Complex spider webs as habitat patches : environmental filtering drives species composition
Bibliographic record
Abstract
Metacommunity theory has advanced understanding of mechanisms shaping community structure. Four main models (neutral, patch-dynamics, species-sorting, and mass-effects) have been recognized to explain these mechanisms, differing in their assumptions about the effects of environmental filtering and species traits on community composition. Here, I focus on complex, three-dimensional spider webs of two social and two solitary species as habitat patches for associated arthropods in a tropical rainforest in Ecuador. I used variance partitioning and various analyses of metacommunity structure to study the role of environmental filtering and dispersal in this system. I found that local patch characteristics, such as patch size and host species, predominantly affected local community composition. Webs of social spider species had higher richness, more variable communities, and proportionally more aggressive (i.e. predatory) web associates. Behavioral characteristics of the host spiders, such as sociality and aggressiveness, seemed to play an important role, as well, in shaping community composition on these patches. In a colonization experiment, there was indication of high dispersal rates at a short temporal scale and some evidence of species dominance at a longer temporal scale. I conclude that environmental filtering is responsible for the patterns of species distribution and that, given the conjunctive high dispersal and species specialization, the metacommunity patterns in this system seem to best be explained by a combination of the species sorting and mass effects models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".