Incorporating vertical movement of fishes in habitat use models
Bibliographic record
Abstract
Abstract Fish telemetry studies now routinely collect positional and depth data, yet analytical approaches that integrate three‐dimensional data are limited. Here we apply the potential path volume (PPV) model, a method previously developed to estimate habitat volume based on rates of avian movement, to free‐swimming fish. Using a telemetry dataset of white sucker ( Catastomus commersonii ) from Turkey Lake (Ontario, Canada), we evaluated the effects of the number of spatial positions and different methods of selecting swim speed (v swim ), a key parameter for PPV models, on habitat volume estimates. We subsequently compared habitat volume estimates and habitat overlap among white sucker pairs from the PPV models to those calculated using kernel utilization distribution‐based approaches. The number of spatial positions in the PPV model had a significant effect on habitat volume estimates, whereas the magnitude of the v swim parameter or its specificity (constant value vs. fish‐season specific parameter values) did not affect habitat volume estimates. The PPV method resulted in larger habitat volume estimates and greater habitat overlap estimates among fish pairs relative to those obtained from a three‐dimensional kernel utilization distribution method. The PPV model is a useful analytical tool that, by incorporating potential animal movement into habitat use evaluations, can help answer key ecological questions and provide insight into fish space use in a wide range of conservation and management applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".