Quantifying change in pelagic plankton network stability and topology based on empirical long-term data |
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Affiliation: | 1. University of Adelaide, School of Biological Sciences, Adelaide 5005, Australia;2. Israel Oceanographic and Limnological Research, Kinneret Limnological Laboratory, Migdal 14950, Israel;3. Center for Eco-Environmental Research, Nanjing Hydraulic Research Institute, Nanjing 210000, China |
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Abstract: | Over the last 34 years, Lake Müggelsee has experienced concurrent warming and nutrient reduction. While the effects of environmental change on single taxonomic or physical–chemical variables have been relatively well researched in isolation, understanding how environmental change propagates through the ecological network remains a major challenge. Capitalizing on the long-term monitoring program of the German Long-Term Ecosystem Research Network site Lake Müggelsee (1979-ongoing), we identified three time periods (1979–1995; 1996–2005; 2006–2013) which differed significantly in phytoplankton biomass and relative plankton community composition. Using multivariate first order autoregressive (MAR1) modeling on 13 pelagic plankton groups and four abiotic variables, we quantified interaction networks and indicators of stability and centrality for each period. Our results suggested that the Müggelsee network was bottom-up regulated in all periods and that stability increased over time. Moreover, in all three networks, non-trophic and indirect interactions appeared to be as commonly present as trophic and direct interactions. Using network centrality measures of betweenness and closeness, we identified keystone plankton groups and groups particularly responsive to environmental change based on variation in centrality ranks over time. Given a more comprehensive understanding of the interaction network at hand, MAR1 model-derived stability and centrality measures may potentially be used as integrated ecological indicators to monitor changes in stability of lake ecosystems and to identify particularly vulnerable components of the network. |
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Keywords: | Community stability Interaction networks Long-term research Network centrality |
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