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Record W6987815071

Understanding links between flow regime and fish populations in the Saskatchewan River Delta

2024· article· en· W6987815071 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typearticle
Languageen
FieldMathematics
TopicFixed Point Theorems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEscapementHydroelectricityBayFish migrationPopulationStreamflowHydrology (agriculture)Abiotic componentSpawn (biology)
DOInot available

Abstract

fetched live from OpenAlex

Successful spawning and survival to adulthood (i.e., recruitment) are essential to maintain sustainable fish populations. Abiotic environmental conditions can influence recruitment and the resulting ability to harvest adult fish. In rivers, the flow regime dictates water depths and velocities that subsequently trigger spawning and enable the survival of juveniles. However, anthropogenic activities such as the operation of hydroelectric generating stations can change the natural hydrograph with effects on physical and biological processes. I investigated the relationship between the river flow regime and sustainable fish population sizes for two species of economic and cultural importance (Walleye Sander vitreus and Lake Sturgeon Acipenser fulvescens) in the Saskatchewan River Delta. To estimate the annual recruitment of Walleye, aging structures were removed from fish sampled from the commercial fishery at Cumberland Lake, Saskatchewan. Cohort strength was then estimated by assigning the year of hatch to individual fish. The cohort strength was compared against discharge from a gauge below the E.B. Campbell hydroelectric generating station, located ~100 km upstream from Cumberland Lake. I found a significant effect of hydrology with an estimated 69% increase (28–105% credible interval) in recruitment with every 100 m3·s-1 increase in discharge over the fry growth period (weeks 30–42) in Walleye. Also, based on the estimated Bayesian posterior distribution, there was a very high probability (p > 0.99) that the effect was different from zero. To estimate long-term harvest numbers for Lake Sturgeon, data was drawn from multiple sources (Hudson Bay Company records, government commercial fishery records, and recent mark-recapture programs). During the pre-dam (1774 to 1960) and post-dam (1965 to 2019) eras, the annual total harvest of Lake Sturgeon was estimated and compared to determine if harvest levels differed before and after flow modification. I observed no significant difference between the pre- and post-dam eras when all data was combined, but using only a subset of the 20th-century commercial catch data revealed a significant difference in Lake Sturgeon catch before and after dams were built. Discharge during the first ten years of the Lake Sturgeon’s life before recruitment to the fishery was a significant predictor of catch, but only when backdating to the period 25–35 years prior to catch and only when using more recent gauge data rather than tree-ring records. I found an estimated 59% increase in Lake Sturgeon catch with every 100 m3·s-1 increase in mean annual discharge. The study of these two species strengthens our understanding of the relationship between interannual and multidecadal changes in flow and fish population sizes, with implications for maximum sustainable harvest levels in the Saskatchewan River Delta. As upstream hydropower operations and irrigation withdrawals continue to alter spring and summer flows, data suggest that re-naturalization of the flow regime could improve recruitment of Walleye and Lake Sturgeon.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.495

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.062
GPT teacher head0.229
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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