1 1.10 Application of estuarine and coastal classifications in marine ...

approach. New classifications will be required that represent land-sea interactions and are
spatially seamless between landscapes and seascapes.
1.10.12.2 Understanding and Communicating Errors and Uncertainty
An important challenge for management is to ensure that decision making where
ecological goals and objectives are central, such as conservation prioritization, are objective and
science driven and not driven primarily by data availability or technological advancement. In
reality the desire for a rapid, quantitative and defensible approach can sometimes override
uncertainty in the data. Very rarely are the uncertainties quantified and communicated in a
spatially explicit way before marine classifications are applied in management. For robust
decision making to take place, more effort must be focused on evaluating data errors, bias and
uncertainty and this will become very important in MSP where a diverse array of spatial datasets
are being assembled together with novel untested procedures for developing classified maps.
Uncertainty comes in a variety of forms and representations and requires different techniques for
presentation. It is important to acknowledge that a classification scheme is a model of reality and
in many cases is a 2 nd or even 3 rd derivative of the original data. Misclassifications occur and can
be due to spatial accuracy, observer bias, environmental variability, processing techniques,
thematic, temporal and spatial resolution, etc. Some will say that all maps are a “lie” and that
decisions can be made at various stages to make maps “lie” (Monmonier 1996). Inevitably some
maps represent more of the true reality than others. Error is particularly problematic when a
highly dynamic system is represented by a static map, yet management decisions will and must
be made on best-available data. As noted by Alfred Russell Wallace in 1876 “nothing like a
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