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Record W2799281361 · doi:10.7939/r32j6890m

Identifying seasonal Lake Sturgeon (Acipenser fulvescens) movement patterns and habitat selection in the South Saskatchewan River Basin

2016· article· en· W2799281361 on OpenAlexaboutno aff
Donnette Thayer

Bibliographic record

VenueUniversity of Alberta Library · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLake sturgeonAcipenserFisheryGeographyHabitatMovement (music)Structural basinSelection (genetic algorithm)SturgeonFish <Actinopterygii>EcologyGeologyBiologyGeomorphology

Abstract

fetched live from OpenAlex

Lake Sturgeon (Acipenser fulvescens Rafinesque 1817) have experienced population declines throughout their range. In Alberta, low density age-class distributions, irregular recruitment, critically low spawning potential ratios and other factors led to a 2007 designation of “Threatened” for this species under the Provincial Wildlife Act. As a protected species, efforts to develop a comprehensive recovery strategy require an understanding of species’ biology, behavior, and critical habitat requirements. My objectives were to a) identify movement behaviors and habitat changes before and after an extreme flood event, and b) ascertain overwintering movement behaviors and habitat selection of South Saskatchewan River Basin Lake Sturgeon. Multiple-receiver acoustic telemetry was used to obtain triangulated, fine-scale movement patterns at the confluence of the Bow and Oldman rivers, a potential critical spawning habitat site, before, during and after an extreme flood, and at a study site on the South Saskatchewan River known to be critical overwintering habitat, before, during, and after the ice-on period. Substrate analysis was performed pre- and post-flood for Grand Forks, and substrate, bottom velocity, and depth analyses were performed for the South Saskatchewan River overwintering study site. In the extreme flood event study, movement behaviors were captured by receivers positioned to triangulate movements and provide positions at a fine scale. Receivers had been deployed to assess spawning behavior when the 2013 Alberta ‘Flood of Floods’ occurred, providing an opportunity to document fine-scale Lake Sturgeon movement behaviors before, during and after an extreme disturbance event. Lake Sturgeon showed a tendency to avoid high water velocity areas. Increases in flow rate showed a significant negative relationship to mean positional distance from shore. Substrate assessment prior to and following the flood showed a significant change in substrate composition. Mean gravel- and larger-substrate area increased, providing evidence that high flow rates in 2013 and 2014 exposed larger particle fluvial substrate. Complexity as measured by patch density increased, indicating a measureable overall increase in heterogeneity. Lake Sturgeon benefitted from improved spawning habitat following the extreme flood by gaining larger, cleaner substrate with more complexity. Although extreme flood events may be costly to human life and infrastructure, they may be ecologically beneficial to some freshwater organisms, particularly plesiomorphic, long-lived species such as Lake Sturgeon whose survival has depended upon a capability to adapt to erratic and occasionally extreme peak flow events. In the overwintering study, fine-scale movements by adult and juvenile Lake Sturgeon were tracked for a previously identified overwintering habitat in the South Saskatchewan River using acoustic telemetry from late October 2013 through April 2014 for a 1.5 kilometer reach of the South Saskatchewan River identified in a previous study as overwintering habitat. Period-segmented movement rates, depth selection, aggregation behavior and preferential overwintering habitat selection by adult and juvenile Lake Sturgeon were evaluated for 23-26 individuals (20-23 adults and 3 juveniles, depending on residency during period assessed – individuals not present during all segments were excluded) detected within the study area. Environmental parameters included depth, depth standard deviation (rugosity), bottom velocity, bottom velocity standard deviation, and substrate type (silt, sand, and gravel or larger substrate). Boosted regression tree (BRT) analyses were performed to identify habitat selection for Lake Sturgeon for period-aggregated core (50th percentile) and range (95th percentile) positions. Movement rates paralleled temperature variation, becoming highly restricted during periods of deepest cold. Increased aggregation was also noted for these periods. The deepest habitat positions occurred during ice formation and breakup. Depth and bottom velocity were the most important variables used to identify sturgeon habitat for all periods assessed. Since Lake Sturgeon display site fidelity and aggregation behaviors independent of resource availability, winter habitat may limit the population. While spawning habitat is crucial to ensuring successful recruitment, data suggests that a substantial portion of the population spends the greater part of the year in overwintering locations, making this habitat potentially more critical to the survival of the population as a whole than any other habitat. If winter habitat is degraded or otherwise compromised, it could hinder species recovery, and should be considered critical habitat, receiving appropriate protection. This was the first study to use fine-scale acoustic telemetry analysis of movement behavior and habitat selection for a Lake Sturgeon population occupying fluvial winter habitat.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.493
Threshold uncertainty score0.992

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.161
Teacher spread0.155 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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