Stranding data can significantly bias marine mammal habitat suitability models
Bibliographic record
Abstract
• Stranding data overestimate manatee habitat suitability by over three times. • Sightings and telemetry data provide the most reliable habitat suitability models. • First study showing stranding data bias habitat modeling. • Carcass drift influences stranding locations, misaligning them with habitat use. • Stranding data should be cautiously used and combined with drift models when possible. In the absence of sightings, stranding records can be used to parameterize habitat models for marine mammal conservation. However, their reliability for identifying suitable habitat remains uncertain. We assessed how stranding data influence the habitat predictions for American manatee ( Trichechus manatus ) in the Potiguar Basin, Brazil. Using MaxEnt, we compared models built using: (1) sightings and telemetry data, (2) stranding records alone, and (3) all three data sources combined. We found that the first model based solely on sightings and telemetry produced the most accurate and ecologically meaningful predictions. In contrast, the stranding-only model overestimated suitable habitat by more than threefold, while the combined model overpredicted it by more than twofold. These differences between model predictions are best explained by carcass drift and detection biases and indicate that stranding locations do not reliably reflect areas of actual habitat use. This quantitative assessment provides new insights into the significant biases stranding data can introduce into habitat suitability models. Our findings also highlight the need to prioritize direct observations, and to apply drift modeling and validation against direct observations when using stranding data, to ensure accurate and actionable conservation planning.
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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 | 0.004 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".