Time-varying habitat selection analysis: a model and applications for studying diel, seasonal and post-release changes

Dejeante, Romain; Chamaillé‐Jammes, Simon

doi:10.1101/2023.01.04.522602

Cited by 4 publications

(5 citation statements)

References 22 publications

(22 reference statements)

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“…Here we fitted the dynamic logistic model using the “ddhazard” function from the dynamichazard R package by Christoffersen (2021), which implements the method described by Fahrmeir (1992). In Dejeante et al (2023a), we provide the script needed to run a time‐varying HSA on a simulated trajectory; the model fitting takes ~3 s on this dataset, on a PC with an Intel(R) Xeon(R) CPU E5‐1650 0 @ 3.20GHz.…”

Section: Methodsmentioning

confidence: 99%

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Time‐varying habitat selection analysis: A model and applications for studying diel, seasonal, and post‐release changes

Dejeante,

Valeix,

Chamaillé‐Jammes

2024

Ecology

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Resource selection functions are commonly used to evaluate animals' habitat selection, for example, the disproportionate use of habitats relative to their availability. While environmental conditions or animal motivations may vary over time, sometimes in an unknown manner, studying changes in habitat selection usually requires an a priori segmentation of time in distinct periods. This limits our ability to precisely answer the question “When is an animal's habitat selection changing?” Here, we present a straightforward and flexible alternative approach based on fitting dynamic logistic models to used/available data. First, using simulated datasets, we demonstrate that dynamic logistic models perform well in recovering temporal variations in habitat selection. We then show real‐world applications for studying diel, seasonal, and post‐release changes in the habitat selection of the blue wildebeest (Connochaetes taurinus). Dynamic logistic models allow the study of temporal changes in habitat selection in a framework consistent with resource selection functions but without the need to segment time in distinct periods, which can be a difficult task when little is known about the process studied or may obscure interindividual variability in timing of change. These models should undoubtedly find their place in the movement ecology toolbox. We provide R scripts to facilitate their adoption. We also encourage future research to focus on how to account for temporal autocorrelation in location data, as this would allow statistical inference from location data collected at a high frequency, an increasingly common situation.

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Section: Methodsmentioning

confidence: 99%

“…Data and code (Dejeante et al, 2023b) are archived in a Figshare collection at https://doi.org/10.6084/m9.figshare.c.6365415.v5.…”

Section: Data Availability Statementmentioning

confidence: 99%

Time‐varying habitat selection analysis: A model and applications for studying diel, seasonal, and post‐release changes

Dejeante,

Valeix,

Chamaillé‐Jammes

2024

Ecology

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“…Data available from the figshare repository: https://doi.org/10.6084/m9.figshare.24786867.v2 (Dejeante et al., 2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

Counter‐strategies to infanticide: The importance of cubs in determining lion habitat selection and social interactions

Dejeante,

Loveridge,

Macdonald

et al. 2024

Journal of Animal Ecology

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Animal social and spatial behaviours are inextricably linked. Animal movements are driven by environmental factors and social interactions. Habitat structure and changing patterns of animal space use can also shape social interactions. Animals adjust their social and spatial behaviours to reduce the risk of offspring mortality. In territorial infanticidal species, two strategies are possible for males: they can stay close to offspring to protect them against rivals (infant‐defence hypothesis) or patrol the territory more intensively to prevent rival intrusions (territorial‐defence hypothesis). Here, we tested these hypotheses in African lions (Panthera leo) by investigating how males and females adjust their social and spatial behaviours in the presence of offspring. We combined datasets on the demography and movement of lions, collected between 2002 and 2016 in Hwange National Park (Zimbabwe), to document the presence of cubs (field observations) and the simultaneous movements of groupmates and competitors (GPS tracking). We showed a spatial response of lions to the presence of offspring, with females with cubs less likely to select areas close to waterholes or in the periphery of the territory than females without cubs. In contrast, these areas were more selected by males when there were cubs in the pride. We also found social responses. Males spent more time with females as habitat openness increased but the presence of cubs in the pride did not influence the average likelihood of observing males with females. Furthermore, rival males relocated further after an encounter with pride males when cubs were present in the prides, suggesting that the presence of cubs leads to a more vigorous repulsion of competitors. Males with cubs in their pride were more likely to interact with male competitors on the edge of the pride's home range and far from the waterholes, suggesting that they are particularly assiduous in detecting and repelling rival males during these periods. In general, the strategies to avoid infanticide exhibited by male lions supported the territorial‐defence hypothesis. Our study contributes to answer the recent call for a behavioural ecology at the spatial‐social interface.

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“…This method could be further extended to allow for non-linear habitat preferences and non-linear interaction effects (e.g., seasonal selection patterns). These ideas have been explored for RSFs (e.g., McCabe et al, 2021; Dejeante et al, 2023), but not yet for SSFs, which have the ability to assess variability in both habitat selection and movement.…”

Section: Introductionmentioning

confidence: 99%

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

Klappstein,

Michelot,

Fieberg

et al. 2024

Preprint

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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 animal's movement decisions. We provide a broad overview of how smooth effects can be applied to increase the flexibility and biological realism of step selection analysis. 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.

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Time-varying habitat selection analysis: a model and applications for studying diel, seasonal and post-release changes

Cited by 4 publications

References 22 publications

Time‐varying habitat selection analysis: A model and applications for studying diel, seasonal, and post‐release changes

Time‐varying habitat selection analysis: A model and applications for studying diel, seasonal, and post‐release changes

Counter‐strategies to infanticide: The importance of cubs in determining lion habitat selection and social interactions

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

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