Estimating effective landscape distances and movement corridors: comparison of habitat and genetic data

Abstract

Abstract. Resistance models provide a key foundation for landscape connectivity analyses and are widely used to delineate wildlife corridors. Currently, there is no general consensus regarding the most effective empirical methods to parameterize resistance models, but habitat data (species' presence data and related habitat suitability models) and genetic data are the most widely used and advocated approaches. However, the practical consequences of applying one or the other approach have not been well studied. To address this knowledge gap, we performed a comparative study on the implications of using habitat suitability versus genetic data for determining effective landscape distances (a proxy inversely related to isolation among patches) based on least-cost and circuit-theoretic approaches, and for identifying potential movement corridors. For our comparison, we used data for the Cantabrian brown bear in Spain, an endangered population for which connectivity has been identified as a major conservation concern. Our results show that for brown bears, habitat models tend to overestimate resistance to movement through non-optimal areas, whereas genetic data yield higher estimates of effective distances within habitat areas. Therefore, our results suggest that (1) dispersal might be generally less constrained by landscape conditions than habitat utilization in home ranges, and that (2) dispersing animals might be more flexible in their movement behavior than residents are in their habitat resource utilization behavior, with records for residents dominating species occurrence data and subsequent habitat models. The assessed approaches provided dissimilar connectivity models with notable differences in patterns of predicted corridors across the study area, mainly due to differences in predicted connections between subpopulations. Our results highlight that the functional differences in habitat vs. genetic data, as well as the assumptions and potential limitations of different analytical approaches that use these data, need to be considered more carefully in connectivity modeling and subsequent corridor delineation.

Assessing differences in connectivity based on habitat versus movement models for brown bears in the Carpathians

“…More importantly though, our results showed that the differences between multiscale habitat model and movement models differed across the study area, i.e., within optimal and sub-optimal areas. Congruent with Mateo Sánchez et al (2015a), who analyzed movement corridors identified based on a multiscale habitat model against genetic data, we found that in areas with low habitat suitability, multiscale habitat model greatly overestimated resistance compared to models based on movement data, especially if only active steps were considered. In such sub-optimal areas, multiscale habitat model resulted in corridors with much higher effective distances and absolute resistances than movement models (Wasserman et al 2010;Mateo Sánchez et al 2015a).…”

“…Congruent with Mateo Sánchez et al (2015a), who analyzed movement corridors identified based on a multiscale habitat model against genetic data, we found that in areas with low habitat suitability, multiscale habitat model greatly overestimated resistance compared to models based on movement data, especially if only active steps were considered. In such sub-optimal areas, multiscale habitat model resulted in corridors with much higher effective distances and absolute resistances than movement models (Wasserman et al 2010;Mateo Sánchez et al 2015a). On the other hand, in core habitat areas, movement models predicted comparable (in case of movement models with active steps) or greater (in case of movement models with all steps) resistance than the multiscale habitat model, similar to findings for movement models based on genetic data (Mateo Sánchez et al 2015a).…”

“…4 and 5), sub-optimal areas (e.g., connections no. 2 and 3) selection and habitat selection at different scales (Roever et al 2014;Mateo Sánchez et al 2015a). The magnitude and spatial patterns of these differences depended on models' parameterization, i.e., the type of movement steps (active versus all), and the method of measuring environmental covariates (averaged along steps versus measured at endpoints of steps) used in the movement models, as well as on the scale of predictor variables used in the habitat models.…”

“…In connectivity assessments, the landscape permeability is typically represented via a so-called resistance surface reflecting the local cost of movement for a given species through a particular environment due to behavioral and physiological factors such as aversion, energy expenditure, or mortality risk (Zeller et al 2012). However, how resistance surfaces are defined can greatly influence the delineation of movement corridors, including their length and locations (Rayfield et al 2011;Ziółkowska et al 2014;Mateo Sánchez et al 2015a). Defining ecologically meaningful resistance surfaces is thus critical (Trainor et al 2013).…”

“…This assumption remains largely untested though, and it is unlikely that the same environmental factors determine both animal movements and habitat selection, and at the same scales (Roever et al 2014). This suggests that different input data, and potentially different modeling frameworks, should be used when modeling general habitat selection versus movement (Naves et al 2003;Moe et al 2007;Fernández et al 2012;Mateo Sánchez et al 2015a). Understanding how connectivity assessments are affected by relying on habitat models to define resistance surfaces is thus important for efficient corridor planning (Trainor et al 2013;Elliot et al 2014;Roever et al 2014;Mateo Sánchez et al 2015a, b).…”