Increased avian bioacoustic diversity without lost profit after planting perennial vegetation in marginal cropland
Bibliographic record
Abstract
Expansion of arable cropland and intensification of agriculture has driven substantial losses of habitat, biodiversity, and ecosystem services. Balancing biodiversity conservation and environmental priorities with farm economics and food production is particularly challenging. However, many areas of crop fields contain marginal, wet, or saline soils that consistently produce relatively low crop yields. These suboptimal growing areas may be ideal targets for perennial restoration to address biodiversity conservation goals without reducing crop yield and profitability. We tested the value of restoring marginal areas within 20 treatment fields and 31 matched reference fields (65 ha) growing primarily canola, cereal, and legume crops in Saskatchewan, Canada. Using prior-year yield maps, we asked participating producers to delineate and convert 10-20% of their crop fields to a diverse forage grass mix near low yielding wetlands and/or marginal saline areas, and then compared these to matched nearby reference fields that were cropped as usual. We tracked changes in acoustic biodiversity and crop yields over the next three years. Autonomous recording units (ARUs) recorded over 2450 hours of environmental soundscapes in treatment and reference fields. Multiple bioacoustic diversity indices (bioacoustics index (BIO), acoustic diversity index (ADI), and normalized difference soundscape index (NDSI)) significantly increased in treatment relative to reference fields, with the most substantial increases from the first to second year after planting. Total field level crop yields were reduced on average by 9% in treatment fields which was less than the 14% area converted, and producer profitability did not differ between treatment and reference fields. This suggests that restoring marginal areas within cropland adds landscape diversity and is a nature-based solution to address biodiversity loss from agriculture that has significant promise to provide environmental, economic, and agronomic benefits.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".