Ecological predictors of spider sociality in the<scp>A</scp>mericas
Bibliographic record
Abstract
Abstract Aim To evaluate the relative importance of environmental factors relevant to specific hypotheses that have been proposed to explain the distribution of social and subsocialAnelosimusspiders in the American continent andEcuador. For social species, we test the seasonality and prey size hypotheses, which posit that species forming long‐lived multifemale colonies require aseasonal conditions and warm and productive environments in which large insects can develop. For subsocial species, we test the rain intensity and predation risk hypotheses, which posit that species whose colonies contain a single female and her offspring cannot occupy areas where strong rains cause frequent damage to their webs and where warm temperatures and high productivity promote greater predation. Location America,Ecuador. Methods Using generalized linear models, hierarchical variance partitioning,Maxent distribution modelling and phylogenetically controlled regressions, we analysed the relative importance of environmental variables that either directly or indirectly relate to these non‐exclusive hypotheses – temperature and precipitation seasonality (seasonality hypothesis), annual temperature and net primary productivity (insect size and predation hypotheses) and rain intensity (rain intensity hypothesis). Results Temperature seasonality, followed by annual temperature and rain intensity, were the most important predictors of the distribution of spider sociality across America, whereas temperature and rain intensity predominated in the largely aseasonalEcuador. In general, social species were associated with lower temperature seasonality, warmer temperatures and higher rain intensity than subsocial species. Main conclusion The association of socialAnelosimuswith warm and wet areas in the tropics is consistent with both the seasonality and prey size hypotheses, i.e. both aseasonal conditions and warm temperatures, which allow large insects to develop, are needed for large social colonies to form. That subsocialAnelosimusdrop‐out from tropical areas with warm temperatures and high rain intensity is consistent with the hypotheses that high predation risk and disturbance by strong rains exclude subsocialAnelosimusfrom the lowland rain forest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".