Anthropogenic impacts at the interface of animal spatial and social behaviour
Bibliographic record
Abstract
Human disturbance is contributing to widespread, global changes in the distributions and densities of wild animals. These anthropogenic impacts on wildlife arise from multiple bottom-up and top-down pathways, including habitat loss, resource provisioning, climate change, pollution, infrastructure development, hunting and our direct presence. Animal behaviour is an important mechanism linking these disturbances to population outcomes, although these behavioural pathways are often complex and can remain obscured when different aspects of behaviour are studied in isolation from one another. The spatial-social interface provides a lens for understanding how an animal's spatial and social environments interact to determine its spatial and social phenotype (i.e. measurable characteristics of an individual), and how these phenotypes interact and feed back to reshape environments. Here, we review studies of animal behaviour at the spatial-social interface to understand and predict how human disturbance affects animal movement, distribution and intraspecific interactions, with consequences for the conservation of populations and ecosystems. By understanding the spatial-social mechanisms linking human disturbance to conservation outcomes, we can better design management interventions to mitigate undesired consequences of disturbance.This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".