Inferring plankton community structure from marine and freshwater long‐term data using multivariate autoregressive models

Abstract

Multivariate autoregressive (MAR) models have been useful in elucidating food web dynamics and stability from freshwater plankton monitoring data, but their applicability to marine datasets has not been as well explored. Characteristics of marine systems, such as the movement of water masses by tides and currents, may present unique challenges to MAR modeling of data gathered in marine environments. To explore the behavior of MAR models with marine plankton data, in the context of what we know about applying MAR to freshwater data, we applied MARs to each of three freshwater and four marine long-term datasets and compared results among them. We generated sets of replicate MAR models for each dataset and used the consistency of models within each set of replicates as a measure of MAR performance. Overall, replicate MAR models generated from the marine datasets were less consistent than those generated from the freshwater datasets, suggesting that MAR methods need fundamental reconfigurations to be applied to standard marine plankton data. Higher variability observed within the marine MAR results may be attributable to weaker biotic interactions as represented by the data, and to overparameterization when the criteria for lumping freshwater plankton taxa into model variables are directly applied to marine plankton taxa. Adjustments to dataset preparation for MAR application and to the modeling framework itself may address these issues associated with analyzing data from highly dynamic systems.

Coastal phytoplankton community dynamics and coexistence driven by intragroup density‐dependence, light and hydrodynamics

“…Salinity is another dominant factor in other coastal areas, that can prove at times even more influential than nutrient loads (Irwin et al 2012, Gasiūnaitė et al 2005. Scheef et al (2013) called for the use of salinity as a discriminating factor for estuarine environments in MAR analyses. Some diatoms have indeed lower growth rates when salinity increases (Balzano et al 2011), which could explain our mostly negative effects on plankton growth.…”

“…Therefore, any framework examining competition among planktonic species should account for both the broad variations in environmental conditions and in planktonic abundances over time. Multivariate autoregressive (MAR) modeling is one such dynamic framework that has been increasingly used to examine interactions between planktonic groups in a dynamic environment (Klug et al 2000, Ives et al 2003, Hampton and Schindler 2006, Huber and Gaedke 2006, Scheef et al 2013, Griffiths et al 2015, Gsell et al 2016. MAR models enable the estimation of interaction strengths between taxa (Ives et al 2003, Mutshinda et al 2009) as well as the dependence of population growth rates on abiotic variables (Hampton et al 2013), both necessary to model planktonic dynamics.…”

Quantifying change in pelagic plankton network stability and topology based on empirical long-term data

“…Here, we make use of the long-term research program installed at the German Long-Term Ecosystem Research Network (LTER-D) site Müggelsee (Germany) to explore how changes in the phyto-and zooplankton biomass and community composition due to anthropogenic pressure affect the structure and stability of the pelagic interaction network utilizing multivariate first order autoregressive modelling (MAR1) and ecological network analysis. MAR1 modeling (Ives et al, 2003) allows the identification and quantification of network interactions and the derivation of stability metrics of ecological networks from long-term data (Hampton et al, 2013;Ives et al, 1999;Scheef et al, 2013). The resulting interaction matrix can also be used to inform ecological network analysis.…”

Spatio-temporal population dynamics of six phytoplankton taxa

“…It is not uncommon that spatio-temporal populationmonitoring data lack replicates for a given location and point in time, which may hamper our ability to estimate observation error variance at the desired scale of observation, and hence, to fit state-space models (Dennis et al, 2010). Observation error is a common issue in marine time-series (Hampton et al, 2013;Scheef et al, 2012), and quality control information from all work stages (from sampling to taxonomical analysis) needs to be considered when plankton data are used for analyses (Zingone et al, 2015).…”