Biotic index

Journal of South American Earth Sciences 113 (2022) 103638
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Journal of South American Earth Sciences
journal homepage: www.elsevier.com/locate/jsames
Selection of macroinvertebrate metrics for rapid assessment of the human
impact by biotic conditions of Bolivian Altiplano streams
Carlos I. Molina a, *, Julio Pinto b, Dario Achá c
a
Universidad Mayor de San Andrés (UMSA), Facultad de Ciencias Puras y Naturales (FCPyN), Carrera de Biología, Instituto de Ecología, Unidad de Ecología Animal y
Zoología, Convenio Colección Boliviana de Fauna, Bolivia
b
Universidad Mayor de San Andrés (UMSA), Facultad de Ciencias Puras y Naturales (FCPyN), Carrera de Biología, Instituto de Ecología, Unidad de Ecología Acuática,
Laboratorio de Limnología, Bolivia
c
Universidad Mayor de San Andrés (UMSA), Facultad de Ciencias Puras y Naturales (FCPyN), Carrera de Biología, Instituto de Ecología, Unidad de Ecología Acuática,
Laboratorio de Calidad Ambiental, Bolivia
A R T I C L E I N F O
A B S T R A C T
Keywords:
Multimetric index
Macroinvertebrates
Organic pollution
Hydraulic disturbance
Monitoring program
We developed a rapid multimetric index using macroinvertebrates as bioindicators of ecological conditions for
the Bolivian Altiplano streams. A total of 64 taxa and 30 environmental variables were collected from 29 sampled
sites. After identifying redundant metrics through Principal Components Analysis (PCA) and considering prac­
tical application criteria, four biological attributes were selected to compose the Altiplano Integrity Benthic Index
(AIBI): Genus richness, Filter-Collector, Gatherer-Collector, and Swimmer groups. The sensitivity of the index
was tested in 17 rivers using a Co-Inertia Analysis (CoIA), and its first axis of analysis was strongly correlated to
the new AIBI scores (r = 0.8, p < 0.001). At Pacajes Rivers, the macroinvertebrate Gatherers-Collector group was
common in organic pollution sites and the Swimmer group with hydraulic disturbance sites. In contrast, the
Scraper group becomes a specific group for the reference conditions. The AIBI responded to environmental
variables associated with a gradient of human disturbance affecting the running freshwater’s ecological condi­
tion. The proposed index could allow the structuration a biomonitoring program.
1. Introduction
The growing demand for water by human populations induces
habitat loss, water pollution, the introduction of invasive species,
overharvesting, and flow modification (Dudgeon et al., 2006; Silva et al.,
2017). Many approaches were developed to evaluate the running
freshwater condition and ensure a better quality of the response to
management practices and rehabilitate the ecosystem (Buijse et al.,
2005; Pond et al., 2011).
Some of the most well-known ecological tools used to monitor and
manage freshwater ecosystems are multimetric indices (or Indexes of
Biotic Integrity) (Karr, 1981; Kerans and Karr, 1994; Oberdorff et al.,
2002; Li et al., 2018). These approaches use biological assemblages of
diverse taxonomic groups such as algae, fishes, and macroinvertebrates
(Barbour et al., 1999; Karr, 1981; Kerans and Karr, 1994), but the last
taxonomic group has gained popularity for their ease of use (Baptista
et al., 2011; Moya et al., 2011; Pond et al., 2013; Villamarína et al.,
2013; Silva et al., 2017; Tampo et al., 2020). Nevertheless, the difficulty
of taxonomic determination at low level in many groups is difficult. The
genus- or species-level represents more accurately the composition of
the aquatic community and increases our ability to detect a variety of
impacts (Jones, 2008; Pond et al., 2011, 2013).
In South America, during the 90s and at the beginning of the new
millennium, there was a growth in the use of qualitative biological
indices to qualify running freshwater (Roldán, 1999; Figueroa et al.,
2003; Molina et al., 2006) since the implementation of the Biological
Monitoring Working Party developed in England (BMWP, Hellawell
1978). However, there is concern about the empirical modification of
this index, mainly due to the high variability and lifestyles within the
same macroinvertebrate families (Fierro et al., 2012). Consequently,
many researchers prefer to develop indices adapted to local conditions;
the Integrity Biological Index (IBI) allows this, but tools as this demand a
; CoIA, Co-Inertia Analysis; AIBI, Altiplano Integrity Benthic Index; IBI, Integrity Biological Index; IHF, Fluvial Habitat Index; FFGs, Functional Feeding Groups;
FHGs, Functional Habitat Groups; BMWP, Biological Monitoring Working Party.
* Corresponding author. Casilla Postal #10077, Campus Universitario de Cota Cota, La, Paz, Bolivia.
E-mail addresses: [email protected], [email protected] (C.I. Molina).
https://doi.org/10.1016/j.jsames.2021.103638
Received 15 September 2020; Received in revised form 25 October 2021; Accepted 18 November 2021
Available online 30 November 2021
0895-9811/© 2021 Elsevier Ltd. All rights reserved.
Journal of South American Earth Sciences 113 (2022) 103638
C.I. Molina et al.
lot of economic and logistical resources that are often not given priority
by our local environmental authorities.
Linnaeus species concept (or morphologic concept) is the basis of the
classical taxonomic determination, which has limitations when applied
in several fields of knowledge. Genus- or species-level information,
combined with functional trait groups (feeding and/or habitat), can
detect more subtle and complex effects of the environmental impact
(Jones, 2008; Shieh et al., 2012; Akamagwuna and Odume, 2020). The
state of knowledge of the many taxonomic groups has improved in the
Neotropical region (mainly contributions of Dominguez and Fernández
2009), as have the molecular tools that are expediting taxonomic de­
terminations (Molina et al., 2017). Usually, the definitions of functional
features of macroinvertebrate communities are described at genus- or
species-level, and for this reason, many standardized IBI used to be
robust. In contrast to this, in studies at the coarser family level, re­
searchers have to increase the biological attributes (e.g. biological traits)
in order to improve the power to detect impact and define reference
conditions in the running freshwater.
In Bolivia, there were some attempts to calibrate and extend the
BMWP use to the whole country by the Ministry of Environment and
Water (MMAyA, 2011). In the best-case scenario, there were research
initiatives to standardize the IBI from the Amazon (Moya et al., 2007) to
the Andean region (Moya et al., 2011). Nevertheless, there are practical
limitations in using these indices that do not consider environmental
technicians’ training, and the incorporation of this capacity could draw
the attention of policymakers. Therefore, there is the need to develop a
new and more comprehensive index for specific human disturbances.
For the first time in Bolivian Altiplano we evaluated biotic conditions
using macroinvertebrates at genus level identification for small and
mid-sized rivers to develop a rapid and comprehensive benthic index to
distinguish the different human disturbance pressures.
2. Material and methods
2.1. Study area
The Pacajes province is in the Southeastern region of the Department
of La Paz (around 36 km from La Paz City). Its population, estimated at
around 590,000 inhabitants, suffers from the low quantity and quality of
water resources. Pacajes province is part of a semiarid plateau region
with high solar radiation and low precipitation incidence (Fig. 1).
Physiographically, it is made up of hills, plains, and mountainous areas
with residual shapes, which means that is divided by shallow organic
soils with the predominance of rocky outcrops or loose rocks. Despite
having a tropical latitude, the climate shows extreme daily variability
due to the high altitude (average 4013 m.a.s.l.) and high radiation
levels. There are two marked seasons, a wet season where precipitation
can reach between 15 and 20 mm from November to March (tempera­
tures range between 3 and 18 ◦ C) and dry season with almost null pre­
cipitation and frequent frosts from May to September (temperatures
range between − 5 and 13 ◦ C). The soil practices are split by high
mountain use, occupied for grazing. The plain and small mountainous
areas sometimes are occupied by agriculture and grazing. The slopes and
valleys are used for agriculture and mining activity (exploiting mainly
gypsum and other minerals such as copper and zinc). Since October
2011, biological, physical and chemical water parameters are
monitored.
Fig. 1. Map of the study area showing the twenty-nine selected sampling sites within the Pacajes province.
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C.I. Molina et al.
Journal of South American Earth Sciences 113 (2022) 103638
2.2. Study sites and sampling design
described by taxa recorded. The five FFGs were Predator (PR), GathererCollector (GC), Filter-Collector (FC), Scraper (SC) and Shredder (SH);
the six FHGs were Burrowers (BUR), Climbers (CLB), Clingers (CLG),
Skaters (SKT), Sprawlers (SPR) and Swimmers (SMW). The qualitative
assessments of the biological conditions of each site were evaluated by
Biological Monitoring Working Party indices modified and applied in
Central Altiplano region: BMWPIbe (Iberian Peninsula (Alba-Tercedor
and Sánchez-Ortega, 1988),), BMWPCol (Colombian rivers (Roldán,
1999),) and ABI (Andean Peruvian streams, Acosta et al., 2009) and
BMWPBol (Bolivian rivers (MMAyA, 2011),). By the end, we regarded
these 17 biological metrics for further analysis.
We used the talking map methodology by setting up several meetings
with local indigenous communities. This allowed us to identify several
issues related to water supply and what activities may be leading to
water degradation. Thereby, we defined our work with 16 rivers and
planned it around 42 sites. Thus, the sites were selected if they were
minimally impacted areas (reference sites) and impacted areas (between
intermediate and severe disturbance sites). Later, we reduced our sites to
29 because some of the sites reflected the same disturbed conditions,
according to the Fluvial Habitat Index (IHF, in its Spanish acronym).
This index was designed to characterize the physical heterogeneity of
the stream channel (Pardo et al., 2002), and we used the modified
version of the Altiplano and Central Andes rivers according to Acosta
et al. (2009).
2.6. Index construction and validation
Data were analyzed in three steps: selecting the metrics to compose
the index, building the multimetric index, and testing the new index
proposed.
The selection of the metrics was made exploring the linear relation
within environmental variables and biological metrics, by correlation
circle evaluation (an extension mode of Principal Component Analysis,
PCA). That way, highly correlated metrics often provided similar in­
formation (redundancy analysis), allowing us to choose a set of variables
with better score contributions to the analysis and those variables with a
high frequency of correlations among other variables. In order to obtain
the spatial gradient (discrimination between reference and disturbed
sites), as the consequence of the relation of the environmental variables
and biological metrics the Co-Inertia Analysis was performed (-acronym
CoIA- Dolédec and Chessel, 1994). The significance of these relation­
ships was studied with a Monte Carlo test (with 999 random permuta­
tions). All the multivariable analyses were carried out using the ade4
package (Dray and Dufour, 2007) available in R 3.6.3 software (RCor­
eTeam, 2020).
To build the multimetric index, we compared the best biological
metrics between reference and disturbed sites obtained by CoIA. When
the distribution of the biological metrics increase from reference sites to
disturbed sites, we assigned values of 20 (above 25% quartile of the
reference sites), 15 (below 75% quartile of highest disturbance sites),
and 5 (below 50% quartile of lowest disturbance sites). Such served as
indicator scores per each biological attribute recommended by Karr
et al. (1986) and Barbour et al. (1999) (“three-section method”). When
the distribution of boxplot metrics decreases from reference sites to
disturbance sites, we assigned the inverse values (5, 15, and 20 values,
respectively).
Finally, the index’s sensitivity was tested by the median score dis­
tribution that we obtained by each biological metric and correlated with
CoIA F1 scores of the 29 sampled sites (spatial co-structures). Four
interpretive water condition categories were established based on
reference and disturbed sites (very good, good, degraded, and severely
degraded).
2.3. Environmental characterization
Substrate composition of the streambeds of the rivers was evaluated
by visual examination as Pebble (16–64 mm diameter), Gravel (2–16
mm diameter), Sand (1–2 mm diameter), and Silt (<1 mm diameter).
The water was characterized and sampled before the macro­
invertebrates collection. Water temperature, dissolved oxygen (DO), %
oxygen saturation (OS), pH, electrical conductivity (Cond) were deter­
mined using a multi-probe meter (WTW, Multi 350i, Weilheim, Ger­
many). Water samples were collected in sterile plastic bottles (2L
approximately) at each defined site and transported to the Quality
Environmental Laboratory (LCA, in its Spanish acronym). Laboratory
measurements evaluated Alkalinity (Alk), Harness (Har), Chemical Ox­
ygen Demand (COD), Sulfate (SO42-), Chlorides (Cl− ), Nitrate (NO3− ),
Ammonium (NH4+), Arsenic (As), Sodium (Na+), Calcium (Ca2+),
Magnesium (Mg2+), and Potassium (K+) following standard EPA
methods (EPA, 1982).
According to Hudson (1997), the hydraulic parameters were deter­
mined from several measurements such as Width (Wid), Depth (Dep),
and we obtained the transversal section area of the river (A). Later we
evaluate the average of current velocity (V) per area. Thus, we estimated
the Discharge (Q) of each sampled point according to the following
formula: Q (m3. s− 1) = A (m2) x V (m. s− 1).
2.4. Macroinvertebrate sampling and identification
We used the qualitative method (handle net) since the quantitative
method, such as the Surber sampler, could not be used in all sites
because we got high heterogeneity of the current and depth. These nets
were dragged against the water flow, kicking the substrate for 3 or 5 min
to try to catch the free-living invertebrates from benthic substrates, and
then pooled together in a 500 mL plastic bottle. The collected materials
were preserved with an added 80% ethanol and transported to the
Limnology laboratory for further analysis (Ecology Institute). All or­
ganisms were completely separated from detritus and small substrate
particles and transferred to 10 mL bottles for identification to genus
level as well as possible, using the following identifications keys:
Ephemeroptera (Domínguez et al., 2009), Odonata (Ellenrieder and
Garrison, 2009), Plecoptera (Froehlich, 2009), Hemiptera (Mazzucconi
et al., 2009), Trichoptera (Angrisano and Sganga, 2009), Diptera
(Coscarón-Arias, 2009; Paggi, 2009), Coleoptera (Archangelsky et al.,
2009), Crustacea (Peralta and Grosso, 2009), Acari (Ferradas and
Fernández, 2009), Annelida (Marchese, 2009) and Gastropoda (Cuezzo,
2009).
3. Results
3.1. Environmental variables
Geographic references and environmental variables recorded in 29
sampled sites in Pacajes provinces are summarized in Appendix A. Some
metals were under the detection limit (Cd, Cu, Cr, Pb, and Zn) and were
not considered for further analysis. The 25 variables left were incorpo­
rated into the first PCA, and to reduce the redundancy of the metric, we
explored its contribution to the axis component and correlation within
variables. Fig. 2A shows the first correlation circle of the PCA, where the
sulfate as the hardness of water had a high score contribution to the F1
axis (80%). At the same time, hardness was well correlated with con­
ductivity and some anions like chlorides, sodium, and calcium (r > 0.8).
Opposite to the F1 axis, the pebble substrate was associated with the
dissolved oxygen, also FHI index and Saturate oxygen show an
2.5. Data analysis
The macroinvertebrates’ composition was evaluated in terms of the
total family number and genus richness. We assigned functional feeding
groups (FFGs) and habitat groups (FHGs) according to the literature
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C.I. Molina et al.
Journal of South American Earth Sciences 113 (2022) 103638
Table 1
Results of principal components analysis of environmental variables from 29
sites sampled.
Environmental variables
Component 1
Component 2
Depth (Dep)
Wide (Wid)
Water velocity (Vel)
Discharge (Q)
pH
Chemical oxygen demand (COD)
Oxygen saturate (OS)
Saturate oxygen (SO)
Conductivity (Cond)
Alkalinity (Alk)
Hardness (Har)
Sulfate (SO4)
Chlorides (Cl)
Nitrate (NO3)
Ammonium (NH4)
Arsenic (As)
Sodium (Na)
Calcium (Ca)
Magnesium (Mg)
Potassium (K)
Pebble (Peb)
Gravel (Gra)
Sand (San)
Silt (Sil)
Fluvial Habitat Index (FHI)
Biological variables
− 0.118
0.311
− 0.460
0.060
− 0.115
0.611
− 0.375
− 0.420
0.874
− 0.216
0.870
0.835
0.835
0.417
0.446
− 0.096
0.846
0.869
0.133
0.230
− 0.463
− 0.244
0.347
0.100
− 0.670
Component 1
0.593
0.275
0.490
0.859
0.068
0.281
− 0.056
− 0.312
− 0.198
0.419
− 0.114
− 0.004
− 0.126
0.066
− 0.118
0.230
− 0.128
− 0.129
0.722
0.332
− 0.066
− 0.627
− 0.337
0.732
− 0.405
Component 2
Richness (Rich)
Predator (PR)
Gatherer/Collector (GC)
Filter/Collector (FC)
Scraper (SC)
Shredder (SH)
Burromers (BUR)
Clingers (CLG)
Sprawlers (SPR)
Swimmers (SMW)
− 0.176
0.857
− 0.121
− 0.480
− 0.777
− 0.522
0.457
− 0.744
0.647
− 0.473
− 0.713
− 0.371
0.728
− 0.261
− 0.121
− 0.656
0.468
− 0.239
− 0.669
0.507
by genus richness and all biotic indices (high correlation between them
with r > 0.8). The second axis summarizes the majority of the functional
biotic metrics that we keep for subsequent analyses and avoid the
metrics with the lowest factorial contribution and SKT and CLB
(Table 2).
3.3. Biological metrics and environmental variables relationships
The goal was to find the best synthetic metrics that provide the
optimal interpretation of the abiotic-biotic relationships; we have cho­
sen metrics with a high correlation with other variables and high spatial
heterogeneity explanation to both previous PCA components. Among
the environmental variables were: FHI index, hardness, sulfates, water
velocity, saturate oxygen, silt sediment, gravel and sand substrate. In
terms of the biological metrics, they were: richness, FFGS like scraper,
gatherer-collector, shredder, filter-collector, and FHGs like swimmers.
The CoIA emphasizes the importance of the first axis, explaining a
high variance of the analysis with 76%. Accordingly, there was clearly a
significant co-structure between environmental variables and biological
metrics (permutation test significant, p < 0.001). The spatial costructure of analysis results shown on the first axis were able to sepa­
rate no disturbance sites “Reference” (sites 7, 17, 18, and 26), charac­
terized by high scores of FHI index, saturate oxygen (SO), and gravel
substrate (Gra). In such sites, macroinvertebrates had by high genus
richness (Rich) and filter-collector groups (FC). Likewise, we identified
two types of disturbances. In the same opposing first axis, we were able
to typify as “Disturbance A" (sites 3, 14, and 28 with organic pollution)
the one related to high values of harness (Har) and sulfate (SO4) and
related to gatherer-collector groups (GC). In the second axis, the high
values of silt sediment (Sil) and Velocity (Vel) were related to the
Fig. 2. Correlation plot of the environmental variables. A Total environmental
variables measured. B Biotic metrics. The coordinates of the arrow tips are
correlations between the variables and the PCA axes and are contained within
the circle of radius equal 1. The grey arrows shown the redundancy variables
that we avoided to use for the subsequent analysis. The legends of variables are
described on Tables 1 and 2
important score contribution. The F2 axis showed as much discharge as
silt sediment, and magnesium cation had a high score contribution
(70%), but these last variables exhibited high association (r > 0.8).
Although other variables do not show import contribution to any
component, we tried to keep them for the subsequent analysis because
they reflected the heterogeneity of variances, such as depth, alkalinity,
water velocity, and gravel substrate (Table 1).
3.2. Biological metrics
Invertebrate taxa collected are summarized in Appendix B, and on
the basis of this data set, we calculate the biological metrics for each site
listed in Table 2. In much the same way as the environmental variables,
we explored the correlation within the biological metrics. Fig. 2B shows
on the opposite section of the F1 axis the high score contributions given
4
Journal of South American Earth Sciences 113 (2022) 103638
Kerans and Karr (1994)
Kerans and Karr (1994), Moya et al., (2007)
Moya et al. (2007)
Kerans and Karr (1994), Moya et al., (2007)
Kerans and Karr (1994), Moya et al., (2007)
Kerans and Karr (1994), Moya et al., (2007)
Kerans and Karr (1994), Moya et al., (2007)
Pond et al. (2011)
Pond et al. (2011)
Pond et al. (2011)
–
Pond et al. (2011)
Pond et al. (2011)
Alba-Tercedor and Sánchez-Ortega (1988)
Roldán (1999)
Acosta et al. (2009)
MMAyA (2011)
swimmers group (SMW). We typified them as “Disturbance B" (sites 1, 5,
11, 20, and 21 with hydraulic stress) (Fig. 3A, E, and 3F).
3.4. Altiplano Integrity Benthic Index (AIBI)
Through the assigned scores of 20, 15, and 5 when Richness and
Filter-Collector groups decreased (Fig. 4), and inverse scores of 5, 15,
and 20 when Gatherer-Collector and Swimmer groups increased from
the references to disturbances sites (Fig. 5). We have chosen the median
distribution score per site from these indicator values and correlated
them with the environmental gradient that we calculated from the first
CoIA axis (r = 0.863, p < 0.001). All these median scores express the
new index that we called Altiplano Integrity Benthic Index (AIBI), which
reflects the water conditions per each site worked (Fig. 6).
Decline
Decline
Decline
Increase
Increase
Decline
Decline
Increase
Increase
Decline
–
Increase
Increase
Decline
Decline
Decline
Decline
4. Discussion
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Our study attempted to manage a considerable number of evaluated
sites while using the appropriate multivariate statistic tools to select the
best metrics for the final index to be sensitive, reproducible, and accu­
rate. Traditionally, the standardization of a multimetric index requires
extensive logistical support and a high sample collection effort. From an
economic perspective, these efforts are not a priority in relatively poor
South American countries. Consequently, and given our socio-economic
reality, the implementation of rapid methodologies is urgent for the
adequate assessment of the state of health of running waters.
The IHF morphostructural index allowed us to optimize the number
of sites evaluated by seeking an environmental gradient and comparing
reference and disturbance sites. This index seems to work quite well
through slight modifications made by Acosta et al. (2009). Afterward,
the environmental gradient was given by the PCA’s first axis, which, in
turn, highly correlated with the IHF index. Lineal methods are
commonly used to standardize and calibrate a multimetric index (e.g.,
multiple regression or PCA) to create an appropriate index for evalu­
ating anthropic pressures (Barbour et al., 1999; Pond et al., 2013). The
strength of the PCA relies on the simultaneous exploration of the many
variables that can be related to multidimensional space, and at the same
time, reducing the high number of samples that tend to be demanded by
multiple regression to solve a one-dimensional problem. The CoIA works
like two PCA conducted separately, one for environmental data and
another one for biological metrics. The final outcome results from the
conjunction of both analyzes, maximizing the covariance structure,
leading to a maximal correlation (co-structure) (Dolédec and Chessel,
1994; Franquet et al., 1995).
Conventionally, most of the studies that calibrate a multimetric
index use quantitative sampling methods (such as Kick-sampling or
Surber network), since they give us other biological metrics (mainly
abundance of invertebrates). As was clarified in our macroinvertebrate
method section, the different rivers sampled were highly heterogeneous
in terms of the water current (some sites with an almost null current).
Consequently, we had to appeal towards qualitative sampling so that the
whole unit of sampling would be comparable. There were previous ex­
periences comparing the qualitative and quantitative methods for bio­
indication purposes in rivers in the Andean regions, and both methods
do not give different water interpretations (Molina et al., 2006). On the
other hand, these qualitative samples could be considered quick tools to
assess the state of health of freshwater bodies, and local communities
can easily appropriate its use.
BMWPIbe, BMWPCol, BMWPBol, and ABI indices fulfilled their role to
evaluate the disturbance related to organic pollution (urbane source).
However, such indices cannot discriminate other disturbances such as
the hydrological force as evidenced by our proposed index. Conse­
quently, the traditional changes of the original BMWP index by empir­
ical or analytical adjustment of scores of macroinvertebrate (Acosta
et al., 2009; Alba-Tercedor and Sánchez-Ortega, 1988; MMAyA, 2011;
Roldán, 1999), does not represent a significant resource of variation in
Richness
No. families
% PR
% GC
% FC
% SC
% SH
% BUR
% CLB
% CLG
% SKT
% SPR
% SMW
BMWP-Ibe
BMWP-Col
ABI
BMWP-Bol
Biology metrics S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 Expected response References
Table 2
Biological analyzed metrics and their expected qualitative responses to the human impact. Richness (Rich), Number of families (No.F), Predator (PR), Gatherer-Collector (GC), Filter-Collector (FC), Scraper (SC), Shredder
(SH), Burrowers (BUR), Climbers (CLB), Clingers (CLG), Skaters (SKT), Sprawlers (SPR), Swimmers (SMW) and four Biological Monitoring Working Party indices modified in different regions (BMWP and AIBI).
C.I. Molina et al.
5
C.I. Molina et al.
Journal of South American Earth Sciences 113 (2022) 103638
Fig. 3. Results of the Co-Inertia analysis processed. A. Spatial standardized Co-Inertia scores of sites sampled from the relationship of environmental and biological
metric data sets. The arrows show the trend of the sampled sites between the relationships of environmental variables and biological metrics and, thereby, we
obtained a triangular assemblage. B. Cononical weight of biological metrics. C. Cononical weight of environmental variables.
sulfate, and chloride concentrations (above to 1000 mg. l− 1). Most of the
taxa found in these organic impaired sites were Oligochaetes (Dero) and
Diptera larvae (Paratrichocladius), and in general, GC groups dominated
samples. Like many studies around the world have demonstrated GC
groups are tolerant to organic pollution (Arimoro et al., 2007; Marchese,
2009).
Reference sites were found at the Corocoro region’s headstream and
other sites far from rural centers (sites 7 and 26 of the Blanco and Mauri
rivers, respectively). Good oxygenation and moderate current of their
waters typified these sites, and the other physical and chemical pa­
rameters were in the lowest concentrations. In these reference condi­
tions, the fauna were more diverse, and some taxa were represented
mainly by SC groups corresponding to EPT orders (Andesiops, Claudio­
perla and Metrichia, respectively). The larvae Simulium were identified as
a CF group related to the good conditions of the reference sites. Previous
studies have recognized the sensitivity of the CF group against sus­
pended sediments (Akamagwuna and Odume, 2020), and we are
the response of the bioindication power; because they have been shown
to be highly correlated with each other, but does not allow different
anthropogenic disturbances discriminate.
According to Townsend et al. (1989), the hydrological force is
considered a natural disturbance event. However, in our case, there was
also a high proportion of Silt sediment and Magnesium concentration
found in River Desaguadero (Sites 1, 2, 9 and 11). The silt sediments
come from the runoffs of the Desaguadero basin related partly to agri­
culture practices in which the fine material course is released in the form
of suspended sediments and subsequently deposited on the river bed
(Vallejos et al., 2018). Most of the taxa that we found in these impacted
sites were invertebrates related to SMW and BUR groups such as
Amphipod (Hyalella) and Ostracoda (Patamocypris) respectively.
Although some impacted sites showed a dispersed human population
influence, the proposed index was able to identify sites with organic
pollution (sites 14 and 28 nearby to Corocoro village), the water quality
had COD values larger than 100 mg. l− 1 and the highest hardness,
6
C.I. Molina et al.
Journal of South American Earth Sciences 113 (2022) 103638
Fig. 4. Boxplots of calibration decreasement scores from reference and disturbance sites. A. Richness (Rich). B. Filter-collector group (FC). The dotted line represents
the approximate 5th percentile of the calibration reference distribution for each stratum.
proposing the sensibility of EPT taxa to organic pollution.
Our data analysis at the genus level resolution allowed us to
discriminate between the most and least impacted sites. At least there is
a consensus that the genus or species level of the macroinvertebrates
provides robust tools for evaluating impacts to water quality (Jones,
2008; Pond et al., 2008, 2011), because the taxonomic resolution is
linked to the roles of the functional status of invertebrate assemblages in
streams or rivers (Akamagwuna and Odume, 2020; Merritt et al., 2002;
Shieh et al., 2012).
In Neotropical freshwater, when using macroinvertebrates as bio­
indicators there is a concern about the difficulty of taxonomic deter­
mination in many groups. With the intent to decrease this bias of the
bioindication, they often calculate several metrics instead of improving
the state of knowledge about the biology of the macroinvertebrates and
enhancing our local capacity to make a biodiversity inventory. Our
study has been constituted as a model case study, where local commu­
nities actively participated in the definition of threats that disturb
running water, and locals were trained to collect samples. The public
university contributed to the biological study of invertebrates to develop
a quick, precise, and effective index to survey the health status of
running freshwater in the province of Pacajes.
According to Karr (1981), Karr et al. (1986), and Li et al. (2016),
water scores are generally classified into six grades from highest
(excellent condition) to lowest values (no living organisms). Our study
finds these six categories of water quality as inappropriate since we
cannot consider the lowest category or the worst condition because we
always find macroinvertebrates. Although such organisms are very
resistant to define disturbances, establishing the high or excellent
category is more complex since no previous studies have been carried
out in the work area, and the definition of “pristine” sites is still difficult
to establish as control sites until other complementary studies are con­
ducted. So far, four interpretive water conditions would be properly
established based on reference and disturbed sites, which correspond to:
very good, good, degraded, and severely degraded.
The only thing missing from the spread of the proposed index would
be to test its operation in different seasons on the Altiplano-Puna plateau
and developing countries.
5. Conclusion
Our results provided further insight into the responses of taxonomic
genus resolution on macroinvertebrates related to functional feeding
and habitat groups. We demonstrate that co-structure analysis can be
useful to explore a space typology as the consequence of human impact
and to help us define a metric index. Such knowledge will contribute to
developing more predictive trait-based biomonitoring tools for better
management of streams and rivers affected by the extensive and diverse
human pressures on the Altiplano-Puna plateau freshwater systems.
7
C.I. Molina et al.
Journal of South American Earth Sciences 113 (2022) 103638
Fig. 5. Boxplots of calibration increasement scores from reference and disturbance sites. A. Proportion of Gatherer-Collector groups (GC). B. Proportion of Swimmier
groups (SWM). The dotted line represents the approximate 75th percentile of the calibration reference distribution for each stratum.
Credit author statement
Carlos I. Molina: Conceptualization, Methodology, Statistical anal­
ysis, Investigation, Writing -Original Draft, Writing -Review & Editing.
Julio Pinto: Supervision, Funding, Writing -Review & Editing. Dario
Achá: Conceptualization, Funding, Resources, Writing-Review &
Editing.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgments
The authors thank Jach’a Suyo Pacajak’e of Pacajes province for
supporting our research. The study was financially supported by the
DIPGIS-UMSA program through the IDH taxes (in its Spanish acronym).
We also thank Guillermina Miranda, Francisco Osorio, Roberto Apaza,
Claudio Rosales, and others for their technic-scientific assistance on this
project. Without the support of two current French projects (JEAI-Ferria
and JEAI-Titicaca), it would not have been possible to carry out the
present manuscript. Finally, thanks to Sofia Lana from the University of
California for providing useful comments on the manuscript.
Fig. 6. Correlation of the Altiplano Integrity Benthic Index (AIBI) and CoIA F1
scores obtained from the 29 sampled sites. The sloping line and grey area shown
the tendency relationship between variables exploited.
8
C.I. Molina et al.
Appendix A. Geographic references and environmental variables recorded in sampled sites in Pacajes province: Decimal coordinates and altitude (Alt), mean depth (Dep), mean
width (Wid); mean velocity (V), mean flow (Q); pH, electrical conductivity (Cond), alkalinity (Alk), Harness (Har), Chemical Oxygen Demand (COD), dissolved oxygen (DO), Oxygen
saturation (OS); Sulfate (SO42¡), Chlorides (Cl¡), Nitrate (NO3¡), Ammonium (NH4þ), Sodium (Naþ), Magnesium (Mg2þ), Arsenic (As), Potassium (Kþ) Percent pebble (Peb), percent
gravel (Gra), percent sand (San), percent silt (Sil) and Fluvial Habitat Index (FHI)
9
River/
stream
Latitude Longitude Alt
(m.s.
n.m.)
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
S15
S16
S17
S18
S19
S20
S21
S22
S23
S24
S25
S26
S27
S28
S29
Desaguadero
Desaguadero
Ballivian
Ballivian
Ballivian
Mauri
Mauri
Challuyo
Desaguadero
Mauri
Desaguadero
Putuni
Putuni
Corocoro
Cachaca
Pontosuelo
Pontosuelo
Pontosuelo
JachaJawira
Titikani
Copalaka
Butijlaca
Colorado
Colorado
Achuta
Blanco
Jalluma
Corocoro
Cosekani
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
68,768
68,782
68,537
68,615
68,728
69,324
68,922
68,826
68,609
68,644
68,342
68,333
68,411
68,453
68,418
68,482
68,443
68,365
68,541
68,599
68,631
68,450
68,422
68,421
69,068
69,170
68,612
68,440
68,361
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
16,942
17,115
17,072
17,113
17,167
17,478
17,469
17,416
17,247
17,292
17,486
17,187
17,187
17,214
17,226
17,192
17,113
17,113
17,012
17,023
17,024
16,861
16,936
17,004
17,891
17,936
16,982
17,168
17,242
3919
3827
3969
3930
3837
4004
3943
3925
3838
3819
3786
4175
4000
3948
3990
3966
3971
4195
4034
4112
4006
4084
4046
4116
4007
5098
3890
3972
4163
Depth
(m)
Width
(m)
Vel
(m
s− 1 )
Q (m3 pH Cond
s− 1 )
(μS
cm− 1)
Alk
(mg
l− 1)
Har
(mg
l− 1)
COD
(mg
l− 1)
DO
(mg
l− 1)
OS SO42−
(%) (mg
l− 1)
Cl−
(mg
l− 1)
NO3−
(mg
l− 1)
NH4+
(mg
l− 1)
Na+
(mg
l− 1)
Mg2+
(mg l1)
Ca2+
(mg
l− 1)
As
(mg
l− 1)
K+
(mg
l− 1)
Peb
(%)
Gra
(%)
San
(%)
Sil FHI
(%)
0,73
0,33
0,35
0,59
0,50
0,22
0,19
0,70
0,35
0,09
0,20
0,05
0,48
0,02
0,49
0,03
0,55
0,03
0,53
0,39
0,48
0,52
0,56
0,60
0,25
0,13
0,56
0,05
0,02
0,73
15,23
9,34
1,68
3,20
19,93
19,93
0,80
21,25
137,41
22,14
1,82
3,63
10,85
3,45
0,83
2,22
2,49
2,61
6,96
3,67
2,87
2,10
1,67
4,45
6,68
2,15
2,49
1,48
3,93
2,04
2,79
3,11
2,89
1,34
3,41
3,68
1,40
2,00
0,20
2,70
1,49
0,13
1,59
1,80
1,58
2,60
1,39
1,62
1,23
1,58
1,46
1,79
3,18
1,34
1,80
0,60
1,40
78,81
8,63
36,13
56,71
52,63
13,13
22,67
59,08
64,24
39,18
5,10
0,27
6,84
0,13
6,79
0,04
6,44
0,18
6,55
7,78
6,85
6,62
6,40
6,28
3,90
1,48
6,42
0,06
0,05
115
72
125
135
11
173
120
125
139
89
187
160
59
22
19
20
43
115
192
252
144
125
125
153
67
58
139
58
60
53
644
496
795
805
135
200
137
697
315
278
370
65
1228
537
203
541
38
1012
328
625
406
505
654
77
35
214
1361
193
31,00
56,00
60,50
45,00
106,27
44,00
33,00
70,00
28,00
151,27
59,92
22,00
5,60
103,00
35,00
19,00
39,00
10,00
12,00
30,00
15,00
13,00
40,00
33,00
23,00
35,00
40,00
120,00
6,40
8,14
6,14
5,99
6,46
5,57
6,86
8,00
5,41
8,07
5,31
6,39
9,14
7,52
5,34
5,76
7,08
6,96
6,48
10,88
8,07
10,60
10,65
7,16
7,42
10,60
7,41
6,99
6,78
9,44
115
95
94
99
91
104
113
90
114
77
98
123
111
89
93
105
104
99
117
114
123
116
106
108
123
107
104
101
125
345
582
863
1313
999
375
385
3
685
424
732
516
3
1764
1414
276
1646
15
160
0
619
140
239
267
16
6
33
4310
2
0,60
0,62
0,30
0,30
0,42
0,30
0,30
0,44
0,47
0,40
0,41
0,30
0,30
1,70
0,30
0,30
0,48
0,30
0,30
0,34
0,90
0,30
0,03
0,61
0,30
0,48
0,30
0,30
0,30
0,64
0,58
0,30
0,30
0,47
0,30
0,30
0,30
0,30
0,45
0,39
0,30
0,30
3,20
0,30
0,30
0,30
0,54
0,30
0,30
1,10
0,30
0,58
0,03
0,30
0,30
0,30
0,54
0,30
296
555
744
932
775
274
339
7
544
354
580
457
9
857
1126
189
1007
9
150
10
533
142
1100
264
36
18
35
2706
18
36,40
49,00
20,00
42,00
30,47
14,00
21,00
5,90
52,00
27,42
19,71
30,00
0,02
0,02
0,02
0,02
0,02
0,01
26,00
31,00
75,00
26,00
25,00
23,00
8,00
4,40
5,40
0,15
0,09
20
178
166
249
256
32
46
45
193
94
100
99
19
386
177
68
188
11
363
80
126
120
160
223
18
7
77
478
70
0,572
0,023
0,001
0,001
0,003
0,840
1700
0,001
0,001
0,363
0,392
0,530
0,016
0,004
0,004
0,011
0,005
0,016
0,001
0,001
0,018
0,001
0,023
0,001
0,100
0,056
0,001
0,002
0,006
15,15
23,00
11,00
13,00
7,05
33,00
27,00
6,30
27,00
6,61
14,99
21,00
4,20
9,40
6,40
4,10
7,20
2,40
23,00
6,40
36,00
4,70
6,10
7,40
35,00
17,00
4,60
10,00
2,50
0
0
0
0
0
20
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
70
50
30
0
0
0
0
0
80
60
30
90
0
0
0
0
50
40
0
50
70
100
100
70
0
0
50
40
30
30
25
60
50
60
0
0
20
0
0
50
10
0
90
90
10
50
50
100
10
30
0
0
30
0
10
50
60
70
0
25
20
50
40
100
100
80
20
40
0
0
100
10
10
90
0
10
0
40
0
0
0
0
100
90
0
0
0
0
0
0
0
0
8,8
8,9
8,6
8,5
9,3
8,7
8,3
9,0
8,6
9,7
8,8
8,5
8,8
8,8
9,0
9,4
6,8
9,5
7,9
7,9
7,8
8,6
8,2
8,1
9,2
9,3
7,6
8,3
8,2
340
3070
4380
4950
4270
1534
1700
260
3410
1645
2837
2380
230
5360
4610
1295
5460
178
1034
595
2120
1314
1580
2110
316
150
520
12860
458
262
533
827
296
277
37
72
15
617
289
315
259
1
1253
534
121
150
24
146
146
498
339
334
610
76
1
80
1166
154
31
25
27
25
34
42
38
26
23
25
36
41
15
29
22
43
39
53
28
34
30
48
37
29
75
48
32
34
30
Journal of South American Earth Sciences 113 (2022) 103638
Code
site
Taxa
10
Cnidaria
Hydridae
Hydra
Tricladida
Dugesiidae
Dugesia
Nematoda
Tylenchydae
Tylenchus
Oligochaeta
Naididae
Chaetogaster
Dero
Homochaeta
Pristina
Hirudinea
Glossiphoniidae
Helobdella
Molusca
Hydrobiidae
Heleobia
Planorbidae
Biomphalaria
Sphaeriidae
Sphaerium
Crustacea
Ostracoda
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 Feeding
groups
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Barbour et al. (1999)
Merritt et al. (2002)
GC
GC
GC
GC
BUR
BUR
BUR
BUR
–
Barbour et al. (1999)
–
–
PR
SPR
Barbour et al. (1999), Merritt et al. (2002)
1
SC
CLG
Merritt et al. (2002)
SC
CLG
Merritt et al. (2002)
FC
BUR
Barbour et al. (1999)
GC
SWM
Mourguiart (1992), Barbour et al. (1999), Merrit et al.
(2002)
1
GC
GC
SWM
SWM
–
–
1
GC
SWM
–
GC
SWM
–
1
SC
SWM
Merritt et al. (2002), Saigo et al. (2009)
1
1
PR
PR
PR
SWM
SWM
SWM
Merritt et al. (2002)
–
–
1
1
PR
SWM
–
GC
SKT
Christiansen and Snider 1996
PR
CLB
Barbour et al. (1999), Merrit et al., (2002)
1
1
1
1
1
1
1
1
1
1
1
CLG
BUR
1
1
1
PR
GC
1
1
1
Barbour et al. (1999)
–
1
1
1
CLB
1
1
1
1
GC
GC
1
1
1
1
1
Deserti et al. (2017), Pennak (1989)
1
1
1
1
1
1
1
CLG
1
1
1
PR
1
1
1
1
1
1
References
1
(continued on next page)
Journal of South American Earth Sciences 113 (2022) 103638
Cyprididae
Cyprinotus
Potamocypris
1
Ilyocypridae
Ilyocypris
Limnocytheridae
Elpidium
Amphipoda
Hyalellidae
Hyalella
1
Arachnida
Hidracarina
Hydracaridae
spA8
Oribatidae
spA9
Hexapoda
Collembola
Isotomidae
Odonata
Aeshnidae
Aeshna
1
1
1
1
1
Habitat
groups
C.I. Molina et al.
Appendix B. Invertebrates presence, composition in different sites sampled and functional groups that were defined by survey references (feeding and habitat lives): Predator (PR),
Gatherer/Collector (GC), Filter/Collector (FC), Scraper (SC), Shredder (SH), Burromers (BUR), Climbers (CLB), Clingers (CLG), Skaters (SKT), Sprawlers (SPR) and Swimmers (SMW)
C.I. Molina et al.
(continued )
Taxa
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 Feeding
groups
11
Chironomidae
Podonomidae
Podonomus
Tanypodinae
Alotanypus
Apsectrotanypus
Pentaneura
Orthocladinae
Corynoneura
Cricotopus
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References
PR
PR
CLB
CLB
Merritt et al. (2002)
Merritt et al. (2003)
GC
SC
SWM
SWM
Merritt et al. (2002)
Molina (2004)
SH
SWM
Molina (2004)
SC
CLG
Molina (2004)
SC
SC
SC
GC
CLB
CLB
CLB
CLB
Merritt et al. (2002)
Wiggins (1996), Barbour et al. (1999)
Motta et al. (2016)
Wiggins (1996)
PR
PR
PR
PR
SWM
SWM
SKT
SKT
Merritt et al. (2002)
–
Merritt et al. (2002)
–
PR
PR
PR
PR
PR
PR
PR
GC
GC
GC
GC
CLB
CLB
CLB
CLB
CLB
CLB
CLB
BUR
BUR
CLG
CLG
Merritt et al. (2002)
White and Brigham (1996)
White and Brigham (1996)
White and Brigham (1996)
–
Michat and Alarie 2009
White and Brigham (1996)
Barbour et al. (1999)
White and Brigham (1996)
Babour et al. (1999)
White and Brigham (1996)
PR
GC
GC
CLB
CLG
CLG
White and Brigham (1996), Merritt et al., (2002)
White and Brigham (1996), Barbour et al. (1999)
PR
BUR
Barbour et al., (1999), Merrit et al. (2002), Ronderos
et al. (2010)
1
GC
CLB
Martyniuk et al. (2019)
1
PR
PR
PR
SPR
SPR
SPR
Caleño et al. (2018)
Barbour et al. (1999)
Barbour et al. (1999)
GC
SH
CLG
CLG
Barbour et al. (1999)
Barbour et al., (1999), Merritt et al. (2002)
1
(continued on next page)
Journal of South American Earth Sciences 113 (2022) 103638
Gomphidae
Neogomphus
Ephemeroptera
Baetidae
Andesiops
Leptophebiidae
Meridialaris
Plecoptera
Gripopterygidae
Claudioperla
1
Trichopera
Hydroptilidae
Hydroptila
Metrichia
Oxyethira
Hemiptera
Corixidae
Ectemnostega
Macroveliidae
Chepuvelia
1
Coleoptera
Dytiscidae
Celina
Derovatellus
Lancetes
Leuronectes
Notaticus
Rhantus
Elmidae
Austrelmis
1
Hydraenidae
Hydraena
Hydrophilidae
Tropisternus
Staphylinidae
SpA1
Diptera
Ceratopogonidae
Culicoides
Habitat
groups
C.I. Molina et al.
Barbour et al. (1999)
Barbour et al. (1999), Motta et al. (2016)
Barbour et al. (1999)
Barbour et al., (1999), Merritt & Wallace
Barbour et al. (1999)
Barbour et al. (1999)
Barbour et al. (1999)
1
1
1
1
1
1
GC
GC
PR
PR
PR
PR
GC
GC
SH
SH
PR
PR
PR
PR
FC
FC
1
1
1
1
1
1
1
1
1
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1
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1
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1
Oliveridia
Orthocladius
Parametriocnemus
Paratanytarsus
Paratrichocladius
Pseudosmittia
Chironominae
Polypedilum
Tanytarsus
Dolychopodidae
spA2
Empididae
spA3
Ephydridae
spA4
Limoniidae
spA5
Muscidae
Limnophora
Sciomyzidae
spA6
Simuliidae
Simulium
(continued )
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1
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