Skip to content

Research at St Andrews

Comparing pre- and post-construction distributions of long-tailed ducks Clangula hyemalis in and around the Nysted offshore wind farm, Denmark: a quasi-designed experiment accounting for imperfect detection, local surface features and autocorrelation

Research output: Book/ReportCommissioned report


Ib Krag Petersen, Monique Lea MacKenzie, Eric Rexstad, Mary S. Wisz, Anthony D. Fox

School/Research organisations


We report a novel technique to model abundance patterns of wintering seaducks in relation to the construction of an offshore wind farm (OWF) based on seven years of aerial survey transect data.
Distance sampling was used to estimate seaduck densities adjusted for covariates affecting detection probabilities. A generalized additive model (GAM) generated seaduck densities in sampling units in relation to spatially explicit covariates, using bootstrapping to account for uncertainties in both processes. Generalized estimating equations generated precision measures for the GAM robust to spatial and temporal autocorrelation. Comparison of pre- and post-construction model generated surfaces showed significant reductions in long-tailed duck numbers only within the OWF (despite the fact that the model was uninformed about the OWF location), although the absolute numbers involved were trivial in a flyway population context. This method provides quantification of distributional effects on organisms over a gradient in space and time that offers an alternative to Before-After/Control-Impact designs in environmental impact assessment.


Original languageEnglish
PublisherUniversity of St Andrews
Number of pages16
Publication statusPublished - 2011

Publication series

NameCREEM Technical Report

    Research areas

  • Aerial surveys, Distance sampling, Environmental impact assessment, Feeding desities, Generalized additive models, Parametric bootstrap , Spatially-adaptive model, Generalized estimating equations, Seaducks, Spatial autocorrelation, Temporal autocorrelation

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

ID: 14419791