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Ecohydrology & Hydrobiology xxx (xxxx) xxx
Contents lists available at ScienceDirect
Ecohydrology & Hydrobiology
journal homepage: www.elsevier.com/locate/ecohyd
Original Research Article
Understanding the effect of environment on macrobenthic
invertebrate in naturally occurring repeated mesohabitats
from the warm-temperate zone river
Amit Jagannath Patil
a
, Zhenhong Wang
a , †
, Xiaole He
a , b
, Pangen Li
a , c
, Ting Yan
a
,
He Li
a
a
Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, School of Wat er and Environment,
Chang’an University, No.126, Yan ta Road, Xi’an, Shaanxi 710064, China
b
Shanghai Majorbio Bio-pharma Technology Co., Ltd., China
c
Tianjin Lvyin Landscape and Ecology Construction Co., Ltd., China
a r t i c l e i n f o
Article history:
Received 19 April 2022
Revised 5 September 2022
Accepted 17 October 2022
Available online xxx
Keywo rds:
River functioning
Macroinvertebrate
Diversity indices
Mesohabitat
Rapid-pool-benchland
a b s t r a c t
Spatial heterogeneity causes the natural variability of the physical factors which are critical
to the functioning of the riverine ecosystem. Repeatedly occurring heterogeneous meso-
habitats in the rapid-pool-benchland system (RPBS), are universally seen along natural
rivers. The heterogeneity of the mesohabitat mosaic is a key factor determining the mac-
robenthic invertebrate (MBI) communities of the river ecosystem. MBI communities serve
multiple functions of river ecosystems, however, limited studies have been done on the
effect heterogeneous mesohabitats of RPBS on abundance and distribution pattern of MBI
communities. In this paper, we examined abundance and distribution patterns of MBI com-
munities in the RPBS mesohabitats along five rivers of the Shaanxi province in the North-
west China. A total of 112 mesohabitats comprising 40 rapids, 37 pools, and 35 benchlands
were sampled. The MBI-environmental relationships were analyzed using the piecewise
structural equation model ( pSEM ) at two level; the overall and habitat-specific. MBI abun-
dance explained 61% variation in the overall system. Whereas, for the same pSEM, the total
number of taxa explained 25% variation which increased to 38% including the variance ac-
counted for by random effects (i.e. river). Habitat-specific models such as rapid showed a
direct effect of soil moisture and system width on the MBI abundance and total number
of taxa. In pool, only soil texture and Total Nitrogen (TN) showed a direct effect, while in
benchland soil moisture, pH, Total Phsphorous (TP), and TN influenced the MBI abundance
and total number of taxa. pSEM models allowed to identify the main drivers of MBI com-
munities in a naturally occurring repeated mesohabitats. Further detailed studies on MBI
(e.g., functional trait) from naturally occurring meoshabitats are needed to better elucidate
the factor governing the MBI community, and in turn ecosystem functioning.
©2022 European Regional Centre for Ecohydrology of the Polish Academy of Sciences.
Published by Elsevier B.V. All rights reserved.
Abbreviations: Width_R, River width; Length_H, Mesohabitat length;
Width_H, Mesohabitat width; moist/Moisture
s
, Soil moisture; Elev, Eleva-
tion; pH
s
, Soil pH; Textu re
s
, Soil texture; TP
s
, Soil total phosphorus; TN
s
,
Soil tota l nitrogen.
† Corresponding author.
E-mail address: w_zhenhong@126.com (Z. Wang ) .
1. Introduction
Natural freshwater ecosystems represent the terrestrial
phases of the global hydrological cycle and, include rivers,
streams, lakes, ponds, wetlands as well as groundwater
( Reid et al., 2020 ). They play a fundamental ecological
https://doi.org/10.1016/j.ecohyd.2022.10.001
1642-3593/© 2022 European Regional Centre for Ecohydrology of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Please cite this article as: A.J. Patil, Z. Wang , X. He et al., Understanding the effect of environment on macrobenthic in-
vertebrate community structure in naturally occurring repeated mesohabitats from the warm-temperate zone river, Eco-
hydrology & Hydrobiology, https://doi.org/10.1016/j.ecohyd.2022.10.001
A.J. Patil, Z. Wan g, X. He et al. Ecohydrology & Hydrobiology xxx (xxxx) xxx
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role and provide economically important products and ser-
vices ( Covich et al., 2004 ; Postel and Carpenter, 1997 ). In
addition, they provide critical habitats for a large vari-
ety of aquatic plants, fishes, reptiles, birds, and mammals
( Nyingi et al., 2013 ). However, river ecosystems are influ-
enced by changes at different spatial and temporal scales.
Investigating these complex natural interactions provides
context and guidance for simulating the impact of changes
in the river ecosystem ( Kiesel et al., 2019 ). In nature, river
functioning is a complex organized system characterized
by river flow, sedimentation, distribution of organic mat-
ter (OM), organisms, humans, climate change, and eco-
nomic activity, consequently ensuing habitat heterogeneity
( Kovalchuk et al., 2020 ).
Environmental heterogeneity in a river is formed by ge-
omorphological units, river basin vegetation, sediment dis-
tribution, and hydrological connectivity which plays an im-
portant role in the biodiversity and functioning of the river
system ( Palmer et al., 2010 ). Such heterogeneity forms nat-
ural mesohabitats, visually distinct areas of habitat iden-
tifiable from banks to streams, to river beds, to vegeta-
tion along the river ( Armitage et al., 1995 ; Armitage and
Pardo, 1995 ). In river environment, high biodiversity is
commonly associated with mesohabitat ( Silva et al., 2014 )
and hence mesohabitat is an ecologically important struc-
tural unit to understand the river system ( Pardo and Ar-
mitage, 1997 ). The repeated naturally occurring mesohabi-
tats are a combination of regular structures i.e. the rapid-
pool-benchland system (RPBS) in the natural or semi-
natural rivers ( Wang et al., 2019 , Chen et al., 2015 , 2014b ,
2014a ). The pool is a deeper habitat that is generally close
to the bank, while the benchland is formed by settling of
sediment and other materials delivered by the river action
and the rapid is generally seen in the middle line of the
stream ( Wan g et al., 2019 , 2014b , 2014a ). These RPBS occur
due to the geo-physio-dynamical variabilities and are also
very important features for sustaining biological diversity,
especially in terms of beta diversity ( Silva et al., 2014 ).
The macrobenthic invertebrate (MBI) communities are
ubiquitous and abundant in most of the river ecosys-
tem ( López-López and Sedeño-Díaz, 2015 ). The MBI com-
munities also play a critical role in the food chain
by participating in the natural flow of energy and nu-
trients ( Hauer and Resh, 2017 ; Vannote et al., 198 0 ;
Wallace and Webster, 1996 ). In the last few decades, MBI
have been the most commonly used biological indicator
( Selvanayagam and Abril, 2016 ) to assess the ecological sta-
tus of the aquatic system as they have limited movement,
a relatively long life cycle ( Mathuriau et al., 2012 ), and a
wide range of tolerance to environmental stressors ( López-
López and Sedeño-Díaz, 2015 ). The distribution and abun-
dance of MBI often respond to the interaction of multi-
ple environmental variables including discharge, channel
width, river bed sediment size, and anthropogenic activi-
ties, consequently changing the taxonomic and functional
diversity of local communities. In addition, MBI abun-
dance and richness (e.g. family richness) have been widely
used to detect environmental responses (e.g. Ferreira et al.,
2014 ; Friberg et al., 2011 ; Rasifudi et al., 2018 ).
Most riverine ecosystem studies focus on the changes
in species and their functional relationships in various
flow regimes ( Biles et al., 2003 ; Cardinale et al., 2002 ).
Comprehensive studies have been carried out on the
MBI community structure and diversity pattern from rif-
fles and pools system. These include the effect of fine
accumulated sediment ( Harrison et al., 2007 ), structure
and functional organization of hyporheic communities
( Jones and Lim, 2005 ), multi substratum sampling ap-
proach ( Beauger and Lair, 2008 ), influence of visually as-
sessment of mesohabitat ( Silva et al., 2014 ) and taxonomic
trait structure ( Herbst et al., 2018 ).
Species and functional patterns of MBI in various flow
regimes using multihabitat approach are also well docu-
mented ( Fornaroli et al., 2016 ; Kemp et al., 20 0 0 ). How-
ever, the abundance and distribution pattern of MBI and
how the morphology and environmental variables influ-
ence it are poorly investigated in the RPBS. Further, com-
parative studies on MBI in different mesohabitats are prac-
tically unknown from the warm-temperate zone rivers to
the best of our knowledge, yet elucidating these patterns
and biota-environmental interactions is critical for man-
agement as anthropogenic and climate change impacts on
ecosystems intensify.
It is recognized that studying riverine system with dif-
ferent habitat heterogeneity may be useful in determin-
ing the drivers of MBI community structure and function.
Despite the high complexity of RPBS, the differences in
the MBI communities and the factors that govern the ob-
served pattern have been scarcely explored. The avoid-
ance to study mesohabitat in the river by most freshwater
benthic ecological researchers is due to the preference of
the stable substrata (rapid) over unstable ( Harrison et al.,
2007 ). The warm-temperate zone river systems of China
are extremely intricate and multifaceted systems. However,
in recent years, with population growth, the expansion
of industrial and agricultural activities, and global climate
change, several ecological disturbances have been reported
in many of the river systems of China ( Han et al., 2018 ;
Li et al., 2020 ; Liu et al., 2021 ; Wang et al., 2019 ; Xia et al.,
2020 ).
Despite the surge of interest in the warm-temperate
zone river system of China (27,192 searched articles; Web
of Science Database), there are relatively few studies (57
published literature) on the MBI community structure and
environmental relationship in different mesohabitat. The
studies in the RPBS river mesohabitat in China focused on
microbial abundance and enzymatic activities ( Wa ng et al.,
2019 ) and the effect of mesohabitat on river water qual-
ity ( Chen et al., 2015 ). Disentangling the complex rela-
tionships between different aspects of river environments,
such as the morphological, chemical, and anthropogenic
drivers and their influence on the biological components
is critical for understanding the direct and indirect effects
of natural and human-driven changes in the ecosystem.
Structural equation modeling (SEM) has been frequently
used to explore the mechanism of how independent vari-
ables (environmental or anthropogenic disturbances) can
affect the response variable (biological) when mediated
through a third set of variables. Additionally, the struc-
tural equation model has been used to discover the link
between the effect of land use on climate change and on
stream macroinvertebrate ( Li et al., 2018 ), biodiversity pat-
2
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Figure 1. Study area showing all the sampling sites along the Wei, Chan, Ba, Feng and Jing River in the Shaanxi province of Northwest China. Here, in this
map W represents Wei River, C indicates Chan River, B is for the Ba River, F is Feng River and J represents the Jing River.
tern and understanding the river functioning by means of
macroinvertebrate functional diversity ( Liu et al., 2021 ). Al-
though the environmental influences on the river ecosys-
tem are widely recognized (River biodiversity and ecology
assessment, flood management and climate change), stud-
ies have not assessed and compared these interrelated im-
pacts on the MBI of RPBS. Therefore, in this study, we focus
on the RPBS from the five warm-temperate zone rivers in
the Shaanxi province of Northwest China. The objectives of
the present study are i) to identify the factors influencing
the MBI abundance and total number of taxa in the RPBS
meoshabitats and ii) to compare the total system and indi-
vidual mesohabitat (RPBS) for MBI distribution. We used
the piecewise structural equation model to interpret the
mechanisms driving this relationship.
2. Methodology
2.1. Study area
The study was conducted in the Wei River basin (33
O
59’–35
O 58’N and 106
O 41’–10 9
O 32’E; basin covers about
124,130 Km
2
; Fig. 1 ) from Shaanxi province of Northwest
China which falls under the warm temperate zone. Here,
the word "temperate" represents the areas with mild tem-
peratures and this warm temperate zone is located in
patches of western, south central, and eastern China. Due
to its location in the arid and semi-arid area of the north-
west China, the climate type is mainly continental mon-
soon and other seasons include cold and dry winter, hot
and rainy summer, mild climate in spring, and autumn. The
origin of the Wei River is in the north of Niaoshu Mountain
in Gansu province which converges into the Yellow River
in Xi’an. Five rivers entailing Feng, Chan, Ba, Wei, and Jing
during the spring of 2019 (April to July) were selected for
the present study. The Jing from the north, Feng, Chan, and
Ba from the south form the Wei river basin which is sup-
ported by the Qinling mountain range in the North. A total
of 45 sites were identified based on the presence of rapid,
pool, and benchland mesohabitats ( Fig. 2 ) at each site.
2.2. River physical features
The site positions were recorded with the help of GPS
(HORIBA U-50). The major river physical features for the
present study are river width, RPBS length and width mea-
surements (Fig. S1 in the Supplementary Materials). Prior
to the field sampling, all the five ri vers have been vis-
ited for identifying the RPBS availability in the system. The
rapid mesohabitat length measurements were considered
from the beginning of the fast-flowing water (FP311 Flow-
Probe, Global Wate r) till where it dissipates. The shallow
water area with the high water velocity was marked for
the measurements of rapid width. The stream pool was
3
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Figure 2. This figure displays morphological structure and RPBS distributed along the different River in Shaanxi province, China. a) Schematic representa-
tion of the RPBS in river b) RPBS along the Jing River c) RPBS along the Chan River d) RPBS along the Jing River e) RPBS along the Wei River. Red rectangle
represent rapid, yellow circle represent pool and green triangle represent benchland.
4
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firstly physically located, later initial point of the slow wa-
ter movement and rise in depth was considered as the
starting point to measure the pool length while the end
point was taken just before the beginning of riffle or runs.
Each pool side which was parallel to the river bank with
maximum area and depth was referenced for width mea-
surements. Finally, the biggest landmass closest to the river
(wet areas of the river bank or sandbars) was consid-
ered for the sampling of the environmental, biological, and
physical features. During all the measurements, there was
always one person standing on the highest point of the
bank side to guide and verify the shape of the systems.
The smaller systems were manually measured (length and
width). In the larger mesohabitats where the manual mea-
surements were not possible the reference points were
taken by GPS. At each mesohabitat minimum 6 reference
points were collected. Later, all the measured distances
were combined in the Google earth system (Google earth
pro 7.3.3.7786). Each physical feature had been vectorized
using GIS and statistical tools were used for measuring the
distances (ArcMap 10 .3).
2.3. Sediment collection and analysis
In each river, sites with all the three mesohabitats
rapid, pool, and benchland were selected. A total of 135
sediment samples were collected. Sediments were taken
with the help of scoop, Peterson’s grab, and metal quadrate
for rapid, pool, and benchland, respectively. The sediment
for texture analysis were kept at room temperature and
those for soil chemistry pH, To ta l Organic Carbon (TOC),
Total Nitrogen (TN) and Tota l Phosphorous (TP) were kept
in the icebox. Samples for soil moisture content were
stored in aluminum containers, and each container was
kept in a separate zip lock bag in order to avoid desicca-
tion.
In the laboratory, soil pH was measured in 1:1 soil: dis-
tilled water (W-V) ( Mclean, 198 3 ). TOC was determined
through wet combustion titrimetric method ( Nelson and
Sommers, 198 3 ), TP was measured using the Molybde-
num blue colorimetric method ( HJ 632-2011, 2011 ) and
TN was estimated by the Kjeldahl method ( HJ 717-2014,
2014 ). For soil moisture analysis, containers were imme-
diately measured ( Gardner, 198 6 ). Pre-dried soil was used
for sediment texture analysis using the sieve method and
particle size classification was carried out ( Folk, 198 0 ;
Wentworth, 1922 ).
2.4. River macroinvertebrates
A total of 135 (45 rapid, 45 pool and 45 benchland)
macroinvertebrate samples were collected using a Surber
sampler (mesh size 250 μm; 900 cm
2
), Peterson’s grab
(640 cm
2
), and metal frame quadrant (625 cm
2
) for rapid,
pool, and benchland, respectively. A sieve (mesh size 250
μm) was used for the sample derived from the pool and
benchland. Boulders and pebbles were removed before
washing the sediment. Materials retained on the sieve
were preserved in 90% ethanol. In the laboratory, all the
samples were washed to remove excess soil, sorted un-
der a magnifying lens, and preserved in fresh 90% ethanol.
All sorted samples were identified to the lowest taxo-
nomic level (genus/family) by using literature ( Epler, 2001 ;
Gooderham and Tsyrlin, 2002 ; Kriska, 2013 ; Thorp and
Rogers, 2015 ; William, 1967 ).
2.5. Data analysis
The final analysis, was carried out on 112 samples
(40 rapids, 37 pools and 35 benchland). Samples with no
faunal record or dominance of single species were not
considered. Occurrence of dominant species were repre-
sented using percentage composition from three mesohab-
itats (RPBS). Univariate analysis was carried out on the
MBI abundance using di v ersity function from the package
“vegan’’ ( Oksanen et al., 2020 ). The total abundance (N)
and total number of taxa (S) in each sample were calcu-
lated. The Shannon Wiener index ( H
) was calculated to
compare the species diversity across varied mesohabitats
(RPBS) along streams. Whereas, to check how evenly MBI
species is distributed along different habitat Pielou’s even-
ness ( J
) was calculated. Later, the distribution of the en-
vironmental variables and biological indices were visual-
ized using Box and Whisker plot from the “ggpubr” pack-
age ( Kassambara, 2020 ). To further test differences in en-
vironmental and MBI diversity indices, the non-parametric
Kruskal–Wallis statistical test was performed. In addition,
Mann–Whitney U tests was used for pairwise comparisons
of RPBS ( Kassambara, 2021 ).
We fitted a piecewise structural equation ( pSE M)
mixed-effects generalized linear ( glmer model) and linear
models ( lme model) to evaluate the hypothesized path-
ways while observing how mesohabitat (RPBS) and the en-
vironment influence the abundance and total number of
taxa of river MBI ( Fig. 3 ). As most of the diversity in-
dices were derived from the abundance and total num-
ber of taxa, these two indices can be good representa-
tive for understanding the distribution of the MBI along
RPBS. In this model, habitat was selected as the fixed ef-
fect and river was the random effect. SEM tool is ex-
tensively used for understanding the complex structure
of nature ( Lefcheck et al., 2016 ). Here, pSE M is permit-
ted to list the structured equations which are identified
by linear ( lme ) and generalized linear model ( gl mer ) func-
tions. As well as pSEM function in R library “piecewis-
eSEM” ( Lefcheck, 2016 ) allows to fit non-normally dis-
tributed models. Reduction in physico-chemical variables
was done by removing variables with strong correlation
(r > 0.7). From available soil classes, PCA 1 axis for texture
was obtained and used for further analysis. We also ap-
plied data transformations to meet the assumptions of nor-
mality and homogeneity of variances ( O’Brien, 2007 ). To
study the overall system, the mesohabitat was set as an
exogenous variable, whereby to see behavior and habitat
functioning we built habitat-specific pSEM. The other pa-
rameters, based on their general nature and prior knowl-
edge of the variables were considered as response and
predictors. We also checked the multicollinearity in each
component model by calculating the variance inflation fac-
tor (VIF) for each predictor (VIF > 3 indicates possible
collinearity) and the variables were removed. Further re-
duction in the number of variables and model comparison
5
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Figure 3. Conceptual structural equation of the RPBS functioning for the natural river system
Tabl e 1
Piecewise structural equation model selection procedure by AICc criteria. The final model with lowest AICc
values were selected where full model fail to reject based on the model comparison test (ANOVA).
Model selection steps variable removed AICc Fisher’s C p
Metamodel
1 Width_R 500.943 4.168 0.384
2 Width_R + Length_H 440.03 3.943 0.414
3 Width_R + Length_H + Width_H 353.127 2.556 0.635
Final Width_R + Length_H + Width_H + TN 273.597 2.614 0.271
5 Width_R + Length_H + Width_H + TN + TP 318.018 5.907 0.052
full - 729.11 2.618 0.624
Rapid model
1 moist 467.622 1.934 0.38
2 moist + Width_H 444.795 1.144 0.564
full/final - 467.274 1.674 0.433
Pool model
1 moist 650.137 2.929 0.57
2 moist + Width_H 631.446 2.709 0.608
3 moist + Width_H + TP 633.995 5.098 0.608
Final moist + Width_H + TP + Length_H 610.767 0.072 0.965
Full - 652.561 2.223 0.695
Bench model
Final Elev 349.786 1.165 0.559
Full Elev + Moist 386.502 0.869 0.929
was done by using Akaike Information Criteria corrected
(AICc) for small sample size ( Table 1 ). The directed sepa-
ration ( dSep) test was conducted on the fitted models for
any significant missing paths and based on Akaike Infor-
mation Criterion (AIC) with the lowest values the claimed
path was selected ( Shipley, 2013 ). These recognized miss-
ing claims were added to optimize the model. Once the
model was updated, the evaluation was carried out using
global goodness of fit indices (Fisher’s C statistics, p < 0.05).
Whereas, for categorical variables rather than a model-
derived coefficient effect the marginal means were used.
These marginal means along with groups were derived
through emmeans function in R ( Lenth, 2021 ). Finally, the
summary, marginal R
2 and conditional R
2 have been ex-
tracted for the response variables. All statistical analyses
were performed by using Program R ( R Core Team, 2020 ).
3. Results
3.1. Mesohabitat characterization
The range values of the environmental variables indi-
cate the environmental variability of the study area ( Fig. 4
and Table S1).
Amongst the environmental variables measured, in the
pool habitat, soil TP (1096.89 ±774.53 mg/kg; Mean ±SD;
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Figure 4. Distribution of river physico-chemical parameter along RPBS mesohabitats. a) Tota l phosphorous b) Total nitrogen c) Total organic carbon d)
Soil pH e) Soil moisture f) Habitat length g) Habitat width h) River width i) Elevation j) granule k) Very coarse sand l) Coarse sand m) Medium sand
n) Fine sand o) Very fine sand p) Silt and clay. Red box and dot represents rapid mesohabitat, yellow indicates pool and green color for the benchland
mesohabitat. The line above the boxplot represent the comparison between two habitats and the significance is displayed by asterisks (
∗p < 0.05, ∗∗ p < 0.01,
∗∗∗ p < 0.0 01).
Fig. 4 a), TN (56.67 ±49.50 mg/kg; Mean ±SD; Fig. 4 b),
and moisture (21.23 ±12.17 %; Mean ±SD; Fig. 4 e)
showed the highest variation. Whereas, TOC (1.58 ±1. 64
%; Mean ±SD; Fig. 4 c) and pH (8.42 ±0.50; Mean ±
SD; Fig. 4 d) showed high variation in the benchland and
rapid, respectively. Among the river characteristics, highest
variation for habitat length (75.60 ±119.16 ; Mean ±SD;
Fig. 4 f) was observed in the benchland, the habitat width
(17.89 ±22.25; Mean ±SD; Fig. 4 g) and river width (54.12
±65.89; Mean ±SD; Fig. 4 h) in the pool habitat, while
the highest mean value for elevation was recorded in the
rapid. Benchland showed higher variation in sediment tex-
ture with very coarse sand (13.09 ±10.74; Mean ±SD;
Fig. 4 l), fine sand (13.87 ±14.31; Mean ±SD; Fig. 4 n)
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Figure 5. Distribution of macrobenthic indices along RPBS mesohabitats. a) To ta l abundance ( N ), b) Total number of taxa ( S ), c) Shannon’s diversity ( H
),
d) Pielou’s evenness ( J
). Here, red box and dot represents rapid, yellow indicates pool and green color for the benchland mesohabitat. The line above the
boxplot represents the comparison between two habitats and the significance is displayed by asterisks (
∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001).
and very fine sand (9.20 ±11.28; Mean ±SD; Fig. 4 o).
While, the granule (14.82 ±12.83; Mean ±SD; Fig. 4 j)
and medium (14.57 ±13. 76; Mean ±SD; Fig. 4 m) sand
showed higher variation in the rapid. Variation in silt and
clay (11.15 ±17. 09; Mean ±SD; Fig. 4 p) was observed in
the pool.
The Kruskal–Wallis test performed between RPBS for
the environmental variables, showed significant differences
(p < 0.05; Fig. 4 ) for TP, mesohabitat length, mesohabitat
width, and very fine sand particles. Furthermore, pairwise
comparison test between habitat revealed that the TP was
significantly different between rapid and pool (p < 0.001)
whereas, second significant difference was found be-
tween bench and pool (p < 0.05). Similarly, habitat length
showed significant differences between rapid and pool
(p < 0.01), and benchland and pool (p < 0.05). Rapid and
pool showed the significant differences for very fine sand
alone (p < 0.05).
The MBI abundance (7315 ±7989 ind/m
2
; Mean ±SD;
Fig. 5 a) and evenness (0.68 ±0.22; Mean ±SD; Fig. 5 d)
showed highest variation in pool, while total number of
taxa were similar in both rapid (9 ±4; Mean ±SD;
Fig. 5 b) and pool (6 ±4; Mean ±SD; Fig. 5 b). MBI di-
versity showed the highest variation in the pool (1.5 ±
0.7; Mean ±SD; Fig. 5 c) and benchland (1.6 ±0.7; Mean
±SD; Fig. 5 c). The statistical comparison test showed
that the MBI abundance, total number of taxa and Shan-
non’s diversity along RPBS (p < 0.05; Fig. 5 ) showed sig-
nificant differences. Furthermore, pairwise comparison test
showed that abundance significantly varied between rapid
and pool (p < 0.0 0 01) later followed by rapid and benchland
(p < 0.001). Whereas, MBI total species number was signifi-
cantly different between the rapid and pool (p < 0.001) and
between benchland and pool (p < 0.001). The Shannon’s di-
versity indices followed the same pattern to that of total
number of taxa in statistical comparison test (p < 0.05).
During our study the rapid habitat was dominated
(Taxa > 10 %; Table S2) by Hydropsychae spp ., Baetis spp. and
Cricotopus trifsciatus which represent the order Trichoptera,
Ephemeroptera and Diptera respectively . Whereas, the pool
habitat showed highest composition (Taxa > 10% ) of taxa
from the order Diptera such as Chironomus spp. and
Polypedilum spp.. In the benchland mesohabitat, the taxa
from the order Tubificida ( Tubifex sp. ), Diptera (Tipulidae)
and Gastropoda (Lymnaeidae) were recorded as the major
contributors (Taxa > 10%) .
3.2. Causal pathway of MBI structure along RPBS
The pSEM with the aggregated data showed that meso-
habitat had a significant role in structuring the MBI com-
munities ( Fig. 6 ). However, habitat-specific models showed
how MBI abundance and total number of taxa were af-
fected via different pathways in RPBS. In the overall model,
where habitat is exogenous variable, texture, TP and pH
were the most important variable affecting the abundance
(marginal-R
2 = 0.61, conditional-R
2 = 0.61, Fig. 6 a) and
total number of taxa (marginal-R
2 = 0.25, conditional-
R
2 = 0.38, Fig. 6 a). Additionally, the large difference in
habitat marginal means was observed for MBI abundance
(a, b, c; Fig. 6 a and Table S4) while, for total number
of taxa, marginal mean differences show a distinction be-
tween the rapid and benchland. The direct positive effect
of TP on abundance and, negative of soil pH on total num-
ber of taxa was observed in the overall pSE M ( Fig. 6 a and
Table S3). The pSEM for rapid showed the direct negative
effect of system width for both, total number of taxa and
abundance, but soil moisture negatively affected only the
total number of taxa ( Fig. 6 b and Table S5). There was no
significant effect of TN and TP on total number of taxa and
abundance in the rapid mesohabitat ( Fig. 6 b). In the pool
pSEM, total number of taxa and abundance were signifi-
cantly affected by soil texture and TN, respectively ( Fig. 6 c
and Table S6). Benchland pSEM followed different path-
ways wherein abundance is directly affected by TN, TP and
pH though, total number of taxa is affected by TP, soil
moisture, and pH. The soil texture effect on total number
of taxa and abundance was seen via moisture and pH in
the benchland habitat ( Fig. 6 d and Table S7).
4. Discussion
In this study, we examined the effect of river physi-
cal characteristics and soil environment parameters on the
MBI abundance and total number of taxa along repeated
RPBS mesohabitat of five ri ver ecosystems in the Shaanxi
8
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Figure 6. Final structural equation models after optimization. a) Metamodel b) Rapid mesohabitat c) Pool mesohabitat d) Benchland mesohabitat. Black
thick arrow represent strong positive correlation, red thick arrow represents negative correlation, black thin arrow for the moderate positive effect, red
thin arrow for the moderate negative effect, grey arrow for positive weak correlation, pink arrow for the weak negative correlation while dashed arrow
represent the insignificant paths. R
2
m = variance explained only by fixed effects in the model. R
2
c = conditional variance explained by the random effect
(i.e. including the random effect for the different Rivers). The label on arrow is the estimates between predictor and response variables.
province of Northwest China. The rapid and pool support
different MBI communities while benchland can accommo-
date fauna from both these mesohabitats. We have also ex-
plored the connection between mesohabitat environment
and physical characters which shows that in higher ele-
vated mesohabitats, if rivers or streams have smaller chan-
nel widths then soil TN can promote the MBI abundances.
On the other hand, higher MBI taxa were favored by soil
with less water holding capacity.
In rivers, the physical characteristics of the river and
sediment texture are inextricably linked ( Belletti et al.,
2017 ). In the present study, river mesohabitat length and
width, river width, and altitude were considered as the
major physical characteristics. The rapid system showed
variation in length due to the slope of the riverbed and
flow velocity. The differentiation in the river width and
elevation in benchland could be due to the irregular
shape and structure of the system ( Fryirs et al., 2018 ;
Wheaton et al., 2015 ). Compared to the other mesohabiats,
the pool had a higher quantity of fine-particle sediments
and even sludge due to the slow water flow, relatively
rich plants, and deeper water bodies ( Wang et al., 2017 ).
The variation in sediment particles observed in the present
mesohabitats corroborates findings from other riverine sys-
tems ( Anonymous, 2014b , 2014a ).
Consistent with sediment texture, TN and TP were high-
est in the pool followed by the bench and rapid indicating
that slow movement of water, rich vegetation coupled with
relatively homogenous sediment allow the accumulation of
nutrients in the pools ( Wa ng et al., 2017 ). On the other
hand, benchland with transitional phases between partially
exposed and exposed sediment helps to degrade the accu-
mulated pollutant and nutrients ( Chen et al., 2015 ). How-
ever, at some rapid and pool, TN and TP values were sim-
ilar because both these mesohabitats are characterized by
fine sediments, which promote the production or accumu-
lation of these nutrients in the system ( Bonada et al., 2020 ;
Lin et al., 2021 ). Some of the extreme values of TP and
TN in the present mesohabitats may be due to the small
headwater streams which are easily susceptible to hu-
man impacts ( Dalu and Wasserman, 2018 ; Kristensen and
Globevnik, 2014 ; Weigelhofer, 2017 ).
From the metamodel marginal means, rapid and pool
vary in the MBI abundance and total number of taxa. How-
ever, benchland showed similarities in marginal means
with the other two mesohabitats (pool and rapid) indicat-
ing that habitat heterogenity accounts for the MBI commu-
nity dynamics. In general, MBI abundance, diversity, and
evenness were higher in the pool followed by rapid and
benchland. However, MBI total number of taxa was higher
in rapid followed by pool and bench. In general, the MBI
in the benchland meoshabitat had low MBI communities.
In the river environment, habitat structure and environ-
mental variables have a significant influence on the river
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MBI abundance and number of taxa. As seen in the overall
pSEM, the factors for the variation in the MBI abundance
and total number of taxa were pH, soil texture and TP in-
dicating that these parameters play a key role in the spa-
tial variation of MBI distribution along the mesoahabitats
of the studied river system.
Further, among the environmental variables, the meta-
model identified finer soil particles can lower the soil pH
which can support MBI total number of taxa, while TP
production positively influences the MBI abundance. This
lower soil pH condition in the river may be due to the
sampling time, as rivers undergo episodic lower pH levels,
especially in the springtime ( Feeley et al., 2013 ; Feeley and
Kelly-Quinn, 2014 ). Moreover, stream with peaty soil can
also lower the pH during the same season ( Mc Conigley
et al., 2017 ). Besides low pH which may have caused the
production of TP in fine soil, there are other variables that
may be responsible for controlling the nutrient formation
in the soil such as soil moisture. During the field study,
we recorded pool and benchland possess larger areas as
compared to rapid and had soft substrata which have the
capacity to hold moisture that allows the formation of TP
in the soil. Soil moisture is also responsible for the degra-
dation of the leaf litter in the soil thus enhancing the
organic content and other biogeochemical cycles. Lack of
soil moisture can definitely impact the soil microbial fauna
and hence the biogeochemical activity ( Merritt and Law-
son, 1992 ). As represented in our pSEM, higher production
of TN in pool mesohabitat can affect the MBI community
positively. It is likely that microbial conditioning occurs
more rapidly in-stream habitat due to optimal moisture
and uniform stream temperature. However, a reverse trend
has been observed in the rapid mesohabitat where despite
the smaller width size and less water holding capacity,
substrata promote the growth of the MBI communities in
the river. Mostly at the hyporheic zone, positive interaction
between sediment texture and moisture for microbial res-
piration and activity, as well as the physical protection of
OM promotes a suitable conditions for heterotrophic via-
bility ( Moyano et al., 2013 ). The pool has been identified as
a highly active zone for microbial activities than the rapid
and benchland system ( Wang et al., 2019 ). On the contrary,
available finer soil can retain moisture for a longer time in
the benchland. The benchlands can also under go wet and
dry conditions because of their positioning in the riverine
system. Furthermore, exposure to the environmental con-
dition and available finer soil particles along with lower
soil moisture can increase the pH, consequently increasing
the TN production in the soil which ultimately helps the
TP production in the benchland ( Chen et al., 2015 ).
MBI distribution was also related to abiotic features
along the landscape. Abiotic variables related to topogra-
phy, such as sediment size appeared to be more impor-
tant than physico-chemical variables to define river types
and predict invertebrate composition ( Pero et al., 2020 ).
In rivers, stones and similar physical objects provide food
for the animals by trapping particulate organic matter
( Hawkins, 19 84 ; Stewart et al., 2003 ), while habitats with
fine material and decaying vegetation can also support
the high abundance and diversity of MBI ( Kamboj et al.,
2020 ). In the present study, even though rapid and pool
mesohabitat provide majority of the shelter for inverte-
brates such as collector-gatherers (Trichoptera and Diptera)
and scrapers (Ephemeroptera) the dominance of Tubificida
taxon were found in the benchland. This is because the
trapped leaf litter and organic matter can attract varieties
of MBI in the rapid and pool habitat ( Baptista et al., 2001 ).
Additionally, the invertebrate abundance increases with
the proportion of fine sediment and soil moisture compo-
nent ( Churchwell et al., 2016 ) which corroborates our find-
ings (pool habitat). The similarities of pool taxa in bench-
land may occur due to the many floodplain species hav-
ing adaptations to survive in the moist areas (Oligochaeta,
Gastropoda, Diplopoda, Isopoda Diptera, and Coleoptera)
with higher diversity and abundance especially Diptera
( Merritt and Lawson, 1992 ).
The variation in the physico-chemical parameters
mostly occurred because of the system morphology and
soil texture composition which vary for all the three meso-
habitats (RPBS). In the pool habitat, the system width and
river width varies along with silt and clay composition of
the soil, which ultimately supports a wide variation in the
TP, TN, and soil moisture values. Whereas, in the bench
system, variation of pH and TOC can be due to the expo-
sure and higher variation of sediment type (coarse sand,
fine and very fine sand). Past studies of the Wei river basin
showed that TN and agricultural land can affect the MBI
assemblages, in particularly the species richness and diver-
sity indices suggesting MBI could be indicators of nutrition
status in the Wei River basin ( Liu et al., 2020 ). Similarly
using these nutrients on mesohabitat level can help us to
understand the functioning of the ecosystem in detail.
5. Future work in RPBS direction
The ecological study on riverine mesohabitat has been
neglected due to its heterogeneous substrata and infre-
quent occurrence along the River. As mesohabitat influ-
ences the MBI total number of taxa and abundances as
shown in the present study identification and studying of
heterogeneous habitats can reveal the structural and func-
tional pattern of MBI community. Therefore, studying such
mesohabitat in the riverine system can improve the under-
standing of MBI in this system. Thus, we propose the fol-
lowing research directions to improve the understanding of
the riverine system:
1. Further study with taxonomic resolution can answer
species-specific supports for different mesohabitat.
2. Research considering their functional traits can help to
understand the system-specific role in the river envi-
ronment.
3. Seasonal comparative studies of these RPBS can further
help to understand about the behaviors of these meso-
habitats and their influence on the MBI.
6. Conclusion
To our knowledge, this is the first study to report the
relationship between river physico-chemical and MBI di-
versity indices from the RPBS along the river. If we are
to predict the future impacts of disturbance in the river-
10
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ARTICLE IN PRESS
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ine system, we need to have a better mechanistic under-
standing of the various mesohabitats of this system using
various approaches including models, experiments and, in
situ data across multiple spatial and temporal scales. The
RPBS is one of the least studied habitats in the riverine
system. In this article, along with multihabitat sampling
the pSEM approach was used to study the environmen-
tal variables shaping the MBI community in the RPBS. Our
results indicate that the RPBS has a significant effect on
the MBI community in the present studied locations. Sub-
strata was the most critical factor for the heterogeneity of
the MBI community in the RPBS. The MBI community het-
erogeneity may affect the other variables in the stream,
in turn influencing the various ecological process such as
the biogeochemical process. Further, although benchland
has been the most neglected riverine habitat, the present
study demonstrated that benchland can also be an im-
portant habitat supporting a heterogeneous MBI commu-
nity. Overall, these mesohabitat presented relatively dis-
tinct macroinvertebrate community structures. Therefore,
the present study reveals that mesohabitats can also in-
crease the biodiversity and improve the biological produc-
tivity of the river. Furthermore, including the RPBS meso-
habitat in bioassesment and ecological surveys can help
to understand role of mesohabitat in shaping MBI assem-
blages. However, the role of different environmental con-
ditions between mesohabitats in determining macroinver-
tebrate response still needs further investigation. Never-
theless, our results improve the understanding of the MBI
community and factors that affect them and, such studies
are particularly relevant given the current threats to the
rivers from existing and emerging anthropogenic threats
and climate change.
Declaration of Competing Interest
The authors declare that there was no conflict of inter-
est while carrying out this study.
CRediT authorship contribution statement
Amit Jagannath Patil: Conceptualization, Investigation,
Methodology, Formal analysis, Writing – original draft.
Zhenhong Wang: Supervision, Writing –review & edit-
ing. Xiaole He: Investigation, Methodology, Formal analy-
sis, Writing –review & editing. Pangen Li: Investigation,
Formal analysis, Writing –review & editing. Ting Yan: In-
vestigation, Formal analysis, Writing –review & editing. He
Li: Investigation, Formal analysis, Writing –review & edit-
ing.
Acknowledgments
We express our sincere thanks to the Key Laboratory of
Subsurface Hydrology and Ecological Effects in Arid Region,
Ministry of Education, School of Wate r and Environment,
Chang’an University, for the use of all facilities. The first
author would like to thank, the Chinese Scholarship Coun-
cil (CSC) and Ministry of Human Resource Development
(MHRD), India for the research fellowship. The authors are
very grateful to the editor and two anonymous review-
ers for their constructive suggestions. The first author sin-
cerely thanks to PR statistics and Dr. Jonathan Lefcheck for
the statistical training and support during the data anal-
ysis. The authors also thanks Dr. Sanitha K. Sivadas for
the valuable suggestions and further improvement of the
manuscript. Finally, first author thanks Dr. Goldin Quadros,
Ji Yaqi and Zhang Cong for their valuable support.
Funding
The Fundamental Research Funds for the Central Uni-
versities ( 300102292902 ).
Supplementary materials
Supplementary material associated with this article can
be found, in the online version, at doi: 10.1016/j.ecohyd.
2022.10.001 .
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