Description
Predicting, managing and optimizing the provision of biodiversity-driven ecosystem services in croplands is a critical component of sustainable environmental management. Increasing losses to pests due to direct and indirect effects of climate change is predicted to hamper our future ability to produce enough food, feed and fibre. Natural biological control of insect pests remains difficult to predict beyond the crop level and system-specific attempts. This is likely due to the diversity in cropping systems and species communities, functional traits of key species, and the contrasting spatio-temporal landscape dynamics. In order to bridge this gap we have already, with BECC support (workshop April 2018) (i) set up a network of ecologists and biological control experts from Europe and North America (ii) developed archetypes of herbivore-natural-enemy cropping systems (iii) developed and validated joint qualitative and quantitative models that use a general framework but allow for differences in model structure between different crops and agroecosystems (i.e. between the archetypes), generating robust and testable predictions.This workshop was funded by BECC.
Period | 2019 Jun 17 → 2019 Jun 19 |
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Event type | Workshop |
Location | Lund, SwedenShow on map |
Documents & Links
Related content
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Activities
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Modelling natural pest control across the world’s agroecosystems – an archetype approach
Activity: Talk or presentation › Presentation
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Trait-based models of natural pest control across agricultural landscapes
Activity: Talk or presentation › Presentation
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Projects
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Research output
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Modelling natural pest control across the world’s agroecosystems – an archetype approach
Research output: Contribution to conference › Abstract
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Models of natural pest control: Towards predictions across agricultural landscapes
Research output: Contribution to journal › Review article › peer-review
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Archetype models upscale understanding of natural pest control response to land-use change
Research output: Contribution to journal › Article › peer-review