay rates increasing with stand age. Studies in P. brutia plantations have not identified a significant effect of stand structure on litter decomposition even though litter nutrients stocks vary under different treatment intensities (Erkan et al. 2023). The above suggests that although in general an increase in the decomposition rate is expected at denser stands, important interactions with microenvironmental conditions could ultimately determine the process (Latterini et al. 2023). The effect of climate, soil, litter quality and stand structure on the decomposition of organic material within forests remains an important ecological question. A clear understanding of the relative importance of these key abiotic and biotic factors is often hindered by their interconnections. Ongoing climate change could influence the process of decomposition and the way it is modeled (Ranucci et al. 2022) and highlight the importance of non-rainfall water sources and their incorporation in carbon cycle models (Logan et al. 2022), especially for Mediterranean ecosystems, where climatic variations are more pronounced. Common decomposition studies for dominant Mediterranean pine species under varying canopy status and management treatments could create comparable outcomes and disentangle the species versus environmental effects on litter decomposition. Adding chemical analysis, and/or mixed litter influence (where the latter occurs) throughout the decomposition process, can further inform valuable data-driven simulations of carbon fluxes in terrestrial ecosystems under climate change. Abbreviations AIC Akaike Information Criterion BA Plot basal area (m 2 /ha) C Carbon C/N Ratio of carbon to nitrogen DBH Tree diameter at breast height (cm) H Tree height (m) LAI Leaf Area Index (m 2 /m 2 ) N Soil nitrogen (%) NLMM Nonlinear mixed effects model P Soil phosphorus (mg/kg) PAR Photosynthetically Active Radiation (μmol quanta m -2 s -1 ) RH Relative Humidity (%) sdD Standard deviation of trees diameters for each plot SOC Soil Organic Carbon SWC Mean daily soil water content (m 3 /m 3 ) T air Mean daily atmospheric temperature ( o C) T soil Mean daily soil temperature ( o C) VIF Variation Inflation Factor VPD Mean daily Vapor Pressure Deficit (kPa) WHC Soil Water Holding Capacity Declarations Acknowledgements This work is part of the “PineOptim” project, which is implemented in the framework of H.F.R.I. call “Basic Research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – NextGenerationEU (H.F.R.I. Project Number: 016258). The research was conducted under the license given by the General Secretariat for Forestry of the Hellenic Ministry of the Environment and Energy (protocol number: 75638/5179/10-07-2024). Authors’ Contributions Conceptualization: EDM, PGD, NMF, MF, KR; Methodology: EDM, PGD, CS, NMF, MF; Investigation: EDM, NE, GX, AG, NK, NM, CS, EBF, GS; Data curation: EDM, NE, GX, AG, EBF; Visualization: EDM, NMF; Formal Analysis: EDM, PGD, NMF; Writing- original draft: EDM, PGD, NMF; Writing - review & editing: NK, PGD, MF, EV, NMF; Project administration: MNF, PGD, KR; Funding acquisition: MNF, PGD, KR. Data availability The datasets generated and analyzed during the current study are available from the corresponding authors on reasonable request. Conflict of interest Authors declare no conflict of interest. References Aber, J.D., Melillo, J.M. and McClaugherty, C.A. 1990. “Predicting Long-Term Patterns of Mass Loss, Nitrogen Dynamics, and Soil Organic Matter Formation from Initial Fine Litter Chemistry in Temperate Forest Ecosystems.” Canadian Journal of Botany 68 (10): 2201–8. https://doi.org/10.1139/b90-287. Aerts, R. 1997. “Climate, Leaf Litter Chemistry and Leaf Litter Decomposition in Terrestrial Ecosystems: A Triangular Relationship.” Oikos 79 (3): 439–49. https://doi.org/10.2307/3546886. Arianoutsou, M. and Radea, C. 2000. “Litter Production and Decomposition in Pinus halepensis Forests”. In: Ne’eman, G., Trabaud, L., eds. Ecology, biogeography and management of Pinus halepensis and Pinus brutia forest ecosystems in the Mediterranean Basin : pp. 183–190. Backhuys Publishers, Leiden, The Netherlands Arianoutsou, M. 1993. “Leaf Litter Decomposition and Nutrient Release in a Maquis (Evergreen Sclerophyllous) Ecosystem of North-Eastern Greece.” Pedobiologia 2 (37): 65–71. Arslan, H., Güleryüz, G. and Kırmızı, S. 2010. “Nitrogen Mineralisation in the Soil of Indigenous Oak and Pine Plantation Forests in a Mediterranean Environment.” European Journal of Soil Biology 46 (1): 11–17. https://doi.org/10.1016/j.ejsobi.2009.08.002. Ball, B. A., Hunter, M. D., Kominoski, J. S., Swan, C. M. and Bradford, M. A. 2008. “Consequences of Non-Random Species Loss for Decomposition Dynamics: Experimental Evidence for Additive and Non-Additive Effects.” Journal of Ecology 96 (2): 303–13. https://doi.org/10.1111/j.1365-2745.2007.01346.x. Bar-On, Y.M., Phillips, R. and Milo, R. 2018. “The Biomass Distribution on Earth.” Proceedings of the National Academy of Sciences 115 (25): 6506–11. https://doi.org/10.1073/pnas.1711842115. Berg, B. 2014. “Decomposition Patterns for Foliar Litter – A Theory for Influencing Factors.” Soil Biology and Biochemistry 78 (November): 222–32. https://doi.org/10.1016/j.soilbio.2014.08.005. Berg, B., Berg, M., Bottner, P. et al. 1993. “Litter Mass Loss Rates in Pine Forests of Europe and Eastern United States: Some Relationships with Climate and Litter Quality.” Biogeochemistry 20 (September): 127–59. https://doi.org/10.1007/BF00000785. Blanco, J.A., Imbert, B.J. and Castillo, F.J. 2006. “Influence of Site Characteristics and Thinning Intensity on Litterfall Production in Two Pinus sylvestris L. Forests in the Western Pyrenees.” Forest Ecology and Management 237 (1): 342–52. https://doi.org/10.1016/j.foreco.2006.09.057. Bonanomi, G., Incerti, G., Giannino, F.,Mingo, A., Lanzotti, V. and Mazzoleni, S. 2013. “Litter Quality Assessed by Solid State 13C NMR Spectroscopy Predicts Decay Rate Better than C/N and Lignin/N Ratios.” Soil Biology and Biochemistry , Special Issue: Interactions of Soil Minerals with Organic Components and Microorganisms VII and Enzymes in the Environment IV, vol. 56 (January): 40–48. https://doi.org/10.1016/j.soilbio.2012.03.003. Bonanomi, G., Motti, R., De Marco, A. and Idbella, M. 2023. “Temperature Sensitivity and Decomposition Rate of 101 Leaf Litter Types from Mediterranean Ecosystems.” Science of The Total Environment 894 (October): 165026. https://doi.org/10.1016/j.scitotenv.2023.165026. Bonanomi, G., Zotti, M., Cesarano, G. et al. 2021. “Decomposition of Woody Debris in Mediterranean Ecosystems: The Role of Wood Chemical and Anatomical Traits.” Plant and Soil 460 (1): 263–80. https://doi.org/10.1007/s11104-020-04799-4. Boydak, M. 2004. “Silvicultural Characteristics and Natural Regeneration of Pinus brutia Ten. — a Review.” Plant Ecology 171 (1–2): 153–63. https://doi.org/10.1023/B:VEGE.0000029373.54545.d2. Bravo-Oviedo, A., Ruiz-Peinado, R., Onrubia, R. and del Río, M. 2017. “Thinning Alters the Early-Decomposition Rate and Nutrient Immobilization-Release Pattern of Foliar Litter in Mediterranean Oak-Pine Mixed Stands.” Forest Ecology and Management 391 (May): 309–20. https://doi.org/10.1016/j.foreco.2017.02.032. Bueis, T., Bravo, F., Pando, V. and Turrión, M.B. 2018. “Local Basal Area Affects Needle Litterfall, Nutrient Concentration, and Nutrient Release during Decomposition in Pinus Halepensis Mill. Plantations in Spain.” Annals of Forest Science 75 (1): 21. https://doi.org/10.1007/s13595-018-0699-5. Buer, C.S., Imin, N. and Djordjevic, M.A. 2010. “Flavonoids: New Roles for Old Molecules.” Journal of Integrative Plant Biology 52 (1): 98–111. https://doi.org/10.1111/j.1744-7909.2010.00905.x. Cao, H., Chen, R., Wang, L. et al. 2016. “Soil pH, Total Phosphorus, Climate and Distance Are the Major Factors Influencing Microbial Activity at a Regional Spatial Scale.” Scientific Reports 6 (1): 25815. https://doi.org/10.1038/srep25815. Chambel, M.R., Climent, J., Pichot, C. and Ducci, F. 2013. “Mediterranean Pines ( Pinus halepensis Mill. and brutia Ten.).” In Forest Tree Breeding in Europe: Current State-of-the-Art and Perspectives , edited by Luc E Pâques. Springer Netherlands. https://doi.org/10.1007/978-94-007-6146-9_5. Cortez, J. 1998. “Field Decomposition of Leaf Litters: Relationships between Decomposition Rates and Soil Moisture, Soil Temperature and Earthworm Activity.” Soil Biology and Biochemistry 30 (6): 783–93. https://doi.org/10.1016/S0038-0717(97)00163-6. De Meo, I., Lagomarsino, A., Agnelli, A.E. and Paletto, A. 2019. “Direct and Indirect Assessment of Carbon Stock in Deadwood: Comparison in Calabrian Pine ( Pinus brutia Ten. Subsp. brutia ) Forests in Italy.” Forest Science 65 (4): 460–68. https://doi.org/10.1093/forsci/fxy051. Dimitrakopoulos, P.G. 2010. “Influence of Evenness on the Litter-Species Richness–Decomposition Relationship in Mediterranean Grasslands.” Journal of Plant Ecology 3 (2): 71–78. https://doi.org/10.1093/jpe/rtq009. Doukalianou, F., Spyroglou, G., Orfanoudakis, M., et al. 2022. “Effects of Forest Thinning on Soil Litter Input Nutrients in Relation to Soil CO2, CH4, and N2O Fluxes in Greece.” Atmosphere 13 (3): 376. https://doi.org/10.3390/atmos13030376. Erkan, N., Güner, S.T. and Aydın, A.C. 2023. “Thinning Effects on Stand Growth, Carbon Stocks, and Soil Properties in Brutia Pine Plantations.” Carbon Balance and Management 18 (March): 6. https://doi.org/10.1186/s13021-023-00226-0. FAO. 2022. Global Soil Organic Carbon Map – GSOCmap v.1.6. Rome. https://openknowledge.fao.org/items/612deb84-1cb3-41cc-97cb-72e219e9f5d0. Fioretto, A., Papa, S., Sorrentino, G. and Fuggi, A. 2001. “Decomposition of Cistus Incanus Leaf Litter in a Mediterranean Maquis Ecosystem: Mass Loss, Microbial Enzyme Activities and Nutrient Changes.” Soil Biology and Biochemistry 33 (3): 311–21. https://doi.org/10.1016/S0038-0717(00)00142-5. Fioretto, A., Papa, S. and Fuggi, A. 2003. “Litter-Fall and Litter Decomposition in a Low Mediterranean Shrubland.” Biology and Fertility of Soils 39 (1): 37–44. https://doi.org/10.1007/s00374-003-0675-5. Fletcher, R. and Fortin, M.J. 2018. Accounting for Spatial Dependence in Ecological Data: Applications with R . https://doi.org/10.1007/978-3-030-01989-1_6. Fotelli, M.N., Lyrou, F.G., Avtzis, D.N., et al. 2020. “Effective Defense of Aleppo Pine Against the Giant Scale Marchalina Hellenica Through Ecophysiological and Metabolic Changes.” Frontiers in Plant Science 11 (December). https://doi.org/10.3389/fpls.2020.581693. Fox, J., Weisberg, S. and Price, B. 2001. “Car: Companion to Applied Regression.” May 1. https://doi.org/10.32614/CRAN.package.car. Freedman, Z. and Zak, D.R. 2015. “Soil Bacterial Communities Are Shaped by Temporal and Environmental Filtering: Evidence from a Long-Term Chronosequence.” Environmental Microbiology 17 (9): 3208–18. https://doi.org/10.1111/1462-2920.12762. Gallardo, A. and Merino, J. 1993. “Leaf Decomposition in Two Mediterranean Ecosystems of Southwest Spain: Influence of Substrate.” Ecology 74: 152–61. https://doi.org/10.2307/1939510. Ganatsas, P., Tsakaldimi, M., Karydopoulos, T., Papaemannuil, A., Papadopoulos, S.. 2024. “Long-Term Effect of Different Forest Thinning Intensity on Carbon Sequestration Rates and Potential Uses in Climate Change Mitigation Actions | Mitigation and Adaptation Strategies for Global Change.” Mitigation and Adaptation Strategies for Global Change 23. https://doi.org/10.1007/s11027-023-10102-4. García-Plé, C., Vanrell, A. and Morey, M. 1995. “Litter Fall and Decomposition in a Pinus halepensis Forest on Mallorca.” Journal of Vegetation Science 6 (1): 17–22. https://doi.org/10.2307/3236251. Gartner, T.B. and Cardon, Z.G. 2004. “Decomposition Dynamics in Mixed-Species Leaf Litter.” Oikos 104 (2): 230–46. https://doi.org/10.1111/j.0030-1299.2004.12738.x. Gliksman, D., Haenel, S., and Grünzweig, J.M. 2018a. “Biotic and Abiotic Modifications of Leaf Litter during Dry Periods Affect Litter Mass Loss and Nitrogen Loss during Wet Periods.” Functional Ecology 32 (3): 831–39. https://doi.org/10.1111/1365-2435.13018. Gliksman, D., Haenel, S., Osem, Y., et al. 2018b. “Litter Decomposition in Mediterranean Pine Forests Is Enhanced by Reduced Canopy Cover.” Plant and Soil 422 (1): 317–29. https://doi.org/10.1007/s11104-017-3366-y. Grünzweig, J.M., and Gliksman, D. 2021. “Litter Decomposition in Mediterranean Pine Forests Subjected to Climate Change.” In Pines and Their Mixed Forest Ecosystems in the Mediterranean Basin , edited by Gidi Ne’eman and Yagil Osem. Springer International Publishing. https://doi.org/10.1007/978-3-030-63625-8_16. Hashimoto, S., Carvalhais, N., Ito, A., Migliavacca, M., Nishina, K. and Reichstein, M. 2015. “Global Spatiotemporal Distribution of Soil Respiration Modeled Using a Global Database.” Biogeosciences 12 (13): 4121–32. https://doi.org/10.5194/bg-12-4121-2015. Hättenschwiler, S., Tiunov, A.V. and Scheu, S. 2005. “Biodiversity and Litter Decomposition in Terrestrial Ecosystems.” Annual Review of Ecology, Evolution, and Systematics 36 (Volume 36, 2005): 191–218. https://doi.org/10.1146/annurev.ecolsys.36.112904.151932. Houminer, N., Riov, J., Moshelion, M., Osem, Y. and David-Schwartz, R. 2022. “Comparison of Morphological and Physiological Traits between Pinus brutia , Pinus halepensis , and Their Vigorous F1 Hybrids.” Forests 13 (9): 1477. https://doi.org/10.3390/f13091477. Hutson, B.R. and Veitch, L.G. 1985. “Relationships between Litterfall Rate, Litter Mass and Decomposition Rate in Eucalyptus Forests in Southeastern Australia.” Australian Journal of Ecology 10 (4): 443–50. https://doi.org/10.1111/j.1442-9993.1985.tb00905.x. Incerti, G., Bonanomi, G., Giannino, F., et al. 2011. “Litter Decomposition in Mediterranean Ecosystems: Modelling the Controlling Role of Climatic Conditions and Litter Quality.” Applied Soil Ecology 49 (September): 148–57. https://doi.org/10.1016/j.apsoil.2011.06.004. Incerti, G., Cartenì, F., Cesarano, G., et al. 2018. “Faster N Release, but Not C Loss, From Leaf Litter of Invasives Compared to Native Species in Mediterranean Ecosystems.” Frontiers in Plant Science 9 (April). https://doi.org/10.3389/fpls.2018.00534. Jabiol, J., and Chauvet, E. 2015. “Biodiversity and Litter Decomposition: A Case Study in a Mediterranean Stream.” Freshwater Science 34 (2): 423–30. https://doi.org/10.1086/680094. Jarvis, P., Rey, A., Petsikos, C., et al. 2007. “Drying and Wetting of Mediterranean Soils Stimulates Decomposition and Carbon Dioxide Emission: The ‘Birch Effect’ ” Tree Physiology 27 (7): 929–40. https://doi.org/10.1093/treephys/27.7.929. Joly, F.X., Scherer-Lorenzen, M. and Hättenschwiler, S. 2023. “Resolving the Intricate Role of Climate in Litter Decomposition.” Nature Ecology & Evolution 7 (2): 2. https://doi.org/10.1038/s41559-022-01948-z. Kaundun, S.S., Fady, B. and Lebreton, P. 1997. “Genetic Differences between Pinus halepensis, Pinus brutia and Pinus eldarica Based on Needle Flavonoids.” Biochemical Systematics and Ecology 25 (6): 553–62. https://doi.org/10.1016/S0305-1978(97)00049-5. Kazakou, E., Vile, D., Shipley, B., Gallet, C. and Garnier, E. 2006. “Co-Variations in Litter Decomposition, Leaf Traits and Plant Growth in Species from a Mediterranean Old-Field Succession.” Functional Ecology 20 (1): 21–30. https://doi.org/10.1111/j.1365-2435.2006.01080.x. Kim, C. 2016. “Basal area effects on a short-term nutrient status of litter fall and needle litter decomposition in a Pinus densiflora stand.” Journal of Ecology and Environment 39 (1): 51–60. Lado-Monserrat, L., Lidón, A., Bautista, I. 2015. “Litterfall, Litter Decomposition and Associated Nutrient Fluxes in Pinus halepensis : Influence of Tree Removal Intensity in a Mediterranean Forest.” European Journal of Forest Research 134 (5): 833–44. https://doi.org/10.1007/s10342-015-0893-z. Lado-Monserrat, L., Lidón, A., Bautista, I. 2016. “Erratum to: Litterfall, Litter Decomposition and Associated Nutrient Fluxes in Pinus halepensis : Influence of Tree Removal Intensity in a Mediterranean Forest.” European Journal of Forest Research 135 (1): 203–14. https://doi.org/10.1007/s10342-015-0923-x. Lagomarsino, A., Mazza, G., Agnelli, A.E., et al. 2020. “Litter Fractions and Dynamics in a Degraded Pine Forest after Thinning Treatments.” European Journal of Forest Research 139 (2): 295–310. https://doi.org/10.1007/s10342-019-01245-8. Laskowski, R. 2012. “What Determines Forest Litter Decomposition? Global Trends and Local Variance.” Geographia Polonica 85 (2): 39–46. https://doi.org/10.7163/GPol.2012.2.9. Latterini, F., Dyderski, M.K., Horodecki, P., et al. 2023. “The Effects of Forest Operations and Silvicultural Treatments on Litter Decomposition Rate: A Meta-Analysis.” Current Forestry Reports 9 (4): 276–90. https://doi.org/10.1007/s40725-023-00190-5. Liu, G., Wang, L., Jiang, L., et al. 2018. “Specific Leaf Area Predicts Dryland Litter Decomposition via Two Mechanisms.” Journal of Ecology 106 (1): 218–29. https://doi.org/10.1111/1365-2745.12868. Logan, J.R., Todd-Brown, K.E., Jacobson, K.M., Jacobson, P.J., Vogt, R. and Evans, S.E. 2022. “Accounting for Non-Rainfall Moisture and Temperature Improves Litter Decay Model Performance in a Fog-Dominated Dryland System.” Biogeosciences 19 (17): 4129–46. https://doi.org/10.5194/bg-19-4129-2022. Malik, A.A., Puissant, J., Buckeridge, K.M., et al. 2018. “Land Use Driven Change in Soil pH Affects Microbial Carbon Cycling Processes.” Nature Communications 9 (1): 3591. https://doi.org/10.1038/s41467-018-05980-1. Matos, P.S., Barreto-Garcia, P.A.N.B., Gama-Rodrigues, E.F., de Paula, A. and de Oliveira, A.M. 2022. “Short-Term Effects of Forest Management on Litter Decomposition in Caatinga Dry Forest.” Energy, Ecology and Environment 7 (2): 130–41. https://doi.org/10.1007/s40974-021-00231-4. Mauri, A., Di Leo,M., de Rigo, D. and Caudullo, G.. 2016. Pinus halepensis and Pinus brutia in Europe: Distribution, Habitat, Usage and Threats . McClaugherty, C. and Berg, B.. 1987. “Cellulose, Lignin and Nitrogen Concentrations as Rate Regulating Factors in Late Stages of Forest Litter Decomposition.” Pedobiologia 30 (2): 101–12. https://doi.org/10.1016/S0031-4056(23)00361-X. Michelaki, C., Fyllas, N.M., Galanidis, A., et al. 2019. “An Integrated Phenotypic Trait-Network in Thermo-Mediterranean Vegetation Describing Alternative, Coexisting Resource-Use Strategies.” Science of The Total Environment 672 (July): 583–92. https://doi.org/10.1016/j.scitotenv.2019.04.030. Moore, A.M. 1986. “Temperature and Moisture Dependence of Decomposition Rates of Hardwood and Coniferous Leaf Litter.” Soil Biology and Biochemistry 18 (4): 427–35. https://doi.org/10.1016/0038-0717(86)90049-0. Navarro, F. B., Romero-Freire, A., Del Castillo, T., et al. 2013. “Effects of Thinning on Litterfall Were Found after Years in a Pinus halepensis Afforestation Area at Tree and Stand Levels.” Forest Ecology and Management 289 (February): 354–62. https://doi.org/10.1016/j.foreco.2012.09.026. Ne’eman, G. and Trabaud, L. 2000. Ecology, Biogeography and Management of Pinus halepensis and P. brutia Forest Ecosystems in the Mediterranean Basin . In Forest Ecology and Management - FOREST ECOL MANAGE , vol. 154. https://doi.org/10.1016/S0378-1127(00)00635-6. Ni, Y., Yang, T., Ma, Y., et al. 2021. “Soil pH Determines Bacterial Distribution and Assembly Processes in Natural Mountain Forests of Eastern China.” Global Ecology and Biogeography 30 (11): 2164–77. https://doi.org/10.1111/geb.13373. Oberpriller, J., de Souza Leite, M. and Pichler, M. 2022. “Fixed or Random? On the Reliability of Mixed-Effects Models for a Small Number of Levels in Grouping Variables.” Ecology and Evolution 12 (7): e9062. https://doi.org/10.1002/ece3.9062. Olson, J.S. 1963. “Energy Storage and the Balance of Producers and Decomposers in Ecological Systems.” Ecology 44 (2): 322–31. https://doi.org/10.2307/1932179. Petraglia, A., Cacciatori, C., Chelli, S., et al. 2019. “Litter Decomposition: Effects of Temperature Driven by Soil Moisture and Vegetation Type.” Plant and Soil 435 (1): 187–200. https://doi.org/10.1007/s11104-018-3889-x. Pinheiro, J., Bates, D. and R Core Team. 2025. “Nlme: Linear and Nonlinear Mixed Effects Models.” March 31. https://doi.org/10.32614/CRAN.package.nlme. Purahong, W., Kapturska, D., Pecyna, M.J., et al. 2014. “Influence of Different Forest System Management Practices on Leaf Litter Decomposition Rates, Nutrient Dynamics and the Activity of Ligninolytic Enzymes: A Case Study from Central European Forests.” PLOS ONE 9 (4): e93700. https://doi.org/10.1371/journal.pone.0093700. R Core Development Team. 2025. “R: The R Project for Statistical Computing.” https://www.r-project.org/. Radea, C., and Arianoutsou, M. 2000. “Cellulose Decomposition Rates and Soil Arthropod Community in a Pinus halepensis Mill. Forest of Greece after a Wildfire.” European Journal of Soil Biology 36 (January): 57–64. https://doi.org/10.1016/S1164-5563(00)01045-1. Ranucci, M., Perez, M., Lombardi, D. and Vitale, M. 2022. “Is the Current Modelling of Litter Decomposition Rates Reliable under Limiting Environmental Conditions Induced by Ongoing Climate Change?” Soil Systems 6 (4): 81. https://doi.org/10.3390/soilsystems6040081. Riva, E.G. de la, Prieto, I. and Villar, R. 2019. “The Leaf Economic Spectrum Drives Leaf Litter Decomposition in Mediterranean Forests.” Plant and Soil 435 (1): 353–66. https://doi.org/10.1007/s11104-018-3883-3. Romanowicz, K.J., Freedman, Z.B., Upchurch, R.A., Argiroff, W.A. and Zak, D.R. 2016. “Active Microorganisms in Forest Soils Differ from the Total Community yet Are Shaped by the Same Environmental Factors: The Influence of pH and Soil Moisture.” FEMS Microbiology Ecology 92 (10): fiw149. https://doi.org/10.1093/femsec/fiw149. Rousk, J., Bååth, E., Brookes, P.C., et al. 2010. “Soil Bacterial and Fungal Communities across a pH Gradient in an Arable Soil.” The ISME Journal 4 (10): 1340–51. https://doi.org/10.1038/ismej.2010.58. Sani Environmental Observatory – LTER-Greece . n.d. Accessed August 27, 2025. https://www.lter-greece.gr/sites/sani-environmental-observatory/. Santonja, M., Fernandez, C., Proffit, M., et al. 2017. “Plant Litter Mixture Partly Mitigates the Negative Effects of Extended Drought on Soil Biota and Litter Decomposition in a Mediterranean Oak Forest.” Journal of Ecology 105 (3): 801–15. https://doi.org/10.1111/1365-2745.12711. Santonja, M., Pereira, S., Gauquelin, T., et al. 2022. “Experimental Precipitation Reduction Slows Down Litter Decomposition but Exhibits Weak to No Effect on Soil Organic Carbon and Nitrogen Stocks in Three Mediterranean Forests of Southern France.” Forests 13 (9): 1485. https://doi.org/10.3390/f13091485. Santos, G.R., Gentil Otto, M.S., Passos, J.R. de Souza, et al. 2019. “Changes in Decomposition Rate and Litterfall in Riparian Zones with Different Basal Area of Exotic Eucalyptus in South-Eastern Brazil.” Southern Forests: A Journal of Forest Science 81 (4): 285–95. https://doi.org/10.2989/20702620.2019.1633503. Sazeides, C., Christopoulou, A. and Fyllas, N. 2021a. “Litter Decomposition across a Post-Fire Chronosequence in Mediterranean Pine Forests.” Paper presented at CEST2021, Athens, Greece. https://doi.org/10.30955/gnc2021.00060. Sazeides, C.I., Christopoulou, A., and Fyllas, N.M. 2021b. “Coupling Photosynthetic Measurements with Biometric Data to Estimate Gross Primary Productivity (GPP) in Mediterranean Pine Forests of Different Post-Fire Age.” Forests 12 (9): 9. https://doi.org/10.3390/f12091256. Schwieger, S., Dorrepaal, E., Petit Bon, M., et al. 2025. “Environmental Conditions Modulate Warming Effects on Plant Litter Decomposition Globally.” Ecology Letters 28 (1): e70026. https://doi.org/10.1111/ele.70026. Sheffer, E., Canham, C.D., Kigel, J. and Perevolotsky, A. 2015. “Countervailing Effects on Pine and Oak Leaf Litter Decomposition in Human-Altered Mediterranean Ecosystems.” Oecologia 177 (4): 1039–51. https://doi.org/10.1007/s00442-015-3228-3. Sierra, C.A., Malghani, S. and Loescher, H.W. 2017. “Interactions among Temperature, Moisture, and Oxygen Concentrations in Controlling Decomposition Rates in a Boreal Forest Soil.” Biogeosciences 14 (3): 703–10. https://doi.org/10.5194/bg-14-703-2017. Sinsabaugh, R., Lauber, C., Weintraub, M., et al. 2008. “Stoichiometry of Soil Enzyme Activity at Global Scale.” Ecology Letters 11 (October): 1252–64. https://doi.org/10.1111/j.1461-0248.2008.01245.x. Soil and Water Resources Institute (SWRI) by Hellenic Agricultural Organization “DEMETER.” n.d. “SWRI, by Hellenic Agricultural Organization ‘Demeter.’” Accessed November 2, 2025. https://www.swri.gr/index.php/en/. Villazón-Orozco, F.L., Ferrín, M., Márquez, L., et al. 2025. “Drought and Litter Quality Effects on the Contribution of Soil Fauna to Decomposition in a Mediterranean Forest.” European Journal of Soil Biology 125 (June): 103729. https://doi.org/10.1016/j.ejsobi.2025.103729. Viros, J., Santonja, M., Temime-Roussel, B., Wortham, H., Fernandez, C., and Ormeño, E. 2021. “Volatilome of Aleppo Pine Litter over Decomposition Process.” Ecology and Evolution 11 (11): 6862–80. https://doi.org/10.1002/ece3.7533. Wallace, K.J., Laughlin, D.C., Clarkson, B.D. and Schipper, L.A. 2018. “Forest Canopy Restoration Has Indirect Effects on Litter Decomposition and No Effect on Denitrification.” Ecosphere 9 (12): e02534. https://doi.org/10.1002/ecs2.2534. Wang, Q.W., Pieristè, M., Liu, C., Kenta, T., Robson, T.M. and Kurokawa, H. 2021. “The Contribution of Photodegradation to Litter Decomposition in a Temperate Forest Gap and Understorey.” New Phytologist 229 (5): 2625–36. https://doi.org/10.1111/nph.17022. Wang, Q.W., Robson, T.M., Pieristè, M., Kenta, T., Zhou, W. and Kurokawa, H. 2022. “Canopy Structure and Phenology Modulate the Impacts of Solar Radiation on C and N Dynamics during Litter Decomposition in a Temperate Forest.” Science of The Total Environment 820 (May): 153185. https://doi.org/10.1016/j.scitotenv.2022.153185. Whitford, W.G. and Duval, B.D. 2019. Ecology of Desert Systems . Academic Press. Zhao, Y.Y., Li, Z.T., Xu, T. and Lou, A. 2022. “Leaf Litter Decomposition Characteristics and Controlling Factors across Two Contrasting Forest Types.” Journal of Plant Ecology 15 (6): 1285–301. https://doi.org/10.1093/jpe/rtac07 Supplementary Files MantzarietalSupplementaryInformation.pdf 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":1789170,"visible":true,"origin":"","legend":"\u003cp\u003eStudy sites and plots across Greece: a) Study areas, b) P. brutia stands in Xanthi, c) P. halepensis stands in Sani, Chalkidiki, d) P. brutia stands on Lesvos Island\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8310680/v1/4e066fec810f8d881bcd4a02.png"},{"id":100574040,"identity":"4119fea4-0876-4046-977e-9c661bc2ad77","added_by":"auto","created_at":"2026-01-19 10:09:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":900786,"visible":true,"origin":"","legend":"\u003cp\u003eClimate data per site and plot. Plots illustrate: a) the mean daily temperature (\u003csup\u003eo\u003c/sup\u003eC), b) the mean daily relative humidity (%), c) the average daily vapor pressure deficit (kPa) and d) the mean daily water content (m\u003csup\u003e3\u003c/sup\u003em\u003csup\u003e-3\u003c/sup\u003e). Number of days with VDP\u0026lt;1.5 kPa and SWC\u0026lt;0.15 m\u003csup\u003e3\u003c/sup\u003e/m\u003csup\u003e3\u003c/sup\u003e for each plot respectively: LES1: 102, 107; LES2: 104, 175; LES3: 94, 166; LES4: 96, 102; SAN1: 15, 316; SAN2: 34, 38; SAN3: 25, 57; XAN1: 50, 166; XAN2: 61, 206; XAN3: 59, 169.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8310680/v1/50f095125958bfd20b18ce05.png"},{"id":100574038,"identity":"33bf2933-409c-4d9d-bdfe-852601994183","added_by":"auto","created_at":"2026-01-19 10:09:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":264118,"visible":true,"origin":"","legend":"\u003cp\u003ePine needle litter proportion of remaining mass over the incubation period. Dots represent pine needle litter samples collected and curves represent predicted mass loss using the plot-specific nonlinear regression. Standard errors are not illustrated due to aesthetic reasons. Proportions’ standard errors per region: Lesvos varies from 0.98092 ± 0.00168 (days=93) to 0.71609 ± 0.03186 (days=370), Sani varies from 0.89910 ± 0.01719 (day=90) to 0.46857 ± 0.04771 (days=364) and Xanthi varies from 0.98523 ± 0.00117 (days=90) to 0.790121 ± 0.02002 (days=361).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8310680/v1/595a907be9c16dcca5e011c9.png"},{"id":100574053,"identity":"5ac59a35-2888-476e-8d23-9740141293d0","added_by":"auto","created_at":"2026-01-19 10:09:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":311785,"visible":true,"origin":"","legend":"\u003cp\u003ePaper sheet\u003cstrong\u003e \u003c/strong\u003eproportion of remaining mass over the incubation period. Dots represent paper sheet samples collected and curves represent predicted mass loss using the plot-specific nonlinear regression. Standard errors are not illustrated due to aesthetic reasons. Proportions’ standard errors per region: Lesvos varies from 0.98956 ± 0.00118 (days=59) to 0.75312 ± 0.03436 (days=370), Sani varies from 0.89411 ± 0.02419 (day=60) to 0.42886 ± 0.10963 (days=272) and Xanthi varies from 0.85409 ± 0.016222 (days=62) to 0.40849 ± 0.04151 (days=301).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8310680/v1/f7e34fdfd3fe08c8ecc82aac.png"},{"id":100595895,"identity":"a8ac25cb-8435-4998-824b-82447d59439d","added_by":"auto","created_at":"2026-01-19 13:49:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":277441,"visible":true,"origin":"","legend":"\u003cp\u003eWood stick\u003cstrong\u003e \u003c/strong\u003eproportion of remaining mass over the incubation period. Dots represent wood stick samples collected and lines represent predicted mass loss using the plot-specific linear regression. Standard errors are not illustrated due to aesthetic reasons. Proportions’ standard errors per region: Lesvos varies from 0.93289 ± 0.00020 (days=64) to 0.89595 ± 0.01400 (days=365), Sani varies from 0.90856 ± 0.01203 (day=60) to 0.48456 ± 0.11644 (days=246) and Xanthi varies from 0.93455 ± 0.0.00503 (days=91) to 0.71982 ± 0.05161 (days=301).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8310680/v1/072e136c2d267281622a180a.png"},{"id":100574010,"identity":"487de410-b87c-4e1f-bd5b-8dcb3e1832f0","added_by":"auto","created_at":"2026-01-19 10:09:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":601897,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation of decomposition rates k in each site, as inferred from the NLMM analysis. Solid lines represent paper sheets; dotted lines represent woods sticks and dashed lines represent pine needle litter decomposition.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8310680/v1/6927e7acd4a96692e4685df1.png"},{"id":104781044,"identity":"f0e5117d-24e2-4517-9657-5cad79ffdcc8","added_by":"auto","created_at":"2026-03-17 07:54:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5446056,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8310680/v1/e7025c8e-0603-42dc-8d07-47980dfe4a3a.pdf"},{"id":100574063,"identity":"75584fec-a914-49a2-8647-b042c6c617f3","added_by":"auto","created_at":"2026-01-19 10:09:41","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":649041,"visible":true,"origin":"","legend":"","description":"","filename":"MantzarietalSupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8310680/v1/a4ed55c7d307dedc0f7a245b.pdf"}],"financialInterests":"","formattedTitle":"Early stage litterfall decomposition dynamics in Pinus halepensis and Pinus brutia stands: Disentangling the effect of climate, species identity and management practices","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eForests account for the largest terrestrial carbon (C) pool on the planet, playing a key role in removing anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions from the atmosphere and storing them in their soil and biomass. At the global scale, approximately 1500 Pg C are stored at the top one meter of soil (FAO 2022) and ~320 Gt C in the aboveground plant biomass (Bar-On et al. 2018), while soil respiration releases approximately 87-95 Pg C yr\u003csup\u003e-1\u003c/sup\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e(Hashimoto et al. 2015). Shifting rainfall patterns, drought events and increased air and soil temperatures are modifying carbon and nitrogen balance, by affecting microclimatic conditions, litter chemistry and the decomposers\u0026rsquo; community. In the context of climate change, where Mediterranean ecosystems are considered highly vulnerable (IPCC 2023), a clear understanding of litter decomposition is thus needed for designing efficient forest adaptation practices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMediterranean ecosystems, such as low elevation pine forests, are characterized by remarkable dry-to-wet cycles that affect decomposition rates (Gallardo and Merino 1993; Fioretto et al. 2001, 2003; Kazakou et al. 2006; Jarvis et al. 2007; Arslan et al. 2010; Dimitrakopoulos et al. 2010; Incerti et al. 2011, 2018; de la Riva et al. 2019). During the wet season, microbial degradation is substantially enhanced by the incoming precipitation and other non-rainfall water sources such as fog or dew (Gliksman et al. 2018a). In dry season, photodegradation is strongly driven by the high radiation fluxes and the prevailing high temperatures (Gr\u0026uuml;nzweig and Gliksman 2021). In general, the decomposition rates in Mediterranean ecosystems are considered optimal within a temperature range of 20-35\u003csup\u003eo\u003c/sup\u003eC and a litter moisture range between 40-90% (Incerti et al. 2011). At the same time, recent studies highlight the role of microbial community biomass in regulating decomposition process, with mixed-species litter effects ranging from synergistic (where higher species richness accelerates decay to antagonistic effects that slow down decomposition rates (Gartner and Cardon 2004; H\u0026auml;ttenschwiler et al. 2005; Ball et al. 2008; Dimitrakopoulos 2010; Sheffer et al. 2015; Zhao et al. 2022). Studies that systematically control the effect of all the above abiotic and biotic factors on the process of decomposition are highly relevant in the context of climate change, and its potential response to global change.\u003c/p\u003e\n\u003cp\u003eForest management practices, such as canopy gap creation and understory removal, influence decomposition through different mechanisms. For example, canopy gaps, occurring either naturally or through stand thinning can accelerate litter decay through photodegradation (Gliksman et al. 2018b; Wang et al. 2021; 2022). Stand thinning can also enhance soil fertility and nutrients circulation (Lado-Monserrat et al. 2016), but reduce litterfall production and thus the forest floor biomass intake (Blanco et al. 2006; Doukalianou et al. 2022), affect water availability (Bueis et al. 2018), rain interception and soil microclimatic conditions (Whitford and Duval 2019; Gr\u0026uuml;nzweig and Gliksman 2021), and shape the composition of understory vegetation (Navarro et al. 2013). Findings from studies of forest management practices\u0026rsquo; influence on litter decomposition dynamics are accumulating (Purahong et al. 2014; Jabiol and Chauvet 2015; Santonja et al. 2017; Lagomarsino et al. 2020; Matos et al. 2022), with their results suggesting that specific silvicultural treatments can mitigate fluctuations in regional temperature and humidity conditions (Bravo-Oviedo et al. 2017). In the context of adaptive forest management, the potential effect of different forest management practices on decomposition is highly relevant (Latterini et al. 2023).\u003c/p\u003e\n\u003cp\u003eAround the Mediterranean basin, \u003cem\u003ePinus brutia\u003c/em\u003e and \u003cem\u003ePinus halepensis\u003c/em\u003e forests cover approximately 7 million ha and play a major ecological and economic role (Chambel et al. 2013; Mauri et al. 2016). Decomposition studies in these forests have focused on their post-fire decomposition dynamics (Radea and Arianoutsou 2000; Sazeides et al. 2021a), the effect of different precipitation regimes on litter decomposition rates (Santonja et al. 2022), the nutrient release during decomposition (Garc\u0026iacute;a-Pl\u0026eacute; et al. 1995; Viros et al. 2021), as well as the effect of different silviculture practices on nutrient availability (Lado-Monserrat et al. 2015). Most of these studies have used methods that monitor the rate of mass loss of litterbags (Berg 2014) and/or of standardized materials (cellulose papers, fabric clots, wood sticks etc.), aiming to distinguish environmental effects from those regulated by the quality of litter. Some studies have implemented a common decomposition protocol for several climatic regions (Joly et al. 2023) however Mediterranean pine forests were not included or the variety of management practices implemented were not specifically taken into account. Even though recent studies have investigated litterfall composition and carbon accumulation in Mediterranean pine forests under discrete management regimes (Doukalianou et al. 2022; Ganatsas et al. 2024; Bueis et al. 2018), none has focused on the process of decomposition through the implementation of common across-sites decomposition protocol.\u003c/p\u003e\n\u003cp\u003eIn the present study we implemented a common decomposition monitoring protocol in \u003cem\u003eP. brutia\u003c/em\u003e and \u003cem\u003eP.\u003c/em\u003e \u003cem\u003ehalepensis\u003c/em\u003e dominated forests at three different geographic regions in Greece. Within these regions, different plots are characterized by discrete management practices and thus provide a natural laboratory to disentangle the effect of climate, dominant species identity and thus litter quality, and management on decomposition dynamics. Our aim was to identify the main drivers of decomposition dynamics in Mediterranean pine forests and to rank the magnitude of their effects and their interactions. To the best of our knowledge, this study represents the first multisite and multiregional decomposition experiment in Greece.\u0026nbsp;\u003c/p\u003e"},{"header":"2.\tMaterials \u0026 methods","content":"\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e2.1 Study sites\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe used a network of 10 permanent monitoring plots (ca 0.1 ha each) established across three study sites in northern Greece. The four (4) plots on the island of Lesvos and the three (3) plots at the continental area of Xanthi are dominated by \u003cem\u003eP. brutia.\u003c/em\u003e The three (3) plots at Sani are dominated by the sympatric \u003cem\u003eP. halepensis\u003c/em\u003e pine (Figure 1). At Lesvos there is no understory vegetation, at Sani there is an understory shrub layer (0.5-2.0 m height), consisting mainly of the evergreens \u003cem\u003ePistacia lentiscus, Phyllirea latifolia\u003c/em\u003e and \u003cem\u003eQuercus coccifera\u003c/em\u003e, and at Xanthi there is an at least 5 m-high floor below the pines, consisting of deciduous broadleaves trees (mainly \u003cem\u003eCarpinus orientalis, Fraxinus ornus, Quercus coccifera, Q. frainetto\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSani experiences a Mediterranean climate with typical hot dry summers and mild, cool winters (with temperatures ranging from 2\u003csup\u003eo\u003c/sup\u003eC to 10\u003csup\u003eo\u003c/sup\u003eC in the winter and 20-32\u003csup\u003eo\u003c/sup\u003eC in the summer, with maximum precipitation of ~60mm in November and December). In the area, three \u003cem\u003eP. halepensis\u003c/em\u003e plots, all at sea level, are systematically monitored as part of the European LTER network (\u003cem\u003eSani Environmental Observatory \u0026ndash; LTER-Greece\u003c/em\u003e). Each plot is managed with a different intensity of overstory thinning / understory removal aiming to reduce fire risk (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt Xanthi, three study plots are established approximately 4 km north of the city, at an elevation of 290m asl. The climate of Xanthi approaches a continental status with Mediterranean features (with mean temperatures ranging from 2 to 8\u003csup\u003eo\u003c/sup\u003eC during winter and from 21 to 31\u003csup\u003eo\u003c/sup\u003eC in the summer). Summers are short, hot and dry, while winters are cold, with significant rainfall and snowfall, particularly in nearby mountains. Xanthi also faces strong stormy winds, especially during winter months. The forest plots at Xanthi are dominated by \u003cem\u003eP. brutia\u003c/em\u003e, with a significant contribution of various deciduous and broadleaves species. These plots have been subjected to three different thinning practices since 2016 (Table 1): no thinning (control), traditional thinning (~20% removal of total basal area) and intensive thinning (~40% removal of the total basal area) (Doukalianou et al. 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn Lesvos Island, four \u003cem\u003eP. brutia\u0026nbsp;\u003c/em\u003eplots are established within the island \u0026lsquo;s central pine forest. Lesvos experiences typical Mediterranean climate conditions with mild, rainy winters and hot dry summers. Temperatures range from 6-12\u003csup\u003eo\u003c/sup\u003eC during winter and 27.5 up to 39\u003csup\u003eo\u003c/sup\u003eC during the summer, while most precipitation of 115-140mm occurs from December to February. Throughout the year the climate is relatively humid, except during the summer months, when aridity increases due to high temperatures along with strong northerly winds from the Sea of Marmara. The four monitoring plots represent a post-fire chronosequence ranging from approximately 20 to 90 years after stand-replacing fires. These plots are relatively unmanaged and thus provide an example of natural post-fire Pine forest dynamics (Sazeides et al. 2021b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVariability of stand structure among the plots, as expressed through variation in Basal Area (BA) and Leaf Area Index (LAI), has been shaped by the different management practices, disturbance history and microclimatic conditions. Across all plots, soil, tree and stand characteristics are systemically monitored (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Characteristics of the permanent monitoring plots.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"956\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAI *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasal area (m\u003csup\u003e2\u003c/sup\u003e/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTree DBH (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTree height (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil texture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil pH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrigin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eManagement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eP. brutia\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLesvos\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLES1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26.36179\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e39.17160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.69 \u0026plusmn; 3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 89px;\"\u003e\n \u003cp\u003eNatural post-fire regeneration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLES2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26.29545\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e39.12662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e22.2 \u0026plusmn; 5.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLES3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26.38450\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e39.15641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31.8 \u0026plusmn; 9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eLES4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26.37402\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e39.16189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31.2 \u0026plusmn; 8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSiL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eP. halepensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSAN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23.31355\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e40.1085\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.14 (1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e23.5 \u0026plusmn; 15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 89px;\"\u003e\n \u003cp\u003eNatural forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNone (low density)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSAN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23.33419\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e40.07637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e27.4 \u0026plusmn; 7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eUnderstory removal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSAN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23.31331\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e40.09282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.06 (4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e25.3 \u0026plusmn; 7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNone (high density)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eP. brutia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003eXanthi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eXAN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e24.88772\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e41.15615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e49.3 \u0026plusmn; 16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 89px;\"\u003e\n \u003cp\u003ePlanted forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eXAN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e24.88650\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e41.15597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e43.3 \u0026plusmn; 12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eModerate thinning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eXAN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e24.88765\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e41.15548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e36.3 \u0026plusmn; 11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eIntensive thinning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;(*) In plots SAN1 and SAN3 the LAI of the understory shrub vegetation is given in parentheses\u003c/em\u003e\u003cem\u003e\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e2.2 Plot-level measurements\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAcross the ten study plots tree diameter at breast height (DBH- cm) and (H- m) are systematically recorded (since 2017 at Sani, since 2016 in Xanthi and since 2020 on Lesvos) for all individuals with D \u0026gt; 5cm. Tree diameters were used to calculate the plot-level basal area (BA- m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e-1\u003c/sup\u003e). \u0026nbsp;LAI (m\u003csup\u003e2\u003c/sup\u003e\u0026nbsp; m\u003csup\u003e-2\u003c/sup\u003e) was estimated using a ceptometer (ACCUPAR LP-80; Decagon Devices Inc, WA, USA), by systematically measuring photosynthetic active radiation (PAR \u0026ndash; \u0026mu;mol quanta m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) at 36 points above the forest floor (1.3 m) and comparing each PAR with that measured outside of the canopy under full light conditions. All LAI measurements were made around solar noon, and the leaf distribution parameter was set to X = 1. Key soil parameters (soil texture, soil pH, soil organic carbon (SOC), soil nitrogen (N), total plant available phosphorus (Olsen P) and soil water holding capacity (WHC)) were measured at soil samples collected at a depth ranging from 5 to 20 cm, depending on local conditions in each plot, using ISO lab techniques (Soil and Water Resources Institute (SWRI) of the Hellenic Agricultural Organization Dimitra).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e2.3 Experimental design\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA common experimental design that included two decomposition protocols was implemented simultaneously across all regions and study plots. The litterbag method focuses on the decomposition of pine needles from each site (Berg 2014; Krishna and Mohan 2017), including the effect of litter quality on the decomposition rate. The standard material protocol, using paper sheets and wood sticks (Joly et al. 2017) focuses on the decomposition of common materials across sites, emphasizing the effect of microenvironmental conditions on decomposition dynamics.\u003c/p\u003e\n\u003cp\u003eIn the litterbag protocol eighteen (18) litterbags (15x15 cm) containing pine needles (~1g of air-dry mass per litterbag) were placed in a subplot within each study plot. The litterbags were placed below the surface of the forest floor and above the humus layer. They were fixed with fine plastic mesh parallel to the ground. Each litterbag featured a double mesh design, with the upper side made of plastic wire (mesh size 0.15x0.15 cm), to facilitate micro-fauna interaction with litter, and the underside constructed from non-degradable organic material with a finer mesh size of approximately 0.05x0.05 cm. Every 90 days 3 litterbag samples were collected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe same type of litterbags was used for the paper sheets. We used non-recycled, chlorine-free, A3 printing paper, of an indicative size of 297 x 420 mm and weight ~10g, folded in 1/6 of their size to fit the litterbags These represent decomposition materials of high cellulose concentration. For wood sticks (low lignin, recalcitrant compounds of organic matter proxy), we used common tongue depressors (made of birch wood) of indicative size of 149 x 19 x 1 mm and weight of 2.5g. In the standard material protocol, eighteen (18) paper sheets and eighteen (18) wood sticks were placed adjacent to the pine litter subplot within each study plot. Every 90 days, three (3) paper sheets and three (3) wood sticks samples were collected. For this protocol, a supplementary subplot was also installed, to determine the incubation period needed in each plot to reach the same proportion of mass loss across all sites. This threshold was defined at 30-45% for cellulose papers and 7-28% of initial mass for wood sticks (Joly et al. 2017). Samples from the supplementary subplot were collected every 60 days.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter retrieval, all samples (both pine needles and standard material) were dried for 48 hours at a temperature of 70\u003csup\u003eo\u003c/sup\u003eC and then weighed after carefully removing them from the litterbags and separating them from soil debris. The experiment was initiated in all study sites in July of 2024.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e2.4 Monitoring of micro-climate conditions\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eMicro-climate conditions on each plot were measured using a set of HOBO\u0026reg; data loggers. The MX230x soil moisture and temperature sensors were established at a depth of 5cm in every subplot. The MX2031A air temperature and relative humidity sensors were placed on trees adjacent to the decomposition subplots. Soil sensors measure soil temperature (\u003csup\u003eo\u003c/sup\u003eC) and soil water content (m\u003csup\u003e3\u003c/sup\u003em\u003csup\u003e-3\u003c/sup\u003e), while air sensors measure air temperature (\u003csup\u003eo\u003c/sup\u003eC), relative humidity (%) and dew point (\u003csup\u003eo\u003c/sup\u003eC), at a 30 minute step. Mean, maximum and minimum values of all parameters were calculated both daily and for the 90 days period on each plot. The latter occurred to follow the number of samples collected to ensure their incorporation into the statistical analysis.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e2.5 Statistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe initially used non-linear regression to fit the Olson decay model (Olson 1963), for each study plot and type of decomposition material:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1768816659.png\" width=\"543\" height=\"61\"\u003e\u003c/p\u003e\n\u003cp\u003ewith \u003cem\u003eM(t)\u003c/em\u003e (g) the mass remaining after incubation time \u003cem\u003et\u003c/em\u003e (days), \u003cem\u003eM\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e is the initial incubated mass (g), \u003cem\u003eRM\u003c/em\u003e the remaining mass portion (\u003cem\u003eM(t)/M\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e) and \u003cem\u003ek\u003c/em\u003e the decomposition rate (day\u003csup\u003e-1\u003c/sup\u003e). In this model, incubation time (t) was the single predictor and the estimated \u003cem\u003ek\u003c/em\u003e\u0026lsquo;s represents the average (constant) decomposition rate over the study period. Non- linear regression models were fitted using the \u0026ldquo;nlsList\u0026rdquo; function in R (R Core Development Team 2025).\u003c/p\u003e\n\u003cp\u003eWe then used a non-linear mixed effects model (NLMM) analysis, in which the decomposition rate was allowed to vary with the microenvironmental conditions, and the hierarchical structure of our dataset was specifically considered. Mixed effect analysis was chosen since it is considered suitable for handling ecological data, due to its resilience to spatial dependence (Fletcher et al. 2018), its ability to produce consistent results from clustered data, and its flexibility to incorporate site-specific parameters and their contribution to decomposition dynamics (Oberpriller et al. 2022).\u003c/p\u003e\n\u003cp\u003eFor each type of material, we initially fitted alternative NLMMs setting plot, species and plots nested in species as random effects, using the \u0026ldquo;nlme\u0026rdquo; package (Pinheiro et al. 2025). By considering the variance associated with each random term and the overall model performance we decided to proceed with our analysis using plot as the only random term. For the fixed part of the model, we initially tested for the multicollinearity of all potential fixed terms i.e. \u003cem\u003eT\u003c/em\u003e\u003csub\u003eair\u003c/sub\u003e \u003cem\u003eRH\u003c/em\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003esoil\u003c/sub\u003e \u003cem\u003eSWC\u003c/em\u003e, elevation, total stand BA and average LAI, soil pH, soil organic carbon (\u003cem\u003eSOC\u003c/em\u003e), soil nitrogen (N) and phosphorus (P) concentration as well as soil water holding capacity (WHC), by considering the Variance Inflation Factors (VIF) through the \u0026ldquo;\u003cem\u003ecar\u003c/em\u003e\u0026rdquo; package (Fox et al., 2001). We then fitted a maximal nonlinear mixed effect model, including all non-collinear fixed effects, and proceeded to a stepwise elimination by sequentially removing nonsignificant predictors (at p\u0026lt;0.05) and comparatively considering the value of the model\u0026rsquo;s Akaike Information Criterion (AIC). During the elimination process of collinear variables (from a maximum to minimal structure), we chose to keep at least one from each group (e.g. micro-climatic, soil, stand structure) providing us with ecological meaning. In all cases we retained the simplest model that had a \u0026Delta;AIC lower than 4 from the previous model. The outcome of our model fits represents the decomposition rate as a linear function of the fixed effect terms (\u003cem\u003eF\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e), i.e.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1768816720.png\" width=\"439\" height=\"165\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e3.1 Microclimatic Conditions\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eDuring the July 2024 \u0026ndash; August 2025 period, the average daily temperature ranged from -0.2 to 32.9 \u003csup\u003eo\u003c/sup\u003eC at Lesvos, from 3.8 to 32.9 \u003csup\u003eo\u003c/sup\u003eC at Sani and from -1.2 to 33.1 \u003csup\u003eo\u003c/sup\u003eC at Xanthi (Fig 2a). Average daily RH ranged from 25.9 to 98.2% at Lesvos, from 37.1 to 96.9 % at Sani and from 27.9 to 96.5 % at Xanthi (Fig 2b). We estimated that at Lesvos there were 94 to 104 days with an average atmospheric vapor pressure deficit (VPD \u0026ndash; kPa) higher than 1.5 kPa, thus the forest there seem to be under the driest atmospheric conditions, compared with 50 to 61 days at Xanthi and 15 to 34 \u0026nbsp;days at Sani (Fig 2c), probably due to proximity of the stands at Sani to the coastline.\u003cstrong\u003e\u003cem\u003e\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e3.2 Average Decomposition Rates\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAmong the used materials, decomposition rates followed the expected ranking (from fast to slow) across all sites: cellulose papers \u0026gt; pine litter \u0026gt; wood (lignin material). At Lesvos and Xanthi, the needle litter of \u003cem\u003eP. brutia\u003c/em\u003e had an almost three times lower decomposition rate compared to the \u003cem\u003eP. halepensis\u003c/em\u003e litter at Sani (\u003cem\u003ek\u003csub\u003elitter\u003c/sub\u003e\u003c/em\u003e\u0026asymp;0.0017 d\u003csup\u003e-1\u003c/sup\u003e vs \u003cem\u003ek\u003csub\u003elitter\u003c/sub\u003e\u003c/em\u003e \u0026asymp;0.0006 d\u003csup\u003e-1\u003c/sup\u003e,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eTable 2). At Sani and Xanthi, cellulose papers had a similar incubation period of 180 days and presented a faster decomposition compared to Lesvos (Xanthi: \u003cem\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/em\u003e\u0026asymp;0.0026 d\u003csup\u003e-1\u003c/sup\u003e, Sani \u003cem\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/em\u003e\u0026asymp;0.0022 d\u003csup\u003e-1\u003c/sup\u003e vs Lesvos \u003cem\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/em\u003e\u0026asymp;0.0005 d\u003csup\u003e-1\u003c/sup\u003e, Table 3). On the other hand, the decomposition of recalcitrant wood sticks followed a more distinct regional pattern. On Lesvos, the recorded wood sticks\u0026rsquo; decomposition rates were the slowest (incubation period of ~240 days), at Sani the fastest (~120 days) with Xanthi in between (~180 days). When data were grouped per species, litter of \u003cem\u003eP. halepensis\u003c/em\u003e stands decomposed ~2.83 times faster than litter of \u003cem\u003eP. brutia stands\u003c/em\u003e, papers in \u003cem\u003eP. halepensis\u003c/em\u003e stands decomposed ~1.8 times faster than papers in \u003cem\u003eP. brutia\u003c/em\u003e stands. The wood sticks in \u003cem\u003eP. halepensis\u003c/em\u003e stands decomposed ~2.8 times faster than the woods in \u003cem\u003eP. brutia\u003c/em\u003e stands.\u003c/p\u003e\n\u003cp\u003eThe plot-specific non-linear regression models provided a good fit for the needle litter (Fig.3) and paper sheets (Fig. 4) data. For wood sticks, nonlinear regression did not provide a good fit and thus linear regression was applied (Fig 5). For the pine needles litter and papers, the estimated decomposition rate was highest at SAN1 and lowest at LES2. For wood sticks the fastest rate was also at SAN1, while the slowest at LES1 (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Decomposition rates (day\u003csup\u003e-1\u003c/sup\u003e) for pine litter, papers and wood sticks estimated through nonlinear regression, grouped by region (left columns) and dominant species (right columns). R\u003csup\u003e2\u003c/sup\u003e was traditionally calculated by the ratio of the sum of squares of the difference between observed and predicted values to the sum of squares of the difference between the observed values and the mean observed value per grouping variable.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"954\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eRegion\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003elitter\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003ewood\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eDominant species\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003elitter\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003ewood\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLesvos\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000607 \u0026plusmn; 0.000048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e66.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000504 \u0026plusmn; 0.000006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e55.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000357 \u0026plusmn; 0.000012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e86.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP. brutia\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.000617 \u0026plusmn; 0.000037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e53.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001243 \u0026plusmn; 0.000079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e53.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.000495 \u0026plusmn; 0.000026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e62.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSani\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001745 \u0026plusmn; 0.000079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e71.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002245 \u0026plusmn; 0.000118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e43.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001387 \u0026plusmn; 0.000073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e80.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP. halepensis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.001745 \u0026plusmn; 0.000079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e71.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002245 \u0026plusmn; 0.000168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e71.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001387 \u0026plusmn; 0.000073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e80.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eXanthi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0006312 \u0026plusmn; 0.000058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e40.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002612 \u0026plusmn; 0.000124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e66.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.000689 \u0026plusmn; 0.000054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e64.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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 3:\u003c/strong\u003e Decomposition rates (day\u003csup\u003e-1\u003c/sup\u003e) for pine litter, papers and wood sticks estimated through plot-specific nonlinear regression (pine litter and papers) and linear regression (wood sticks). Coefficient of determination R\u003csup\u003e2\u003c/sup\u003e is also provided (and calculated similarly as mentioned in Table 2).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"879\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ePlot\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003elitter\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ek\u003csub\u003ewood\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLES1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.000764 \u0026plusmn; 0.000095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e74.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.000654 \u0026plusmn; 0.000092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e64.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.000295 \u0026plusmn; 0.000024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e83.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLES2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.000485 \u0026plusmn; 0.000089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e68.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.000322 \u0026plusmn; 0.000085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e69.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.000391 \u0026plusmn; 0.000027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e87.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLES3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.000556 \u0026plusmn; 0.000089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e79.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.000422 \u0026plusmn; 0.000086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e67.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.000344 \u0026plusmn; 0.000024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e87.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLES4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.000624 \u0026plusmn; 0.000092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e63.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.000623 \u0026plusmn; 0.000093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e74.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.000400 \u0026plusmn; 0.000022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e92.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSAN1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.002011 \u0026plusmn; 0.000144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e72.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.004146 \u0026plusmn; 0.000267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e69.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.001657 \u0026plusmn; 0.000111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e88.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSAN2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.001633 \u0026plusmn; 0.000126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e78.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.000924 \u0026plusmn; 0.000103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e68.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.001128 \u0026plusmn; 0.000095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e83.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSAN3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.001513 \u0026plusmn; 0.000122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e79.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.002582 \u0026plusmn; 0.000162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e78.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.001368 \u0026plusmn; 0.000152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e74.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eXAN1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.000793 \u0026plusmn; 0.000099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e44.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.002668 \u0026plusmn; 0.000165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e78.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.000611 \u0026plusmn; 0.000059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e78.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eXAN2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.000593 \u0026plusmn; 0.000094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e58.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.002908 \u0026plusmn; 0.000176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e79.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.000713 \u0026plusmn; 0.000075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e75.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eXAN3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.000515 \u0026plusmn; 0.000092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e22.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.002278 \u0026plusmn; 0.000149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e45.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.000742 \u0026plusmn; 0.000136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e52.00%\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\u003ch3\u003e\u003cstrong\u003e\u003cem\u003e3.3 Environmental Effects on Decomposition Rates\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA summary of the mixed effect model analysis used to decide for the optimum hierarchical structure of our dataset is presented in Supplementary Material (A). Using plot as the only random term, the NLMMs yielded the best fitting results with similar estimates for the plots\u0026rsquo; decomposition rates to the nonlinear simple plot-level analysis (Table S1). By using species as the only random term, thus grouping together all data from Xanthi and Lesvos (\u003cem\u003eP. brutia\u003c/em\u003e) in one group and all data from Sani (\u003cem\u003eP. halepensis\u003c/em\u003e) in another one, the NLMM analysis suggested that litter from the \u003cem\u003eP. halepensis\u003c/em\u003e stands decomposed 2.79 times faster than the litter from \u003cem\u003eP. brutia\u003c/em\u003e stands (\u003cem\u003ek\u003csub\u003ePhalepensis\u003c/sub\u003e\u003c/em\u003e=0.001734 d\u003csup\u003e-1\u003c/sup\u003e, \u003cem\u003ek\u003csub\u003ePbrutia\u003c/sub\u003e\u003c/em\u003e=0.0006195 d\u003csup\u003e-1\u003c/sup\u003e) (Fig. S1). Similar results were obtained when a random structure of plot- nested within species was used (Table S2). Even though the \u0026ldquo;plots nested in species\u0026rdquo; random term probably represents in a more informative way the structure of our dataset, the simpler \u0026ldquo;plot only\u0026rdquo; random effect model was used to avoid unnecessary complexity when micro-environmental fixed effects terms were added.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe correlation and the VIF analysis revealed intercorrelation between the micro-climatic, edaphic and stand structure variables (Supplementary Material B, Fig. S2). Thus \u003cem\u003eRH\u003c/em\u003e, \u003cem\u003eVPD\u003c/em\u003e and\u003cem\u003e\u0026nbsp;WHC\u003c/em\u003e were excluded from the set of variables used as fixed terms. For all materials, the elimination process was finalized when the most significant factors were identified, multicollinearity among predictors was low VIF \u0026lt;5, and the optimum R\u003csup\u003e2\u003c/sup\u003e and AIC were achieved (Supplementary Material C, Table S3, S4 and S5).\u003c/p\u003e\n\u003cp\u003eThe effect of the maintained fixed effect terms on the decomposition rate of each material is summarised in Table 4.\u003cem\u003e\u0026nbsp;pH\u003c/em\u003e presented a universal positive effect on the decomposition rate across all materials. \u003cem\u003eT\u003csub\u003eair\u003c/sub\u003e\u003c/em\u003e and \u003cem\u003eSWC\u003c/em\u003e presented positive effects on the decomposition rate for pine litter and paper sheets. The effect of stand structure, expressed through total stand \u003cem\u003eBA\u003c/em\u003e and \u003cem\u003eLAI\u003c/em\u003e, affected significantly only the decomposition rate of paper sheets in a positive and a negative way respectively. On the other hand, the decomposition rate of woods sticks was only significantly (positively) affected by the \u003cem\u003epH\u003c/em\u003e of the soil.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo summarize the seasonal variation in the decomposition rate of each material we used the estimated coefficients of the environmental (\u003cem\u003eT\u003csub\u003eair\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003eSWC, pH\u003c/em\u003e) and stand structure (\u003cem\u003eBA\u003c/em\u003e, \u003cem\u003eLAI\u003c/em\u003e) predictors and inferred the plot specific \u003cem\u003ek\u003c/em\u003e\u0026rsquo;s for each decomposing material (Fig. 6). The predicted decomposition rates at Sani present the wider variation, in contrast to Lesvos. For example, \u003cem\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/em\u003e varies from less than 0.001 d\u003csup\u003e-1\u003c/sup\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003eto SAN2 to more than 0.004 d\u003csup\u003e-1\u003c/sup\u003e at SAN1, underlying the importance of plot specific conditions, even within the site. The smallest seasonal variation in the decomposition rates was found at Lesvos, with the \u003cem\u003ek\u003c/em\u003e\u0026rsquo;s for all materials not exceeding in any season the value of 0.001 d\u003csup\u003e-1\u003c/sup\u003e. Xanthi on the other hand illustrated systematically low \u003cem\u003ek\u003c/em\u003e\u0026rsquo;s for needle litter and wood sticks (below of 0.001 d\u003csup\u003e-1\u003c/sup\u003e) but had higher decomposition rates for paper (from ca 0.002 to 0.003 d\u003csup\u003e-1\u003c/sup\u003e). For plots at Lesvos and Sani, the fastest decomposition rates of litter and papers were shown for the plots with the smallest \u003cem\u003eBA\u003c/em\u003e (LES1 and SAN1 respectively), a pattern which was not observed at Xanthi \u0026lsquo;s plots.\u003c/p\u003e\n\u003cp\u003eIn terms of seasonality, the decomposition rate at Lesvos seemed to be higher during the winter 2025 period, reaching a plateau or even reducing during the spring and summer 2025 periods. At Sani the estimated decomposition rates seem to progressively increase from autumn 2024 to summer 2025. At Xanthi the decomposition rates seem to progressively increase at XAN1 and XAN2 but reach a plateau during spring 2025 at XAN3. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Optimum fixed effect structure for decomposition of each material. Marginal (R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eM\u003c/sub\u003e) and conditional (R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eC\u003c/sub\u003e) coefficient of determination are also reported. Both \u003cem\u003eR\u003csub\u003eM\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e and conditional \u003cem\u003eR\u003csub\u003eC\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e coefficients use the same total variance (sum of predicted fixed, random and residuals variances) as denominator. \u003cem\u003eR\u003csub\u003eM\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e uses only the predicted fixed effect variance in its numerator, while (\u003cem\u003eR\u003csub\u003eC\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e) uses the sum of fixed and random effect variance in its numerator.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eIntercept\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eT\u003csub\u003eair\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eSWC\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003epH\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eBA\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eLAI\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eM\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eC\u003c/sub\u003e\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ek \u003csub\u003elitter\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.005896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e+0.000029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e+0.003226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e+0.000560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e64.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e81.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ek \u003csub\u003epaper\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.020061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e+0.000054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e+0.005676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e+0.002902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e+0.000130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.002035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e65.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e82.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ek \u003csub\u003ewoods\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.003055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e+0.000594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e28.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e64.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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"},{"header":"4.\tDiscussion","content":"\u003cp\u003eIn this study we present the results of an early-stage decomposition experiment in Mediterranean pine forests. The common monitoring protocol identified between regions and dominant species differences in the decomposition rate of different materials. Across regions, soil pH emerged as a consistent predictor of the process of early-stage decomposition. Within the relatively narrow range of climatic conditions that our monitoring protocol was implemented, microclimate conditions seem to partially control the rates of early-stage decomposition. The effect of litter quality as expressed through the chemical composition of litter from the two study species could only be indirectly considered as the latter was not measured throughout the implementation period. However, differences in the decomposition rates of standard material, where the effect of litter quality is by-passed, highlighted the effect of soil pH as a common predictor of across region differences in the rates of decomposition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results show that litter \u003cem\u003eP. halepensis\u003c/em\u003e stands decomposed significantly faster than litter of \u003cem\u003eP. brutia\u003c/em\u003e stands. We estimated an average decomposition rate of \u003cem\u003ek\u003csub\u003eNLR\u003c/sub\u003e\u003c/em\u003e= 0.001745417 d\u003csup\u003e-1\u003c/sup\u003e for \u003cem\u003eP. halepensis\u003c/em\u003e and \u003cem\u003ek\u003csub\u003eNLR\u003c/sub\u003e\u003c/em\u003e=0.000617105 d\u003csup\u003e-1\u003c/sup\u003e for \u003cem\u003eP. brutia\u003c/em\u003e. The estimated decomposition rate for \u003cem\u003eP. halepensis\u003c/em\u003e is close to those reported in other studies. For example, Gliksman et al. (2018b) reported an average decomposition rate ranging from 0.001338 d\u003csup\u003e-1\u003c/sup\u003e in large forest gaps, to -0.001057 d\u003csup\u003e-1\u003c/sup\u003e in shady areas, for \u003cem\u003eP. halepensis\u003c/em\u003e after a one-year incubation period under relatively warmer conditions. In Greece, Arianoutsou and Radea (2000) reported a \u003cem\u003ek\u003c/em\u003e around 0.00029 d\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003efor \u003cem\u003eP. halepensis\u003c/em\u003e litter, estimated through monthly litterfall production and accumulation. Plots at Sani presented decomposition rates ranging from 0.002011 d\u003csup\u003e-1\u003c/sup\u003e to 0.001633 d\u003csup\u003e-1\u003c/sup\u003e, which is an order of magnitude greater than the decomposition rates of \u003cem\u003eP. halepensis\u003c/em\u003e stands of various silvicultural treatments reported in Lado-Monserrat et al. (2016) (from k=0.00078 d\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003eof the densest plot to k=0.00066 d\u003csup\u003e-1\u003c/sup\u003e on the clear-cut plot). On the other hand, decomposition rates of \u003cem\u003eP. halepensis\u003c/em\u003e measured by Garc\u0026iacute;a-Pl\u0026eacute; et al. (1995) were significantly faster than the ones reported here (ranging from 0.0170 d\u003csup\u003e-1\u003c/sup\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003eto 0.0120 d\u003csup\u003e-1\u003c/sup\u003e in their case). Litter mass loss in \u003cem\u003eP. brutia\u003c/em\u003e stands yielded an average decomposition rate from \u003cem\u003ek\u003csub\u003eNLR\u003c/sub\u003e\u003c/em\u003e =0.000485 d\u003csup\u003e-1\u003c/sup\u003e to \u003cem\u003ek\u003csub\u003eNLR\u003c/sub\u003e\u003c/em\u003e =0.000764 d\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003eacross our monitoring plots at Lesvos and Xanthi. Our estimates are within the range reported by Tsiafouli et al. (2018) under different drought treatments (from 0.0001999 d\u003csup\u003e-1\u003c/sup\u003e to 0.0009001 d\u003csup\u003e-1\u003c/sup\u003e) for similar incubation period, but with their litterbags intercepting micro-fauna. Unfortunately, few decomposition studies on \u003cem\u003eP. brutia\u003c/em\u003e were found, with some concerning woods decomposition (De Meo et al. 2019). This variation in the observed decomposition rates at different areas around the Mediterranean basin suggests that microenvironmental conditions could significantly affect the process of early decay, as also highlighted by the results of this study.\u003c/p\u003e\n\u003cp\u003eFrom the two to three times higher \u003cem\u003ek\u003csub\u003elitter\u003c/sub\u003e\u003c/em\u003e in Sani compared to Lesvos and Xanthi and assuming \u0026ldquo;all else being equal\u0026rdquo;, we could hypothesize that \u003cem\u003eP. halepensis\u003c/em\u003e litter should be of higher quality than that of \u003cem\u003eP. brutia\u003c/em\u003e. \u003cem\u003eP. brutia\u003c/em\u003e is encountered in more arid and semi-arid zones in the eastern Mediterranean basin, hence it could be considered to require lower amounts of precipitation and humidity to grow than \u003cem\u003eP. halepensis\u003c/em\u003e (Boydak 2004; Ne\u0026rsquo;eman and Trabaud 2000). Differences in their needle water retention have been reported, with \u003cem\u003eP. brutia\u003c/em\u003e being able to retain more water under drought stress (Houminer et al. 2022). Although we did not specifically measure litter quality, needle chemical concentration data from previous studies (Michelaki et al. 2019; Fotelli et al. 2020) suggest that fresh \u003cem\u003eP. brutia\u003c/em\u003e needles have a slightly non-significant higher C/N ratio compared to \u003cem\u003eP. halepensis\u003c/em\u003e across different sites in Greece. Nevertheless, this difference does not seem rigid enough to explain the observed differences in the decomposition rates between our study species, in the lengths that higher leaf C/N ratios could slow down decomposition rates (McClaugherty and Berg 1987; Aber et al. 1990; Arianoutsou 1993; Berg et al. 1993; Arianoutsou and Radea 2000), even before moisture conditions and their attribution to C/N ratio are considered (Aerts 1997; Petraglia et al. 2019). Furthermore, C/N ratio has been reported as partially insufficient to estimate predicted mass loss of decomposed material (Bonanomi et al. 2013). Additionally, differences in leaf flavonoids concentration that control litter UV sensitivity or microbial activity (Buer et al. 2010), could potentially explain the between species \u003cem\u003ek\u003csub\u003elitter\u003c/sub\u003e\u003c/em\u003e difference, but do not seem to be of an adequate magnitude between these two sympatric species (Kaundun et al. 1997). Unfortunately, since a chemical analysis (including C/N, lignin, cellulose composition etc.) of leaf litter during the implementation period of this set-up was not available, a definitive distinction of species versus microclimatic conditions effects cannot be derived. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, by considering the results from the decomposition of the standardized material, where the effect of litter quality has been excluded, \u0026ldquo;all else is not equal\u0026rdquo;. Cellulose papers decomposed significantly faster at Sani and Xanthi with \u003cem\u003ek\u003csub\u003epaper\u003c/sub\u003e\u0026nbsp;\u003c/em\u003eat SAN1 being almost an order of magnitude higher than the average \u003cem\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/em\u003e at Lesvos. During the incubation period SAN1 had the least atmospherically dry conditions across all plots, and in general plots at Sani and Xanthi experienced a lower amount of atmospheric dry days. Thus, microclimatic conditions and in particular \u003cem\u003eRH\u003c/em\u003e and/or \u003cem\u003eVPD\u003c/em\u003e could be considered a candidate for explaining differences across sites. However, in the case of paper sheets, the per-plot ranking of \u003cem\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/em\u003e does not follow the same order as the estimated indexes of atmospheric dryness, with \u003cem\u003ek\u003csub\u003epaper\u003c/sub\u003e\u003c/em\u003e at Sani illustrating a wide range that included all the respective decomposition rates at the relatively drier plots at Xanthi. For wood sticks however, a clear regional separation based on atmospheric dryness is achieved, although no climate variable was maintained as significant in the NLMM analysis. In general, the effect of microclimatic conditions on the decomposition process seems to agree with previous studies, identifying air temperature and soil moisture as important predictors (Moore 1986; Laskowski 2012; Sierra et al. 2017; Wallace et al. 2018; Schwieger et al. 2025), through their influence on micro-fauna activity and thus decomposition rates (Cortez 1998; Bonanomi et al. 2023; Villaz\u0026oacute;n-Orozco et al. 2025).\u003c/p\u003e\n\u003cp\u003eSoil \u003cem\u003epH\u003c/em\u003e was the single predictor that was systematically retained in our NLMM analysis and could be used to separate regional level differences in the early decomposition stage. All plots at Sani had a \u003cem\u003epH\u003c/em\u003e\u0026gt; 7 in contrast with the more acidic plots at Lesvos and Xanthi. Thus, the higher atmospheric wetness and the favorable edaphic condition at Sani could explain the regional differences in the observed decomposition rates, suggesting that between these two sympatric species, litter quality could not be a strong determinant. Microbial activity is relatively higher in more alkaline soils rather than acidic ones (Cao et al. 2016). Soil \u003cem\u003epH\u003c/em\u003e is related to local bacterial and fungal diversity (Romanowicz et al. 2016), their phylogenetic structure (Freedman and Zak 2015) and has been suggested as the primary driver for microbial communities\u0026rsquo; composition and richness, even stronger than plants\u0026rsquo; phylogenetic diversity (Ni et al. 2021). This could be particularly important for wood sticks, with the positive effect of \u003cem\u003epH\u003c/em\u003e likely linked to the composition of recalcitrant compounds at this early- decay stage, with the influence of climatic conditions and nutrients availability expected to emerge over longer incubation periods (Bonanomi et al. 2021), probably explaining the better fit of linear regression at this stage of decay. The common positive effect of \u003cem\u003epH\u003c/em\u003e across all materials could be associated with the synthesis and activity of fungal and soil bacteria communities (Sinsabaugh et al. 2008; Rousk et al. 2010), with Malik et al. (2018) also highlighting the role of \u003cem\u003epH\u003c/em\u003e in explaining two fundamental mechanisms of soil organic carbon accumulation in soils with varying land use intensities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe direct effect of stand structure, through variation in total basal area and leaf area index, was only identified in the paper sheet decomposition rates. A positive effect of \u003cem\u003eBA\u003c/em\u003e on the decomposition process has been found in other studies (Hutson and Veitch 1985; Kim 2016; Bueis et al. 2018; Liu et al. 2018; Santos et al. 2019). In our study the stand thinning practices at Xanthi (XAN1- XAN3 from control to heavier thinning) seem to affect the decomposition rate with XAN3 experiencing a slower decomposition for needle litter and paper sheets, but a higher rate for wood sticks. This trend aligns with the findings of Bravo-Oviedo et al. (2017) for mixed oak and pine forests, where heavy thinning led to decrease in decomposition rates. On the contrary, across the post-fire chronosequence gradient on Lesvos, the youngest and least dense plot (LES1) illustrated faster decomposition of paper sheet and needle litter, with wood decay rates increasing with stand age. Studies in \u003cem\u003eP. brutia\u003c/em\u003e plantations have not identified a significant effect of stand structure on litter decomposition even though litter nutrients stocks vary under different treatment intensities (Erkan et al. 2023). The above suggests that although in general an increase in the decomposition rate is expected at denser stands, important interactions with microenvironmental conditions could ultimately determine the process (Latterini et al. 2023).\u003c/p\u003e\n\u003cp\u003eThe effect of climate, soil, litter quality and stand structure on the decomposition of organic material within forests remains an important ecological question. A clear understanding of the relative importance of these key abiotic and biotic factors is often hindered by their interconnections. Ongoing climate change could influence the process of decomposition and the way it is modeled (Ranucci et al. 2022) and highlight the importance of non-rainfall water sources and their incorporation in carbon cycle models (Logan et al. 2022), especially for Mediterranean ecosystems, where climatic variations are more pronounced. Common decomposition studies for dominant Mediterranean pine species under varying canopy status and management treatments could create comparable outcomes and disentangle the species versus environmental effects on litter decomposition. Adding chemical analysis, and/or mixed litter influence (where the latter occurs) throughout the decomposition process, can further inform valuable data-driven simulations of carbon fluxes in terrestrial ecosystems under climate change.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"557\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eAIC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eAkaike Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eBA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003ePlot basal area (m\u003csup\u003e2\u003c/sup\u003e/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eCarbon\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eC/N\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eRatio of carbon to nitrogen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eDBH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eTree diameter at breast height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eTree height (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eLAI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eLeaf Area Index (m\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eSoil nitrogen (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eNLMM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eNonlinear mixed effects model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eSoil phosphorus (mg/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003ePAR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003ePhotosynthetically Active Radiation (\u0026mu;mol quanta m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eRH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eRelative Humidity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003esdD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eStandard deviation of trees diameters for each plot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eSOC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eSoil Organic Carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eSWC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eMean daily soil water content (m\u003csup\u003e3\u003c/sup\u003e/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003csub\u003eair\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eMean daily atmospheric temperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003csub\u003esoil\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eMean daily soil temperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eVIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eVariation Inflation Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eVPD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eMean daily Vapor Pressure Deficit (kPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eWHC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003eSoil Water Holding Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis work is part of the \u0026ldquo;PineOptim\u0026rdquo; project, which is implemented in the framework of H.F.R.I. call \u0026ldquo;Basic Research Financing (Horizontal support of all Sciences)\u0026rdquo; under the National Recovery and Resilience Plan \u0026ldquo;Greece 2.0\u0026rdquo; funded by the European Union \u0026ndash; NextGenerationEU (H.F.R.I. Project Number: 016258). The research was conducted under the license given by the General Secretariat for Forestry of the Hellenic Ministry of the Environment and Energy (protocol number: 75638/5179/10-07-2024).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eConceptualization: EDM, PGD, NMF, MF, KR; Methodology:\u0026nbsp;EDM, PGD, CS, NMF, MF; Investigation: EDM, NE, GX, AG, NK, NM, CS, EBF, GS; Data curation: EDM, NE, GX, AG, EBF; Visualization: EDM, NMF; Formal Analysis: EDM, PGD, NMF; Writing- original draft: EDM, PGD, NMF; Writing - review \u0026amp; editing: NK, PGD, MF, EV, NMF; Project administration: MNF, PGD, KR; Funding acquisition: MNF, PGD, KR.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAuthors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAber, J.D., Melillo, J.M. and McClaugherty, C.A. 1990. \u0026ldquo;Predicting Long-Term Patterns of Mass Loss, Nitrogen Dynamics, and Soil Organic Matter Formation from Initial Fine Litter Chemistry in Temperate Forest Ecosystems.\u0026rdquo; \u003cem\u003eCanadian Journal of Botany\u003c/em\u003e 68 (10): 2201\u0026ndash;8. https://doi.org/10.1139/b90-287.\u003c/li\u003e\n \u003cli\u003eAerts, R. 1997. \u0026ldquo;Climate, Leaf Litter Chemistry and Leaf Litter Decomposition in Terrestrial Ecosystems: A Triangular Relationship.\u0026rdquo; \u003cem\u003eOikos\u003c/em\u003e 79 (3): 439\u0026ndash;49. https://doi.org/10.2307/3546886.\u003c/li\u003e\n \u003cli\u003eArianoutsou, M. and Radea, C. 2000. \u0026ldquo;Litter Production and Decomposition in \u003cem\u003ePinus halepensis\u003c/em\u003e Forests\u0026rdquo;.\u0026nbsp;In: Ne\u0026rsquo;eman, G., Trabaud, L., eds. \u003cem\u003eEcology, biogeography and management of Pinus halepensis and Pinus brutia forest ecosystems in the Mediterranean Basin\u003c/em\u003e: pp. 183\u0026ndash;190. Backhuys Publishers, Leiden, The Netherlands\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eArianoutsou, M. 1993. \u0026ldquo;Leaf Litter Decomposition and Nutrient Release in a Maquis (Evergreen Sclerophyllous) Ecosystem of North-Eastern Greece.\u0026rdquo; \u003cem\u003ePedobiologia\u003c/em\u003e 2 (37): 65\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eArslan, H., G\u0026uuml;lery\u0026uuml;z, G. and Kırmızı, S. 2010. \u0026ldquo;Nitrogen Mineralisation in the Soil of Indigenous Oak and Pine Plantation Forests in a Mediterranean Environment.\u0026rdquo; \u003cem\u003eEuropean Journal of Soil Biology\u003c/em\u003e 46 (1): 11\u0026ndash;17. https://doi.org/10.1016/j.ejsobi.2009.08.002.\u003c/li\u003e\n \u003cli\u003eBall, B. A., Hunter, M. D., Kominoski, J. S., Swan, C. M. and Bradford, M. A. 2008. \u0026ldquo;Consequences of Non-Random Species Loss for Decomposition Dynamics: Experimental Evidence for Additive and Non-Additive Effects.\u0026rdquo; \u003cem\u003eJournal of Ecology\u003c/em\u003e 96 (2): 303\u0026ndash;13. https://doi.org/10.1111/j.1365-2745.2007.01346.x.\u003c/li\u003e\n \u003cli\u003eBar-On, Y.M., Phillips, R. and Milo, R. 2018. \u0026ldquo;The Biomass Distribution on Earth.\u0026rdquo; \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 115 (25): 6506\u0026ndash;11. https://doi.org/10.1073/pnas.1711842115.\u003c/li\u003e\n \u003cli\u003eBerg, B. 2014. \u0026ldquo;Decomposition Patterns for Foliar Litter \u0026ndash; A Theory for Influencing Factors.\u0026rdquo; \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 78 (November): 222\u0026ndash;32. https://doi.org/10.1016/j.soilbio.2014.08.005.\u003c/li\u003e\n \u003cli\u003eBerg, B., Berg, M., Bottner, P. et al. 1993. \u0026ldquo;Litter Mass Loss Rates in Pine Forests of Europe and Eastern United States: Some Relationships with Climate and Litter Quality.\u0026rdquo; \u003cem\u003eBiogeochemistry\u003c/em\u003e 20 (September): 127\u0026ndash;59. https://doi.org/10.1007/BF00000785.\u003c/li\u003e\n \u003cli\u003eBlanco, J.A., Imbert, B.J. and Castillo, F.J. 2006. \u0026ldquo;Influence of Site Characteristics and Thinning Intensity on Litterfall Production in Two \u003cem\u003ePinus sylvestris\u003c/em\u003e L. Forests in the Western Pyrenees.\u0026rdquo; \u003cem\u003eForest Ecology and Management\u003c/em\u003e 237 (1): 342\u0026ndash;52. https://doi.org/10.1016/j.foreco.2006.09.057.\u003c/li\u003e\n \u003cli\u003eBonanomi, G., Incerti, G., Giannino, F.,Mingo, A., Lanzotti, V. and Mazzoleni, S. 2013. \u0026ldquo;Litter Quality Assessed by Solid State 13C NMR Spectroscopy Predicts Decay Rate Better than C/N and Lignin/N Ratios.\u0026rdquo; \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, Special Issue: Interactions of Soil Minerals with Organic Components and Microorganisms VII and Enzymes in the Environment IV, vol. 56 (January): 40\u0026ndash;48. https://doi.org/10.1016/j.soilbio.2012.03.003.\u003c/li\u003e\n \u003cli\u003eBonanomi, G., Motti, R., De Marco, A. and Idbella, M. 2023. \u0026ldquo;Temperature Sensitivity and Decomposition Rate of 101 Leaf Litter Types from Mediterranean Ecosystems.\u0026rdquo; \u003cem\u003eScience of The Total Environment\u003c/em\u003e 894 (October): 165026. https://doi.org/10.1016/j.scitotenv.2023.165026.\u003c/li\u003e\n \u003cli\u003eBonanomi, G., Zotti, M., Cesarano, G. et al. 2021. \u0026ldquo;Decomposition of Woody Debris in Mediterranean Ecosystems: The Role of Wood Chemical and Anatomical Traits.\u0026rdquo; \u003cem\u003ePlant and Soil\u003c/em\u003e 460 (1): 263\u0026ndash;80. https://doi.org/10.1007/s11104-020-04799-4.\u003c/li\u003e\n \u003cli\u003eBoydak, M. 2004. \u0026ldquo;Silvicultural Characteristics and Natural Regeneration of \u003cem\u003ePinus brutia\u003c/em\u003e Ten. \u0026mdash; a Review.\u0026rdquo; \u003cem\u003ePlant Ecology\u003c/em\u003e 171 (1\u0026ndash;2): 153\u0026ndash;63. https://doi.org/10.1023/B:VEGE.0000029373.54545.d2.\u003c/li\u003e\n \u003cli\u003eBravo-Oviedo, A., Ruiz-Peinado, R., Onrubia, R. and del R\u0026iacute;o, M. 2017. \u0026ldquo;Thinning Alters the Early-Decomposition Rate and Nutrient Immobilization-Release Pattern of Foliar Litter in Mediterranean Oak-Pine Mixed Stands.\u0026rdquo; \u003cem\u003eForest Ecology and Management\u003c/em\u003e 391 (May): 309\u0026ndash;20. https://doi.org/10.1016/j.foreco.2017.02.032.\u003c/li\u003e\n \u003cli\u003eBueis, T., Bravo, F., Pando, V. and Turri\u0026oacute;n, M.B. 2018. \u0026ldquo;Local Basal Area Affects Needle Litterfall, Nutrient Concentration, and Nutrient Release during Decomposition in Pinus Halepensis Mill. Plantations in Spain.\u0026rdquo; \u003cem\u003eAnnals of Forest Science\u003c/em\u003e 75 (1): 21. https://doi.org/10.1007/s13595-018-0699-5.\u003c/li\u003e\n \u003cli\u003eBuer, C.S., Imin, N. and Djordjevic, M.A. 2010. \u0026ldquo;Flavonoids: New Roles for Old Molecules.\u0026rdquo; \u003cem\u003eJournal of Integrative Plant Biology\u003c/em\u003e 52 (1): 98\u0026ndash;111. https://doi.org/10.1111/j.1744-7909.2010.00905.x.\u003c/li\u003e\n \u003cli\u003eCao, H., Chen, R., Wang, L. et al. 2016. \u0026ldquo;Soil pH, Total Phosphorus, Climate and Distance Are the Major Factors Influencing Microbial Activity at a Regional Spatial Scale.\u0026rdquo; \u003cem\u003eScientific Reports\u003c/em\u003e 6 (1): 25815. https://doi.org/10.1038/srep25815.\u003c/li\u003e\n \u003cli\u003eChambel, M.R., Climent, J., Pichot, C. and Ducci, F. 2013. \u0026ldquo;Mediterranean Pines (\u003cem\u003ePinus halepensis\u003c/em\u003e Mill. and \u003cem\u003ebrutia\u003c/em\u003e Ten.).\u0026rdquo; In \u003cem\u003eForest Tree Breeding in Europe: Current State-of-the-Art and Perspectives\u003c/em\u003e, edited by Luc E P\u0026acirc;ques. Springer Netherlands. https://doi.org/10.1007/978-94-007-6146-9_5.\u003c/li\u003e\n \u003cli\u003eCortez, J. 1998. \u0026ldquo;Field Decomposition of Leaf Litters: Relationships between Decomposition Rates and Soil Moisture, Soil Temperature and Earthworm Activity.\u0026rdquo; \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 30 (6): 783\u0026ndash;93. https://doi.org/10.1016/S0038-0717(97)00163-6.\u003c/li\u003e\n \u003cli\u003eDe Meo, I., Lagomarsino, A., Agnelli, A.E. and Paletto, A. 2019. \u0026ldquo;Direct and Indirect Assessment of Carbon Stock in Deadwood: Comparison in Calabrian Pine (\u003cem\u003ePinus brutia\u003c/em\u003e Ten. Subsp. \u003cem\u003ebrutia\u003c/em\u003e) Forests in Italy.\u0026rdquo; \u003cem\u003eForest Science\u003c/em\u003e 65 (4): 460\u0026ndash;68. https://doi.org/10.1093/forsci/fxy051.\u003c/li\u003e\n \u003cli\u003eDimitrakopoulos, P.G. 2010. \u0026ldquo;Influence of Evenness on the Litter-Species Richness\u0026ndash;Decomposition Relationship in Mediterranean Grasslands.\u0026rdquo; \u003cem\u003eJournal of Plant Ecology\u003c/em\u003e 3 (2): 71\u0026ndash;78. https://doi.org/10.1093/jpe/rtq009.\u003c/li\u003e\n \u003cli\u003eDoukalianou, F., Spyroglou, G., Orfanoudakis, M., et al. 2022. \u0026ldquo;Effects of Forest Thinning on Soil Litter Input Nutrients in Relation to Soil CO2, CH4, and N2O Fluxes in Greece.\u0026rdquo; \u003cem\u003eAtmosphere\u003c/em\u003e 13 (3): 376. https://doi.org/10.3390/atmos13030376.\u003c/li\u003e\n \u003cli\u003eErkan, N., G\u0026uuml;ner, S.T. and Aydın, A.C. 2023. \u0026ldquo;Thinning Effects on Stand Growth, Carbon Stocks, and Soil Properties in Brutia Pine Plantations.\u0026rdquo; \u003cem\u003eCarbon Balance and Management\u003c/em\u003e 18 (March): 6. https://doi.org/10.1186/s13021-023-00226-0.\u003c/li\u003e\n \u003cli\u003eFAO. 2022. \u003cem\u003eGlobal Soil Organic Carbon Map \u0026ndash; GSOCmap v.1.6.\u003c/em\u003e Rome. https://openknowledge.fao.org/items/612deb84-1cb3-41cc-97cb-72e219e9f5d0.\u003c/li\u003e\n \u003cli\u003eFioretto, A., Papa, S., Sorrentino, G. and Fuggi, A. 2001. \u0026ldquo;Decomposition of \u003cem\u003eCistus Incanus\u003c/em\u003e Leaf Litter in a Mediterranean Maquis Ecosystem: Mass Loss, Microbial Enzyme Activities and Nutrient Changes.\u0026rdquo; \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 33 (3): 311\u0026ndash;21. https://doi.org/10.1016/S0038-0717(00)00142-5.\u003c/li\u003e\n \u003cli\u003eFioretto, A., Papa, S. and Fuggi, A. 2003. \u0026ldquo;Litter-Fall and Litter Decomposition in a Low Mediterranean Shrubland.\u0026rdquo; \u003cem\u003eBiology and Fertility of Soils\u003c/em\u003e 39 (1): 37\u0026ndash;44. https://doi.org/10.1007/s00374-003-0675-5.\u003c/li\u003e\n \u003cli\u003eFletcher, R. and Fortin, M.J. 2018. \u003cem\u003eAccounting for Spatial Dependence in Ecological Data: Applications with R\u003c/em\u003e. https://doi.org/10.1007/978-3-030-01989-1_6.\u003c/li\u003e\n \u003cli\u003eFotelli, M.N., Lyrou, F.G., Avtzis, D.N., et al. 2020. \u0026ldquo;Effective Defense of Aleppo Pine Against the Giant Scale Marchalina Hellenica Through Ecophysiological and Metabolic Changes.\u0026rdquo; \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e 11 (December). https://doi.org/10.3389/fpls.2020.581693.\u003c/li\u003e\n \u003cli\u003eFox, J., Weisberg, S. and Price, B. 2001. \u0026ldquo;Car: Companion to Applied Regression.\u0026rdquo; May 1. https://doi.org/10.32614/CRAN.package.car.\u003c/li\u003e\n \u003cli\u003eFreedman, Z. and Zak, D.R. 2015. \u0026ldquo;Soil Bacterial Communities Are Shaped by Temporal and Environmental Filtering: Evidence from a Long-Term Chronosequence.\u0026rdquo; \u003cem\u003eEnvironmental Microbiology\u003c/em\u003e 17 (9): 3208\u0026ndash;18. https://doi.org/10.1111/1462-2920.12762.\u003c/li\u003e\n \u003cli\u003eGallardo, A. and Merino, J. 1993. \u0026ldquo;Leaf Decomposition in Two Mediterranean Ecosystems of Southwest Spain: Influence of Substrate.\u0026rdquo; \u003cem\u003eEcology\u003c/em\u003e 74: 152\u0026ndash;61. https://doi.org/10.2307/1939510.\u003c/li\u003e\n \u003cli\u003eGanatsas, P., Tsakaldimi, M., Karydopoulos, T., Papaemannuil, A., Papadopoulos, S.. 2024. \u0026ldquo;Long-Term Effect of Different Forest Thinning Intensity on Carbon Sequestration Rates and Potential Uses in Climate Change Mitigation Actions | Mitigation and Adaptation Strategies for Global Change.\u0026rdquo; \u003cem\u003eMitigation and Adaptation Strategies for Global Change\u003c/em\u003e 23. https://doi.org/10.1007/s11027-023-10102-4.\u003c/li\u003e\n \u003cli\u003eGarc\u0026iacute;a-Pl\u0026eacute;, C., Vanrell, A. and Morey, M. 1995. \u0026ldquo;Litter Fall and Decomposition in a \u003cem\u003ePinus\u003c/em\u003e \u003cem\u003ehalepensis\u003c/em\u003e Forest on Mallorca.\u0026rdquo; \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e 6 (1): 17\u0026ndash;22. https://doi.org/10.2307/3236251.\u003c/li\u003e\n \u003cli\u003eGartner, T.B. and Cardon, Z.G. 2004. \u0026ldquo;Decomposition Dynamics in Mixed-Species Leaf Litter.\u0026rdquo; \u003cem\u003eOikos\u003c/em\u003e 104 (2): 230\u0026ndash;46. https://doi.org/10.1111/j.0030-1299.2004.12738.x.\u003c/li\u003e\n \u003cli\u003eGliksman, D., Haenel, S., and Gr\u0026uuml;nzweig, J.M. 2018a. \u0026ldquo;Biotic and Abiotic Modifications of Leaf Litter during Dry Periods Affect Litter Mass Loss and Nitrogen Loss during Wet Periods.\u0026rdquo; \u003cem\u003eFunctional Ecology\u003c/em\u003e 32 (3): 831\u0026ndash;39. https://doi.org/10.1111/1365-2435.13018.\u003c/li\u003e\n \u003cli\u003eGliksman, D., Haenel, S., Osem, Y., et al. 2018b. \u0026ldquo;Litter Decomposition in Mediterranean Pine Forests Is Enhanced by Reduced Canopy Cover.\u0026rdquo; \u003cem\u003ePlant and Soil\u003c/em\u003e 422 (1): 317\u0026ndash;29. https://doi.org/10.1007/s11104-017-3366-y.\u003c/li\u003e\n \u003cli\u003eGr\u0026uuml;nzweig, J.M., and Gliksman, D. 2021. \u0026ldquo;Litter Decomposition in Mediterranean Pine Forests Subjected to Climate Change.\u0026rdquo; In \u003cem\u003ePines and Their Mixed Forest Ecosystems in the Mediterranean Basin\u003c/em\u003e, edited by Gidi Ne\u0026rsquo;eman and Yagil Osem. Springer International Publishing. https://doi.org/10.1007/978-3-030-63625-8_16.\u003c/li\u003e\n \u003cli\u003eHashimoto, S., Carvalhais, N., Ito, A., Migliavacca, M., Nishina, K. and Reichstein, M. 2015. \u0026ldquo;Global Spatiotemporal Distribution of Soil Respiration Modeled Using a Global Database.\u0026rdquo; \u003cem\u003eBiogeosciences\u003c/em\u003e 12 (13): 4121\u0026ndash;32. https://doi.org/10.5194/bg-12-4121-2015.\u003c/li\u003e\n \u003cli\u003eH\u0026auml;ttenschwiler, S., Tiunov, A.V. and Scheu, S. 2005. \u0026ldquo;Biodiversity and Litter Decomposition in Terrestrial Ecosystems.\u0026rdquo; \u003cem\u003eAnnual Review of Ecology, Evolution, and Systematics\u003c/em\u003e 36 (Volume 36, 2005): 191\u0026ndash;218. https://doi.org/10.1146/annurev.ecolsys.36.112904.151932.\u003c/li\u003e\n \u003cli\u003eHouminer, N., Riov, J., Moshelion, M., Osem, Y. and David-Schwartz, R. 2022. \u0026ldquo;Comparison of Morphological and Physiological Traits between \u003cem\u003ePinus brutia\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Pinus halepensis\u003c/em\u003e, and Their Vigorous F1 Hybrids.\u0026rdquo; \u003cem\u003eForests\u003c/em\u003e 13 (9): 1477. https://doi.org/10.3390/f13091477.\u003c/li\u003e\n \u003cli\u003eHutson, B.R. and Veitch, L.G. 1985. \u0026ldquo;Relationships between Litterfall Rate, Litter Mass and Decomposition Rate in Eucalyptus Forests in Southeastern Australia.\u0026rdquo; \u003cem\u003eAustralian Journal of Ecology\u003c/em\u003e 10 (4): 443\u0026ndash;50. https://doi.org/10.1111/j.1442-9993.1985.tb00905.x.\u003c/li\u003e\n \u003cli\u003eIncerti, G., Bonanomi, G., Giannino, F., et al. 2011. \u0026ldquo;Litter Decomposition in Mediterranean Ecosystems: Modelling the Controlling Role of Climatic Conditions and Litter Quality.\u0026rdquo; \u003cem\u003eApplied Soil Ecology\u003c/em\u003e 49 (September): 148\u0026ndash;57. https://doi.org/10.1016/j.apsoil.2011.06.004.\u003c/li\u003e\n \u003cli\u003eIncerti, G., Carten\u0026igrave;, F., Cesarano, G., et al. 2018. \u0026ldquo;Faster N Release, but Not C Loss, From Leaf Litter of Invasives Compared to Native Species in Mediterranean Ecosystems.\u0026rdquo; \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e 9 (April). https://doi.org/10.3389/fpls.2018.00534.\u003c/li\u003e\n \u003cli\u003eJabiol, J., and Chauvet, E. 2015. \u0026ldquo;Biodiversity and Litter Decomposition: A Case Study in a Mediterranean Stream.\u0026rdquo; \u003cem\u003eFreshwater Science\u003c/em\u003e 34 (2): 423\u0026ndash;30. https://doi.org/10.1086/680094.\u003c/li\u003e\n \u003cli\u003eJarvis, P., Rey, A., Petsikos, C., et al. 2007. \u0026ldquo;Drying and Wetting of Mediterranean Soils Stimulates Decomposition and Carbon Dioxide Emission: The \u0026lsquo;Birch Effect\u0026rsquo; \u0026rdquo; \u003cem\u003eTree Physiology\u003c/em\u003e 27 (7): 929\u0026ndash;40. https://doi.org/10.1093/treephys/27.7.929.\u003c/li\u003e\n \u003cli\u003eJoly, F.X., Scherer-Lorenzen, M. and H\u0026auml;ttenschwiler, S. 2023. \u0026ldquo;Resolving the Intricate Role of Climate in Litter Decomposition.\u0026rdquo; \u003cem\u003eNature Ecology \u0026amp; Evolution\u003c/em\u003e 7 (2): 2. https://doi.org/10.1038/s41559-022-01948-z.\u003c/li\u003e\n \u003cli\u003eKaundun, S.S., Fady, B. and Lebreton, P. 1997. \u0026ldquo;Genetic Differences between \u003cem\u003ePinus halepensis, Pinus brutia\u003c/em\u003e and \u003cem\u003ePinus eldarica\u003c/em\u003e Based on Needle Flavonoids.\u0026rdquo; \u003cem\u003eBiochemical Systematics and Ecology\u003c/em\u003e 25 (6): 553\u0026ndash;62. https://doi.org/10.1016/S0305-1978(97)00049-5.\u003c/li\u003e\n \u003cli\u003eKazakou, E., Vile, D., Shipley, B., Gallet, C. and Garnier, E. 2006. \u0026ldquo;Co-Variations in Litter Decomposition, Leaf Traits and Plant Growth in Species from a Mediterranean Old-Field Succession.\u0026rdquo; \u003cem\u003eFunctional Ecology\u003c/em\u003e 20 (1): 21\u0026ndash;30. https://doi.org/10.1111/j.1365-2435.2006.01080.x.\u003c/li\u003e\n \u003cli\u003eKim, C. 2016. \u0026ldquo;Basal area effects on a short-term nutrient status of litter fall and needle litter decomposition in a \u003cem\u003ePinus densiflora\u003c/em\u003e stand.\u0026rdquo; \u003cem\u003eJournal of Ecology and Environment\u003c/em\u003e 39 (1): 51\u0026ndash;60.\u003c/li\u003e\n \u003cli\u003eLado-Monserrat, L., Lid\u0026oacute;n, A., Bautista, I. 2015. \u0026ldquo;Litterfall, Litter Decomposition and Associated Nutrient Fluxes in \u003cem\u003ePinus halepensis\u003c/em\u003e: Influence of Tree Removal Intensity in a Mediterranean Forest.\u0026rdquo; \u003cem\u003eEuropean Journal of Forest Research\u003c/em\u003e 134 (5): 833\u0026ndash;44. https://doi.org/10.1007/s10342-015-0893-z.\u003c/li\u003e\n \u003cli\u003eLado-Monserrat, L., Lid\u0026oacute;n, A., Bautista, I. 2016. \u0026ldquo;Erratum to: Litterfall, Litter Decomposition and Associated Nutrient Fluxes in \u003cem\u003ePinus halepensis\u003c/em\u003e: Influence of Tree Removal Intensity in a Mediterranean Forest.\u0026rdquo; \u003cem\u003eEuropean Journal of Forest Research\u003c/em\u003e 135 (1): 203\u0026ndash;14. https://doi.org/10.1007/s10342-015-0923-x.\u003c/li\u003e\n \u003cli\u003eLagomarsino, A., Mazza, G., Agnelli, A.E., et al. 2020. \u0026ldquo;Litter Fractions and Dynamics in a Degraded Pine Forest after Thinning Treatments.\u0026rdquo; \u003cem\u003eEuropean Journal of Forest Research\u003c/em\u003e 139 (2): 295\u0026ndash;310. https://doi.org/10.1007/s10342-019-01245-8.\u003c/li\u003e\n \u003cli\u003eLaskowski, R. 2012. \u0026ldquo;What Determines Forest Litter Decomposition? Global Trends and Local Variance.\u0026rdquo; \u003cem\u003eGeographia Polonica\u003c/em\u003e 85 (2): 39\u0026ndash;46. https://doi.org/10.7163/GPol.2012.2.9.\u003c/li\u003e\n \u003cli\u003eLatterini, F., Dyderski, M.K., Horodecki, P., et al. 2023. \u0026ldquo;The Effects of Forest Operations and Silvicultural Treatments on Litter Decomposition Rate: A Meta-Analysis.\u0026rdquo; \u003cem\u003eCurrent Forestry Reports\u003c/em\u003e 9 (4): 276\u0026ndash;90. https://doi.org/10.1007/s40725-023-00190-5.\u003c/li\u003e\n \u003cli\u003eLiu, G., Wang, L., Jiang, L., et al. 2018. \u0026ldquo;Specific Leaf Area Predicts Dryland Litter Decomposition via Two Mechanisms.\u0026rdquo; \u003cem\u003eJournal of Ecology\u003c/em\u003e 106 (1): 218\u0026ndash;29. https://doi.org/10.1111/1365-2745.12868.\u003c/li\u003e\n \u003cli\u003eLogan, J.R., Todd-Brown, K.E., Jacobson, K.M., Jacobson, P.J., Vogt, R. and Evans, S.E. 2022. \u0026ldquo;Accounting for Non-Rainfall Moisture and Temperature Improves Litter Decay Model Performance in a Fog-Dominated Dryland System.\u0026rdquo; \u003cem\u003eBiogeosciences\u003c/em\u003e 19 (17): 4129\u0026ndash;46. https://doi.org/10.5194/bg-19-4129-2022.\u003c/li\u003e\n \u003cli\u003eMalik, A.A., Puissant, J., Buckeridge, K.M., et al. 2018. \u0026ldquo;Land Use Driven Change in Soil pH Affects Microbial Carbon Cycling Processes.\u0026rdquo; \u003cem\u003eNature Communications\u003c/em\u003e 9 (1): 3591. https://doi.org/10.1038/s41467-018-05980-1.\u003c/li\u003e\n \u003cli\u003eMatos, P.S., Barreto-Garcia, P.A.N.B., Gama-Rodrigues, E.F., de Paula, A. and de Oliveira, A.M. 2022. \u0026ldquo;Short-Term Effects of Forest Management on Litter Decomposition in Caatinga Dry Forest.\u0026rdquo; \u003cem\u003eEnergy, Ecology and Environment\u003c/em\u003e 7 (2): 130\u0026ndash;41. https://doi.org/10.1007/s40974-021-00231-4.\u003c/li\u003e\n \u003cli\u003eMauri, A., Di Leo,M., de Rigo, D. and Caudullo, G.. 2016. \u003cem\u003ePinus halepensis and Pinus brutia in Europe: Distribution, Habitat, Usage and Threats\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eMcClaugherty, C. and Berg, B.. 1987. \u0026ldquo;Cellulose, Lignin and Nitrogen Concentrations as Rate Regulating Factors in Late Stages of Forest Litter Decomposition.\u0026rdquo; \u003cem\u003ePedobiologia\u003c/em\u003e 30 (2): 101\u0026ndash;12. https://doi.org/10.1016/S0031-4056(23)00361-X.\u003c/li\u003e\n \u003cli\u003eMichelaki, C., Fyllas, N.M., Galanidis, A., et al. 2019. \u0026ldquo;An Integrated Phenotypic Trait-Network in Thermo-Mediterranean Vegetation Describing Alternative, Coexisting Resource-Use Strategies.\u0026rdquo; \u003cem\u003eScience of The Total Environment\u003c/em\u003e 672 (July): 583\u0026ndash;92. https://doi.org/10.1016/j.scitotenv.2019.04.030.\u003c/li\u003e\n \u003cli\u003eMoore, A.M. 1986. \u0026ldquo;Temperature and Moisture Dependence of Decomposition Rates of Hardwood and Coniferous Leaf Litter.\u0026rdquo; \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 18 (4): 427\u0026ndash;35. https://doi.org/10.1016/0038-0717(86)90049-0.\u003c/li\u003e\n \u003cli\u003eNavarro, F. B., Romero-Freire, A., Del Castillo, T., et al. 2013. \u0026ldquo;Effects of Thinning on Litterfall Were Found after Years in a \u003cem\u003ePinus halepensis\u003c/em\u003e Afforestation Area at Tree and Stand Levels.\u0026rdquo; \u003cem\u003eForest Ecology and Management\u003c/em\u003e 289 (February): 354\u0026ndash;62. https://doi.org/10.1016/j.foreco.2012.09.026.\u003c/li\u003e\n \u003cli\u003eNe\u0026rsquo;eman, G. and Trabaud, L. 2000. \u003cem\u003eEcology, Biogeography and Management of Pinus halepensis and P. brutia Forest Ecosystems in the Mediterranean Basin\u003c/em\u003e. In \u003cem\u003eForest Ecology and Management - FOREST ECOL MANAGE\u003c/em\u003e, vol. 154. https://doi.org/10.1016/S0378-1127(00)00635-6.\u003c/li\u003e\n \u003cli\u003eNi, Y., Yang, T., Ma, Y., et al. 2021. \u0026ldquo;Soil pH Determines Bacterial Distribution and Assembly Processes in Natural Mountain Forests of Eastern China.\u0026rdquo; \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e 30 (11): 2164\u0026ndash;77. https://doi.org/10.1111/geb.13373.\u003c/li\u003e\n \u003cli\u003eOberpriller, J., de Souza Leite, M. and Pichler, M. 2022. \u0026ldquo;Fixed or Random? On the Reliability of Mixed-Effects Models for a Small Number of Levels in Grouping Variables.\u0026rdquo; \u003cem\u003eEcology and Evolution\u003c/em\u003e 12 (7): e9062. https://doi.org/10.1002/ece3.9062.\u003c/li\u003e\n \u003cli\u003eOlson, J.S. 1963. \u0026ldquo;Energy Storage and the Balance of Producers and Decomposers in Ecological Systems.\u0026rdquo; \u003cem\u003eEcology\u003c/em\u003e 44 (2): 322\u0026ndash;31. https://doi.org/10.2307/1932179.\u003c/li\u003e\n \u003cli\u003ePetraglia, A., Cacciatori, C., Chelli, S., et al. 2019. \u0026ldquo;Litter Decomposition: Effects of Temperature Driven by Soil Moisture and Vegetation Type.\u0026rdquo; \u003cem\u003ePlant and Soil\u003c/em\u003e 435 (1): 187\u0026ndash;200. https://doi.org/10.1007/s11104-018-3889-x.\u003c/li\u003e\n \u003cli\u003ePinheiro, J., Bates, D. and R Core Team. 2025. \u0026ldquo;Nlme: Linear and Nonlinear Mixed Effects Models.\u0026rdquo; March 31. https://doi.org/10.32614/CRAN.package.nlme.\u003c/li\u003e\n \u003cli\u003ePurahong, W., Kapturska, D., Pecyna, M.J., et al. 2014. \u0026ldquo;Influence of Different Forest System Management Practices on Leaf Litter Decomposition Rates, Nutrient Dynamics and the Activity of Ligninolytic Enzymes: A Case Study from Central European Forests.\u0026rdquo; \u003cem\u003ePLOS ONE\u003c/em\u003e 9 (4): e93700. https://doi.org/10.1371/journal.pone.0093700.\u003c/li\u003e\n \u003cli\u003eR Core Development Team. 2025. \u0026ldquo;R: The R Project for Statistical Computing.\u0026rdquo; https://www.r-project.org/.\u003c/li\u003e\n \u003cli\u003eRadea, C., and Arianoutsou, M. 2000. \u0026ldquo;Cellulose Decomposition Rates and Soil Arthropod Community in a \u003cem\u003ePinus halepensis\u003c/em\u003e Mill. Forest of Greece after a Wildfire.\u0026rdquo; \u003cem\u003eEuropean Journal of Soil Biology\u003c/em\u003e 36 (January): 57\u0026ndash;64. https://doi.org/10.1016/S1164-5563(00)01045-1.\u003c/li\u003e\n \u003cli\u003eRanucci, M., Perez, M., Lombardi, D. and Vitale, M. 2022. \u0026ldquo;Is the Current Modelling of Litter Decomposition Rates Reliable under Limiting Environmental Conditions Induced by Ongoing Climate Change?\u0026rdquo; \u003cem\u003eSoil Systems\u003c/em\u003e 6 (4): 81. https://doi.org/10.3390/soilsystems6040081.\u003c/li\u003e\n \u003cli\u003eRiva, E.G. de la, Prieto, I. and Villar, R. 2019. \u0026ldquo;The Leaf Economic Spectrum Drives Leaf Litter Decomposition in Mediterranean Forests.\u0026rdquo; \u003cem\u003ePlant and Soil\u003c/em\u003e 435 (1): 353\u0026ndash;66. https://doi.org/10.1007/s11104-018-3883-3.\u003c/li\u003e\n \u003cli\u003eRomanowicz, K.J., Freedman, Z.B., Upchurch, R.A., Argiroff, W.A. and Zak, D.R. 2016. \u0026ldquo;Active Microorganisms in Forest Soils Differ from the Total Community yet Are Shaped by the Same Environmental Factors: The Influence of pH and Soil Moisture.\u0026rdquo; \u003cem\u003eFEMS Microbiology Ecology\u003c/em\u003e 92 (10): fiw149. https://doi.org/10.1093/femsec/fiw149.\u003c/li\u003e\n \u003cli\u003eRousk, J., B\u0026aring;\u0026aring;th, E., Brookes, P.C., et al. 2010. \u0026ldquo;Soil Bacterial and Fungal Communities across a pH Gradient in an Arable Soil.\u0026rdquo; \u003cem\u003eThe ISME Journal\u003c/em\u003e 4 (10): 1340\u0026ndash;51. https://doi.org/10.1038/ismej.2010.58.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eSani Environmental Observatory \u0026ndash; LTER-Greece\u003c/em\u003e. n.d. Accessed August 27, 2025. https://www.lter-greece.gr/sites/sani-environmental-observatory/.\u003c/li\u003e\n \u003cli\u003eSantonja, M., Fernandez, C., Proffit, M., et al. 2017. \u0026ldquo;Plant Litter Mixture Partly Mitigates the Negative Effects of Extended Drought on Soil Biota and Litter Decomposition in a Mediterranean Oak Forest.\u0026rdquo; \u003cem\u003eJournal of Ecology\u003c/em\u003e 105 (3): 801\u0026ndash;15. https://doi.org/10.1111/1365-2745.12711.\u003c/li\u003e\n \u003cli\u003eSantonja, M., Pereira, S., Gauquelin, T., et al. 2022. \u0026ldquo;Experimental Precipitation Reduction Slows Down Litter Decomposition but Exhibits Weak to No Effect on Soil Organic Carbon and Nitrogen Stocks in Three Mediterranean Forests of Southern France.\u0026rdquo; \u003cem\u003eForests\u003c/em\u003e 13 (9): 1485. https://doi.org/10.3390/f13091485.\u003c/li\u003e\n \u003cli\u003eSantos, G.R., Gentil Otto, M.S., Passos, J.R. de Souza, et al. 2019. \u0026ldquo;Changes in Decomposition Rate and Litterfall in Riparian Zones with Different Basal Area of Exotic Eucalyptus in South-Eastern Brazil.\u0026rdquo; \u003cem\u003eSouthern Forests: A Journal of Forest Science\u003c/em\u003e 81 (4): 285\u0026ndash;95. https://doi.org/10.2989/20702620.2019.1633503.\u003c/li\u003e\n \u003cli\u003eSazeides, C., Christopoulou, A. and Fyllas, N. 2021a. \u0026ldquo;Litter Decomposition across a Post-Fire Chronosequence in Mediterranean Pine Forests.\u0026rdquo; Paper presented at CEST2021, Athens, Greece. https://doi.org/10.30955/gnc2021.00060.\u003c/li\u003e\n \u003cli\u003eSazeides, C.I., Christopoulou, A., and Fyllas, N.M. 2021b. \u0026ldquo;Coupling Photosynthetic Measurements with Biometric Data to Estimate Gross Primary Productivity (GPP) in Mediterranean Pine Forests of Different Post-Fire Age.\u0026rdquo; \u003cem\u003eForests\u003c/em\u003e 12 (9): 9. https://doi.org/10.3390/f12091256.\u003c/li\u003e\n \u003cli\u003eSchwieger, S., Dorrepaal, E., Petit Bon, M., et al. 2025. \u0026ldquo;Environmental Conditions Modulate Warming Effects on Plant Litter Decomposition Globally.\u0026rdquo; \u003cem\u003eEcology Letters\u003c/em\u003e 28 (1): e70026. https://doi.org/10.1111/ele.70026.\u003c/li\u003e\n \u003cli\u003eSheffer, E., Canham, C.D., Kigel, J. and Perevolotsky, A. 2015. \u0026ldquo;Countervailing Effects on Pine and Oak Leaf Litter Decomposition in Human-Altered Mediterranean Ecosystems.\u0026rdquo; \u003cem\u003eOecologia\u003c/em\u003e 177 (4): 1039\u0026ndash;51. https://doi.org/10.1007/s00442-015-3228-3.\u003c/li\u003e\n \u003cli\u003eSierra, C.A., Malghani, S. and Loescher, H.W. 2017. \u0026ldquo;Interactions among Temperature, Moisture, and Oxygen Concentrations in Controlling Decomposition Rates in a Boreal Forest Soil.\u0026rdquo; \u003cem\u003eBiogeosciences\u003c/em\u003e 14 (3): 703\u0026ndash;10. https://doi.org/10.5194/bg-14-703-2017.\u003c/li\u003e\n \u003cli\u003eSinsabaugh, R., Lauber, C., Weintraub, M., et al. 2008. \u0026ldquo;Stoichiometry of Soil Enzyme Activity at Global Scale.\u0026rdquo; \u003cem\u003eEcology Letters\u003c/em\u003e 11 (October): 1252\u0026ndash;64. https://doi.org/10.1111/j.1461-0248.2008.01245.x.\u003c/li\u003e\n \u003cli\u003eSoil and Water Resources Institute (SWRI) by Hellenic Agricultural Organization \u0026ldquo;DEMETER.\u0026rdquo; n.d. \u0026ldquo;SWRI, by Hellenic Agricultural Organization \u0026lsquo;Demeter.\u0026rsquo;\u0026rdquo; Accessed November 2, 2025. https://www.swri.gr/index.php/en/.\u003c/li\u003e\n \u003cli\u003eVillaz\u0026oacute;n-Orozco, F.L., Ferr\u0026iacute;n, M., M\u0026aacute;rquez, L., et al. 2025. \u0026ldquo;Drought and Litter Quality Effects on the Contribution of Soil Fauna to Decomposition in a Mediterranean Forest.\u0026rdquo; \u003cem\u003eEuropean Journal of Soil Biology\u003c/em\u003e 125 (June): 103729. https://doi.org/10.1016/j.ejsobi.2025.103729.\u003c/li\u003e\n \u003cli\u003eViros, J., Santonja, M., Temime-Roussel, B., Wortham, H., Fernandez, C., and Orme\u0026ntilde;o, E. 2021. \u0026ldquo;Volatilome of Aleppo Pine Litter over Decomposition Process.\u0026rdquo; \u003cem\u003eEcology and Evolution\u003c/em\u003e 11 (11): 6862\u0026ndash;80. https://doi.org/10.1002/ece3.7533.\u003c/li\u003e\n \u003cli\u003eWallace, K.J., Laughlin, D.C., Clarkson, B.D. and Schipper, L.A. 2018. \u0026ldquo;Forest Canopy Restoration Has Indirect Effects on Litter Decomposition and No Effect on Denitrification.\u0026rdquo; \u003cem\u003eEcosphere\u003c/em\u003e 9 (12): e02534. https://doi.org/10.1002/ecs2.2534.\u003c/li\u003e\n \u003cli\u003eWang, Q.W., Pierist\u0026egrave;, M., Liu, C., Kenta, T., Robson, T.M. and Kurokawa, H. 2021. \u0026ldquo;The Contribution of Photodegradation to Litter Decomposition in a Temperate Forest Gap and Understorey.\u0026rdquo; \u003cem\u003eNew Phytologist\u003c/em\u003e 229 (5): 2625\u0026ndash;36. https://doi.org/10.1111/nph.17022.\u003c/li\u003e\n \u003cli\u003eWang, Q.W., Robson, T.M., Pierist\u0026egrave;, M., Kenta, T., Zhou, W. and Kurokawa, H. 2022. \u0026ldquo;Canopy Structure and Phenology Modulate the Impacts of Solar Radiation on C and N Dynamics during Litter Decomposition in a Temperate Forest.\u0026rdquo; \u003cem\u003eScience of The Total Environment\u003c/em\u003e 820 (May): 153185. https://doi.org/10.1016/j.scitotenv.2022.153185.\u003c/li\u003e\n \u003cli\u003eWhitford, W.G. and Duval, B.D. 2019. \u003cem\u003eEcology of Desert Systems\u003c/em\u003e. Academic Press.\u003c/li\u003e\n \u003cli\u003eZhao, Y.Y., Li, Z.T., Xu, T. and Lou, A. 2022. \u0026ldquo;Leaf Litter Decomposition Characteristics and Controlling Factors across Two Contrasting Forest Types.\u0026rdquo; \u003cem\u003eJournal of Plant Ecology\u003c/em\u003e 15 (6): 1285\u0026ndash;301. https://doi.org/10.1093/jpe/rtac07\u003c/li\u003e\n\u003c/ol\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":"decomposition drivers, carbon sequestration, litterbag method, pine forests","lastPublishedDoi":"10.21203/rs.3.rs-8310680/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8310680/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and aims\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding the main drivers of decomposition, a fundamental ecosystem process, is crucial for predicting carbon dynamics in Mediterranean pine forests in the context of climate change. Litter decomposition is highly influenced by micro-environmental conditions, litter chemistry and forest management practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multi-plot decomposition experiment was conducted in \u003cem\u003ePinus brutia\u003c/em\u003e and \u003cem\u003ePinus halepensis\u003c/em\u003edominated forests at three regions in Greece. In each plot, different forest management practices have been implemented over the last decades (overstory thinning, understory removal and lack of management). The mass loss of pine needles and standardized material (cellulose papers, wood sticks) was systematically measured along with micro-environmental conditions. Nonlinear mixed-effect models were used to explore the influence of species, stand structure, and micro-climatic and soil conditions on the early decomposition rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeedle litter in \u003cem\u003eP. halepensis\u003c/em\u003e dominated stands decomposed approximately twice as fast as litter in\u003cem\u003e P. brutia\u003c/em\u003e stands; however, these species-specific differences were probably masked by micro-environmental variation and therefore could not be attributed to differences in litter chemistry. The analysis of the decomposition rates of cellulose papers revealed an across-region positive effect of pH that interacts with variation in microclimate (air temperature and soil water content) and stand structure (total basal area and leaf area index). For wood sticks, pH was the single across-plot predictor of decomposition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe process of early-stage decomposition across regions could be primarily controlled by variation in soil conditions and their microbial communities, with microclimate and stand structure operating at a smaller (plot-level) scale.\u003c/p\u003e","manuscriptTitle":"Early stage litterfall decomposition dynamics in Pinus halepensis and Pinus brutia stands: Disentangling the effect of climate, species identity and management practices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 10:08:54","doi":"10.21203/rs.3.rs-8310680/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":"c17ca51e-007b-4c73-bd61-1ac076843a4a","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T07:31:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 10:08:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8310680","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8310680","identity":"rs-8310680","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}