Using GIS-linked Bayesian Belief Networks as a tool for modelling urban biodiversity.
Journal article
Authors | 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., Norton, Briony, A. and Darren R., Grafius |
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Abstract | 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. |
Keywords | BBN; Bird; Invertebrate; Fragmentation; Model; Species richness |
Year | 2019 |
Journal | Landscape and Urban Planning |
Journal citation | 189, pp. 382-395 |
Publisher | Elsevier |
ISSN | 0169-2046 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.landurbplan.2019.05.012 |
Web address (URL) | http://hdl.handle.net/10545/623822 |
http://creativecommons.org/licenses/by/3.0/us/ | |
hdl:10545/623822 | |
Publication dates | 30 May 2019 |
Publication process dates | |
Deposited | 10 Jun 2019, 08:59 |
Accepted | 20 May 2019 |
Rights | © 2019 The Authors. Published by Elsevier B.V. |
Attribution 3.0 United States | |
Contributors | University of Sheffield, Cranfield University, British Trust for Ornithology, Centre for Ecology and Hydrology and University of Exeter |
File | File Access Level Open |
File | File Access Level Open |
File | File Access Level Open |
File | File Access Level Open |
https://repository.derby.ac.uk/item/94q94/using-gis-linked-bayesian-belief-networks-as-a-tool-for-modelling-urban-biodiversity
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