Biology subjects

Klappstein, N. J.

Publications and source records attributed to Klappstein, N. J..

3 recordsLinked to original sources

bioRxiv2024

Maternal den site fidelity of polar bears in western Hudson Bay

Seasonal migrations allow to access temporally varying resources and individuals may show fidelity to specific locations. Polar bears (Ursus maritimus) are a sea ice dependent species that migrate between marine and terrestrial habitats, the latter being important for parturition and early cub rearing. However, fidelity to maternity den sites is poorly understood. We assessed polar bear maternal den site fidelity of the Western Hudson Bay subpopulation in Manitoba, Canada. Using capture and telemetry data collected between 1979 - 2018, we examined site fidelity from 188 maternity den locations from 78 individuals. We calculated within-individual inter-year distances between dens, and compared these to between-individual distances via non-parametric bootstrapping. We used generalised additive models to assess how maternal age, years between denning events, and sea ice conditions affected site fidelity. We found some evidence of site fidelity, as within-individual inter-year distances were smaller than between-individual den distances by approximately 18.5 km. As time between captures increased, inter-den distances also increased (ranging from approximately 25 km to 55 km), but no other variables significantly affected fidelity. Our findings suggest that western Hudson Bay polar bears show a moderate amount of fidelity to denning areas, but not necessarily to specific sites.

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↗

bioRxiv2022

Flexible hidden Markov models for behaviour-dependent habitat selection

There is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters. For this purpose, a two-stage modelling approach is often taken: (i) classify behaviours with a hidden Markov model (HMM), and (ii) fit a step selection function (SSF) to each subset of data. However, this approach does not properly account for the uncertainty in behavioural classification, nor does it allow states to depend on habitat selection. An alternative approach is to estimate both state switching and habitat selection in a single, integrated model called an HMM-SSF. We build on this recent methodological work to make the HMM-SSF approach more efficient and general. We focus on writing the model as an HMM where the observation process is defined by an SSF, such that well-known inferential methods for HMMs can be used directly for parameter estimation and state classification. We extend the model to include covariates on the HMM transition probabilities, allowing for inferences into the temporal and individual-specific drivers of state switching. We demonstrate the method through an illustrative example of African zebra (Equus quagga), including state estimation, and simulations to estimate a utilisation distribution. In the zebra analysis, we identified two behavioural states, with clearly distinct patterns of movement and habitat selection ("encamped" and "exploratory"). In particular, although the zebra tended to prefer areas higher in grassland across both behavioural states, this selection was much stronger in the fast, directed exploratory state. We also found a clear diel cycle in behaviour, which indicated that zebras were more likely to be exploring in the morning and encamped in the evening. This method can be used to analyse behaviour-specific habitat selection in a wide range of species and systems. A large suite of statistical extensions and tools developed for HMMs and SSFs can be applied directly to this integrated model, making it a very versatile framework to jointly learn about animal behaviour, habitat selection, and space use.

ecology↗