Landscape Ecology

, Volume 32, Issue 1, pp 99–113 | Cite as

Characterising landscape connectivity for conservation planning using a dispersal guild approach

  • Alex Mark Lechner
  • Daniel Sprod
  • Oberon Carter
  • Edward C. Lefroy
Research Article

Abstract

Context

Land use changes have modified the extent and structure of native vegetation, resulting in fragmentation of native species habitat. Connectivity is increasingly seen as a requirement for effective conservation in these landscapes, but the question remains: ‘connectivity for which species?’.

Objective

The aim of this study was to develop and then apply a rapid, expert-based, dispersal guild approach where species are grouped on similar fine-scale dispersal behaviour (such as between scattered trees) and habitat characteristics.

Methods

Dispersal guilds were identified using clustering techniques to compare dispersal and habitat parameters elicited from experts. We modelled least-cost paths and corridors between patches and individual movement probabilities within these corridors for each of the dispersal guilds using Circuitscape. We demonstrate our approach with a case study in the Tasmanian Northern Midlands, Australia.

Results

The dispersal guild approach grouped the 12 species into five dispersal guilds. The connectivity modelling of those five guilds found that broadly dispersing species in this landscape, such as medium-sized carnivorous mammals, were unaffected by fragmentation while from the perspective of the three dispersal guilds made up of smaller mammals, the landscape appeared highly fragmented.

Conclusions

Our approach yields biologically defensible outputs that are broadly applicable, particularly for conservation planning where data and resources are limited. It is a useful first step in multi-species conservation planning which aims to identify those species most in need of conservation efforts.

Keywords

Conservation biology Conservation planning Connectivity Dispersal Guilds Least-cost paths Landscape planning Corridor Mammals Cluster analysis Multi-species model 

Supplementary material

10980_2016_431_MOESM1_ESM.pdf (1.9 mb)
Supplementary material 1 (PDF 1953 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Alex Mark Lechner
    • 1
    • 2
  • Daniel Sprod
    • 3
  • Oberon Carter
    • 4
  • Edward C. Lefroy
    • 1
  1. 1.Centre for EnvironmentUniversity of TasmaniaHobartAustralia
  2. 2.Regional Water and Land Resources, Sustainable Minerals InstituteThe University of QueenslandBrisbaneAustralia
  3. 3.Tasmanian Land ConservancyLower Sandy BayAustralia
  4. 4.Natural and Cultural Heritage DivisionDepartment of Primary Industries, Parks, Water and EnvironmentHobartAustralia

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