Assessing the detectability of road crossing effects in streams: mark–recapture sampling designs under complex fish movement behaviours
Bibliographic record
Abstract
Summary Most reviews of stream fish connectivity have highlighted the urgent need for standardized methods to quantify the effects of barriers such as road crossings on fish movement and incorporate the complexity of fish behaviours. A question that has not been fully addressed yet in field assessments of fish stream connectivity is which conditions influence the detectability of road crossing effects. Failure to detect existing road crossing effects can result from shortcomings in sampling design that lead to low statistical power. Here, we propose general barrier dispersal models to allow for asymmetry in barrier permeability and changes in movement behaviours of fish confronting a barrier. Despite the increased realism of these ecological assumptions, it remains to be determined whether asymmetric barriers and altered movement behaviours can be unambiguously detected using mark–recapture trials. We used simulations within a modelling framework that explicitly incorporates barrier and behavioural effects to assess the statistical power of various mark–recapture sampling designs under different combinations of design and ecological constraints. Key insights from our simulations are that (i) the spatial extent of the study reach critically affects detectability of barrier effects; (ii) the number of recaptured individuals that cross a barrier has greater effect on detectability than the total number of recaptures on both sides of the barrier; and (iii) detectability of asymmetry in barrier permeability and of altered movement behaviours increases with both linear fish density and effect size. Synthesis and applications. The proposed dispersal models, incorporating asymmetric barrier permeability and changes in movement behaviours of fish confronting a barrier, are of broad importance in the quantification of habitat connectivity in streams and rivers. Our simulation approach provides precise guidelines for improving the sampling design by adjusting the spatial extent of the study reach based ona prioriknowledge of ecological constraints. This study highlights the importance of evaluating the detectability of the effects of barriers such as road crossings and carefully planning the sampling design of mark–recapture studies before conducting costly field trials and provides quantitative tools to help achieve these goals.
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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 | 0.049 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".