Minimizing effects of wind development on bat species in the Northeast

Wind energy development is promoted as an environmentally friendly alternative to greenhouse gas-emitting fossil fuels, however wind farms can have negative impacts on wildlife, including bats. A recent study estimated 600,000 bats were killed by wind power in the United States in 2012. Fatalities appear to be heavily skewed towards migratory bats, which have comprised about 75% of documented fatalities in North America. Migratory bats are at greatest risk as they head south during the late-summer to fall migration season. In addition, a number of hibernating bat species, particularly Myotis species, have suffered losses of up to 99% in recent years due to the spread of white-nose fungus, leaving these populations vulnerable to any further loss of individuals. Several possibilities for minimizing the effects of wind development on migratory and at-risk bat species have been proposed, including mounting deterrent devices on wind turbines, practicing curtailment of wind turbine operation during periods of high bat activity, and siting wind farms away from major bat flyways and migration routes. However, an effective deterrent device has not yet been identified, and major routes of migratory bats are entirely unknown. This presentation explores the use of radar as a bat deterrent, and addresses the use of acoustic and automated telemetry data as a means of understanding the timing and location of bat movement in the coastal and offshore environment.

The application of fuzzy logic based hydrometeor classification techniques and polarimetric weather radar for the identification of airborne fauna

To assess the potential for detrimental effects to wildlife posed by wind turbines, there is a need to collect information on distribution and behavior for a broad suite of birds and bats. Avian monitoring systems based on marine navigation radar are often used to quantify bird and bat migration near both potential and established wind sites; however, the capability to distinguish between bats and different varieties of birds has still not been practically achieved. Hydrometeor classification algorithms (HCAs), based on fuzzy logic rules, are commonly used to identify different types of precipitation in radar data. I will discuss the potential application of these meteorological techniques to enable better classification of biological targets.