Anthony Kaufman

University Honors with Honors in Marine Biology

Major: Marine Biology Minor: Statistics

Supervisor: Stuart Borrett, Biology and Marine Biology

Ecosystem Network Analysis (ascendant perspective) Indicators are Robust to Flux Uncertainty in Lake Sidney Lanier, USA

Understanding how data uncertainty influences ecosystem analysis is critical as we move toward ecosystem based management. Here, we investigate how 17 Ecosystem Network Analysis (ENA) indicators that characterize ecosystem condition are affected by uncertainty in an ecosystem model of Lake Sidney Lanier (USA). We applied ENA to 122 plausible model of the ecosystem developed by Borrett and Osidele (2007, Ecological Modelling, vol 200: pgs 371-387), and then used the coefficient of variation (CV) to compare system indicator variability. We considered Total System Throughput (TST) as a measure of the underlying model uncertainty. We first hypothesized that indicators whose calculation includes the TST would be at least as variable as TST. Second, indicators calculated as ratios, with TST in the numerator and denominator, would tend to be less variable than TST because its influence will cancel. Lastly, the Average Mutual Information (AMI) would be less variable because, unlike TST, it is a bounded function. Our results show that the 17 indicators group into three categories based on 95% confidence intervals determined for the CVs. The first group shows no significant difference from TST. Two clusters consist of CV values lower than that for TST. Ratio indicators along with the AMI are the least variable and thus the most robust to the flux uncertainty. No indicator shows significantly higher CV values. Thus we can be as certain, or more certain, in our output of ENA indicators as we are in the model we put in for the Lake Sidney Lanier, USA ecosystem.