<scp>NLM</scp>py: a <scp>python</scp> software package for the creation of neutral landscape models within a general numerical framework

Etherington, Thomas R.; Holland, E. Penelope; O’Sullivan, David

doi:10.1111/2041-210x.12308

Cited by 62 publications

(49 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…The function util_merge merges a weighted combination of multiple rasters allowing for even more sophisticated landscape patterns. The merging of NLMs, such as planar gradients with less autocorrelated landscapes, provides an established method for deriving ecotones (Etherington et al., ; Travis & Dytham, ). util_rescale is used internally in all algorithms implemented in NLMR, but is a public function in landscapetools to linearly rescale raster cell data into a range between zero and one.…”

Section: Functionalitymentioning

confidence: 99%

“…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. Packages developed for common programming languages, such as NLMpy (Etherington, Holland, & O’Sullivan, ) for PYTHON, fulfill these criteria. However, currently there is no NLM software package available for R, the programming language most widely used by ecologists (R Core Team, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Functionalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Ecosystem distributions were simulated with the mid-point displacement algorithm using the NLMpy Python package (Etherington et al, 2015). linear, clustered and dispersed).…”

Section: Ecosystem Distributionsmentioning

confidence: 99%

The use of range size to assess risks to biodiversity from stochastic threats

Murray

Keith

Bland

et al. 2017

Diversity and Distributions

View full text Add to dashboard Cite

Aim Stochastic threats such as disease outbreak, pollution events, fire, tsunami and drought can cause rapid species extinction and ecosystem collapse. The ability of a species or ecosystem to persist after a stochastic threat is strongly related to the extent and spatial pattern of its geographical distribution. Consequently, protocols for assessing risks to biodiversity typically include geographic range size criteria for assessing risks from stochastic threats. However, owing in part to the rarity of such events in nature, the metrics for assessing risk categories have never been tested. In this study, we investigate the performance of alternative range size metrics, including the two most widely used, extent of occurrence (EOO) and area of occupancy (AOO), as predictors of ecosystem collapse in landscapes subject to stochastic threats. Methods We developed a spatially explicit stochastic simulation model to investigate the impacts of four threat types on a dataset of 1350 simulated geographic distributions of varying pattern and size. We empirically estimated collapse probability in response to each threat type and evaluated the ability of a set of spatial predictors to predict risk. ResultsThe probability of ecosystem collapse increased rapidly as range size declined. While AOO and EOO were the most important predictors of collapse risk for the three spatially explicit threats included in our model (circle, swipe and cluster), core area, patch density and mean patch size were better predictors for edge effect threats.Main conclusions Our study is the first to quantitatively assess the range size metrics employed in biodiversity risk assessment protocols. We show that the current methods for measuring range size are the best spatial metrics for estimating risks from stochastic threats. Our simulation framework delivers an objective assessment of the performance of hitherto untested but widely used measures of geographic range size for risk assessment.

show abstract

“…Therefore, to enable a two dimensional array of any size to be created, we create an array that is larger than the distribution area extent, but is the minimum-sized square that will cover the desired extent, from which a slice of the required dimensions is then extracted. The two kinds of null neutral models are created by a PYTHON package of NLMpy [41]. The fragmentation degree parameter for the null neutral model algorithms is the same as parameter p in Equation (1).…”

mentioning

confidence: 99%

Modified Neutral Models as Benchmarks to Evaluate the Dynamics of Land System (DLS) Model Performance

Xiu

Liu

Yang

2017

IJGI

View full text Add to dashboard Cite

Abstract:Assessing model performance is a continuous challenge for modelers of land use change.Comparing land use models with two neutral models, including the random constraint match model (RCM) and growing cluster model (GrC) that consider the initial land use patterns using a variety of evaluation metrics, provides a new way to evaluate the accuracy of land use models. However, using only two neutral models is not robust enough for reference maps. A modified neutral model that combines a density-based point pattern analysis and a null neutral model algorithm is introduced. In this case, the modified neutral model generates twenty different spatial pattern results using a random algorithm and mid-point displacement algorithm, respectively. The random algorithm-based modified neutral model (Random_MNM) results decrease regularly with the fragmentation degree from 0 to 1, while the mid-point displacement algorithm-based modified neutral model (

show abstract

NLMpy: a python software package for the creation of neutral landscape models within a general numerical framework

Cited by 62 publications

References 20 publications

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

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

The use of range size to assess risks to biodiversity from stochastic threats

Modified Neutral Models as Benchmarks to Evaluate the Dynamics of Land System (DLS) Model Performance

Contact Info

Product

Resources

About