We monitored bat activity on the upper Coastal Plain of Virginia using mist nets and acoustic detection (ANABAT) during April-October 2000 and April-August 2001. We classified forty sites into three forest-cover classes (pine forest, mixed pine, and hardwood forest) and three landscape-feature classes (permanent water, riparian corridor, and upland). We captured 406 bats (8 species) in mist nets; red bats (Lasiurus borealis; n = 281), big brown bats (Eptesicus fuscus; n = 47), and eastern pipistrelles (Pipistrellus subflavus; n = 36) were the most commonly captured species. We captured fewer than 30 individuals of five other species. There were no significant differences in captures per 100 net nights for overall captures or for individual species among forest-cover classes. Overall captures per 100 net nights differed significantly among landscape-feature classes; however, post-hoc analyses could not tease out significantly different pairs. Captures of L. borealis were higher over permanent waters than along riparian corridors or in uplands. Bray-Curtis polar ordination suggested that landscape features such as beaver ponds and impoundments influenced habitat use by bats more than forest-cover type. Discriminant function analysis identified 713 bat calls (≥ 95% confidence) using ANABAT II detectors. Lasiurus borealis and P. subflavus were more frequently recorded by ANABAT II than northern myotis (Myotis septentrionalis) among the three forest-cover classes and among the three landscape-feature classes. Planned, a priori, contrast indicated that for 25 nights when mists nets and acoustic detectors were used simultaneously, mean number of bat species detected for the pooled results of both techniques was higher than the average number of species detected by the mean of each of the two techniques separately. Mean number of bat species detected by the ANABAT II system was higher than mean number detected by mist netting.


This is the online version published ahead of print. Initial submission: April 2017; revised submission: September 2017.