Universal multifractal analysis as a tool to characterize multiscale intermittent patterns: example of phytoplankton distribution in turbulent coastal waters

The phosphorus transfer continuum: Linking source to impact with an interdisciplinary and multi-scaled approach

“…Analyses of high-frequency data in such waters (at least daily; preferable hourly or at scales of minutes) have revealed multi-fractal scaling properties and the presence of information buried in the correlation structures (Harris, 2003;Harris and Heathwaite, 2005;Seuront et al, 1999). So dnoisyT water quality data, which are often collected at weekly intervals or longer, should not be averaged to reduce the dnoiseT, and traditional statistical and empirical approaches to limnology may be also missing much important fine scale information (Harris, 2003;Harris and Heathwaite, 2005).…”

Microscale Complexity in the Ocean: Turbulence, Intermittency and Plankton Life

“…In a 74-page chapter devoted to intermittency, Frisch [21] then only states that a process is intermittent when it "displays activity during only a fraction of the time, which decreases with the scale under consideration." Intermittency has similarly been described as "the active turbulent regions do not fill the whole volume, but only a subvolume in a very irregular way" [31] and "active regions occupy tiny fractions of the space available" [6].…”

“…Numerous experimental data analyses done in different frameworks and geographical regions have shown that physical and biological patterns and processes in marine sciences display high intermittency [1][2][3][4][5][6][7][8][9][10][11]. This intermittency may correspond to a basic property of aquatic ecosystems, as sharp, local fluctuations are ubiquitously observed in space-time distributions of turbulent dissipation rates, temperature, salinity, nutrient and plankton concentration [3][4][5][6][7][10][11][12] and the motion behaviour of both benthic and pelagic organisms [13][14][15].…”

“…This intermittency may correspond to a basic property of aquatic ecosystems, as sharp, local fluctuations are ubiquitously observed in space-time distributions of turbulent dissipation rates, temperature, salinity, nutrient and plankton concentration [3][4][5][6][7][10][11][12] and the motion behaviour of both benthic and pelagic organisms [13][14][15]. Practically, the occurrence of high density peaks positively skews a given probability distribution, causing it to deviate from Gaussianity (in which case the skewness is nil, and the probability distribution symmetric).…”

Sub-kilometer length scales in coastal waters

“…Considerable attention has been given to determine scales of biological and physical oceanographic processes and to identify the drivers of plankton patchiness. These questions have been addressed using a variety of different methods, including spectral analysis (Denman and Platt, 1976;Losee et al, 1989;Lovejoy et al, 2001;Washburn et al, 1998;Wiebe et al, 1996), autocorrelation or autocovariance functions (Chang et al, 2002;Mackas, 1984;Yu et al, 2002) correlograms or variograms (Dustan and Pinckney, 1989;Mackas, 1984;Yoder et al, 1987), wavelet analysis (Deutschman et al, 1993;Machu et al, 1999;Charria et al, 2003), and multifractal analysis (Seuront et al, 1996(Seuront et al, , 1999. Better understanding of the scales of variability can be used to help identify the physical and biological processes structuring biomass distribution and community structure, and help distinguish which processes are responsible at different scales (Chang et al, 2002;Cunningham et al, 2003;Dustan and Pinckney, 1989;Seliger et al, 1981).…”