Diversity and Distributions

1. Species monitoring programmes regularly encounter missing data, which complicates tasks such as estimating population size or detecting temporal trends. Selecting an imputation method suited to the properties of the data is therefore an important practical challenge, particularly for species exhibiting overdispersion and zero-inflation. 2. We compare seventeen imputation methods, comprising thirteen Poisson-based statistical models (accounting for overdispersion and zero-inflation, with fixed, random and multivariate structures) and four contrast-based approaches (LORI, MICE, missForest, correspondence analysis). Using four complete monitoring datasets of waterbird species surveyed across France and Italy over 21 years, illustrating a variety of abundance distributions, we introduced missing observations under a realistic Missing At Random mechanism, at rates from 5% to 70%. We evaluated all methods on computational burden, prediction accuracy for positive and null counts, quantification of uncertainty, and accuracy of population-size estimates. 3. No single method dominates across all criteria. Statistical models and contrast-based approaches yield similar point predictions, but only statistical models provide a genuine measure of uncertainty. Models that jointly account for overdispersion and zero-inflation perform best at predicting zero counts and achieving reliable prediction intervals, and the most suitable method ultimately depends on the abundance distribution of the target species. 4. These results provide practical guidance for ecologists selecting an imputation strategy for incomplete count data, highlighting the trade-offs between predictive accuracy, computational cost, and the ability to propagate uncertainty through subsequent ecological analyses such as population-trend estimation.

Arctic ecosystems are undergoing rapid climate warming, providing an opportunity to examine molecular evolution under environmental stress. Here, we used whole-genome resequencing to assess population structure, genetic diversity, and recent demographic history in the snowy owl (Bubo scandiacus), a highly mobile Arctic apex predator classified as Vulnerable by the IUCN. We analysed genome-wide data from individuals sampled across the species' circumpolar range using a chromosome-level reference genome. Population genomic analyses revealed an absence of detectable population structure, indicating extensive gene flow and near-panmixia across continents. Genome-wide nucleotide diversity was low ({pi}= 2.47 x 10^(-4)), while runs of homozygosity were dominated by short tracts, indicating limited standing genetic variation despite little evidence for recent close inbreeding. Our demographic inference revealed a sustained decline in effective population size over the past ~1,600 years, a pronounced reduction coinciding with the Medieval Warm Period followed by a partial recovery during the subsequent cooler interval. This direct, measurable effect of past climate warming on effective population size raises concerns for the long-term survival of the snowy owl. Our results demonstrate that high connectivity and low levels of recent inbreeding have not necessarily buffered this species against climate-driven demographic erosion of genetic diversity. This study highlights the value of using whole-genome population genomics to inform genomic monitoring and conservation decision-making in rapidly changing environments.

Understanding the demographic processes shaping population connectivity is essential for conservation. Yet disentangling the effects of dispersal and local effective density remains challenging because traditional genetic approaches integrate signals over evolutionary timescales. Identity-by-descent (IBD) segments provide opportunities to reconstruct recent demographic connectivity over timescales relevant to contemporary conservation challenges, but their reliability under realistic ecological conditions remains poorly evaluated. Here, we assess the ability of two promising spatiall IBD-based methods, IBD-Analysis and MAPS, to estimate effective density and dispersal variance. Using a simulation-inference framework combining spatially explicit simulations, IBD segment extraction, and demographic inference, we evaluated both methods across a wide range of demographic, ecological and methodological scenarios. Both approaches reliably recovered demographic parameters under homogeneous landscapes, with IBD-Analysis providing accurate estimates of global effective density and dispersal across a broad range of conditions. Spatial heterogeneity emerged as the main driver of inference performance. When demographic parameters varied across space, IBD-Analysis integrated local variation into biased global estimates, whereas MAPS captured spatial patterns but showed substantial variability and generated spurious spatial structure in homogeneous landscapes. Methodological factors had comparatively weaker effects than ecological complexity, highlighting the importance of accounting for landscape structure when applying IBD-based demographic inference. These results highlight the potential of IBD-based approaches to move beyond descriptive patterns of genetic connectivity toward mechanistic inference of demographic processes. By distinguishing the contributions of dispersal and density, these methods can improve conservation decisions by linking genomic patterns to key demographic parameters relevant for conservation.

1. Occurrence records underpin biodiversity analyses, but their coordinates are often erroneous or missing. Misplaced coordinates distort environmental predictors and range estimates used in conservation assessment. Existing tools flag suspect records or georeference locality text, but rarely correct coordinates, provide calibrated uncertainty, or quantify downstream consequences. 2. We present GeoMend, a deterministic, rule-based method that flags and corrects suspect coordinates and georeferences records lacking coordinates from locality text, assigning each a precision tier or uncertainty radius. It uses no machine learning or artificial intelligence. We evaluate it against expert coordinates for 16,913 records and 736 species on Hispaniola, compare georeferencers using 5,000 localities from 72 countries, and measure changes in environmental predictors and the extent of occurrence. 3. On held-out Hispaniola records, GeoMend flags 92% of coordinates more than 5 km from expert truth, versus 27% for CoordinateCleaner. Among the accepted corrections with paired coordinates, the median error falls from 2.19 to 0.82 km, and 68% of the complete held-out pairs improve. On records placed by both GeoMend and GEOLocate that also have original coordinates, the median error is 0.67 km for GeoMend versus 10.69 km for GEOLocate. For records lacking coordinates, evaluated uncertainty radii contain expert truth about 92% of the time in well-supported gazetteer classes. Correction reduces the mean standardized environmental distance from expert truth by 38% and moves the extent of occurrence toward expert truth by a median of 8% (mean of 79%). Corrections and newly georeferenced records give 17 Hispaniola species their first range estimate. 4. On the global benchmark, GeoMend is more accurate than GPT-4o for two-thirds of the records placed by all four comparators, with median errors of 4.4 and 10.8 km, respectively. Applied to Global Biodiversity Information Facility (GBIF) records lacking coordinates, it places records from 82,180 previously unmapped species across 233 countries, ranging from precise locality interpretations to administrative centroids. Automated placement makes the global backlog more approachable, while explicit tiers distinguish uses supported by precise and coarse coordinates. Further georeferencing requires regional expertise to combine automated placement with local knowledge.

1. Plant populations at high elevation persist near their physiological limits, and treeline position is one of the clearest expressions of this constraint. In many mountain regions, however, this limit reflects historical land use as much as climate. Treeline shift is most often tracked as the displacement of a boundary line (1D) overlooking structural changes only visible when the ecotone is analysed as a surface (2D). 2. We reconstructed treeline ecotone dynamics at nine sites in the Italian Alps using aerial imagery from 1954 and 2017. At each site we detected the timberline and tree species line, quantified elevational displacement, and measured ecotonal surface change, relating them to climatic, land-use, and topographic predictors. 3. Both boundaries advanced upslope at all nine sites, with timberlines shifting faster (1.6 m/yr) than tree species lines (1.1 m/yr). This decoupled velocity caused the ecotonal surface to contract by ~17%. Land-use variables consistently explained more variance than climatic ones. 4. Land-use legacy and abandonment have a major role in shaping treeline dynamics. Boundary displacement reveals vegetation advance at both margins, while the surface view reveals concurrent narrowing of the habitat available for tree establishment. Both perspectives are needed to fully characterize how plant populations respond to shifting limiting factors at the treeline ecotone.

Information on the long-term dynamics of species distributions is essential for assessing global biodiversity change and its causes, and for informed conservation decisions. A valuable source of such historical information are atlases. They provide spatial standardisation, extensive geographic coverage, temporal replication, documented sampling protocols, and span multiple species. Here we present model-based occupancy probabilities for 1,055 bird and 58 butterfly species, derived from nine gridded atlas datasets spanning several editions over the past 50 years. These are: Japan, New Zealand, New York state (US), the states of Alberta, Quebec, and The Maritimes (Canada), Czech Republic, United Kingdom, and Europe. Despite standardisation and coordination during data collection, the raw atlas data still contain some variation in sampling effort. We accounted for this using a two-step process: (1) we estimated a proxy for sampling effort in each grid cell within each atlas period using the Frescalo Algorithm, and (2) we ran a Bayesian spatial occupancy model for each species and atlas period, using the estimated sampling effort as the single predictor of detection in the observation process of the model. As the main product, we provide occupancy probabilities for species across grid cells and atlas periods. We also report uncertainty around each probability estimate and flag ~21% (rare) species per atlas for which the model did not fit well. The probability distributions are fit for purpose and served in a user-friendly database with standardised geographic projection, metadata, and taxonomy.

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

Aim: Given that much of Earth's biodiversity is contained within species, inferring intraspecific genetic diversity and identifying its ecological correlates is essential for understanding evolutionary processes and informing conservation. Here, we investigate broad-scale variation in species-level mitochondrial genetic diversity and evaluate the relative importance of alternative ecological hypotheses proposed to explain it. Location: Brazilian Atlantic Forest, Iberian Peninsula, northwestern Africa, Arabian Peninsula, Madagascar and Australian Monsoonal Tropics. Taxon: Amphibians and reptiles Methods: We assembled a comprehensive dataset by integrating and manually curating mitochondrial sequences, occurrence records, environmental data and species traits from multiple open-access sources. We used multiple mitochondrial genes to estimate mean nucleotide diversity for each study species and quantified regional variation in genetic diversity. We then used random forests models to evaluate the relative importance of selected variables representing hypotheses related to water-energy availability, topography, historical climatic stability, land-cover characteristics, species geographic characteristics, life-history traits, evolutionary history and conservation status. Results: The final dataset comprised 247 amphibians and 295 reptiles, for which 43 and 52 explanatory variables were evaluated, respectively. Overall, the strongest support was found for hypotheses related to life-history traits, topographic heterogeneity and water-energy availability. In amphibians, higher genetic diversity was associated with smaller body size and greater environmental heterogeneity, particularly broader elevational and precipitation gradients. In reptiles, genetic diversity was primarily associated with life-history traits, with species of smaller body size and lower reproductive output exhibiting higher diversity. Long-term climatic stability showed comparatively little explanatory power for both taxa. Main conclusions: Our comprehensive study demonstrates that integrating extensive manual data curation with comparative macrogenetic analyses provides a robust framework for identifying broad-scale ecological correlates of mitochondrial genetic diversity. These findings improve our understanding of the factors associated with contemporary patterns of genetic diversity and provide a foundation for future conservation assessments in data-limited systems.

Megadiverse countries concentrate most of the World's biodiversity. These countries encompass different levels of research infrastructure, and nearly all of them have been historically committed to the Convention of Biological Diversity, often setting precedents and leading new approaches. Here, we ask whether the assessment of genetic diversity indicators provide novel insights for biodiversity management and conservation in one of those countries, Mexico. We evaluated the first national application of the Kunming-Montreal Global Biodiversity Framework (GBF), genetic diversity indicators in Mexico. The data covered 97 taxa across diverse ecosystem types and taxonomic groups, including crop wild relatives, to assess the capacity of genetic diversity indicators to provide novel insights, as well as their relevance for strengthening national conservation strategies. We calculated two indicators: the proportion of populations with an effective population size greater than 500 (Ne 500) and the proportion of populations maintained (PM). Data were sufficient to estimate the Ne 500 indicator for 47 taxa and the PM for 28 taxa, with mean indicator values of 0.217 and 0.936, respectively. Species' geographic range was positively associated with Ne 500, whereas national endemicity had no significant effect. Comparisons with the IUCN Red List and Mexico's national extinction-risk legislation revealed that genetic vulnerability occurs even among taxa not currently recognized as threatened. Our findings highlight a gap in legal recognition of genetic diversity protection that could be strengthened by incorporating population-level assessments focused on maintaining adaptive potential in the long-term. Additionally, the indicators allowed to highlight conservation concerns within crop wild relatives, though fully realizing their potential requires integrating the entire wild-to-domesticated complex. The Mexican experience highlights that the main challenge for scaling the monitoring of genetic diversity is not only generating new data, but integrating existing information into systems that strengthen collaboration and communication among researchers, local communities, and government institutions.

Climate change will alter habitat suitability for plant communities in mountain ecosystems of the Western Himalaya. There will be contrasting magnitudes and spatial configurations of these changes for different plant species, and it is an important question how to translate these contrasts into effective climate-proof multi-species conservation planning. This study assessed current habitat associations and projected changes in suitable habitat for two contrasting species, Meconopsis aculeata and Rhododendron campanulatum, in Himachal Pradesh, India. Field surveys and spatially filtered occurrence records were combined with MaxEnt modelling using 10 non-collinear environmental predictors. Habitat suitability was projected to 2050 and 2070 under Shared Socioeconomic Pathways; SSP1-2.6, SSP2-4.5 and SSP5-8.5 using HadGEM3-GC31-LL. Field observations showed contrasting habitat associations, with M. aculeata occurring mainly in open alpine herbaceous communities and R. campanulatum in subalpine woody communities. Elevation was an important predictor for both species while climatic predictors contributed differently to their relative importance and response patterns. Future projections indicated reductions in suitable habitat of approximately 25% for M. aculeata and 27-28% for R. campanulatum. Habitat-change maps showed heterogeneous patterns of loss, gain and persistence rather than uniform contraction, and centroid displacement remained small (7.7-8.5 km). We performed proximity analyses to show which currently suitable areas are most close to areas of future gain, in order to support the design of climate-proof protected areas. We discuss how interspecific differences in response are relevant to multi-species conservation planning that considers the spatial relationship between current high-suitability areas and the gain, loss and redistribution of future suitable habitat.

Arboreal camera trapping is an emerging remote sensing tool for building plant-frugivore interaction networks with enormous potential to provide new insights into seed dispersal as an eco-evolutionary process. Interaction data derived from camera trap videos broadly falls into two categories: visitation, indicating the presence of an animal in a tree, and intake, indicating true fruit consumption. These linked approximations of seed dispersal may or may not provide distinct sets of information, shaping our understanding of network dynamics, yet the extent to which emergent network structure depends on variable choice is unknown. To address this gap, we deployed camera traps in the canopy of 21 plant species in the Costa Rican tropical dry forest, an endangered biome with sparse community-wide plant-frugivore interaction data. Our objectives were: 1) Quantify plant-frugivore network structure in the tropical dry forest via network, group, and species-level metrics; and 2) Compare metrics between visitation and intake networks. We find strong structural patterns of low connectance, low nestedness, high modularity, and low niche overlap that differ significantly from null expectations in the same direction in both networks. The visitation network exhibited significantly greater modularity & species degree than the intake network, while the intake network exhibited significantly greater plant niche overlap than the visitation network. These findings suggest that the likelihood of consuming fruit on camera differs across species, which may have downstream consequences on other emerging metric estimations. Researchers seeking to build quantitative plant-frugivore networks should therefore consider visitation and intake variables in concert when relying on camera trap data. This work provides foundational plant-frugivore interaction data for a critically endangered biome and enhances our understanding of camera trapping as a remote sensing tool in frugivory and seed dispersal research.

Species are dramatically shifting their geographical ranges, yet the consequences of these spatial redistributions on species interactions have not been examined at broad spatial and temporal scales. Here, we investigate how the redistribution of species abundance over the past 50 years affects the potential for predator-prey interactions among North American birds. We first predict potential predator-prey interactions based on existing data and functional traits, then construct a novel, abundance-weighted metric of interaction potential for each pair of predator and prey in local communities. Across North America, the potential for predator-prey interactions has been increasing over time. We further show that this widespread increasing trend in interaction potentials is mostly driven by higher abundances of predator species. The spatial redistributions of predators and prey lead to turnover of interactions within local communities, and the turnover is less associated with changes in climate but more associated with human-induced land use changes. Our novel approach and results highlight the importance of considering traits, abundance, and species interactions - including known and potential interactions - when studying the ecological consequences of species range shifts.

Aim Regional climate-change refugia - areas where abiotic conditions related to hydrology and topography sustain unique plant communities despite an otherwise unsuitable regional macroclimate - have been essential for maintaining biodiversity through past climatic transitions. At fine spatial scales, regional refugia can also contain microrefugia, where canopy structure and topography strongly buffer understory microclimate. We tested whether such microrefugia structure understory plant communities within a regional climate-change refugium, where sheltered, groundwater-fed canyons have sustained a biogeographically distinctive flora since the Last Glacial Maximum. Location Temperate forest in the North American Great Plains, Nebraska, USA. Methods In a 20.2-ha forest inventory plot spanning a sharp topographic gradient, we quantified diversity, composition, and distributions and of understory woody and herbaceous plants across seven functional groups along fine-scale topographic, light, and soil moisture gradients. Results Woody species density and richness were significantly higher within sheltered microrefugia in canyon bottoms and on forested slopes and strongly decreased with increasing canopy openness, whereas C4 grass richness was highest along the exposed, high-light forest-prairie ecotone, reflecting divergent functional group responses to microclimate buffering. Soil moisture structured richness across functional groups: woody plant and C3 grass richness peaked at moisture extremes, sedge richness at intermediate moisture variability, and C4 grass richness declined with increasing moisture variability. Both woody and herbaceous understory plants showed strong variation in composition with topography; in particular, woody seedlings and graminoids exhibited strong species turnover with increasing elevation. Main Conclusions These results demonstrate that topographically driven microrefugia support disproportionate understory biodiversity and suggest that protecting both regional refugia and finer-scale microrefugia will be critical for conserving plant communities, including species documented exclusively in microrefugia, as macroclimate continues to change.

1. Assessing the conservation value of secondary forests and reforestation is important for conservation planning. Butterflies are widely used as indicators, and fruit-feeding butterflies are often used in biodiversity assessments in tropical landscapes. 2. We examined fruit-feeding butterfly diversity, abundance, and community composition across a primary forest, a secondary forest, and a plantation forest in northern Vietnam. We sampled butterflies nearly monthly during one year using baited traps. 3. Primary and secondary forest supported higher species richness than plantation forest. Moreover, while secondary forest had mainly a subset of species found in primary forest, the plantation forest had a distinct butterfly fauna. 4. Butterfly abundance and community composition varied across months, without showing a clear seasonal pattern. 5. Our results suggest that secondary and plantation forests do not fully replace primary forests, but they contribute meaningfully to butterfly conservation in human-modified tropical landscapes.

As ecosystems face rapid environmental change, understanding the interplay between landscape structure, genetic diversity, and adaptive potential is critical for predicting resilience. We investigated how biome-specific connectivity and environmental heterogeneity mediate adaptive strategies and resilience in the tropical rainbowfish (Melanotaenia splendida splendida) across contrasting rainforest and savannah ecosystems in tropical Australia. Using an integrative framework combining genomic, morphological, and environmental analyses, we inferred that hydroclimatic variation has been a key driver of genetic and phenotypic divergence. Rainforest populations exhibited strong local adaptation to fragmented, stable environments, while savannah populations showed less genomic differentiation and morphological divergence, patterns consistent with weaker local adaptation. We hypothesise that greater reliance on phenotypic plasticity may facilitate adaptive responses in savannah populations, reflecting their exposure to more connected and variable environments. Variance partitioning highlighted the contrasting roles of neutral and adaptive processes across biomes, with implications for managing resilience in freshwater systems under climate change. Our findings underscore the trade-offs between connectivity, local adaptation, and plasticity, providing a model for understanding adaptation and resilience across diverse ecosystems.

Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.

Habitat fragmentation and destruction in old-growth forests represents a primary threat to specialist species, frequently resulting in the erosion of genetic diversity. The Hermit Beetle, Osmoderma barnabita, is a protected relic of primeval forests and an obligate saproxylic specialist whose larval development depends exclusively on the wood-mould of hollow trees. This study provides the first high-resolution, genome-wide assessment of O. barnabita across seven Polish forest complexes using double-digestion Restriction-site Associated DNA sequencing data from 49 individuals. Following rigorous quality control and reference bias assessment, we evaluate contemporary population structure. To distinguish between evolutionary processes, we isolated 2,920 putatively adaptive outliers from the neutral background, evaluating both neutral and adaptive genomic signals. The analyses revealed four distinct genetic clusters, confirming that the Polish metapopulation is severely subdivided. Crucially, the westernmost population in the Lower Silesian Forest emerged as the most divergent evolutionary unit (FST up to 0.360), suggesting potential hybrid origin or long-term isolation. We observed a striking linear correlation (R2=0.986) between neutral diversity and adaptive potential, indicating that genetic drift in small and isolated forest fragments is actively depleting the variation required for long-term persistence of this species. Our findings demonstrate significant gene flow restriction across all sites, suggesting that current conservation frameworks fail to encompass these highly differentiated lineages. To mitigate this genetic vulnerability, we recommend reclassifying these clusters as independent Management Units and Evolutionary Significant Units. Maintaining forest continuity and protecting local micro-habitats are essential to prevent further isolation and preserve the genomic integrity of this saproxylic flagship species.

BACKGROUND: The Argentine ant (Linepithema humile) is among the world's worst invasive species. In the Donana Biological Reserve (Spain), it is associated with human structures that provide the mild, moist conditions it needs within the hot, dry scrubland. We evaluated a habitat-modification treatment, predicting that removing this suitable habitat would eradicate the invader locally. Over an 18-year period, we monitored ant communities at a treated and an untreated site, at different distances from the human structures, before and after treatment, and analysed how temperature and worker numbers affected encounters at baits between the Argentine ant and native ants. RESULTS: Distance to the human structures shaped ant communities at both sites, but only the treated site changed after treatment. There, the Argentine ant decreased close to the human structure and almost disappeared farther from it, whereas the endemic Cataglyphis tartessica and C. floricola recovered, especially close to the structure. At baits, the outcome of encounters depended mainly on how temperature changes favoured each species relative to its maximum activity temperature: Cataglyphis spp. displaced the Argentine ant in the morning, as temperature increased, and the Argentine ant recovered the baits in the late afternoon. Numerical advantage favoured only the Argentine ant. CONCLUSION: Habitat modification reduced the Argentine ant and allowed endemic ants to recover, but did not eradicate the invader, which persisted at low abundance 13 years after treatment. In protected areas, habitat management should be combined with eradication, monitoring and prevention of spread.

Mediterranean forests are emerging as one of the clearest hotspots of climate-change impacts, where rapid warming and increasingly severe droughts and heatwaves are pushing forest ecosystems toward their physiological and functional limits. These pressures threaten productivity, carbon dynamics, ecosystem stability, and the services forests provide. In this context, a process-based forest ecosystem model was used to assess climate-driven changes in carbon dynamics and productivity stability across Italian forests within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework. 5135 plots were simulated, corresponding to ~80% of the field plots included in the second Italian National Forest Inventory (INFI). Model simulations were first evaluated against independent observations of gross primary production (GPP) from satellite-based products and forest carbon variables from the third INFI. Forest dynamics from 2015 to 2100 were simulated under three CMIP6-based emission scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) using two climate forcings (GFDL-ESM4 and UKESM1-0-LL). Future responses were assessed for GPP, aboveground biomass, growing stock volume, and carbon stocks, while variability in GPP was quantified using the coefficient of variation as an indicator of forest stability. Results show that the model reproduced the main spatial and temporal patterns of forest productivity and carbon stocks. Future projections revealed strong spatial heterogeneity and increasing divergence among scenarios after mid-century. GPP showed the earliest and strongest response to climate change, with mountain forests generally maintaining or increasing productivity and exhibiting greater stability, whereas coastal forests showed pronounced productivity declines and increasing variability, particularly under higher-emission scenarios and more extreme climate forcing. In contrast, carbon stocks generally continued to increase throughout the century, although carbon accumulation progressively slowed toward the end of the century, particularly under higher-emission scenarios.

Aims In forest ecosystems, tree species composition and diversity are fundamental drivers of biodiversity and many species are closely associated to specific tree species as hosts. Saproxylic diversity across tree species may be shaped by interactions between ecological and environmental factors, such as resource availability. However, a throughout and unbiased quantification of diversity across tree species is missing. We investigated patterns an drivers of saproxylic beetle diversity across European tree genera while accounting for differences in sampling effort. Location Europe Methods We used a large dataset, of 25 studies, comprising 500,000 deadwood-dependent beetles, representing 1041 saproxylic beetle species, sampled from 29 tree genera. We accounted for sampling effort by estimating diversity using coverage-based standardization across Hill numbers. We than used linear models to identify drivers of saproxylic beetle diversity across tree genera and assessed differences in community composition using Nonmetric Multidimensional Scaling (NMDS). Additionally, the most abundant beetle families were identified for a subset of 14 genera. Results Quercus supported the highest richness of infrequent, frequent, and highly frequent species, while Fagus exhibited the highest observed (unstandardized) species richness. Beetle communities were less differentiated when focusing on infrequent species, whereas tree genera were separated when focusing on frequent and highly frequent species. In addition, we found that resource availability and maximum tree height best predicted infrequent beetle diversity, whereas resource availability was the main driver of frequent species. In contrast, the same analyses for unstandardized data indicated postglacial occurrence as an important factor for infrequent species. Main conclusions Although resource availability supported the species area hypothesis, standardized data revealed higher richness on smaller trees, partly contradicting this pattern. Our results highlight the importance of accounting for sampling bias and promoting tree diversity, particularly oaks, to sustain saproxylic beetle diversity.