Data from: Drivers of contemporary lacustrine fish species richness in the glacial Lake Agassiz basin
Bibliographic record
Abstract
Aim: Biological communities are the result of a stepwise spatiotemporal filtering process, driven by large-scale historical and local contemporary determinants. The biogeographical pattern and species richness of North American fishes are predominantly determined by historical processes of past glaciations and postglacial dispersal and by contemporary environmental and ecological processes. Here, we evaluate the effects of postglacial dispersal through glacial Lake Agassiz and habitat heterogeneity, as represented by lake surface area, on contemporary freshwater fish species richness patterns of northwestern Ontario lakes. Location: Northwestern Ontario, Canada Taxon: Freshwater fishes Methods: We applied the theory of island biogeography and species-area curves to examine the effects of isolation from the past dispersal corridor of glacial Lake Agassiz and habitat heterogeneity on species richness across 264 contemporary lakes in northwestern Ontario, Canada. While controlling for correlations among the predictor variables, generalized linear models were constructed between species richness, as the response variable and the explanatory variables of lake elevation and surface area and connection to the dispersal corridor of Lake Agassiz. Results: Differential cover by glacial Lake Agassiz led to variation in fish species richness across contemporary lake basins and species richness is higher in lakes that were covered by Lake Agassiz relative to basins remaining outside of the boundaries of the glacial lake. Lake surface area is the strongest predictor of species richness, while lake elevation is the strongest factor predicting isolation as species richness decreases with increasing altitudes. Main Conclusions: Habitat heterogeneity and postglacial colonization have led to differences in fish richness within the same geographical region. Fish species richness increases with lake surface area and decreases with elevation, likely driven by greater niche diversity facilitating the assembly of more diverse communities and isostatic rebound and fluctuating levels of Lake Agassiz isolating lakes at high elevations from the dispersal route earlier during the colonization process, respectively. These patterns underscore the importance of incorporating historical and environmental community determinants in biodiversity studies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".