Materials and Methods
Sample preparation
Diatom sampling was performed at 114 sites along the South Korean coast
(Fig. 1 and Table S1) during January and February 2010. We chose sites
accessible from the coast for phytoplankton netting, and that were
< 40 km apart in order to achieve full coverage of Korean
waters. One liter water samples were collected in clean polyethylene
bottles for quantitative analysis, and phytoplankton samples were taken
using a 20 μm mesh net for qualitative analysis. Collected samples were
immediately fixed with 5% Lugol’s solution (Sigma, St. Louis, MO, USA)
and transported to the laboratory. Environmental data, including
temperature, salinity, and pH were measured in-situ , using a
YSI-6600 portable meter (YSI; Yellow Springs, OH, USA).
Diatom assemblage
analysis
The fixed water samples were allowed to settle for 1 d, and then the
supernatant was removed to concentrate the phytoplankton. Total diatom
abundance in each 1 L water sample was determined (the minimum found was
600 cells per sample) using a Sedgwick–Rafter counting chamber under a
light microscope (LM, Axioskop 40; Zeiss, Germany), and diatom diversity
and sample composition were determined.
To identify the diatom species positively, cellular organic material was
removed using equal amounts of KMnO4 and HCl in a 70℃
water bath until the sample became clear, and then the acid was removed
using five rinses. Selected cleaned samples were mounted in a Pleurax
(cat. no.139-06682, Wako, Japan) and observed under the LM equipped with
a CCD camera (AxioCamMRc5; Zeiss, German). For examination using a
scanning electron microscope (SEM, JSM7600F, Jeol, Tokyo, Japan), the
rest of the cleaned samples were filtered onto a polycarbonate membrane
(3.0-μm pore size; TSTP02500, Millipore, Bedford, MA, USA), which was
then dried in air. The filtrated membranes were attached to an aluminium
stub using carbon tape and then sputter-coated with gold. The SEM was
operated at accelerating voltages of 5 kV using a 10 mm working
distance.
Statistical analysis
Species that contributed ≥ 1% of the total diatomic assemblage in at
least one sample were selected for numerical analysis resulting in 156
diatom taxa being used. Diatom assemblage diversity was calculated using
the Shannon–Wiener diversity index (Shannon and Weaver 1949). The
absolute abundance of each species was transformed by its fourth root
into normalizing skewed composition, data and pairwise distances between
sampling sites were calculated using the Bray–Curtis similarity
algorithm.
To identify spatial similarity between sampling sites, we performed
eight hierarchical clustering methods based on the pairwise distance
matrix. These were as follows: the single linkage method, the complete
linkage method, the unweighted pair-group method using arithmetic
averages (UPGMA), the weighted pair-group method using arithmetic
averages (WPGMA), the unweighted pair-group method using centroids
(UPGMC), the weighted pair-group method using centroids (WPGMC), and two
variants of ward’s minimum variance method (ward.D and ward.D2). The
degrees of data distortion from the eight methods were then assessed
based on cophenetic correlation coefficients (Sokal and Rohlf 1962).
Pairwise distances between sampling sites were calculated in the
“vegan” package ((Oksanen, et al. 2013), and clustering was visualized
using the “factoextra” package (Kassambara and Mundt 2017), both in R
(the R Project for Statistical Computing, supported by the R Foundation
for Statistical Computing).
The indicator value method (IndVal) was applied to identify indicator
species among the groups of sites using the “indicspecies” method in R
(De Cáceres 2013). The IndVal values ranged from zero for “not an
indicator species” to one for “maximum indicator ability.”