Ecography

Anthropogenic climate and land-use change are driving an emerging extinction crisis that is expected to intensify in the future. Species climatic niche limits shape their sensitivity to these pressures, potentially leading to disproportionate extinction risk among more climatically vulnerable species. We test whether realized climatic niche limits are associated with current and projected extinction risk across >23,000 terrestrial vertebrate species. We assessed the phylogenetic structure of thermal and aridity niche limits and related them to IUCN threat status and simulated future extinctions. We show that realized niche limits are phylogenetically conserved, indicating evolutionary clustering of climatic tolerances. Species with colder upper thermal limits were more likely to be classified as threatened across taxa. Aridity niche limits show weaker and less consistent relationships with current threat status. Simulated extinction scenarios reveal taxon-specific patterns of climatic niche loss compared to random species extinctions. We also show significant reductions in phylogenetic diversity relative to randomized expectations based on simulated species extinctions. We find that extinction risk is systematically associated with species climatic niche limits, reflecting evolutionary constraints on environmental tolerance. These results indicate that future extinctions will disproportionately affect climatically vulnerable lineages, with cascading consequences for phylogenetic diversity and ecosystem functioning.

AO_SCPLOWBSTRACTC_SCPLOWMountain gradients facilitate our understanding of species range limits, competition dynamics, stress-resilience trade-offs, and determinants of vegetation zone boundaries. Forest compositional models often use altitude as the main predictor, a proxy for temperature that is defensible where floristic transitions are gradual and climate relationships are linear. However, mountains with distinct assemblages, representing tropical gradients or areas with complex biogeographic history, require a modeling framework that reflects non-linear dynamics or interactions between environmental factors, including outlier events (rather than mean conditions). Our study system encompasses both tropical and temperate forests along a broad ([~]3000 m) altitudinal gradient, positioned within a narrow latitudinal band (< 1{degrees}) and composed of mature, continuous forest in the Bhutan Himalaya. To represent the breadth of climatic conditions experienced over a trees lifetime, we used a Bayesian modeling paradigm and integrated multi-generational field knowledge to develop a priori hypotheses and informed priors, with consideration of monsoon seasonality and possible ecophysiological thresholds. Our approach followed three stages (the Pattern, the Mechanism, the Test). Specifically, we interpolated microclimate data and derived custom metrics based on thermodynamics, propagating uncertainty into subsequent models to test whether climate posteriors outperformed altitude in explaining growth form partitioning. For spatial patterns, we identified six distinct vegetation zones (encompassing 145 species from 57 families), with a mid-gradient peak in richness at the tropical-temperate transition zone, and convergence of deciduousness at either end of the gradient. For individual growth forms, abundance was tied to different ecological mechanisms, explained by adaptations to climatic stressors and competition trade-offs. For instance, evergreen broad-leaved dominance was linked to ephemeral cloud immersion, whereas tropical deciduous species were affiliated with higher vapor pressure deficit at lower altitudes. Most importantly, compositional (between-group) models showed that the interaction between frost events and fog probability (air saturation prior to the dry season) governed growth form partitioning more than any single factor; temperate deciduous species, confined to a narrow altitudinal band, exemplified this finding. Our methodological approach is transferable to other data-sparse mountain systems, and our results highlight the vulnerability of unique habitat types and montane endemics under climate change scenarios that alter the fog-frost dynamics. Second abstract in DzongkhaTo see the second abstract in Dzongkha, the official language of Bhutan, please visit our Zenodo site: https://doi.org/10.5281/zenodo.19081441.

Aim: To forecast near-future arrivals of non-native terrestrial and freshwater vertebrates at the regional level. Location: Global (geopolitical regions worldwide, including countries and main administrative divisions). Methods: We compiled first regional record data and assembled functional and macroecological variables for 1,931 non-native vertebrate species. For each region, we identified recently arrived non-native species using retrospective windows of thirty and twenty years ending in 2015 (1986-2015; 1996-2015). We then fitted region-specific random-forest models classifying recently arrived species versus those not yet arrived using as predictors: (i) harmonised species traits (e.g., habitat, diet, body size and native-range attributes) and (ii) spread history, capturing time since first record elsewhere. Predictive performance was evaluated using leave-one-out cross-validation, comparing full models with trait-only and spread-only variants. We also assessed relationships between predictive accuracy, predictor importance, and the geographic positioning and trade connectedness of regions. Finally, we predicted region-specific probabilities of arrival for species not yet recorded. Results: Forecasting accuracy was consistently high across regions and taxa, with AUC values above 0.9 in more than half of the focal regions. Full models substantially outperformed models using either predictor set alone, and spread-history-only models typically exceeded trait-only models. Relative importance of spread-history predictors declined with geographic distance to the focal region, whereas predictability was lower in highly trade-connected regions. Predicted near-future high-risk arrivals were dominated by birds and freshwater fishes and showed strong regional structuring. A small set of species ranked highly across many regions (e.g., birds: Phasianus colchicus, Acridotheres tristis, Amandava amandava, Colinus virginianus, Corvus splendens and Lonchura malacca; fishes: Coregonus peled and Oreochromis mossambicus; mammal: Oryctolagus cuniculus), suggesting substantial unrealised spread potential. Main conclusions: Near-future regional arrivals of non-native vertebrates are predictable from spread history and species traits. This enables scalable, updateable regional watchlists to support prevention, early detection and horizon scanning.

O_LIThermal performance curves (TPCs) predict species vulnerability to climate change, but standard respirometry assumes that measured oxygen consumption reflects physiological state. Sessile invertebrates that retract their tentacles and contract under thermal stress violate this assumption, with unmeasured consequences for thermal limit estimates. C_LIO_LIWe tested this behavioral confound in an undescribed cold-water intertidal anemone (Urticina sp.) in the Northwest Atlantic by integrating a negative binomial encounter-rate regression, a maximum entropy species distribution model (both from effort-corrected iNaturalist data), and closed-chamber respirometry across seven temperatures (1-30{degrees}C, 18 individuals, 126 trials). C_LIO_LIThe strongest distributional predictors were cloud cover and coastal urbanization, with a weaker association with winter minimum SST; direct evidence for warm-edge thermal limitation came from the experiment. Anemone expansion state (scored 0-1 from fully closed to fully expanded) was variable and without a clear trend across the coldest treatments but declined above 20{degrees}C before collapsing at the 30{degrees}C treatment, which proved lethal to all individuals. C_LIO_LIStandard TPC models extrapolated the thermal maximum far beyond the lethal bracket ([~]74{degrees}C symmetric Gaussian; 45.9{degrees}C asymmetric). A Bayesian multiplicative model that separated physiology from behavior showed that physiology continued to track temperature while expansion state declined above 20{degrees}C; a fully expanded anemone respired about twice as fast as a fully closed one at the same temperature. The decline in measured respiration is therefore both behavioral and physiological, and disentangling the two requires recording expansion state alongside oxygen consumption. C_LIO_LIBecause a closed anemone cannot feed or exchange gases, the ecologically relevant thermal limit is the temperature at which the animal can no longer maintain its normal expanded posture, not a curve-fitted thermal maximum. That behavioral threshold leaves warm-edge populations within a few degrees of functional thermal failure. C_LIO_LIFuture thermal physiology studies of organisms capable of modulating oxygen consumption through behavior should incorporate quantitative behavioral covariates to separate physiological from behavioral components of the metabolic response. C_LI

Climate is a strong predictor of global species richness, but the effects of climatic conditions are difficult to separate from the geography of the climates themselves. Recent work in climate space has shown that the area and isolation of discrete climatic conditions explain broad-scale richness gradients, yet the internal spatial cohesion of those climates remains poorly characterized. Here, we introduce climate percolation as a complementary descriptor of climate geography, measuring the degree to which the total area of a climate bin is concentrated within effectively connected fragments. Using global range maps for amphibians, birds, mammals and reptiles, we quantified species richness across a two-dimensional climate space defined from 12 climatic variables and evaluated the independent and joint effects of climate area, climate isolation and climate percolation across multiple climate-space resolutions. Climate isolation and percolation were strongly coupled: their first joint axis explained, on average, more than 95% of their shared variation, revealing a dominant gradient of climate fragmentation along which geographically isolated climates are also internally subdivided. Despite this collinearity, percolation consistently outperformed isolation in cross-validation across all four vertebrate groups, with particularly strong predictive gains for birds and mammals. The largest improvements, however, came from the shared isolation-percolation axis, indicating that vertebrate richness in climate space is more strongly associated with the integrated geographical structure of climates than with either inter-fragment distance or internal cohesion alone. These results suggest that climate fragmentation is a multidimensional property of environmental space, combining both the distance among climate fragments and the dominance structure of connected areas. By extending climate-space approaches from area and isolation to percolation, our framework provides a more complete description of how the geography of climate may shape global richness gradients and offers a structural basis for anticipating how future changes in climate connectivity could alter biodiversity patterns.

Species distribution models (SDMs) increasingly inform conservation and biosecurity decisions in freshwater systems, where the reliability of its uncertainty estimates matters as much as its point predictions. Ensemble SDMs derive prediction intervals from across-replicate variance, but this variance captures systematic error only when replicates disagree about it, an assumption that fails when training data are contaminated with low-accuracy records, the norm in citizen-science datasets. Whether this failure is spatially uniform or concentrates in identifiable parts of a range is unknown. Using a panel of European freshwater crayfish spanning native headwater-associated species and invasive lowland colonizers, we show that contamination-induced calibration failure is strongly spatially structured: it concentrates at stream-network headwaters, the topological tops of the network, where upstream-aggregated predictors are structurally undefined, and scales with contamination severity, replicated across four species and both dominant ensemble protocols (replicate and consensus). The failure is driven by upward prediction bias, not by intervals failing to widen: contaminated ensembles overpredict suitability in headwaters, and because the bias is shared across ensemble members, the intervals do not flag it. This is a conservation-relevant blind spot, because headwaters are both refugia for threatened native crayfish and front lines for invasion; an SDM that silently overpredicts suitability there misdirects survey and management effort toward the segments where its predictions are least trustworthy. Standard leave-one-basin-out conformal calibration, the recommended panel-wide remedy, repairs marginal coverage but leaves headwaters undercovered, because a single calibration threshold is dominated by the abundant non-headwater segments. A group-conditional (Mondrian) variant, calibrating the two populations separately, restores reliable coverage in both at no extra cost and reallocates width where it is needed. We recommend network-position-stratified calibration as a default for ensemble SDMs in dendritic freshwater systems.

Ecological theory states that the geographic ranges and coexistence of species are determined by fundamental and realized niches - the sets of environments where a species intrinsic population growth rate is positive in the absence and presence of competitors, respectively. Yet large-scale tests of niche theory have been hampered by the challenge to obtain sufficient data on demography and competition. Here, we quantify fundamental and realized niches by combining data on variation in fundamental demographic rates, community composition and the abiotic environment across the global geographic ranges of 29 shrub species from the South African Fynbos biome (a global biodiversity hotspot). Estimated pairwise competition coefficients and fundamental-realized niche contrasts reveal multi-scale mechanisms of species coexistence. At small scales, species generally exert stronger competition on themselves than on other species. At biogeographical scales, more competitive species have narrower fundamental niches but are not significantly better dispersed, which provides evidence for a generalist-specialist trade-off rather than a competition-colonization trade-off. Under both present and future climates, interspecific competition more strongly limits the realized niches and geographic ranges of generalist species. The large-scale application of niche theory thus identifies key forces shaping biodiversity and indicates that generalist species may be more strongly impacted by climate change than previously thought.

Understanding how habitat connectivity shapes biodiversity remains a major ecological challenge. In particular, the roles of connectivity and ecological heterogeneity on co-variation in plant species diversity and intraspecific genetic diversity is not understood. We combined species distribution modelling, resistance-to-movement mapping, landscape connectivity analysis and population genomics to investigate diversity patterns in three wet meadow herbs, Scorzonera humilis, Oenanthe peucedanifolia and Lychnis flos-cuculi, and their surrounding plant communities. Genetic diversity patterns differed strongly among co-occurring species. Connectivity metrics explained genetic diversity only in O. peucedanifolia, and environmental drivers of genetic diversity were highly species specific. Importantly, genetic diversity changed with the presence of some species in the community, but it was consistently unrelated to indicators of local plant community diversity. Overall, the processes shaping within-species biodiversity may differ fundamentally from those structuring habitat connectivity and plant species communities, with important implications for conservation.

Aims: Species' distributions are determined by the interplay between ecological niche and dispersal ability, constrained by biogeographical barriers. Bald-headed uakaris (Cacajao spp.) are highly specialized primates often associated with seasonally flooded forests. In this study, we used ecological niche models to assess changes in habitat suitability and geographic distribution of uakari species under future scenarios. Location: Western Amazonia. Methods: We integrated ecological niche models, current deforestation data, and dispersal ability to estimate habitat suitability under two Shared Socioeconomic Pathway (SSP) scenarios: intermediate (SSP2-4.5) and very high (SSP5-8.5) greenhouse gas (GHG) emissions. Results: Our models project shifts in suitable conditions for all species. Three of the five species are projected to experience substantial reductions ([&ge;]62%) in suitable habitat conditions within their current ranges by 2050 under both future scenarios. Across the western Amazonia, up to 219,189 km2 and 211,276 km2 of land are projected to be unsuitable within the uakari ranges under the intermediate and very high emissions scenarios, respectively. This is particularly relevant for C. calvus, C. rubicundus, and C. ucayalii. At the species level, the uakaris may lose between 343 km2 and 84,531 km2 of their ranges in the intermediate scenario and 858 km2 and 76,216 km2 in the very high scenario. Shifts in suitability due to climate change are expected to vary from 6 to 191 km in the intermediate scenario and from 5 to 168 km in the very high scenario. Furthermore, the uakaris may lose between 0.5% and 8% of their current ranges due to deforestation in all scenarios. Main conclusions: Our findings reveal a high sensitivity of the uakaris to climate change impacts. It is projected that all species may experience contractions in the suitable areas and spatial suitability within their ranges by 2050, underscoring climate change as a relevant threat to these taxa.

Freshwater monitoring programmes like the European Unions Water Framework Directive (WFD) provide a wealth of data on European lake status, including water quality and macrophytes (aquatic plants) as critical habitat features that support health of humans and wildlife. Easier WFD data access can enable external management and research to better safeguard human and natural freshwater use. We demonstrate a replicable workflow to easily download and process multi-year (2007-2024) observations of lake macrophytes (425 sites) and complementary water quality variables (202 sites) from Swedish WFD data. Then, we illustrate the value of improved data access to address ecological questions that drive conservation, investigating how spatial scales influence macrophyte richness and associated water quality relationships using a spatial random intercept model. Decomposing the spatial intercept links small scales (<10 km) to site-level gradients and large scales (>100 km) to biogeographical drivers. Stochastic and environmentally-structured processes coexisted at intermediate scales (10-100 km). Adding water quality rarely improved overall predictive performance of macrophyte diversity models but consistently influences the role of different spatial scales. Water quality variables showed consistent spatially structured variation at intermediate scales and unique spatial patterns in tandem, overlapping with large-scale biogeographical influences. Altogether, we show context-dependencies for spatial model interpretation and provide guidance in accounting for spatial confounding to improve inferential and predictive performance. Our workflow and results show a clear way forward for accessing high-quality macrophyte and water quality data sets and their utility for addressing ecological questions that guide macrophyte protection under the WFD. HighlightsO_LIyears Swedish of macrophyte and water quality monitoring data were extracted. C_LIO_LIrichness showed scale-specific patterns linked to geographic gradients. C_LIO_LIbest predictive models for richness had no water quality at all. C_LIO_LIoverlap in their spatial scales and must be carefully separated. C_LIO_LIpen access data and multiscale analysis can apply to many ecological questions. C_LI

Circuit theory has been successfully applied to ecological connectivity modelling, notably via the Circuitscape software, which is typically run locally on a laptop or via a server. For downstream geospatial web applications relying on connectivity analysis, backend infrastructure is required, which can be costly and require advanced data governance. Recent developments in WebAssembly now allow fast C++ or Rust code to be run directly in a sandboxed browser environment for edge computing. We present a WebAssembly/Rust toolset with a geospatial data pipeline and efficient edge-computing implementation of connectivity analysis. This approach may be useful for geospatial modelling software where rasters and memory footprint are small enough for the browser context. Our results show that as expected, Circuitscape solves 1000x1000 raster networks 1-2x faster, but requires further file writes. Accounting for total program runtime, our web implementation can be faster for the given context.

Protected areas cannot halt biodiversity loss in isolation; integrating them with surrounding human-dominated landscapes is critical. However, this integration is challenged by substantial landscape heterogeneity at their borders, hindering our understanding of cross-border changes in ecosystem service provision. We introduce a novel framework for characterizing these dynamics by analyzing ecosystem service gradients along protected area borders. For 16 protected areas in the French Alps, we assessed 12 ecosystem services using a mix of established biophysical models and novel connectivity-based models for mobile species. These were aggregated into three stakeholder-driven domains reflecting respectively rural, cultural, and urban management priorities. Automated polynomial regression analysis classified borders into five gradient types. The most common were 'Decreasing Gradients', representing a decline in ecosystem services outside the protected area, and 'Increasing Gradients', with the opposite pattern. Our analysis reveals these patterns are driven by specific landscape configurations, uncovering frequent trade-offs between the three management priorities, where, for instance, landscapes supporting rural priorities often degrade cultural and urban ones. We also identify key opportunities for synergies, by identifying areas where ecosystem services for all three priority domains increase simultaneously outside the protected area. This spatially explicit typology provides a powerful diagnostic tool for designing targeted interventions, such as prioritizing habitat restoration where ecosystem services decline or managing agricultural landscapes to mitigate conflicts across management priorities, supporting a more effective integration of protected areas into the wider landscape.

Biodiversity is predicted to stabilize ecosystems if species have different environmental responses. How this response diversity is shaped by ecological and evolutionary processes remains poorly understood. We determine the drivers of thermal response diversity of 16 Swiss peri-alpine lake-fish communities. We report the first evidence that evolutionary diversification of lineages through adaptive radiation can increase the response diversity of an ecosystem. In-situ diversification increases response diversity in the cold-deep lake environment, but non-endemic and non-native species contributed only weakly to response diversity. The loss of endemic species during historical anthropogenic eutrophication led to a negative legacy on present thermal response diversity in cold and deep lake strata. Overall, the interplay of evolutionary diversification, ecological assembly and anthropogenic impacts drives variation in response diversity. Conserving and restoring processes that generate diversity may help maintain ecosystem stability beyond the Anthropocene.

This study investigates changes in habitat connectivity and meta-ecosystem resilience between 1817 and 2022 along a 150 km section of a large European river (Danube) and its adjacent floodplains. The analysis was based on a time series of functional habitat networks (graphs) constructed from historical records and remote sensing data, integrating habitat suitability and dispersal modes of functional organism groups. The results indicate that overall habitat availability declined by about 50% since 1817, leading to the near-complete loss of functional connectivity among dynamic habitats by 1910. Less dynamic habitats persisted or expanded but became functionally less connected. Connectivity-based habitat classification further revealed four distinct functional connectivity clusters and the near-complete loss of an originally dominant, dynamic, and highly connected habitat type. Furthermore, the results indicated a fundamental loss of meta-ecosystem resilience. This development was reflected by increased spatial modularity among functionally similar habitats and by reduced layer dissimilarity and structural robustness in multilayer networks representing the spatial habitat structure and functional habitat connectivity across different functional organism groups.

The diversity of species responses to environmental change (response diversity) is a key mechanism of ecological stability. However, anticipating where strong or weak stabilizing responses emerge is challenging because species responses can depend on the local community and the specific stability metric. Whether species traits can consistently inform on how species respond to disturbances, enabling less context-dependent predictions, remains an open question. To address this gap, we use microcosm experiments on marine phytoplankton to test how response diversity supports multiple aspects of community stability under pulse temperature changes, testing both an increase (heatwave) and a decrease in temperature (coldspell). We then map species traits to their responses in a community to identify which traits modulate and predict species' sensitivities. Fundamental response diversity, based on the diversity of species responses to temperature measured in isolation, was a weak predictor of community stability, and relationships differed between disturbances (i.e., heatwave and coldspell). Instead, species traits were consistent predictors of species responses in communities. Small, fast-growing species were more tolerant and benefited from the disturbance, while large, slow-growing species were less tolerant and decreased in proportion - these patterns were consistent across disturbances and community compositions. These results suggest that strong trait-performance relationships might reduce the importance of response diversity for stability. But these findings also show that general species traits, such as size and growth rate, can predict which species, and how, contribute to community responses, providing an empirical basis to relate species traits to stability outcomes under climate change.

AO_SCPLOWBSTRACTC_SCPLOWO_LICommunity assembly graphs (CAGs) summarize which species combinations can coexist and how single-species invasions drive transitions between them, encoding the pathways, alternative endpoints, and cycles that make up a communitys assembly history. Constructing CAGs from dynamical models requires methods that are both computationally tractable and faithful to the underlying ecological dynamics. However, existing methods rely on restrictive assumptions, such as global stability, that exclude alternative stable states and non-equilibrium dynamics known to occur in empirical systems. C_LIO_LIWe develop a computational pipeline that constructs CAGs from any generalized Lotka-Volterra model. Building on the invasion graph framework and its connection to permanence, the pipeline verifies that community dynamics are bounded, identifies which subsets of species coexist in the sense of permanence, determines which single-species invasions are dynamically realized, and assigns each community a topographic height equal to the length of the longest assembly path leading to it. We also provide a numerical algorithm to simulate the dynamics of community assembly. C_LIO_LIWe prove several general properties of the resulting graphs, including that a successful invader is never subsequently excluded and that, in the absence of assembly cycles, permanent communities can be reassembled by introducing their species one at a time in the right order. We prove that the CAG faithfully reproduces the compositional shifts seen in the numerically simulated dynamics of assembly. Applying the pipeline to three empirically based models (a New Zealand grassland, a European pasture, and a Puerto Rican ant community), we show how competition strength and mutualistic feedbacks reshape the assembly landscape and how intransitive competition generates assembly cycles. C_LIO_LIOur approach accommodates alternative stable states and non-equilibrium dynamics without requiring global stability, and it turns the long-standing landscape metaphor into a quantitative, mechanistically grounded object by resolving what "height" means. More broadly, it makes the topography of the assembly pathways measurable, providing a way to compare the historical contingency and predictability of the assembly in ecological systems. C_LI

Rock surface weathering is a critical element in the process of early soil formation, in which lichens are thought to play a significant role. Crustose lichens, with a large area of rock-surface contact, are generally considered more influential in rock weathering, while foliose and fruticose growth forms, with more developed three-dimensional structure and less rock-surface contact, are rarely considered in this context. Here, we test the extent to which all three growth forms contribute to granitic rock surface weathering in Maritime Antarctic ecosystems, by quantifying rock hardness beneath foliose (n = 2 species), fruticose (n = 2) and crustose lichens (n= 5). Our data confirm that foliose lichens reduced rock surface hardness by 9%, to a lesser extent than crustose and foliose lichens (40% and 31% reduction, respectively). To disentangle whether these effects result from lichen-induced weathering or lichen preference for pre-weathered rock, we also analyzed a dated deglaciation sequence on granitic rocks from the Morteratsch Glacier forefield in the Swiss Alps. At this location, the impact of crustose lichens on rock substrate hardness generally increased with time since exposure from glacial retreat and with lichen thallus size. We conclude that lichen presence on rock surfaces significantly reduces rock hardness, with crustose lichens having a greater impact than foliose and fruticose forms, highlighting the potential role of lichens of all three growth forms in driving substrate breakdown and shaping early-stage ecosystem processes in polar and alpine regions.

Antarctic benthic organisms are highly diverse and play a critical role in the Southern Ocean ecosystem. Despite decades of sampling, vast areas of the Antarctic continental shelf remain biologically unsurveyed due to logistical and financial constraints, limiting baseline knowledge essential for effective conservation planning. Species distribution models (SDMs) allow biodiversity to be inferred in the absence of biological data by linking benthic community patterns to environmental predictors. However, the resolution of the environmental predictors, particularly bathymetry, varies significantly between regions, casting doubt about how reliably SDMs can be used to predict into regions where only coarse-resolution data are available. Here, we show that SDMs trained on high-resolution data underestimate Antarctic benthic morphospecies richness by up to 18% when applied to aggregated coarse-resolution environmental data (and up to 50% when using satellite-derived ETOPO bathymetry). Using six systematically degraded versions of high-resolution multibeam bathymetry and annotated seafloor imagery across three Antarctic regions, we evaluate SDM performance both with and without additional environmental variables. High-resolution bathymetry captures terrain complexity most effectively, but we find that the spatial distribution of richness hotspots and the median richness per cell remain consistent, provided models are applied at the same resolution at which they were trained. Our results suggest that while high-resolution bathymetry may enhance local predictions, coarse-resolution data may be more robust for regional-scale predictions, such as those used for Antarctic shelf-wide spatial planning.

Agroecosystems are dynamic ecosystems, constituted by patches of vegetation and agricultural use, where biodiversity is shaped by spatial and temporal variability. While most studies have focused on spatial composition and configuration, the role of temporal variability remains poorly understood. Yet, temporal dynamics can strongly modify species composition, abundance, and persistence in ecological communities. Temporal variability is particularly relevant in agroecosystems with rainfed agriculture where environmental conditions shift dramatically between rainy and dry seasons. In this paper, we assess the role of temporal variability on biodiversity maintenance in an agricultural matrix using a metacommunity model that simulates an agricultural landscape under rainfed conditions, that is, with abrupt seasonal changes in the agricultural patches. This model couples a local community network dynamic with a migration dynamic and is based on empirically documented features of rainfed agricultural matrices. Our results show that temporal variability provides new opportunities for species to recover from low densities. However, the effect of temporal variability is not straightforward. It depends on the initial and final conditions, the migration and mortality rates and the intensity of temporal variability. Overall, our findings highlight the need to further investigate temporal variability to better understand its role in shaping biodiversity in agricultural landscapes.

Environmental DNA (eDNA) has emerged as a promising tool for estimating fish abundance, yet linking eDNA concentration to true density remains a significant challenge in seasonal systems, where the signal is strongly influenced by temperature. We investigated whether eDNA can serve as an abundance index for three-spined stickleback (Gasterosteus aculeatus) in four coastal bays of the Baltic Sea (5.7-20.5{degrees}C, April-July 2023), by pairing eDNA sampling with two trap types of contrasting catchability. Light traps capture fish by phototactic attraction during darkness, so their catchability is driven primarily by night duration rather than temperature, while benthic traps respond to temperature through the same activity-driven mechanism as eDNA production. The temperature sensitivity of eDNA estimated from field data was far higher than physiological expectation (Q10 = 12.4, against a maximum metabolic rate benchmark of Q10 = 3.5), indicating that the field temperature signal reflects ecological change in addition to metabolism. We then compared how well three eDNA predictors tracked a combined trap-based abundance index: uncorrected eDNA, eDNA corrected with the temperature response constrained to the laboratory metabolic rate (a first-principles correction), and eDNA corrected with the response estimated from the field data. Uncorrected and first-principles-corrected eDNA were both strong predictors of abundance (standardised slopes of 0.45 and 0.43), whereas the field-corrected predictor was not (0.08). Uncorrected and first-principles-corrected eDNA performed comparably because temperature and abundance increased together over the season; the first-principles correction is nonetheless preferable, as it remains reliable when this covariation is unknown a priori. We conclude that estimating a temperature correction from field data should be avoided in seasonal eDNA monitoring, because it removes the abundance signal together with the temperature effect and assumes a stability in abundance that cannot be verified without independent reference data.