Evidence map›Paper›PMID 41029473›Full record

ArticleMovement ecology2025

Effects of urbanisation on the movements of an arboreal specialist using hidden Markov models.

Caio Santos Neto, Ross G Dwyer, Theo Michelot, Romane H Cristescu

Abstract read
In one paragraph

Article in Movement ecology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Caio Santos NetoQueensland University of Technology, Queensland, Australia. caio.santosneto@qut.edu.au.
Ross G DwyerUniversity of the Sunshine Coast, Queensland, Australia.
Theo MichelotDalhousie University, Nova Scotia, Canada.
Romane H CristescuUniversity of the Sunshine Coast, Queensland, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSpecies with specialised ecological niches rely heavily on specific resources or conditions, making them less resilient to habitat fragmentation and land-use changes. For specialists with limited mobility, the challenges are even greater, as they may struggle to locate new habitats for their survival. While some highly mobile species adjust their movement behaviours in human-modified environments by either avoiding areas with faster, straighter paths or adapting to forage for human-related resources, little is known about how arboreal species with low mobility adapt to urban landscapes. Koalas (Phascolarctos cinereus) are highly susceptible to the impacts of urbanisation due to their unique adaptations and reliance on tree canopy cover, which is thought to be a major factor driving population decline in the increasingly urbanised Australian coast.

methodsIn this study, we applied biotelemetry to track the movements of 72 koalas in urban and nonurban environments. We then applied hidden Markov models (HMMs) to these data to investigate how environmental factors (such as human land use), as well as biological factors (e.g., sex) and temporal cycles (e.g., time of day), influenced koala movement behaviours.

resultsWe detected little effect of land use type on the movement behaviours of koalas in urban and nonurban landscapes, suggesting that the type of land use does not play a substantial role in how koalas shift between different movement behaviours. However, urban-dwelling koalas exhibited faster and more directed movements at night (rather than at dusk) and showed less pronounced changes in their movement behaviours across seasons than those typically observed in natural environments.

conclusionsOur findings highlight the adaptability and flexibility of koalas in modifying their movement behaviours to navigate human-modified environments. By focusing their movements during times when human activity is lower, koalas may be able to reduce the likelihood of agonistic interactions with humans. We suggest that creating low-disturbance areas in urban and peri-urban environments could allow wildlife to maintain more natural behaviours, potentially improving their overall well-being.

Indexed as

Animal movementAnimal telemetryArboreal speciesHidden Markov modelHuman activitiesKoalaMovement behavioursSpecialist speciesUrbanisationUrban wildlife

Identifiers

PMID41029473
PMCPMC12487388

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LicenceCC BY-NC-ND
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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.