Principal response curves technique for the analysis of multivariate biomonitoring time series

Abstract

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrated.

Application of multivariate statistical techniques in microbial ecology

“…PRC is especially useful for the analysis of longitudinal series of measurements, since time-dependent effects can be clearly isolated in PRC from other effects (see Figure 6B). PRC was further extended by (van den Brink et al , 2009) to use a single reference point as control for each series of measurements, allowing longitudinal analysis of individual communities. Multiple sets of objects can be displayed on the same chart as separate response curves, and time can be substituted by other gradient present in the experimental design or the dataset (ter Braak, Šmilauer, 2015; van den Brink et al , 2009).…”

“…PRC was further extended by (van den Brink et al , 2009) to use a single reference point as control for each series of measurements, allowing longitudinal analysis of individual communities. Multiple sets of objects can be displayed on the same chart as separate response curves, and time can be substituted by other gradient present in the experimental design or the dataset (ter Braak, Šmilauer, 2015; van den Brink et al , 2009). The congruency between each variable response pattern and computed principal response curve is provided by variable weights which are displayed on a separate chart (see Figure 6B; (van den Brink, ter Braak, 1999)).…”

Impacts of an Invasive Non-Native Annual Weed, Impatiens glandulifera, on Above- and Below-Ground Invertebrate Communities in the United Kingdom

“…Differences in the vegetation community composition, as well as temporal differences between invaded and uninvaded plots were analysed using principal response curves (PRC) [42], [43]. The PRC analysis is a special type of redundancy analysis (RDA) that allows for an evaluation of the temporal differences of the community.…”

“…Sampling dates (months) were treated as repeated measurements and data for 2007 and 2008 were treated separately. Monte Carlo permutations [42] were used to test the overall significance of the PRC. To analyse differences in plant species richness between invaded and uninvaded plots, a two factor ANOVA was performed on the total number of plant species per plot, with invasion status and year as fixed effects.…”

Mesocosm validation of the marine No Effect Concentration of dissolved copper derived from a species sensitivity distribution

“…For the evaluation of the response of the plankton community to the treatment, Principal Response Curve (PRC) analyses were performed, as described in (van den Brink et al, 2009;Van den Brink and Ter Braak, 1999). The dominant plankton species were selected and the less abundant species were combined in taxonomic groups.…”