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Spatial modelling approaches to aid land‐use decisions which benefit both wildlife and humans are often limited to the comparison of pre‐determined landscape scenarios, which may not reflect the true optimum landscape for any end‐user. Furthermore, the needs of wildlife are often under‐represented when considered alongside human financial interests in these approaches. We develop a method of addressing these gaps using a case‐study of wild bees in the UK, an important group whose declines may adversely affect both human economies and surrounding ecosystems. By combining the genetic algorithm NSGA‐II with a process‐based pollinator model which simulates bee foraging and population dynamics, Poll4pop, we ‘evolve’ a typical UK agricultural landscape to identify optimum land cover configurations for three different guilds of wild bee. These configurations are compared to those resulting from optimisations for farm income alone, as well as optimisations that seek a compromise between bee populations and farm income objectives. We find that the land cover proportions in landscapes optimised for each bee guild reflect their nesting habitat preferences rather than foraging preferences, highlighting a limiting resource within the study landscape. The spatially explicit nature of these optimised landscapes illustrates how improvement for a given target species may be limited by differences between their movement range and the scale of the units being improved. Land cover composition and configuration differ significantly in landscapes optimised for farm income and bee population growth simultaneously and illustrate how human agents are required to compromise much more when the multifaceted nature of biodiversity is recognised and represented by multiple objectives within an optimisation framework. Our methods provide a way to quantify the extent to which real‐life landscapes promote or compromise objectives for different landscape end‐users. Our investigation suggests that optimisation set‐up (decision‐unit scales, traditional choice of a single biodiversity metric) can bias outcomes towards human‐centric solutions. It also demonstrates the importance of representing the individual requirements of different actors with different landscape‐level needs when using genetic algorithms to support biodiversity‐inclusive decision‐making in multi‐functional landscapes.
Spatial modelling approaches to aid land‐use decisions which benefit both wildlife and humans are often limited to the comparison of pre‐determined landscape scenarios, which may not reflect the true optimum landscape for any end‐user. Furthermore, the needs of wildlife are often under‐represented when considered alongside human financial interests in these approaches. We develop a method of addressing these gaps using a case‐study of wild bees in the UK, an important group whose declines may adversely affect both human economies and surrounding ecosystems. By combining the genetic algorithm NSGA‐II with a process‐based pollinator model which simulates bee foraging and population dynamics, Poll4pop, we ‘evolve’ a typical UK agricultural landscape to identify optimum land cover configurations for three different guilds of wild bee. These configurations are compared to those resulting from optimisations for farm income alone, as well as optimisations that seek a compromise between bee populations and farm income objectives. We find that the land cover proportions in landscapes optimised for each bee guild reflect their nesting habitat preferences rather than foraging preferences, highlighting a limiting resource within the study landscape. The spatially explicit nature of these optimised landscapes illustrates how improvement for a given target species may be limited by differences between their movement range and the scale of the units being improved. Land cover composition and configuration differ significantly in landscapes optimised for farm income and bee population growth simultaneously and illustrate how human agents are required to compromise much more when the multifaceted nature of biodiversity is recognised and represented by multiple objectives within an optimisation framework. Our methods provide a way to quantify the extent to which real‐life landscapes promote or compromise objectives for different landscape end‐users. Our investigation suggests that optimisation set‐up (decision‐unit scales, traditional choice of a single biodiversity metric) can bias outcomes towards human‐centric solutions. It also demonstrates the importance of representing the individual requirements of different actors with different landscape‐level needs when using genetic algorithms to support biodiversity‐inclusive decision‐making in multi‐functional landscapes.
Context Implementing heterogeneous rural landscapes with high agricultural diversity and a substantial proportion of natural habitats has been proposed to ensure food production while reducing negative impacts on ecosystem services. However, evidence of an increased supply of ecosystem services (ES) in more heterogeneous landscapes remains limited, with no consensus. Objectives To evaluate the effect of the spatial cropland system’s diversity and landscape configuration on the supply of key ES in agricultural landscapes of the Rio de la Plata Grasslands region. Methods We analyzed the relationship between the supply of ES and the heterogeneity of 1121 micro-watersheds. We assessed the Ecosystem Service Supply Index (ESSI), the Hydrological Yield (HY), and the Absorbed Photosynthetically Active Radiation (APAR) in agricultural areas. We calculated the average grassland patch area, the structural and functional cropland diversity, the cropland percentage, and the grassland’s juxtaposition to assess landscape heterogeneity. Results Cropland functional diversity increased the supply of ES at the micro-watershed level. It positively affected the ESSI and APAR, and reduced the HY. In contrast, the juxtaposition of grasslands had opposite effects to those of cropland functional diversity, so the spatial segregation of grasslands favored the ES supply. Conclusions The functional cropland diversification and the segregation of natural grasslands improved the supply of ES and counteracted the negative effects of agricultural expansion. These findings contribute to designing multifunctional landscapes and suggest that cropland functional diversity and grassland configuration should be considered in food production systems aimed to preserve ES supply.
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