Decision evaluation in complex risk network systems (DecernsSDSS) demo and Black River case study

The selection of metrics for ecosystem restoration programs is critical for improving the quality of monitoring programs and characterizing project success. Moreover it is oftentimes very difficult to balance the importance of multiple ecological, social, and economical metrics. Metric selection process is a complex and must simultaneously take into account monitoring data, environmental models, socio-economic considerations, and stakeholder interests. We propose multicriteria decision analysis (MCDA) methods, broadly defined, for the selection of optimal sets of metrics to enhance evaluation of ecosystem restoration alternatives. Two MCDA methods, a multiattribute utility analysis (MAUT), and a probabilistic multicriteria acceptability analysis (ProMAA), are applied and compared for a hypothetical case study of a river restoration involving multiple stakeholders. Overall, the MCDA results in a systematic, unbiased, and transparent solution, informing restoration alternatives evaluation. The two methods provide comparable results in terms of selected metrics. However, because ProMAA can consider probability distributions for weights and utility values of metrics for each criteria, it is suggested as the best option if data uncertainty is high. Despite the increase in complexity in the metric selection process, MCDA improves upon the current ad-hoc decision practice based on the consultations with stakeholders and experts, and encourages transparent and quantitative aggregation of data and judgement, increasing the transparency of decision making in restoration projects. We believe that MCDA can enhance the overall sustainability of ecosystem by enhancing both ecological and societal needs.

(Figure 2) Mean discharge per day (total and baseflow part) of the San Francisco River and daily precipitation at station ECPL (Planta), in 2007

We investigated controls on the water chemistry of a South Ecuadorian cloud forest catchment which is partly pristine, and partly converted to extensive pasture. From April 2007 to May 2008 water samples were taken weekly to biweekly at nine different subcatchments, and were screened for differences in electric conductivity, pH, anion, as well as element composition. A principal component analysis was conducted to reduce dimensionality of the data set and define major factors explaining variation in the data. Three main factors were isolated by a subset of 10 elements (Ca2+, Ce, Gd, K+, Mg2+, Na+, Nd, Rb, Sr, Y), explaining around 90% of the data variation. Land-use was the major factor controlling and changing water chemistry of the subcatchments. A second factor was associated with the concentration of rare earth elements in water, presumably highlighting other anthropogenic influences such as gravel excavation or road construction. Around 12% of the variation was explained by the third component, which was defined by the occurrence of Rb and K and represents the influence of vegetation dynamics on element accumulation and wash-out. Comparison of base- and fast flow concentrations led to the assumption that a significant portion of soil water from around 30 cm depth contributes to storm flow, as revealed by increased rare earth element concentrations in fast flow samples. Our findings demonstrate the utility of multi-tracer principal component analysis to study tropical headwater streams, and emphasize the need for effective land management in cloud forest catchments.

Habitat map of Danajon Bank, Philippines, derived from a high-spatial-resolution multi-spectral satellite image and georeferenced point intercept transect and spot-check survey field data, using an object-based image classification method

A mosaic of two WorldView-2 high resolution multispectral images (Acquisition dates: October 2010 and April 2012), in conjunction with field survey data, was used to create a habitat map of the Danajon Bank, Philippines (10°15'0'' N, 124°08'0'' E) using an object-based approach. To create the habitat map, we conducted benthic cover (seafloor) field surveys using two methods. Firstly, we undertook georeferenced point intercept transects (English et al., 1997). For ten sites we recorded habitat cover types at 1 m intervals on 10 m long transects (n= 2,070 points). Second, we conducted geo-referenced spot check surveys, by placing a viewing bucket in the water to estimate the percent cover benthic cover types (n = 2,357 points). Survey locations were chosen to cover a diverse and representative subset of habitats found in the Danajon Bank. The combination of methods was a compromise between the higher accuracy of point intercept transects and the larger sample area achievable through spot check surveys (Roelfsema and Phinn, 2008, doi:10.1117/12.804806). Object-based image analysis, using the field data as calibration data, was used to classify the image mosaic at each of the reef, geomorphic and benthic community levels. The benthic community level segregated the image into a total of 17 pure and mixed benthic classes.