Do spatial models of growth rate potential reflect fish growth in a heterogeneous environment? A comparison of model results
Bibliographic record
Abstract
Abstract – Spatial models of fish growth rate potential have been used to characterize a variety of environments including estuaries, the North American Great Lakes, small lakes and rivers. Growth rate potential models capture a snapshot of the environment but do not include the effects of habitat selection or competition for food in their measures of environment quality. Here, we test the ability of spatial models of fish growth rate potential to describe the quality of an environment for a fish population in which individual fish may select habitats and local competition may affect per capita intake. We compare growth rate potential measurements to simulated fish growth and distributions of model fish from a spatially explicit individual‐based model of fish foraging in the same model environment. We base the model environment on data from Lake Ontario and base the model fish population on alewife in the lake. The results from a simulation experiment show that changes in the model environment that caused changes in the average growth rate potential correlated extremely highly ( r 2 ≥0.97) with changes in simulated fish growth. Unfortunately, growth rate potential was not a reliable quantitative predictor of simulated fish growth nor of the fish spatial distribution. The inability of the growth rate potential model to quantitatively predict simulated fish growth and fish distributions results from the fact that growth rate potential does not consider the effects of habitat selection or of competition on fish growth or distribution, processes that operate in our individual‐based model and presumably also operate in nature. The results, however, do support the use of growth rate potential models to describe the relative quality of habitats and environments for fish populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".