Effects of urbanisation on the movements of an arboreal specialist using hidden Markov models
Bibliographic record
Abstract
BACKGROUND: Species 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. METHODS: In 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. RESULTS: We 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. CONCLUSIONS: Our 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".