Movement ecology of white sturgeon in the regulated upper Columbia River
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".