Effects of functional diversity on ecosystem services in cocoa agroforestry systems in Costa Rica
Bibliographic record
Abstract
Cocoa agroforestry systems (AFS) provide multiple ecosystem services, which are influenced by features of the shade tree community. By strategically selecting and managing shade trees based on their functional traits, cocoa farmers can affect functional diversity of AFS and potentially enhance the benefits they deliver. In this research, we applied functional trait ecology to better understand the effect of functional diversity of cocoa AFS on three ecosystem services: carbon storage, soil fertility, and provisioning of cocoa and other products. To achieve this, we characterized 30 AFS across a gradient of ecological complexity and established relationships between functional diversityw indices and ecosystem services using ANOVA and multiple regression models. As a result, two contrasting ecological dynamics were observed: low-complexity AFS, dominated by resource-conservative traits (higher leaf dry matter content, higher stem specific density, and low leaf nitrogen concentration), were associated with lower carbon stocks and soil fertility, while high-complexity AFS, characterized by resource-acquisitive traits (low leaf dry matter content, low stem specific density, and high leaf nitrogen concentration), delivered greater ecosystem services. Through the multiple regression analysis, we found that AFS dominated by species with greater maximum potential height, greater leaf nitrogen concentration, lower leaf dry matter content, lower leaf mass per area, and lower leaf nitrogen-phosphorus ratio were associated with higher carbon storage (R 2 = 0.84), soil fertility (R 2 = 0.7 for soil nitrogen), and multiple ecosystem services (R 2 = 0.78). Additionally, cocoa yields were negatively correlated with shade cover and the dominance of large-leaf shade trees, revealing a potential trade-off between maximizing yields and enhancing ecosystem services. Nevertheless, the models indicated that a win-win scenario can be achieved when shade trees are productive, generating additional benefits. Finally, our study highlights critical relationships between shade tree traits and delivery of key ecosystem services for farm sustainability and farmer livelihoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".