Modelling non-Euclidean movement and landscape connectivity in highly structured ecological networks

Chris Sutherland*, Angela K. Fuller, J. Andrew Royle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)


1. Movement is influenced by landscape structure, configuration and geometry, but measuring distance as perceived by animals poses technical and logistical challenges. Instead, movement is typically measured using Euclidean distance, irrespective of location or landscape structure, or is based on arbitrary cost surfaces. A recently proposed extension of spatial capture‐recapture (SCR) models resolves this issue using spatial encounter histories of individuals to calculate least‐cost paths (ecological distance: Ecology, 94, 2013, 287) thereby relaxing the Euclidean assumption. We evaluate the consequences of not accounting for movement heterogeneity when estimating abundance in highly structured landscapes, and demonstrate the value of this approach for estimating biologically realistic space‐use patterns and landscape connectivity.

2. We simulated SCR data in a riparian habitat network, using the ecological distance model under a range of scenarios where space‐use in and around the landscape was increasingly associated with water (i.e. increasingly less Euclidean). To assess the influence of miscalculating distance on estimates of population size, we compared the results from the ecological and Euclidean distance based models. We then demonstrate that the ecological distance model can be used to estimate home range geometry when space use is not symmetrical. Finally, we provide a method for calculating landscape connectivity based on modelled species‐landscape interactions generated from capture‐recapture data.

3. Using ecological distance always produced unbiased estimates of abundance. Explicitly modelling the strength of the species‐landscape interaction provided a direct measure of landscape connectivity and better characterised true home range geometry. Abundance under the Euclidean distance model was increasingly (negatively) biased as space use was more strongly associated with water and, because home ranges are assumed to be symmetrical, produced poor characterisations of home range geometry and no information about landscape connectivity.

4. The ecological distance SCR model uses spatially indexed capture‐recapture data to estimate how activity patterns are influenced by landscape structure. As well as reducing bias in estimates of abundance, this approach provides biologically realistic representations of home range geometry, and direct information about species‐landscape interactions. The incorporation of both structural (landscape) and functional (movement) components of connectivity provides a direct measure of species‐specific landscape connectivity.
Original languageEnglish
Pages (from-to)169-177
Number of pages9
JournalMethods in Ecology and Evolution
Issue number2
Early online date30 Dec 2014
Publication statusPublished - Feb 2015


  • Abundance
  • Animal movement
  • Dendritic ecological network
  • Density
  • Ecological distance
  • Functional connectivity
  • Habitat network
  • Stream distance
  • Structural connectivity


Dive into the research topics of 'Modelling non-Euclidean movement and landscape connectivity in highly structured ecological networks'. Together they form a unique fingerprint.

Cite this