Advancing Restoration Ecology for Freshwater Fish Habitat of the Laurentian Great Lakes
Bibliographic record
Abstract
Following decades of anthropogenic activity in Lake Ontario (LO), Canada, ecological restoration on coastal wetlands has been aimed at preserving biodiversity. However, as resources are typically limited for conservation, it is essential to ensure that such efforts achieve associated goals. Knowledge co-production, a collaborative process where research is conducted in a respectful manner, has been identified as an approach to maximize efficacy for ecological restoration. Aquatic Habitat Toronto (AHT) is a unique consensus-based partnership with diverse member agencies that engage in knowledge co-production and ecological restoration Toronto Harbour (TH). One ecological concern identified by AHT are the negative impacts associated with common carp, Cyprinus carpio, which are a non-native species that established within LO and have negatively impacted freshwater ecosystems by reducing native fish diversity. With acoustic telemetry, I identified potential spawning sites throughout LO, which could be targeted for management actions such as exclusion barriers. I then completed a review examining the implementation of selective fragmentation with exclusion barriers based on biological traits including phenology, sensory capability, morphology, and behaviour. Exclusion barriers have been found to effectively decrease access to common carp spawning sites, but can also hinder movements of native species. To mitigate this issue, I explored differences in phenologies of two native species (largemouth bass Micropterus nigricans, and northern pike Esox lucius) and common carp with predictive modelling of spawning movements to operate barriers seasonally, where native fishes are permitted passage through barriers while common carp movements are inhibited. In addition to common carp exclusion barriers, other restoration techniques have been used throughout TH. To evaluate the efficacy of such restoration efforts, I used discrete and continuous sampling methods. I found that ecological restoration achieved the goals of providing habitat to largemouth bass and decreasing access to common carp with exclusion barriers; however, these barriers likely also had a negative impact on large-bodied northern pike in that their movements into wetlands were hindered. Results from this thesis add to the growing literature base of restoration ecology, providing evidence for environmental managers to mitigate adverse impacts associated with habitat alteration and non-native species for the benefit freshwater biodiversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".