The luxury effect beyond cities: bats respond to socioeconomic variation across landscapes
Bibliographic record
Abstract
BACKGROUND: The luxury effect describes the positive relationship between affluence and organism diversity or activity in urban ecosystems. Driven by human activities, the luxury effect can potentially be found at a broader scale across different landscapes. Previously, the luxury effect relationship has been established within a city for two bat species, the red bat (Lasiurus borealis) and the evening bat (Nycticeius humeralis). We examined landscape-scale patterns of bat activity distribution-using empirical data for seven bat species for the luxury effect. We also identified bat-land cover associations for each species. Across North Carolina, USA, we used the mobile transect survey protocol of the North American Monitoring Program to record bat activity at 43 sites from 2015 to 2018. We collected land cover and income data at our transect locations to construct generalized linear mixed models to identify bat-land cover and bat-income relationships. RESULTS: We found that across landscapes, activity of the red bat and the evening bat was positively correlated to income independent of land cover, consistent with previous single-city results. We found a negative relationship between hoary bat (Lasiurus cinereus) activity and income. All seven species had specific land cover associations. Additionally, we found a positive interaction term between income and evergreen forest for the red bat and a positive interaction term between income and woody wetland for hoary bat. CONCLUSIONS: Our results demonstrated that the luxury effect is an ecological pattern that can be found at a broad spatial scale across different landscapes. We highlight the need for multi-scale ecology studies to identify the mechanism(s) underlying the luxury effect and that the luxury effect could cause inequity in how people receive the ecosystem services provided by bats.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".