Retrospective Analysis Of A Multi-Decadal Phytoplankton Time-Series In Naragansett Bay: Stressors, Resilience, Change, And Ecological ThresholdsEPA Grant Number: R832443
Title: Retrospective Analysis Of A Multi-Decadal Phytoplankton Time-Series In Naragansett Bay: Stressors, Resilience, Change, And Ecological Thresholds
Investigators: Smayda, Theodore J. , Borkman, David G.
Institution: University of Rhode Island
EPA Project Officer: Hiscock, Michael
Project Period: July 1, 2005 through June 30, 2007
Project Amount: $296,574
RFA: Exploratory Research: Understanding Ecological Thresholds In Aquatic Systems Through Retrospective Analysis (2004) RFA Text | Recipients Lists
Research Category: Ecosystems , Water , Aquatic Ecosystems
A retrospective analysis of a 38-year (1959-1996) phytoplankton and habitat time series in Narragansett Bay based on weekly quantitative measurements will evaluate changes in ecological thresholds induced by patterns and trends in anthropogenic and climate change variables leading to altered short- and long-term behavior observed in phytoplankton biomass and species. The data indicate that the ecosystem resilience and ecological thresholds of Narragansett Bay, a representative coastal marine habitat, have varied over the four decade period of observations.
Four main objectives will be pursued: (1) to detect and identify regime shifts in phytoplankton species and biomass behavior; (2) to quantify phytoplankton community resilience to change and identify phytoplankton change thresholds utilizing an energetic modeling approach (exergy analysis); (3) to test the hypothesis that abrupt ecological switches (thresholds) leading to altered stable states are underlain by a series of intersecting, gradual long-term changes in one or more combinations of physical-chemical and biological parameters (phytoplankton, zooplankton, grazers); (4) to apply a phyotoplankton community ordination approach seeking to develop an “early warning” system presaging regime shifts for use in estuarine systems.
These objectives will be achieved through a combination of statistical and time series analyses, ecological and energetic models, and multivariate ordination procedures. The analyses will improve our basic understanding of aquatic ecosystem resilience, thresholds, and the role of external v. internal drivers.
The results will aid management practices, such as updating monitoring procedures, use of early warning indices, and improved risk assessment. The results will also help to establish whether the global epidemic of harmful algal blooms is symptomatic of disequilibrated coastal ecosystems undergoing transition into a new, stable state, or has lesser ecological significance. Insight into this can influence mitigation strategies.