Verification of an Environmental Impact Assessment Using a Multivariate Statistical Model

Abstract

Environmental impact assessment is a means of preventing and mitigating the adverse effects of economic development activities on the natural environment. It is meant to ensure that decision-makers have sufficient information to consider environmental impacts before proceeding with new projects. Despite their important role in public policy, verification of environmental impact assessments has seldom been conducted. In this study, we used principal component analysis (PCA) to identify the major sources of influence on the coastal waters adjacent to a major tourist facility (an aquarium) in southern Taiwan, followed by the construction of a structural equation model (SEM) to determine the direct and indirect effects of the abiotic factors on phytoplankton and zooplankton density and diversity. Based on the loadings of principal components 1–3, we identified that river input, suspended matter, and seasonal changes were the major factors affecting the coastal area. The SEM further suggested that phytoplankton density and diversity were affected directly by seasonal changes and suspended matter, but only indirectly by river input, owing to the latter’s effect on suspended matter. In contrast, the SEM suggested that zooplankton density and diversity were affected directly by seasonal changes, but indirectly by both river input and suspended matter owing to their effects on phytoplankton density and diversity. Q2 was the season with the highest number of visitors to the aquarium, but none of the abiotic or biotic parameters showed particular differences, implying that the variations in those parameters in the adjacent coastal waters were not related to the visitors. We suggest that PCA and SEM be used in the future in other contexts to verify environmental impact assessments.

Full text

Citation: Chou, W.-R.; Hsieh, H.-Y.;
Hong, G.-K.; Ko, F.-C.; Meng, P.-J.;
Tew, K.S. Verification of an
Environmental Impact Assessment
Using a Multivariate Statistical Model. J.
Mar. Sci. Eng. 2022,10, 1023. https://
doi.org/10.3390/jmse10081023
Academic Editor: Michele Arienzo
Received: 28 June 2022
Accepted: 23 July 2022
Published: 26 July 2022
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Journal of
Marine Science
and Engineering
Article
Verification of an Environmental Impact Assessment Using a
Multivariate Statistical Model
Wei-Rung Chou 1, Hung-Yen Hsieh 2, Guo-Kai Hong 2, Fung-Chi Ko 1,2 , Pei-Jie Meng 1,2
and Kwee Siong Tew 1,2,3,4,*
1National Museum of Marine Biology & Aquarium, #2 Houwan Rd., Checheng, Pingtung 944401, Taiwan;
weirung@nmmba.gov.tw (W.-R.C.); ko@nmmba.gov.tw (F.-C.K.); pjmeng@gms.ndhu.edu.tw (P.-J.M.)
2Graduate Institute of Marine Biology, National Dong Hwa University, #2 Houwan Rd., Checheng,
Pingtung 944401, Taiwan; hyhsieh@gms.ndhu.edu.tw (H.-Y.H.); hong.guokai@gmail.com (G.-K.H.)
3Institute of Marine Ecology and Conservation, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
4International Graduate Program of Marine Science and Technology, National Sun Yat-sen University,
Kaohsiung 80424, Taiwan
*Correspondence: kweestew@gmail.com
Abstract:
Environmental impact assessment is a means of preventing and mitigating the adverse
effects of economic development activities on the natural environment. It is meant to ensure that
decision-makers have sufficient information to consider environmental impacts before proceeding
with new projects. Despite their important role in public policy, verification of environmental impact
assessments has seldom been conducted. In this study, we used principal component analysis (PCA)
to identify the major sources of influence on the coastal waters adjacent to a major tourist facility (an
aquarium) in southern Taiwan, followed by the construction of a structural equation model (SEM) to
determine the direct and indirect effects of the abiotic factors on phytoplankton and zooplankton
density and diversity. Based on the loadings of principal components 1–3, we identified that river
input, suspended matter, and seasonal changes were the major factors affecting the coastal area. The
SEM further suggested that phytoplankton density and diversity were affected directly by seasonal
changes and suspended matter, but only indirectly by river input, owing to the latter’s effect on
suspended matter. In contrast, the SEM suggested that zooplankton density and diversity were
affected directly by seasonal changes, but indirectly by both river input and suspended matter owing
to their effects on phytoplankton density and diversity. Q2 was the season with the highest number
of visitors to the aquarium, but none of the abiotic or biotic parameters showed particular differences,
implying that the variations in those parameters in the adjacent coastal waters were not related to the
visitors. We suggest that PCA and SEM be used in the future in other contexts to verify environmental
impact assessments.
Keywords:
phytoplankton; zooplankton; species diversity; coastal waters; principal component
analysis; structural equation modeling
1. Introduction
The National Environmental Policy Act 1970 (NEPA) of the United States of America
was the first formal incorporation of environmental impact assessment into a piece of legis-
lation [1]. Since then, countries around the world have incorporated some form of impact
assessment into their formal procedures or legislation relating to planning or other areas
of environmental decision making [
2
]. In Taiwan, the Environmental Impact Assessment
Act was promulgated by presidential order in 1994 to prevent and mitigate the adverse
impact of development activity on the environment, and to ensure that decision-makers
considered environmental impacts before deciding whether to proceed with new projects.
An ecosystem is usually affected by a combination of different environmental fac-
tors [
3
], making it difficult to determine which factors are more important than others,
J. Mar. Sci. Eng. 2022,10, 1023. https://doi.org/10.3390/jmse10081023 https://www.mdpi.com/journal/jmse

J. Mar. Sci. Eng. 2022,10, 1023 2 of 11
and to construct an ecological model that includes both physical and biological factors.
Principal component analysis (PCA), coupled with structural equation modeling (SEM),
has proved a reliable tool in identifying the major contributors to water quality in certain
areas [
4
,
5
] and can specify the relationships among variables [
6
], thereby providing insight
into the relationships between correlated physical, chemical, and biological variables in an
ecosystem [7,8].
The National Museum of Marine Biology & Aquarium (NMMBA) is a public aquarium
situated on a 58-hectare site within Kenting National Park near the southern tip of Taiwan
(Figure 1). Since its completion in 2000, the NMMBA has attracted millions of visitors each
year. The environmental impact assessment submitted prior to land clearing and construc-
tion [
9
] stated that the operation of the aquarium would not affect the adjacent coastal area.
Furthermore, the Environmental Impact Comparative Analysis Report submitted prior to
the construction of an additional exhibition hall at the site in 2010–2012 [
10
] also stated that
the construction would have little effect on the coastal ecosystem. Over the years, environ-
mental monitoring of the adjacent coastal area has been conducted seasonally, but specific
verification of these two environmental impact assessments has never been conducted.
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 2 of 12
An ecosystem is usually affected by a combination of different environmental factors
[3], making it difficult to determine which factors are more important than others, and to
construct an ecological model that includes both physical and biological factors. Principal
component analysis (PCA), coupled with structural equation modeling (SEM), has proved
a reliable tool in identifying the major contributors to water quality in certain areas [4,5]
and can specify the relationships among variables [6], thereby providing insight into the
relationships between correlated physical, chemical, and biological variables in an ecosys-
tem [7,8].
The National Museum of Marine Biology & Aquarium (NMMBA) is a public aquar-
ium situated on a 58-hectare site within Kenting National Park near the southern tip of
Taiwan (Figure 1). Since its completion in 2000, the NMMBA has attracted millions of
visitors each year. The environmental impact assessment submitted prior to land clearing
and construction [9] stated that the operation of the aquarium would not affect the adja-
cent coastal area. Furthermore, the Environmental Impact Comparative Analysis Report
submitted prior to the construction of an additional exhibition hall at the site in 2010–2012
[10] also stated that the construction would have little effect on the coastal ecosystem.
Over the years, environmental monitoring of the adjacent coastal area has been conducted
seasonally, but specific verification of these two environmental impact assessments has
never been conducted.
Figure 1. Study area and sampling stations located in southern Taiwan.
Since the operation of the aquarium could potentially increase nutrient-rich
wastewater that would be drained into the coastal area, and because the construction of
an exhibition hall could potentially increase the turbidity of the seawater, we would ex-
pect an impact on the phytoplankton and zooplankton in the adjacent coastal ecosystem.
We hypothesize that the abiotic and biotic parameters would be different near the drain-
age pipe, especially during the peak season. In this study, we constructed a structural
equation model (SEM) for the coastal ecosystem of NMMBA based on four years of envi-
ronmental monitoring data (2011–2014) to determine the environmental factors that affect
the phytoplankton and zooplankton dynamics and diversity in the area. The objective was
to verify the above-mentioned environmental impact assessments, which stated that the
Figure 1. Study area and sampling stations located in southern Taiwan.
Since the operation of the aquarium could potentially increase nutrient-rich wastewa-
ter that would be drained into the coastal area, and because the construction of an exhibition
hall could potentially increase the turbidity of the seawater, we would expect an impact
on the phytoplankton and zooplankton in the adjacent coastal ecosystem. We hypothesize
that the abiotic and biotic parameters would be different near the drainage pipe, especially
during the peak season. In this study, we constructed a structural equation model (SEM)
for the coastal ecosystem of NMMBA based on four years of environmental monitoring
data (2011–2014) to determine the environmental factors that affect the phytoplankton
and zooplankton dynamics and diversity in the area. The objective was to verify the
above-mentioned environmental impact assessments, which stated that the operation of
the aquarium, as well as the construction of an exhibition hall, would not affect the adjacent

J. Mar. Sci. Eng. 2022,10, 1023 3 of 11
coastal ecosystem. The results will provide a model and case study for the verification of
other environmental impact assessments elsewhere in the future.
2. Materials and Methods
2.1. Study Area
Six stations were monitored adjacent to NMMBA, of which four stations were located
at a depth of around 5 m and two stations at around 20 m (Figure 1). Domestic wastewater
produced by NMMBA and its visitors enters a sewage treatment plant on-site at NMMBA,
from which it is recycled for irrigation, with the excess being drained into nearby coastal
waters through a drainage pipe near S10 (Figure 1). Nutrient-rich waste seawater from the
aquarium facilities goes through a series of artificial wetlands before entering the coastal
waters, also through the drainage pipe (Figure 1). Two third-order streams, the Sichong
River and the Baoli River, enter the study area from the north (Figure 1). The Sichong River
goes through a forest area, whereas the Baoli River mostly goes through farmlands and
residential areas. The pH and turbidity were 8.3
±
0.1 and 23
±
7 ntu in the Sichong River,
respectively, and 8.5
±
0.1 and 75
±
16, respectively, in the Baoli River during the study
period [11].
2.2. Measurement of Abiotic and Biotic Parameters
Between 2011 and 2014, abiotic (i.e., water temperature, salinity, transparency, tur-
bidity, and dissolved oxygen) and biotic data (i.e., phytoplankton and zooplankton) were
collected every quarter (Q1: Mar–May, Q2: Jun–Aug, Q3: Sep–Nov, Q4: Dec–Feb), on
16 occasions altogether, at a depth of 1 m below the water surface at 6 stations off the
NMMBA (Figure 1). Water depth at the stations was ~5–25 m, and their distance from
the shore ranged from 0.2 to 1.5 km. Temperature and salinity were measured in situ
using a handheld meter (YSI Model-30, YSI, Yellow Springs, OH, USA), and a multipa-
rameter meter (YSI Model-556, YSI, OH, USA) was used to measure dissolved oxygen
(DO) and pH. Turbidity was measured using a turbidimeter (2100P Turbidimeter, Hach,
Loveland, CO, USA) [
12
]. Nutrients (NO
2
-N + NO
3
-N, NH
3
-N, PO
4
-P, and SiO
2
-Si) were
also measured according to [
13
]: nitrate was reduced to nitrite using a cadmium redac-
tor, and nitrite was determined via diazotation with sulfanilamide and coupling with
N-(1-naphtha)-ethylenediamine dihydrochloride [
14
]; ammonia was determined using
the indophenol blue spectrophotometric method [
15
]; phosphate was determined using
the molybdate–antimony method [
16
]; silicate was determined using the silicomolybdate
method, which was reduced by a metal–oxalic acid solution [
17
]. The nitrate concentration
in this study was nitrate + nitrite; however, the nitrite concentration in the area was very
low [
9
,
18
] and, thus, negligible. Phytoplankton samples were taken using a Niskin water
sampler, and 1 L of each sample was preserved using formalin to a final concentration of
5%. In the laboratory, 200 mL of each such sample was filtered onto a 0.45-
µ
m piece of
filter paper, which was dried in an oven at 50
◦
C for 24 h. The filter paper was mounted
onto a slide using immersion oil, and then, examined for phytoplankton taxa under a
light microscope [
8
]. Individual phytoplankters were identified to the lowest taxonomic
level possible. Zooplankton were collected using horizontal tows at cruising speeds of
about 2 knots for 10 min, using a Norpac net (0.45-m diameter opening, 330-
µ
m mesh size)
equipped with a flowmeter (Model 438 110, Hydro-bios, Kiel, Germany). Zooplankton
samples were preserved onboard immediately in 5% borax-buffered formalin in seawa-
ter [
19
]. Zooplankton were later examined under a dissecting microscope (Askania model
GSZ 2) and their different classes identified.
2.3. Data Analysis
The biotic and abiotic parameters were analyzed using principal component analysis
to identify their influences on the coastal area. All data were ln-transformed before analysis.
Principal components with eigenvalues larger than 1 and components explaining at least
10% of the variability were considered to represent the factors that influence the study area.

J. Mar. Sci. Eng. 2022,10, 1023 4 of 11
The interpretation of each PC axis was determined based on the factor loadings of the vari-
ables [
8
] and the temporal and spatial variations of each score, which were analyzed using
two-way ANOVA and multiple comparisons (Supplementary Materials). For explanation
of the method, please refer to https://www.statistixl.com/features/principal-components/
(accessed on 28 June 2022).
After the main environmental forces had been identified, we introduced phytoplank-
ton and zooplankton densities and diversity indices [
20
] to predict the interactions between
environmental factors and the biotic parameters in a conceptual model (Figure 2). We
assumed that the environmental factors could affect both phytoplankton and zooplankton,
and that phytoplankton and zooplankton could affect each other reciprocally. Lastly, we
used LISREL 8 [
21
] to verify the model. Detailed assumptions and the concept of SEM are
explained in [6,22,23].
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 4 of 12
2.3. Data Analysis
The biotic and abiotic parameters were analyzed using principal component analysis
to identify their influences on the coastal area. All data were ln-transformed before anal-
ysis. Principal components with eigenvalues larger than 1 and components explaining at
least 10% of the variability were considered to represent the factors that influence the
study area. The interpretation of each PC axis was determined based on the factor load-
ings of the variables [8] and the temporal and spatial variations of each score, which were
analyzed using two-way ANOVA and multiple comparisons (Supplementary Materials).
For explanation of the method, please refer to https://www.statistixl.com/features/princi-
pal-components/ (accessed on 28 June 2022).
After the main environmental forces had been identified, we introduced phytoplank-
ton and zooplankton densities and diversity indices [20] to predict the interactions be-
tween environmental factors and the biotic parameters in a conceptual model (Figure 2).
We assumed that the environmental factors could affect both phytoplankton and zoo-
plankton, and that phytoplankton and zooplankton could affect each other reciprocally.
Lastly, we used LISREL 8 [21] to verify the model. Detailed assumptions and the concept
of SEM are explained in [6,22,23].
Figure 2. The hypothesized structural model.
3. Results
Table 1 summarizes the means (±SD) and ranges of the biotic and abiotic variables
measured quarterly at all six stations at the study site off the NMMBA between 2011 and
2014. The water temperature ranged from 24.1 to 31.6 °C, typical of a subtropical region.
The salinity varied greatly between 18.8 and 34.6, as did various nutrient concentrations,
indicating a strong effect of freshwater input. The transparency, turbidity, and suspended
solids also fluctuated greatly, suggesting that suspended matter may play an important
role in the region (Table 1). Spatial and/or temporal variation in phytoplankton and zoo-
plankton were also apparent in both the taxon number and diversity index (Table 1).
Figure 2. The hypothesized structural model.
3. Results
Table 1summarizes the means (
±
SD) and ranges of the biotic and abiotic variables
measured quarterly at all six stations at the study site off the NMMBA between 2011 and
2014. The water temperature ranged from 24.1 to 31.6
◦
C, typical of a subtropical region.
The salinity varied greatly between 18.8 and 34.6, as did various nutrient concentrations,
indicating a strong effect of freshwater input. The transparency, turbidity, and suspended
solids also fluctuated greatly, suggesting that suspended matter may play an important
role in the region (Table 1). Spatial and/or temporal variation in phytoplankton and
zooplankton were also apparent in both the taxon number and diversity index (Table 1).

J. Mar. Sci. Eng. 2022,10, 1023 5 of 11
Table 1.
Ranges and means (
±
SD) of the hydrological and biological variables in the study area from
2011 to 2014 (N = 96).
Range Mean ±SD
Temperature (◦C) 24.1–31.6 27.8 ±2.1
Salinity 18.8–34.6 32.9 ±1.8
Dissolved oxygen (mg L−1)4.7–10.0 6.7 ±0.7
pH 7.87–8.40 8.18 ±0.11
Transparency (m) 1.0–20.0 8.1 ±4.4
Turbidity (ntu) 0.20–16.4 0.90 ±2.02
Suspended solids (mg L−1)1.84–16.70 6.55 ±3.25
NO3-N (mg L−1)0.001–0.193 0.024 ±0.033
NH3-N (mg L−1)0.001–0.289 0.018 ±0.042
PO4-P (mg L−1)0.002–0.008 0.004 ±0.002
SiO2-Si (mg L−1)0.020–1.382 0.121 ±0.217
Chl a (µg L−1)0.02–2.53 0.33 ±0.50
Phytoplankton density (cell L−1)260–180,400 27,537 ±36,921
Phytoplankton species number 4–23 13 ±4
Phytoplankton Shannon–Weaver diversity index
0.17–2.53 1.57 ±0.50
Zooplankton density (ind 100 m−3)299–93,231 12,826 ±20,171
Zooplankton class number 9–25 19 ±4
Zooplankton Shannon–Weaver diversity index 0.62–2.30 1.60 ±0.35
The first three principal components explained 63.69% of the abiotic variables at
the study site (Table 2). The eigenvalues of PC1, PC2, and PC3 were larger than 1 and
explained 33.94%, 15.74%, and 14.01% of the variance, respectively. The factor loadings for
salinity (
−
0.822), nutrients such as NO
3
-N, SiO
2
-Si, and NH
3
-N (0.763~0.916), and turbidity
(0.683) in PC1 (Table 2) indicated that low salinity was coupled with high turbidity and
high nutrient content, which suggests that PC1 represents the influence of river input
(i.e., high river discharge may have reduced the salinity while increasing the nutrient
load and turbidity of the coastal seawater). Both the temporal (two-way ANOVA, DF = 3,
F = 3.76, p= 0.014 *) and spatial (two-way ANOVA, DF = 5, F = 3.91, p= 0.003 *) variabilities
of the principal component scores of PC1 were significantly different between seasons and
sites, respectively (Table 3A). Further analysis using Tukey’s test revealed that Q4 (the dry
season) was significantly different, in this respect, from Q2 and Q3 (the rainy seasons) [
11
]
and that station S1 (closest to the river mouths: Figure 1) was significantly different from
other stations (Table 3A).
Table 2.
Loadings of principal components 1–3 (after varimax rotation) for abiotic variables measured
in the study area (N = 96).
Variable PC 1 PC 2 PC 3
Transparency −0.277 −0.639 −0.057
Temperature 0.211 0.311 −0.472
Salinity −0.822 −0.090 0.358
NO3-N 0.763 0.226 0.122
PO4-P 0.186 0.012 0.094
SiO2-Si 0.916 0.141 0.071
NH3-N 0.800 0.195 0.174
Turbidity 0.683 0.092 −0.412
Chl a −0.024 0.728 −0.083
Suspended solids 0.180 0.643 −0.143
Eigenvalues 3.393 1.574 1.401
Total variance (%) 33.94 15.74 14.01

J. Mar. Sci. Eng. 2022,10, 1023 6 of 11
Table 3.
Results from two-way ANOVA tests and all pairwise multiple comparisons (Tukey’s HSD
Test) on principal components 1 to 3. * p< 0.05. Different superscripts indicate significant differences.
(A) PC1
Two-way ANOVA Multiple Comparison
DF F p
Season 3 3.76 0.014 * Q3 aQ2 aQ1 ab Q4 b
Station 5 3.91 0.003 * S1 aS10 bS4 bS7 bS11 bS2 b
Season ×Station 15 1.39 0.175
(B) PC2
Two-way ANOVA Multiple Comparison
DF F p
Season 3 7.98 <0.001 * Q2 aQ4 aQ3 aQ1 b
Station 5 1.36 0.252
Season ×Station 15 0.27 0.996
(C) PC3
Two-way ANOVA Multiple Comparison
DF F p
Season 3 12.77 <0.001 * Q1 aQ4 bQ3 bc Q2 c
Station 5 0.72 0.611
Season ×Station 15 0.70 0.778
PC2 showed a negative factor loading for transparency (
−
0.639) coupled with positive
loadings for chl a (0.728) and suspended solids (0.643) (Table 2), which suggests that PC2
represents the suspended matter in the water. The temporal variability in the principal
component scores of PC2 was significant (two-way ANOVA, DF = 3, F = 7.98, p<
0.001 *
)
(Table 3B). Further analysis using Tukey’s test revealed that Q1 (the dry season) [
11
] was
significantly different from other seasons, whereas there was no significant difference
between the sampling stations (Table 3B).
PC3 showed a negative factor loading for water temperature (
−
0.472) and turbidity
(
−
0.412), while the loading for salinity was high (0.358) (Table 2). The temporal variability
of the principal component scores of PC3 was significant (two-way ANOVA, DF = 3,
F = 12.77, p< 0.001 *) (Table 3C). Multiple comparisons using Tukey’s test revealed that
Q1 (the dry season) was significantly different from other seasons, Q2 was significantly
different from Q4, and Q3 and Q4 were not significantly different from each other (Table 3C).
This suggests that the water temperature per se, not the rainy/dry season alternation, was
mainly responsible for the differences, and that PC3 represents the changes in season.
Based on the loadings of principal components 1–3, we identified that river input,
suspended matter, and seasonal changes were the main factors influencing the biotic and
abiotic parameters in the study area. We modified the simple conceptual model of Figure 2
into a more detailed model (Figure 3), with the influence of river input expressed in terms
of salinity, turbidity, and various nutrients; the influence of suspended matter expressed
in terms of transparency, chl a, and suspended solids; and the influence of the changes
in season expressed in terms of temperature, salinity, and turbidity. The phytoplankton
and zooplankton densities and taxon numbers are treated in terms of their respective
Shannon–Weaver diversity indices.

J. Mar. Sci. Eng. 2022,10, 1023 7 of 11
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 7 of 12
Figure 3. The conceptual model used for exploring the influence of abiotic parameters on phyto-
plankton and zooplankton density and diversity in the study area.
Using the 18 kinds of observed biotic and abiotic data obtained from six stations dur-
ing 16 cruises (N = 96) in 2011–2014, a final SEM model was constructed (Figure 4). The
weighted-least-squares Chi-square test (χ2 = 86.17, df = 58, p = 0.0096) was significant, pos-
sibly due to the large sample sizes [24]. The low root-mean-square error of approximation
(RMSEA = 0.071) suggests that the model was statistically significant and substantively
meaningful [23]. Other model-fit indices were the standardized root-mean-square resid-
ual (SRMR) = 0.074; the goodness of fit index (GFI) = 0.88; and the adjusted goodness of fit
index (AGFI) = 0.81, all indicating that the model was moderately acceptable [23].
Figure 3.
The conceptual model used for exploring the influence of abiotic parameters on phyto-
plankton and zooplankton density and diversity in the study area.
Using the 18 kinds of observed biotic and abiotic data obtained from six stations
during 16 cruises (N = 96) in 2011–2014, a final SEM model was constructed (Figure 4). The
weighted-least-squares Chi-square test (
χ2
= 86.17, df = 58, p= 0.0096) was significant, pos-
sibly due to the large sample sizes [
24
]. The low root-mean-square error of approximation
(RMSEA = 0.071) suggests that the model was statistically significant and substantively
meaningful [
23
]. Other model-fit indices were the standardized root-mean-square residual
(SRMR) = 0.074; the goodness of fit index (GFI) = 0.88; and the adjusted goodness of fit
index (AGFI) = 0.81, all indicating that the model was moderately acceptable [23].
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 8 of 12
Figure 4. Final structural equation model after modification, with standardized coefficients. Solid
arrows: significant influence; broken arrow: insignificant influence.
The model showed no direct influence of river input on the planktonic communities
(Figure 4). River input, however, did affect the amount of suspended matter (0.45), which
negatively influenced the phytoplankton density and species number. The changes in sea-
son affected both phytoplankton and zooplankton densities, while the phytoplankton
density also affected zooplankton density, showing a bottom-up effect in this coastal area
[25].
Between 2011 and 2014, the highest average number of visitors entering NMMBA
was in Q2, significantly greater than in other quarters (two-way ANOVA, F = 90.90, p <
0.001 *). The average number of visitors in Q3 was also significantly higher than in Q1 and
Q4, whereas the latter two quarters were not significantly different from each other
(Figure 5). There were no significant differences in visitor numbers between different
years.
Figure 4.
Final structural equation model after modification, with standardized coefficients. Solid
arrows: significant influence; broken arrow: insignificant influence.
The model showed no direct influence of river input on the planktonic communities
(Figure 4). River input, however, did affect the amount of suspended matter (0.45), which

J. Mar. Sci. Eng. 2022,10, 1023 8 of 11
negatively influenced the phytoplankton density and species number. The changes in
season affected both phytoplankton and zooplankton densities, while the phytoplankton
density also affected zooplankton density, showing a bottom-up effect in this coastal
area [25].
Between 2011 and 2014, the highest average number of visitors entering NMMBA was
in Q2, significantly greater than in other quarters (two-way ANOVA, F = 90.90, p<
0.001 *
).
The average number of visitors in Q3 was also significantly higher than in Q1 and Q4,
whereas the latter two quarters were not significantly different from each other (Figure 5).
There were no significant differences in visitor numbers between different years.
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 9 of 12
Figure 5. The quarterly average (mean ± SD, N = 4) number of visitors entering the National Mu-
seum of Marine Biology & Aquarium between 2011 and 2014. Different letters above bars indicate
significant differences (p < 0.001).
4. Discussion
Environmental monitoring has become a common practice in safeguarding natural
ecosystems. In many countries, when a development project might be detrimental to the
surrounding environment, governmental regulations require an environmental impact
assessment in advance to aid in decision-making [2]. After the environmental assessment
has been conducted, typically with the inclusion of baseline environmental monitoring,
and a conclusion of mild impact has been reached, the project will ordinarily be approved
by government regulators and carried out. After the project has been completed, environ-
mental monitoring will usually be continued for some time to ensure that the impact is
acceptable, but long-term verification of the conclusions of environmental impact assess-
ments has rarely been conducted.
In this study, we examined the temporal and spatial changes in abiotic and biotic
parameters in the coastal area off the National Museum of Marine Biology & Aquarium,
a major tourist facility in southern Taiwan. The PCA scores led us to conclude that river
input, suspended matter, and the changes in season were the main factors influencing the
study area. The input of freshwater from two rivers caused a simultaneous decrease in
salinity and increase in turbidity and nutrient concentrations, with the greatest impact
being detected at the sampling station closest to the river mouths. The abiotic and biotic
parameters were different in the dry season (Q4 and Q1) when compared to the wet sea-
son (Q2 and Q3), suggesting that river input was the main contributor to the variations in
the coastal marine environment. Similar results have also been reported at a locality along
the west coast of central Taiwan, where river discharge contributed the most to the phy-
toplankton dynamics in the area [8].
Diatoms are the most dominant phytoplankton group in the study area [26,27]. Dia-
toms produce extracellular acidic polysaccharides [28] in the form of transparent exopol-
ymer particles (TEPs) that can slow the sinking of solid particle aggregations and prolong
pelagic residence time [29]. Our results showed parallel dynamics between chl a and sus-
pended solids, which increased and decreased together, but an opposite trend in trans-
parency (Table 2). This suggests that in the study area, the phytoplankton and suspended
solids might be trapped by TEPs [30], thereby decreasing the transparency of the water.
Figure 5.
The quarterly average (mean
±
SD, N = 4) number of visitors entering the National
Museum of Marine Biology & Aquarium between 2011 and 2014. Different letters above bars indicate
significant differences (p< 0.001).
4. Discussion
Environmental monitoring has become a common practice in safeguarding natural
ecosystems. In many countries, when a development project might be detrimental to the
surrounding environment, governmental regulations require an environmental impact as-
sessment in advance to aid in decision-making [
2
]. After the environmental assessment has
been conducted, typically with the inclusion of baseline environmental monitoring, and a
conclusion of mild impact has been reached, the project will ordinarily be approved by gov-
ernment regulators and carried out. After the project has been completed, environmental
monitoring will usually be continued for some time to ensure that the impact is acceptable,
but long-term verification of the conclusions of environmental impact assessments has
rarely been conducted.
In this study, we examined the temporal and spatial changes in abiotic and biotic
parameters in the coastal area off the National Museum of Marine Biology & Aquarium,
a major tourist facility in southern Taiwan. The PCA scores led us to conclude that river

J. Mar. Sci. Eng. 2022,10, 1023 9 of 11
input, suspended matter, and the changes in season were the main factors influencing the
study area. The input of freshwater from two rivers caused a simultaneous decrease in
salinity and increase in turbidity and nutrient concentrations, with the greatest impact
being detected at the sampling station closest to the river mouths. The abiotic and biotic
parameters were different in the dry season (Q4 and Q1) when compared to the wet season
(Q2 and Q3), suggesting that river input was the main contributor to the variations in
the coastal marine environment. Similar results have also been reported at a locality
along the west coast of central Taiwan, where river discharge contributed the most to the
phytoplankton dynamics in the area [8].
Diatoms are the most dominant phytoplankton group in the study area [
26
,
27
]. Di-
atoms produce extracellular acidic polysaccharides [
28
] in the form of transparent exopoly-
mer particles (TEPs) that can slow the sinking of solid particle aggregations and prolong
pelagic residence time [
29
]. Our results showed parallel dynamics between chl a and
suspended solids, which increased and decreased together, but an opposite trend in trans-
parency (Table 2). This suggests that in the study area, the phytoplankton and suspended
solids might be trapped by TEPs [
30
], thereby decreasing the transparency of the water.
Such a phenomenon has been reported recently, based on satellite observations of the Bohai
and Yellow Seas [
31
]: low transparency in winter and spring and high transparency in
summer and autumn were strongly correlated with the total suspended matter.
The final SEM (Figure 4) indicated a significant influence of the abiotic environment
factors on phytoplankton density and diversity, but not on zooplankton density and diver-
sity, a result similar to that found at the above-mentioned site in central Taiwan [
8
]. The high
river input during the rainy season, despite bringing in higher concentrations of various
nutrients that could enhance phytoplankton growth [
26
], also increased the concentration
of suspended matter. This could have negatively affected the phytoplankton density and
diversity owing to a concomitant decrease in light penetration [
32
]. Furthermore, strong
katabatic wind occurred sporadically in Q4 in the study area [
33
], and the wind-mixed
layer could reach the bottom and cause resuspension of the bottom sediments. Had we not
considered the indirect effects of river input on phytoplankton via suspended matter, we
would have concluded that nutrient concentrations did not affect phytoplankton when, in
fact, they did. Similar situations have also been reported in other estuaries [
34
,
35
], although
phytoplankton can sometimes increase under conditions of low light penetration [36].
Suspended matter did not directly affect the zooplankton during this study; however,
the SEM results revealed an indirect influence through phytoplankton density, a clear
bottom-up effect [
37
]. Contrary to our results, other studies have shown that abiotic factors
such as temperature are usually the main drivers of plankton communities in aquatic
ecosystems [
38
,
39
]. Our SEM model clearly shows both direct and indirect influences
among the biotic and abiotic parameters, and thus, enhances our understanding of the
complex ecosystem in the area. Since the model is robust, we expect to be able to further
modify it and confirm additional interactions by considering other factors as data become
available. This would greatly improve our understanding of the disturbances faced in the
area, regardless of whether they represent natural or anthropogenic perturbations.
During the monitoring period, Q2 was always the season with the highest number of
visitors to the NMMBA, and we would expect higher nutrient concentrations during the
quarter near S10. However, none of the abiotic or biotic parameters showed any significant
increase or decrease at the station during Q2 compared to other quarters. This implies that
the observed changes in various environmental parameters in the coastal waters adjacent
to the NMMBA were not related to the visitors. Based on the results from the multivariate
statistical model analysis, we verified that the environmental impact assessment concluded
before the construction of the NMMBA was reliable and accurate. We suggest that using
the methods of PCA, to determine sources of influence, and SEM, to confirm their direct
and indirect effects, should be applied to other areas of interest to verify environmental
impact assessments in the future.

J. Mar. Sci. Eng. 2022,10, 1023 10 of 11
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/jmse10081023/s1. Table S1: The principle component scores used
in two-way ANOVA.
Author Contributions:
Conceptualization, K.S.T. and W.-R.C.; methodology, W.-R.C.; software,
W.-R.C.; validation, K.S.T. and H.-Y.H.; formal analysis, H.-Y.H., F.-C.K. and P.-J.M.; investigation,
H.-Y.H., G.-K.H., F.-C.K. and P.-J.M.; resources, K.S.T.; data curation, K.S.T.; writing—original draft
preparation, W.-R.C.; writing—review and editing, H.-Y.H., F.-C.K., P.-J.M. and K.S.T.; visualization,
G.-K.H.; supervision, K.S.T.; project administration, K.S.T.; funding acquisition, K.S.T. All authors
have read and agreed to the published version of the manuscript.
Funding:
This research was funded by Taiwan’s Ministry of Science and Technology (MOST) to K.S.T.
(MOST 103-2911-M-291-003) and intramural funding from the National Museum of Marine Biology
& Aquarium to K.S.T. (100100311 and 1031003).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
This work was supported by grants from Taiwan’s Ministry of Science and
Technology (MOST) to K.S.T. (MOST 103-2911-M-291-003), and by intramural funding from the
National Museum of Marine Biology & Aquarium to K.S.T. (100100311 and 1031003). The authors
would like to thank Mark J. Grygier for his careful English proofreading of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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