Landscape Ecology

, Volume 25, Issue 10, pp 1479–1487

Detecting human-driven deviations from trajectories in landscape composition and configuration

Research Article

DOI: 10.1007/s10980-010-9523-9

Cite this article as:
Proulx, R. & Fahrig, L. Landscape Ecol (2010) 25: 1479. doi:10.1007/s10980-010-9523-9


While landscape trajectories are increasingly used for tracking change in processes such as agricultural intensification and urbanization, analyses that combine environmental and human disturbances remain scarce. The aim of this study was to investigate the relationship between Shannon evenness, a measure of landscape composition, and spatial contagion, a measure of landscape configuration, within sixteen Canadian regions covering a gradient of land-uses and human disturbances: natural, semi-natural, urban, and agricultural. The agricultural regions showed generally lower variation in contagion and evenness and overall lower contagion values (smaller patches), leading to steeper contagion-evenness slopes than in the other region categories. In addition, the sampled agricultural regions were much more similar to each other than were the sampled regions within each of the other three region categories. These results indicate that the process of agricultural development (at least in western Canada) leads to a reduction in pattern variation and an alteration of the expected relationships among pattern metrics in agricultural regions. This possibility is supported by a neutral model of patch dynamics, suggesting that the characteristic scale of disturbances is a generic structuring process of landscape trajectories.


Landscape composition Landscape configuration Contagion Landscape development Disturbance Spatial scale Agriculture Urbanization Neutral model 

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Geomatics and Landscape Ecology LaboratoryCarleton UniversityOttawaCanada
  2. 2.Max Planck Institute for BiogeochemistryOrganismic BiogeochemistryJenaGermany

Personalised recommendations