Estimating landscape resistance to movement: a review

By K. Zeller, K. McGarigal, Andrew R Whiteley. Landscape Ecology, 2012

970 citations recorded by the source corpus.

Subfields: Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/181dd676a92c6e1db1e78dea76ac469208dc8118

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In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Circuit‐theory applications to connectivity science and conservation (2018, background): “Use of genetic data to inform models of landscape resistance to movement with circuit theory has since become standard practice (Zeller et al. 2012; Simpkins et al. 2018).”
Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus) (2013, neutral): “For most studies to date, the assignment of resistance values has been largely subjective, often relying on expert opinion (Beier et al. 2008; Spear et al. 2010; Zeller et al. 2012).”
Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, background): “, dispersal leading to successful reproduction), either because individuals respond to environmental heterogeneity during dispersal movements, or because the environment experienced during dispersal alters their survival (Zeller et al. 2012).”
Movement patterns of three arboreal primates in a Neotropical moist forest explained by LiDAR-estimated canopy structure (2016, background): “high temporal resolution) as in our case, SSFs provide a means of parsing out individual movement decisions relative to random locations in the environment (Milton 1981; Boyce et al. 2002; Asensio et al. 2011; Zeller et al. 2012; Squires et al. 2013).”
Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data (2018, background): “…derived from home- range movement in the form of steps, paths or movement- only points, or movement inferred from genetic data, are considered the closest proxies for estimating dispersal movements and may be more appropriate for estimating resistance (Cushman & Lewis, 2010; Zeller et al., 2012).”
Estimating utilization distributions from fitted step-selection functions (2017, background): “…movement resistance surfaces (Benz et al. 2016, Zeller et al. 2016) from telemetry data, rather than deriving such resistance surfaces from expert opinions or resource-selection ❖ www.esajournals.org 8 April 2017 ❖ Volume 8(4) ❖ Article e01771 functions as is commonly done (Zeller et al. 2012).”
Why Did the Bear Cross the Road? Comparing the Performance of Multiple Resistance Surfaces and Connectivity Modeling Methods (2014, background): “Increasing recognition of the importance of connectivity to population persistence, combined with the development of new algorithms, fast computers and user-friendly software [4], has led to a proliferation of research on population connectivity [5] and conservation actions applying this knowledge a”
Integrating dispersal proxies in ecological and environmental research in the freshwater realm (2017, background): “Environmental 302 variables used to produce cost surfaces typically include land use, human constructions and 303 topography (Zeller et al. 2012).”
Estimating effective landscape distances and movement corridors: Comparison of habitat and genetic data (2015, methodology): “Formally, landscape resistance represents an integration of multiple behavioral and physiological factors such as aversion, energy expenditure, or mortality risk when moving through a particular environment (Zeller et al. 2012).”
Looking beyond the mountain: Dispersal barriers in a changing world (2016, background): “But predicting how species respond to dispersal barriers – based on species’ distributions – is complicated because doing so requires extensive mapping, correctly interpreting what “suitable habitat” or movement costs are (Zeller et al. 2012), and knowing about dispersal distances and dynamics.”
Amphibian decline, pond loss and reduced population connectivity under agricultural intensification over a 38 year period (2017, background): “As a result, the taxa chosen and the study area are suitable to illustrate how landscape change can affect habitat occupancy, connectivity and metapopulation structure through its impacts on dispersal.”
Evaluating population connectivity for species of conservation concern in the American Great Plains (2013, neutral): “Most of the published studies using landscape resistance maps have relied on expert opinion to estimate resistance of landscape features to movement (Zeller et al. 2012) given Biodivers Conserv (2013) 22:2583–2605 2599”
Fencing solves human‐wildlife conflict locally but shifts problems elsewhere: A case study using functional connectivity modelling of the African elephant (2018, result): “This is similar to other studies that have used the inverse of habitat suitabilities to reflect resistances, except that our resistances are based on actual movement data (i.e., step- selection), rather than on presence data (i.e., point-selection; Zeller et al., 2012).”