Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 November 2024 | 16(11): 26089–26103
ISSN 0974-7907 (Online)
| ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.8954.16.11.26089-26103
#8954 | Received 08
February 2024 | Final received 11 October 2024 | Finally accepted 28 November
2024
Watershed survey of streams in
western Bhutan with macroinvertebrates, water chemistry, bacteria and DNA
barcodes
Juliann M. Battle 1 , Bernard W.
Sweeney 2, Bryan Currinder 3,
Anthony Aufdenkampe 4, Beth A. Fisher
5 & Naimul Islam 6
1,2 Stroud Water Research Center, 970
Spencer Road, Avondale, PA 19311, USA.
3 Department of Environmental
Science and Policy, 3134 Wickson Hall, University of
California, Davis, CA 95616, USA.
4 LimnoTech, 7300 Hudson Blvd, Suite 295,
Oakdale, MN 55128, USA.
5 Minnesota State University, 142
Ford Hall, Mankato, MN 56001, USA.
6 The City of Philadelphia, 1101
Market St, Philadelphia, PA 19107, USA.
1 jbattle@stroudcenter.org
(corresponding author), 2 sweeney@stroudcenter.org, 3 bcurrinder@ucdavis.edu,
4 aaufdenkampe@limno.com, 5 beth.fisher@mnsu.edu,
6 naimul.islam@phila.gov
Editor: J.A. Johnson, Wildlife Institute of India,
Dehradun, India. Date of publication: 26 November
2024 (online & print)
Citation:
Battle, J.M., B.W. Sweeney, B. Currinder, A. Aufdenkampe, B.A. Fisher & N. Islam (2024). Watershed
survey of streams in western Bhutan with macroinvertebrates, water chemistry,
bacteria and DNA barcodes. Journal of Threatened Taxa 16(11): 26089–26103. https://doi.org/10.11609/jott.8954.16.11.26089-26103
Copyright: © Battle et al. 2024. Creative Commons Attribution 4.0 International License.
JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: The 2015 work was supported by gifts from Peter Kjellerup, Mandy Cabot, W.B. Dixon Stroud Jr., Lisa
Stroud, and the Stroud Endowment for Environmental Research. The 2016 work was funded by the Greg and Susan Walker Endowment, a Trustees’ Council of Penn Women Grant, and a Graduate & Professional Student Assembly Grant from the University of Pennsylvania.
Barcode work was funded with help from a donation from MilliporeSigma, as facilitated by Tim Currinder. The preparation of this publication was supported by the Stroud Endowment for Environmental Research. This is Stroud Publication 2023001.
Competing interests: The authors declare no competing interests.
Declarations: Data availability any data presented in this paper will be available from the corresponding author upon request. Barcode data is available on GenBank and Barcode of Life Data systems.
Author contributions: Conceptualization: BS, AA & BC; developing methodology: BS, AA & BC. Conducting the research: BS, BC, AA, BF, & NI. Formal analysis: JB; visualization: BS, AA, BC, & JB. Project administration: BS, AA, BC & NI. First-draft elaboration: JB, BC, & BS. Writing: JB and BS; review and editing: BC & BF. Funding acquisition: BS, AA & BC. All authors read and approved the final manuscript.
Author details: J. Battle is a staff scientist at Stroud Water
Research Center in the entomology group. She has been doing research in rivers,
streams, and wetlands for >30 years. B.
Sweeney is distinguished research scientist and executive director
emeritus at Stroud Water Research Center. B.
Currinder is a PhD candidate in ecology at the
University of California, Davis. His research investigates population and
community dynamics of lakes and ponds in the Sierra Nevada mountains. A. Aufdenkampe
is a senior environmental scientist at LimnoTech,
leading teams to develop and synthesize biogeochemical and hydrological data,
models, and software to support scientifically-sound decision making. He was
formerly a senior scientist at the Stroud Water Research Center where he
oversaw the Organic and Isotope Geochemistry laboratory group. B. Fisher is an associate professor at
Minnesota State University, Mankato. Her teaching and research focus on soil
health, regenerative agriculture, climate change, and water quality. She works
at the intersection of environmental science and biogeochemistry and mineralogy
in soil and aqueous environments.
Acknowledgements: The Waterkeeper Alliance was instrumental in
organizing this project. Nedup Tshering
(Clean Bhutan) and Pem Dekhi
(Waterkeeper Alliance Bhutan) provided valuable assistance in Bhutan. We are
grateful for the input from Dr. Sarah Willig, Dr. Yvette Bordeaux,
Heather Kostick, Sally Cardy,
Amanda Johnston, and William Morrissey from the University of Pennsylvania. We
thank Peter Kjellerup, Mandy Cabot, and Dixon &
Lisa Stroud for their assistance with the 2015 field collection. Special thanks
to Dr. John Jackson from the Stroud Center for his support and access to various resources
within the Stroud Center entomological section
throughout the project. Stroud Center employees Kelly
McIntyre and Mike Broomall were responsible for the EPT species identification,
additional help from Stroud was provided by Katie McFadden Billé,
Sally Peirson, and Matt Wilson for sample processing,
and Charlie Dow, Tim Smith, and Ellie Kissis for data
analyses. The 2016 data was from the Capstone Projects of B. Currinder and N. Islam from the University of Pennsylvania.
Abstract: Bhutan in the eastern Himalaya
contains some of the last pristine watersheds in the world, yet there has been
limited monitoring of streams and rivers. Eighteen streams in three watersheds
were surveyed for chemistry, bacteria, and macroinvertebrates in post-monsoon
(2015) and monsoon (2016) seasons. Many water quality variables, including
temperature, pH, specific conductivity, nitrite, nitrate, E. coli, and
total coliform bacteria differed between seasons and between areas upstream and
downstream of anthropogenic disturbance. In both seasons, total coliform
bacteria and E. coli were significantly higher downstream of
anthropogenic disturbance, with many urban sites having high coliform levels
(>2000 cfu/100 ml) indicative of sewage inflow. A
total of 50 insect families and six non-insect taxa were identified. During the
post-monsoon, eight of 13 metrics (e.g., total richness, Ephemeroptera, Plecoptera, and Trichoptera (EPT)
richness, % EPT, % non-insects, HKHbios, BMWP1983,
ASPT1983, and ASPT2021) based on kick samples (qualitative) indicated
impairment, while in the monsoon season composite Surbers
(quantitative) had two metrics (e.g., total richness and Shannon) that differed
between sites up and downstream of disturbance. DNA barcoding for cytochrome c
oxidase subunit I (COI) in 63 morphological species of mayfly, stonefly,
and caddisfly indicated 18 additional species, 17 mayflies and one stonefly.
Forty-two barcode species were new additions to the Barcode of Life Data
database. Results suggest macroinvertebrates are a viable method for evaluating
human impacts on Bhutan streams. Bhutan faces future challenges of sanitation
management, climate change, and shared river systems, and monitoring will need
to be expanded. The monsoon season may be an ideal time to measure water
chemistry and bacteria due to increased runoff, but macroinvertebrate sampling
should occur in the post-monsoon season to obtain the best sampling conditions
and larger individuals. Increasing the knowledge of species in the region,
potentially with the help of DNA barcodes, will document the diversity of the
region and help amplify the capacity for macroinvertebrates with future
biomonitoring.
Keywords: Biomonitoring, coliform, COI
gene, diversity, eastern Himalaya, EPT, hotspot, seasons, water quality.
INTRODUCTION
Located in the eastern Himalaya,
Bhutan is mountainous, 70% forested, and considered a world biodiversity
“hotspot” (Wangdi et al. 2013). Human populations
historically occurred largely in rural areas throughout the low to
mid-elevations of the western part of the country (Worldometers
2022). Bhutan’s urban population has grown by 40% from 2005 to 2017, and it is
projected to comprise half of the country’s population by 2037 (NSBB 2019). As
of 2020, the capital Thimphu accommodated around 28%
of the total urban population and ~13% of the total population (Worldometers 2022). This increased urbanization in the
western part of the country has put significant stress on Bhutan’s abundant,
but fragile, forests and water resources (Wangdi et
al. 2013). Thus, despite the country’s relatively small population (791,817
people in 38,177 km2; Worldometers 2022)
and large forested areas, significant challenges currently exist in maintaining
adequate water quality, particularly for humans and aquatic wildlife living
downstream of population centers (WBMP 2016).
Although urban development tends
to take up less area in the watershed than agriculture, it often has a larger
impact on stream conditions (WBMP 2016). A recent survey of Bhutan wastewater
management reported that only eight out of 35 towns (~7% of Bhutan’s
population) have a public sewage system, with the majority (80%) of the
remaining urban population depending on on-site sanitation systems, with many
being both inadequately designed and maintained (Dorji
et al. 2019). Climate change poses additional threats to water quality in
Bhutan through its impact on hydrology (WBMP 2016), causing localized droughts
and frequent flooding (Tariq et al. 2021). Water scarcity, construction of
roads, and hydropower dams are all factors that will impact water quality in
Bhutan’s streams and rivers (WBMP 2016; Thapa et al. 2020; Tariq et al. 2021).
Because of its shared river systems (i.e., those flowing through multiple
countries; sensu Price et al. 2014), the capacity to
manage water quality becomes more complicated because of political challenges.
Agricultural land use and its
impacts on water quality in Bhutan, as in other watersheds worldwide, varies
with factors like intensity, region, livestock, and crop type, with virtually
all water quality loss being due to modified flows, degraded channel habitat,
altered temperature regimes, and high inputs of nutrients, pesticides, and
sediments (Allan 2004). In Bhutan, the primary mode of livelihood in rural
areas has historically consisted of traditional rain-fed and irrigated row crop
agriculture, but recent times have seen a transition to more intensive
agriculture using inorganic fertilizers (Dorji et al.
2011). Forestry (commercial and traditional firewood collection) and industry
(mining, cement industry, fishery) are also impacting water quality in Bhutan
(WBMP 2016; Tariq et al. 2021). Several studies in the Bhutan region examining
water quality have pointed to the discharge of untreated sewage directly into
streams as the major source of pollution in urban areas, while nutrient levels
have been indicative of agricultural disturbance (Korte et al. 2010; Giri & Singh 2013; Dorji et
al. 2021).
The use of aquatic
macroinvertebrates (i.e., insects, crustaceans, molluscs,
and worms) has been shown to be a powerful tool for monitoring freshwater
around the world because of their high diversity, high abundance, and spectrum
of pollution tolerances (Allan 2004). In Bhutan, macroinvertebrates have been
shown to be of use for monitoring agricultural and urban impacts but most
studies have focused on one stream and did not use a watershed approach (Moog
et al. 2008; Ofenböck et al. 2010; Wangyal et al. 2011; Giri &
Singh 2012; Dorji 2014a; Dorji
et al. 2014, 2021; Gurung & Dorji 2014; Wangchuk
& Dorji 2018).
This study was designed to
further investigate the water quality of stream and river systems in western
Bhutan for many sites throughout three watersheds. The study focused on how
water quality responded to the presence and activities of human development
within the districts of Thimphu and Paro in the Wangchhu basin and the districts of Punakha and Wangduephodrang in the Punatsangchhu
basin. Family-level identifications were used to describe the macroinvertebrate
assemblage of sites while species-level data on three major aquatic insect
Orders (Ephemeroptera, Plecoptera, and Trichoptera; also known as EPT) were barcoded using the
mitochondrial cytochrome c oxidase 1 (COI) gene. EPT has been
shown worldwide to be the most sensitive (i.e., intolerant) of pollution and
therefore most indicative of stream and water health (Resh
& Jackson 1993). Species-level knowledge of macroinvertebrates needs to be expanded
in Bhutan and the south Asian region to better connect taxa to water quality
parameters. Recent advances in the use of deoxyribonucleic acid (DNA) barcoding
to identify aquatic macroinvertebrate species have enhanced their use for
biomonitoring (Sweeney et al. 2011; Jackson et al. 2014; Li et al. 2022).
METHODS
In this
study, 18 streams and rivers in three watersheds were selected to measure water
chemistry, bacteria, and macroinvertebrates (Table 1). Water quality was
measured at 16 sites from 6–13 November 2015 (post-monsoon season) and 12 of
the same sites were sampled from 15–20 August 2016 (monsoon season) with an
additional two sites added (Figure 1; Table 1). Stream sites represented a
gradient of anthropogenic disturbance (e.g., an undisturbed, forested upstream
area was contrasted with a downstream area impacted by agriculture or
urbanization) and were labeled as being either upstream or downstream of major
human disturbance (Table 1).
Temperature,
conductivity, pH, and dissolved oxygen were measured with an Orion 5-Star
portable meter and turbidity was measured with a Campbell Scientific OBS3+
turbidity sensor. Water samples were analyzed for ammonia, nitrite, nitrate,
and phosphorus using API-brand freshwater test kits and quantified using an
open- source colorimeter by IO-Rodeo
(http://iorodeo.com/pages/colorimeter-project accessed August 2016). Total
coliform bacteria and Escherichia coli bacteria were measured within 24
h of collection using the 3M™ Petrifilm™ E.coli/Coliform
Count Plate kit and expressed as colony-forming units (cfu)/100ml.
Wilcoxon rank-sum test (t approximation, 2-sided test) was used to examine
differences in water quality variables between sites classified upstream or
downstream of disturbance for each year, and both years of data were combined
to examine if differences existed upstream or downstream within the Paro and Thimphu watersheds. As BT03 was a drinking well, only
chemistry and bacteria were sampled (Table 1).
In 2015,
macroinvertebrates were qualitatively sampled using a 500-μm D-frame net in
riffle and run areas. The stream bottom was disturbed by kicking the substrate
and collecting downstream, in addition, rocks, leaf packs, and woody material
were examined. In the field, collected material was placed in a tray, and
specimens were picked by hand before preserving in 95% ethanol, which was
changed within 24 h of collection.
In 2016,
macroinvertebrates were quantitatively collected with a Surber sampler (0.093 m2;
250-µm mesh net). For each site, 16 individual Surber samples were taken in
riffle areas (and some run areas if riffle habitat was scarce) and the contents
(macroinvertebrates and organic debris) were split evenly between two large
buckets containing stream water. The content of each of the two buckets was
then transferred to a field sample splitter and the sample was split evenly
into four subsamples (0.1858 m2; Arscott
et al. 2006). Two subsamples were preserved in 70% isopropyl alcohol resulting
in four samples per site. In the laboratory, the entire 2015 sample was
identified but, in 2016 three of the four preserved samples were further
subsampled and processed under a microscope until a minimum of 200
macroinvertebrate specimens were obtained (>600 individuals per site). For three
sites (BT06, BT11, BT13), only 1–2 preserved samples were processed because of
limited time. Macroinvertebrate insects were identified to family level and
some non-insects (e.g., oligochaetes, planarians, nematodes, bivalves, snails,
and mites) were identified to order level or higher.
In order to
ensure that taxon richness metrics were not biased by the number of individuals
examined, samples were standardized (i.e., rarefaction) using the SAS
statistical package (version 9.4, SAS Institute Inc., Cary, North Carolina).
The 2015 qualitative samples were standardized to 100 individuals (except sites
BT06, BT12, and BT14, which had <100 individuals), and the 2016 quantitative
samples were standardized to 200 individuals/sample with both datasets being resampled
to 1,000 random draws. Macroinvertebrate samples were used to calculate
richness and percentage metrics, as well as the Shannon and Simpson diversity
indices (Resh & Jackson 1993). Using samples in
their entirety to best mimic the original index methods, the Hindu
Kush-Himalaya Index (HKHbios; Ofenböck
et al. 2010), the Biological Monitoring Working Party (BMWP), and the Average
Score per Taxon (ASPT) were calculated. BMWP and ASPT were based on Armitage et
al. (1983) method (ASPT 1983; BMWP 1983) and a Bhutan version following Dorji et al. (2021), BMWP (2021), and ASPT (2021). Within
each year, a Wilcoxon rank-sum test (normal distribution, one-sided) was used
to examine differences in macroinvertebrate metrics between sites classified
upstream or downstream of disturbance.
Non-metric
multidimensional scaling (NMS) was used to examine how macroinvertebrate taxa
assemblages differed among years and in relation to various types of
disturbance (i.e., upstream or downstream) using PC-ORD (version 6.22, MjM Software, Gleneden Beach, Oregon). This analysis was
done using Sorenson distance, the step length was set at 0.20, and Monte Carlo
was used to determine the optimal number of axes. NMS was performed using
presence/absence data of 42 common taxa (i.e., taxa found in at least 2
samples) and was run with 41 iterations, an r2 set at 0.28, a final
stress of 12.0, and a final instability was <0.00001.
In an
effort to better document the EPT diversity, DNA was sequenced (COI gene) for a
subset of EPT specimens to evaluate if species could be separated by morphology
alone, or whether there were cryptic species present. The process of selecting
EPT specimens for barcoding involved inspecting all individuals and choosing
specimens that could be identified to genus level, and further dividing them
into groups based on morphology. Common mayfly specimens were selected from
forested and urban streams with the goal of barcoding four individual larvae
from both stream types (undisturbed vs. disturbed) and a variety of sites and
drainages where possible. Caddisflies and stoneflies were also separated based
on morphology and 3–6 individuals were barcoded where possible albeit not from
both stream types. The majority of the barcoded specimens were from 2015
because 2016 specimens were mostly small and immature, and therefore difficult
or impossible to identify to a low level. Leg tissue from each specimen was
sent to the Canadian Centre for DNA Barcoding at the University of Guelph,
where genomic mitochondrial DNA was extracted and the 658-base pair (bp) barcoding region of the COI gene was amplified and
sequenced (Sweeney et al. 2011). Sequences and detailed information about all
specimens including photographs are stored on the GenBank and Barcode of Life
Data systems (BOLD) website (https://boldsystems.org/). Of the 458 individuals
submitted for barcoding, COI sequences ≥200 bp were
determined for 281 specimens (61% of the total, 25 individuals with 200–350 bp; nine individuals with 351–450 bp;
247 individuals with 451–658 bp). The number of
barcoded species and variance determined by BOLD Barcode Index Number (BIN) was
based on their criteria for compliant barcode sequences (data accessed 8/2023).
The study included 230 barcode-compliant individuals and 51 non-compliant (mainly
because of short sequences). Sequences were aligned with a BOLD aligner and
neighbor-joining trees (pairwise deletion and Kimura-2-parameter distance) were
used to identify genetically distinct barcode species, which were confirmed
using BINs where possible.
RESULTS
Water
chemistry and bacteria variables for all study sites are summarized in Table 2.
Results of the Wilcoxon rank-sum test showed that many of the 2015 water
quality variables (specifically, pH, dissolved oxygen, specific conductivity,
turbidity, ammonia, nitrite, nitrate, and phosphate) did not differ
significantly (p>0.05) between sites upstream vs. downstream of disturbance.
Temperature, E. coli, and total coliform were all significantly lower
upstream of disturbance relative to downstream sites (Table 2). In 2016, pH,
specific conductivity, and nitrite were all significantly higher upstream
compared to sites downstream of disturbance, whereas E. coli, total
coliform, and nitrate were significantly lower upstream than downstream. It is
notable that differences in coliform bacteria, both total and E. coli,
between upstream and downstream sites differed in both 2015 and 2016 by an
average of thousands of cfu/100 ml. In contrast, for
2016 the differences in water quality variables were relatively small between
upstream and downstream sites [e.g., pH (±0.2), nitrate and nitrite (±0.2
ppm)]. In the Paro and Thimphu watersheds, when the
2015 and 2016 data were combined, the coliform (total and E. coli) had
the same patterns as the individual years with higher levels downstream than
upstream. In addition, ammonia levels in the Thimphu
watershed were significantly higher downstream (0.19 ppm) than upstream (0.04
ppm), while specific conductivity was higher upstream (181 µS/cm) than downstream
(126 µS/cm).
A total of
50 insect families and six non-insect taxa were identified in 2015 and 2016;
specifically, 36 taxa in 2015 and 49 taxa in 2016. The mayfly Baetidae was the only taxa collected from all 26 samples,
while the mayflies Ephemerellidae and Heptageniidae, the caddisfly Hydropsychidae,
and the true flies Chironomidae, Simuliidae,
and Tipulidae were also common (>80% of the 26
samples). There were 22 rare taxa (i.e., 13 taxa were only recorded from one
sample, and nine taxa were only recorded from two samples). Based on counts, Baetidae, Ephemerellidae, Heptageniidae, and Hydropsychidae
were the most abundant in 2015 and Baetidae, Chironomidae, and Simuliidae were
most abundant in 2016. For the 2015 data, the Wilcoxon test showed that for the
13 metrics examined, total richness, EPT richness, % EPT, % non-insects, HKHbios, BMWP 1983, ASPT 1983, and ASPT 2021 were
significantly (p≤0.05) different between upstream and downstream sites (Table
3). In 2016, total richness and Shannon diversity were higher in upstream sites
than downstream ones, while EPT richness and % EPT were only slightly (p≤0.09)
different between the upstream and downstream sites.
The NMS
revealed sites clustered by year and disturbance with years separating sites along
axis 1 (32%) and disturbance separating sites along axis 2 (39%; Figure 2).
Differences between years are likely related to the contrast in sampling
seasons (post-monsoon vs. monsoon) and methods (qualitative dip net vs.
quantitative Surber). For differences between years (axis 1), Stenopsychidae was the key taxa for 2015 whereas Acari, Empididae, Lepidostomatidae, Psychodidae,
and individuals of mayflies, true flies, and caddisflies too small to identify
beyond the family were the key taxa for 2016. Macroinvertebrate diversity was
higher in 2016 (when samples were processed in the laboratory with a
microscope) than in 2015 (when samples were processed in the field by eye).
Microscope processing allows the counting of both small individuals (e.g., Acari, Ceratopogonidae,
oligochaetes) as well as individuals that were too small to identify beyond
order (e.g., Ephemeroptera, Trichoptera, Diptera). There were also more individuals examined in 2016
(>600 specimens per site) vs. 2015 (>100 specimens per site), increasing
the likelihood of greater diversity. The NMS also showed sites upstream of
disturbance were characterized by Perlodidae, Nemouridae, Rhyacophilidae, and Athericidae, whereas sites downstream of disturbance had
fewer taxa and were more likely to have oligochaetes.
Although
there were more morphological EPT taxa (76) than barcoded taxa (63), the actual
barcode total may be underrepresented, because only 60% of the 458 individuals
were successfully sequenced (Table 4). The 40% failure rate for barcoding may
have resulted from the challenge of obtaining high-grade ethanol (95%,
non-denatured) in Bhutan, making it difficult to properly preserve the DNA in
samples. When only sequenced taxa were examined, there were 17 more taxa
revealed with barcode than morphology alone, specifically 16 mayflies (in the
families Baetidae, Ephemerellidae,
Heptageniidae, Leptophlebiidae)
and one stonefly (Nemoura). Barcoding
indicated no additional caddisfly species. The average intraspecific variance
across all groups was relatively low (average 0.36%; range = 0.0–1.18 %), in
contrast to the interspecific variance (average 10.1%; range = 1.0–17.8 %).
There were 19 barcoded species with <3 individuals so intraspecific variance
could not be determined for those species. There were four taxa (Acentrella sp. C, Drunella
sp. A, Hydropsyche sp. D, Paragnetina) that appeared to be morphologically
distinct but grouped with another barcode species suggesting multiple
morphotypes. Based on the BOLD database, there were 13 unique species that were
considered non-compliant, nine of them because sequences were too short
(<500 bp) and they were not assigned to a BIN, and
four are awaiting compliance with metadata requirements (Table 4). There were
42 barcode taxa (24 mayflies, six stoneflies, and 12 caddisflies) that were new
sequences (e.g., new BINs) to the BOLD database (Table 4). One mayfly and seven
caddisfly species had already been barcoded in other studies and had a species
name available in BOLD (Table 4).
When mayfly sequences were compared between
multiple sites (e.g., upstream vs. downstream and among drainage basins), they
revealed differences that morphology failed to uncover. Overall, 201 of the 342
mayfly specimens (59%) were successfully barcoded, resulting in a total of 42
species versus 27 species based on morphology. There were several morphological
taxa that looked similar but barcoding revealed that they did not occur at the
same site (i.e., no spatial overlap; Figure 3). For example, barcodes indicated
the presence of two species of Epeorus sp.
C (29 individuals barcoded from eight streams) but one species was found in all
the drainages (in small to medium streams) while the other species was only
found at the large river sites of the Punatsangchhu
drainage. This pattern of two (or more) species being morphologically similar
but not overlapping geographically also occurred for Cincticostella
sp. B (i.e., Paro and Thimphu sites vs. Punatsangchhu; 13 specimens), Notacanthurus
sp. B (Paro vs. Punatsangchhu; 13 specimens),
and Epeorus sp. B (i.e.,
upstream sites in the Paro vs. Thimphu; seven
specimens). One caveat is that all of these spatial differences among species
may be influenced by small sample sizes.
Barcoding
revealed that one taxon, Baetis sp. A,
was made up of five barcode species (34 specimens sequenced from 12 sites),
with two common species being found in small to medium streams of the Paro and Thimphu watersheds, while a third common species preferred
large sites in the Punatsangchhu and the confluence of
the Paro and Thimphu Rivers. In addition, for some
taxa only identified to a specific genus via morphology, barcoding was able to
reveal multiple species, i.e., Fallceon (2
species), Iron (3 species), and Paraleptophebia
(3 species), while in other cases morphology and barcoding were aligned
(e.g.., Acentrella species A, B and C,
Baetis sp. D, Drunella
sp. A).
DISCUSSION
Based on
health concerns and other management reasons, the World Health Organization
(WHO 2017) has provided guidelines for drinking water that can be used as a
baseline for stream conditions. They set levels not to be exceeded for nitrite
(3 mg/L), nitrate (50 mg/L), and E. coli (zero cfu/100
ml) based on health concerns, a pH range (6.50–8.50) for sewage treatment
operation, and an ammonia level (1.5 mg/L) for odor (WHO 2017). Using WHO
criteria, many water quality parameters in this study were largely within the
acceptable range (Table 2): specifically, nitrite, nitrate, and ammonia levels
were below these limits and only one site had a pH slightly above the limit
(8.56). E. coli was the only variable that was above the recommended
limit (WHO 2017) and it was exceeded for the majority of the downstream sites
(94%), while all the upstream sites registered no E. coli or extremely
low levels (1 cfu/100 ml). This is good news for
those living upstream, given that much of Bhutan’s rural population draws
untreated water for consumption directly from stream or river systems (Giri et al. 2010; Rahut et al.
2015) and E coli is a measure of fecal coliform and thus an indicator of
fecal contamination. This is not good news for those living downstream of
disturbance, because high fecal coliform indicates an increased risk of
pathogen-borne illnesses (USEPA 2012; WHO 2017). In the United States, the
Environmental Protection Agency (USEPA 2012) guideline for streams is that E.
coli should not exceed a geometric mean of 126 cfu/100
ml to be considered safe for swimming. Unfortunately, 20% of the Bhutan samples
exceeded this level, and for four of the 2016 sites (BT11, BT10, BT14, BT15) in
the urban area of Thimphu watershed, levels were
anywhere from 23 to 35 times higher (2900–4533 cfu/100
ml). This indicates that untreated sewage was entering the river at or near
those sites. The results suggest fecal coliform or E. coli could be a
powerful, yet easy and inexpensive tool to regularly monitor the safety of
Bhutan streams for various public activities.
A previous
study of four headwater streams in Bhutan reported that most environmental
variables (i.e., temperature, conductivity, stream width, depth, velocity) did
not differ between monsoon and pre-monsoon seasons (Dorji
2014b). Similarly, a 2008–2009 study of the river Wang Chhu
near Thimphu city sampled in pre-monsoon, monsoon,
and post-monsoon indicated similar patterns in response to urban pollution in
all three seasons and that nitrate, total coliform, and biochemical oxygen
demand [BOD] were the best parameters for monitoring urban impacts (Giri & Singh 2013). The results suggest that water
chemistry in the monsoon season was better able to discern impacts than in the
post-monsoon season. A study examining agricultural practices in a Bhutan
stream in the Samtse district also indicated that the
monsoon season was the optimal time to measure the highest levels of nitrate,
BOD, and total dissolved solids (Giri et al. 2010).
It is
important to note for this study that not all the same sites were measured in
both years (e.g., Punatsangchhu sites were only
sampled in 2015). Nevertheless, there were more water quality variables that
differed between upstream and downstream sites in the monsoon season than the
post-monsoon (3 vs. 6). This might be related to the fact that higher discharge
in the monsoon season may result in more pollution entering the stream than for
the pre- or post-monsoon seasons (Giri et al. 2010).
Typically, 70% of the annual precipitation is concentrated during the monsoon
season that occurs from June to September and a major portion of the water
volume in the basins is attributed to rain-fed recharge (WBMP 2016).
In Bhutan,
septic tanks are commonly reported to overflow into the environment due to poor
design and maintenance and this problem is exacerbated by heavy monsoon rains
because soak-pits and waste stabilization ponds can become full and overflow
(Taylor-Dormond et al. 2018; Dorji
et al. 2019). Also, because agriculture across this country occurs in steep
topography, erosion is extensive in Bhutan and is exacerbated by heavy rain
showers during the pre-monsoon season falling on bare soils prior to crop
emergence (Dorji et al. 2011; WBMP 2016). Although
the dominant soil type, gneissic, is resistant to erosion, the loss of fertile
soils during storms results in increased nutrients and sediments washing into
streams and rivers (Baillie et al. 2004; Dorji et al.
2011). Rapid runoff into Bhutan waters during flood events is further
exacerbated by forest fires and overgrazing (Tariq et al. 2021). The above
factors suggest that most of the water quality differences measured in this
study (Table 2) between upstream and downstream sites were indicative of
pollution (i.e., higher temperatures, coliform, and E. coli downstream
than upstream in 2015 and higher coliform, E. coli, nitrite, and nitrate
downstream than upstream in 2016). In contrast, the higher pH and conductivity
levels in the upstream sites versus the downstream sites in 2016 are likely due
to a geological influence since these variables typically increase with pollution
but were found to decrease downstream of disturbance areas.
For the
post-monsoon season, the 2015 kick samples sorted in the field resulted in
larger, more mature macroinvertebrate specimens, and many metrics indicated
significant differences between upstream and downstream sites. The best metrics
were related to sensitive groups known to become less abundant in response to
disturbance (i.e., EPT richness and % EPT; Table 3). Other important metrics
capable of measuring disturbance in 2015 were taxon richness (on average having
two more taxa upstream than downstream, often families belonging to EPT) and %
non-insects (averaging 1% upstream vs. 6% downstream). In addition, the metric
BMWP1983 indicated the upstream sites had better environmental conditions than
the downstream sites in 2015. It is noteworthy that although the BMWP1983 was
initially designed for European streams, it worked better than the version
(BMWP 2021) modified specifically for Bhutan (Dorji
et al. 2021). To this end, BMWP1983 characterized some insect families (i.e., Ephemerellidae and Heptageniidae)
as sensitive to disturbance even though they were found in nearly all the sites
(including degraded sites) suggesting those families contain taxa somewhat
pollution-tolerant, while other families (e.g., Perlidae
and Perlodidae) seemed to be better indicators of
“good” water quality or sites that lack major human disturbance. Also, both the
1983 and 2021 versions of the metric ASPT, which is the BMWP modified to
account for richness, were sensitive to disturbance in 2015. The HKHbios was designed to monitor streams in the region
(Bangladesh, Bhutan, Nepal, India, and Pakistan) and worked well in indicating
impact in 2015, although it rated all the sites as “good”, even the disturbed
ones, but the sampling method in this study was modified, which may have
inflated the scores (Ofenböck et al. 2010).
In 2016,
the fact that taxon richness and Shannon were the only metrics associated with
the Surber sampling to indicate a disturbance is likely related to multiple
factors (Table 3). The monsoon season is a difficult time to sample, presenting
a safety issue, and high-water levels may have scoured some streams more than
others. Also challenging is achieving equal sampling effort at sites across a gradient
of small streams to large rivers, especially since high flow limited sampling
in some cases to only the stream edges. Regional studies of monsoon effects on
macroinvertebrates are not all in agreement (Brewin et al. 2000; Ofenböck et al. 2010; Dorji
2014b; Wangchuk & Dorji 2018; Thapa et al. 2020).
Most studies in tropical Asian streams suggest a tendency for an overall
decline in macroinvertebrates abundance and richness during the monsoon versus
drier seasons (see Dudgeon 1999; Brewin et al. 2000). In Bhutan, one study
reported macroinvertebrate abundance in headwater streams also decreased after
flash floods but found no difference in macroinvertebrate diversity between
pre- and post-monsoon seasons (Dorji 2014b). In
contrast, a study of springs in nearby Nepal found EPT richness was higher in
the post-monsoon versus the pre-monsoon season (Thapa et al. 2020). In a
relatively large survey, Ofenböck et al. (2010)
studied 198 streams in the Hindu Kush-Himalayan region and found that both pre-
and post-monsoon macroinvertebrate data were able to differentiate non-impacted
and impacted sites. To evaluate disturbance, they recommended sampling in the
pre-monsoon season to avoid the many complications (noted above) associated
with flooding effects in the post-monsoon period (Ofenböck
et al. 2010).
The NMS
indicated distinct differences in the 2015 and 2016 macroinvertebrate
assemblages, which may be attributed to both time of year and sampling methods
(Figure 2). More importantly, both sampling years, independent of the method,
resulted in the separation of upstream and downstream sites. Given that for
2016, only two of the 12 metrics showed a significant difference between
upstream and downstream sites (Table 3), perhaps metrics more specific to the
Bhutan macroinvertebrate assemblages like % Baetidae
or % Plecoptera (or possibly % Nemouridae
and % Perlodidae), might be more sensitive measures
of disturbance but this would require a larger dataset to put it to the test.
The level
of disturbance was not well-defined in this study. Not all sites designated
downstream of disturbance had the same level of degradation. Hopefully, going
forward, land use types may be quantified to better understand the relationship
between disturbance in the watershed and its impact on macroinvertebrate
assemblages (Giri & Singh 2013). Many
macroinvertebrate studies in Bhutan are still using the higher family level
identification, and although this level of identification is useful in
instances of high degradation (Giri & Singh 2012;
Dorji 2014a, Dorji et al.
2014; Gurung & Dorji 2014; Wangchuk & Dorji 2018), it has been shown in other studies not to be
as sensitive as genus or species level identification in discerning small
levels of disturbance (Arscott et al. 2006). Although
progress has begun in creating species-level checklists for Bhutan (Wangdi et al. 2018; Dorji et al.
2021; Gyeltshen & Prasad 2022), research on the
taxonomy of most of the aquatic macroinvertebrate groups is very limited and
lacks baseline data. Bhutan seems to have a high diversity of
macroinvertebrates belonging to 18 orders and 89 families (Dorji
& Gurung 2017), with current species counts of 38 stoneflies, 172
caddisflies, 33 dipterans, 41 beetles, five mites, 12 hemipterans, 114
dragonflies and damselflies, and one megalopteran (Wangdi
et al. 2018). As of 2017, at least 566 new species of flora and fauna have been
recorded for Bhutan, including 77 aquatic species (Takaoka & Somboon 2008; Gyeltshen et al.
2018).
The biggest
challenge in species-level identification for aquatic macroinvertebrates is
that taxonomic keys still need to be expanded or developed for many groups.
This study shows that DNA barcoding may help in this regard. DNA barcoding
expanded the EPT list by 17 species and highlighted the presence of cryptic
taxa (e.g., four species for Baetis sp. A;
Table 4, Figure 3). Moreover, it suggested that morphologically similar species
of mayflies often segregate according to either drainage or disturbance. Other
studies have shown DNA barcoding improves macroinvertebrate monitoring (Jackson
et al. 2014; Li et al. 2022) and have shown that morphologically similar mayfly
species were spatially separated within the same river based on pollution
(Sweeney et al. 2011). The barcoding results (42 “new” DNA sequences) represent
only a start for EPT and highlight the need for further additions to the DNA
reference library for the region.
The largest
water quality challenges Bhutan faces going forward are sanitation management,
climate change, and shared river systems (WBMP 2016). Urban areas of Bhutan
will have to provide adequate sanitation infrastructure and sufficient
regulatory pollution control measures to be enforced to protect water quality (Karn & Harada 2001; Dorji et
al. 2019). For example, macroinvertebrate monitoring, in conjunction with
chemical and bacteria parameters, could help evaluate the effectiveness of the
new 2021 biological processing plant in Thimphu city
that replaced their outdated sewage facility (Lhaden
2021). Bioassessment with macroinvertebrates could also help in managing
changes in hydrology due to climate change and guide policy in managing river
systems shared with neighboring countries. Given its inexpensive and
straightforward nature, biomonitoring of streams with macroinvertebrates seems
to be an accessible tool for both public officials and community/citizen
science. The study shows water chemistry and bacteria were best sampled in the
monsoon season to have the greatest measure of human disturbance, while
macroinvertebrates were most effective in detecting impacts when sampled in the
post-monsoon season. The DNA findings (e.g., 18 more EPT species using barcode
versus morphology and 42 new sequences added to the BOLD database) suggest the
diversity of stream macroinvertebrates in this region is presently
underestimated and the continued expansion of species identifications (either
morphologically or through DNA barcoding) will greatly aid in the future
assessments of Bhutan waterways.
Table 1.
Description of the Bhutan sampling locations in 2015 and 2016. Sites in similar
watersheds are listed in pairs or groups indicating ones that were upstream
(US) or downstream (DS) of disturbance. Stream type (tributary or mainstem),
size, and land use are general descriptors. Years of water chemistry, bacteria,
and macroinvertebrates were sampled are provided.
|
Location |
US or DS |
Stream type |
Size (discharge m3/s
Nov 2015) |
Land use |
Chem & bacteria yrs |
Macroinvert yrs |
Elevation (m) |
Latitude |
Longitude |
|
|
Paro River watershed |
|
|
|
|
|
|
|
|
|
|
|
BT03 |
groundwater well accessed at
Tiger's Nest Tea House |
US |
other |
|
Forest |
2015, 2016 |
|
2976 |
27.4884 |
89.3586 |
|
BT04 |
Stream below Tiger's Nest |
US |
trib |
small |
Forest |
2015, 2016 |
2016 |
2982 |
27.4859 |
89.3621 |
|
BT02 |
Holy Water stream near Chilai La pass |
US |
trib |
small (0.03) |
Forest |
2015, 2016 |
2015, 2016 |
3235 |
27.3709 |
89.3620 |
|
BT07 |
Woo Chhu
at Woo Chhu village |
DS |
trib |
small (0.30) |
Suburban/agriculture |
2015, 2016 |
2015, 2016 |
2412 |
27.3912 |
89.4244 |
|
|
|
|
|
|
|
|
|
|
|
|
|
BT05 |
Stream 1 by Ramzi |
US |
trib |
small (0.13) |
Forest |
2015, 2016 |
2015, 2016 |
2866 |
27.5415 |
89.3295 |
|
BT06 |
Stream 2 by Ramzi |
DS |
trib |
small |
Forest/agriculture |
2016 |
2015, 2016 |
2692 |
27.5226 |
89.3283 |
|
BT01 |
Paro Chhu
at Udumwara Resort |
US |
main |
medium |
Suburban/agriculture |
2015, 2016 |
2015 |
2355 |
27.4651 |
89.3558 |
|
BT08 |
Paro Chhu
at Shaba |
DS |
main |
large |
Suburban/agriculture |
2015, 2016 |
2015, 2016 |
2432 |
27.3548 |
89.4643 |
|
Thimphu River watershed |
|
|
|
|
|
|
|
|
|
|
|
BT13 |
Thimphu Chhu at Chagri Dorjeden Monastery |
US |
main |
medium |
Forest |
2015, 2016 |
2015, 2016 |
2599 |
27.5961 |
89.6304 |
|
BT09 |
Thimphu Chhu at Dodena |
US |
main |
medium |
Forest/suburban |
2015, 2016 |
2015, 2016 |
2523 |
27.5792 |
89.6348 |
|
BT15 |
Thimphu Chhu at Chanjiji Football
Ground |
DS |
main |
large |
Urban |
2016 |
2015, 2016 |
2293 |
27.4565 |
89.6491 |
|
BT12 |
Thimphu Chhu at Lungtenphug |
DS |
main |
large |
Urban |
2015 |
2015 |
2296 |
27.4502 |
89.6547 |
|
BT14 |
Ola Rong
Chhu at Semtokha |
DS |
trib |
medium (1.94) |
Urban |
2015, 2016 |
2015 |
2283 |
27.4434 |
89.6603 |
|
BT11 |
Thimphu Chhu at Zimda |
DS |
main |
large |
Urban |
2015, 2016 |
2015, 2016 |
2283 |
27.4302 |
89.6426 |
|
Paro & Thimphu watersheds |
|
|
|
|
|
|
|
|
|
|
|
BT10 |
Wangchhu at Tamchu |
DS |
main |
large |
Urban |
2015, 2016 |
2015 |
2021 |
27.2503 |
89.5252 |
|
Punatsangchhu watershed |
|
|
|
|
|
|
|
|
|
|
|
BT16 |
Mochhu River upstream of
Punakha Dzong |
US |
main |
medium |
Forested |
2015 |
2015 |
1481 |
27.7117 |
89.7652 |
|
BT17 |
Punatsangchu below Khuruthang |
DS |
main |
large |
Urban/agriculture |
2015 |
2015 |
1209 |
27.5452 |
89.8699 |
|
BT18 |
Punatsangchu at Wangdue Phodrang |
DS |
main |
large |
Urban/agriculture |
2015 |
2015 |
1203 |
27.4863 |
89.8959 |
Table 2.
Water quality variables from November 2015 and August 2016 at Bhutan streams
and rivers. Range of variables given for sites considered to be upstream (US)
or downstream (DS) of a disturbance. Wilcoxon rank-sum test results (** p≤0.01;
* 0.05≤p>0.01; ns not significant) within years and watersheds.
|
Variables |
|
2015 |
|
|
2016 |
|
|
Paro |
|
|
Thimphu |
|
|
|
US (n = 8) |
DS (n = 8) |
t-test |
US (n = 7) |
DS (n = 7) |
t-test |
US (n = 9) |
DS (n = 6) |
t-test |
US (n = 4) |
DS (n = 8) |
t-test |
|
pH |
7.47–8.39 |
7.55–8.56 |
ns |
8.06–8.51 |
7.40–8.18 |
* |
7.47–8.51 |
7.40–8.29 |
ns |
8.06–8.39 |
7.41–8.56 |
ns |
|
Temp (°C) |
4.4–9.4 |
7.9–12.5 |
* |
8.3–13.7 |
12.8–16.1 |
ns |
4.4–13.7 |
9.2–14.9 |
ns |
5.8–10.1 |
7.9–16.1 |
ns |
|
Dissolved Oxygen (mg/L) |
6.5–10.1 |
8.6–13.1 |
ns |
7.7–8.6 |
7.3–8.5 |
ns |
7.7–9.6 |
7.3–9.2 |
ns |
6.5–9.0 |
7.3–9.8 |
ns |
|
Spec. Conductivity (μS/cm) |
21–198 |
90–156 |
ns |
114–187 |
40–138 |
* |
21–179 |
110–141 |
ns |
166–198 |
40–156 |
* |
|
Turbidity (NTU) |
1–18 |
2–70 |
ns |
1–13 |
2–20 |
ns |
1–18 |
2–70 |
ns |
1–13 |
2–20 |
ns |
|
Total Coliform (cfu/100 ml) |
0–44 |
21–443 |
** |
0–267 |
367–16,000 |
** |
0–267 |
22–2,400 |
* |
0–100 |
107–16,000 |
* |
|
E. coli (cfu/100ml) |
0–1 |
<1–58 |
** |
0 |
0–3933 |
** |
0–1 |
1–433 |
* |
0 |
14–4533 |
* |
|
Ammonia (ppm) |
0–0.28 |
0.06–0.30 |
ns |
0.02–0.22 |
0.06–0.28 |
ns |
0.07–0.28 |
0.07–0.28 |
ns |
0.02–0.09 |
0.06–0.30 |
* |
|
Nitrite (ppm) |
0.03–0.41 |
0.03–0.10 |
ns |
0.02–0.09 |
0.04–0.25 |
* |
0.02–0.09 |
0.03–0.11 |
ns |
0.04–0.41 |
0.05–0.25 |
ns |
|
Nitrate (ppm) |
0.95–2.31 |
1.45–1.96 |
ns |
0.94–1.50 |
1.22–1.81 |
* |
0.97–2.31 |
1.17–1.69 |
ns |
0.94–1.69 |
1.22–1.96 |
ns |
|
Phosphate (μM) |
0.33–0.46 |
0.30–0.76 |
ns |
0.25–0.41 |
0.32–0.81 |
ns |
0.30–0.46 |
0.33–0.57 |
ns |
0.25–0.49 |
0.30–0.81 |
ns |
Table 3.
Macroinvertebrate metrics for 2015 qualitative (dip nets) and 2016 quantitative
(Surbers) sampling in Bhutan. Range of metrics
provided for sites considered to be upstream (US) or downstream (DS) of
disturbance. Wilcoxon rank-sum test results (*** p≤0.001; ** 0.01≤p>0.001; *
0.05≤p>0.01; ● 0.09≤p>0.05; ns not significant) indicate if metrics
differed based on disturbance.
|
Metrics |
|
2015 |
|
|
2016 |
|
|
|
US (n = 6) |
DS (n = 10) |
t-test |
US (n = 5) |
DS (n = 5) |
t-test |
|
Richness |
12–14 |
9–16 |
* |
14–18 |
12–15 |
* |
|
EPT Richness |
8–11 |
5–8 |
*** |
6–8 |
4–7 |
● |
|
Diptera richness |
2–4 |
2–3 |
ns |
3–6 |
3–5 |
ns |
|
% EPT |
79–95 |
35–95 |
* |
38–46 |
21–80 |
● |
|
% Chironomidae |
0–10 |
0–20 |
ns |
13–47 |
5–19 |
ns |
|
% Non-insects |
0–4 |
0–25 |
* |
5–10 |
4–28 |
ns |
|
Shannon Diversity |
1.88–2.14 |
1.42–2.38 |
ns |
1.81–2.40 |
1.58–2.06 |
* |
|
Simpson Diversity |
0.79–0.86 |
0.64–0.88 |
ns |
0.70–0.88 |
0.70–0.81 |
ns |
|
HKHbios |
7.6–8.7 |
6.2–7.6 |
*** |
5.9–7.8 |
6.7–7.8 |
ns |
|
BMWP 1983 |
68–111 |
48–76 |
** |
47–71 |
42–71 |
ns |
|
BMWP 2021 |
46–86 |
41–77 |
ns |
46–69 |
47–79 |
ns |
|
ASPT 1983 |
6.5–7.6 |
6.0–7.3 |
** |
5.4–7.4 |
5.2–6.8 |
ns |
|
ASPT 2021 |
6.1–7.2 |
5.5–7.4 |
** |
4.5–6.7 |
5.0–6.2 |
ns |
Table 4.
Morphological name followed by a letter is the designation of unique species.
Variance is show as maximum within a barcode species (% intra) and distance to
nearest neighbor (% inter). Number of barcode species and variance determined
by BOLD BINs based on their criteria for compliant barcode (data accessed
8/2023). Intraspecific variance was listed as not available (na) if there was only one individual in the BIN. If there
were multiple barcode species for a single morphological name then the range
for % variance is shown. Instances where barcode species was based on
noncompliant specimens are denoted with “b” followed by a number of basepairs (bp) in sequence; all
of these were 1 individual with the exception of Skwala
and Mystacides (2 individuals). Asterisks indicate a
new sequence to the BOLD library.
|
Morphological name |
No. individual barcoded |
No. sequences (>200 bp) |
No. barcode species |
% Variance Intra Inter |
a Not unique
sequence
b Not barcode compliant c Name on BOLD
BIN |
|||
|
EPHEMEROPTERA - 27 of 31
morphological taxa sequenced |
|
|
|
|||||
|
Total |
342 |
201 |
42 |
|
|
|
||
|
Baetidae |
|
|
|
|
|
|
||
|
|
Acentrella sp. A |
17 |
14 |
1 |
1.96 |
15.2 |
|
|
|
|
Acentrella sp. B |
29 |
19 |
1 |
0.51 |
17.0 |
|
|
|
|
Acentrella sp. C* |
4 |
1 |
1 |
na |
13.5 |
|
|
|
|
Acentrella sp. D |
1 |
1 |
0 |
|
|
a Acentrella
sp. C |
|
|
|
Baetis sp. A* |
47 |
34 |
5 |
0–2.51 |
5.8–15.6 |
|
|
|
|
Baetis sp. B* |
2 |
1 |
1 |
na |
15.5 |
|
|
|
|
Baetis sp. C* |
7 |
1 |
1 |
na |
14.6 |
|
|
|
|
Baetis sp. D |
13 |
6 |
1 |
2.16 |
15.4 |
|
|
|
|
Fallceon** |
14 |
9 |
2 |
Na–0 |
16.5–16.9 |
|
|
|
Caenidae |
|
|
|
|
|
|
||
|
|
Caenis sp. A |
4 |
0 |
— |
|
|
|
|
|
|
Caenis sp. B* |
2 |
2 |
1 |
na |
12.7 |
|
|
|
Ephemerellidae |
27 |
12 |
2 |
0–0.73 |
16.3–16.5 |
c Spinorea
gilliesi |
||
|
|
Cincticostella sp. A |
3 |
0 |
— |
|
|
|
|
|
|
Cincticostella sp. B |
27 |
13 |
2 |
0.36–0.92 |
4.8 |
|
|
|
|
Cincticostella sp. C |
6 |
0 |
— |
|
|
|
|
|
|
Drunella sp. A* |
10 |
9 |
1 |
0.18 |
11.1 |
|
|
|
|
Drunella sp. B |
1 |
1 |
0 |
|
|
a Drunella
sp. A |
|
|
|
Drunella sp. C |
2 |
1 |
1 |
|
|
b 268 bp |
|
|
|
Teloganopsis |
4 |
0 |
— |
|
|
|
|
|
Ephemeridae |
|
|
|
|
|
|
||
|
|
Ephemera* |
1 |
1 |
1 |
na |
4.7 |
|
|
|
Heptageniidae |
|
|
|
|
|
|
||
|
|
Afronurus |
1 |
1 |
1 |
0.17 |
13.4 |
|
|
|
|
Cinygmula* |
6 |
4 |
1 |
0.7 |
9.2 |
|
|
|
|
Epeorus sp. A |
11 |
1 |
1 |
|
|
b 329 bp |
|
|
|
Epeorus sp. B* |
10 |
7 |
3 |
1.61–1.77 |
3.7–11.8 |
b 217 bp,
c E. aculeatus |
|
|
|
Epeorus sp. C* |
38 |
29 |
2 |
0–1.46 |
7.8–11.5 |
|
|
|
|
Iron*** |
4 |
4 |
3 |
na–0.96 |
12.2–14.3 |
|
|
|
|
Notacanthurus sp. A* |
6 |
5 |
1 |
1.0 |
17.0 |
|
|
|
|
Notacanthurus sp. B*** |
19 |
13 |
3 |
na–0 |
2.5–14.0 |
|
|
|
|
Rhithrogena** |
7 |
4 |
2 |
na–0.16 |
3.9 |
|
|
|
Leptophlebiidae |
|
|
|
|
|
|
||
|
|
Paraleptophlebia** |
9 |
5 |
3 |
0.17–0.89 |
9.6 |
b 484 bp |
|
|
Neoephemeridae* |
10 |
3 |
1 |
0.96 |
9.6 |
|
||
|
PLECOPTERA - 10 of 11
morphological taxa sequenced |
|
|
|
|||||
|
Total |
29 |
19 |
10 |
|
|
|
||
|
Capniidae |
1 |
1 |
1 |
|
|
b 260 bp |
||
|
|
Leuctridae |
|
|
|
|
|
|
|
|
|
Paraleuctra |
1 |
1 |
1 |
|
|
b 441 bp |
|
|
Nemouridae |
|
|
|
|
|
|
||
|
|
Amphinemura* |
4 |
2 |
1 |
0.2 |
10.7 |
|
|
|
|
Nemoura** |
3 |
2 |
2 |
na |
6.6 |
|
|
|
Peltoperlidae |
|
|
|
|
|
|
||
|
|
Cryptoperla* |
3 |
3 |
1 |
0.37 |
12.7 |
|
|
|
Perlidae |
|
|
|
|
|
|
||
|
|
Calineuria |
3 |
0 |
— |
|
|
|
|
|
|
Kiotina sp. A* |
2 |
2 |
1 |
0 |
15.7 |
|
|
|
|
Kiotina sp. B * |
2 |
2 |
1 |
na |
15.6 |
|
|
|
|
Paragnetina |
5 |
3 |
1 |
2.5 |
14.6 |
|
|
|
|
Tetropina |
1 |
1 |
0 |
|
|
a Paragnetina |
|
|
Perlodidae |
|
|
|
|
|
|
||
|
|
Skwala |
4 |
2 |
1 |
|
|
b 202 & 459 bp |
|
|
TRICHOPTERA - 26 of 34
morphological taxa sequenced |
|
|
|
|||||
|
Total |
87 |
61 |
25 |
|
|
|
||
|
Brachycentridae |
|
|
|
|
|
|
||
|
|
Brachycentrus* |
3 |
3 |
1 |
0 |
6.0 |
|
|
|
|
Micrasema* |
1 |
1 |
1 |
na |
10.5 |
|
|
|
Glossosomatidae |
|
|
|
|
|
|
||
|
|
Agapetus* |
6 |
6 |
1 |
1.37 |
13.0 |
|
|
|
|
Glossosoma |
3 |
3 |
1 |
1.12 |
9.1 |
c Glossosoma
dentatum |
|
|
Hydropsychidae |
|
|
|
|
|
|
||
|
|
Arctopsyche |
3 |
3 |
1 |
0.48 |
5.8 |
c Arctopsyche
lobata |
|
|
|
Hydropsyche sp. A |
5 |
0 |
— |
|
|
|
|
|
|
Hydropsyche sp. B* |
2 |
2 |
1 |
0.17 |
8.5 |
|
|
|
|
Hydropsyche sp. C |
4 |
1 |
0 |
|
|
a Hydropsyche
sp. D |
|
|
|
Hydropsyche sp. D* |
3 |
3 |
1 |
0.33 |
2.7 |
|
|
|
|
Hydropsyche sp. E |
3 |
2 |
1 |
0.17 |
5.8 |
|
|
|
|
Hydropsyche sp. F* |
3 |
2 |
1 |
0.17 |
11.7 |
|
|
|
|
Hydropsyche sp. G* |
3 |
3 |
1 |
0.64 |
2.7 |
|
|
|
|
Lepidostoma* |
3 |
3 |
1 |
1.36 |
10.1 |
|
|
|
|
Mystacides* |
2 |
2 |
1 |
na |
3.1 |
b 586 & 594 |
|
|
Limnephilidae |
2 |
2 |
1 |
0.18 |
10.8 |
c Phylostenax
himalus |
||
|
|
Chimarra* |
3 |
3 |
1 |
0.34 |
2.7 |
|
|
|
|
Neurocyta* |
1 |
1 |
1 |
na |
3.6 |
b 637 bp |
|
|
Psychomyiidae |
1 |
0 |
— |
|
|
|
||
|
Rhyacophilidae |
|
|
|
|
|
|
||
|
|
Himalopsyche sp. A |
3 |
3 |
1 |
1.19 |
2.5 |
c Himalopsyche
digitata |
|
|
|
Himalopsyche sp. B |
2 |
1 |
1 |
1.81 |
8.5 |
|
|
|
|
Himalopsyche sp. C |
3 |
1 |
1 |
0.17 |
11.0 |
c Himalopsyche
horai |
|
|
|
Himalopsyche sp. D |
1 |
0 |
— |
|
|
|
|
|
|
Rhyacophila sp. A* |
3 |
1 |
1 |
na |
2.3 |
|
|
|
|
Rhyacophila sp. B |
3 |
3 |
1 |
0.38 |
7.5 |
|
|
|
|
Rhyacophila sp. C |
1 |
0 |
— |
|
|
|
|
|
|
Rhyacophila sp. D |
1 |
0 |
— |
|
|
|
|
|
|
Rhyacophila sp. E |
1 |
1 |
1 |
|
|
b 317 bp |
|
|
|
Rhyacophila sp. F |
2 |
0 |
— |
|
|
|
|
|
|
Rhyacophila sp. G |
2 |
2 |
1 |
0.17 |
1.0 |
c Himalopsyche tibetana |
|
|
|
Rhyacophila sp. H |
4 |
0 |
— |
|
|
|
|
|
|
Rhyacophila sp. I |
1 |
0 |
— |
|
|
|
|
|
|
Rhyacophila sp. J |
2 |
2 |
1 |
|
|
b 202 & 257 bp |
|
|
Stenopsychidae |
|
|
|
|
|
|
||
|
|
Stenopsyche sp. A |
4 |
4 |
1 |
0.64 |
6.2 |
|
|
|
|
Stenopsyche sp. B |
3 |
3 |
1 |
0.32 |
10.6 |
|
|
For
figures - - click here for full PDF
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