Using resistance distance from circuit theory to model dispersal through habitat corridors

Journal of Plant Ecology VOLUME 11, NUMBER 3, PAGES 385–393 June 2018 doi: 10.1093/jpe/rtx004 Advance Access publication 31 March 2017 available online at academic.oup.com/jpe Using resistance distance from circuit theory to model dispersal through habitat corridors Jan Thiele1,*, Sascha Buchholz2,3 and Jens Schirmel4 1 Institute of Landscape Ecology, University of Münster, Heisenbergstr. 2, Münster 48149, Germany; Department of Ecology, TU Berlin, Rothenburgstr. 12, Berlin 12165, Germany 3 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Altensteinstr. 34, Berlin 14195, Germany 4 Institute of Environmental Sciences, University Koblenz-Landau, Fortstr. 7, Landau 76829, Germany *Correspondence address: Institute of Landscape Ecology, University of Münster, Heisenbergstr. 2, Münster 48149, Germany. Tel: +49-251-8330113; Fax: +49-251-8338338; E-mail: 2 Aims Resistance distance (RD), based on circuit theory, is a promising metric for modelling effects of landscape configuration on dispersal of organisms and the resulting population and community patterns. The values of RD reflect the likelihood of a random walker to reach from a source to a certain destination in the landscape. Although it has successfully been used to model genetic structures of animal populations, where it most often outperforms other isolation metrics, there are hardly any applications to plants and, in particular, to plant community data. Our aims were to test if RD was a suitable metric for studying dispersal processes of plants in narrow habitat corridors (linear landscape elements [LLE]). This would be the case, if dispersal processes (seed dispersal and migration) resembled random walks. Further, we compared the model performance of RD against least-cost distance (LCD) and Euclidean distance (ED). Finally, we tested the suitability of different cost surfaces for calculations of LCD and RD. Methods We used data from 50 vegetation plots located on semi-natural LLE (field margins, ditches, road verges) in eight agricultural landscapes of Northwest Germany. We mapped LLE, including hedges and tree rows, from aerial images in a Geographic Information System, converted the maps into raster layers, and assigned resistance values to the raster cells, where all cells outside of LLE received infinite resistance and, thus, represented barriers to dispersal. For all pairs of plots within study areas, we calculated Jaccard similarity assuming that it INTRODUCTION For a dispersing organism or propagule, the likelihood of reaching a certain location generally decreases with distance from the source (Hanski and Gilpin 1997). Hence, the Euclidean distance (ED) between two points or patches is a was a proxy (or correlate) of dispersal events between plots. Further, we calculated RD and LCD of the network of LLE and ED between the plots. We modelled the effects of distance metrics on community similarity using binomial generalized linear mixed models. Important Findings ED was clearly the least suitable isolation metrics. Further, we found that RD performed better than LCD at modelling Jaccard similarity. Predictions varied markedly between the two distance metrics suggesting that RD comprises additional information about the landscape beyond spatial distance, such as the possible presence of multiple pathways between plots. Cost surfaces with equal celllevel resistances for all types of LLE performed better than more complex ones with habitat-specific resistances. We conclude that RD is a highly suitable measure of isolation or, inversely, connectivity for studying dispersal processes of plants within habitat corridors. It is likely also suitable for assessing landscape permeability in other landscape types with areal habitats instead of narrow corridors. RD holds the potential to improve assessments of isolation (or connectivity) for models of regional population and meta-community dynamics. Keywords: connectivity, floristic similarity, isolation, landscape, migration, least-cost distance Received: 19 May 2016, Revised: 4 January 2017, Accepted: 13 January 2017 classical measure of their isolation from each other that may serve well in case the intervening landscape is uniformly suitable for dispersal (isolation by distance, Jenkins et al. 2010). However, in many cases the landscape in between habitats is heterogeneous, consisting of different land-cover or ecosystem types, which may vary considerably in how much they © The Author(s) 2017. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China. All rights reserved. For permissions, please email: Abstract 386 landscape-resistance surfaces for certain species or species groups (Spear et al. 2010; Zeller et al. 2012), if environmental variation is controlled for in the analysis. Although hitherto only few studies directly compared the performance of RD with other isolation metrics (Kershenbaum et al. 2014), there is evidence that RD may substantially outperform ED and LCD at modelling genetic distance of populations (e.g., McRae and Beier 2007; Kershenbaum et al. 2014). However, there are also some contradictory results. For instance, a continental-scale study of the wolverine (Gulo gulo) in North America found markedly better prediction of genetic differentiation when using RD as compared to LCD (McRae and Beier 2007), whereas another study of the same species, conducted at regional scale, found LCD to perform better (Schwartz et al. 2009). Yet other studies found only minor differences in the performance of LCD and RD, e.g., studying reptiles and amphibians at regional and landscape scale ( Moore et al. 2011; Row et al. 2010). Thus, there seems to be a high potential in the application of RD, but the relative performance of different measures of isolation appears to vary among types of organisms and scales, which calls for further comparative empirical testing. Numerous studies applied RD to model genetic distance of animal populations, but only few studies have tested its applicability and relative performance at modelling plants. RD performed better at modelling genetic distance of populations of American mahogany (Swietenia macrophylla) and Pitcher’s thistle (Cirsium pitcheri) compared to LCD (Fant et al. 2014; McRae and Beier 2007). Further, RD correlated stronger with genetic distance in studies of prairie sunflower (Helianthus petiolaris) and canyon live oak (Quercus chrysolepis) compared to ED (Andrew et al. 2012; Ortego et al. 2015). These studies were conducted at different scales from supra-regional (Central America, American mahogany) to landscape scale (Great Sand Dunes in Colorado, prairie sunflower), respectively. Another supra-regional study, conducted in coastal marshes, found RD to outperform LCD and ED with salt marsh grass (Puccinellia maritima), but there was no substantial difference between RD and LCD with sea arrowgrass (T (...truncated)