Vegetation Sampling & Analysis of Ruffed Grouse Habitat in Western Rhode Island Introduction In the past fifty years, populations of ruffed grouse (Bonasa umbellus) (Figure 1) have declined in Rhode Island 7. It is hypothesized that this decline is mainly due to the loss of prime early successional forest habitat 2. This hypothesis is supported by the fact that other species are facing similar declines in early successional forest types 4,5,6. Early successional forests have declined in Rhode Island over the last half century due primarily to fire suppression and high grade forestry practices 1. Because of the lack of available early successional forests, ruffed grouse now may be occupying surrogate habitats in more mature forests which provide less cover from predators, increase foraging time, and decreased reproductive output. We used a habitat suitability model to predict suitable ruffed grouse habitat (Figure 2). In order to test the model, we conducted vegetation surveys to compare habitat characteristics at sites of varying stand ages that were predicted to be more or less suitable for grouse based on a habitat suitability model. Knowledge of suitable grouse habitat will be useful for wildlife managers. Conclusion The weak correlations between the vegetation parameters and habitat suitability implies that either the suitability model is inadequate at predicting grouse habitat or that site specific vegetation data is inadequate at predicting grouse habitat. The weak correlations could also result from a combination of both of these factors. The correlations between vegetation characteristics suggest that mature forests (with high basal area and percent canopy cover) will be less suitable for grouse since they require more dense understory with high stem density and herbaceous cover. Given that most of Arcadia’s forests are mature, these forests are likely too old to provide optimal grouse habitat. Further studies should be conducted to determine if there are prime habitat locations in Rhode Island. Amy Wynia 1, Erik Blomberg 2, & Dr. Scott McWilliams 3 1 URI Coastal Fellow, 2 Masters of Science Candidate, & 3 NRS Department Figure 1. Female Bonasa umbellus nesting in the Arcadia State Forest located in Washington County, Rhode Island, USA during summer Photo courtesy of Erik Blomberg. Literature Cited 1.Butler, B.J., and E.H. Wharton The Forests of Rhode Island. Northeastern Research Station Report NE-INF US Department of Agriculture, Newton Square, PA 2.Dessecker, D.R., and D.G. McAuley Importance of early successional habitat to ruffed grouse and American woodcock. Wildlife Society Bulletin. 29: Devers, P.K Population ecology of and the effects of hunting on ruffed grouse (Bonasa umbellus) in the southern and central Appalachians. Dissertation. Virginia Polytechnic Institute and State University. Blacksburg, VA, USA. 4.Fettinger, J.L Ruffed grouse nesting and brood habitat in western North Carolina. M.S. Thesis. University of Tennessee, Knoxville. Knoxville, TN, USA. 5.Haulton, G.C., D.F. Stauffer, R.L. Kirkpatrick, and G.W. Norman Ruffed grouse (Bonasa umbellus) brood microhabitat selection in the southern Appalachians. American Midland Naturalist. 150: Livaitis, J.A Importance of early successional habitats to mammals in eastern forests. Wildlife Society Bulletin. 29: _____, J.A. Sherburne, and J.A. Bissonette Bobcat habitat use and home-range size in relation to prey density. Journal of Wildlife Management. 50: Thompson, F.R., D.A. Freiling, and E.K. Fritzell Drumming, nesting and brood habitats of ruffed grouse in an oak-hickory forest. Journal of Wildlife Management. 51: Tirpak, J.M. and B. Giuliano Influence of nest site selection on ruffed grouse nest success in the Appalachians. Pp in G.W. Norman et al., ed. Ruffed Grouse Ecology and Management in the Appalachian Region. Final Project Report of the Appalachian Cooperative Grouse Research Project, Blacksburg, VA. Acknowledgements The Coastal Fellows Program is supported by the URI Offices of the President and the Provost and by the College of the Environment and Life Science. Methods Data were collected during summer 2006 in the Arcadia State Forest, located in Washington County, Rhode Island. GIS was used to select randomly stratified points for sampling with each point consisting of one anchor plot and two nested plots, 100 m north and south of the anchor plot. Two intersecting 20 m transects were run in the cardinal directions, and used at each plot for measurements (Figure 3). Vegetation characteristics were chosen based on relative importance to grouse reproduction 3 and were measured using methods presented in Table 1. The vegetation parameters were correlated to a ruffed grouse suitability index. Table 1. Presents the vegetation characteristics measured for vegetation surveys conducted during summer 2006, how we measured each characteristic, and the literature cited for each characteristic. Process of Selecting Plots: Vegetation Sampling within each Plot Figure 3. GIS-based map displaying location of vegetation surveys conducted summer 2006 in the Arcadia State Forest located in Washington County, Rhode Island, USA. Nested Plots were located 100 m north and south of center Plot. Locus Map Figure 2. GIS-based ruffed grouse habitat suitability model used to predict suitable grouse habitat in the Arcadia State Forest located in Washington County, Rhode Island, USA. Locus Map ParameterCorrelation Stem Density0.044 Basal Area % Canopy Cover % CWD0.115 % Bare Cover0.005 % Overall Understory Cover % Herbaceous Cover % Grass Cover % Woody Cover % Slash Cover0.108 Herbaceous Height0.015 Grass Height0.112 Woody Height0.002 Table 2. Results from Pearson Correlations between vegetation parameters measured in the field and a ruffed grouse habitat suitability index. Percent Cover Correlation Values Overall Understory Woody Herbaceous Percent Cover Correlation Values Overall Understory Woody Herbaceous Results Figure 4b. A Pearson Correlation Coefficient for a correlation between % Canopy Cover and % Overall Understory Cover. Correlation Value = The negative correlation indicates as % Canopy increases, % Overall Understory Cover decreases. Figure 4a. Pearson Correlation Coefficients for correlations between Basal Area and % Overall Understory Cover, Basal Area and % Woody Cover, and Basal Area and % Herbaceous Cover. The negative correlations indicate as Basal Area increases, % Overall Understory Cover decreases. Figure 5b. A Pearson Correlation Coefficient for a correlation between % Herbaceous Cover and % Overall Understory Cover. Correlation Value = The positive correlation indicates as % Overall Understory Cover increases, % Herbaceous Cover increases as well. Figure 5a. Pearson Correlation Coefficients for correlations between Stem Density and % Overall Understory Cover, Stem Density and % Woody Cover, and Stem Density and & % Herbaceous Cover. The negative correlations indicate as Stem Density increases, % Overall Understory Cover descreases. Table 4. Results from Pearson Correlations between Percent Cover and Stem Density. Table 3. Results from Pearson Correlations between Basal Area and Percent Cover ParameterMeasuring MethodCitation Percent Canopy CoverSpherical densiometer5 Stem Density/ha Count trees/shrubs >1m tall & within 1m of the transect 2,4,7,8 Basal Area/acreWedge prism9 Percent Coarse Woody Debris Count downed trees and bushes >15cm in height intersecting each transect at 2m intervals9 Percent Understory Cover Daubenmire frame4,5,7,8 a. Bare b. Overall c. Herbaceous d. Woody e. Grass f. Slash Plant Height (cm) Meter stick a. Herbaceous b. Woody c. Grass Midstory CompositionIdentiy all shrub and tree species that lie within the extent of the transects Overstory Composition