Moving across the border: modeling migratory bat populations

Abstract

Abstract. The migration of animals across long distances and between multiple habitats presents a major challenge for conservation. For the migratory Mexican free-tailed bat (Tadarida brasiliensis mexicana), these challenges include identifying and protecting migratory routes and critical roosts in two countries, the United States and Mexico. Knowledge and conservation of bat migratory routes is critical in the face of increasing threats from climate change and wind turbines that might decrease migratory survival. We employ a new modeling approach for bat migration, network modeling, to simulate migratory routes between winter habitat in southern Mexico and summer breeding habitat in northern Mexico and the southwestern United States. We use the model to identify key migratory routes and the roosts of greatest conservation value to the overall population. We measure roost importance by the degree to which the overall bat population declined when the roost was removed from the model. The major migratory routes-those with the greatest number of migrants-were between winter habitat in southern Mexico and summer breeding roosts in Texas and the northern Mexican states of Sonora and Nuevo Leon. The summer breeding roosts in Texas, Sonora, and Nuevo Leon were the most important for maintaining population numbers and network structure -these are also the largest roosts. This modeling approach contributes to conservation efforts by identifying the most influential areas for bat populations, and can be used to as a tool to improve our understanding of bat migration for other species. We anticipate this approach will help direct coordination of habitat protection across borders.

Operationalizing the telecoupling framework for migratory species using the spatial subsidies approach to examine ecosystem services provided by Mexican free-tailed bats

“…To estimate D SA , the proportional dependence of Mexican freetailed bats on different roosts and habitat regions, we used a network model for the species that was developed by Wiederholt et al (2013). The model simulates bat migration between four winter regions in southern Mexico and 25 major summer breeding roosts in northern Mexico and the southwestern United States, with each site receiving individuals from several locations (Fig.…”

“…Expected migratory routes are determined based on distancebased formulations of migration costs. The approach allows us to simulate changes in migratory population size associated with the loss of any particular habitat region or roost (Wiederholt et al 2013(Wiederholt et al , 2015.…”

“…If a summer site was connected to more than one route, the circle representing the summer site was filled and outlined with the colors of the two different routes (from Wiederholt et al [2013]). …”

“…2). Removal of these large southern breeding roosts caused the bats to migrate farther distances, which in the model increased energetic costs and reduced the probability of survival during migration (Wiederholt et al 2013). Table 1 shows the rescaled proportional dependency values, D S , from the habitat removal exercise (Fig.…”

A management-oriented framework for selecting metrics used to assess habitat- and path-specific quality in spatially structured populations

“…Many metrics within these categories measure similar characteristics, and while there may be some differences in the predictive ability of metrics under different circumstances (Wu et al, 2002;Pascual-Hortal and Saura, 2007;Blazquez-Cabrera et al, 2014), we advocate choosing the simplest adequate metric that reflects the location, scale and objectives (Rayfield et al, 2010). While we make the distinction here between graph-based metrics and metrics based on occupancy or demographic data (see below), it is important to note that some studies have used a hybrid approach by adopting graph theory terminology while using demographic-based models (e.g., Taylor and Norris (2010), Wiederholt et al (2013), Erickson et al (2014)). This hybrid approach borrows concepts from graph theory but uses demographic information to represent the dynamics of both nodes and edges.…”