Integrating evolution in the management of captive zoo populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Both natural animal populations and those in captivity are subject to evolutionary forces. Evolutionary changes to captive populations may be an important, but poorly understood, factor that can affect the sustainability of these populations. The importance of maintaining the evolutionary integrity of zoo populations, especially those that are used for conservation efforts including reintroductions, is critical for the conservation of biodiversity. Here, we propose that a greater appreciation for an evolutionary perspective may offer important insights that can enhance the reproductive success and health for the sustainability of captive populations. We provide four examples and associated strategies that highlight this approach, including minimizing domestication (i.e., genetic adaptation to captivity), integrating natural mating systems into captive breeding protocols, minimizing the effects of translocation on variation in photoperiodism, and understanding the interplay of parasites/pathogens and inflammation. There are a myriad of other issues that may be important for captive populations, and we conclude that these may often be species specific. Nonetheless, an evolutionary perspective may mitigate some of the challenges currently facing captive populations that are important from a conservation perspective, including their sustainability.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it