Fish Community Dynamics and Spatial Overlap in Lakes Across Ontario, Canada
Bibliographic record
Abstract
Inland fisheries are an important source of employment, nutrition, and recreation around the world that are often overshadowed by their marine counterparts. Given the importance of these fisheries, observational and modelling approaches are constantly being refined to improve monitoring and management approaches. Multi-species size spectrum models, which are size-structured models that take species interactions into account, have been increasingly used in marine systems to address important fisheries questions; however, they have yet to be applied to freshwater systems. In this thesis, I develop, to my knowledge, the first multi-species size spectrum model for a freshwater fishery and determine if this approach can be used to enhance the ecosystem-based fisheries management of inland fisheries. Through sensitivity analyses, I show that size and growth parameters of large predators have a strong influence on size-spectrum model output, and model uncertainty may be reduced by paying special attention to the estimation of these parameters. Further, this work highlights the possibility of simplifying multi-species size spectrum models by grouping less influential species into guilds. My calibrated model of the Lake Nipissing fishery demonstrates that multi-species spectrum models are an appropriate method to apply ecosystem-based fisheries management to inland fisheries and highlights the importance of considering species interactions under different management scenarios. In order to better understand how to simplify future analyses, I reviewed the methods and data required to partition fish communities into guilds and created a step-by-step guide to facilitate these analyses by others. Lastly, I explored patterns of spatial overlap among thermal guilds within lakes across Ontario, Canada. My findings suggest that temperature and lake depth are strong drivers of spatial overlap patterns and these patterns can be used to inform future size spectrum models. Altogether, my thesis has demonstrated the potential for multi-species size spectrum model usage in freshwater systems and highlighted approaches that could further increase the applicability of these models for temperate lakes.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".