Confidence in ecological indicators: A framework for quantifying uncertainty components from monitoring data. (August 2016) - Carstensen, Jacob
Highlights: Monitoring data are affected by many different sources of uncertainty. Large datasets are needed to quantify all the different uncertainty components. Indicator bias and uncertainty can be reduced through improved modeling. Monitoring programs can be optimized to obtain more precise indicators. Abstract: The value of an ecological indicator is no better than the uncertainty associated with its estimate. Nevertheless, indicator uncertainty is seldom estimated, even though legislative frameworks such as the European Water Framework Directive stress that the confidence of an assessment should be quantified. We introduce a general framework for quantifying uncertainties associated with indicators employed to assess ecological status in waterbodies. The framework is illustrated with two examples: eelgrass shoot density and chlorophyll a in coastal ecosystems. Aquatic monitoring data vary over time and space; variations that can only partially be described using fixed parameters, and remaining variations are deemed random. These spatial and temporal variations can be partitioned into uncertainty components operating at different scales. Furthermore, different methods of sampling and analysis as well as people involved in the monitoring introduce additional uncertainty. We have outlined 18 different sources of variation that affect monitoring data to a varying degree and are relevant to consider when quantifying the uncertainty of an indicator calculated from monitoring data. However, in most cases it is not possible to estimate all relevant sources of uncertainty from monitoring data from a single ecosystem, and those uncertainty components that can be quantified will not be well determined due to the lack of replication at different levels of the random variations (e.g. number of stations, number of years, and number of people). For example, spatial variations cannot be determined from datasets with just one station. Therefore, we recommend that random variations are estimated from a larger dataset, by pooling observations from multiple ecosystems with similar characteristics. We also recommend accounting for predictable patterns in time and space using parametric approaches in order to reduce the magnitude of the unpredictable random components and reduce potential bias introduced by heterogeneous monitoring across time. We propose to use robust parameter estimates for both fixed and random variations, determined from a large pooled dataset and assumed common across the range of ecosystems, and estimate a limited subset of parameters from ecosystem-specific data. Partitioning the random variation onto multiple uncertainty components is important to obtain correct estimates of the ecological indicator variance, and the magnitude of the different components provide useful information for improving methods applied and design of monitoring programs. The proposed framework allows comparing different indicators based on their precision relative to the cost of monitoring.