Aarhus University Seal / Aarhus Universitets segl

No. 186: Comparison of the iPCoD and DEPONS models for modelling population consequences of noise on harbour porpoises

Nabe-Nielsen, J. & Harwood, J. 2016. Comparison of the iPCoD and DEPONS models for modelling population consequences of noise on harbour porpoises. Aarhus University, DCE – Danish Centre for Environment and Energy, 22 pp. Scientific Report from DCE – Danish Centre for Environment and Energy No. 186 http://dce2.au.dk/pub/SR186.pdf

 

Summary

The Interim Population Consequences of Disturbance (iPCoD) and Disturbance Effects of Noise on the Harbour Porpoise Population in the North Sea (DEPONS) frameworks were both developed to assess the potential effects of noise associated with offshore renewable energy developments on harbour porpoise populations. Although both models simulate population dynamics based on the birth and survival rates of individual animals, they model survival in a different way. iPCoD uses average survival rates derived from data from North Sea animals. In the DEPONS model, survival emerges from the individuals’ ability to continuously find food. The models also differ in the way they model the consequences of exposure to noise and the kinds of output they can provide.

The DEPONS approach is based on a more realistic model of porpoise biology than iPCoD, and it can provide detailed predictions of the short-term effects of disturbance that are likely to be valuable for spatio-temporal planning and mitigation. However, iPCoD runs faster than the DEPONS model, making it possible to compare a larger number of different scenarios and to take account of a wider range of uncertainties. The structural differences between the two modelling frameworks make each model better suited to answer a different set of questions. These differences between the two models are likely to result in different predictions of the population effects of particular development scenarios, and a direct comparison of model predictions is only likely to be informative if input parameters are aligned and model outputs are carefully analysed.