The translocation trade-off for eastern sand darter (<i>Ammocrypta pellucida</i>): balancing harm to source populations with the goal of re-establishment<sup>1</sup>
Bibliographic record
Abstract
The use of translocations to recover populations requires a sufficiently large number of individuals from source populations, but removing too many individuals could lead to source population collapse. To understand the trade-off between the probabilities of source population extirpation and translocation success, matrix population models that incorporate Allee effects, density dependence, and demographic and environmental stochasticity were combined with a model that simulates removals from source populations. We apply these models to eastern sand darter (Ammocrypta pellucida; Species at Risk Act status: Threatened) translocation scenarios in Canada. The results suggest that translocations most often require source populations >20 000 individuals, as source population extirpation probability increased with the number and frequency of removals. Transport mortality or losses immediately following introduction further affected translocation success. Uncertainty around life-history parameters and the strength of Allee effects led to additional uncertainty about the required source population size. Although stochastic processes affected the probability of translocation success, factors such as stocking density and frequency can be controlled, and translocation may therefore be a viable strategy for eastern sand darter recovery, even when applying cautious thresholds to guard against uncertainty.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".