Random subsamples of animal populations can reveal intrinsic differences in sociality with key implications in ecology, conservation and disease transmission
Bibliographic record
Abstract
Abstract Animal populations are under mounting stress from the dual threats of climate change and rapid global human population growth, raising significant concerns about declining wildlife and the rising risk of zoonotic diseases. In many species, social interactions can be a highly plastic suite of behaviours that are responsive to these disturbances and are consequential to other processes like disease transmission and population dynamics. Studying social interactions can be challenging in that researchers often rely on wildlife population subsamples due to practical constraints and costs, which can introduce biases in the reliability of social network metrics. We investigated the extent to which subsamples can depict intrinsic characteristics of wildlife populations using data from three distinct species: peri-urban fallow deer, Alpine ibex and Angolan giraffe. We showed that random subsamples of these populations could still reveal differences in their social behaviour, indicating that, as long as researchers have a reliable estimate of population size, subsampling animal populations can be an effective and precise method to infer their sociality and offer valuable empirical data for management, conservation and zoonotic disease ecology. Furthermore, we demonstrate that non-random sampling, influenced for instance by animal personality and related trappability, can introduce significant biases in social network estimates. These findings underscore the importance of accounting for sampling biases in social network analysis and offer a robust framework for using partial networks in ecological studies and conservation management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".