Literature DB >> 28062112

A modelling framework to predict bat activity patterns on wind farms: An outline of possible applications on mountain ridges of North Portugal.

Carmen Silva1, João Alexandre Cabral1, Samantha Jane Hughes2, Mário Santos3.   

Abstract

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.
Copyright © 2016 Elsevier B.V. All rights reserved.

Keywords:  Acoustic monitoring; Bat activity patterns; Ecological modelling; Environmental risk assessment; Stochastic Dynamic Methodology (StDM); Wind farms

Year:  2017        PMID: 28062112     DOI: 10.1016/j.scitotenv.2016.12.135

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Ecobat: An online resource to facilitate transparent, evidence-based interpretation of bat activity data.

Authors:  Paul R Lintott; Sophie Davison; John van Breda; Laura Kubasiewicz; David Dowse; Jonathan Daisley; Emily Haddy; Fiona Mathews
Journal:  Ecol Evol       Date:  2017-12-12       Impact factor: 2.912

  1 in total

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