Worldwide ecological impact assessments of wind farms have gathered relevant information on bat activity patterns. Since conventional bat study methods require intensive field work, the prediction of bat activity might prove useful by anticipating activity patterns and estimating attractiveness concomitant with the wind farm location. A novel framework was developed, based on the stochastic dynamic methodology (StDM) principles, to predict bat activity on mountain ridges with wind farms. We illustrate the framework application using regional data from North Portugal by merging information from several environmental monitoring programmes associated with diverse wind energy facilities that enable integrating the multifactorial influences of meteorological conditions, land cover and geographical variables on bat activity patterns. Output from this innovative methodology can anticipate episodes of exceptional bat activity, which, if correlated with collision probability, can be used to guide wind farm management strategy such as halting wind turbines during hazardous periods. If properly calibrated with regional gradients of environmental variables from mountain ridges with windfarms, the proposed methodology can be used as a complementary tool in environmental impact assessments and ecological monitoring, using predicted bat activity to assist decision making concerning the future location of wind farms and the implementation of effective mitigation measures.
A modelling framework to predict bat activity patterns on wind farms: An outline of possible applications on mountain ridges of North Portugal
Title: A modelling framework to predict bat activity patterns on wind farms: An outline of possible applications on mountain ridges of North Portugal
March 01, 2017
Journal: Science of the Total Environment
Silva, C.; Cabral, J.; Hughes, S.; Santos, M. (2016). A modelling framework to predict bat activity patterns on wind farms: An outline of possible applications on mountain ridges of North Portugal. Science of the Total Environment, 581-582, 337-349.