Biology subjects

Signer, J.

Publications and source records attributed to Signer, J..

6 recordsLinked to original sources

bioRxiv2026

Segmentation and profile-based classification of movement strategies from animal tracking data

O_LIClassifying animal movement strategies from GPS tracking data is essential for understanding space use, population dynamics and conservation planning. However, existing approaches either require strong parametric assumptions about trajectory shape, large labelled datasets (i.e. expert-annotated) for machine learning, or lack formal uncertainty quantification. These limitations create barriers for researchers working with novel species or limited sample sizes. C_LIO_LIWe present a profile-based classification framework consisting of three steps. First, trajectories are segmented using breakpoint detection applied to Net Squared Displacement (NSD) time series. Movement metrics are then extracted from each segment and classified by comparing them to empirically derived behavioural profiles via Z-score distances transformed to softmax probabilities. Bootstrap resampling quantifies uncertainty in the resulting classifications from both training and test data. We validated the framework through simulation experiments and applied it to GPS tracking data from two ecologically contrasting species: gray wolf (Canis lupus;43 individuals) and northern lapwing (Vanellus vanellus;15 individuals). C_LIO_LISimulations showed that 5-10 training segments per movement strategy suffice for reliable classification, with overall accuracy of 91.1%across residential, floating and dispersal strategies. Segment duration of 30-60 days was required for confident discrimination of residential and floating behaviour. For wolves, the framework clearly distinguished residency, floating or dispersal (91.2%of segments classified with >50%probability). For lapwings, migration was identified with high confidence, while residential-floating discrimination reflected genuine ecological ambiguity confirmed by domain experts, with bootstrap confidence intervals transparently flagging uncertain cases. C_LIO_LIThe profile-based framework provides an accessible, interpretable alternative to parametric NSD fitting and machine learning approach, requiring modest training data while delivering probabilistic classifications with honest uncertainty estimates. An R package (moveprofile) implementing the complete workflow is freely available. The framework is applicable to any tracked species where distinct movement strategies can be identified by experts knowledge. C_LI

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

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↗

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↗