Land Sparing Can Maintain Bird Diversity in Northeastern Bangladesh
Bibliographic record
Abstract
One of humanity’s most significant challenges in the process of attaining the established sustainability goals is balancing the growing human demand for food and the need to conserve biodiversity. This challenge requires appropriate land uses that are able to conserve biodiversity while ensuring ample food supply. This study compares bird species diversity and abundance in areas undergoing land sharing and land sparing in northeastern Bangladesh (West Bhanugach Reserved Forest). Birds serve as useful biologic indicators because of their presence within different trophic levels and their well-studied ecology. To survey birds, we selected a total of 66 sampling sites within land-sharing (33) and land-sparing (33) land-use areas. Between May and June 2017, we observed and recorded bird calls within a 50-m radius around each sampling site. We counted 541 individuals from 46 species of birds. The Shannon bird diversity was higher in the land-sparing sites (1.52) than in the land-sharing sites (1.23). We found approximately 30% more bird species (39 vs. 30) and 40% more individuals (318 vs. 223) in the land-sparing areas than land-sharing areas. Three bird species, Arachnothera longirostra, Micropternus brachyurus and Copsychus malabaricus, were significantly associated with the land-sparing sites. This study shows that land sharing negatively affects bird diversity, richness and abundance compared to land-sparing. The use of chemical fertilizers and the lack of food, such as insects, for birds can explain the lower diversity, richness and abundance of birds in the land-sharing areas. Although land sharing is an effective means of producing food, land sparing is the most effective land-use practice for preserving bird diversity in northeastern Bangladesh.
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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 | 0.001 |
| 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) | 0.002 | 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".