Wolves in lower Michigan: habitat modeling and capacity estimation
Abstract
Gray wolf populations have been rebounding in the Great Lakes region, after being
nearly extirpated from the conterminous United States. Breeding populations of wolves
have established in the Upper Peninsula of Michigan but have not yet become widely
established in the Lower Peninsula (LP) of Michigan, despite several sightings of
wolves in the LP since 2004. The objective of this analysis was to determine the
quantity of potential habitat for wolves in the LP using wolf occurrence data obtained
by radio telemetry, Maxent software, and ArcGIS. The habitat model was evaluated to
determine if the potential habitat could support a viable population. A species distribution
model (SDM) was created in Maxent and analyzed in ArcGIS to estimate the amount of
potential habitat for wolves in the LP. Connectivity of potential habitat was evaluated
by looking at least cost paths, corridors, and potential dispersal land. I found
that there is 2,674-3,246 km2 of potential habitat in the LP, which could support
52-63 wolves. This is below the minimum of 100 necessary to sustain a viable population.
All habitat patches at least 50 km2 in size are in the northern LP, but are not connected
by land suitable for dispersal. Thus, although there are habitat patches of sufficient
size in lower Michigan, it is likely that packs of wolves that may inhabit these patches
will remain relatively isolated and have difficulties dispersing throughout lower
Michigan. It is unlikely that they will be able to inhabit or disperse through the
southern LP without land management geared towards creating larger unfragmented habitat
patches and suitable dispersal corridors. This analysis highlights the need for land
management geared towards wolves in order to allow them to continue to recover their
former range in the United States.
Type
Master's projectPermalink
https://hdl.handle.net/10161/2858Citation
Claeys, Gina (2010). Wolves in lower Michigan: habitat modeling and capacity estimation. Master's project, Duke University. Retrieved from https://hdl.handle.net/10161/2858.Collections
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