Urbanization and the abundance and diversity of Prairie bats
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) The effects of urbanization on biodiversity are generally considered to be negative, but the potential for landscape context to modulate these effects has not been adequately examined because most urban ecology research has been conducted in one biome: the temperate forest. This bias also applies to studies of the urban ecology of bats, whose diversity is correlated with habitat heterogeneity. We investigated the hypothesis that in the fairly flat, homogeneous Prairies, urbanization, by creating structurally complex islands, benefits bats by increasing access to the vertical landscape elements (buildings and trees) in which they roost. From 2006 to 2008, we surveyed bat assemblages in and around Calgary, Alberta, using mist nets to capture them and bat detectors to record their echolocation activity. Our data supported the prediction that urbanization increases the abundance of Prairie bats, but not the prediction that it increases their diversity. Instead, the urban bat assemblage was less diverse, and exhibited decreased species evenness compared to non-urban assemblages. Although Myotis lucifugus dominated bat assemblages throughout our study area, this was most evident in the city, and this species drove the increased urban abundance of bats. Ultimately, we reject our hypothesis and conclude that urbanization in the Prairies may create attractive habitat for one synanthropic bat, but is detrimental to others.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.013 | 0.001 |
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".