An overview of computational tools for preparing, constructing and using resistance surfaces in connectivity research

Abstract

Context Connectivity between habitat patches is a recognized conservation action to conserve biodiversity in a rapidly changing world. Resistance surfaces, a spatial representation of cost of movement across the landscape, are often the foundation for connectivity analyses but working with them can be daunting due to the diversity and complexity of software tools. Objectives We present an overview of the steps involved when working with resistance surfaces, identify tools that perform specific tasks, evaluate user experience with the tools, identify needs of the user community, and present some recommendations for users and developers. Methods We identified tools applicable at each of the three steps (i) preparing data, (ii) constructing and optimizing surfaces, and (iii) using resistance surfaces. We conducted an online survey of the connectivity user community to assess the popularity and experience with tools on five criteria and identified characteristics important in the selection of connectivity tools. Results We reviewed a total of 43 tools, of which 10 are useful for data preparation, 27 allow construction, and 30 tools that use resistance surfaces. A total of 148 survey participants working in 40 countries were familiar with 37 tools. Tools are ranked heterogeneously for the five criteria. Crucial avenues for future development of connectivity tools identified by respondents are incorporation of uncertainties, dynamic connectivity modelling, and automated parameter optimization. Conclusions Since resistance surfaces are used for a variety of applications, it is important that users are aware about the appropriate tools. We anticipate that future tools for connectivity research will incorporate more complex and biologically more realistic analytical approaches.

Connecting tiger (Panthera tigris) populations in Nepal: Identification of corridors among tiger‐bearing protected areas

“…One of the primary steps in corridor modeling is to develop a travel‐cost resistance surface based on the key environmental variables which influence travel effort, thereby quantifying the expected difficulty the target species encountered in traversing the landscape (Zeller et al, 2012). There are various approaches available to optimize resistance surfaces, including but not limited to expert opinion (Dutta et al, 2016; Rathore et al, 2012), habitat suitability (Balbuena‐Serrano et al, 2022; González‐Saucedo et al, 2021), movement patterns (Carvalho et al, 2016; Proctor et al, 2015), genetic data (Dutta et al, 2022; Jennings et al, 2020), or combinations of these methods (Zeller et al, 2018; Ziółkowska et al, 2016). A resistance surface is generally developed by using empirical data on species distribution or movement in combination with key environmental and anthropogenic variables that may influence the movement of the species (Hilty et al, 2019; Wade et al, 2015).…”

Unravelling the effects of individual heterogeneity on connectivity estimates: Insights from Spatial Capture-Recapture modelling

“…Population-level connectivity can be based on a map called a resistance surface that quantifies the cost of moving through each pixel of the landscape. This surface is estimated from spatial environmental variables and information on the cost of movement or space use derived from expert opinion or empirical data (Dutta et al, 2022; Zeller et al, 2012). In landscape management, resistance surfaces are used to map corridors, detect barriers or predict population range shift due to climate change (Dutta et al, 2022).…”

“…This surface is estimated from spatial environmental variables and information on the cost of movement or space use derived from expert opinion or empirical data (Dutta et al, 2022; Zeller et al, 2012). In landscape management, resistance surfaces are used to map corridors, detect barriers or predict population range shift due to climate change (Dutta et al, 2022). However, connectivity does not only have a spatial dimension, but also varies with time (Zeller et al, 2019), or with individual characteristics: e.g.…”

Improving ecological connectivity assessment : a step forward to consider connectivity concerns in landscape planning

“…Most connectivity models represent the effect of landscape structure on species' movements with a resistance surface (Spear et al 2010;Zeller et al 2012;Dutta et al 2022). A resistance surface is a raster map that assigns a resistance value to each pixel of the landscape matrix and represents the difficulty or energy cost that involves crossing each cell for the focal species.…”

“…Many methods based on resistance models are available to characterize the possibilities for species´ movement (Cushman et al 2009;Dutta et al 2022). Among the existing techniques, least-cost path is one of the most used approaches to evaluate the preferential movement pathways (Adriaensen et al 2003;Etherington 2016).…”