Behavioural state‐dependent habitat selection and implications for animal translocations

Picardi, Simona; Coates, Peter S.; Kolar, Jesse L.; O’Neil, Shawn T.; Mathews, Steven R.; Dahlgren, David K.

doi:10.1111/1365-2664.14080

Cited by 38 publications

(33 citation statements)

References 74 publications

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“…To assess behaviour-specific habitat selection, some applied studies have employed a two-stage design, in which HMMs are used sequentially with SSFs (e.g., Suraci et al, 2019; Ellington et al, 2020; Clontz et al, 2021; Picardi et al, 2022). In this case, the animal path is segmented into discrete behavioural states using an HMM, and segments of each behaviour are jointly analysed with an SSF to produce state-dependent habitat selection parameters.…”

Section: Introductionmentioning

confidence: 99%

Flexible hidden Markov models for behaviour-dependent habitat selection

Klappstein

Thomas

Michelot

2022

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Flexible hidden Markov models for behaviour-dependent habitat selection

Klappstein

Thomas

Michelot

2022

Preprint

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“…By using natural areas, even when they are not protected, limited in size, and adjacent to settlements, as stepping-stones, striped hyenas can survive even in a highly fragmented landscape, which stresses the importance of remnant natural fragments (Singh et al, 2010(Singh et al, , 2014Gantchoff et al, 2020;Bhandari et al, 2021). The fact that males can be found further away from settlements than females, and are less likely to stay and feed in human-dominated areas, emphasizes the need to identify important areas to preserve for each sex specifically (Zeller et al, 2018) while considering their different motivational states (Maiorano et al, 2017;Picardi et al, 2021). In this case, it seems that preserving the open areas adjacent to settlements will be more beneficial for females, while preserving extended areas further away from settlements will be beneficial for males.…”

Section: Discussionmentioning

confidence: 99%

Sex Differences Dictate the Movement Patterns of Striped Hyenas, Hyaena hyaena, in a Human-Dominated Landscape

Bar‐Ziv

Picardi

Kaplan³

et al. 2022

Front. Ecol. Evol.

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Large-carnivore populations have experienced significant declines in the past centuries in extended parts of the world. Habitat loss, fragmentation, and depletion of natural resources are some of the main causes of this decline. Consequently, behavioral flexibility, enabling the exploitation of anthropogenic food resources in highly disturbed human-dominated landscapes, is becoming critical for the survival of large carnivores. These behavioral changes increase the potential for human-large carnivore conflict and can further intensify carnivore persecution. Here, we examine how land cover types (representing a gradient of anthropogenic disturbance) alter the behavior of striped hyenas (Hyaena hyaena) in a human-dominated landscape in Israel, and whether differences in life history between males and females affect their reaction to such disturbances and consequently their level of exposure to humans. We used a Hidden Markov Model on GPS-tracking data for seven striped hyenas to segment individual-night trajectories into behavioral states (resting, searching, and traveling). We then used multinomial logistic regression to model hyenas’ behavioral state as a function of the interaction between land cover and sex. Females traveled less than males both in terms of average distance traveled per hour, per night, and nightly net displacement. Most steps were classified as “searching” for females and as “traveling” for males. Female hyenas spent a higher proportion of time in human-dominated areas and a lower proportion in natural areas compared to males, and they were also more likely to be found close to settlements than males. Females changed their time allocation between natural and human-dominated areas, spending more time resting than traveling in natural areas but not in human-dominated ones; males spent more time searching than resting in human-dominated areas but were equally likely to rest or search in natural ones. The differences in life history between male and female hyenas may reflect different motivations for space use as a means to optimize fitness, which affects their exposure to humans and therefore their potential involvement in human-hyenas conflict. Understanding the mechanisms that lead to behavioral change in response to human disturbance is important for adaptive management and promoting human large-carnivores co-existence in general.

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“…Removing data potentially discards useful information, which may already be scarce in studies using VHF telemetry or with small sample sizes (Girard et al, 2002). Alternatively, quantifying behavioural responses post-capture can provide useful speciesspecific information on altered behaviour and inform future studies (Dechen Quinn et al, 2012;Picardi et al, 2022). For example, Stabach et al (2020) examined effects of GPS collar deployment on scimitar-horned oryx Oryx dammah to quantify the short-term responses in activity, behaviour, stress levels and the length of time before these effects subsided.…”

Section: Identification Of Altered Behaviour Post-capturementioning

confidence: 99%

Using piecewise regression to identify biological phenomena in biotelemetry datasets

Wolfson

Andersen

Fieberg

2022

Journal of Animal Ecology

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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 behaviour over time or respond to external stimuli. A wide variety of animal‐borne sensors can provide information on an animal's 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 multiple biotelemetry data streams. Different biological latent states can be inferred by partitioning a time‐series into multiple segments based on changes in modelled responses (e.g. their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short‐term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator 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 signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood‐rearing (i.e. hatching) for a breeding trumpeter swan. 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 criteria and cross‐validation measures. These simple modelling approaches are accessible to a wide audience and offer a straightforward means of assessing a variety of biologically relevant changes in animal behaviour.

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Behavioural state‐dependent habitat selection and implications for animal translocations

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 38 publications

References 74 publications

Flexible hidden Markov models for behaviour-dependent habitat selection

Flexible hidden Markov models for behaviour-dependent habitat selection

Sex Differences Dictate the Movement Patterns of Striped Hyenas, Hyaena hyaena, in a Human-Dominated Landscape

Using piecewise regression to identify biological phenomena in biotelemetry datasets

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