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Estimating food production in an urban landscapeGrafius, Darren R.; Edmondson, Jill L.; Norton, Briony A.; Clark, Rachel; Mears, Meghann; Leake, Jonathan R.; Corstanje, Ron; Harris, Jim A.; Warren, Philip H.; University of Sheffield; et al. (Springer Science and Business Media LLC, 2020-03-20)There is increasing interest in urban food production for reasons of food security, environmental sustainability, social and health benefits. In developed nations urban food growing is largely informal and localised, in gardens, allotments and public spaces, but we know little about the magnitude of this production. Here we couple own-grown crop yield data with garden and allotment areal surveys and urban fruit tree occurrence to provide one of the first estimates for current and potential food production in a UK urban setting. Current production is estimated to be sufficient to supply the urban population with fruit and vegetables for about 30 days per year, while the most optimistic model results suggest that existing land cultivated for food could supply over half of the annual demand. Our findings provide a baseline for current production whilst highlighting the potential for change under the scaling up of cultivation on existing land.
Using GIS-linked Bayesian Belief Networks as a tool for modelling urban biodiversity.Corstanje, Ron; Warren, Philip H.; Evans, Karl L.; Siriwardena, Gavin M.; Pescott, Oliver L.; Plummer, Kate E.; Mears, Meghann; Zawadzka, Joanna; Richards, J. Paul; Harris, Jim A.; et al. (Elsevier, 2019-05-30)The ability to predict spatial variation in biodiversity is a long-standing but elusive objective of landscape ecology. It depends on a detailed understanding of relationships between landscape and patch structure and taxonomic richness, and accurate spatial modelling. Complex heterogeneous environments such as cities pose particular challenges, as well as heightened relevance, given the increasing rate of urbanisation globally. Here we use a GIS-linked Bayesian Belief Network approach to test whether landscape and patch structural characteristics (including vegetation height, green-space patch size and their connectivity) drive measured taxonomic richness of numerous invertebrate, plant, and avian groups. We find that modelled richness is typically higher in larger and better-connected green-spaces with taller vegetation, indicative of more complex vegetation structure and consistent with the principle of ‘bigger, better, and more joined up’. Assessing the relative importance of these variables indicates that vegetation height is the most influential in determining richness for a majority of taxa. There is variation, however, between taxonomic groups in the relationships between richness and landscape structural characteristics, and the sensitivity of these relationships to particular predictors. Consequently, despite some broad commonalities, there will be trade-offs between different taxonomic groups when designing urban landscapes to maximise biodiversity. This research demonstrates the feasibility of using a GIS-coupled Bayesian Belief Network approach to model biodiversity at fine spatial scales in complex landscapes where current data and appropriate modelling approaches are lacking, and our findings have important implications for ecologists, conservationists and planners.