Bridging disciplines to advance elasmobranch conservation: applications of physiological ecology
Bibliographic record
Abstract
A strength of physiological ecology is its incorporation of aspects of both species' ecology and physiology; this holistic approach is needed to address current and future anthropogenic stressors affecting elasmobranch fishes that range from overexploitation to the effects of climate change. For example, physiology is one of several key determinants of an organism's ecological niche (along with evolutionary constraints and ecological interactions). The fundamental role of physiology in niche determination led to the development of the field of physiological ecology. This approach considers physiological mechanisms in the context of the environment to understand mechanistic variations that beget ecological trends. Physiological ecology, as an integrative discipline, has recently experienced a resurgence with respect to conservation applications, largely in conjunction with technological advances that extended physiological work from the lab into the natural world. This is of critical importance for species such as elasmobranchs (sharks, skates and rays), which are an especially understudied and threatened group of vertebrates. In 2017, at the American Elasmobranch Society meeting in Austin, Texas, the symposium entitled `Applications of Physiological Ecology in Elasmobranch Research' provided a platform for researchers to showcase work in which ecological questions were examined through a physiological lens. Here, we highlight the research presented at this symposium, which emphasized the strength of linking physiological tools with ecological questions. We also demonstrate the applicability of using physiological ecology research as a method to approach conservation issues, and advocate for a more available framework whereby results are more easily accessible for their implementation into management practices.
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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".