Geographic, Taxonomic and Metric Gaps in Biodiversity Research Limit Evidence‐Based Conservation in Agricultural Landscapes: An Umbrella Review
Bibliographic record
Abstract
Agriculture is fundamentally dependent on biodiversity, yet unsustainable management practices increasingly threaten various organisms and ecosystem services. Confronting the global crisis of biodiversity loss requires a thorough understanding of the gaps, clusters and biases in existing knowledge across various management practices, spatial scales, and taxonomic groups. We undertook a comprehensive literature review, synthesising secondary data from 200 meta-analyses on agricultural management impacts on biodiversity in croplands. Our systematic map covers 1885 comparisons (mean effect sizes), from over 9000 primary studies. In the latter, seven high-income countries prevail (notably the USA, China and Brazil), with particular focus on fertiliser use, phytosanitary interventions and crop diversification. This emphasis on individual practices overshadows research at the farm and landscape levels. In secondary evidence, arthropods and microorganisms are most frequently studied, while annelids, vertebrates and plants are less represented. Evidence predominantly stems from averaged abundance data, revealing substantial gaps in studies on functional and phylogenetic diversity. Our findings highlight the need to analyse combinations of multiple practices to accurately reflect real-world farming contexts, and covering a wider range of taxa, biodiversity metrics and spatial levels, to enable evidence-based conservation strategies in agriculture. Given the uneven evidence on agricultural impacts, caution is required when applying meta-analytical findings to public policies and global assessments.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".