An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

Maarten J Van Strien,Cornelis T J Slager,Adrienne Grêt‐Regamey,Bauke De Vries

doi:10.1002/ece3.2145

Maarten J Van Strien, Cornelis T J Slager + Show 2 more

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https://doi.org/10.1002/ece3.2145

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Abstract

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.

Highlights

Ecology and Evolution published by John Wiley & Sons Ltd
We found that Landscape Generator (LG) successfully recreated the pattern of the real landscape with regard to most, but not all, landscape metrics (Fig. 4; Table 3)
Whereas traditional neutral landscape models implement a relatively small set of landscape-level pattern metrics, LG implements a more complete set of class- and patch-level pattern metrics, which gives LG users more control over the final landscape pattern and allows them to generate a wider variety of patterns

Summary

Introduction

The spatial arrangement and composition of anthropogenic land uses and natural habitats can have a significant impact on a range of ecological processes (Turner 1989), such as plant dispersal (e.g., Higgins et al 2003), metapopulation viability (e.g., With et al 2006), population abundance (e.g., Flather and Bevers 2002), species richness (e.g., Steiner and Ko€hler 2003), habitat connectivity (e.g., Mimet et al 2013), or genetic differentiation between populations (e.g., Bruggeman et al 2010; Van Strien et al 2015). Instead of using real landscapes, many of these studies have simulated these ecological processes in computer-generated landscapes (Gardner and Urban 2007; Wang and Malanson 2008; but see, for instance, Mimet et al 2013). Compared to using real landscapes, computer-generated landscapes are less restricted in the range of possible landscape patterns and capable of capturing only those aspects of landscape patterns that are relevant to the study objectives (Wang and Malanson 2008).

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