A replicated study on the response of spider assemblages to regional and local processes
Bibliographic record
Abstract
Abstract Understanding species richness variation among local communities is one of the central topics in ecology, but the complex interplay of regional processes, environmental filtering, and local processes hampers generalization on the importance of different processes. Here, we aim to unravel drivers of spider community assembly in temperate forests by analyzing two independent data sets covering gradients in elevation and forest succession. We test the following four hypotheses: (H1) spider assemblages within a region are limited by dispersal, (H2) local environment has a dominant influence on species composition and (H3) resources, and (H4) biotic interactions both affect species richness patterns. In a comprehensive approach, we studied species richness, abundance, taxonomic composition, and trait‐phylogenetic dissimilarity of assemblages. The decrease in taxonomic similarity with increasing spatial distance was very weak, failing to support H1. Functional clustering of species in general and with canopy openness strongly supported H2. Moreover, this hypothesis was supported by a positive correlation between environmental and taxonomic similarity and by an increase in abundance with canopy openness. Resource determination of species richness (H3) could be confirmed only by the decrease of species richness with canopy cover. Finally, decreasing species richness with functional clustering indicating effects of biotic interactions (H4) could only be found in one analysis and only in one data set. In conclusion, our findings indicate that spider assemblages within a region are mainly determined by local environmental conditions, while resource availability, biotic interactions and dispersal play a minor role. Our approach shows that both the analysis of different aspects of species diversity and replication of community studies are necessary to identify the complex interplay of processes forming local assemblages.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".