Phylogenetic comparative methods: Harnessing the power of species diversity to investigate welfare issues in captive wild animals
Bibliographic record
Abstract
This paper reviews a way of investigating health and welfare problems in captive wild animals (e.g., those in zoos, aviaries, aquaria, or aquaculture systems) that has great potential, but to date has been little used: systematically comparing species with few or no health and welfare issues to those more prone to problems. Doing so empirically pinpoints species-typical welfare risk and protective factors (such as aspects of their natural behavioral biology): information which can then be used to help prevent or remedy problems by suggesting new ways to improve housing and husbandry, and by identifying species intrinsically best suited to captivity. We provide a detailed, step-by-step "how to" guide for researchers interested in using these techniques, including guidance on how to statistically control for the inherent similarities shared by related species: an important concern because simple, cross-species comparisons that do not do this may well fail to meet statistical assumptions of non-independence. The few relevant studies that have investigated captive wild animals' welfare problems using this method are described. Overall, such approaches reap value from the great number and diversity of species held in captivity (e.g., the many thousands of species held in zoos); can yield new insights from existing data and published results; render previously intractable welfare questions (such as "do birds need to fly?" or "do Carnivora need to hunt?") amenable to study; and generate evidence-based principles for integrating animal welfare into collection planning.
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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 | 0.042 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".