Life History, Maternal Quality and the Dynamics of Harvested Fish Stocks
Bibliographic record
Abstract
Knowledge of offspring production (recruitment) is fundamental to understanding and forecasting the dynamics of a population. In this thesis, I focus on two demographic characteristics of fish stocks that are important to recruitment: population density and age structure. First, populations produce more recruits at low density, but quantifying this response has proven difficult. Using data from hundreds of populations of walleye (Sander vitreus), an economically important freshwater fish, I demonstrate that the growing-degree-day metric (a temperature index) is better than age at explaining variation in density-dependent growth and maturity both within and among populations. I then incorporate multi-lake measures of density-dependent life history change into a temperature-based biphasic model of growth and reproduction to predict sustainable rates of mortality for walleye throughout most of their range. Second, the age (or size) structure of a population may also affect recruitment because of positive effects of maternal age on offspring production and survival; however, evidence for these ‘maternal influences’ on recruitment is limited. Using both an analytical model and a meta-analysis of stock-recruitment data from 25 species of exploited marine fish, I show that (i) maximum reproductive rate increased with the mean age of adults in a population, and (ii) the importance of age structure increased with a species’ longevity. I then demonstrate a similar effect of maternal influences on reproductive rate in a detailed study of Lake Erie walleye. By highlighting the importance of fisheries-induced demographic change to recruitment, this thesis provides insight into past and present failures. However, it also demonstrates clearly the benefits of proactive management strategies that (i) identify and respect the limits of exploitation, (ii) protect from exploitation reproductively valuable individuals—principles that apply generally to any freshwater, marine, or terrestrial species that is of recreational, commercial, or conservation value.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".