Human land use, agriculture, pesticides and losses of imperiled species
Bibliographic record
Abstract
ABSTRACT Aim Anthropogenic habitat loss is usually cited as the most important cause of recent species’ extinctions. We ask whether species losses are in fact more closely related to habitat loss than to any other aspect of human activity such as use of agricultural pesticides, or human population density (which reflects urbanization). Location Canada. Methods We statistically compared areas in Canada where imperiled species currently occur, versus areas where they have been lost. Using multiple regressions, we relate the numbers of species that had suffered range reductions in an ecoregion to variables that represent present habitat loss, pesticide use and human population density. Results We find high losses of imperiled species in regions with high proportions of agricultural land cover. However, losses of imperiled species are significantly more strongly related to the proportion of the region treated with agricultural pesticides. The relationship between species losses and area treated with pesticides remains significant after controlling for area in agriculture. Main conclusions Our results are consistent with the hypothesis that agricultural pesticide use, or something strongly collinear with it (perhaps intensive agriculture more generally), has contributed significantly to the decline of imperiled species in Canada. Habitat conversion per se may be a less important cause of species declines than how that converted habitat is used.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".