Demographic Variation Among Unexploited Alpine Brook Trout (Salvelinus fontinalis) Populations and Responses to Experimental Fisheries
Bibliographic record
Abstract
Primary demographic attributes such as somatic growth, mortality, size- and age-at-maturity, fecundity, and recruitment drive the structure of fish populations, and these processes are also influenced by density-dependent and -independent processes. Here I study multiple populations of unexploited (i.e. assumed equilibria), lentic Brook Trout to describe variation in population structure. Studying unexploited populations gives fisheries managers and scientists an understanding of the intrinsic variation in systems absent from harvest pressure. First, I identify how much primary demographic attributes vary among populations, then attempt to attribute the observed variation to a suite of nested models containing terms for ecosystem productivity (total phosphorus concentration), climate (growing degree days or GDD), and density (fish biomass per hectare) in 9 lakes. I found that (1) populations varied substantially in somatic growth parameters (two-fold), natural mortality (three-fold), age-at-maturity (three-fold), length-at-maturity (two-fold) and recruitment (three-fold), (2) growth early in life was negatively correlated with density (r = -0.58), but maximum length was positively correlated with GDD (r = 0.61), and (3) spawning stock density was negatively correlated with recruitment (r = -0.57), but positively correlated with GDD (r = 0.55). I then experimentally harvested these previously unexploited populations with size selective gillnets by removing 30-70 % of the largest individuals from 5 of the 9 populations over 2 consecutive years. I tested the compensatory response to harvest with a BACI analysis, where I looked to see if absolute growth rate, size- and age-at-maturity, reproductive investment, and recruitment compensate for fisheries harvest. I found (1) strong evidence of recruitment compensation, (2) that overall (i.e. site-wide) stock-recruitment relationship was strongly density dependent and over-compensatory (i.e. a humped, Ricker type relationship), (3) positive but nonsignificant compensation in growth and age-at-maturity, and (4) no change in reproductive investment, but noted that populations may compensate for reproductive capacity in other ways (e.g. a combination of increased somatic growth and younger age-at-maturity). Comparing observed variation in unexploited populations’ demography with environmental variables helps fisheries managers and scientists understand intrinsic variability, and drivers of said variability; further exploiting these populations in an experimental fishery shows the initial mechanisms behind compensation. Examining both unexploited and responses to fisheries will help fisheries managers and scientists understand which populations can have their density reduced (via setting appropriate harvest rates), set realistic targets to recover populations, and increase understanding of the mechanisms that structure 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".