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Understanding decision making in a food-caching predator using hidden Markov models

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Understanding decision making in a food-caching predator using hidden Markov models. / Farhadinia, Mohammad S.; Michelot, Théo; Johnson, Paul J.; Hunter, Luke T. B.; Macdonald, David W.

In: Movement Ecology, Vol. 8, 9, 10.02.2020.

Research output: Contribution to journalArticlepeer-review

Harvard

Farhadinia, MS, Michelot, T, Johnson, PJ, Hunter, LTB & Macdonald, DW 2020, 'Understanding decision making in a food-caching predator using hidden Markov models', Movement Ecology, vol. 8, 9. https://doi.org/10.1186/s40462-020-0195-z

APA

Farhadinia, M. S., Michelot, T., Johnson, P. J., Hunter, L. T. B., & Macdonald, D. W. (2020). Understanding decision making in a food-caching predator using hidden Markov models. Movement Ecology, 8, [9]. https://doi.org/10.1186/s40462-020-0195-z

Vancouver

Farhadinia MS, Michelot T, Johnson PJ, Hunter LTB, Macdonald DW. Understanding decision making in a food-caching predator using hidden Markov models. Movement Ecology. 2020 Feb 10;8. 9. https://doi.org/10.1186/s40462-020-0195-z

Author

Farhadinia, Mohammad S. ; Michelot, Théo ; Johnson, Paul J. ; Hunter, Luke T. B. ; Macdonald, David W. / Understanding decision making in a food-caching predator using hidden Markov models. In: Movement Ecology. 2020 ; Vol. 8.

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@article{c131ed4946694347a03d345987c877db,
title = "Understanding decision making in a food-caching predator using hidden Markov models",
abstract = "BackgroundTackling behavioural questions often requires identifying points in space and time where animals make decisions and linking these to environmental variables. State-space modeling is useful for analysing movement trajectories, particularly with hidden Markov models (HMM). Yet importantly, the ontogeny of underlying (unobservable) behavioural states revealed by the HMMs has rarely been verified in the field.MethodsUsing hidden Markov models of individual movement from animal location, biotelemetry, and environmental data, we explored multistate behaviour and the effect of associated intrinsic and extrinsic drivers across life stages. We also decomposed the activity budgets of different movement states at two general and caching phases. The latter - defined as the period following a kill which likely involves the caching of uneaten prey - was subsequently confirmed by field inspections. We applied this method to GPS relocation data of a caching predator, Persian leopard Panthera pardus saxicolor in northeastern Iran.ResultsMultistate modeling provided strong evidence for an effect of life stage on the behavioural states and their associated time budget. Although environmental covariates (ambient temperature and diel period) and ecological outcomes (predation) affected behavioural states in non-resident leopards, the response in resident leopards was not clear, except that temporal patterns were consistent with a crepuscular and nocturnal movement pattern. Resident leopards adopt an energetically more costly mobile behaviour for most of their time while non-residents shift their behavioural states from high energetic expenditure states to energetically less costly encamped behaviour for most of their time, which is likely to be a risk avoidance strategy against conspecifics or humans.ConclusionsThis study demonstrates that plasticity in predator behaviour depending on life stage may tackle a trade-off between successful predation and avoiding the risks associated with conspecifics, human presence and maintaining home range. Range residency in territorial predators is energetically demanding and can outweigh the predator{\textquoteright}s response to intrinsic and extrinsic variables such as thermoregulation or foraging needs. Our approach provides an insight into spatial behavior and decision making of leopards, and other large felids in rugged landscapes through the application of the HMMs in movement ecology.",
keywords = "Caching behaviour, Hidden Markov models, Life-stage, Multistate animal movement, Panthera pardus saxicolor, Range residency, Satellite telemetry, Viterbi algorithm",
author = "Farhadinia, {Mohammad S.} and Th{\'e}o Michelot and Johnson, {Paul J.} and Hunter, {Luke T. B.} and Macdonald, {David W.}",
note = "Financial support was provided by the People{\textquoteright}s Trust for Endangered Species (PTES), Zoologische Gesellschaft f{\"u}r Arten- und Populationsschutz (ZGAP), Quagga Conservation Fund and IdeaWild.",
year = "2020",
month = feb,
day = "10",
doi = "10.1186/s40462-020-0195-z",
language = "English",
volume = "8",
journal = "Movement Ecology",
issn = "2051-3933",
publisher = "BioMed Central",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Understanding decision making in a food-caching predator using hidden Markov models

AU - Farhadinia, Mohammad S.

AU - Michelot, Théo

AU - Johnson, Paul J.

AU - Hunter, Luke T. B.

AU - Macdonald, David W.

N1 - Financial support was provided by the People’s Trust for Endangered Species (PTES), Zoologische Gesellschaft für Arten- und Populationsschutz (ZGAP), Quagga Conservation Fund and IdeaWild.

PY - 2020/2/10

Y1 - 2020/2/10

N2 - BackgroundTackling behavioural questions often requires identifying points in space and time where animals make decisions and linking these to environmental variables. State-space modeling is useful for analysing movement trajectories, particularly with hidden Markov models (HMM). Yet importantly, the ontogeny of underlying (unobservable) behavioural states revealed by the HMMs has rarely been verified in the field.MethodsUsing hidden Markov models of individual movement from animal location, biotelemetry, and environmental data, we explored multistate behaviour and the effect of associated intrinsic and extrinsic drivers across life stages. We also decomposed the activity budgets of different movement states at two general and caching phases. The latter - defined as the period following a kill which likely involves the caching of uneaten prey - was subsequently confirmed by field inspections. We applied this method to GPS relocation data of a caching predator, Persian leopard Panthera pardus saxicolor in northeastern Iran.ResultsMultistate modeling provided strong evidence for an effect of life stage on the behavioural states and their associated time budget. Although environmental covariates (ambient temperature and diel period) and ecological outcomes (predation) affected behavioural states in non-resident leopards, the response in resident leopards was not clear, except that temporal patterns were consistent with a crepuscular and nocturnal movement pattern. Resident leopards adopt an energetically more costly mobile behaviour for most of their time while non-residents shift their behavioural states from high energetic expenditure states to energetically less costly encamped behaviour for most of their time, which is likely to be a risk avoidance strategy against conspecifics or humans.ConclusionsThis study demonstrates that plasticity in predator behaviour depending on life stage may tackle a trade-off between successful predation and avoiding the risks associated with conspecifics, human presence and maintaining home range. Range residency in territorial predators is energetically demanding and can outweigh the predator’s response to intrinsic and extrinsic variables such as thermoregulation or foraging needs. Our approach provides an insight into spatial behavior and decision making of leopards, and other large felids in rugged landscapes through the application of the HMMs in movement ecology.

AB - BackgroundTackling behavioural questions often requires identifying points in space and time where animals make decisions and linking these to environmental variables. State-space modeling is useful for analysing movement trajectories, particularly with hidden Markov models (HMM). Yet importantly, the ontogeny of underlying (unobservable) behavioural states revealed by the HMMs has rarely been verified in the field.MethodsUsing hidden Markov models of individual movement from animal location, biotelemetry, and environmental data, we explored multistate behaviour and the effect of associated intrinsic and extrinsic drivers across life stages. We also decomposed the activity budgets of different movement states at two general and caching phases. The latter - defined as the period following a kill which likely involves the caching of uneaten prey - was subsequently confirmed by field inspections. We applied this method to GPS relocation data of a caching predator, Persian leopard Panthera pardus saxicolor in northeastern Iran.ResultsMultistate modeling provided strong evidence for an effect of life stage on the behavioural states and their associated time budget. Although environmental covariates (ambient temperature and diel period) and ecological outcomes (predation) affected behavioural states in non-resident leopards, the response in resident leopards was not clear, except that temporal patterns were consistent with a crepuscular and nocturnal movement pattern. Resident leopards adopt an energetically more costly mobile behaviour for most of their time while non-residents shift their behavioural states from high energetic expenditure states to energetically less costly encamped behaviour for most of their time, which is likely to be a risk avoidance strategy against conspecifics or humans.ConclusionsThis study demonstrates that plasticity in predator behaviour depending on life stage may tackle a trade-off between successful predation and avoiding the risks associated with conspecifics, human presence and maintaining home range. Range residency in territorial predators is energetically demanding and can outweigh the predator’s response to intrinsic and extrinsic variables such as thermoregulation or foraging needs. Our approach provides an insight into spatial behavior and decision making of leopards, and other large felids in rugged landscapes through the application of the HMMs in movement ecology.

KW - Caching behaviour

KW - Hidden Markov models

KW - Life-stage

KW - Multistate animal movement

KW - Panthera pardus saxicolor

KW - Range residency

KW - Satellite telemetry

KW - Viterbi algorithm

U2 - 10.1186/s40462-020-0195-z

DO - 10.1186/s40462-020-0195-z

M3 - Article

VL - 8

JO - Movement Ecology

JF - Movement Ecology

SN - 2051-3933

M1 - 9

ER -

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