Local and regional‐scale effects of hedgerows on grassland‐ and forest‐associated bird populations within agroecosystems
Bibliographic record
Abstract
Linear woody features (LWFs), like hedgerows along field edges, provide wildlife habitat and support biodiversity in agroecosystems. Assessments of LWFs usually focus on community-level indices, such as species richness. However, effective conservation actions need to balance the contrasting habitat preferences of different wildlife species, necessitating a focus on population-level effects in working landscapes. We assessed associations between LWFs and abundance for 45 bird species within an intensive agroecosystem in eastern Ontario, Canada. We used distance- and removal-sampling methods across 4 years (2016-2019) to estimate local bird abundance in habitats representing a range of LWF densities. We also predicted abundance across a subset of the study region with and without LWFs to understand their contribution to regional population density. Associations between local bird abundance and LWFs were variable among species, but overall community effects were clearly positive, particularly for forest and shrubland species. At the site level, 20/45 species (44%) had higher densities associated with greater LWF presence on average, compared to 5/45 (11%) with negative associations. At the regional scale, LWFs had predicted benefits on total abundance for 31 species (69%), contributing to an estimated 20% increase on average. Positive effects were most pronounced in areas with greater agricultural land use (primarily field crops), suggesting LWFs may provide crucial habitat in heavily modified landscapes but have little to no additional benefit for the avian community in areas with greater existing heterogeneity and habitat retention. Species that responded negatively tended to be at risk with strong habitat preferences for intact forests or large, open grasslands and, thus, greater sensitivity to potential edge effects. With rapidly declining songbird populations and a global need for food security, conservation strategies that amplify biodiversity and enhance agricultural productivity through ecosystem services such as pest control, pollination, and water regulation are vital. We demonstrate the benefits of habitat heterogeneity in agroecosystems on songbird densities and highlight the need to integrate local and landscape-level assessments in conservation planning. An effective, balanced strategy includes concentrating LWFs in areas of extensive arable crops, with habitat retention patches where possible, while maintaining heterogeneity through mixtures of natural habitats and pastoral farming in less intensive regions.
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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 | 0.000 | 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) | 0.000 | 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".