Direct and indirect effects of agricultural land cover on avian biodiversity in eastern Canada
Bibliographic record
Abstract
Abstract Agriculture is one of the largest threats to global biodiversity. However, most studies have focused only on the direct effects of agriculture on biodiversity, and few have addressed the indirect effects, potentially over or under-estimating the overall impacts of agriculture on biodiversity. The indirect effect is the response not to the agricultural cover types or operations per se, but instead, to the way that agriculture influences the extent and configuration of different types of natural land cover in the landscape. We used structural equation modelling (SEM) to evaluate the direct, indirect, and total effects of agriculture on species richness of three bird guilds: forest birds, shrub-edge birds, and open country birds. We found that forest bird richness was driven by the negative indirect effect of cropland via forest loss. Shrub-edge and open country bird richness increased with the amount of agriculture land covers; however, importantly, we found negative indirect effects of agriculture on both guilds via a reduction in more natural land covers. This latter result highlights how we would have over-estimated the positive effects of agriculture on shrub-edge and open country bird richness had we not measured both direct and indirect effects (i.e., the total effect size is less than the direct effect size). Overall, our results suggest that a bird-friendly agricultural landscape in our region would have forest that is configured to maximize forest edge, and a high proportion of perennial forage within the agricultural portion of the landscape.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".