
DMP provide the statistical analysis services for many environmental impact assessments. Our clients include the European Marine Energy Centre Limited (EMEC Ltd.), Aurora Environmental Limited, and the Sea Mammal Research Unit Limited (SMRU Limited). We currently provide the EIA analysis to both the Strangford Lough and Falls of Warness undersea turbine installations.

Falls of
Warness-style
Our clients typically seek answers to the following:
For example, the Strangford Lough data comprise of continual observer sightings of marine mammals and birds –species, numbers and locations. Oceanographic conditions are also recorded or interpolated. In total the data represent visual scans every few minutes, taken over many days/months/years. The data can require extensive cleaning and validation prior to analysis.
Answering the client’s questions requires relatively advanced statistical models that use custom-coded components in the statistical programming language R. Specifically DMP employ:
Statistical models, reports and simulation software.
The DMP models provide baseline relationships between the monitored species and their environment through time. These are accompanied with measures of uncertainty and collectively allow the client to determine what sequences of observations would be ‘unusual’ i.e. would indicate an impact.
The simulation models provide a range of installation-impact scenarios and the corresponding probability of detecting these. It is clear from this whether the client needs to consider alterations of the sampling strategies. For example, the client may:
Future post-installation observations can be fed to the models/simulation to determine if these represent an impact, as well as the associated uncertainty with this conclusion.
These projects require DMP to custom build regression models for cyclical non-linear patterns with spatial components and long-term repeated measurements. Bespoke simulation software is also required for projections of possible effects of turbine installation and the effectiveness of current monitoring regimes. No aspect of this is statistically trivial and naïve analysis of the problem would produce incorrect conclusions.