Stochastic life history modeling for managing regional‐scale freshwater fisheries: an experimental study of brook trout
Bibliographic record
Abstract
Environmental heterogeneity can combine with evolutionary responses to create very dynamic and often locally independent populations across a landscape. Such complexity creates difficulties for managers trying to conserve populations across large areas. This study develops, applies, and tests the use of stochastic life history modeling and Monte Carlo simulation to assess management scenarios related to the realities of regional fisheries management and conservation. We apply this approach to the management of recreational brook trout (Salvelinus fontinalis) fishing; an activity that can severely impact species balance, abundance, and the size structure of fish communities. Specifically, the model incorporates population-specific life-history information (e.g., growth rate, reproductive effort, and survival) to allow forecasts of the impact of various management strategies and/or changes to environmental conditions on a population's ecological characteristics (e.g., size structure, abundance, and probability of persistence). Sampling was carried out in 16 water bodies spread across four sites in Atlantic Canada. Each water body was sampled in 2005 and reassessed in 2008. This sampling had two primary objectives: (1) define a significant proportion of life-history variation of brook trout in Atlantic Canada, and (2) to test the precision and accuracy of model predictions of population responses to experimental exploitation and management changes. The model successfully predicted population responses to changes in adult survival in 12 of 13 populations having sufficient data for validation testing, while also proving to be a useful tool when engaging stakeholders regarding management options and their associated risk. We suggest that such models are cost-effective and have great potential for informing proactive management of jurisdictions with numerous and diverse populations.
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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 | 0.002 |
| 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 | 0.001 | 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 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".