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Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models
Author(s) -
Hampton Stephanie E.,
Holmes Elizabeth E.,
Scheef Lindsay P.,
Scheuerell Mark D.,
Katz Stephen L.,
Pendleton Daniel E.,
Ward Eric J.
Publication year - 2013
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/13-0996.1
Subject(s) - abiotic component , ecology , multivariate statistics , population , autoregressive model , biotic component , geography , computer science , econometrics , biology , machine learning , mathematics , demography , sociology
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.