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

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.

NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R

“…It can be applied in all landscape analyses in which one wants to test the influence of NLMs on ecological dynamics. Although existing tools are capable of simulating some of the NLMs contained in NLMR (Gardner, ; Gardner & Urban, ; Saura & Martínez‐Millán, ; van Strien et al., ; Etherington et al., ), none of them combine as many different types and none are as well integrated in a native geospatial workflow. Hence, the majority of the limitations that previous NLM software exhibit, such as developing own methods for spatial operations like masking and extracting, are overcome.…”

“…NLMR is the first software to allow the simulation of NLMs within the self-contained, reproducible framework of R. It can be applied in all landscape analyses in which one wants to test the influence of NLMs on ecological dynamics. Although existing tools are capable of simulating some of the NLMs contained in NLMR (Gardner, 1999;Gardner & Urban, 2007;Saura & Martínez-Millán, 2000;van Strien et al, 2016;Etherington et al, 2015), none of them combine as many different types and none are as well integrated in a native geospatial workflow. Hence, the majority of the limitations that previous We believe that being capable of simulating NLMs natively in R is highly beneficial for the field of landscape ecology.…”

“…NLMs are now used in a variety of ways to examine landscape-scale observations and metrics of ecological patterns and processes (With & King, 1997;Turner, Gardner, & O'Neill, 2001). With and King (1997) outlined three uses for NLMs which were further highlighted by Turner and Gardner (2015): (a) coupling ecological models with NLMs to predict changes in ecological processes, (b) analyse the extent of structural deviation between real and neutral landscapes, and (c) development and testing of novel landscape metrics.…”

“…The large number of applications and their increased uptake has led to the development of a variety of software designed to generate NLMs, using a range of algorithms. Options include standalone software such as RULE (Gardner, 1999), its successor QRULE (Gardner & Urban, 2007) and SIMMAP (Saura & Martínez-Millán, 2000), as well as LG (van Strien, Slager, de Vries, & Grêt-Regamey, 2016). However, arguably the most useful pieces of software are those that offer a collection of different NLM algorithms based in an open-source programming language which allow for reproducible and consistent workflow.…”

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

“…At present, more and more attentions are paid to the changing trend, vulnerability, sensitivity, and heterogeneity analysis methods of landscape pattern, such as landscape pattern index, the particle size effect, and spatial statistics algorithm [2,[5][6][7]. The wildly accepted ecological security assessment system include the "ecological footprint" concept and model, the concept of pressure-state-response (PSR) framework model, and the concept of natureeconomy-society framework model [8,9].…”