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- Title
Omic-style statistical clustering reveals old and new patterns in the Gulf of Maine ecosystem.
- Authors
Record, Nicholas R.; O'Brien, John D.; Stamieszkin, Karen; Runge, Jeffrey A.
- Abstract
The burgeoning of omic technology has spawned a new subfield of statistics aimed at interpreting the complex information contained in omic data. Some of these statistical methods can be applied to any data set with taxonomic counts, and they have the potential to provide additional insights over traditional approaches. We test this potential by reanalyzing a well-studied zooplankton data set - the Gulf of Maine continuous plankton recorder series - using a modified Dirichlet-multinomial mixture (DMM) model. The data set has ∼50 years of approximately monthly samples along a transect from Boston, USA, to Yarmouth, Canada. The results from the DMM analysis were largely consistent with previous analyses but also provided new insights. Notably, the Calanus-dominated communities that returned following a reduction in the 1990s showed a loss of background diversity, suggesting a shift in sources and possibly higher vulnerability of these communities. The DMM analysis also revealed a breakdown of seasonal ecological succession in the 1990s. These changes could be a precursor to similar changes in other Calanus-dominated systems. The approach demonstrates a path toward linking traditional analyses with recent omic-style analyses.
- Subjects
GULF of Maine; UNITED States; FISHERIES; FISHERY sciences; FISHERY laws; WILDLIFE conservation laws; TWENTIETH century; ECOLOGY
- Publication
Canadian Journal of Fisheries & Aquatic Sciences, 2017, Vol 74, Issue 7, p973
- ISSN
0706-652X
- Publication type
Article
- DOI
10.1139/cjfas-2016-0151