Human impacts on environment–diversity relationships: evidence for biotic homogenization from butterfly species richness patterns
Bibliographic record
Abstract
ABSTRACT Aim Broad‐scale spatial variation in species richness relates to climate and physical heterogeneity but human activities may be changing these patterns. We test whether climate and heterogeneity predict butterfly species richness regionally and across Canada and whether these relationships change in areas of human activity. Location Canada. Methods We modelled the ranges of 102 butterfly species using genetic algorithms for rule‐set production (GARP). We then measured butterfly species richness and potentially important aspects of human activity and the natural environment. These were included in a series of statistical models to determine which factors are likely to affect butterfly species richness in Canada. We considered patterns across Canada, within predominantly natural areas, human‐dominated areas and particular ecozones. We examined independent observations of butterfly species currently listed under Canada's endangered species legislation to test whether these were consistent with findings from statistical models. Results Growing season temperature is the main determinant of butterfly species richness across Canada, with substantial contributions from habitat heterogeneity (measured using elevation). Only in the driest areas does precipitation emerge as a leading predictor of richness. The slope of relationships between all of these variables and butterfly species richness becomes shallower in human‐dominated areas, but butterfly richness is still highest there. Insecticide applications, habitat loss and road networks reduce butterfly richness in human‐dominated areas, but these effects are relatively small. All of Canada's at‐risk butterfly species are located in these human‐dominated areas. Main conclusions Temperature affects butterfly species richness to a greater extent than habitat heterogeneity at fine spatial scales and is generally far more important than precipitation, supporting both the species richness–energy and habitat heterogeneity hypotheses. Human activities, especially in southern Canada, appear to cause surprisingly consistent trends in biotic homogenization across this region, perhaps through range expansion of common species and loss of range‐restricted 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 | 0.001 | 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".