Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models
Stephanie E. Hampton, Elizabeth E. Holmes, Lindsay P. Scheef, Mark D. Scheuerell, Stephen L. Katz, Daniel E. Pendleton, and Eric J. Ward
Long-term
ecological data sets present opportunities for identifying drivers of
community dynamics and quantifying their effects through time series
analysis. Multivariate autoregressive (MAR) models are well known in
many other disciplines, such as econometrics, but widespread adoption of
MAR methods in ecology and natural resource management has been much
slower despite some widely cited ecological examples. Here we review
previous ecological applications of MAR models and highlight their
ability to identify abiotic and biotic drivers of population dynamics,
as well as community-level stability metrics, from long-term empirical
observations. Thus far, MAR models have been used mainly with data from
freshwater plankton communities; we examine the obstacles that may be
hindering adoption in other systems and suggest practical modifications
that will improve MAR models for broader application. Many of these
modifications are already well known in other fields in which MAR models
are common, although they are frequently described under different
names. In an effort to make MAR models more accessible to ecologists, we
include a worked example using recently developed R packages (MAR1 and
MARSS), freely available and open-access software.
Read More: http://www.esajournals.org/doi/abs/10.1890/13-0996.1
