High densities of hatchery-origin white sturgeon suppress somatic growth rates of an endangered wild population
Bibliographic record
Abstract
Success in conserving or recovering endangered species requires a detailed understanding of vital rates that control population trends. Sturgeons (Acipenseridae) are the most imperiled taxon globally, with ongoing population declines despite significant conservation and restoration efforts in recent decades. Here, a long-term dataset is analyzed to describe growth rates of endangered white sturgeon (Acipenser transmontanus) in the transboundary section of the upper Columbia River over a 32-year period where conservation aquaculture was initiated in year 12 to prevent extirpation. The extent of spatial (Canada and US), temporal (1990-2021), and seasonal variation (summer and winter) in growth dynamics was modelled to understand the potential impact of increased competition due to stocking on somatic growth of wild-origin fish. von Bertalanffy growth models were fit to observations of growth in length and weight from recaptures of tagged fish. Overall, growth rates of wild- and hatchery-origin white sturgeon were similar, with growth in length higher in summer than in winter, especially in the US. Growth in weight was similar in winter and summer for wild- and hatchery-origin fish weighing less than ~30 kg, but growth was considerably higher in winter than summer for heavier fish. Examination of temporal random effects showed an abrupt decrease in growth part way through the time series. To evaluate the magnitude and uncertainty of the change, we fit a Bayesian change point model and found evidence for suppressed growth in length and weight of wild fish in years following the peak in densities of hatchery-origin sturgeon released from conservation aquaculture. Results provide an understanding of how an endangered species responds to a conservation action commonly applied for imperiled fish species worldwide and may provide insight into other important population dynamics (e.g., reproductive metrics) that are critical for recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.169 | 0.001 |
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; both teacher heads agree on what is shown here.
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".