Spatial and life history variation in a trait-based species vulnerability and impact model.

Anthropogenic pressures threaten biodiversity, necessitating conservation actions founded on robust ecological models. However, prevailing models inadequately capture the spatiotemporal variation in environmental pressures faced by species with high mobility or complex life histories, as data are often aggregated across species' life histories or spatial distributions. We highlight the limitations of static models for dynamic species and incorporate life history variation and spatial distributions for species and stressors into a trait-based vulnerability and impact model. We use green sea turtles in the Greater Caribbean Region to demonstrate how vulnerability and anthropogenic impact for a dynamic species change across four life stages. By incorporating life stages into a trait-based vulnerability model, we observed life stage-specific vulnerabilities that were otherwise unnoticed when using an aggregated trait value set. Early life stages were more vulnerable to some stressors, such as inorganic pollution or marine heat waves, and less vulnerable to others, such as bycatch. Incorporating spatial distributions of stressors and life stages revealed impacts differ for each life stage across spatial areas, emphasizing the importance of stage-specific conservation measures. Our approach showcases the importance of incorporating dynamic processes into ecological models and will enable better and more targeted conservation actions for species with complex life histories and high mobility.

Hunting mode and habitat selection mediate the success of human hunters.

BACKGROUND: As a globally widespread apex predator, humans have unprecedented lethal and non-lethal effects on prey populations and ecosystems. Yet compared to non-human predators, little is known about the movement ecology of human hunters, including how hunting behavior interacts with the environment. METHODS: We characterized the hunting modes, habitat selection, and harvest success of 483 rifle hunters in California using high-resolution GPS data. We used Hidden Markov Models to characterize fine-scale movement behavior, and k-means clustering to group hunters by hunting mode, on the basis of their time spent in each behavioral state. Finally, we used Resource Selection Functions to quantify patterns of habitat selection for successful and unsuccessful hunters of each hunting mode. RESULTS: Hunters exhibited three distinct and successful hunting modes ("coursing", "stalking", and "sit-and-wait"), with coursings as the most successful strategy. Across hunting modes, there was variation in patterns of selection for roads, topography, and habitat cover, with differences in habitat use of successful and unsuccessful hunters across modes. CONCLUSIONS: Our study indicates that hunters can successfully employ a diversity of harvest strategies, and that hunting success is mediated by the interacting effects of hunting mode and landscape features. Such results highlight the breadth of human hunting modes, even within a single hunting technique, and lend insight into the varied ways that humans exert predation pressure on wildlife.

Mammal responses to global changes in human activity vary by trophic group and landscape.

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human-wildlife interactions along gradients of human influence.

Limited spatiotemporal niche partitioning among mesocarnivores in Gorongosa National Park, Mozambique.

Competition drives community composition and structure in many ecosystems. Spatial and temporal niche partitioning, in which competing species divide the environment in space or time, are mechanisms that may allow for coexistence among ecologically similar species. Such division of resources may be especially important for carnivores in African savannas, which support diverse carnivore assemblages. We used camera traps to explore patterns of spatial and temporal niche partitioning among four mesocarnivore species in Mozambique's Gorongosa National Park: large-spotted genet (Genetta maculata), African civet (Civettictis civetta), honey badger (Mellivora capensis) and marsh mongoose (Atilax paludinosus). We applied a multispecies occupancy model to evaluate spatial partitioning among mesocarnivores and to quantify the environmental factors that affect species-specific habitat use, including relative lion (Panthera leo) activity. We also analyzed the temporal activity overlap of the four focal species. We identified species-specific habitat covariates that influenced detection probabilities but found no evidence of spatial or temporal partitioning among mesocarnivores in the study system. Indeed, we found some evidence for spatial co-occurrence between two of our focal species: African civet and marsh mongoose. There may be limited competition among mesocarnivores in this system, perhaps due to niche and diet differentiation among these species and an abundance of resources. While we found limited evidence that lion activity impacts mesocarnivores, ongoing monitoring of intraguild interactions is vital as apex predator populations recover in the system. This study adds to a growing understanding of African mesocarnivore ecology and highlights the importance of understanding these dynamics for effective multispecies conservation and restoration.

Effects of human disturbances on wildlife behaviour and consequences for predator-prey overlap in Southeast Asia.

Some animal species shift their activity towards increased nocturnality in disturbed habitats to avoid predominantly diurnal humans. This may alter diel overlap among species, a precondition to most predation and competition interactions that structure food webs. Here, using camera trap data from 10 tropical forest landscapes, we find that hyperdiverse Southeast Asian wildlife communities shift their peak activity from early mornings in intact habitats towards dawn and dusk in disturbed habitats (increased crepuscularity). Our results indicate that anthropogenic disturbances drive opposing behavioural adaptations based on rarity, size and feeding guild, with more nocturnality among the 59 rarer specialists' species, more diurnality for medium-sized generalists, and less diurnality for larger hunted species. Species turnover also played a role in underpinning community- and guild-level responses, with disturbances associated with markedly more detections of diurnal generalists and their medium-sized diurnal predators. However, overlap among predator-prey or competitor guilds does not vary with disturbance, suggesting that net species interactions may be conserved.

A gentle introduction to computer vision-based specimen classification in ecological datasets.

Classifying specimens is a critical component of ecological research, biodiversity monitoring and conservation. However, manual classification can be prohibitively time-consuming and expensive, limiting how much data a project can afford to process. Computer vision, a form of machine learning, can help overcome these problems by rapidly, automatically and accurately classifying images of specimens. Given the diversity of animal species and contexts in which images are captured, there is no universal classifier for all species and use cases. As such, ecologists often need to train their own models. While numerous software programs exist to support this process, ecologists need a fundamental understanding of how computer vision works to select appropriate model workflows based on their specific use case, data types, computing resources and desired performance capabilities. Ecologists may also face characteristic quirks of ecological datasets, such as long-tail distributions, 'unknown' species, similarity between species and polymorphism within species, which impact the efficacy of computer vision. Despite growing interest in computer vision for ecology, there are few resources available to help ecologists face the challenges they are likely to encounter. Here, we present a gentle introduction for species classification using computer vision. In this manuscript and associated GitHub repository, we demonstrate how to prepare training data, basic model training procedures, and methods for model evaluation and selection. Throughout, we explore specific considerations ecologists should make when training classification models, such as data domains, feature extractors and class imbalances. With these basics, ecologists can adjust their workflows to achieve research goals and/or account for uncertainty in downstream analysis. Our goal is to provide guidance for ecologists for getting started in or improving their use of machine learning for visual classification tasks.

A big-headed problem drives an ecological chain reaction.

Disruption of key species interactions reverberates across an African savanna.

Animal cognition and culture mediate predator-prey interactions.

Predator-prey ecology and the study of animal cognition and culture have emerged as independent disciplines. Research combining these disciplines suggests that both animal cognition and culture can shape the outcomes of predator-prey interactions and their influence on ecosystems. We review the growing body of work that weaves animal cognition or culture into predator-prey ecology, and argue that both cognition and culture are significant but poorly understood mechanisms mediating how predators structure ecosystems. We present a framework exploring how previous experiences with the predation process creates feedback loops that alter the predation sequence. Cognitive and cultural predator-prey ecology offers ecologists new lenses through which to understand species interactions, their ecological consequences, and novel methods to conserve wildlife in a changing world.

Trait-based sensitivity of large mammals to a catastrophic tropical cyclone.

Extreme weather events perturb ecosystems and increasingly threaten biodiversity1. Ecologists emphasize the need to forecast and mitigate the impacts of these events, which requires knowledge of how risk is distributed among species and environments. However, the scale and unpredictability of extreme events complicate risk assessment1-4-especially for large animals (megafauna), which are ecologically important and disproportionately threatened but are wide-ranging and difficult to monitor5. Traits such as body size, dispersal ability and habitat affiliation are hypothesized to determine the vulnerability of animals to natural hazards1,6,7. Yet it has rarely been possible to test these hypotheses or, more generally, to link the short-term and long-term ecological effects of weather-related disturbance8,9. Here we show how large herbivores and carnivores in Mozambique responded to Intense Tropical Cyclone Idai, the deadliest storm on record in Africa, across scales ranging from individual decisions in the hours after landfall to changes in community composition nearly 2 years later. Animals responded behaviourally to rising floodwaters by moving upslope and shifting their diets. Body size and habitat association independently predicted population-level impacts: five of the smallest and most lowland-affiliated herbivore species declined by an average of 28% in the 20 months after landfall, while four of the largest and most upland-affiliated species increased by an average of 26%. We attribute the sensitivity of small-bodied species to their limited mobility and physiological constraints, which restricted their ability to avoid the flood and endure subsequent reductions in the quantity and quality of food. Our results identify general traits that govern animal responses to severe weather, which may help to inform wildlife conservation in a volatile climate.

The diversity of animals identified as keystone species.

Although the keystone species concept was conceived of over 50 years ago, contemporary efforts to synthesize related literature have been limited. Our objective was to create a list of keystone animal species identified in the literature and to examine the variation in the traits of species and the ecosystem influences they elicit. We documented 230 species considered keystones. A clustering analysis classified them into five archetypes based on combinations of their taxonomic class, body size, trophic level, and role (consumers, modifiers, or prey). Although conservation and public perception of keystones primarily focuses on large vertebrate consumers, our analysis reveals that researchers have defined a wide diversity of keystone species, with large variation in associated ecosystem processes. Future research may confront ambiguity in the definition of keystone status, as well as clarify the type, abundance, and quality of data required to assign the term. Identifying keystones with increased rigor would not only enrich the literature but also inform intervention to safeguard threatened keystones and their associated influences on ecosystems.

Championing inclusive terminology in ecology and evolution.

Amid a growing disciplinary commitment to inclusion in ecology and evolutionary biology (EEB), it is critical to consider how the use of scientific language can harm members of our research community. Here, we outline a path for identifying and revising harmful terminology to foster inclusion in EEB.

The influence of human activity on predator-prey spatiotemporal overlap.

Despite growing evidence of widespread impacts of humans on animal behaviour, our understanding of how humans reshape species interactions remains limited. Here, we present a framework that draws on key concepts from behavioural and community ecology to outline four primary pathways by which humans can alter predator-prey spatiotemporal overlap. We suggest that predator-prey dyads can exhibit similar or opposite responses to human activity with distinct outcomes for predator diet, predation rates, population demography and trophic cascades. We demonstrate how to assess these behavioural response pathways with hypothesis testing, using temporal activity data for 178 predator-prey dyads from published camera trap studies on terrestrial mammals. We found evidence for each of the proposed pathways, revealing multiple patterns of human influence on predator-prey activity and overlap. Our framework and case study highlight current challenges, gaps, and advances in linking human activity to animal behaviour change and predator-prey dynamics. By using a hypothesis-driven approach to estimate the potential for altered species interactions, researchers can anticipate the ecological consequences of human activities on whole communities.

Why don't we share data and code? Perceived barriers and benefits to public archiving practices.

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

Dynamic landscapes of fear: understanding spatiotemporal risk.

The landscape of fear (LOF) concept posits that prey navigate spatial heterogeneity in perceived predation risk, balancing risk mitigation against other activities necessary for survival and reproduction. These proactive behavioral responses to risk can affect individual fitness, population dynamics, species interactions, and coexistence. Yet, antipredator responses in free-ranging prey often contradict expectations, raising questions about the generality and scalability of the LOF framework and suggesting that a purely spatial, static LOF conceptualization may be inadequate. Here, we outline a 'dynamic' LOF framework that explicitly incorporates time to account for predictable spatiotemporal variation in risk-resource trade-offs. This integrated approach suggests novel predictions about predator effects on prey behaviors to refine understanding of the role predators play in ecological communities.

Contrasting patterns of risk from human and non-human predators shape temporal activity of prey.

Spatiotemporal variation in predation risk arises from interactions between landscape heterogeneity, predator densities and predator hunting mode, generating landscapes of fear for prey species that can have important effects on prey behaviour and ecosystem dynamics. As widespread apex predators, humans present a significant source of risk for hunted animal populations. Spatiotemporal patterns of risk from hunters can overlap or contrast with patterns of risk from other predators. Human infrastructure can also reshape spatial patterns of risk by facilitating or impeding hunter or predator movement, or deterring predators that are themselves wary of humans. We examined how anthropogenic and natural landscape features interact with hunting modes of rifle hunters and mountain lions Puma concolor to generate spatiotemporal patterns of risk for their primary prey. We explored the implications of human-modified landscapes of fear for Columbian black-tailed deer Odocoileus hemionus columbianus in Mendocino County, California. We used historical harvest records, hunter GPS trackers and camera trap records of mountain lions to model patterns of risk for deer. We then used camera traps to examine deer spatial and temporal activity patterns in response to this variation in risk. Hunters and mountain lions exhibited distinct, contrasting patterns of spatiotemporal activity. Risk from rifle hunters, who rely on long lines of sight, was highest in open grasslands and near roads and was confined to the daytime. Risk from mountain lions, an ambush predator, was highest in dense shrubland habitat, farther from developed areas, and during the night and crepuscular periods. Areas of human settlement provided a refuge from both hunters and mountain lions. We found no evidence that deer avoided risk in space at the scale of our observations, but deer adjusted their temporal activity patterns to reduce the risk of encounters with humans and mountain lions in areas of higher risk. Our study demonstrates that interactions between human infrastructure, habitat cover and predator hunting mode can result in distinct spatial patterns of predation risk from hunters and other predators that may lead to trade-offs for prey species. However, distinct diel activity patterns of predators may create vacant hunting domains that reduce costly trade-offs for prey. Our study highlights the importance of temporal partitioning as a mechanism of predation risk avoidance.

Site fidelity and behavioral plasticity regulate an ungulate's response to extreme disturbance.

With rapid global change, the frequency and severity of extreme disturbance events are increasing worldwide. The ability of animal populations to survive these stochastic events depends on how individual animals respond to their altered environments, yet our understanding of the immediate and short-term behavioral responses of animals to acute disturbances remains poor. We focused on animal behavioral responses to the environmental disturbance created by megafire. Specifically, we explored the effects of the 2018 Mendocino Complex Fire in northern California, USA, on the behavior and body condition of black-tailed deer (Odocoileus hemionus columbianus). We predicted that deer would be displaced by the disturbance or experience high mortality post-fire if they stayed in the burn area. We used data from GPS collars on 18 individual deer to quantify patterns of home range use, movement, and habitat selection before and after the fire. We assessed changes in body condition using images from a camera trap grid. The fire burned through half of the study area, facilitating a comparison between deer in burned and unburned areas. Despite a dramatic reduction in vegetation in burned areas, deer showed high site fidelity to pre-fire home ranges, returning within hours of the fire. However, mean home range size doubled after the fire and corresponded to increased daily activity in a severely resource-depleted environment. Within their home ranges, deer also selected strongly for patches of surviving vegetation and woodland habitat, as these areas provided forage and cover in an otherwise desolate landscape. Deer body condition significantly decreased after the fire, likely as a result of a reduction in forage within their home ranges, but all collared deer survived for the duration of the study. Understanding the ways in which large mammals respond to disturbances such as wildfire is increasingly important as the extent and severity of such events increases across the world. While many animals are adapted to disturbance regimes, species that exhibit high site fidelity or otherwise fixed behavioral strategies may struggle to cope with increased climate instability and associated extreme disturbance events.

Disturbance type and species life history predict mammal responses to humans.

Human activity and land use change impact every landscape on Earth, driving declines in many animal species while benefiting others. Species ecological and life history traits may predict success in human-dominated landscapes such that only species with "winning" combinations of traits will persist in disturbed environments. However, this link between species traits and successful coexistence with humans remains obscured by the complexity of anthropogenic disturbances and variability among study systems. We compiled detection data for 24 mammal species from 61 populations across North America to quantify the effects of (1) the direct presence of people and (2) the human footprint (landscape modification) on mammal occurrence and activity levels. Thirty-three percent of mammal species exhibited a net negative response (i.e., reduced occurrence or activity) to increasing human presence and/or footprint across populations, whereas 58% of species were positively associated with increasing disturbance. However, apparent benefits of human presence and footprint tended to decrease or disappear at higher disturbance levels, indicative of thresholds in mammal species' capacity to tolerate disturbance or exploit human-dominated landscapes. Species ecological and life history traits were strong predictors of their responses to human footprint, with increasing footprint favoring smaller, less carnivorous, faster-reproducing species. The positive and negative effects of human presence were distributed more randomly with respect to species trait values, with apparent winners and losers across a range of body sizes and dietary guilds. Differential responses by some species to human presence and human footprint highlight the importance of considering these two forms of human disturbance separately when estimating anthropogenic impacts on wildlife. Our approach provides insights into the complex mechanisms through which human activities shape mammal communities globally, revealing the drivers of the loss of larger predators in human-modified landscapes.