Plant Diversity and Agroecosystem Function in Riparian Agroforests: Providing Ecosystem Services and Land-Use Transition
Bibliographic record
Abstract
Achieving biologically diverse agricultural systems requires a commitment to changes in land use. While in-field agrobiodiversity is a critical route to such a transition, riparian systems remain an important, yet understudied, pathway to achieve key diversity and ecosystem services and targets. Notably, at the interface of agricultural landscapes and aquatic systems, the diversification of riparian buffers with trees reduces the non-point source pollution in waterways. However, in riparian agroforestry systems, little is known about herbaceous community patterns and, importantly, the herbaceous community’s role in governing carbon (C) and nitrogen (N) cycling. Our study investigated herbaceous community taxonomic and phylogenetic diversity patterns in riparian (i) grasslands (GRASSLAND), (ii) rehabilitated agroforests (AGROFOREST-REHAB), and (iii) remnant forests (AGROFOREST-NATURAL). We then determined the biodiversity-ecosystem function relationships between community functional diversity metrics, C and N cycling, and greenhouse gas fluxes. We observed significant differences in taxonomic and phylogenetic diversity among riparian buffer types. We found that herbaceous plant communities in riparian agroforestry systems expressed plant trait syndromes associated with fast-growing, resource acquiring strategies, while grassland buffer plants exhibited slow-growing, resource conserving strategies. Herbaceous communities with high functional diversity and resource acquiring trait syndromes, such as those in the agroforestry riparian systems, were significantly correlated with lower rates of soil CO2 efflux and N mineralization, both of which are key fluxes related to ecosystem service delivery. Our findings provide further evidence that functionally diverse, and not necessarily taxonomically diverse, plant communities are strongly correlated to positive ecosystem processes in riparian agroforestry systems, and that these communities contribute to the transition of agricultural lands toward biologically and functionally diverse landscapes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".