2024-03-29T08:14:48Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1401642019-03-05T11:15:56Zcom_10261_123com_10261_8col_10261_376
00925njm 22002777a 4500
dc
Aguilera, Rosa
author
Livingstone, David R.
author
Marcé, R.
author
Jennings, Eleanor
author
Piera, Jaume
author
Adrian, Rita
author
2016
Currently, many water quality variables can be automatically monitored at subdaily frequencies, but most environmental datasets still rely heavily on comparatively low-frequency (e.g., monthly) sampling campaigns. Taking advantage of the long-term ecological data available from polymictic Lake Müggelsee (Germany) at weekly temporal scales, we tested whether dynamic factor analysis (DFA), a dimension-reduction technique especially designed for time series with data gaps, was able to reproduce the same underlying patterns if the resolution of the observations was artificially reduced from weekly to once every 2 or 4 weeks (2-weekly or 4-weekly, respectively) and whether the same results were obtained for different variables (water temperature, water chemistry, and plankton). As expected, the reduction in data resolution and the addition of gaps increased the amount of variance in our case studies; however, water temperature patterns, which generally vary only slowly if considered at broader temporal scales, were represented well in the 4-weekly data model. By contrast, total algal biomass and nutrient dynamics, which usually fluctuate more rapidly, were not. We further assessed the representativeness of spot samples taken by selection at 4-weekly and seasonal intervals compared to samples obtained by averaging the weekly data over these 2 intervals. In this case, the resulting observation variance was more pronounced in DFA models of the samples taken by selection than of those obtained by averaging. The introduction of additional artificial gaps in one univariate and one multivariate model decreased the amount of information that could be explained by the pattern(s) extracted by DFA. Overall, we found that a too low sampling frequency could be a limiting factor when studying interactions and responses to changing conditions in aquatic systems, particularly when biological variables are involved
Inland Waters : Journal of the International Society of Limnology 6(3): 284-294 (2016)
http://hdl.handle.net/10261/140164
10.5268/IW-6.3.948
Dynamic factor analysis
Müggelsee
Sampling resolution
Time series
Using dynamic factor analysis to show how sampling resolution and data gaps affect the recognition of patterns in limnological time series