Modeling Habitat of Freshwater Mussels (Bivalvia:Unionidae) in the Lower Great Lakes 25 Years after the Dreissena Invasion
Bibliographic record
Abstract
Finding remnant populations of species that are of conservation concern can be difficult, particularly in aquatic habitats. Models of ecological niches can aid in the discovery of refuges. Remnant populations of native freshwater mussels (unionids) have been found in Lakes Erie and St Clair. Our goals were to predict undiscovered refuges in Lake Ontario based on habitat analysis from Lake Erie and to conduct surveys to test those predictions. We built a presence-only model on environmental data including attributes of the benthic zone and shoreline where mussels occurred in Lake Erie. We found a link between small- and large-scale variables related to unionid persistence. Bathymetry, fetch, and shoreline geomorphology contributed most to the model. These variables correspond to local-scale environmental factors important for unionid survival, including presence of vegetation and substrate composition, which explained ∼22% of the variance in presence, abundance, and richness. The model predicted that 0.8% of the near-shore area of Lake Ontario should be habitat for unionids. In surveys at 34 locations on the USA shore of Lake Ontario, we found 1800 unionids of 11 species and showed that areasOntario, a result signifying generality of our model for conservation approaches to freshwater mussels.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".