Modelling Marten (Martes americana) Movement Costs in a Boreal Forest: Effects of Grain Size and Thematic Resolution
We investigated landscape resistance to movements of American marten (Martes americana) based on snow-tracking data. We generated movement cost maps of the study area with different grain size, thematic resolution, and habitat-specific resistance to movements. We compared simulated tracks obtained f...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2012-01-01
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Series: | International Journal of Ecology |
Online Access: | http://dx.doi.org/10.1155/2012/742913 |
Summary: | We investigated landscape resistance to movements of American marten (Martes americana) based on snow-tracking data. We generated movement cost maps of the study area with different grain size, thematic resolution, and habitat-specific resistance to movements. We compared simulated tracks obtained from resistance maps to real tracks plotted along transects that we surveyed in winters 2004 to 2008 at the Montmorency Forest, Quebec, Canada. Simulated tracks were located at the intersection between least-cost paths simulated across the study area and transects. We used nearest-neighbour distances between simulated and real tracks to assess the performance of resistance maps and estimate landscape resistance parameters. Simulations with specified costs to movement for open areas, young forest, and mature forest performed better than simpler resistance scenarios, suggesting that resistance to marten movements differed among those landscape attributes that were considered. Simulations with a map grain size of 100 m performed significantly better than 5, 25, and 300 m, possibly because of gap crossing avoidance. Model performance (compared to null model) was maximal when resistance to movement in open habitat was set to 20 times higher than in mature forest, but uncertainty around this estimate was large. This research demonstrates that presence-only (point) data can be used to parameterize movements using spatially explicit modelling. |
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ISSN: | 1687-9708 1687-9716 |