Telemetry-based spatial–temporal fish habitat models for fishes in an urban freshwater harbour
Bibliographic record
Abstract
Fish habitat associations are important measures for effective aquatic habitat management, but often vary over broad spatial and temporal scales, and are therefore challenging to measure comprehensively. We used a 9-year acoustic telemetry dataset to generate spatial-temporal habitat suitability models for seven fish species in an urban freshwater harbour, Toronto Harbour, Lake Ontario. Fishes generally occupied the more natural regions of Toronto Harbour most frequently. However, each species exhibited unique habitat associations and spatial-temporal interactions in their habitat use. For example, largemouth bass exhibited the most consistent seasonal habitat use, mainly associating with shallow, sheltered embayments with high aquatic vegetation (SAV) cover. Conversely, walleye seldom occupied Toronto Harbour in summer, with the highest occupancy of shallow, low-SAV habitats in the spring, which corresponds to their spawning period. Others, such as common carp, shifted between shallow summer and deeper winter habitats. Community level spatial-temporal habitat importance estimates were also generated, which can serve as an aggregate measure for habitat management. Acoustic telemetry provides novel opportunities to generate robust spatial-temporal fish habitat models based on wild fish behaviour, which are useful for the management of fish habitat from a fish species and community perspective. Supplementary Information: The online version contains supplementary material available at 10.1007/s10750-023-05180-z.
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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) | 0.001 | 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 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".