Optimising UK Landscapes for Agroecosystem Resilience

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The UK's withdrawal from the EU's Common Agricultural Policy (CAP) provides an opportunity to enhance the resilience of agricultural landscapes. Agroecosystem resilience is interlinked with the provision of non-market ecosystem services, such as soil health, pest regulation and pollination, and the functional biodiversity that underpins them. However, the interactive effects of agricultural practices and landscape complexity on functionally important biological communities are not well known. This project will develop a mechanistic landscape-scale model for several bioindicators (earthworms, collembola, hoverflies) of agroecosystem multifunctionality. These bioindicators represent diverse life histories, behaviours, and dispersal capabilities, and so exhibit varying sensitivities to environmental conditions, agricultural management practices and landscape features. The model allows us to test the central hypothesis that while different bioindicators show differing sensitivities to specific landscape (configuration of land use types) and management practices, agricultural landscapes can be designed to benefit each bioindicator simultaneously. We expect this new understanding of the interplay between populations, agricultural practices, and landscapes to inform optimisation management plans, such as the upcoming Environmental Land Management (ELM) scheme that replaces the CAP Ecological Focus Area's (EFA's).The specific objectives of the project include: 1) Identify the most representative species for each bioindicator, giving weight to their functional importance in agroecosystems, existing models for each species, and data availability for model development and validation. Individual life history and behavioural traits for each species will be synthesised from the literature, the majority of which can be found in existing models. 2) Develop the standardised mechanistic submodels that inform the population models. These include established energy budget modelling approaches (the parameters for which are available for many species, and for others can be easily obtained from individual-level data) and behavioural submodels which will use a state-space modelling approach to identify key drivers of movement patterns at their relevant spatial scales. Individual-level movement data is limited in the literature, and so here we envisage a need to collect new data for hoverflies from ongoing experiments at Sonning Farm.3) Design the agent-based modelling framework, which represents spatially explicit landscapes, heterogeneous exposure to agricultural practices, individual-environment interactions, and the individual traits from which species population dynamics emerge. In the first instance, the model environment will replicate the time-series of conditions (e.g. weather, management) of the validation datasets together with land cover and soil maps held by CEH (LCMs, open source) and Cranfield (LandIS). 4) Validate the model, using extensive datasets held by Reading, Cranfield, and Syngenta together with published datasets such as the UK Environmental Change Network and the National Biodiversity Network atlas. Approximate Bayesian Computation, a powerful tool for developing complex ecological models, will be used to streamline model analysis. Model validation across diverse agroecosystems, for which population data is available, will enable quantification of the interactive effects (e.g. antagonistically or synergistically) of landscape composition and agricultural management on the bioindicator populations. 5) Simulate novel landscape composition and management scenarios, using the Cranfield high-performance computer, to quantify the effect of each scenario on each bioindicator population. Scenarios will compare the current tiered ELM scheme in the UK and EFA's under the EU's CAP.