Comparing correlative and bioenergetics‐based habitat suitability models for drift‐feeding fishes
Bibliographic record
Abstract
Abstract Quantitative habitat suitability models (HSMs) are frequently used to inform the conservation and management of lotic organisms, often in the context of instream flow management. Correlative statistical models relating hydraulic variables to habitat preferences (habitat suitability curves based on use:availability ratios) are the most common form of HSM, but face significant criticism on the grounds that habitat preference may not reflect the fitness consequences of habitat use. Consequently, there has been a drive to develop mechanistic approaches that link habitat to direct correlates of fitness. Bioenergetic foraging models relating hydraulic conditions to energy balance are particularly well‐developed for drift‐feeding fishes (e.g. salmonids) and show promise as a more mechanistic approach to modelling suitability. However, these models are rarely validated empirically or quantitatively compared with correlative HSMs. We addressed these gaps by comparing the ability of a bioenergetics‐based HSM and two correlative HSMs (a traditional suitability index and a resource selection function) to predict density and growth of stream salmonids (juvenile steelhead, Oncorhynchus mykiss , and coastal cutthroat trout, Oncorhynchus clarki ). Suitability estimates differed between the approaches, with both correlative models predicting higher suitability relative to the bioenergetic model at shallow depths and low to intermediate velocities, but lower suitability as depth increased. The bioenergetic model explained over 90% of variation in trout growth, compared to c . 50% for the correlative model. The bioenergetic model was also better at predicting fish density; however, the improvement was less striking and a high proportion of variation remained unexplained by either method. Differences in suitability estimates between approaches probably reflect biotic interactions (e.g. territorial displacement or predation risk) that decouple realised habitat use from energetics‐based estimates of habitat quality. Results highlight fundamental differences between correlative HSMs, based on observed habitat use, and mechanistic HSMs, based on the physiology and behaviour of the focal taxa. They also suggest that mechanistic bioenergetics‐based models provide more rigorous estimates of habitat suitability for drift‐feeding stream fishes. The bioenergetics approach is readily accessible to instream flow practitioners because model predictions are expressed in terms of traditional habitat suitability curves.
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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".