Why does home range size predict captive Carnivora welfare
Bibliographic record
Abstract
Zoos and sanctuaries currently house all 286 species of order Carnivora. Here, some respond well to these captive conditions, while other species are prone to high levels of stereotypic behaviour (SB; mainly route-tracing), and high rates of infant mortality. To understand why, we built on and replicated previous research (Kroshko et al. 2016 in Animal Behaviour) by updating a SB database to cover 1960-2016 inclusive; this now contained data from 2337 individuals across 57 Carnivora species, for 28 of which data were available from 5 or more subjects. Using phylogenetic generalized least square regressions and 21 species with natural home range size (HRS) data, we analyzed the effect of HRS on a species’ median time spent performing SB. Like Kroshko et al., our analyses showed that wide-ranging species spent more time route-tracing (P= 0.042, F2,18=2.351). More data are currently being collected and analyzed to address two new questions. First, are any potential determinants of natural HRS (e.g. metabolic rates, population density, population group size, and territoriality) better at predicting route-tracing SB than natural HRS is? If yes, these variables could help better identify those species that are best suited for captive life. Second, are any potential consequences of a wide-ranging lifestyle (e.g. having long daily travel distances, small day range to annual range ratios, and large hippocampal volumes) better predictors of route-tracing SB than natural HRS? If yes, then these variables could help inspire new, effective, evidence-based enrichments for preventing or reducing carnivore SB.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".