Using GIS-linked Bayesian Belief Networks as a tool for modelling urban biodiversity
Author(s): Grafius, D.R., Corstanje, R., Warren, P.H., Evans, K.L., Norton, B.A., Siriwardena, G.M., Pescott, O.L., Plummer, K.E., Mears, M., Zawadzka, J., Richards, J.P. & Harris, J.A.
Published: 3 May 2019 Pages: 14pp
Journal: Landscape and Urban Planning Volume: 189
Digital Identifier No. (DOI): 10.1016/j.landurbplan.2019.05.012
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.