Evidence map›Paper›PMID 32071720›Full record

ArticleMovement ecology2020

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

Mohammad S Farhadinia, Théo Michelot, Paul J Johnson, Luke T B Hunter, David W Macdonald

Abstract read
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Article in Movement ecology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mohammad S Farhadinia1Oxford Martin School and Department of Zoology, University of Oxford, 34 Broad St, Oxford, OX1 3BD UK.ORCID 0000-0002-5385-6254
Théo Michelot2School of Mathematics and Statistics, University of St Andrews, The Observatory, Buchanan Gardens, St Andrews, KY169LZ UK.
Paul J Johnson3Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney House, Oxford, Oxfordshire OX13 5QL UK.
Luke T B Hunter4Wildlife Conservation Society, Bronx, NY 10460 USA.
David W Macdonald3Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney House, Oxford, Oxfordshire OX13 5QL UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

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.

Indexed as

Caching behaviourHidden Markov modelsLife-stageMultistate animal movementPanthera pardus saxicolorRange residencySatellite telemetryViterbi algorithm

Identifiers

PMID32071720
PMCPMC7011357

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.