Freshwater conservation planning in the far north of Ontario, Canada: identifying priority watersheds for the conservation of fish biodiversity in an intact boreal landscape
Bibliographic record
Abstract
Freshwater ecosystems show more biodiversity loss than terrestrial or marine systems. We present a systematic conservation planning analysis in the Arctic Ocean drainage basin in Ontario, Canada, to identify key watersheds for the conservation of 30 native freshwater fish, including four focal species: lake sturgeon, lake whitefish, brook trout, and walleye. We created species distribution models for 30 native fish species and accounted for anthropogenic impacts. We used the “prioritizr” package in R to select watersheds that maximize species targets, minimize impacts, and meet area-based targets based on the Convention on Biological Diversity commitment to protect 17% of terrestrial and freshwater areas by 2020 and the proposed target to protect 30% by 2030. We found that, on average, 17.4% and 29.8% of predicted species distributions were represented for each of the 30 species in the 17% and 30% area-based solutions, respectively. The outcomes were more efficient when we prioritized for individual species, particularly brook trout, where 24% and 36% of its predicted distribution was represented in the 17% and 30% solutions, respectively. Future conservation planning should consider climate change, culturally significant species and areas, and the importance of First Nations as guardians and stewards of the land in northern Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".