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Projects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. The estimation of space-use and movement properties is often necessary to answer basic movement ecology questions, but designing an animal tracking study to produce reliable estimates is often done in an ad hoc manner. We developed 'movedesign', a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design. Leveraging the 'ctmm' R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS devices, which may result in a compromise between reliably estimating home range or speed and distance. 'movedesign' has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.

Animals moving through landscapes need to strike a balance between finding sufficient resources to grow and reproduce while minimizing encounters with predators 1,2. Because encounter rates are determined by the average distance over which directed motion persists 1,3–5, this trade-off should be apparent in individuals’ movement. Using GPS data from 1,396 individuals across 62 species of terrestrial mammals, we show how predators maintained directed motion ~7 times longer than for similarly-sized prey, revealing how prey species must trade off search efficiency against predator encounter rates. Individual search strategies were also modulated by resource abundance, with prey species forced to risk higher predator encounter rates when resources were scarce. These findings highlight the interplay between encounter rates and resource availability in shaping broad patterns mammalian movement strategies.

Direct encounters, in which two or more individuals are physically close to one another, are a topic of increasing interest as more and better movement data become available. Recent progress, including the development of statistical tools for estimating robust measures of changes in animals’ space use over time, facilitates opportunities to link direct encounters between individuals with the long-term consequences of those encounters. Working with movement data for coyotes (Canis latrans) and grizzly bears (Ursus arctos horribilis), we investigate whether close intraspecific encounters were associated with spatial shifts in the animals’ range distributions, as might be expected if one or both of the individuals involved in an encounter were seeking to reduce or avoid conflict over space. We analyze the movement data of a pair of coyotes in detail, identifying how a shift in home range location resulting from altered movement behavior was apparently a consequence of a close intraspecific encounter. With grizzly bear 50 movement data, we approach the problem from the perspective of a set of encounter pairs within a population. We find support for the hypotheses that 1) close intraspecific encounters between bears are, on average, associated with subsequent shifts in range distributions and 2) encounters defined at finer spatial scales are followed by greater changes in space use. Our results suggest that animals can undertake long-term, large-scale spatial shifts in response to close intraspecific encounters that have the potential for conflict. These results lend support for existing theory on the evolution of territories and space use (e.g., Maynard-Smith’s bourgeois strategy regarding low-conflict coexistence). Overall, we find that analyses of movement data in a pairwise context can 1) identify distances at which individuals’ proximity to one another may alter behavior and 2) facilitate testing of population-level hypotheses concerning the potential for direct enc

Animal encounters are key components of population dynamics, community dynamics, and evolutionary processes. Consequently, measuring encounter rates (i.e. encounters per time) can be insightful. Encounter rates can be measured from animal tracking data, using metrics that can be split into two groups. The first group consists of trajectory-based metrics, i.e. measures based on serial records of animal locations. This first group includes PROX, the number of observed per number of samples. The second group, in contrast, consists of metrics based on home range overlap, including the Bhattacharyya coefficient (BC). In this study, we argue both types of metrics are limited. Trajectory-based metrics are direct measures of encounter rates but have statistical estimation issues due to their dependency on the frequency of location sampling. Meanwhile, home-rangebased metrics are statistically sound but are not proportional to encounter rates. To overcome both challenges, we proposed a new metric, Relative Encounter Rate (RER). RER increases linearly with the number of encounters and does not depend on the frequency of sampling (i.e. it is scale-invariant). In an individual-based simulation, we measured how RER, BC, and PROX relative error under different sample sizes and sampling frequencies. Further, we compared these metrics in three empirical case studies. We tested Jaguars for polygyny, deforestation effects on tapir connectivity, and an extension of the dearest enemy hypothesis with brown bears. We also compared partner hierarchy according to BC and RER in Jaguar mating clusters. In the simulation study, we found PROX overestimates the encounter rate when data has a low sampling frequency. The simulation also indicates BC overestimated encounters. Furthermore, PROX led to false positives in the Tapir and Bear case studies. In addition, PROX was incapable of detecting many individual relationships in the jaguar polygyny study. RER does not depend on sampling frequency (

The study of movement behavior and home range in animals is fundamental to understanding ecological dynamics, yet designing efficient tracking studies remains a challenge due to the lack of clear guidelines. To address this knowledge gap we created 'movedesign,' a user-friendly application that enables researchers to evaluate the precision of three commonly reported movement and spatial ecology estimates: home range area, speed, and distance traveled. Leveraging the 'ctmm' R package, our application employs autocorrelated kernel density estimators (AKDEs) and continuous-time speed and distance (CTSD) estimators, effectively addressing biases inherent in animal movement datasets. By evaluating the interplay between data resolution and battery life, 'movedesign' guides researchers in making informed decisions to achieve reliable estimates of home range and velocity while considering trade-offs. With broad applications for researchers and decision-makers, this tool empowers efficient deployment strategies, optimizing sampling design for insightful and robust outcomes in movement ecology studies.

A Shiny R application that aims to assist researchers in designing animal tracking projects related to two main research questions: the estimation of home range and of speed and distance traveled. The application makes use of simulations to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration).

The study of habitat selection is a foundational component of basic and applied animal ecology. Today, habitat selection in mammals is primarily studied using resource selection functions, a class of models that uses logistic regression to compare “used” to “available” habitat. However, these models have several statistical problems, including rampant pseudoreplication from failing to account for autocorrelation in modern animal movement data, no clear guidelines for sampling available habitat, and large amounts of numerical error from sampling too few available points. These problems are widely acknowledged but have no generally accepted solutions, so we propose three new methods for addressing them: likelihood weighting, Gaussian availability sampling, and numerical convergence checks. We demonstrate the practical advantages of these methods over conventional approaches using simulations and empirical data on a water mongoose (Atilax paludinosus), a caracal (Caracal caracal), and a serval (Leptailurus serval), and briefly demonstrate how to apply our methods to animal tracking data using the ‘ctmm’ R package. Broad uptake of these methods could substantially improve our estimates of habitat selection in mammals.

1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. 2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. 3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. 4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

Data and code that can be used to reproduce the analyses underlying 'Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting' by Alston, Fleming, et al. (Preprint: https://doi.org/10.1101/2022.04.21.489059) For more detailed information, please visit the README file.

1. Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. 2. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. 3. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. 4. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

Estimating animal home ranges is a primary purpose of collecting tracking data. Many widely used home range estimators, including conventional kernel density estimators, assume independently sampled data. In stark contrast, modern GPS animal tracking datasets are almost always strongly autocorrelated. The incongruence between estimator assumptions and empirical reality often leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) directly models the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command-line ctmm R package. Here, we introduce ctmmweb, which provides a point-and-click graphical interface to ctmm and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstrate ctmmweb’s capabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal. We intend ctmmweb to open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.

Encounter rates link movement strategies to intra- and inter-specific interactions, and therefore translate individual movement behavior into higher-level ecological processes. Indeed, a large body of interacting population theory rests on the law of mass action, which can be derived from assumptions of Brownian motion in an enclosed container with exclusively local perception. These assumptions imply completely uniform space use, individual home ranges equivalent to the population range, and encounter dependent on movement paths actually crossing. Mounting empirical evidence, however, suggests that animals use space non-uniformly, occupy home ranges substantially smaller than the population range, and are of- ten capable of nonlocal perception. Here, we explore how these empirically supported behaviors change pairwise encounter rates. Specifically, we derive novel analytical expressions for encounter rates under Ornstein-Uhlenbeck motion, which features non-uniform space use and allows individual home ranges to differ from the population range. We compare OU-based encounter predictions to those of Reflected Brownian Motion, from which the law of mass action can be derived. For both models, we further explore how the interplay between the scale of perception and home-range size affects encounter rates. We find that neglecting realistic movement and perceptual behaviors can lead to systematic, non-negligible biases in encounter-rate predictions.