No evidence for environmental filtering of cavity‐nesting solitary bees and wasps by urbanization using trap nests
Bibliographic record
Abstract
Abstract Spatial patterns in biodiversity are used to establish conservation priorities and ecosystem management plans. The environmental filtering of communities along urbanization gradients has been used to explain biodiversity patterns but demonstrating filtering requires precise statistical tests to link suboptimal environments at one end of a gradient to lower population sizes via ecological traits. Here, we employ a three‐part framework on observational community data to test: (I) for trait clustering (i.e., phenotypic similarities among co‐occurring species) by comparing trait diversity to null expectations, (II) if trait clustering is correlated with an urbanization graient, and (III) if species' traits relate to environmental conditions. If all criteria are met, then there is evidence that urbanization is filtering communities based on their traits. We use a community of 46 solitary cavity‐nesting bee and wasp species sampled across Toronto, a large metropolitan city, over 3 years to test these hypotheses. None of the criteria were met, so we did not have evidence for environmental filtering. We do show that certain ecological traits influence which species perform well in urban environments. For example, cellophane bees (Hylaeus: Colletidae) secrete their own nesting material and were overrepresented in urban areas, while native leafcutting bees (Megachile: Megachilidae) were most common in greener areas. For wasps, prey preference was important, with aphid‐collecting (Psenulus and Passaloecus: Crabronidae) and generalist spider‐collecting (Trypoxylon: Crabronidae) wasps overrepresented in urban areas and caterpillar‐ and beetle‐collecting wasps (Euodynerus and Symmorphus: Vespidae, respectively) overrepresented in greener areas. We emphasize that changes in the prevalence of different traits across urban gradients without corresponding changes in trait diversity with urbanization do not constitute environmental filtering. By applying this rigorous framework, future studies can test whether urbanization filters other nesting guilds (i.e., ground‐nesting bees and wasps) or larger communities consisting of entire taxonomic groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".