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Monday, January 25 • 3:00pm - 3:20pm
Assessing Factors Influencing Population Dynamics In Lake Huron Fish Communities Using Dynamic Factor Analysis

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AUTHORS: Andrew Dennhardt*, Michigan State University; James Bence, Michigan State University; Travis Brenden, Michigan State University; Brian Maurer, Michigan State University; William Fetzer, Michigan State University; Catherine Riseng, University of Michigan; Kevin Wehrly, University of Michigan; Danielle Forsyth, University of Michigan

ABSTRACT: An important component to understanding the nature and structure of biodiversity in ecosystems is investigating how communities respond to changes in the environment in both space and time. Hastening our inquiries for better or worse, systems of great value to global biodiversity often contain ecological communities of considerable socioeconomic value as well. One such ecosystem is the Laurentian Great Lakes and its constituent waterways in North America. Amidst the world’s largest surface freshwater system, Lake Huron supports a 5-year commercial fishery worth $4.6 million on average. Though Lake Huron fish communities face numerous pressures from various ecological agents, anthropogenic or otherwise, factors associated with species’ population dynamics are poorly understood due to deficient data across spatiotemporal scales. This motivates an important question: what factors influence fish populations in spatially-structured communities over time? To answer this question, we obtained abundance data on 12 fish species at five sites unevenly summarized during 1979 – 2012 in Lake Huron. Following pilot investigations that described null models of species’ abundance, we assessed these data for their linear association with environmental factors in Lake Huron. To elucidate population relationships to these variables, we applied multiple candidate models using dynamic factor analysis (DFA), a state-space tool for multivariate time series' data. Preliminary results illustrate that factors associated with local harvest biomass as well as remotely-sensed upwelling events and water temperatures disproportionately impact fish populations in the lake. Furthermore, variable influences differ with respect to common temporal trends in fish abundance and associated factor loadings on particular species. In lieu of auxiliary environmental data, more of this ecological story remains to be told. To date, this community-level assessment demonstrates both the power and utility of DFA as a tool for describing ecological patterns that constrain wild populations—factors useful to biodiversity conservation and management in the Anthropocene.

Monday January 25, 2016 3:00pm - 3:20pm EST
Vandenberg B