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Estimating Snowshoe Hare Relative Abundance

In addition to the literature review below, these downloadable files on the publications page address prey species surveys:

M.S. thesis and Journal of Wildlife Management publication by Nick McCann

Final Report on Phase 1 available on the publications page

The snowshoe hare comprises between 76-94% of the lynx diet during periods of hare abundance (Nellis et al. 1972, Brand et al. 1976, O'Donoghue et al. 1998). Red squirrels may have an important secondary role in the lynx diet, particularly when hares are scarce or during snow-free months (O'Donoghue et al. 1998, Aubry et al. 2000). Grouse, small mammals and carrion seem to be less important components of the lynx diet (Aubry et al. 2000). This high reliance on one or two prey species indicates that lynx research should concurrently consider the ecologies of these herbivores.

Snowshoe hares and red squirrels have been studied successfully with indirect, noninvasive methodologies. Indirect methods of studying these species include various protocols designed to provide a population index of abundance without the intensive commitment required with mark-recapture trapping or radiotelemetry. For hares, fecal pellet counts have been commonly used as indirect survey methods (Krebs et al. 1987, 2001a, Murray et al. 2002). Due to the vocal nature of red squirrels, point count transects similar to those performed on birds can be used as an indirect means of obtaining abundance estimates for this species (Mattson and Reinhart 1996, Bayne and Hobson 2000). Densities and distributions of hare obtained from snow-tracking (and pellet counts, see below) will be compared with historical records from track counts conducted by MNDNR.

Figure 4. Snowshoe hare track indices and spring pellet count indices in Minnesota from 1974 to 2002. Data from an unpublished report by J. Erb, MNDNR.

The goal of these prey species surveys is to obtain indices of relative abundance for each species. These indices will be analyzed across various habitats, spatial scales, and temporal scales. Although the data collected on these prey species can be analyzed independently of lynx data, information on relative prey abundance will have a central role in lynx habitat analyses.

Snowshoe Hare Pellet Count Surveys

All hare fecal pellet transects, whether randomly placed in habitats according to availability, systematically placed in lynx home ranges, or stratified in high quality hare habitats, will consist of five 1 m2 circular plots placed at 20 m intervals. Large circular plots have recently been suggested as the preferred method for inferring hare density in southern portions of snowshoe hare range (McKelvey et al. 2002, Murray et al. 2002). All plots will be permanently marked with a reinforcing bar (rebar) stake at 3/8” diameter and revisited each May-June for the duration of the project. During counts all fecal pellets within the plot boundary will be counted and removed. To avoid an inclusion bias among technicians, only 50% of the pellets found directly on the plot boundary will be counted (McKelvey et al. 2002). Vegetation obscuring pellets will be moved as needed but pellets deeply incorporated into the organic layer of the forest floor will not be counted. Over 180 permanent transects were established in the spring of 2003 according to the methods described above. The data sheet is in Appendix 4.

The fecal pellet data will permit the development of an index of relative density across various habitat types that can also be analyzed at a variety of spatial scales, from microhabitat to landscape-scale. Microhabitat analyses will be based on stand-level vegetation measures collected at each pellet plot. Landscape-scale analyses will be performed on a GIS. These multiple-scale analyses of the hare data will allow us to estimate which spatial scale has the greatest influence on hare populations, vital information for forest managers. For statistical analysis, the transect will be made the experimental unit by summing counts across transects. Counts will be normalized with a log-transformation and differences between years and among habitat types will be tested with an analysis of covariance (ANCOVA).