Movement ecology of white sturgeon in the regulated upper Columbia River
Notice bibliographique
Résumé
Advancements in animal tracking technology and analytical techniques have expanded the disciple of movement ecology. An improved understanding of where, when, and why an animal moves can greatly assist in the management and recovery of endangered species. White sturgeon (Acipenser transmontanus) in the Upper Columbia River (UCR) are listed as Endangered due to their considerable decline over the last century. Habitat fragmentation, caused by the construction of dams for hydroelectric generation and river regulation, has greatly impacted white sturgeon populations by preventing access to critical habitat, impacting water quality, and modifying natural flow regimes. The goal of this study was to apply more advanced analytical techniques to white sturgeon movement datasets from a regulated and transboundary reach of the UCR, so as to further understand white sturgeon movement and behaviour in relation to river regulation. Using hidden Markov models (HMMs) and generalized linear mixed models (GLMMs), this study investigated the fine-scale movements of wild adult white sturgeon residing immediately downstream of a hydroelectric operation and the coarse-scale movements of hatchery-origin white sturgeon residing throughout the Transboundary Reach of the UCR. In my fine-scale dataset, two behaviour states were identified: a “residential” behaviour representing short movements with persistent direction, and a “transitory” behaviour representing longer movements with frequent turns. Results revealed that white sturgeon were very likely to remain in a behaviour state, with environmental covariates influencing the probability of a behaviour state only when there was less certainty of the state in the previous timestep (i.e. probability of 50%). Overall, water temperature appeared to influence what behaviour state was more predominantly expressed while discharge appeared to influence where each behaviour state was expressed. Warming water temperatures related to decreased probabilities of residential behaviour and changes in discharge shifted the location of residential behaviour within eddy and tailrace habitats. In my coarse-scale dataset, two behaviour states were also identified: a similar “residential” behaviour state representing localized movement at a single receiver station, and a “transitory” state representing movements between receiver stations. Overall, hatchery-origin white sturgeon moved very little and demonstrated strong site fidelity. When fish did move, location (i.e. country, river zone), discharge, and water temperature had the greatest influence on maximum displacement and behaviour probability. Increases in discharge related to faster declines in maximum displacement in Canada compared to the United States, as well as fish in more channelized river zones having increased probabilities of residential behaviour when previously in a transitory state. Warming water temperatures related to increases in maximum displacement and decreases in the probability of residential behaviour (when previously in a transitory state). In both fine-scale and coarse-scale datasets, trends could be observed seasonally, with increased movement in the summer and fall compared to the winter and spring. Results from this study better described white sturgeon interactions with dams, help describe the effectiveness of recovery measures like conservation aquaculture, and inform management decisions related to river regulation.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».