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Enregistrement 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 sur OpenAlexaboutno aff
Donnette Thayer

Notice bibliographique

RevueUniversity of Alberta Library · 2016
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFish Ecology and Management Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLake sturgeonAcipenserFisheryGeographyHabitatMovement (music)Structural basinSelection (genetic algorithm)SturgeonFish <Actinopterygii>EcologyGeologyBiologyGeomorphology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,493
Score d'incertitude au seuil0,992

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,161
Écart entre enseignants0,155 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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