Browsing by Author "Plummer, Kate E."

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    A bird's eye view: using circuit theory to study urban landscape connectivity for birds
    (Springer, 2017-06-28) Grafius, Darren Ronald; Corstanje, Ronald; Siriwardena, Gavin M.; Plummer, Kate E.; Harris, Jim A.
    Context Connectivity is fundamental to understanding how landscape form influences ecological function. However, uncertainties persist due to the difficulty and expense of gathering empirical data to drive or to validate connectivity models, especially in urban areas, where relationships are multifaceted and the habitat matrix cannot be considered to be binary. Objectives This research used circuit theory to model urban bird flows (i.e. ‘current’), and compared results to observed abundance. The aims were to explore the ability of this approach to predict wildlife flows and to test relationships between modelled connectivity and variation in abundance. Methods Circuitscape was used to model functional connectivity in Bedford, Luton/Dunstable, and Milton Keynes, UK, for great tits (Parus major) and blue tits (Cyanistes caeruleus), drawing parameters from published studies of woodland bird flows in urban environments. Model performance was then tested against observed abundance data. Results Modelled current showed a weak yet positive agreement with combined abundance for P. major and C. caeruleus. Weaker correlations were found for other woodland species, suggesting the approach may be expandable if re-parameterised. Conclusions Trees provide suitable habitat for urban woodland bird species, but their location in large, contiguous patches and corridors along barriers also facilitates connectivity networks throughout the urban matrix. Urban connectivity studies are well-served by the advantages of circuit theory approaches, and benefit from the empirical study of wildlife flows in these landscapes to parameterise this type of modelling more explicitly. Such results can prove informative and beneficial in designing urban green space and new developments.
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    Using GIS-linked Bayesian Belief Networks as a tool for modelling urban biodiversity
    (Elsevier, 2019-05-30) Grafius, Darren R.; Corstanje, Ronald; Warren, Philip H.; Evans, Karl L.; Norton, Briony A.; Siriwardena, Gavin M.; Pescott, Oliver L.; Plummer, Kate E.; Mears, Meghann; Zawadzka, Joanna; Richards, J. Paul; Harris, Jim A.
    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.