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.
Highlights
A central and long-standing objective of landscape ecology is the ability to predict spatial variation in biodiversity
Error rates for 25 m models ranged between 46% for bird species richness and 69% for invertebrate order richness (Table 3), whereas the 5 m models exhibited error rates between 45% for neophyte plant richness and 74% for bird richness
Models at the two different spatial resolutions performed with largely comparable error rates, but we focus here primarily on the 25 m scale, given its comparability with available datasets and relevant scales of inquiry to ecologists and planners seeking to understand landscapescale urban biodiversity
Summary
A central and long-standing objective of landscape ecology is the ability to predict spatial variation in biodiversity This requires accurate spatial biodiversity models at scales relevant to research and planning. Such tools support policies that aid conservation and optimise land-use patterns with minimal negative ecological impacts. Anderson et al, 2009) It is extremely rare, for local scale biodiversity data on species occurrence or abundance to be available systematically across an entire study region (Gillespie et al, 2017). For local scale biodiversity data on species occurrence or abundance to be available systematically across an entire study region (Gillespie et al, 2017) In such cases, there are generally three broad types of approach that can be used to model biodiversity across the region of interest. The first is modelling or extrapolating from species occurrence data, such as combining single species distribution models to predict spatial patterns of species richness across an area
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