Publications: Research reports and publications

Validation and limitations of a cumulative impact model for an estuary

1 February, 2016
CITATION

Clark D, Goodwin E, Sinner J, Ellis J, Singh G 2016. Validation and limitations of a cumulative impact model for an estuary. Ocean & Coastal Management 120: 88-98.

DOI link here.

ABSTRACT

Comprehensive marine management approaches, such as ecosystem based management and marine spatial planning, would benefit from quantitative, spatially explicit estimates of the cumulative impact of human activities on marine ecosystems. In this study, a method to map and quantify cumulative impact was applied to estimate the combined impact of multiple stressors on Tauranga Harbour, a large estuary in New Zealand. The impact of eight stressors on seven ecosystems was assessed at a 100 m resolution, using New Zealand-specific expert judgement on the vulnerability of different ecosystems to each stressor. Estimated cumulative impact tended to be highest in the southern basin and inner estuaries, corresponding with sensitive ecosystems and multiple stressors and reflecting what is known about the distribution of pressures in Tauranga Harbour. Using benthic community data as an independent estimate of ecological condition, we had the novel opportunity to validate cumulative impact predicted from the model. Only a weak relationship was found between the estimated cumulative impact and measured ecological condition and several reasons for this are considered. Substitution of a more realistic sediment layer improved model outputs, highlighting the importance of accurate input data, particularly for stressors or ecosystems with high impact weights. Different standardisation methods did not greatly affect the spatial distribution of cumulative impact patterns in the harbour. The study highlights some fundamental issues for consideration when using this cumulative impact mapping approach, such as the importance of involving the expert panel throughout the course of the study and the availability and quality of the data used to construct the model.