Behavioural and physiological ecology of coastal marine fish: basic and applied perspectives
Bibliographic record
Abstract
Energy is the currency of life, by which we can measure how ecological and anthropogenic factors influence individual fitness, scaling up to population and ecosystem dynamics.Energy is expended and gained by organisms through diverse behavioural tactics aimed at maximizing fitness.My overarching hypothesis for this dissertation is that ecological and anthropogenic factors influence animal behaviour and energetics.I tested this hypothesis in two coastal marine fish species, bonefish (Albula vulpes) and great barracuda (Sphyraena barracuda) using a combination of field studies and controlled experiments.In the wild, landscape features had the greatest impact on bonefish activity and energy expenditure at both fine (i.e., between habitats on a single coral reef crest) and broad (i.e., between coastal habitats and regions) spatial scales.Diel period, water temperature, and tide state also influenced bonefish behaviour and energetics, with some consistent patterns across environments, including greater activity and energy expenditure during the day, as well as ebbing and low tides.Bonefish activity levels and habitat selection also corresponded with temperature-related physiological performance.Comparing two disparate coastal ecogeographic regions, activity-and temperature-based estimates of bonefish energy expenditure were higher in the fringing coral reefs of tropical Culebra, Puerto Rico than the expansive sand flats of sub-tropical Eleuthera, The Bahamas; however, home ranges were significantly larger in Eleuthera than Culebra, which likely has significant energetic costs that may contribute to differences in growth rates between the regions.From a more applied perspective, a common anthropogenic stressor, recreational angling, caused significant locomotory (i.e., iii swimming capabilities) and behavioural (i.e., refuge use) impairment in bonefish and great barracuda, which resulted in increased post-release predation risk.Retaining bonefish for a short period prior to release reduced this impairment and may be a useful strategy for improving post-release survival in environments with high predator burden.Collectively, by examining how ecological and anthropogenic factors influence fish behaviour and energetics, my dissertation has advanced our understanding of fundamental ecology and management of coastal marine fish and their ecosystems.provided me is truly remarkable.They have taught me not only the fundamentals of science, but also the value and skills of social networking, extracurricular activities, science outreach, striking a healthy work-life balance, and how to acquire research funding and scholarships.Since the beginning they treated me not simply as a student, but as a colleague, and their faith in my abilities has strengthened my confidence as a scientist, enabling me to accomplish far more than I could have ever imagined in these past 4 years.I also extend my gratitude to my thesis committee members, Sue Bertram and Pat Walsh, as well as my comprehensive exam external Gabriel Blouin-Demers and dissertation defence examiners Nann Fangue and Murray Richardson, who contributed positive feedback and constructive criticism that helped shape this thesis and contributed to my development as a scientist.They are all busy people, but still took the time to provide critical input to my work while asking for nothing in return, for which I am very thankful.Thank you my partner Caitlin Higginson and my entire family for their unconditional support.I would not do the work that I do had I not grown up fishing and v exploring the wilderness with my father and grandfather.My mother imparted in me a level of determination that is certainly required to spend 10 years in post secondary education.Without the support of Caitlin, there is no way I would have been as productive as have been in these past years.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".