<?xml version="1.0"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title xmlns:dc="http://purl.org/dc/elements/1.1/">2007, BTO, COWRIE Research, Further use of aerial surveys to detect bird displacement by offshore wind farms</dc:title>
  <dc:type xmlns:dc="http://purl.org/dc/elements/1.1/">series</dc:type>
  <dc:identifier xmlns:dc="http://purl.org/dc/elements/1.1/">https://portal.medin.org.uk/portal/start.php?tpc=015_09ef22f7-e029-42bf-acf2-a85db3cea37e</dc:identifier>
  <dc:description xmlns:dc="http://purl.org/dc/elements/1.1/">The series was designed to assess the statistical power of detecting population declines in individual species counts linked to wind farm construction. Employing a methodology based on Generalized Linear Modelling (GLM) and randomization, the study used real count data from random sites by species, year, and month, generating hypothetical scenarios with a model-derived mean-to-variance ratio (P-scale factor). Through 1,000 randomization iterations and comparison of pre- and post-construction counts, the analysis determined the proportion of significant results using P value thresholds. The findings demonstrate that this approach provides robust estimation of statistical power for detecting site-level population declines, and it is recommended for use in similar ecological impact assessments involving temporal and spatial data.</dc:description>
  <dc:date xmlns:dc="http://purl.org/dc/elements/1.1/">20210517T13:33:53 20070101T00:00:00</dc:date>
</oai_dc:dc>