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

Abstract

Neutral landscape models (NLMs) simulate landscape patterns based on theoretical distributions and can be used to systematically study the effect of landscape structure on ecological processes. NLMs are commonly used in landscape ecology to enhance the findings of field studies as well as in simulation studies to provide an underlying landscape. However, their creation so far has been limited to software that is platform dependent, does not allow a reproducible workflow or is not embedded in R, the prevailing programming language used by ecologists. Here, we present two complementary R packages NLMR and landscapetools, that allow users to generate and manipulate NLMs in a single environment. They grant the simulation of the widest collection of NLMs found in any single piece of software thus far while allowing for easy manipulation in a self‐contained and reproducible workflow. The combination of both packages should stimulate a wider usage of NLMs in ecology. NLMR is a comprehensive collection of algorithms with which to simulate NLMs. landscapetools provides a utility toolbox which facilitates an easy workflow with simulated neutral landscapes and other raster data. We show two example applications that illustrate potential use cases for NLMR and landscapetools: First, an agent‐based simulation study in which the effect of spatial structure on disease persistence was studied. The second example shows how increases in spatial scaling can introduce biases in calculated landscape metrics. Simplifying the workflow around generating and handling NLMs should encourage an uptake in the usage of NLMs. NLMR and landscapetools are both generic frameworks that can be used in a variety of applications and are a further step to having a unified simulation environment in R for answering spatial research questions.

Comparative landscape genetics reveals the evolution of viviparity reduces genetic connectivity in fire salamanders

“…Among other applications, NLMs can be used as null models to evaluate the effects of real landscapes on ecological processes (Gardner & Urban, 2007). We used the r package nlmr v0.4 (Sciaini, Fritsch, & Scherer, 2018) to generate four NLM layers: (a) random; (b) random cluster; (c) distance gradient; and (d) fractional brownian motion ( Figure S6). Default options were always used, with the exception of the random cluster model, in which five discrete land use types were processed to simulate land cover heterogeneity.…”

Motif: an open-source R tool for pattern-based spatial analysis

“…R proved to be a main or one of the most important programs in many fields of science that rely on spatial data and spatial data patterns. It includes different subfields of ecology (Sciaini et al 2018;Lai et al 2019), spatial statistics, and GIScience (Lovelace et al 2019). A vast number of existing R packages allow using the motif package not only as an individual tool but also as a part of many possible workflows.…”

Motif: an open-source R tool for pattern-based spatial analysis

“…R proved to be a main or one of the most important programs in many fields of science that rely on spatial data and spatial data patterns. It includes different subfields of ecology (Sciaini et al 2018;Lai et al 2019), spatial statistics, and GIScience (Lovelace, Nowosad, and Muenchow 2019). A vast number of existing R packages allow using the motif package not only as an individual tool but also as a part of many possible workflows.…”