Pure spatial and spatially structured environmental variables explain <i>Skistodiaptomus</i> copepod range limits in the northeastern USA
Bibliographic record
Abstract
We assessed the ability of present-day environmental factors to explain the nonoverlapping range boundaries of Skistodiaptomus copepods in the northeastern USA. Variance partitioning using partial canonical correspondence analysis (CCA) attributed 21% of the variance in species occurrences to spatial location, 20% to spatially structured environmental variation, and 12% to environmental factors that are not spatially structured. Discriminant function analysis (DFA) aided our interpretation of the variance in species' occurrences attributed to spatially structured environmental variation. Skistodiaptomus pallidus lakes were discriminated from Skistodiaptomus oregonensis and Skistodiaptomus pygmaeus lakes along a productivity gradient, with S. pallidus occurring in more productive lakes. In contrast, S. oregonensis and S. pygmaeus lakes were environmentally similar. Thus, a large portion of the spatially structured variation in the variance-partitioning analysis most likely reflected the shared correlations between the spatial locations and environmental conditions of S. pallidus lakes. Taken together, the results from CCA and DFA analyses suggested that S. pallidus' range boundary is controlled by environmental factors (lake productivity), while the range boundaries for S. oregonensis and S. pygmaeus were related more to their biogeographic histories than to present-day environments. We discuss alternative explanations for range limits that are independent of environmental conditions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".