Identifying plant mixes for multiple ecosystem service provision in agricultural systems using ecological networks
Bibliographic record
Abstract
Abstract Managing agricultural environments in a way that maximises the provision of multiple ecosystem services is a significant challenge in the development of sustainable and secure food systems. Advances in network ecology provide a way forward, particularly in arable landscapes, as they incorporate mutualistic and antagonistic interactions associated with crop production. Here, we present an approach to identify mixes of non‐crop plant species that provide multiple ecosystem services while minimising disservices. Genetic algorithms were applied to the Norwood Farm ecological network—a comprehensive dataset of antagonistic and mutualistic species interactions on an organic farm in the United Kingdom. We aimed to show how network analyses can be used to select plants supporting a high diversity of insect pollinators and parasitoids of insect pests, but low diversity of herbivores. Further to this, we wanted to understand the trade‐offs in ecosystem service provision associated with conventional management practices that focus on individual ecosystem services. We show that multilayer network analyses can be used to identify mixes of plant species that maximise the species richness of pollinators and parasitoids (natural enemies of insect pests), while minimising the species richness of herbivores. Trade‐offs between ecosystem processes were apparent with several plant species associated with a high species richness of both positive (pollinators and parasitoids) and negative (herbivores) functional taxonomic groups. As a result, optimal plant species mixes for individual ecosystem services were different from the mix simultaneously maximising pollination and parasitism of pest insects, while minimising herbivory. Synthesis and applications . Plant mixes designed solely for maximising pollinator species richness are not optimal for the provision of other ecosystem services and disservices (e.g. parasitism of insect pests and herbivory). The method presented here will allow for the design of management strategies that facilitate the provision of multiple ecosystem services. To this end, we provide a protocol for practitioners to develop their own plant mixes suitable for farm‐scale management. This avenue of predictive network ecology has the potential to enhance agricultural management, supporting high levels of biodiversity and food production by manipulating ecological networks in specific ways.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".