Midwest Fish & Wildlife Conference 2018

AUTHORS. Beth Wojcik, University of Wisconsin - Madison; Daniel J. Storm, Wisconsin Department of Natural Resources; Timothy R. Van Deelen, University of Wisconsin - Madison

ABSTRACT. White-tailed deer (Odocoileus virginianus) recruitment is highly variable and sensitive to numerous factors; thus deer managers monitor recruitment. In regions of Wisconsin, recruitment measurements have recently declined for unknown reasons. Traditional methods for measuring recruitment involve herd composition counts from roadside surveys. The opportunistic and convenient nature of these surveys has likely resulted in small sample sizes and biased estimates. Our goal was to investigate new survey methods which may provide improved reliability and precision of deer recruitment estimates. Our research occurred during August and September of 2016 and 2017 in 12 counties representing 4 regions (Northern Forest, Central Forest, Central Farmland, and Southern Farmland) of Wisconsin. We standardized roadside surveys by establishing routes, training observers, and creating procedural protocols. We determined the minimum sample size needed for various levels of precision and examined the ability of new survey methods to provide needed sample sizes. Influence of habitat type, survey time, weather, number of observers and deer behavior on detectability, sample size and precision was examined. Standardized roadside surveys in areas with good visibility and high deer populations (woodlots intermixed with hay/alfalfa/soybean fields) resulted in many deer observations, but few deer were observed in habitats with poor visibility from roadsides and low deer populations (heavily forested and corn-dominated landscapes). We compared standardized roadside survey data to recruitment estimates derived from trail camera data for 3 study counties and harvest data for all 12 study counties. Methods providing improved estimates of recruitment allow deer managers to better compare spatial and temporal differences of recruitment, monitor recruitment trends, and model population size.