Modelling occupancy of an imperilled stream fish at multiple scales while accounting for imperfect detection: implications for conservation
Bibliographic record
Abstract
Summary Predictive models of species distribution are useful tools to identify habitats of imperilled species for protection, inventory and restoration. Critical aspects of such models include the influence of scale, uncertainties associated with imperfect detection and spatial autocorrelation and transferability of model predictions. We addressed these issues in developing occupancy models of the imperilled eastern sand darter ( Ammocrypta pellucida ) based on surveys of the Grand and Thames Rivers, Ontario, Canada. Eastern sand darter detection probabilities were remarkably different between streams, but factors affecting site occupancy were similar. The proportion of sand and fine gravel was most important, but water clarity and biotic indices also received support in additive models. Accounting for spatial autocorrelation reduced the effect of important covariates. Occupancy was more closely related to substratum at the site level than factors at broader scales (reach and valley segment), further emphasising the substratum specificity of this species. Almost all of the top‐ranked site and reach occupancy models had good predictive performance based on assessments of transferability. These models indicate that three formerly occupied Ontario catchments have a high probability of supporting the species and deserve consideration for repatriation. Our methods demonstrate how a comprehensive approach to occupancy modelling can be used to help guide recovery efforts for imperilled species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".