Does multi‐level environmental filtering determine the functional and phylogenetic composition of wild bee species assemblages?
Bibliographic record
Abstract
A central goal in ecology is to develop theories that explain the diversity and distribution of species. The evolutionary history of species and their functional traits may provide mechanistic links between community assembly and the environment. Such links may be hierarchically structured such that the strength of environmental filtering decreases in a step‐wise manner from regional conditions through landscape heterogeneity to local habitat conditions. We sampled the wild bee species assemblages in power‐line strips transecting forests in south‐eastern Norway. We used altitude, landscape diversity surrounding sites and plant species composition, together with total plant cover as proxies for regional, landscape and local environmental filters, respectively. The species richness and abundance of wild bees decreased with altitude. The reduction in species richness and abundance was accompanied by a phylogenetic clustering of wild bee individuals. Furthermore, regional filters followed by local filters best explained the structure of the functional species composition. Sites at high altitudes and sites with Ericaceae‐dominated plant communities tended to have larger bees and a higher proportion of social and spring‐emerging bees. When Bombus species were excluded from the analysis, the proportion of pollen specialists increased with the dominance of Ericaceae. Furthermore, we also found that the taxonomic, phylogenetic and functional compositional turnover between sites was higher in the northern region than in the southern part of the study region. Altogether, these results suggest that regional filters drive the species richness and abundance in trait‐groups whereas local filters have more descrete sorting effects. We conclude that the model of multi‐level environmental filters provides a good conceptual model for community ecology. We suggest that future studies should focus on the relationship between the biogeographical history of species and their current distribution, and on the assumption that closely related species do indeed compete more intensely than distantly related species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".