BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Population monitoring requires techniques that produce estimates with low bias and adequate precision. Distance sampling using ground-based thermal infrared imaging (ground imaging) and spotlight surveys is commonly used to estimate population densities of white-tailed deer (Odocoileus virginianus). These surveys are often conducted along roads, which may violate assumptions of distance sampling and result in density estimates that are biased high. Aerial vertical-looking infrared imaging (aerial imaging) is not restricted to roads and therefore enables random sampling and detection. We compared estimates of population density and precision, and evaluated potential sources of bias for these 3 techniques for deer on Arnold Air Force Base in Tennessee, USA, during January-February 2010. Using data from aerial imaging conducted along systematic strip transects, we found that deer were distributed close to roads and deer responded to the landscape along the road edge or to observers driving along roads. As a result of these distributional patterns, estimated deer density based on ground imaging and spotlighting from road-based surveys was 3.0-7.6 times greater than density estimated from strip transects using aerial imaging. Ground imaging did not produce better estimates than spotlighting. Observers on the ground counting all deer seen at test plots with hand-held thermal imagers saw fewer deer than were seen on aerial images, suggesting high detection of deer by aerial imaging. Despite its higher cost (US$10,000) over spotlight surveys, we recommend aerial imaging instead of road-based ground surveys for monitoring populations of deer and discourage the continued use of non-random road-based surveys as a method for estimating white-tailed deer populations. Ó 2014 The Wildlife Society.

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Opossums (Didelphis virginiana) and raccoons (Procyon lotor) were studied at two sites in Tennessee using mark-recapture techniques. Winter density, relationship of trap success with selected habitat variables, interspecific association, and the degree of mutual habitat utilization were determined. Density of opossums was estimated as one animal/9.9 ha at one site and ODe animal/44.5 ha at the second location. Abundance of raccoons was estimated as one animal170.4 ha at one site and one animal/34.S ha at the other. Number of opossums and raccoons was not significantly correlated with any of the habitat variables measured at either site. Interspecific association values for co-occurring populations were 0.111 and 0.267; these values were not statistically significant and indicated that opossums and raccoons occurred independently of one another in regard to spatial distribution. Both species exhibited a wide range of ecological tolerance for habitat.Numerous ecological investigations of opossums (Didelphis virginiana) and raccoons (Procyon lotor) have been conducted in various parts of the United States. McManus (1974) and Gardner (1982) reviewed many of the previous investigations relating to opossums and Lotze and Anderson (1979) and Kaufmann (1982) summarized much of the available literature for raccoons. Previous investigations of the ecology of these species primarily have been works directed toward management questions and, with only a few exceptions (McKeever, 1959;Stuewer, 1943;VanDruff, 1971), prior studies have not assessed populations of D. virginiana and P. lotor collectively, Because opossums and raccoons co-occur throughout much of their geographic ranges, feed on many of the same foods, and utilize similar habitats (Gardner, 1982;Kaufmann, 1982), numerous opportunities for interspecific relationships exist. However, the extent of the interaction between the two species is unclear. The purposes of the present study were to investi-J. Afamm .. 73(4):808-813, 1992 808 gate co-occurring populations of D. virginiana and P. lotor in regard to winter density, relationship of the number captured to selected habitat variables, spatial association, and degree of mutual habitat utilization. STUDY AREAThe study was conducted at two sites separated by ca. 140 km. The first site was a portion of the Milan Army Ammunition Plant (Milan) located in Gibson and Carroll counties in western Tennessee. This site was composed of 68% hardwoods, 20% pasture, 8% pine-hardwoods, and 4% pine. Topography consisted of gently rolling hills interspersed with intermittent streams. Three small ponds « I ha) were the only permanent water sources on the study area. White and black oaks (Quercus sp.), elms (Ulmus sp.) and scattered, small stands of pine (Pinus sp.) dominated the southern two-thirds ofthe site. The northern one-third was pasture, primarily composed of fescue (FeslUca sp.), interspersed by small woodlots (> 1 ha). Adjacent land use was agricultural and livestock-oriented. Cattle were kept on portions ofthe study area dur...

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Landscape and harvest indices are frequently used to represent white-tailed deer (Odocoileus virginianus) density. However, the relationship between deer density and specific landscape indices is unclear. Harvest is another metric often used to estimate deer density. Our objective was to model the relationship among deer density, landscape metrics, and harvest density of deer in TN, USA. We estimated deer density across 11 regions in 2011 using distance sampling techniques. We developed 18 a priori models to assess relationships among deer density, harvest density, and landscape metrics. Estimates of deer density ranged from 1.85 to 19.99 deer/km 2. Deer density was best predicted by harvest density and harvest density + percent woody area. However, harvest density was the only important variable in predicting deer density (Σω i = 0.700). Results of this study emphasize the significance of harvest density in deer management. While the importance of harvest as a management tool for deer is likely to increase as landscapes are fragmented and urbanized, specific management guidelines should be based upon deer densities and landscape metrics when they are important.

Point count data are used increasingly to provide density estimates of bird species. A favored approach to analyze point count data uses distance sampling theory where model selection and model fit are important considerations. We used uniform and half normal models and assessed model fit using χ2 analysis. We were unsuccessful in fitting models to 635 northern bobwhite Colinus virginianus observations from 85 avian point locations spanning 6 y (P ≤ 0.05). Most observations (74%) occurred in the outermost (>100-m) distance radius. Our results violated the assumptions that all observations at the point are detected. The assumption that birds were assigned to the correct distance interval also was probably violated. We caution managers in implementing avian point counts with distance sampling when estimating northern bobwhite population density. We recommend exploring other approaches such as occupancy-estimation and modeling for estimating detection probabilities.