Spatial and life history variation in a trait-based species vulnerability and impact model
Bibliographic record
Abstract
Anthropogenic pressures threaten biodiversity, necessitating conservation actions founded on robust ecological models. However, prevailing models inadequately capture the spatiotemporal variation in environmental pressures faced by species with high mobility or complex life histories, as data are often aggregated across species' life histories or spatial distributions. We highlight the limitations of static models for dynamic species and incorporate life history variation and spatial distributions for species and stressors into a trait-based vulnerability and impact model. We use green sea turtles in the Greater Caribbean Region to demonstrate how vulnerability and anthropogenic impact for a dynamic species change across four life stages. By incorporating life stages into a trait-based vulnerability model, we observed life stage-specific vulnerabilities that were otherwise unnoticed when using an aggregated trait value set. Early life stages were more vulnerable to some stressors, such as inorganic pollution or marine heat waves, and less vulnerable to others, such as bycatch. Incorporating spatial distributions of stressors and life stages revealed impacts differ for each life stage across spatial areas, emphasizing the importance of stage-specific conservation measures. Our approach showcases the importance of incorporating dynamic processes into ecological models and will enable better and more targeted conservation actions for species with complex life histories and high mobility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".