Material and Methods
Study area
The study was conducted in Lianhuan Lake. Lianhuan Lake, formed by
tectonic slump, consists of 18 small lakes around, is located in the
most low-lying center of the Songnen Plain in eastern China (Fig. 1).
The lake has a mean depth of 2.14 m, a maximum depth of 4.6 m, and a
surface area of 580 km2 (G. Yu, Harrison, & Xue,
2001). Lianhuan Lake become the first international waterfowl and
hunting ground since 1985 in China. It lies in the cold temperate zone,
characterized by semiarid climate. The average annual precipitation in
the Lianhuan Lake catchment is 400 mm, 70 % of which occurs in the
summer (June to August). During March to May it is generally dry with a
highest frequency of dust storms. Apart from direct precipitation, the
lake also receives water from Wuyuer and Shuangyang rivers through the
southern part. Rapid economic development around the lake has led to
large quantities of wastewater, from agricultural, industrial, and
domestic sources, being released into the lake. In this study, 13 small
lakes around the Lianhuan Lake were considered. The 13 lakes are
different in natural forms and area and they exhibit different
characters such as nutrients levels, fisheries community, and human
disturbance patterns. Interestingly, these lakes are connected to each
other. Among the 13 small lakes
covered in this study, Habuta lake is geographically located far from
the other lakes. Delong Lake, Yangcaohao Lake and Beiqin Lake are
located in the upper reaches of
Lianhuan lake. Durbote county is
located to the east of these lakes (R. Wang et al., 2019).
Macroinvertebrate sampling
Sampling took place in spring (June), summer (August), and autumn
(October) of 2021 at the at each site (Fig. 1). At each site,
quantitative Petersen grab samples (0.025 m2) were
collected in replicates and processed through a 300 μm mesh size sieve.
Petersen grab and the mesh size sieve were visually inspected to ensure
macroinvertebrates adhering to the grab and sieve were transferred to
the composite sample. All materials were placed into a plastic jar and
preserved in 80% alcohol. In the laboratory, all organisms in the
samples were counted, weighed to the nearest 0.1 g and identified to
genus or to the lowest taxonomic level possible. Species abundance for
each sampling site was calculated as a density (individuals
m-2) and biomass (gm-2) was
calculated by adding the biomass of all species. The identification and
classification of the macroinvertebrates was done according to (Dudgeon,
1999; Morse, Yang, & Tian, 1994; H. Wang, 2002)
Environmental variables
The Lake surface area was calculated using ArcGIS (ver. 10.7). At each
sampling site, water temperature (WT,℃), conductivity (COND,μS/cm), pH,
and dissolved oxygen (DO,mg/L) were measured in the field using a
portable YSI Professional Plus instrument (made in the USA). Water depth
(WD, m) was measured with a Speedtech handheld depth finder (made in the
USA). Water was collected directly into 5-L polypropylene bottles at a
depth of 0.5 m to quantify water chemistry factors. The water chemical
including total phosphorus (TP, mg/L), total nitrogen (TN, mg/L),
ammonium (NH4-N, mg/L), nitrate (NO3-N,
mg/L), nitrite (NO2-N, mg/L), chemical oxygen demand
(CODMn, mg/L), Chlorophyll a concentration (Chla,
mg/L), and Suspended substance (SS, mg/L), were measured in the
laboratory based on standard methods (Federation & Association, 2005).
Chlorophyll a concentration (Chla, mg/L) was determined according
to the protocols for standard observation and measurement in aquatic
ecosystems by filtering 100 mL of the sampled water through GF/C whatman
filter. Pigments extraction was done in 90% aqueous solution of acetone
and Chla concentrations were measured spectrometrically
Classification of macroinvertebrate communities using Self-Organising
Map
In
order to analyze the classification macroinvertebrate communities,
Self-organizing map (SOM) was applied for classifying sampling points
according to the species’ abundance. SOM is an effective method for
cluster analysis, which has a high explanatory ability in the study of
ecological population(Giraudel & Lek, 2001). SOM consists of two layers
of neurons, namely the input layer and the output layer connected by
connection intensities (weights). Input layers acquire information from
a data matrix, and output layers visualize the computational results
(Song et al., 2007). In this study, the input layer was composed of
species abundance and 74 sample points. The number of neurons in the
output layer was determined in advance according as 5
×\(\sqrt{\text{numberofsamplepoints}}\) ≈ 43 (Park, Céréghino, Compin,
& Lek, 2003), and according to the minimum quantization error and the
minimum topographic error (see Appendix S1 in Table S1). The optimal
number of neurons in the output layer was determined to be 49 (Kohonen,
2001). Since SOM output layer has no distinct classification boundaries.K -means clustering analysis was performed on the SOM output layer
neurons and classified the neurons into different groups. The Simple
structure index (SSI) which is the value indicating the relative
importance of each species in determining the distribution patterns of
the samples in the SOM was then used to determine the optimal number of
groups (Park et al., 2006). Therefore, the larger the values of SSI the
higher the clustering quality (Dimitriadou, Dolničar, & Weingessel,
2002; Park et al., 2006).
Indicator species is a species closely related to environmental changes,
used to measure the specificity and fidelity of a species to a certain
environmental state (McGeoch, Van Rensburg, & Botes, 2002). Indicator
values (IndVal) of all species in
each group were calculated to
determine the indicator species in each
group.
A 1000 permutations were performed
to access the significance
(P < 0.05) of the
IndVal observed for each species (Arimoro & Keke, 2021).
The calculation formula of IndVal was done as follows (Dufrêne &
Legendre, 1997):