Effects of landscape complexity on biodiversity of rice agroecosystems: A meta-analysis
Bibliographic record
Abstract
Agricultural intensification is one of the primary drivers of landscape simplification and biodiversity loss. Understanding how different taxa respond to landscape complexity is key to address the challenge of enhancing biodiversity while sustaining food production. We performed a global-scale meta-analysis of biodiversity and landscape relationships, yielding 456 effect sizes from 18 papers across 9 countries. We specifically explored the effect across different taxonomic groups (2 vertebrate and 6 invertebrate orders) and landscape complexity dimensions (composition, configuration, heterogeneity). Despite the high heterogeneity among the data, we found a positive effect of landscape complexity on rice-associated biodiversity, yet the magnitude and sign of effects contrasted among specific orders and landscape dimensions. For instance, among vertebrates only amphibians showed a positive response to landscape composition. Among invertebrates, spiders and beetles responded positively to compositional complexity while true bugs responded negatively to configurational complexity. Our results suggest that promoting landscape compositional complexity at large spatial scales overall benefits biological communities in rice agroecosystems, specifically in major rice-producing regions. Yet the negative impact of configurational heterogeneity observed for certain groups of insects (i.e., true bugs) indicate that the spatial arrangement and degree of fragmentation of rice habitats is also an important factor shaping biodiversity outcomes. This highlights the need of considering different landscape dimensions and multiple groups of animals simultaneously when designing large scale habitat management plans to avoid potential trade-offs and maximize biodiversity in rice agroecosystems. • We did a meta-analysis to assess landscape effects on rice-associated biodiversity. • Landscape complexity overall enhanced biodiversity. • Amphibians responded positively to landscape composition complexity. • Spiders and beetles responded positively to landscape composition complexity. • True bugs responded negatively to landscape configurational complexity.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.042 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".