ESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME‐SERIES DATA

By Anthony R. Ives, Brian Dennis, K. Cottingham, Stephen R. Carpenter. 2003

534 citations recorded by the source corpus.

Natural ecological communities are continuously buffeted by a varying environment, often making it difficult to measure the stability of communities using concepts requiring the existence of an equilibrium point. Instead of an equilibrium point, the equilibrial state of communities subject to environmental stochasticity is a stationary distribution, which is characterized by means, variances, and other statistical moments. Here, we derive three properties of stochastic multispecies communities that measure different characteristics associated with community stability. These properties can be estimated from multispecies time-series data using first-order multivariate autoregressive (MAR(1)) models. We demonstrate how to estimate the parameters of MAR(1) models and obtain confidence intervals for both parameters and the measures of stability. We also address the problem of estimation when there is observation (measurement) error. To illustrate these methods, we compare the stability of the planktonic commun...

Subfields: Ecology, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/ff2da6eff8b9d48333de57d65bf67663d7cca4ec

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Citations in the corpus

How to analyze long-term insect population dynamics under climate change: 50-year data of three insect pests in paddy fields (2005, methodology): “We use an approach similar to the one used by Ives et al. (2003) in analyzing the interaction between species in time-series data on the limnological community.”
ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR (2006, methodology): “Ives et al. (2003) discuss further the concepts of stability and equilibrium in stochastic systems.”
Interaction strengths in food webs: issues and opportunities (2004, background): “Thus, one disconnect between theory and experiment arises from the fact that many theoretical analyses make predictions that are logistically difficult to test empirically (Ives et al. 2003).”
POPULATION TIME SERIES: PROCESS VARIABILITY, OBSERVATION ERRORS, MISSING VALUES, LAGS, AND HIDDEN STATES (2004, background): “Certain types of density dependence can be linearized, the Gompertz model being an example (e.g., Ives et al. 2003).”
Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context (2017, background): “…of species associations are more representative of true interactions than raw co-occurrence indices (Stone & Roberts 1990; Gotelli 2000), especially if inference is derived from partial correlations (Ovaskainen et al. 2016a) or from time-series data (Ives et al. 2003; Thorson et al. 2016).”
Detecting dynamical changes in nonlinear time series using locally linear state‐space models (2012, background): “Values of k near zero imply that the state variable is drawn rapidly towards the mean b0, and as values approach one, the central tendency of the time series diminishes (Ives et al. 2003).”
Slowing down as an early warning signal for abrupt climate change (2008, methodology): “This allows us to use the time series analysis approaches suggested earlier for detecting slowing down (17, 18, 20) on real reconstructed climate records.”
Testing the roles of competition, facilitation and stochasticity on community structure in a species‐rich assemblage (2014, background): “the impact on species abundance) of interspecific interactions in plant communities have been measured in the field, often only weak effects are found (Rees, Grubb & Kelly 1996; Law, Herben & Dieckmann 1997; Freckleton et al. 2000; Ives et al. 2003; Adler et al. 2006; Lamb & Cahill 2008; Mutshinda, ”
MEASUREMENT OF INTERACTION STRENGTH IN NATURE (2005, methodology): “Serial correlation can be easily misinterpreted as an important process, such as density dependence (Ives et al. 2003).”
Fundamentals of Microbial Community Resistance and Resilience (2012, methodology): “Thus, multivariate autoregressive moving-average models, which remove temporal autocorrelation, have been applied to understand the contribution of species and environmental interactions to the stability of communities (e.g., Ives et al., 2003).”
Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data (2012, background): “Values of l near zero imply that the state variable returns rapidly towards the mean; this central tendency diminishes as values approach one [22].”
Sixty years of environmental change in the world's largest freshwater lake – Lake Baikal, Siberia (2008, methodology): “MATLAB (The Mathworks Inc., Natick, MA, USA) code for MAR models has been made publicly available by Ives et al. (2003) through Ecological Archives.”
Joint dynamic species distribution models: a tool for community ordination and spatio‐temporal monitoring (2016, background): “Increased emphasis on mechanism could also be incorporated into future JDSDMs by estimating a ‘species interaction matrix’ that is used to approximate density-dependent interactions among species (Ives et al ., 2003).”