Biology subjects

Avgar, T.

Publications and source records attributed to Avgar, T..

6 recordsLinked to original sources

bioRxiv2026

Prey age modifies risk-taking behavior in a multi-predator environment

Many species of animals undergo senescence, impacting predator-prey relationships, yet how senescing prey adjust their risk-taking behavior is poorly understood. We used integrated step selection analysis to quantify risk-taking from empirical data. A graphical framework of age-dependent adaptive risk-taking predicted - and our empirical analysis found - a reduction in female elk (Cervus canadensis) risk-taking with age toward wolves (Canis lupus) but not cougars (Puma concolor), underscoring the role of predator hunting mode in ecological dynamics. We estimated average risk-taking toward wolves would be 42% lower in a population with median age 10 versus 4 years, highlighting how a prey populations age structure likely impacts risk-induced trait responses and the potential emergence of predation-risk effects. Our findings suggest that altered risk-taking may be an adaptive behavioral shift rather than a passive consequence of physical decline. Such adaptive changes to risk-taking may represent an underappreciated link between individual behavior and community-level coexistence.

ecology↗

bioRxiv2024

Dead zones, obstructions, and missed captures: A novel approach for determining the Effective Capture Area for remote camera traps

1. Camera traps (CTs) have become cemented as an important tool of wildlife research, yet, their utility is now extending beyond academics, as CTs can contribute to more inclusive place-based wildlife management. From advances in analytics and technology, CT-based density estimates of wildlife is an emerging field of research. Most CT-based density methods require an estimate of the size of the viewshed monitored by each CT, a parameter that may be highly variable and difficult to quantify. 2. Here, we developed and tested a standardized field and analytical method allowing us to predict the probability of photographic capture as it varies within CT viewshed. We investigated how capture probability changes due to environmental influences, i.e., vegetation structure, ambient temperature, speed of subject, time of day, in addition to internal factors from CTs themselves, i.e., sensitivity settings, number of photos taken, and CT brand. We then summarize these spatial capture probability kernels into a Realised Viewshed Size (RVS)--the capture-probability corrected size of a CTs viewshed 3. We found that RVS values are heavily influenced by location-specific environmental factors, i.e., vegetation structure, technological delays associated with CTs themselves, i.e., refractory period, and internal CT settings, i.e., sensitivity, number of photographs taken. We also found that the RVS values computed using our methodology are substantially smaller than reported values in the literature. 4. Imprecision surrounding CT viewshed areas can create propagating bias when implementing CT-based density estimators. Our method can change how practitioners consider photographs for use in CT density estimators thus increasing the reliability of CT-based density estimation, and contribute to more accessible wildlife management practices.

ecology↗

bioRxiv2023

Identifying signals of memory from observations of animal movements in Plato's cave

Incorporating memory (i.e., some notion of familiarity or experience with the landscape) into models of animal movement is a rising challenge in the field of movement ecology. The recent proliferation of new methods offers new opportunities to understand how memory influences movement. However, there are no clear guidelines for practitioners wishing to parameterize the effects of memory on moving animals. We review approaches for incorporating memory into Step-Selection Analyses (SSAs), a frequently used movement modeling framework. Memory-informed SSAs can be constructed by including spatial-temporal covariates (or maps) that define some aspect of familiarity (e.g., whether, how often, or how long ago the animal visited different spatial locations) derived from long-term telemetry data. We demonstrate how various familiarity covariates can be included in SSAs using a series of coded examples in which we fit models to wildlife tracking data from a wide range of taxa. We discuss how these different approaches can be used to address questions related to whether and how animals use information from past experiences to inform their future movements. We also highlight challenges and decisions that the user must make when applying these methods to their tracking data. By reviewing different approaches and providing code templates for their implementation, we hope to inspire practitioners to investigate further the importance of memory in animal movements using wildlife tracking data.

ecology↗

bioRxiv2023

Simulating animal space use from fitted integrated Step-Selection Functions (iSSF)

O_LIA standing challenge in the study of animal movement ecology is the capacity to predict where and when an individual animal might occur on the landscape, the so-called, Utilization Distribution (UD). Under certain assumptions, the steady-state UD can be predicted from a fitted exponential habitat selection function. However, these assumptions are rarely met. Furthermore, there are many applications that require the estimation of transient dynamics rather than steady-state UDs (e.g. when modeling migration or dispersal). Thus, there is a clear need for computational tools capable of predicting UDs based on observed animal movement data. C_LIO_LIIntegrated Step-Selection Analyses (iSSAs) are widely used to study habitat selection and movement of wild animals, and result in a fully parametrized individual-based model of animal movement, which we refer to as an integrated Step Selection Function (iSSF). An iSSF can be used to generate stochastic animal paths based on random draws from a series of Markovian redistribution kernels, each consisting of a selection-free, but possibly habitat-influenced, movement kernel and a movement-free selection function. The UD can be approximated by a sufficiently large set of such stochastic paths. C_LIO_LIHere, we present a set of functions in R to facilitate the simulation of animal space use from fitted iSSFs. Our goal is to provide a general purpose simulator that is easy to use and is part of an existing workflow for iSSAs (within the amt R package). C_LIO_LIWe demonstrate through a series of applications how the simulator can be used to address a variety of questions in applied movement ecology. By providing functions in amt and coded examples, we hope to encourage ecologists using iSSFs to explore their predictions and model goodness-of-fit using simulations, and to further explore mechanistic approaches to modeling landscape connectivity. C_LI

ecology↗

bioRxiv2022

Density-dependent habitat selection alters drivers of population distribution in northern Yellowstone elk

Although it is well established that density dependence drives changes in organismal abundance over time, relatively little is known about how density dependence affects variation in abundance over space. We tested the hypothesis that spatial tradeoffs between food and safety can change the drivers of population distribution, caused by opposing patterns of density-dependent habitat selection (DDHS) that are predicted by the multidimensional ideal free distribution. We addressed this using winter aerial survey data of northern Yellowstone elk (Cervus canadensis) spanning four decades. Supporting our hypothesis, we found positive DDHS for food (herbaceous biomass) and negative DDHS for safety (openness and roughness), such that the primary driver of habitat selection switched from food to safety as elk density decreased from 9.3 to 2.0 elk/km2. Our results demonstrate how population density can drive landscape-level shifts in population distribution, confounding habitat selection inference and prediction, and potentially affecting community-level interactions.

ecology↗

bioRxiv2020

A "How-to" Guide for Interpreting Parameters in Resource- and Step-Selection Analyses

O_LIHabitat-selection analyses allow researchers to link animals to their environment via habitat-selection or step-selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat-selection analyses that incorporate movement characteristics, referred to as integrated step-selection analyses, are particularly appealing because they allow modeling of both movement and habitat-selection processes. C_LIO_LIDespite their popularity, many users struggle with interpreting parameters in habitat-selection and step-selection functions. Integrated step-selection analyses also require several additional steps to translate model parameters into a full-fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. C_LIO_LIUsing simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point-process can facilitate parameter interpretation in habitat-selection analyses. Further, we provide a "how to" guide illustrating the steps required to implement integrated step-selection analyses using the amt package. C_LIO_LIBy providing clear examples with open-source code, we hope to make habitat-selection analyses more understandable and accessible to end users. C_LI

ecology↗