Misuse of Checklist Assessments in Endangered Species Recovery Efforts
Bibliographic record
Abstract
Natural resource agencies worldwide must develop species recovery plans that specify threats, propose targets required for recovery, and evaluate the extent to which habitat alteration and restoration may influence species decline and recovery. To evaluate the impacts of proposed habitat alterations on species of conservation concern, standardized protocols may be adopted even when supporting data are scarce. For example, a habitat matrix was developed by the National Marine Fisheries Service (NMFS) to guide consultations under the Endangered Species Act for actions that may affect the functioning of the freshwater habitat used by several federally listed salmonid species. The habitat matrix has also been advocated as a tool for recovery planning by agencies apart from the NMFS, who could use it to define the habitat conditions assumed to be necessary for salmonid population viability and hence recovery. This use of the habitat matrix in a recovery context has not been evaluated, and, despite its widespread use as a regulatory tool, the empirical relationships between many of the habitat matrix variables and salmonid populations remain unexplored. By amassing data on habitat assessments and trends in fish abundance, we empirically evaluate the relationship between habitat matrix scores and salmonid population metrics. We found that abundance trends for populations of three species of threatened and endangered salmonids (chinook, coho, and steelhead) were unrelated to these habitat matrix assessments. This study reveals the danger of assuming quantitative relationships between habitat and organism and cautions against co-opting protocols from the regulatory realm for recovery planning for endangered species.
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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 | 0.131 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".