By living in a landscape of fear, prey species modify their spatial distribution, behaviour, and diet to reduce predation risk, while often compromising food intake. These non-lethal effects often outweigh the lethal effects of predation on prey species. Parasites can have similar effects on their animal hosts, so hosts need to trade-off e.g., resource usage and infection risk in a landscape of disgust. Given these similarities between prey–predator systems and host–parasite systems, we review recent insights from the concepts of the landscape of fear and disgust (combined in a landscape of peril) and suggest novel applications and testable hypotheses for disease ecology. The adaptive behaviours of hosts to avoid parasite infection leads to new predictions for how parasites and predators influence the distribution, behaviour, and food intake of animals.

A major challenge for community ecology is using spatiotemporal data to infer parameters of dynamical models without conducting laborious experiments. We present a framework from statistical physics-Maximum Caliber-to characterize the temporal dynamics of complex ecological systems in spatially extended landscapes and infer parameters from empirical data. As an extension of Maximum Entropy modeling, Maximum Caliber aims at modeling the probability of possible trajectories of a stochastic system, rather than focusing on system states. We demonstrate the ability of the Maximum Caliber framework to capture ecological processes ranging from near to far from equilibrium, using an array of species interaction motifs including random interactions, apparent competition, intraguild predation, and nontransitive competition, along with dispersal among multiple patches. For spatiotemporal data of species occupancy in a metacommunity, the parameters of a Maximum Caliber model can be estimated through a simple logistic regression to reveal migration rates between patches, interactions between species, and local environmental suitabilities. We test the accuracy of the method over a range of system sizes and time periods and find that these parameters can be estimated without bias. We introduce "entropy production" as a measure of irreversibility in system dynamics, and use "pseudo-R2" to characterize predictability of future states. We show that our model can predict the dynamics of metacommunities that are far from equilibrium. The capacity to estimate basic parameters of dynamical metacommunity models from spatiotemporal data represents an important breakthrough for the study of metacommunities with application to practical problems in conservation and restoration ecology.

Decomposition of plant litter, facilitated primarily by microbial decomposers, plays a critical role in biogeochemical cycling and ecosystem function. The rate of litter decomposition can determine its environmental impact, where accelerated decomposition alters the timing and rate of nutrient release and may promote nutrient leaching, whereas slowed decomposition can result in litter accumulation, which impacts seedling recruitment, fire regimes, perennation of microbial communities, and slows nutrient release. Mutualistic endophytes are known to slow litter decomposition, but less is known about the impact that plant pathogens, present in diseased litter, have on decomposition rates. We compared litter decomposition of the invasive annual grass Microstegium vimineum with Bipolaris leaf spot symptoms, a fungal disease, to litter without symptoms of the disease in a year-long common garden experiment. We found leaf tissue with disease symptoms decomposed later in the year compared to litter without symptoms. By summer, 54% of leaf tissue from healthy sites remained compared to 80% of leaf material from diseased litter. Fungal infection did not impact the lignin or C:N content of the litter. There were significant differences in fungal community composition between infected and healthy litter at the start of the experiment that persisted until the end of summer. Disease epidemics prior to senescence contributed to the persistence of infected tissue, which could slow the return of nutrients to the environmental pool and promote the survival and dispersal of pathogen inoculum the following season.

Abstract There is growing recognition that host behavioral responses to disease risk are critical factors driving disease dynamics, but understanding how behavioral responses influence dynamics remains a major challenge. Coupled behavioral and epidemiological models commonly assume that hosts use population prevalence as an indicator of disease risk. However, real-world estimates of prevalence come from data aggregated over coarse spatial scales, while transmission occurs through fine-scale contacts. Fine-scale changes in movement behavior represent an important type of risk response because individuals must use proxies for infection risk, such as host density or environmental factors, whose relationship with actual transmission risk may vary across contexts. In this study, we examine the consequences of using diierent risk proxies to inform fine-scale movement and determine when and if relying on imperfect proxies can cause risk-averse behaviors to increase, rather than decrease, disease transmission relative to no behavioral change. We examine the effect of three risk proxies - local prevalence, local host density, and local transmission coefficient (i.e., “place”) – in the context of “simple trips”, where individuals may respond to disease risk by altering rates of travel from home to “away” locations and back. In one case, individuals stay home more frequently (an absolute risk response) and in the other case, individuals shift their travel to less risky, away locations (a relative risk response). Absolute responses were far more effective in reducing prevalence than relative responses, which were detrimental in some parameter regimes. Detrimental responses occurred when information used to perceive risk was mismatched with the mode of transmission (either density-dependent or frequency-dependent), such that individuals either failed to use pertinent information or used irrelevant information. Imperfect information thus plays a critical role in determining whether behavioral response reduces or elevates disease risk.

Classical theory predicts that, in a temporally static but spatially heterogeneous environment, two species that differ only in dispersal rate will not coexist: the slower disperser excludes the faster. We revisit this result by comparing three models for two competing species on two patches that differ in growth rates and carrying capacities: a traditional state variable Lotka-Volterra (LV) model of simple movement between patches at rates proportional to population sizes on the patches (model 1), a reaction-diffusion (within-patch structured) model of movement both within and between patches (model 2), and a spatially explicit agent-based model (ABM) in which the populations are comprised of individuals, and where movement within and between the two patches follows stochastic Brownian motion (model 3). We also examine a stochastic LV variant (model 1-stoch). Models 1 and 2 predict that the fast disperser is excluded, as does the LV variant model, while model 3 produces long-term coexistence of the fast and slow dispersers, though at low numbers for the former. Coexistence in the ABM appears to result from stochastic Brownian movement, which prevents the slow disperser from reaching its carrying capacity, thus providing an opportunity for a small population of fast dispersers to persist. Although no directed movement is explicitly incorporated in the ABM, individual level discreteness and stochasticity results in coexistence reminiscent of models with some component of directed movement.

Urbanization drives rapid and extreme environmental change, profoundly shaping the ecology and evolution of populations. In this Perspective, we call for the integration and development of evolutionary theory and empirical research through collaboration between theoretical and experimental biologists to provide new insights into urban evolutionary ecology. We argue that mathematical models derived from ecological and evolutionary theory can be tailored to provide a powerful framework for generating predictions that can guide empirical research in urban ecology and evolution. At the same time, empirical results can motivate and inform the development and analysis of new theoretical models specific to urban systems. We illustrate how existing evolutionary theory can be harnessed to generate specific predictions of how urbanization can influence evolution. These predictions span the range of urban impacts on all main evolutionary processes, including mutation, gene flow, genetic drift, non-random mating, and selection. We provide a summary of evidence supporting each prediction and outline empirical approaches available to test them. Importantly, these predictions require distinct modeling approaches that can be applied more broadly to better utilize theory for research on urban environments. To facilitate this, we provide an overview of these existing modeling approaches ranging from the application and syntheses of classic model results to the development of novel probabilistic predictions. We advocate for increased integration of theoretical and empirical research through the development of novel models using parameterization specific to urban systems, empirical tests grounded in theoretical models, model-based empirical tests, and model-based data analysis and inference to advance our understanding of evolution in urban environments.

Adaptive topographies form the foundation for much of our understanding of evolutionary change. Lande's 1976 influential paper on the adaptive topography of phenotypes demonstrated how the concept is inherent in both phenotypic and genetic models of evolution, and how the concept can be used to test evolutionary hypotheses given data. Here, we revisit and generalize Lande's original derivation of an equation analogous to Wright's genotypic adaptive topography to the case of two fitness components. A move to two fitness components yields novel predictions about the shape and mechanistic underpinnings of the adaptive topography. The optimum of this updated fitness function is a weighted average of the optima of the two fitness components, with weights given by the relative strengths of stabilizing selection on each component. Temporal or spatial heterogeneity in the strengths of selection for each fitness component create novel shapes (asymmetry, bi-modality, or lack thereof) of the overall fitness function, a possibility demonstrated with a case-study from the published literature. Finally, when combined with Lande's approach to generate an Ornstein-Uhlenbeck (OU) model for the evolution of the average phenotype, our fitness formulation leads to a previously unrecognized family of stochastic differential equation models of trait evolution. These results provide mechanistic justification for non-Gaussian fitness functions (often observed in natural systems), provide a path for testing alternative models generating non-Gaussian fitness functions, and pave the way for future study of the interplay of ecological and evolutionary dynamics, such as in the study of evolutionary rescue.

Studies have suggested that the quality of the lands surrounding habitat patches can modify the effects of habitat loss and fragmentation on species and influence biodiversity predictions across regions. As landscape matrices tend to be complex and vary with habitat change, isolating such effects is challenging. Here we disentangle the effects of habitat loss, fragmentation and surrounding landscape quality in a large, multiscale manipulative experiment on a plant-herbivore system. We find that habitat loss, fragmentation and surrounding matrix quality all affect survival rates, with the greatest negative effects of fragmentation and lower matrix quality under high habitat loss. Demographic rate changes resulted in strong negative effects of habitat loss, fragmentation and low matrix quality on population size at the landscape scale. Our findings indicate that the benefits of high landscape quality are greater in landscapes with low habitat fragmentation, contesting the common expectation that the surrounding matrix matters only in the most fragmented landscapes. This underscores that the quality of the surrounding landscape can have outsized effects on biodiversity in remaining habitats.

Burton Singer, elected to the National Academy of Sciences in 1994 and the National Academy of Medicine in 2005, died on February 15, 2026, at age 87. Moving seamlessly across statistics, epidemiology, economics, demography, ecology, and public health, his career transcended conventional academic boundaries. He brought the rigor of stochastic process theory to problems ranging from labor market dynamics and demographic change to tropical disease epidemiology, public health policy, and environmental stewardship. He founded the Yale School of Public Health, chaired the National Research Council’s Committee on National Statistics, and led tropical disease research governance for the World Health Organization. Yet colleagues recall his adventurous spirit, indefatigable generosity, and irreverent humor as vividly as his accomplishments.

Modifying travel behavior is often critical for the public health response to infectious disease outbreaks, but depends on consistent, reliable detection. Often, this involves recommending that communities avoid high-risk areas, but if detection is low where transmission is high, these recommendations could lead to a counterproductive response if cases reported by health systems are mostly from low-risk areas. Mismatches between detection and transmission can be further exacerbated by behavioral associations between locations. For example, locations associated with high vectorborne disease (VBD) risk, such as green spaces and parks, are often distant from doctor’s offices, meaning that more time spent in areas with high VBD risk could mean decreased propensity to visit a doctor when sick. These kinds of behavioral correlations between high-risk areas and doctor’s offices carry especially strong risk of causing a counterproductive community response. We combine mathematical models with mobility data analysis to explore how correlations between trips to high-risk areas and healthcare providers can shape disease detection, behavioral response, and outbreak dynamics. Through modeling, we find that a negative correlation between high-risk places and healthcare facilities can cause community response to worsen the outbreak, due to many undetected cases in high-risk areas. We then use smartphone location data to show that there is a clear dichotomy in terms of travel behavior, where the number of trips to places typically associated with urban areas are strongly positively correlated through space and time, and are strongly negatively correlated with places associated with rural areas. This suggests a simple delineation (urban vs. rural) that can be used in future studies to further elucidate how changes in travel behavior could impact the spread and control of infectious diseases.

Understanding how landscape structure influences multiple dimensions of biodiversity is crucial for conservation in fragmented ecosystems. We investigated how bird communities respond to landscape composition and configuration across multiple spatial scales in 20 forest fragments in the Atlantic Forest biome, southern Brazil. Using standardized point counts, we assessed 14 bird community attributes related to species richness, composition, and functional diversity. We modeled the effects of forest cover, forest isolation, crop cover, landscape diversity (at 0.5, 1, 2, and 5 km radii), fragment size, and the presence of valley bottoms. Forest isolation was the strongest negative predictor, reducing total species richness, abundance, and the number of dietary specialists. However, forest cover and diversity of habitats within landscapes partially mitigated these effects by increasing the specialists-to-generalists ratio and the average abundance per species. Although functional richness and dispersion remained stable, declines in functional evenness and divergence with increasing forest cover, along with higher specialists-to-generalists ratio, indicate a simplification of functional structure driven by generalist species. Bird communities responded most strongly to landscape structure at the 5 km scale, suggesting that many species rely on landscape-level connectivity to persist. At the local scale, the presence of valley bottoms decreased functional divergence, suggesting that dominant species in these areas occupy more similar functional roles. These findings underscore the need to maintain and restore forest connectivity and landscape heterogeneity to conserve avian diversity. Conservation strategies should focus on increasing forest cover and restoring ecologically important areas, such as valley bottoms, particularly in simplified agricultural landscapes dominated by monocultures.

Global increases in habitat loss and fragmentation have resulted in non-habitat landcover, or the matrix, becoming an increasingly prominent feature of landscapes. The matrix can influence the population dynamics of species in fragments by modifying processes operating locally on individual patches (e.g. edge effects on survival) or at landscape scales (e.g. inter-patch dispersal). However, the relative magnitude of patch- vs. landscape-scale matrix effects on the populations found in patches remains unclear. We established 12 experimental landscapes in which we controlled for habitat amount and fragmentation while manipulating the quality of the matrix around (i) individual habitat patches and (ii) across the entire landscape in a factorial design. We then compared the magnitude of local- and landscape-scale matrix effects on a specialist herbivore, Chelinidea vittiger (Hemiptera: Coreidae). Population size in fragments was influenced by both patch- and landscape-scale treatments: Population size increased in patches surrounded by high-quality matrix, but only in landscapes dominated by low-quality matrix, due in part to decreased inter-patch movements in these landscapes. In contrast, the effects on both survival and reproductive output were solely at the patch-scale, with both lower in patches surrounded by low-quality matrix. Our results underscore the outsized importance of matrix habitat immediately adjacent to fragment edges-despite the fact that patch-scale manipulations affected only a fraction (3%) of the area that landscape-scale manipulations did, patch-scale effects were more common. The relationship between dispersal, population size and scale-dependent effects of matrix quality emphasizes the need to explicitly consider the spatial scale at which different processes operate when predicting responses to habitat fragmentation. Our results also suggest the matrix immediately adjacent to habitat remnants is of particular importance when considering alternative strategies for landscape conservation or restoration.

Source-sink dynamics are a cornerstone of theory for spatially structured populations. Despite long-standing interest, understanding temporal variation in source-sink dynamics in wild populations remains rare. Biological invasions have the potential to alter source-sink dynamics for native species, which may change over time as invasions proceed. We used 28 years of data on reproduction, movement, and survival to estimate annual source-sink dynamics across the entire range of the endangered Everglade snail kite (Rostrhamus sociabilis plumbeus) during the invasion of a novel prey species, the island apple snail (Pomacea maculata). Snail kite populations underwent striking changes in source-sink dynamics with time since invasion, and no population was consistently a source or sink over time. Some initial benefits of increased prey availability on snail kite demography were diminished in the long term. Populations invaded by P. maculata impacted uninvaded populations via changes in snail kite retention (i.e., lack of movement) and emigration across the metapopulation. Our findings illustrate how effects of biological invasions can change over time and may take decades to fully emerge, and they emphasize how an invasive species can have distant impacts on uninvaded populations via fluctuations in native species' local retention and emigration. In addition, our results demonstrate how fluctuating emigration and retention alter long-term interpretations of source-sink dynamics through variation in local versus landscape contributions of populations to the metapopulation, highlighting that the status of "source" or "sink" can be highly variable through time.

The metapopulation concept offers significant explanatory power in ecology and evolutionary biology. Metapopulations, a set of spatially distributed populations linked by dispersal, and their community and ecosystem level analogs, metacommunity and meta-ecosystem models, tend to be more stable regionally than locally. This fact is largely attributable to the interplay of spatiotemporal heterogeneity and dispersal (the inflationary effect). We highlight this underappreciated (but essential) role of spatiotemporal heterogeneity in metapopulation biology, present a novel expression for quantifying and defining the inflationary effect, and provide a mechanistic interpretation of how it arises and impacts population growth and abundance. We illustrate the effect with examples from infectious disease dynamics, including the hypothesis that policy decisions made during the COVID-19 pandemic generated spatiotemporal heterogeneity that enhanced the spread of disease. We finish by noting how spatiotemporal heterogeneity generates emergent population processes at large scales across many topics in the history of ecology, as diverse as natural enemy-victim dynamics, species coexistence, and conservation biology. Embracing the complexity of spatiotemporal heterogeneity is vital for future research on the persistence of populations.

Warming increases the foraging rates of ectothermic predators, potentially resulting in increased predation pressure on detritivores through top-down effects, thereby influencing decomposition. Trophic cascade effects under warming are shaped by many factors, including temperature, precipitation and trophic structure. Greater species diversity may weaken these cascades through intensified interspecific interactions and, in turn, shape how decomposition responds to warming (the vertical diversity hypothesis), but this process has seldom been examined in natural ecosystems. Here, we experimentally increased warming at three elevations (4650, 4950 and 5200 m) in an alpine meadow ecosystem to test the predator-mediated effects of warming on decomposition, as well as the role of arthropod diversity in these processes. Among the three elevations, arthropod diversity and predator abundance were significantly greater at 4950 m than at 4650 and 5200 m. Warming increased predator abundance at all three elevations, but decreased detritivore abundance only at 4650 and 5200 m. Detritivore abundances at 4650 and 5200 m, but not at 4950 m, were correlated negatively with predator abundance under experimental warming. Based on multigroup structural equation models, warming primarily reduced litter decomposition directly during the cold season when arthropod activity was limited. In contrast, during the warm season and over the whole year, both periods with greater arthropod activity, warming predominantly reduced litter decomposition via predator-driven top-down effects, rather than through direct effects. Although warming increased predator abundance across all elevations, the resulting trophic cascade was observed only at 4650 and 5200 m, where elevated predator abundance suppressed detritivores and reduced litter decomposition. In network analysis, indicators of arthropod diversity had the most correlations with indicators of ecosystem function, suggesting that the warming-driven decline in detritivore abundance could influence ecosystem functionality negatively. Synthesis: We conclude that warming-induced increases in predator abundance can reduce detritivores and decomposition through strong top-down effects, but these effects appear confined to elevations with low arthropod diversity. Our study provides a novel perspective on the factors shaping decomposition responses to warming. Large-scale field studies and mesocosm-based experiments are warranted to assess the generality of this effect.

Emerging infectious plant diseases threaten natural, agricultural and urban systems. Predicting pathogen spillover from one host species to another can reduce disease impacts, but traditional compartment models poorly explain plant disease because plants often experience localized rather than systemic disease. Thus, the amount of infection should be tracked within each host individual, rather than characterizing a host as infected or uninfected. Additionally, annual plants can grow and complete their life cycle on timescales comparable to disease progression, thereby creating potential for feedbacks between tissue growth and pathogen spread that could be important to disease spillover. We hypothesized that, for two plant species that differ in individual-level growth rates and share a pathogen, the faster-growing host supports higher pathogen levels that spill onto, and negatively affect, the slower-growing host. This prediction follows classical apparent competition theory in which prey species with high intrinsic growth rates sustain greater predator abundances, which then suppress other, slower-growing, prey in the community. We explored whether this theory applies to plants sharing pathogens by developing an intra-annual host-pathogen model tracking size structure of, and degree of infection in, plant hosts. We asked how growth rates of annual plant species alter spillover from a reservoir host to focal host species, and disease amplification or dilution in annual plant communities. We found that faster-growing host individuals supported the greatest pathogen loads compared with slower-growing host individuals, yet they experienced smaller pathogen-driven reductions in end-of-season biomass. Consistent with apparent competition predictions, pathogen spillover from reservoir to focal host species caused greatest declines in end-of-season biomass in focal individuals when the reservoir host was fast-growing and focal host was slow-growing. In communities with both fast and slow-growing species, slower-growing hosts generally diluted disease, while faster hosts amplified it. Synthesis. The model predicts that faster-growing hosts are likely to be high impact reservoirs for spillover and could amplify disease in multi-host communities. Slower-growing hosts are likely to bear the greater impact of pathogen spillover via reduced end-of-season biomass. Thus, plant growth rates could be an important factor in driving outcomes of infectious disease spillover in multi-host annual plant communities.

Empirical investigations of source-sink dynamics are needed to relate metapopulation theory to spatially-structured, temporally varying population dynamics in the real world. The difficulty of acquiring the data needed to estimate vital rates has often constrained analyses of source-sink dynamics to static or simulated systems. We used 26 years of data on reproduction, movement, and survival to estimate annual source-sink dynamics across the entire range of the endangered Everglade snail kite (Rostrhamus sociabilis plumbeus) during the invasion of a novel prey species. Populations underwent striking changes in source-sink dynamics over time, varying with time since invasion. Both source and sink populations depended on immigration to offset high emigration rates. Source populations are often prioritized for conservation, but in a metapopulation composed largely of sinks, or sources with high emigration and immigration, an emphasis on maintaining connectivity or expanding available breeding habitat may be more important.

As the Anthropocene proceeds, the matrix in which remaining habitats are embedded is an increasingly dominant component of altered landscapes. The matrix appears to have diverse and far-reaching effects, yet our understanding of the causes and consequences of these effects remains limited. We first synthesize the broad range of perspectives on the matrix, provide a generalized framing that captures these perspectives, and propose hypotheses for how and why the matrix matters for ecological and evolutionary processes. We then summarize evidence for these hypotheses from experiments in which the matrix was manipulated. Nearly all experiments revealed matrix effects, including changes in local spillover, individual movement and dispersal, and use of resources in the matrix. Finally, we discuss how the matrix has been, and should be, incorporated into conservation and management and suggest future issues to advance research on and applications of the matrix in ecology, evolution, and conservation.

Humans greatly influence the ecosystems they live in and the lives of a wide range of taxa they share space with. Specifically, human hunting and harvesting has resulted in many species acclimating via diverse behavioral responses, often quite rapidly. This review provides insights into how hunting and harvesting can elicit behavioral changes. These responses emerge from a species’ previous and evolving ability to assess risk imposed by hunters and respond accordingly; a predator–prey game thus ensues, where both players may change tactics over time. If hunting is persistent, and does not result in the taxa’s extirpation, species are expected to develop adaptations to cope with hunting via natural selection by undergoing shifts in morphology and behavior. This review summarizes the various ways that human hunting intentionally and incidentally alters such evolutionary changes. These changes in turn can influence other species interactions and whole ecosystems. Additionally, alterations in behaviors can provide useful indicators for conservation and evolutionarily enlightened management strategies, and humans should use them to gain insights into our own socio-economic circumstances.