Shade‐growing practices lessen the impact of coffee plantations on multiple dimensions of ant diversity
Bibliographic record
Abstract
Abstract Land use management influence changes in biodiversity beyond the targeted species. Management practices in coffee plantations have shifted from coffee growing below accompanying (shade) trees, to intensified monocultures in which coffee grows fully exposed to the sun. Anthropogenic disturbance causes changes in species composition relative to adjacent natural patches and reduces their biotic heterogeneity. Here, we assessed the impact of coffee plantation management practices on the taxonomical, phylogenetic and functional composition of ant communities, an ecologically dominant group and crucial biological pest controller in these agroecosystems. We hypothesized that shade‐grown coffee plantations would harbour ant communities similar to those of nearby forest patches, but dissimilar to those of intensified monocultures. We surveyed ant diversity in eight shade‐grown coffee farms, eight intensive coffee monocultures and eight forest patches. We used a combination of active and passive sampling methods over two field campaigns spanning 6 months. Our results support our hypothesis for all diversity dimensions. Additionally, ant communities in intensified monocultures were taxonomically and functionally, but not phylogenetically, more homogeneous than those found in forest patches and shade‐grown plantations. Synthesis and applications. Our findings support the idea that practices in shade‐grown coffee plantations buffer the impoverishment of multiple diversity dimensions after forest conversion. Additionally, we identified that leaf‐litter depth and number of twigs mitigate ant diversity loss which, in turn, can favour the presence of potential biocontrol agents. By assessing and integrating multiple biodiversity dimensions into management strategies, farmers and interested parties can minimize future biodiversity and ecosystem service loss.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".