91. Neutral landscape models (NLMs) simulate landscape patterns based 10 on theoretical distributions and can be used to systematically study 11 the effect of landscape structure on ecological processes. NLMs are 12 commonly used in landscape ecology to enhance the findings of field 13 studies as well as in simulation studies to provide an underlying land-14 scape. However, their creation so far has been limited to software 15 that is platform dependent, does not allow a reproducible workflow or 16 is not embedded in R, the prevailing programming language used by 17 ecologists. 18 2. Here, we present two complementary R packages NLMR and land-19 scapetools, that allow users to generate, manipulate and analyse NLMs 20 in a single environment. They grant the simulation of the widest col-21 lection of NLMs found in any single piece of software thus far while 22 allowing for easy manipulation in a self-contained and reproducible 23 workflow. The combination of both packages should stimulate a wider 24 usage of NLMs in landscape ecology. NLMR is a comprehensive col-25 lection of algorithms with which to simulate NLMs. landscapetools 26 provides a utility toolbox which facilitates an easy workflow with sim-27 ulated neutral landscapes and other raster data.283. We show two example applications that illustrate potential use cases 29 for NLMR and landscapetools: First, an agent-based simulation study 30 in which the effect of spatial structure on disease persistence was stud-31 ied. Here, spatial heterogeneity resulted in more variable disease out-32 comes compared to the common well-mixed host assumption. The 33 second example shows how increases in spatial scaling can introduce 34 35 4. Simplifying the workflow around handling NLMs should encourage an 36 uptake in the usage of NLMs. NLMR and landscapetools are both 37 generic frameworks that can be used in a variety of applications and 38 are a further step to having a unified simulation environment in R for 39 answering spatial research questions.40 Keywords: artificial pattern, landscape generator, neutral landscape 41 model, R, spatial visualisation, virtual landscape 42 48 tions and metrics of ecological patterns and processes at landscape scales (With 49
Aim It is widely accepted that biodiversity is influenced by both niche‐related and spatial processes from local to global scales. Their relative importance, however, is still disputed, and empirical tests are surprisingly scarce at the global scale. Here, we compare the importance of area (as a proxy for pure spatial processes) and environmental heterogeneity (as a proxy for niche‐related processes) for predicting native mammal species richness world‐wide and within biogeographical regions. Location Global. Time period We analyse a spatial snapshot of richness data collated by the International Union for Conservation of Nature. Major taxa studied All terrestrial mammal species, including possibly extinct species and species with uncertain presence. Methods We applied a spreading dye algorithm to analyse how native mammal species richness changes with area and environmental heterogeneity. As measures for environmental heterogeneity, we used elevation ranges and precipitation ranges, which are well‐known correlates of species richness. Results We found that environmental heterogeneity explained species richness relationships better than did area, suggesting that niche‐related processes are more prevalent than pure area effects at broad scales. Main conclusions Our results imply that niche‐related processes are essential to understand broad‐scale species–area relationships and that habitat diversity is more important than area alone for the protection of global biodiversity.
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.
Scaling is ubiquitous and persistent in ecology. Following the acclaimed concept of pattern and scale (Levin, 1992), patterns and processes at a certain spatial or temporal scale or organizational level emerge from patterns and processes at finer scales or levels and these, in turn, are influenced by the large-scale patterns (Figure 1, Lischke, Löffler, Thornton, & Zimmermann, 2007). Due to this, scaling, that is, changing from one scale to another, is not always straightforward, and sometimes can cause problems due to scale breaks, nonlinearities, feedbacks and heterogeneity in such patternprocess relationships (Snell et al., 2014). Additionally, scaling is sometimes not explicit, and confusion in terminology adds to scaling-related problems. Here, we (1) address scaling terminology; (2) define three categories of scaling approaches, (a) pre-model scaling, (b) in-model scaling and (c) post-model scaling; and (3) explore examples, problems, and, where available, potential solutions in each category. We also elaborate our main claim that modelling is often confronted with scaling challenges, because modelling-directly or indirectly-always implies scaling.
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