Modelling Impact Assessment in Renewables Development Areas using the New R Package, MRSea v0.1.1

Presentation

Title: Modelling Impact Assessment in Renewables Development Areas using the New R Package, MRSea v0.1.1
Publication Date:
May 01, 2014
Conference Name: Environmental Impact of Marine Renewables 2014
Conference Location: Stornoway, Scotland, UK
Pages: 22
Technology Type:

Document Access

Attachment: Access File
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Citation

Scott-Hayward, L.; Mackenzie, M.; Oedekoven, C.; Walker, C. (2014). Modelling Impact Assessment in Renewables Development Areas using the New R Package, MRSea v0.1.1 [Presentation]. Presented at the Environmental Impact of Marine Renewables 2014, Stornoway, Scotland, UK.
Abstract: 

For both developers and government licensing organisations it is important to have the ability to quantify spatially explicit change in the density and/or distribution of animals in and around marine renewables sites and, in particular, to identify if change occurs near renewables devices[1]. The publicly available MRSea package (Marine Renewable Strategic environmental assessment)[2] has recently been developed for analysing data collected for assessing potential impacts of renewable developments on marine wildlife, although the methods contained in this package have wide applicability. As a part of work commissioned by Marine Scotland, a number of candidate modelling methods were critically compared and the Complex REgion Spatial Smoother (CReSS)[3] with spatially adaptive knot placement using SALSA[4] was the recommended approach due to its success at locating spatially explicit impact-related change. The CReSS/SALSA approach was coupled with Generalised Estimating Equations (GEEs), which accommodate the spatial and temporal correlation that is generally inherent in baseline monitoring and impact assessment data. We present the capabilities of MRSea using an example data set from the package, which is based on offshore data collected from an existing renewables development. Specifically, we analyse a scenario where the animals have re-distributed across the study area between two time points, before and after construction of an offshore wind farm. We begin with correcting the observed counts from the survey data for imperfect detection, fit a spatial model with environmental covariates to the corrected counts, assess the fit of the model, run model diagnostics, make predictions and calculate uncertainty about these predictions. Most importantly for these applications, we identify spatially explicit significant differences in animal density before and after the construction.

 

The Extended Abstract is available here.

 

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