Ecology Letters

Predicting the stability of ecological communities in changing environments is challenging. Classical theory posits that community stability cannot be understood without considering interspecific interactions. A contrasting view is that species environmental responses and their variation (response diversity) influence stability to the extent that effects of interspecific interactions can be ignored. Surprisingly, few studies have evaluated the relative importance of interactions versus species responses. Moreover, trait-based measures of response diversity often show limited predictability. Here, we introduce community performance curves, the aggregate of species performance curves, as a powerful mechanistic link between community composition and stability. This approach reveals that species responses predict most of the variation in community stability in simulated communities, even when the strength of interspecific interactions varies. An experiment with ciliate communities corroborates these findings, while a literature review reveals how rarely both mechanisms are assessed jointly. By moving from summary traits to community performance curves, we reconcile the two perspectives: while species interactions undeniably shape community dynamics, community performance curves are sufficient to predict stability. This provides the opportunity to predict community stability, even when information about the multitude and diversity of interspecific interactions is unavailable.

Natural populations exhibit complex class structures that profoundly shape evolutionary trajectories. While evolutionary demography provides a formal framework to predict adaptation using invasion fitness, the high mathematical dimensionality of these models often precludes analytical solutions, obscuring biological interpretation and hindering the analysis of long-term evolutionary outcomes. Because current reduction techniques remain fragmented, a unifying theoretical foundation is critically needed. Here, we introduce "structural evolutionary invasion analysis," a systematic framework that integrates two complementary tools to simplify complex life cycles. First, we formulate the "invasion determinant," an algebraic method that yields a direct scalar condition for mutant invasion. Second, we develop the Projected Next-Generation Matrix (PNGM), which structurally compresses life-cycle graphs by eliminating secondary classes. We demonstrate that this reduction is mathematically equivalent to separating dynamical timescales, explicitly preserving Fishers reproductive values for the retained focal classes. Crucially, under the standard assumption of weak selection, our synthesized framework guarantees that all properties of evolutionary singularities--including their location, convergence stability, and evolutionary stability--are strictly identical to those derived from the full, unreduced model. Illustrated with diverse ecological examples, this framework provides modellers with a rigorous and tractable toolkit for decoding state-dependent selection in high-dimensional populations.

O_LIUnderstanding functional community structure and the niche-based mechanisms that enable coexistence among sympatric species is essential for explaining how biodiversity is maintained in natural systems, and for anticipating how ecological communities will respond to ongoing environmental change. Stable isotope analysis provides a process-oriented perspective on resource use by integrating information across time and space, thereby allowing reconstruction of realised isotopic niches that reflect multiple dimensions of ecological differentiation. C_LIO_LIWe applied this framework to a community of ungulates in the Central-Eastern Italian Alps, including red deer (Cervus elaphus), roe deer (Capreolus capreolus), and Alpine chamois (Rupicapra rupicapra). Using stable isotope ratios in summer-grown hair segments ({delta}13C, {delta}15N, {delta}34S, {delta}18O, {delta}2H), we quantified species-specific n-dimensional niche hypervolumes within a Bayesian framework and estimated niche regions, overlap probabilities, univariate differentiation and multivariate structure. C_LIO_LIDespite broad dietary overlap typically observed among these ungulates, we found clear isotopic niche segregation, with mean pairwise overlap consistently remaining below 40%. Three dimensions emerged as primary drivers of differentiation: water sourcing ({delta}18O), diet quality ({delta}15N), and habitat openness ({delta}13C). Specifically, chamois appeared to derive more water from plants in their diet rather than from drinking, and to consume a higher-quality diet compared to Cervids. Red deer relied more heavily on forested habitats for resource use compared to roe deer and chamois, and additional isotopic differences between red deer and roe deer may stem from fine-scale abiotic conditions like microclimate and topography. We found no isotopic evidence for differential niche breadth among the three ungulate species. C_LIO_LITogether, these patterns highlight functional differentiation across multiple ecological axes, offering mechanistic insight into how these ungulates segregate realised niche space despite substantial potential for resource overlap. This multi-element isotope perspective underscores the value of integrative, process-based approaches for understanding current coexistence as well as improving predictions of how mammal communities may reorganise under accelerating environmental change. C_LI

Theory suggests that different components of environmental fluctuations, from daily and seasonal cycles to multidecadal trends, can have distinct and even opposing effects on species abundances and community dynamics, depending on their specific adaptations. But empirical research that deconstructs the influence of these different cycles on communities is lacking. Here, we used long-term biological monitoring data together with flow records of rivers across New Zealand to (i) investigate the role of fast, slow, and seasonal river-flow fluctuations in structuring macroinvertebrate communities; and (ii) to assess whether life-history and mobility traits mediate the response. Using joint species distribution models, we found striking differences in taxon and community responses to the different components of river flow variation. Responses to slow fluctuations were generally stronger and better predicted by traits, while responses to seasonal fluctuations were highly heterogeneous. Fast increases in flow, typical of flooding events, had pervasive negative effects on species abundances, but the severity of impact partly depended on mobility traits. Our results suggest that different ecological mechanisms underpin the response to distinct environmental fluctuations, highlighting the value of jointly considering multiple temporal scales of variation and species functional traits to understand and predict how communities reorganise under fluctuating environmental regimes.

Cross-scale integration remains a persistent challenge in ecology. Mechanistic network models have advanced this integration by linking individual behavior to community dynamics. Their complexity, however, often limits exploration to numerical simulations, which tend to be insufficient for fully unveiling the fundamental rules governing system behavior. Extracting these rules requires moving beyond numerical observation to establish exact, analytical constraints. Here, a complete mathematical analysis of a mechanistically detailed plant-pollinator model is presented. This cross-scale analysis decouples transient and equilibrium dynamics, proving that pollination strictly gates plant persistence while recruitment competition caps equilibrium abundance. The precise behavioral mechanisms scaling up to determine network stability are determined: nestedness stabilizes communities by generating floral reward gradients that guide adaptive foraging, whereas connectance destabilizes by eroding these rescue pathways. Additionally, native community persistence and biological invasions are conceptually unified; a single, multi-scale reward threshold (R*) is shown to govern both native survival and alien establishment. These analytical derivations are distilled into conceptual frameworks and visual summaries accessible for empiricists interested in theory and conceptual unification. By translating numerical observations into rigorous, trait-grounded proofs, this analysis demonstrates that complex, cross-scale networks are tractable, revealing the precise conditions under which communities assemble, persist, and collapse.

Species tolerating the same environmental conditions can potentially colonize and thrive in the same habitats and eco-regions. Are any pair of those species equally probable to co-occur in the same community? Can we quantify the propensity of two species to co-occur together? Here, we focus on a simple but largely overlooked community-level pattern: the co-occurrence-occupancy curve, which relates the tendency of species to co-occur with others to their total occupancy across sites. We first define this empirical curve and then derive its expected shape under a random null model that assumes site equivalence and species independence. Building on these results, we introduce the Species Association Index (SAI), an occupancy-standardized measure that quantifies the tendency of a species to associate with others independently of its overall frequency of occurrence. The SAI enables meaningful comparisons among species with contrasting occupancies and provides a transparent benchmark against which departures from neutrality can be assessed. We illustrate the approach using two contrasting systems--tropical rain forest trees on Barro Colorado Island and organisms from Mediterranean rocky shores--highlighting both the generality of the co-occurrence-occupancy framework and its limitations.

Projecting species responses to changing temperatures remains a major challenge in ecology. Central to this effort is harnessing our understanding of species thermal physiological traits, which underlie ectotherm fitness. These traits are typically characterized by describing performance across temperatures (thermal performance curve, TPC), and/or tolerance limits, which capture endpoints of biological failure. Despite their importance, we still lack an understanding of the functional relationship between these traits, limiting our ability to integrate them into comprehensive vulnerability assessments. Using a synthesized dataset of >100 ectotherms, we tested how heat tolerance (CTmax) relates to key TPC traits: thermal optima, thermal maxima, and the supra-optimal range of temperatures where performance is positive. Across taxa, TPC traits were positively related to CTmax, demonstrating a link between heat tolerance and temperature-dependent performance at sub-critical temperatures. While acute locomotor performance scaled proportionally with CTmax, metabolic processes and sustained locomotion scaled sub-proportionally, suggesting decoupling of CTmax and performance among high-CTmax species. This suggests that using CTmax as a comparative metric may overestimate thermal safety margins for metabolic processes critical to growth. Our results indicate that while CTmax and TPCs reflect shared underlying constraints--particularly in acute neuro-muscular traits--their relationship is dependent on timescale and the TPC response trait. Our findings connect our understanding of the processes that maintain performance over thermal gradients with those that cause performance to fail, improving our ability to project species persistence in a warming world. SignificanceClimate warming is increasingly reshaping the thermal environments that govern species persistence worldwide. Predicting vulnerability requires integrating multiple aspects of thermal biology, yet relationships among widely used thermal traits remain poorly understood. By synthesizing data from more than 100 ectotherm species, we quantify links between acute heat tolerance and traits describing sustained biological function across temperatures. We show that performance at relatively benign temperatures and performance at thermal extremes are coupled, but this coupling is strongly process and timescale dependent, with close correspondence for short term locomotion but weaker coupling for metabolic processes. Our results link the processes that maintain performance across temperatures with those that cause failure, fundamentally advancing our projections of species performance in a warming world.

Habitat complexity (HC) in part determines the diversity, stability, and behavior of food webs and can influence predation according to a wide variety of functional relationships. Many aquatic species provide habitat complexity and are also consumed by other species (e.g., macrophytes, corals, mussels). However, food web theory does not readily account for these species that act as edible habitat complexity (EHC). Here, we combine existing theory on predator-prey interactions, HC, and prey switching to describe the role of EHC in benthic food web models. We dissect feedback loops in each model to demonstrate how self-regulation of the prey species, mediated by species densities and HC, drives that food webs behavior. HC can stabilize predator-prey interactions by coupling prey self-regulation with HC self-regulation. EHC can further stabilize predator-prey interactions across a wide variety of "HC functions" that relate HC to predation rates. Significance StatementHabitat complexity (HC) plays a critical role in trophic interactions, population dynamics, and food web stability. However, little theory exists to describe edible habitat complexity (EHC), where a species is both consumed and confers habitat complexity for other species. We provide a series of models demonstrating how HC and EHC alter the population dynamics and stability of simple aquatic food webs. HC is strongly stabilizing in food webs by providing safety in rarity for prey. EHC provides safety in rarity for both prey and the EHC species because their predators are omnivorous. Given the prevalence of EHC species in aquatic systems (e.g., macrophytes, corals, mussels), our models demonstrate the importance of maintaining EHC species in aquatic systems for stable food webs.

Assessing ecosystem resilience at large spatial scales remains a major challenge in ecology and conservation. While resilience is typically inferred from temporal dynamics or perturbation experiments, ecosystems governed by spatial self-organization are thought to encode resilience-related information directly in their spatial structure. Here, we show that the spatial patterns of seagrass meadows can be used to infer ecological deterioration and resilience-related states from a single cartographic snapshot. Using a mechanistic model of Posidonia oceanica self-organization, we generated thousands of synthetic seascapes spanning a mortality-driven gradient from continuous meadows through fragmented and collapsed states and trained deep convolutional neural networks to classify discrete pattern states and estimate continuous levels of deterioration along this gradient. Applied to habitat cartography across the Balearic Islands, the framework revealed ecologically interpretable regional variation in meadow condition, enabling large-scale assessment of seagrass resilience from spatial snapshots alone. Networks trained exclusively on synthetic data generalized effectively to real meadows, showcasing that mechanistic models can substitute for empirical training labels. More broadly, our results establish a transferable strategy for integrating ecological theory and machine learning to monitor the resilience of self-organized ecosystems when direct temporal observations are sparse or unavailable.

O_LISpecies diversity potentially has a dual effect on communities: a generally positive effect on overall community biomass, reflecting the expression of species response and interaction traits, and a poorly characterised effect on mass-specific species contribution to ecosystem functions, reflecting the expression of their effect traits. Disentangling the effects of biodiversity on total biomass from those on effect trait expression would help settle a long-standing debate by clarifying how biodiversity relates to both facets of species effects on ecosystem functioning. C_LIO_LIFollowing the classical BEF approach, we calculate expected ecosystem function based on observed functioning in monoculture. We then derive a net biodiversity effect (NBE) and decompose it into four components: the classical complementarity and selection effects on total community biomass, and complementarity and selection effects on effect trait expression. The latter two reflect, respectively, a complementarity or facilitation in how effect traits influence the function, and how species with the highest potential for increasing the function become dominant in the community. C_LIO_LIWe illustrate this NBE decomposition with three ecosystem functions (nitrogen retention capacity, soil hydraulic conductivity improvement, and forage digestibility) measured in assembled communities under controlled experimental conditions of perennial grassland plants. Regarding nitrogen retention, we find a positive complementary effect via total biomass, but a negative biodiversity effect via effect trait expression. For hydraulic conductivity improvement, biodiversity effects are mostly mediated by total biomass. As for forage digestibility, we found a positive complementarity effect on trait expression, outweighed however by a negative selection effect. This analysis reveals how biodiversity may have contrasting effects on ecosystem functions via its impact on biomass and effect trait expression. C_LI SynthesisSeparating between the effect of biodiversity on plant community biomass and on effect trait expression at the community level is one important step towards understanding the pathways by which diverse plant communities drive ecosystem functioning.

2The genotype-to-phenotype architecture (GPA), defined by complex interactions such as pleiotropy, epistasis, and regulatory control, is a fundamental yet often overlooked driver of biodiversity dynamics. While empirical evidence suggests that traits mediating species interactions (biotic) and environmental responses (abiotic) are frequently correlated, most eco-evolutionary theories treat these traits as independent, leaving a gap in our understanding of how genomic architecture influences community-level outcomes. In this study, we contrast two distinct GPAs, modular (independent trait evolution) and correlated (integrated trait evolution), within a spatially explicit multilayer network framework. We evaluate their impact on biodiversity across varying regimes of selection, migration, and biotic and environmental filtering. Our results reveal a hierarchy of drivers: selection strength dictates the absolute magnitude of the architectural effect, while migration and context-dependent biotic and abiotic effects determine which architecture yields a diversity advantage. Correlated GPAs enhance species coexistence and diversity in low-migration landscapes characterized by strong selection and moderate, balanced biotic and abiotic pressures. In these contexts, trait integration serves as a buffer against selective noise. Conversely, modular GPAs support higher diversity under high migration and strong biotic interactions, where the decoupling of trait modules provides the adaptive flexibility necessary to navigate spatially conflicting selective pressures. Our findings demonstrate that genomic architecture acts as a critical filter for environmental perturbations. Integrating complex GPAs into multispecies models is essential for quantifying the co-evolutionary feedbacks among traits, population adaptation, and species persistence. Our framework provides a path for predicting how biodiversity emerges and persists across biological scales, from genomics to communities and food webs, under the accelerating pressures of global change. 1 ConclusionsO_LIWe integrate trait architecture to spatial biodiversity to show biodiversity patterns are not merely products of ecological interactions, but are fundamentally constrained by Genotype-to-Phenotype Architecture (GPA). By linking GPA to biodiversity we show the interplay between the complexity of an organism and community structure in determining diversity patterns. C_LIO_LIThe hierarchy of Eco-Evolutionary Drivers: We establish a new conceptual hierarchy where selection strength acts as the fundamental governor of architectural impact, while the specific architecture predicting higher diversity (Correlational vs. Modular) is dictated by the interplay of migration scales and context-dependent biotic and abiotic dynamics. C_LIO_LISelection-Migration contingency for coexistence: We provide a new hypothesis for species coexistence: Correlational selection serves as a stabilizing force under dispersal limitation, whereas Modular trait architecture provides the adaptive flexibility to maintain diversity in high-migration, spatially heterogeneous landscapes. C_LIO_LIAdaptive decoupling as a diversity engine: We propose that trait modularity functions as a "buffer" against extinction by decoupling phenotypic responses. This allows populations to navigate conflicting selective pressures, effectively facilitating evolutionary rescue in complex biotic environments. C_LIO_LIMethodological framework for empirical inference: To bridge the gap between theory and data, we provide a novel likelihood-based framework. This enables researchers to infer latent trait architectures from population genomic samplings, turning GPA from a theoretical construct into a measurable sampling variable in natural populations. C_LIO_LIWe define a new roadmap for the next generation of eco-evolutionary modeling. By identifying the gaps between existing simulation engines, we provide a conceptual "blueprint" for a digital ecosystem that fully integrates complex genetic architecture with global bio-diversity dynamics. C_LI

Ecological networks are often analyzed as aggregated structures, an approach that has yielded important insights but implicitly assumes that selection acts uniformly across communities. We refine this perspective by showing that structural selection becomes detectable only when analyses focus on meso-structural scales. Using a detailed trophic network, we quantified local structural environments through order-2 egonets and evaluated how structural traits shape interaction geometry. Aggregated representations captured broad patterns but showed no structural gradients, whereas egonets revealed strong axes of differentiation. Hierarchical asymmetry emerged as the dominant meso-structural trait, with local connectivity contributing secondary structure. Principal component analyses and Manhattan distances showed that meso-structural domains retain the heterogeneity through which selection acts. Structural selection was sparse but concentrated in hierarchical domains, identifying the meso-structural scale as the level at which evolutionary signals become detectable in ecological networks. Together, these results position structural selection as an evolutionary process acting on ecological structure, expanding how selection can be conceptualized in complex systems. One-Sentence SummaryStructural selection becomes detectable only at meso-structural scales, revealing evolutionary gradients that vanish in aggregated networks and expanding how selection is conceptualized in structured ecological systems. Teaser textEcological networks are usually analyzed as large, aggregated structures, but this perspective hides the evolutionary signals that operate at finer scales. By zooming into meso-structural domains, local neighborhoods captured through order-2 egonets, we uncover strong structural gradients that disappear in whole-network representations. These meso domains reveal where structural selection operates, exposing hierarchical asymmetry as the dominant axis of differentiation. Our results show that aggregated networks smooth out the heterogeneity through which selection acts, whereas meso-structural environments retain the variation that shapes interaction geometry. This work reframes how we detect evolutionary processes in ecological networks and shows that structural selection is sparse, localized, and fundamentally meso-structural, converging with classical natural selection when selection is mediated by interaction geometry--a perspective consistent with recent theoretical work on enemy-tracking constraints in tri-trophic systems by Miranda-Perez et al. in prep.

How and why do species interactions produce unstable dynamics? In the simplest models, the answers are straightforward. In the Rosenzweig-MacArthur predator-prey model, resource self-facilitation due to predation mortality triggers oscillations; in Lotka-Volterra competition, positive feedback from stronger interspecific competition underlies alternative states. However, when unstable dynamics arise with three or more species, how and why answers become more opaque. We propose that dissection of feedback loops, chains of direct species interactions, can answer these questions in meso-scale models. To demonstrate, we disentangle instabilities in epidemics using three variations of a general yet mechanistic resource-host-parasite model. Resources introduce destabilizing self-facilitation but also positive interspecific direct effects on propagule production and transmission rate. Those direct effects then produce instabilities through feedback loops. First, we trace how resource self-facilitation catalyzes oscillations by weakening faster, shorter, lower levels of feedback relative to longer, slower feedback of the whole system. Then, we show how resource-dependent propagule yield introduces positive cascade fueling feedback, creating an Allee threshold inhibiting invasion of parasites. In a third variant, we traced how both resource-dependent components produced those unstable dynamics and more complex behaviors, including a period-doubling route to chaos to which we apply a form of loop tracing. Hence, we show how and why direct, positive effects of resources modulate feedbacks underlying oscillations, Allee effects, and more during epidemics. We propose that loop tracing, a generally applicable method, could empower ecologists to glean much deeper insight into dynamics of species interactions.

Trade-offs are central to biodiversity because they prevent the emergence of dominant phenotypes by limiting the simultaneous optimization of multiple fitness components. Yet trade-offs are often difficult to detect empirically when variation in overall performance produces positive correlations that mask underlying constraints. Here we use Pareto fronts--boundaries that capture optimal trade-off solutions--to test for evolutionary constraints on niche-determining traits in phytoplankton, including minimum nutrient requirements, thermal breadth, salt tolerance, and population growth rates. Using experimentally evolved Chlamydomonas reinhardtii populations subjected to nutrient and salt stress, we detected widespread Pareto fronts limiting the joint optimization of growth rate and niche-determining traits, thereby restricting the emergence of multivariate stress tolerance. Importantly, Pareto fronts revealed trade-offs even when underlying trait correlations were positive. We found that the structure of trait covariation behind Pareto fronts strongly predicted evolutionary outcomes: populations moved toward Pareto-optimal phenotypes primarily when trait correlations were neutral or positive, whereas negative trait correlations were associated with limited evolutionary optimization. Extending this framework across phytoplankton diversity, we compiled niche-determining traits for 299 phytoplankton taxa. At a macroevolutionary scale, we detected significant Pareto fronts constraining the evolution of niche-determining traits in phytoplankton. These fronts, however, did not always recapitulate the structure of trade-offs evident among C. reinhardtii populations, suggesting that forces that dictate microevolutionary outcomes, such as genetic correlations, can be resolved across macroevolutionary time. Together, our results highlight that evolutionary trajectories may differ across scales, but that fundamental limits on multivariate trait optimization persist across phytoplankton. Significance StatementTrade-offs among biological traits are central to evolutionary theory but often prove difficult to detect empirically. Here, we apply Pareto fronts--a framework borrowed from economics and engineering--to detect and reveal trade-offs among key niche-determining traits in phytoplankton. By combining experimental evolution in the laboratory with a synthesis of ecological traits across 299 taxa, we demonstrate widespread limits on the simultaneous optimization of growth rate, nutrient competition, salt tolerance, and thermal breadth. Importantly, Pareto fronts reveal trade-offs even when conventional correlation-based approaches fail, uncovering evolutionary constraints that remain hidden in trait correlations. These results show that trade-offs shape phenotypic variation across both micro- and macroevolutionary scales and impose fundamental limits on phytoplankton responses to multiple environmental stressors.

Understanding how biodiversity is structured along tropical elevational gradients requires disentangling the relative roles of regional evolutionary history and local processes shaping ecological assemblies. Here, Ithomiini butterfly communities were studied along repeated elevational gradients in two Neotropical regions with contrasting evolutionary histories: the Amazonian Andes and the Guiana Shield. The study tested whether similar elevational patterns of taxonomic, mimetic, and phylogenetic structure emerge despite distinct regional species pools, and whether abiotic and biotic factors contribute to shaping these patterns. Despite marked regional differences in overall richness, consistent elevational patterns emerged across both regions. Taxonomic and mimetic richness increased with elevation and were accompanied by stronger phylogenetic clustering, indicating that similar habitat filtering processes operate along altitudinal gradients irrespective of regional context. Phylogenetic {beta}-diversity was predominantly driven by lineage turnover, particularly in the Andes, highlighting the role of elevational gradients in promoting replacement of phylogenetically distinct lineages rather than simple species loss. These shared patterns suggest that altitude has a strong and repeatable effect on community structure, with habitat filtering acting locally on regionally distinct species pool. Abiotic factors such as temperature appeared to constrain species distributions at broad spatial scales, whereas biotic interactions acted more locally. In particular, butterfly diversity was positively associated with potential host plant richness and predation pressure, indicating that ecological interactions can further shape local community composition once broad-scale environmental constraints are accounted for. By integrating phylogenetic structure, biotic interactions, and environmental gradients across regions with contrasting evolutionary histories, this study shows how regional species pools and local ecological filtering jointly shape tropical biodiversity and highlights that similar elevational assembly processes could arise independently across the Neotropics.

O_LISeasonal patterns of species appearances constitute a major component of diversity variation. Theory attributes this phenological structuring of communities to the alignment of life cycles to suitable moments and to constraints of seasonality on development, yet the specific mechanisms operating across taxa remain largely unresolved. In insects, body size and colour are key functional traits that contribute to driving spatial community assembly through their link to thermoregulatory performance and development. C_LIO_LIHere we analyse variation in mean body size and colour lightness of 483 butterfly assemblages across Great Britain and throughout the season to test whether trait alignment with seasonal environment and developmental constraints may shape the phenological structuring of communities. C_LIO_LIBoth body size and body colour varied more along season than across space, emphasizing the importance of phenology on diversity variation. Body size was larger early and late in the season, i.e. under conditions of low temperature and solar radiation. This pattern contrasted with the spatial trends found and was driven by species overwintering as adults, which we interpret as being likely due to energetic constraints. Body colour, conversely, was darker early and late in the season, mirroring the spatial pattern found, and suggesting a thermoregulatory alignment with seasonal conditions. Furthermore, covariation between body size and colour suggests a thermoregulatory interaction between both traits. C_LIO_LIOur findings suggest that life-cycle constraints and seasonal thermoregulatory alignment contribute to shaping the phenological structure of insect communities. C_LI

1. Biotic insular systems differ from conventional islands because patch attributes change dynamically as patch-forming organisms develop. It therefore remains unclear whether the assembly mechanisms predicted by island biogeography theory (IBT) operate in such systems. Here, using epiphytic birds nest ferns (BNFs, Asplenium nidus) as a model biotic island system, we tested whether fungal and bacterial community diversity conform to species-area relationships predicted by IBT. With a stratified sampling scheme, we further evaluated the underlying mechanisms (passive sampling, disproportionate effects, and environmental heterogeneity) of species-area relationships, and assessed isolation effects using distance-decay patterns in community similarity. 2. We treated each BNF individual as a microbial island and categorized 24 BNFs into three size classes. Microbial and humus samples from multiple litter layers within each BNF individual were collected; microbial communities were characterized using next-generation sequencing, and humus chemical properties (pH and C:N ratio) were measured to characterize microhabitat conditions. To investigate mechanisms underlying species-area relationships, we applied a multi-scale rarefaction framework to partition diversity components. Spatial distances among BNFs were quantified to evaluate isolation effects. 3. Consistent with IBT predictions, both fungal and bacterial communities exhibited positive species-area relationships, indicating that larger BNFs harbored greater microbial richness. Diversity partitioning suggested that fungal richness increased through both disproportionate effects and environmental heterogeneity, whereas bacterial richness was primarily driven by environmental heterogeneity. Within larger ferns, greater heterogeneity in litter pH was associated with increased species turnover across litter layers, suggesting that decomposition-driven pH gradients create diverse microhabitats that promote microbial diversity. In addition, both microbial communities exhibited distance-decay patterns, indicating that isolation contributes to community assembly through dispersal limitation. 4. Synthesis. Our results demonstrate that BNFs function as a biotic insular system, in which both patch size and spatial isolation structure microbial diversity, consistent with predictions from IBT. Furthermore, we show that environmental heterogeneity generated by the growth of the habitatforming BNF mechanistically links island area to microbial diversity. Our study integrates both local habitat heterogeneity and regional spatial structure, highlighting the potential to extend IBT and metacommunity theory to organism-formed habitats.

Repeated divergence across contrasting habitats is widely used to infer natural selection and local adaptation. However, such inferences remain inherently correlative and capture only adaptation shared within habitat types, thereby missing site-specific adaptation among populations from the same habitat type. Field transplant experiments test adaptation more directly by measuring fitness in nature, but they are typically limited to pairwise reciprocal exchanges between populations and therefore cannot separate the contributions of shared habitat-level and site-specific adaptation to fitness. Here, we overcome these limitations by extending the typical transplant framework to include multiple populations transplanted both within and across habitat types. We apply this framework to lake-stream stickleback, a classic system for studying local adaptation via repeated divergence. Specifically, we transplanted laboratory-reared fish from a panmictic lake population and four independently evolving stream populations into one lake and two stream sites. Stream fish outperformed lake fish in streams and vice versa, providing evidence for adaptive lake-stream divergence. Strikingly, local stream fish also outperformed foreign stream fish at both stream sites. This site-specific advantage was twice as large as the advantage of foreign stream fish over lake fish, which reflects the fitness benefit of shared stream adaptation. These results show that in this system, the majority of fitness-relevant evolutionary variation is site-specific and therefore missed by approaches that rely on repeated divergence to infer adaptation. More broadly, this underscores the importance of ecological scale for understanding adaptation and evolutionary predictability.

AimUnderstanding whether invasive species retain or shift their ecological niches has traditionally relied on scalar overlap metrics that quantify the magnitude of niche change, but not its structure. Here, we test whether biological invasions involve a reorganisation of the environmental axes along which native and invasive ranges are differentiated, and whether the dominant axes of this reorganisation are consistently associated with invasion pathway type (intercontinental vs. within-continent). LocationGlobal (North America, Europe, Africa, Asia, Australasia). Time periodContemporary (environmental variables representing long-term averages, 1980-2021). Major taxa studiedFreshwater crayfish (Decapoda: Astacidea): Procambarus clarkii, Faxonius limosus, Pacifastacus leniusculus, Faxonius virilis, Faxonius rusticus. MethodsWe analysed native and invasive occurrences for five globally important crayfish invaders using [~]400 hydrologically resolved environmental variables from the Global Crayfish Database of Geospatial Traits. Classification models were used to quantify environmental differentiation between native and invasive ranges, and feature contributions were aggregated by environmental domain (climate, topography, soil, land cover). Patterns were evaluated across intercontinental and within-continent invasion pathways and assessed for robustness using cross-validation, permutation tests, sample-size sensitivity, and comparisons with classical niche overlap metrics. ResultsNative and invasive occurrences were consistently distinguishable across all species (accuracy 96.5-99.9%). A pathway-dependent pattern emerged: intercontinental invaders were primarily differentiated along climatic dimensions (58-76% of model importance), whereas within-continent invaders showed a more balanced contribution of climatic and topographic variables ([~]42% each), including strong signals from river network position. This contrast was stable across cross-validation folds (SD < 1.6%), and supported by permutation tests (P = 0.001). Classical niche overlap metrics (Schoeners D = 0.30-0.62) did not capture this qualitative distinction. Main conclusionsBiological invasions involve not only changes in niche position but a reorganisation of the environmental axes that distinguish species distributions. Our results suggest that the dominant axes of this reorganisation differ systematically with invasion pathway, reflecting whether species encounter novel climatic regimes or primarily shift within existing climatic space along topographic and network-position gradients. By resolving which environmental dimensions underpin native-invasive differentiation, this approach provides a complementary perspective to scalar overlap metrics and a basis for more mechanistic interpretations of invasion processes.

Bacterivorous protists are central to aquatic food webs, mediating the transfer of carbon and nutrients to higher trophic levels through the microbial loop. In natural communities, a major challenge remains in linking protist grazing activity to environmental sequences and identifying which taxa are actively feeding at the community level. Here, we present the first application of quantitative stable isotope probing (qSIP) in a grazing experiment. By combining qSIP with 18S rRNA gene amplicon sequencing, we linked prey assimilation to the identity of active protist predators at the operational taxonomic unit (OTU) level. In a replicated 36-h bottle-experiment, live 13C, 15N-labeled Limnohabitans planktonicus cells were added to natural samples from a lake pelagic site and its main inlet stream. Although hydrologically connected, protist richness was higher in the inlet than in the lake, yet a similar number of taxa incorporated prey biomass, comprising 108 OTUs in the inlet and 107 OTUs in the lake, including both rare and abundant taxa. Of these, 26 OTUs were labeled at both sites. The most strongly labeled protist in the inlet was a putative phago-mixotrophic prasinophyte, whereas in the lake it was an uncultured chrysophyte. Across sites, prey incorporation occurred in a broad range of taxa, including heterotrophs (e.g., choanoflagellates, cercozoans, ciliates, centrohelids), putative mixotrophs (e.g., cryptophytes, chrysophytes, dictyochophytes), parasitic protists and fungi. These results demonstrate the potential of qSIP to resolve trophic interactions at fine taxonomic resolution in natural communities and highlight new opportunities to study complex microbial food webs across environmental systems.