Ford. W.M., A. Silvis, J.L. Rodrigue, A. Kniowski and J.B. Johnson. 2016. Deriving habitat models for northern bats (Myotis septentrionalis) from historical detection data: A case study using long-term research on the Fernow Experimental Forest, West Virginia, USA. Journal of Fish and Wildlife Management 7(1): 86-98
The proposed listing of the northern bat (Myotis septentrionalis) following it severe population declines from White-nose Syndrome (WNS) presents considerable challenges to natural resource managers. Because the northern bat is a forest habitat generalist and lacks previous research focus, development of effective conservation measures will depend on appropriate understanding of its habitat relationships. However, severely reduced population sizes make gathering data for such models difficult. As a result, historical data, including acoustic, capture, and radio-telemetry, may be essential in development of habitat models for the northern bat. To date, there has been little evaluation of how effective historical data may be in developing habitat models and it is unclear how models created using different types of bat presence data compare in output. We explored the effect of presence data type on model output by creating presence probability models for the northern bat on the Fernow Experimental Forest in the central Appalachian Mountains of West Virginia using a historical, presence-only dataset. Each presence data type produced outputs that were dissimilar from the other types but corresponded with known traits of the northern bat or are easily explained in the context of the particular data collection protocol involved. However, our results also highlight potential limitations of individual data types. For example, models from mist-net capture data only showed high probability of presence of northern bats along the dendritic network of riparian areas, an obvious artifact of sampling methodology. Development of ecological niche and presence models for northern bat populations would be highly valuable for resource managers. We caution, however, that these efforts should consider the substantial limitations of models derived solely from one data type.