Roosting and Foraging Social Structure of the Endangered Indiana Bat (Myotis sodalis)

Abstract

Social dynamics are an important but poorly understood aspect of bat ecology. Herein we use a combination of graph theoretic and spatial approaches to describe the roost and social network characteristics and foraging associations of an Indiana bat (Myotis sodalis) maternity colony in an agricultural landscape in Ohio, USA. We tracked 46 bats to 50 roosts (423 total relocations) and collected 2,306 foraging locations for 40 bats during the summers of 2009 and 2010. We found the colony roosting network was highly centralized in both years and that roost and social networks differed significantly from random networks. Roost and social network structure also differed substantially between years. Social network structure appeared to be unrelated to segregation of roosts between age classes. For bats whose individual foraging ranges were calculated, many shared foraging space with at least one other bat. Compared across all possible bat dyads, 47% and 43% of the dyads showed more than expected overlap of foraging areas in 2009 and 2010 respectively. Colony roosting area differed between years, but the roosting area centroid shifted only 332 m. In contrast, whole colony foraging area use was similar between years. Random roost removal simulations suggest that Indiana bat colonies may be robust to loss of a limited number of roosts but may respond differently from year to year. Our study emphasizes the utility of graphic theoretic and spatial approaches for examining the sociality and roosting behavior of bats. Detailed knowledge of the relationships between social and spatial aspects of bat ecology could greatly increase conservation effectiveness by allowing more structured approaches to roost and habitat retention for tree-roosting, socially-aggregating bat species.

A method for estimating abundance of mobile populations using telemetry and counts of unmarked animals

“…This is in contrast to double-observer or other methods that condition on animals being present during surveys. Our approach is also practical because the required capture effort is less than in markrecapture surveys and in many studies of bat ecology (e.g., Silvis et al 2014b). For example, using closed-population mark-recapture methods, Nichols et al (1984) marked approximately 80% of a population of voles to estimate abundance, while in our simulations we marked 2-12% of the population.…”

“…We also assumed that bats would move v www.esajournals.org randomly with respect to the roost used during the previous survey period. Extensive data indicate that Indiana bats tend to switch roosts every 2-5 days (Kurta et al 1996, Carter and Feldhamer 2005, Silvis et al 2014b). This tendency to stay in the same roost for several days could be due to a Markovian process in which a bat has a preference for the roost used the previous day.…”

“…Such colonies demonstrate a fission-fusion social structure in which the colony forms temporary groups that roost apart from each other (Kurta et al 2002). The size, composition and locations of these groups can change on a daily basis (Silvis et al 2014b). Given that roosts are highly cryptic and bats are highly mobile, it can be difficult to locate all roosts being used by a single colony (Silvis et al 2014b).…”

“…The size, composition and locations of these groups can change on a daily basis (Silvis et al 2014b). Given that roosts are highly cryptic and bats are highly mobile, it can be difficult to locate all roosts being used by a single colony (Silvis et al 2014b). Because the Indiana bat is a federally endangered species that is now threatened by White-Nose Syndrome (Thogmartin et al 2013), it would be useful to have a reliable method for estimating the size of local populations during summer.…”

“…Our approach combines counts of unmarked animals with coincidence rates estimated from telemetry data. Simultaneous count and location data are already collected in ecological studies of taxa including ungulates (Rivest et al 1998), marine mammals (Huber et al 2001), birds (Dorr et al 2010) and bats (Silvis et al 2014b), and so should be practical and potentially applicable to a variety of species. We derive an estimator that, with adequate sample sizes, gives unbiased abundance estimates.…”