Wildlife conservation and animal temperament: causes and consequences of evolutionary change for captive, reintroduced, and wild populations
Bibliographic record
Abstract
Abstract We argue that animal temperament is an important concept for wildlife conservation science and review causes and consequences of evolutionary changes in temperament traits that may occur in captive‐breeding programmes. An evolutionary perspective is valid because temperament traits are heritable, linked to fitness and potentially subject to intense selection in captivity. Natural, sexual and artificial selection can cause permanent shifts in temperament, reducing the diversity of temperament traits, diversity that may be critical to reintroduction success. Breeding programmes that ignore temperament risk leading the captive population towards domestication. Furthermore, shifts in temperament may involve alterations in linked morphological and physiological traits, and selection may even change functional relationships between traits. Captive‐breeding programmes can reduce changes in temperaments by closely monitoring temperament traits, equalizing reproductive success between temperament morphs and using environmental enrichment to reduce captive stress. Under certain circumstances, knowledge about temperament may also provide a useful tool to optimize captive reproduction and to increase reintroduction success. Outside reintroduction programmes, temperament can mediate responses to human contact, hunting, exploitation, habitat fragmentation and disease transmission. Consideration of temperaments could strengthen both captive and wild conservation efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 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".