Abstract
AbstractThere is a growing need to easily describe and synthesize the dynamics of ecosystems’ components in space and time. Most multivariate analyses provide ordination diagrams or biplots that are too cluttered to allow simple reading and are unfamiliar to most users. To overcome such difficulties, a novel application of principal response curves (PRCs) is proposed. Principal response curves are traditionally used to assess treatment effects on community structure measured repeatedly over time. In this new application, the tested factor and the repeated‐observation axis are replaced by time and space, respectively. The georeferencing of sampling sites permits to produce an easy‐to‐read map that summarizes both the temporal dynamics of the community and the contribution of each species to these dynamics at each sampling site. A 24‐yr‐long time series of scientific surveys monitoring 77 fish and cephalopod species in the Eastern English Channel is used to illustrate the novel application of the PRC method. This new application could prove a relevant tool for the ecosystem approach to human activities management by providing spatialized indicators of community changes, as spatial monitoring tools are increasingly recommended for measuring the effectiveness of management actions.
Highlights
Many ecosystems need to be monitored, notably those that provide services to human communities
The principal response curves (PRCs) method was applied on the table of log-transformed fish abundance data according to the new scheme, that is, with time as the tested factor characterized by two modalities and sampling sites as the repeatedmeasures axis
Sampling sites explained 40.1% of spatiotemporal variance in species abundance data, whereas time was responsible for 6.2%, 36.5% of which is represented by the first canonical axis of the PRC analysis, 10.9% by the second one, and 7.1% by the third one
Summary
Many ecosystems need to be monitored, notably those that provide services to human communities. There is a growing need for integrated assessment and ecosystem-based management (Pikitch et al 2004, Link and Browman 2014) This objective requires methods to describe and summarize the complexity of ecosystems’ ecological components, and notably how communities change over time and space. Many multivariate analyses are available to achieve such objective of describing and summarizing community dynamics (Clarke 1993) and have been popularly used for several decades. These statistical methods are useful for reducing the number of dimensions of community data and characterize both their structural patterns and their spatio-temporal dynamics
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