Biotic interactions and environmental filtering both determine earthworm alpha and beta diversity in tropical rainforests
Bibliographic record
Abstract
Abstract Understanding the relative importance of biotic interactions, multiple environmental drivers, and neutral processes in shaping community diversity and composition is a central question for both theoretical and applied ecology. We analysed a dataset describing 125 earthworm communities sampled in 10 localities in French Guiana. DNA barcodes were used to delimit operational taxonomic units (OTUs) that we considered as species surrogates to avoid the taxonomic deficit and calculate community-scale species richness and pair-wise Sørensen beta-diversity. We used log-ratio and generalised linear models to highlight the effects of biotic interactions and environment as drivers of alpha diversity, and generalised dissimilarity models to figure out the relative contribution of space and environment to beta-diversity at different spatial extents. Community-scale alpha diversity was mainly explained by habitat filtering (soil texture) and interspecific competition that limit the number of locally co-existing species. Beta diversity between pairs of communities was mainly explained by distance when comparing communities in similar habitats, by topography and available soil phosphorus when comparing communities in different habitats, and by distance, elevation and climate when comparing all possible pairs of communities. While community composition is determined locally by neutral processes and environmental filtering, biogeographic processes linked to dispersal limitation and adaptation to local environment are the most influential on a regional scale. This highlights the complex interplay of dispersal limitation, biotic interactions and environmental filtering during the process of community assembly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".