Reconsidering the role of ‘semi‐natural habitat’ in agricultural landscape biodiversity: a case study
Bibliographic record
Abstract
Abstract Semi‐natural habitats are considered as the main source of biodiversity in agricultural landscapes. Most landscape ecology research has focused on the amount (relative surface) and spatial organisation of these habitats. However, these two components of landscape heterogeneity, composition and configuration, are often correlated. Also, landscape structure effects on biodiversity were mostly observed locally, while there is a great need for studying landscape‐scale gamma diversity. We conducted a mensurative experiment to determine the independent effects of semi‐natural habitat amount and configuration on gamma diversity of carabid beetles and vascular plants. The influence of landscape heterogeneity components were tested on species richness, evenness and composition. Local diversity (species richness and composition) was compared across the various cover types to determine their relative contributions. Only carabid species evenness and composition were influenced by semi‐natural habitat amount. Carabid and plant species richness and plant species composition remained unaffected by semi‐natural habitats. Local diversity analysis showed that three types of habitats can be distinguished in agricultural landscapes: grasslands (temporary and permanent ones), woody habitats (woodlands and hedgerows) and row crops. These results beg for a re‐evaluation of the semi‐natural covers. Temporary and permanent grasslands are often similar, probably because of comparable farming management. Permanent grasslands and woody habitats are often combined as semi‐natural covers, although they support very different communities. The lack of effect of semi‐natural habitat amount and configuration on gamma diversity results from a more complex organisation of biodiversity in landscapes and supports the move from semi‐natural vs. farmland to habitat mosaic landscape representations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".