Quantifying allowable harm in species at risk: application to the Laurentian black redhorse (<i>Moxostoma duquesnei</i>)
Bibliographic record
Abstract
Abstract When a species is identified for conservation, often the only way to effect recovery is to reduce the harm imposed by stressors threatening the survival of the species. Ideally all threats would be removed; however, this is often not feasible or practical. Within this context, a demographic approach is presented to assess how much human‐induced harm could be allowed without impairing the persistence of the species. Harm is defined as a negative perturbation that can target one or more vital rates and life stages simultaneously. Allowable harm, defined as a level of harm that will not jeopardize survival or recovery, will be a function of the vital rates affected by human actions, the sensitivity of population growth to changes in these vital rates (their elasticities), the population growth rate prevailing before harm occurs, and the set of demographic parameters considered safe for long‐term persistence. This life‐history based approach requires minimal data, can link demography with habitat‐explicit information, is flexible enough to encompass complex life histories, and follows a precautionary approach. Quantification of allowable harm could be applied to any species at risk. This approach is introduced by applying it to a Canadian population of a freshwater fish, the black redhorse (Moxostoma duquesnei), demonstrating that in the absence of habitat constraints population dynamics of this species are most sensitive to the survival of young adults, but population fitness is particularly sensitive to the loss of habitat used by young‐of‐the‐year fish under current levels of habitat supply. Copyright © 2009 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".