Linking zooplankton richness with energy input and insularity along altitudinal and latitudinal gradients
Bibliographic record
Abstract
Abstract Altitudinal and latitudinal gradients are excellent venues for investigating the direct and indirect effects of air temperature, solar irradiance, and insularity on spatial patterns of aquatic biodiversity. The findings can be used to predict how lake communities will respond to increasingly extreme climate events. We explored hypotheses of energy/climate, geography, and glacial history explaining patterns in species richness in a historical dataset of crustacean zooplankton communities from 436 lakes in the Canadian Rocky Mountains. GIS‐based estimates of solar and thermal energy inputs combined with habitat area and insularity provided the best prediction of local species richness. Energetic and geographic factors explained a moderate proportion of the total variation in species richness (Generalized R2 = 0.50), and were sufficient to account for both altitudinal and latitudinal gradients in zooplankton diversity. History of deglaciation was not supported as a predictor of patterns in species richness. A post hoc analysis with a smaller dataset also found strong support for lake pH, and some support for fish presence as predictors of species richness, but these only increased the proportion of the total variation explained very slightly relative to the model including only energetic and geographic factors (Generalized R2 = 0.55 vs. 0.53). Our findings highlight the multiplicity of local and regional factors of zooplankton species richness in mountain lakes, forecasting that it will increase under a scenario of warmer and drier (i.e., less cloud cover) conditions, especially in high connectivity lakes that cease to be fed by rapidly disappearing glaciers.
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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 | 0.001 |
| 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 | 0.000 |
| 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".