Using null model analysis of species co‐occurrences to deconstruct biodiversity patterns and select indicator species
Bibliographic record
Abstract
Abstract Aim Using total species richness to characterize biodiversity may mask multiple response patterns of species. We propose a null model analysis of species co‐occurrence‐based classification to identify sets of species that may have similar (within‐groups) and distinct (between groups) response patterns to their environment. The classification should also provide an explicit framework for selecting indicator species with characteristic co‐occurrence patterns to predict overall species richness. Location Côte‐Nord, Québec, Canada. Methods We combined null‐model of species co‐occurrence and cluster analysis to identify species groups within diverse assemblages of ground‐dwelling and flying beetles of stands in a boreal forest mosaic; we then examined their co‐occurrence and response patterns to habitat characteristics. Best subset regressions were used to select indicator species of richness within each group, from which indicators of total species richness were selected. Results The identified species groups appeared to display contrasting co‐occurrence and response patterns to at least one of the stand‐level habitat characteristics. Among flying beetles, for example, richness increased with stand‐level heterogeneity for two groups and decreased for two other groups, but the relationship was non‐significant for the total richness. We identified 28 indicator species that explained > 80% (validated by bootstrap analysis) of the variation in total species richness. Predictive performance of indicators was higher than when their co‐occurrence were reshuffled, even under a highly constrained null model, indicating that co‐occurrence patterns contributed to their predictive performance. Main conclusions Co‐occurrence‐based classification appears as a promising and effective tool for deconstructing biodiversity into species groups which reflect their ecological commonalities and differences, thus reducing the risk of making faulty inferences about the causes underlying overall diversity patterns. The method provides an explicit framework for selecting indicator species representing different species groups that may reflect the multiple responses of species co‐occurring with them. Indicator species can be effective for predicting overall species richness.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".