Cc Silva

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

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.

Genes expressed in cotton (Gossypium hirsutum) buds isolated with a subtractive library.

A subtractive cDNA library from cotton buds was constructed to prospect for differentially expressed genes related to early bud development. A library was constructed and 768 cDNA sequences were obtained, comprising 168 clusters, with 126 contigs and 42 singlets. Both the Gossypium as well as Arabidopsis databases were utilized for the in silico analysis, since some genes identified in cotton have not yet been studied for functionality, although they have homology with genes from other species. The transcriptome revealed a large number of transcripts, some of them with unknown function, and others related to pollen development, pollen tubes, ovules, and fibers at different stages. The most populated contig was identified as fiber from 0-10 days after anthesis, with 12 reads. The success and novelty rates generated from the library were 67 and 51%, respectively. The information obtained here will provide a framework for research on functional cotton genomics.