Biology subjects

Fieberg, J.

Publications and source records attributed to Fieberg, J..

15 recordsLinked to original sources

bioRxiv2026

Integrating learning in movement using step-selection analyses

1. Understanding how animals acquire and use information from the environment is critical for linking movement to population dynamics, species distributions and conservation. Advances in tracking technologies and growing interest in learning processes have opened opportunities to study behaviours such as habitat exploration in translocated animals or ontogeny of migration and dispersal movements. However, accessible statistical methods for studying these behavioural processes are lacking. 2. We present a learning-explicit step-selection analysis (SSA) that integrates movement, habitat selection, and learning into a single evolving process. At the start of the movement trajectory, the animal is assumed to have no knowledge of the landscape, i.e. its internal habitat quality map is initialized to a constant. As the animal moves, this map is updated dynamically: with each step, only the habitat within its perceptual range becomes known and contributes to future decisions. 3. Through simulations, we show that this method separates true habitat preferences from learning effects and reveals when large-scale behaviours, such as attraction to resources or avoidance of risks, emerge as knowledge accumulates. Critically, we demonstrate that ignoring learning, as in traditional SSA, can lead to biased estimators of habitat selection. 4. Finally, we apply our approach to a real-world GPS dataset of naive individuals, consisting of 10 juvenile red kites dispersing in Switzerland. Using leave-one-individual-out cross-validation, we show that learner SSFs consistently outperform traditional SSFs in predicting movement decisions. 5. We conclude by discussing methodological considerations and future directions for integrating learning into movement ecology. By explicitly modelling the formation of memory that integrates both spatial information and habitat quality, our approach advances process-based movement ecology while remaining compatible with standard SSA workflows, offering practical tools for both theoretical research and applied wildlife management.

animal behavior and cognition↗

bioRxiv2025

In the face of competition, imperfect knowledge can lead to sustainable exploitation of a shared resource.

Overexploitation of a shared resource is a frequent phenomenon among groups of selfish consumers, known as the tragedy of the commons. To avoid "self-damage through self-interest", humans may either defend exclusive access to resources, or collectively adopt advanced cooperative behaviour and carefully designed spatial harvesting strategies to exploit a shared resource sustainably. However, why do many non-human species that are neither social nor territorial also appear to avoid overexploiting resources? This study shows that, even under highly competitive scenarios, cooperation is not necessary to sustainably exploit shared resources if individuals have imperfect knowledge about the distribution of those resources. Poorly informed individuals are more likely to inadvertently exploit resources in peripheral habitats, which may allow for higher resource renewal in the nearby and low-cost habitats, enhancing the sustainability of resource use. As a result, those consumers can achieve a long-term yield that approaches that of cooperative consumers and is higher than omniscient consumers. Ironically, while there might be an evolutionary drive for individuals to make well-informed decisions, consumers of shared resources may be collectively more prosperous when they make decisions based on less information. These findings might explain why apparently sub-optimal consumer behaviour appears so prevalent in nature.

ecology↗

bioRxiv2024

Perceived and observed biases within scientific communities: a case study in movement ecology

Who conducts biological research, where, and how the results are disseminated varies among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical first step towards addressing them. We documented perceived and observed biases in movement ecology. Movement ecology is a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. First, we surveyed attendees of an international conference, and discussed the results at the conference (comparing uninformed vs informed perceived bias). Although most researchers identified as bias-aware, only a subset of biases were discussed in conversation. Next, by considering author affiliations from publications in the journal Movement Ecology, we found among-country discrepancies between the country of the authors affiliation and study site location related to national economics. At the within-country scale, we found that race-gender identities of postgraduate biology researchers in the USA differed from national demographics. We discuss the role of potential specific causes for the emergence of bias in the sub-discipline, e.g. parachute-science or accessibility to fieldwork. Undertaking data-driven analysis of bias within research sub-disciplines can help identify specific barriers and first steps towards the inclusion of a greater diversity of participants in the scientific process.

ecology↗

bioRxiv2024

High variability of migration strategies in a re-established Trumpeter Swan population

BackgroundThe Interior Population (IP) of trumpeter swans (Cygnus buccinator), formerly extirpated by market hunting, was re-established in eastern North America by releasing individuals from both migratory and non-migratory populations. Their current annual movement patterns are largely unknown. Our goal was to describe their seasonal movements and quantify the proportion of the IP that is migratory, the extent and phenology of seasonal movements, and associations between movement patterns and breeding status and breeding location. MethodsWe deployed 113 GPS-GSM transmitters on IP trumpeter swans in six U.S. states and one Canadian province across the current IP breeding range. Using data from 252 swan-years, we estimated dates of migration events by segmenting the annual cycle using piecewise regression models fit to each yearly time-series of displacement from the breeding site. We fit a latent-state model to characterize population-level associations between breeding latitude and maximum extent of migration, and linear mixed models to quantify associations between individual characteristics (e.g., breeding status, sex) and migration phenology. ResultsAt the individual level, 59% of swans moved to distant non-breeding-period areas (long-distance migration, defined as moving >100 km from the breeding site), 16% exhibited regional migration (25-100 km from breeding site), 19% exhibited non-migratory but local movements (<25 km from breeding site), and 6% exhibited multiple migration strategies. Swans breeding at more-northern latitudes departed their territories earlier in autumn and returned later in the spring than those breeding at more southern latitudes. Breeding swans departed later in the autumn than non-breeders, but breeding status did not have a strong association with arrival in the spring. ConclusionIP trumpeter swans are partial migrants, with a continuum of strategies each year, from local movements to long-distance migration. Much of the variability in movement patterns was related to factors tied to natural history demands (e.g., breeding status) and response to environmental conditions (e.g., through associations with breeding latitude).

ecology↗

bioRxiv2024

Step selection analysis with non-linear and random effects in mgcv

Step selection analysis is used to jointly describe animal movement patterns and habitat preferences. Recent work has extended this framework to model inter-individual differences, account for unexplained structure in animals space use, and capture temporally-varying patterns of movement and habitat selection.In this paper, we formulate step selection functions with penalised smooths (similar to generalised additive models) to unify new and existing extensions, and conveniently implement the models in the popular, open-source mgcv R package. We explore non-linear patterns of movement and habitat selection, and use the equivalence between penalised smoothing splines and random effects to implement individual-level and spatial random effects. This framework can also be used to fit varying-coefficient models to account for temporally or spatially-heterogeneous patterns of selection (e.g., resulting from behavioural variation), or any other non-linear interactions between drivers of the animals movement decisions. We provide the necessary technical details to understand several key special cases of smooths and their implementation in mgcv, showcase the ecological relevance using two illustrative examples, and provide R code (available at https://github.com/NJKlappstein/smoothSSF) to facilitate the adoption of these methods. This paper is a broad overview of how smooth effects can be applied to increase the flexibility and biological realism of step selection analysis.

ecology↗

bioRxiv2023

Methods for Implementing Integrated Step-Selection Functions with Incomplete Data

Integrated step-selection analyses (iSSAs) are versatile and powerful frameworks for studying habitat and movement preferences of tracked animals. iSSAs utilize integrated step-selection functions (iSSFs) to model movements in discrete time, and thus, require animal location data that are regularly spaced in time. However, many realworld datasets are incomplete due to tracking devices failing to locate an individual at one or more scheduled times, leading to slight irregularities in the duration between consecutive animal locations. To address this issue, researchers typically only consider bursts of regular data (i.e., sequences of locations that are equally spaced in time), thereby reducing the number of observations used to model movement and habitat selection. We reassess this practice and explore four alternative approaches that account for temporal irregularity resulting from missing data. Using a simulation study, we compare these alternatives to a baseline approach where temporal irregularity is ignored and demonstrate the potential improvements in model performance that can be gained by leveraging these additional data.

ecology↗

bioRxiv2023

Evaluating goodness-of-fit of animal movement models using lineups

Models of animal movement are frequently fit to animal location data to understand how animals respond to and interact with local environmental features. Several open-source software packages are available for analyzing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness-of-fit. We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness-of-fit of integrated step-selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models, and demonstrate the approach using both methods applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate goodness-of-fit using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g., to visualize variability in model behavior across stochastic simulations or identify areas where the model could be improved). We provide coded examples and two vignettes to demonstrate the flexibility of the approach and encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness-of-fit.

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↗

bioRxiv2023

Modeling individual variability in habitat selection and movement using integrated step-selection analyses

1. Integrated step-selection analyses (ISSAs) are frequently used to study habitat selection using animal movement data. Methods for incorporating random effects in ISSAs have been developed, making it possible to quantify variability among animals in their space-use patterns. Although it is possible to model variability in both habitat selection and movement parameters, applications to date have focused on the former despite the widely acknowledged and important role that movement plays in determining ecological processes from the individual to ecosystem level. One potential explanation for this omission is the absence of readily-available software or examples demonstrating methods for estimating movement parameters in ISSAs with random effects. 2. We demonstrated methods for characterizing among-individual variability in both movement and habitat-selection parameters using a simulated data set and by fitting two models to an acoustic telemetry data set containing locations of 35 red snapper (Lutjanus campechanus). Movement kernels were assumed to depend on either the type of benthic reef habitat in which the fish was located (model 1) or the distance between the fishs current location and nearest edge habitat (model 2). In both models, we also quantified habitat selection for different benthic habitat classes and distance to edge habitat, and we allowed for individual variability in movement and habitat-selection parameters using random effects. 3. The simulation example highlights the benefits of a mixed effects specification, namely we can increase precision when estimating individual-specific movement parameters by borrowing information across like individuals. In our applied example, we found substantial among-individual variability in both habitat selection and movement parameters. Nonetheless, most red snapper selected for hardbottom habitat and for locations nearer to edge habitat. They also moved less when in hardbottom habitat. Turn angles were frequently near {+/-}{pi}, but were more dispersed when fish were far away from edge habitat. 4. We provide code templates and functions for quantifying variability in movement and habitat-selection parameters when implementing ISSAs with random effects. In doing so, we hope to encourage ecologists conducting ISSAs to take full advantage of their ability to model among-individual variability in both habitat-selection and movement patterns.

ecology↗

bioRxiv2022

A comparison of survey method efficiency for estimating densities of Zebra Mussels (Dreissena polymorpha)

Abundance surveys are a common and practical technique to estimate plant or animal densities. Methods used to conduct these surveys often require jointly estimating uncertainty in both counts and detection probability. The estimation of detection probability requires additional measurements that take time, potentially reducing the efficiency of the survey for high-density populations. We conducted quadrat, removal, and distance surveys of zebra mussels (Dreissena polymorpha) in three central Minnesota lakes and used an efficiency measure to help compare survey designs. Survey efficiency depended on both the survey design and the population density. The efficiency of survey designs that required estimates of detection probabilities (relative to designs that did not) decreased with density, leading to a change in the most efficient survey design at high densities. These results demonstrate that the best survey design may be context-specific, requiring some prior knowledge of the underlying population density and the cost/time needed to collect additional information for estimating detection probabilities.

ecology↗

bioRxiv2021

Using Piecewise Regression to Identify Biological Phenomena in Biotelemetry Datasets

Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animals location, movement characteristics, external environmental conditions, and internal physiological status. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. We provide five example applications highlighting a variety of taxonomic species, data streams, timescales, and biological phenomena. These examples include a short-term behavioral response (flee and return) by a trumpeter swan (Cygnus buccinator) immediately following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose (Alces alces); a physiological response (spike in heart-rate) in a black bear (Ursus americanus) to a stressful stimulus (presence of a drone); a mortality event of a trumpeter swan signaled by changes in collar temperature and Overall Dynamic Body Acceleration; and an unsupervised method for identifying the onset, return, duration, and staging use of sandhill crane (Antigone canadensis) migration. We implement analyses using the mcp package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criterion and cross-validation measures. This approach uses simple modeling approaches that are accessible to a wide audience and is a straightforward means of assessing a variety of biologically relevant changes in animal behavior.

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↗

bioRxiv2020

A fresh look at an old concept: Home-range estimation in a tidy world

O_LIA rich set of statistical techniques have been developed over the last several decades to estimate the spatial extent of animal home ranges from telemetry data, and new methods to estimate home ranges continue to be developed. C_LIO_LIHere we investigate home-range estimation from a computational point of view and aim to provide a general framework for computing home ranges, independent of specific estimators. C_LIO_LIWe show how such a workflow can help make home-range estimation easier and more intuitive, and we provide a series of examples illustrating how different estimators can be compared easily, so that one can perform a sensitivity analysis to determine the degree to which the choice of estimator influences qualitative and quantitative conclusions. C_LIO_LIBy providing a standardized, tidy implementation of home-range estimators, we hope to equip analysts with the tools needed to explore how estimator choice influences answers to biologically meaningful questions. C_LI

ecology↗

bioRxiv2018

Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation

1. Popular frameworks for studying habitat selection include resource-selection functions (RSFs) and step-selection functions (SSFs) estimated using logistic and conditional logistic regression, respectively. Both frameworks compare environmental covariates associated with locations animals visit with environmental covariates at a set of locations assumed available to the animal. Conceptually, random coefficients could be used to accommodate inter-individual heterogeneity with either approach, but straightforward and efficient one-step procedures for fitting SSFs with random coefficients are currently lacking. 2. We take advantage of the fact that the conditional logistic regression model (i. e., the SSF) is likelihood-equivalent to a Poisson model with stratum-specific intercepts. By interpreting the intercepts as a random effect with a large (fixed) variance, inference becomes feasible with standard Bayesian techniques, but also with frequentist methods that allow one to fix the variance of a random effect. We compare this approach to other commonly applied alternatives, including random intercept-only models, and to a two-step algorithm for fitting mixed-effects models. 3. We also reinforce the need to weight available points when fitting RSFs, since models fit using "infinitely weighted logistic regression" have been shown to be equivalent to an inhomogeneous Poisson process (IPP). We generalize this result to "infinitely weighted Poisson regression", which converges to the same underlying IPP distribution. 4. Using data from Eurasian otters (Lutra lutra) and mountain goats (Oreamnos americanus), we illustrate that our models lead to valid and feasible inference. In addition, we conduct a simulation study to demonstrate the importance of including random slopes when estimating individual- and population-level habitat-selection parameters. 5. By providing coded examples using integrated nested Laplace approximations (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R-INLA and glmmTMB, we hope to make efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field. SSFs with individual-specific coefficients are particularly attractive since they can provide insights into movement and habitat-selection processes at fine-spatial and temporal scales, but these models had previously been very challenging to fit.

ecology↗