CHALLENGES AND SOLUTIONS TO ANALYZING MARINE COMMUNITIES WITH MULTIVARIATE AUTOREGRESSIVE (MAR) MODELS
Multivariate autoregressive (MAR) models have a long history of use in economics, and have been used successfully to understand trophic interactions and community stability in freshwater plankton communities. Uses of MARs in marine ecosystems have been limited, however, possibly due to several challenges that arise with marine data. For example, many marine monitoring programs 1) use less consistent sampling locations, 2) have more variable time intervals, 3) may have higher observation error, and 4) include highly mobile or transient taxa. Comparison of results from standard MARs applied to point and transect data suggested that information loss in applying standard MARs to transect data can be substantial. Additionally, MAR results typically indicate more trophic links in freshwater communities than comparable marine communities, suggesting that modifications to MARs are necessary when analyzing typical marine data. Development of state-space MARs may help elucidate patterns in marine data by explicitly estimating species interactions in light of both process and observation errors. Extension of Bayesian methods also allows for probabilistic statements about parameters. We demonstrate these improvements in MARs via analyses of several plankton datasets.
This work associated with a CAMEO grant to S. Hampton, S. Katz, E. Holmes, and M. Scheuerell.