The r toolbox grainscape for modelling and visualizing landscape connectivity using spatially explicit networks

Abstract

Networks are widely used for modelling landscape connectivity and have many ecological and conservation applications. The nodes in these models describe geographic locations (such as habitat patches or protected areas) and links describe the potential for organisms (or their propagules) to move among nodes. We present the r package grainscape which facilitates working with these networks within a spatially explicit framework. Package analyses are based on the minimum planar graph, a class of network where links among nodes are influenced by the spatial characteristics of features across the entire landscape. Modelling outputs are compatible with downstream packages including igraph for network analysis and ggplot2 for visualization. Tools for analysis (e.g. finding corridors) and scaling networks (e.g. grains of connectivity) are also provided. Models can be exported for visualization and analysis in Geographic Information System (GIS) or network software. This package provides a computationally‐efficient programmatic toolbox for many landscape connectivity research questions, enabling researchers to easily customize models, work at large geographic extents, generate their own network metrics, conduct sensitivity analyses and seamlessly employ r statistical functions to test models using biological data. A detailed guide, provided as an Appendix, illustrates common analysis and model variants with accompanying r code.

Predicted alteration of vertebrate communities in response to climate‐induced elevational shifts

“…All analyses and spatial layer manipulations were implemented in R (R Core Team, 2021) using the following packages: raster (Hijmans et al, 2015), rgdal (Bivand et al, 2021), grainscape (Chubaty et al, 2020), gdistance (van Etten, 2017), vegan (Oksanen et al, 2020), dgof (Arnold & Emerson, 2011), cooccur (Griffith et al, 2014) and betapart (Baselga et al, 2021).…”

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

“…For instance, in the extreme case when → −∞ and → +∞ , the link removal function becomes deterministic (i.e. P(D ij ) → 1 ), and with the rewiring option in the model (described below), the model can then generate networks that approximate planar networks (McDiarmid et al 2005), which have previously been used to model landscape functional connectivity (Chubaty et al 2020).…”

Open-source Tools in R for Landscape Ecology

“…In graph theory [124], landscapes are described by nodes (i.e., habitat patches) connected by and functional connections called links (or edges) [124]. The grainscape package [125] provides a tool to model connectivity based on spatially explicit networks. More generally, the igraph package [126] provides functionality for graph theoretic analyses.…”