The role of Dynamic Energy Budgets in conservation physiology
Bibliographic record
Abstract
The contribution of knowledge, concepts and perspectives from physiological ecology to conservation decision-making has become critical for understanding and acting upon threats to the persistence of sensitive species. Here we review applications of dynamic energy budget (DEB) theory to conservation issues and discuss how this theory for metabolic organization of all life on earth (from bacteria to whales) is well equipped to support current and future investigations in conservation research. DEB theory was first invented in 1979 in an applied institution for environmental quality assessment and mitigation. The theory has since undergone extensive development and applications. An increasing number of studies using DEB modelling have provided valuable insights and predictions in areas that pertain to conservation such as species distribution, evolutionary biology, toxicological impacts and ecosystem management. We discuss why DEB theory, through its mechanistic nature, its universality and the wide range of outcomes it can provide represents a valuable tool to tackle some of the current and future challenges linked to maintaining biodiversity, ensuring species survival, ecotoxicology, setting water and soil quality standards and restoring ecosystem structure and functioning in a changing environment under the pressure of anthropogenic driven changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".