Random placement models predict species–area relationships in duck communities despite species aggregation
Bibliographic record
Abstract
Species–area relationships are the product of many ecological processes and their interactions. Explanations for the species–area relationship (SAR) have focused on separating putative niche‐based mechanisms that correlate with area from sampling effects caused by patches with more individuals containing more species than patches with fewer individuals. We tested the hypothesis that SARs in breeding waterfowl communities are caused by sampling effects (i.e. random placement from the regional species pool). First, we described observed SARs and patterns of species associations for fourteen species of ducks on ponds in prairie Canada. Second, we used null models, which randomly allocated ducks to ponds, to test if observed SARs and patterns of species associations differed from those expected by chance. Consistent with the sampling effects hypothesis, observed SARs were accurately predicted by null models in three different years and for diving and dabbling duck guilds. This is the first demonstration that null models can predict SARs in waterbirds or any other aquatic organisms. Observed patterns of species association, however, were not well predicted by null models as in all years there was less observed segregation among species (i.e. more aggregation) than under the random expectation, suggesting that intraspecific competition could play a role in structuring duck communities. Taken together, our results indicate that when emergent properties of ecological communities such as the SAR appear to be caused by random processes, analyses of species associations can be critical in revealing the importance of niche‐based processes (e.g. competition) in structuring ecological communities.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".