Ecology Letters

Environmental change alters species growth rates and, through species interactions, can produce complex effects on community functioning and composition. Because functional traits mediate both environmental responses and competition, rapid trait adaptation may modify these effects, but its consequences in multispecies communities remain unclear. We used a trait-based model of competitive communities to test how adaptation affects the mean and diversity of species growth responses to environmental change, and how this influences functional and compositional resistance (how unaffected the community is by the environmental change) and resilience (how well it recovers after it has acclimated to the altered environment). Adaptation altered response variance more strongly than the mean by changing the balance between environmental optimisation and competition-driven trait divergence. As a result, adaptation strongly increased compositional stability, but had less clear effects on abundance. Adaptation also generated a trade-off between resistance and resilience: faster adaptation improved resistance but reduced resilience as communities were displaced less by the environmental change and thus recovered less. Overall, adaptation can be stabilising, but higher adaptation rates do not necessarily produce greater stability across all stability components.

Understanding how species traits shape community properties is a central goal in ecology. But whether traits drive consistent patterns of dominance and assembly regardless of initial species abundances (evenness) remains unclear. We addressed this question using phytoplankton microcosm experiments to disentangle the effects of evenness and traits on community assembly and functioning, focusing on traits related to energy use (photosynthesis, size) alongside demographic parameters. Community assembly was highly deterministic, indicating that initial evenness had little influence on species dominance and final community structure. Instead, evenness drove community functioning. Demography was a stronger predictor of dominance than photosynthetic rates or individual size, suggesting that growth and competitive success depend on multiple metabolic processes that cannot be reduced to simpler proxies. These results show that community structure can emerge predictably across a range of initial conditions, and highlight the importance of demographic metrics over simpler metabolic descriptors for forecasting community outcomes.

In natural ecosystems, community dynamics are driven by diverse interactions operating through both ecological and reproductive processes. Although theory predicts that these processes jointly generate complex and contingent dynamics through the interplay of stabilizing (negative frequency dependence arising from niche differences) and destabilizing effects (positive frequency dependence caused by reproductive interference), empirical tests of this prediction remain scarce. Here, we combined experimental and theoretical approaches to investigate the dynamics of two congeneric annual plant species (Commelina) experiencing both ecological competition and reproductive interference. Our empirically parameterized model revealed a large niche difference and mutual reproductive interference. However, the reproductive interference was not strong enough to generate pronounced contingency of community dynamics on initial conditions. Instead, coexistence was the most likely outcome regardless of initial species composition. Parameter exploration indicated that the weak contingency arose because reproductive assurance strategies of Commelina species buffered the minority disadvantage caused by reproductive interference. These findings provide empirical evidence that reproductive interference does not necessarily destabilize coexistence and that it may be outweighed by ecological niche and fitness differences. Our study highlights the importance of integrating ecological and reproductive processes towards better prediction of community dynamics.

Environmental variability influences the evolution of phenotypic plasticity and the ecological interactions among species in communities, but it is unclear how these processes interact to affect ecological systems across scales. We investigate models describing how plasticity evolves in competitive communities exposed to seasonal variation in abiotic conditions. We conducted eco-evolutionary analyses to identify communities of coexisting species with evolutionarily stable plasticity traits. These analyses revealed that the presence of interspecific competitors constrained the evolution of plasticity in some species, which instead evolved to be specialists. Plasticity often impaired species coexistence by allowing individual species to control more niche space. Communities containing plastic species were usually less species-rich but more productive and less variable. Similar results were obtained when plasticity rate was evolutionarily labile, but plasticity rate altered the patterning of temporal niche partitioning. This work shows how plasticity and competition combine to shape ecological and trait responses to environmental variability.

Short-term population dynamics are shaped by both external environmental stochasticity and endogenous ecological dynamics. The extent to which population trajectories maintain predictable temporal structure reflects underlying determinism and sets limits on inherent forecastability. Permutation entropy quantifies this intrinsic predictability using the relative frequency of short fluctuation motifs. However, requirements for long timeseries have restricted ecological applications. Here, I introduce a Bayesian hierarchical regression framework, incorporating entropy-neutral priors, that estimates expected permutation entropy from shorter, spatially-distributed ecological datasets. I validate the approach's capacity to detect the impact of increasing environmental variance through simulations. Applied to four decades of UK butterfly data, the approach identified increasing permutation entropy across 35 species, consistent with an increasing role of stochastic weather events. Outlier weather events did not have a consistent relationship with permutation entropy, highlighting the potential for environmental forcing to both disrupt and constrain trajectories. With this flexible approach, permutation entropy offers complementary perspectives on longstanding questions around forecasting and population dynamics.

Understanding how species interactions, ecological drift, and immigration jointly shape community composition is a central goal of community ecology. These processes determine not only average community composition, but also how often different community states occur. Here, we ask how competition reshapes the neutral distribution of community composition generated by drift and immigration. We analyze a stochastic two-species competition model with immigration and derive the stationary distribution of relative species composition near neutrality. The stationary distribution shows when communities are usually balanced and when they are instead dominated by one species. Species dominance can become common even when the corresponding deterministic model predicts stable coexistence, particularly in smaller communities. Balanced and dominance states can also occur together, producing a trimodal distribution. Competitive asymmetry further biases the distribution toward the competitively favored species. Thus, the interplay between competition, ecological drift, and immigration determines not only how much community composition varies, but also which community states are most likely to be observed. Our results provide an analytical bridge between neutral and competition theory and a framework for interpreting variation in community composition.

Early-life phenotypes frequently predict adult phenotypes through developmental 'silver-spoon' carry-over effects, but how strongly and under what circumstances? Using 40 years of individual-based data from a great tit (Parus major) population, we used covariance reaction norms to understand how the natal-to-adult body mass association shifts across temperature, rainfall, and population density gradients during both life stages. We show that the natal-adult mass correlation is substantial (r {approx} 0.7) but strongly environment-dependent. Reaction norm shapes across climatic and density axes point to distinct mechanisms, including compensatory growth under benign adult conditions and density-dependent modulation of early-life differences. We further show that the conditions experienced in adulthood largely drive these patterns and determine whether early-life advantages are retained, even when explicitly tested within an environmental-matching paradigm. Decomposing the correlation using 38 generations of pedigree data revealed no evidence of gene-by-environment interactions. Instead, the observed phenotypic correlation reaction norms could be attributed almost entirely to environmentally-sensitive non-genetic individual differences. Overall, our findings show that climatic and demographic shifts can substantially reshape developmental carry-over without altering the genetic architecture tying body mass across life stages, with implications for how reliably early-life mass can predict individual quality and adult traits in wild animal populations.

Ecological coarse-graining of individuals or species into aggregate variables, such as total biomass or regional abundance, is common practice, yet differences among species can still influence aggregate dynamics. We propose a metric for quantifying this irreducible dynamical heterogeneity. For small fluctuations near a stable equilibrium, we compare the aggregate fluctuation spectrum of the full dynamical system with that of the best Ornstein-Uhlenbeck model having the same dimension as the aggregate. The remaining spectral mismatch quantifies the dynamical effects of hidden differences that the fitted aggregate model cannot capture. For weakly interacting random community models at fixed collective interaction strength, we show that increasing species richness can either reduce or increase this heterogeneity, depending on how species interact. Our framework helps understand when ecological and evolutionary differences average out versus when their dynamical effects persist.

Any mechanistic biodiversity-ecosystem functioning theory must explain how biodiversity in natural communities arises, which requires an evolutionary metacommunity perspective. Using a model of adaptive evolution of competitive metacommunities in landscapes with spatially heterogeneous supply of two resources, we studied the evolution of resource-uptake specialization as a function of resource type (substitutable/essential) and trade-off shape between resource-uptake affinities (generalist-/specialist-favouring). We identified "critical essentiality" as a key phenomenon separating parameter regions with fundamentally different biodiversity--ecosystem function relationships. Specifically, while greater spatial heterogeneity in resource supply increases biodiversity, this comes at the cost of reduced productivity when resources are essential. Moreover, when resources are essential, both generalist- and specialist-favoring trade-offs select for phenotypes that prevent efficient resource partitioning, leading to low resource use efficiency and consumer biomass - despite often high species richness. In contrast, when resources are substitutable, a specialist-favoring affinity trade-off selects for phenotypes that partition resources efficiently, promoting a metacommunity of only two species characterized by high functional diversity, resource use efficiency, and biomass. Overall, our analyses thus suggest that conditions promoting the evolution of diverse consumer communities often do not engender higher functional richness, consumer biomass, or resource use efficiency.

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.

Resting metabolic rate, the basal energy required for an organism's maintenance, is central to predicting the eco-evolutionary consequences of environmental and anthropogenic pressures on marine ectotherms. Yet, direct measurements remain extremely scarce, covering less than 1% of marine fish species. Building on the Pace Of Life Syndrome (POLS) hypothesis, which posits a positive correlation between metabolism and life-history speed, we inferred temperature- and mass-specific resting metabolic rate for 18,214 fish species (16,998 Teleostei and 1,216 Elasmobranchii). Estimates were derived from relationships with 15 functional traits and phylogeny using phylogenetic structural equation modelling, a novel approach combining phylogenetic comparative methods with structural equation models. Cross-validations indicated high predictive performance, although some clades require cautious interpretation due to limited empirical data. In line with expectations from life-history theory, ecological niche and morphology, species with the highest metabolic rates were small, narrow, shallow-bodied, and long-jawed pelagic fish with high mortality, short lifespan and rapid growth - traits typical of small epipelagics. The metabolic rate variation aligned with the slow-fast life-history continuum, supporting the POLS hypothesis, but was equally explained by reproductive strategy: highly fecund, fast species exhibited the highest metabolic rate independent of mass and temperature, whereas low-fecundity slow species had the lowest. By filling a critical physiological data gap, our dataset provides a foundation to test long-standing ecological and evolutionary hypotheses such as POLS, and offers a powerful resource to improve models of species and community responses to climate change and exploitation, ultimately supporting physiology-informed ecosystem management.

Ecological stability and function emerge from the many and diverse types of interactions among species. Ecological networks represent the structure and strength of species interactions, but their study is often restricted to single interaction-types, likely due to an absence of models for plausibly (i.e., nonrandomly) structured 'multiplex networks' of multiple interaction types. Here, we present a general but mechanistic model that simulates multiplex networks using the different, but correlated, traits that determine different species interactions. We show that ecological stability varies systematically with these trait correlations by controlling the emergent complexity and positioning of strong interactions in the community. Our model provides a flexible but power tool to study multiplex networks under alternative trait-based hypotheses, offering new insights into the structure and stability of complex natural systems.

Quantifying how individuals adjust the seasonal timings of life-history events (i.e. their phenology) in response to seasonal environmental conditions is fundamental to predicting impacts of changing seasonality. However, the current standard approach of inferring phenological responses by directly analysing observed event dates relies on foundational assumptions that could be invalidated by changing seasonality, and excludes individuals that do not express focal events, potentially generating misleading inferences. To overcome these limitations, we devised a Phenological Threshold Model (PTM) that conceptualizes phenological events as switches between discrete life-history states and primarily estimates variation in underlying latent dynamic responses to seasonal progression, from which event dates secondarily arise. We demonstrate that the PTM can accurately retrieve simulated parameter values, including given incomplete seasonal field observations. Application to multi-year data on annual breeding in black-legged kittiwakes (Rissa tridactyla) demonstrated substantial among-individual variation in latent responses to seasonally varying environmental drivers, and associated daily probabilities that individuals will initiate breeding, revealing how annual distributions of breeding dates and proportions of non-breeders jointly arise. We thereby formalise a new conceptual and statistical framework for quantifying the potential for, and constraints on, rapid plastic and micro-evolutionary changes in phenology in changing seasonal climatic and environmental regimes.

Backgrounds and Aims: Partitioning of biomass to various organs is fundamental to growth and survival strategies of plant species, with broad-scale ecological implications from competition to carbon flux of ecosystems. Although partitioning patterns have been studied extensively in embryophytes, little is known about the variation and evolutionary drivers of biomass partitioning strategies in marine plants, such as seaweeds, that form marine forests analogous to those on land. Methods: We measured developmental variation in standing organ biomass for 18 species of kelp and used a previously published dataset on land plants to draw comparisons between kelps and embryophytes, with respect to developmental allometry, interspecific allometry and the relationship between the two measures. Key results: We report that kelps exhibit large variation in partitioning patterns, with some species differing by more than an order of magnitude in holdfast and stipe investment when controlling for size. The range of biomass partitioning patterns exhibited by kelps includes values not found among embryophytes, highlighting differences in selective pressures on land and in the ocean. However, as previously demonstrated, empirical values of interspecific scaling curves closely match those of land plants. This suggests that despite fundamentally different physical constraints, evolutionary scaling has converged on nearly identical interspecific exponents. We further demonstrate that allometric covariation exists between support organs (stipe and holdfast), highlighting the importance of physical forces in driving functional diversification of marine plants and supporting the notion that covariation of traits is a hallmark of botanical structure and function. Conclusions: The results of our study illuminate the variation and evolutionary patterns of biomass partitioning in marine primary producers. Moreover, these results highlight that universal evolutionary scaling relationships can emerge from fundamentally divergent developmental and biomechanical pathways.

Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a deterministic mathematical model to the average across replicates. We use a stochastic model to fit the variability to the observed variability across replicates. Using a simulation-driven approach, we study the conditions under which our approach allows us to infer a larger fraction of ecological parameters correctly. We observe a substantial improvement in parameter inference. Lastly, our Bayesian approach not only allows us to incorporate prior information about the system, but also provides a distribution of parameters which conveys some idea of the uncertainty of the estimates.

1. Detecting causal relationships from ecological time series remains challenging because community dynamics are often nonlinear, high-dimensional, noisy, and strongly synchronized. These characteristics can reduce the reliability of existing causal inference methods and complicate the identification of ecological interactions in complex natural systems. 2. I introduce Forest-weighted Causal Correlation (FORCC), a machine-learning-based framework that infers directional causality from adaptive neighborhood structures learned by random forests. Rather than evaluating causal direction through cross-map reconstruction, FORCC quantifies directional dependence from the preservation of neighborhood structure using rank-based associations among forest-weight matrices. 3. Simulations of coupled nonlinear systems showed that FORCC accurately recovered causal direction while substantially reducing synchronization-induced false-positive detections relative to conventional convergent cross mapping. Chatterjee's {xi} exhibited the strongest resistance to synchronization-induced errors among the evaluated rank-based statistics. 4. Application to a 12-year marine community time series from Maizuru Bay, Japan, identified asymmetric relationships between individual taxa and community dynamics, revealing candidate driver-like and passenger-like roles that were not readily explained by seasonal synchrony alone. 5. By combining adaptive neighborhood learning with rank-based causal inference, FORCC provides a scalable framework for causal discovery in ecological time series. The method offers a practical tool for screening causal relationships, generating ecological hypotheses, and investigating complex interactions in multivariate natural systems.

Ecological uniqueness, commonly quantified by local contribution to beta diversity (LCBD), is important for understanding and conserving biodiversity. Its widely documented negative relationship with species richness has led to the view that species-poor communities may be disproportionately important for regional diversity. Here, we derive neutral expectations for richness-uniqueness relationships and show that negative slopes can arise from neutral assembly rather than necessarily reflecting an ecological signal of uniqueness. Neutral simulations validate the analytical expectations across abundance- and incidence-based LCBD formulations. Applying the framework to the Barro Colorado Island tropical forest and North American Breeding Bird Survey, we show that accounting for neutral sampling expectations substantially changes the ranking and spatial distribution of ecologically unique sites. By separating neutral sampling expectations from excess uniqueness, our framework provides a principled basis for interpreting ecological uniqueness and informing biodiversity conservation.

Phytoplankton-virus interactions are pivotal in understanding the intertwined dynamics of these key players of the global carbon cycle. Although antiviral resistance is the main hypothesis to solve the apparent paradox of their coexistence, identifying its molecular determinisms and mechanisms, as well as their dynamical consequences, remains challenging. By combining hypotheses about such determinisms and mechanisms, we designed 36 dynamical models and fitted them to experimental infection kinetics in a pioneering selection model approach. Through such a multiple hypotheses testing, we identified that resistance in the model species Ostreococcus tauri is most likely to be predominantly intracellular, to control lysis through budding, and to be acquired by phenotypic plasticity. We further showed that viral kinetics are strongly impacted by both the antiviral resistance's molecular determinants and virions' inactivation through attachment to lysis detritus, as the latter generates a key negative feedback loop during the 'bust' stage of the interaction dynamics.

Unveiling directional causality in complex, high-dimensional dynamical systems remains a fundamental challenge. Conventional nonparametric approaches, such as Convergent Cross Mapping, require computationally intensive surrogate tests and frequently yield false-positive detections under strong coupling due to generalized synchronization. Here, we present F-CODEC (Forest-weighted Conditional Dependence Coefficient), a forward-looking causal discovery framework integrating generalized random forests with adaptive rank conditioning. Nonparametrically conditioning on the target variable's intrinsic delay coordinates systematically neutralizes shared manifold representations, suppressing false positives without surrogate data. Furthermore, an analytical time-series threshold allows instant, rigorous hypothesis testing directly from raw observations. We demonstrate F-CODEC's resilience against generalized synchronization and high-dimensional noise across canonical chaotic benchmarks and multi-species network motifs. Applying F-CODEC to historical lynx-hare cycles resolves delayed trophic forcing, while in-situ IoT honeybee monitoring captures real-time microclimate-acoustic causal surges preceding colony-level behavioral events. F-CODEC establishes a practical, scalable, and surrogate-free tool for causal discovery in nonlinear dynamical systems.

Understanding spatial and temporal variation in community composition is central to protecting and monitoring biodiversity. Similarity in the composition of two communities decreases along spatial and temporal gradients, a relationship known as distance decay. Comparing distance-decay relationships across taxonomic, functional, and bioclimatic facets of diversity provides a means of interrogating niche-based versus neutral influences on community assembly. Furthermore, the context in which these facets yield overlapping or complementary information about global change impacts remains poorly understood. In this study, we examined spatiotemporal distance-decay relationships for taxonomic, functional, and bioclimatic facets of alpine plant diversity leveraging elevation gradients on five peaks in the White Mountains, California, USA, sampled at 3-4 time points between 2007 and 2024. Using multivariate Bayesian beta regression models, we estimated the slopes of elevation distance decay over multiple surveys and across taxonomic, functional and bioclimatic diversity to understand how spatial distance-decay relationships are changing through time and across different facets of diversity. We found strong spatial distance decay of taxonomic similarity across all peaks, with the highest elevation peak showing the strongest decay. Functional and bioclimatic similarities decayed more weakly than taxonomic similarity, suggesting environmental filtering shapes trait and niche distributions. Critically, spatial decay patterns showed no temporal signal across facets of diversity, indicating community stasis rather than biotic homogenization or differentiation. This study adds further evidence that xeric alpine plant communities are resisting climate change impacts through time.