Evaluating community impacts of ocean acidification using qualitative network models

Abstract

We applied Qualitative Network Models (QNMs) to evaluate the potential community effects of ocean acidification (OA) in a major shellfish-producing estuary (Willapa Bay, Washington). QNMs are well-suited to data-limited systems and only require information on the sign (+, -, 0) of the interactions between species. We examined qualitative predictions of community responses to 13 different OA scenarios that corresponded to 3 broad categories of hypothesized OA effects: (1) increased primary productivity, (2) reductions in bivalve populations, and (3) enhanced predation interactions between bivalves and their crab and gastropod predators. The cultivated bivalve Manila clam tended to respond negatively across scenarios, while primary producers (phytoplankton and eelgrasses) and Chinook salmon tended to respond positively. Tradeoffs between species were also assessed: Manila clam and Pacific oyster were predicted to decrease and increase, respectively, when direct OA effects were limited to eelgrasses and the reverse occurred when phytoplankton alone was stimulated by OA. We analyzed the QNMs to identify key linkages that influenced the sign outcome of community members and might therefore warrant future quantitative study. QNMs may be particularly relevant to researchers as a simple method for identifying conditions under which the sign response of species to OA, as inferred from single-species OA experiments, will likely hold in the wild. Given data limitations in most systems, QNMs are a practical alternative or complement to data-intensive quantitative ap - proaches and may help accelerate our understanding of the community-wide effects of OA in marine systems.