Introduction
Biodiversity monitoring is the basal content for ecological research,
ecosystem management and conservation action (Cardinale, et al.,2012;
Hooper, et al.,2012; Carrizo, et al.,2017; Mallick & Chakraborty,2018).
While abiotic environmental conditions and ecosystem property
information are now available at highly resolved spatial and temporal
scales (Vina, et al.,2013; Jetz, et al.,2016), biodiversity is still
often studied from a local and accumulative perspective and is generally
not available at a wide taxonomic breadth, high-resolution temporal
scale and spatial coverage (Anderson,2018; Chase, et al.,2018; Mcglinn,
et al.,2019; Altermatt, et al.,2020). This limitation slows the
development of ecological research, ecosystem management and
conservation action. Now, meta-barcoding and high-throughput sequencing
of environmental DNA (eDNA, i.e., DNA extracted from environmental
samples such as water, soil, and air) provide novel opportunities to
monitor biodiversity (Deiner, et al.,2016; Cristescu & Hebert,2018;
Altermatt, et al.,2020), and this approach is nonlethal for most
classically sampled taxonomic groups, minimizes habitat disruption and
can assess diversity across the tree of life with a single-field
sampling protocol, making it extremely cost effective (Deiner, et
al.,2016; Valentini, et al.,2016; Stat, et al.,2017). As an efficient
and easy-to-standardize monitoring approach (Thomsen & Willerslev,2015;
Valentini, et al.,2016; Lugg, et al.,2018; Seymour,2019), and with the
continuous advancements in DNA sequencing technology, using eDNA
metabarcoding to monitor biodiversity would be an appropriate method to
revolutionize biodiversity monitoring by enabling the census of wide
taxonomic species on a highly resolved spatial and temporal scale in
near real time (Thomsen & Willerslev,2015; Cristescu & Hebert,2018;
Altermatt, et al.,2020).
Streams and rivers connect upstream regions with downstream regions,
connect land with waterbodies, and transport natural and anthropogenic
materials and information through extensive and heterogeneous network
systems (Luo, et al.,2011; Deiner, et al.,2016; Wang, et al.,2016;
Matsuoka, et al.,2019). Riverine water eDNA incorporates biodiversity
information across terrestrial and aquatic biomes (Deiner, et al.,2016;
Matsuoka, et al.,2019; Yang, et al.,2019). By analyzing riverine water
eDNA, information on terrestrial and aquatic biological compositions and
even their spatial structures can be obtained (Deiner, et al.,2016;
Matsuoka, et al.,2019). Therefore, the eDNA that is transported in river
networks offers a novel and spatially integrated component that can be
used to simultaneously monitor aquatic and terrestrial biodiversity and
will transform biodiversity data acquisition in research, management and
conservation (Deiner, et al.,2016; Yang, et al.,2019). However, to
achieve this objective, the monitoring effectiveness (i.e., the
proportion of aquatic and terrestrial biodiversity information that
could be detected using limited riverine water eDNA samples) needs to be
assessed first. Until now, there has been no systemic research on
monitoring effectiveness.
The concept of watershed biological information flow (WBIF) (Yang, et
al.,2019) is a good conceptual framework to assist with the assessment
of monitoring effectiveness. In the WBIF framework, the monitoring
effectiveness depends on the transportation effectiveness of the WBIF
from the upstream to downstream regions and from the land to river
(Deiner & Altermatt,2014; Sansom & Sassoubre,2017; Pont, et al.,2018;
Seymour,2019; Yang, et al.,2019). The WBIF framework integrates the
processes of origin, state, transport, and fate of eDNA (Barnes &
Turner,2016; Shogren, et al.,2017). Many studies have shown that eDNA
degrades over time in a logistic manner (Barnes, et al.,2014; Nukazawa,
et al.,2018; Seymour,2019; M, et al.,2020), and the detection distance
varies from less than 1 km in a small stream to more than 100 km in a
large river (Deiner & Altermatt,2014; Stoeckle, et al.,2016; Pont, et
al.,2018; Seymour,2019). Deiner and colleagues (2016) indicated that
much terrestrial biological information has been input to rivers and
that a large number of eukaryotic phyla from terrestrial taxa can be
detected from riverine water eDNA (Deiner, et al.,2016). Our previous
WBIF study conducted on the Qinghai-Tibet Plateau in summer showed that
the transportation effectiveness from adjacent riparian sites to river
sites was 62.76% on rainy days and 44.16% on sunny days; additionally,
the transportation effectiveness from adjacent upstream sites to
downstream sites (distance varied from 7 km to 23.5 km) was
approximately 80.30% (Yang, et al.,2019). Moreover, it was shown that
the transportation effectiveness of WBIF relied on transport capacity,
degradation rate and environmental filtration (Yang, et al.,2019). The
transport capacity mainly depended on erosion and runoff, and
degradation rate mainly depended on environmental features, and
environmental filtration mainly depended on environmental change, all of
which were related to season and weather conditions (Yang, et al.,2019).
Because the monitoring effectiveness depends on the transportation
effectiveness of WBIF and WBIF is related to season and weather
conditions, we propose that the monitoring effectiveness varies with
season and weather conditions.
The aim of this study was to identify the effectiveness of monitoring
the biodiversity information of upstream and riparian zones using
riverine water eDNA in different seasons and weather conditions. In this
study, based on the WBIF framework, we conducted a case study in the
Shaliu River basin, which is a typical watershed on the Qinghai-Tibet
Plateau; specifically, we analyzed the transportation effectiveness of
two types of WBIFs, i.e., the WBIF from upstream to downstream regions
and the WBIF from riparian zones to rivers, in different seasons and
weather conditions, as indicated by environmental microbes. Then, we
used the transportation effectiveness of the WBIF to identify the
corresponding monitoring effectiveness. Our objectives were twofold:
first, we sought to establish whether riverine water eDNA was viable for
monitoring the biodiversity information of upstream and riparian zones.
Second, we sought to identify which season and weather conditions were
optimal for monitoring the biodiversity information of the upstream and
riparian zones.
Materials and Methods
Study Area
The Shaliu River basin (37°10′-37°52′ N, 100°17′-99°32′ E), as a
sub-basin of the Qinghai Lake basin, is located 3196 m above sea level
on the Qinghai-Tibet Plateau (Fig. 1). The mean annual precipitation is
423.4 mm, and the average annual evaporation is 1674.7 mm. The annual
mean temperature is −0.6°C, the monthly mean temperature of January is
-17.5°C and that of July is 11.0°C. The Shaliu River freezes in October
and unfreezes in the following April. The Shaliu River is 106 km long,
with a catchment area of 1320 km2. Grassland is the
main land cover type, accounting for more than 90% of the watershed
area. Less than 5% of the watershed area was seriously changed by human
activity, such as transformation into cultivated land and building
land11http://www.gangcha.gov.cn/html/2125/item.html. Due to its
simple ecosystem assemblages and weak disturbance by human activity, the
Shaliu River basin is a natural simplified model for investigating the
effectiveness of monitoring aquatic and terrestrial biodiversity
information using riverine water eDNA.
Field Sampling
We collected eDNA samples three times, including 27 soil eDNA samples
and 27 water eDNA samples, from 9 transects (Fig. 1) in the Shaliu River
on April 8 and 9, June 25 and 26 and September 19 and 20, 2019. A 1.5 L
surface water sample was collected from the river site of each transect
and transported at 0°C to the laboratory of the Rescue and
Rehabilitation Center of Naked Carps of Qinghai Lake. Then, water
samples were filtered using 0.2-μm membrane filters to obtain the eDNA
sample in the laboratory, and the water eDNA samples were frozen,
transported at -20°C and stored at -80°C until DNA extraction. A 5 mL
soil eDNA sample was collected from the riparian zone site (5 m distance
from the river) of each transect and transported to the laboratory at
0°C. Then, the soil eDNA samples were frozen, transported at -20°C and
stored at -80°C until DNA extraction.
In the first sampling period (spring group), during April 8 and 9, the
air temperature was -6~8°C, the water temperature was
-0.5~0.7°C, the frozen river was starting to thaw, the
runoff volume was 1.8~3.9 m³/s, the soil was still
frozen, and both days were cloudy with freezing, heavy winds. On the
frozen days of April 8 and 9, the river was clear; on these days, we
sampled 18 samples (9 water samples and 9 soil samples) at 9 transects
from the downstream to upstream regions.
In the second sampling period (summer group), during June 25 and 26, the
air temperature was 7~17°C, the water temperature was
4.3~16.4°C, and the runoff volume was
29.9~45.5 m³/s. On the sunny day of June 25, the river
was clear; on this day, we collected 4 samples (2 water samples and 2
soil samples) from the transects of SL1 and SL2. It began to rain on the
night of June 25, and on the rainy day of June 26, the river was turbid;
we collected samples (7 water samples and 7 soil samples) from the last
7 transects from the downstream to upstream regions.
In the third sampling period (autumn group) on September 19 and 20, the
air temperature was 0~10°C, the water temperature was
0.2~8.8°C, part of the transects started to freeze, and
the runoff volume was 5.7~12.8 m³/s. On the rainy day of
September 19, the river was turbid; we collected 4 samples (2 water
samples and 2 soil samples) from the SL1 and SL2 transects. On the
cloudy day of September 20, the river was clear, and we collected 14
samples (7 water samples and 7 soil samples) from the last 7 transects
from the downstream to upstream regions.
DNA Extraction and Sequence
Analysis
Microbial DNA was extracted from eDNA samples using an E.Z.N.A.® Stool
DNA Kit (Omega BioTek, Norcross, GA, USA) according to the
manufacturer’s protocols. Then, the final DNA concentration and purity
were determined by a NanoDrop 2000 UV-vis spectrophotometer (Thermo
Scientific, Wilmington, USA), and DNA quality was checked by 1% agarose
gel electrophoresis. The V3-V4 hypervariable regions of the bacterial
16S rRNA gene were amplified with the primers 338F (5’-
ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) by using
a PCR thermocycler system (GeneAmp 9700, ABI, USA). The PCRs were
conducted using the following program: 3 min of denaturation at 95°C; 29
cycles of 30 s at 95°C, 30 s for annealing at 55°C, and 45 s for
elongation at 72°C; and a final extension at 72°C for 10 min. The PCRs
were performed in triplicate 20-μL mixtures containing 4 μL of 5×
FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4
μL of FastPfu Polymerase, 0.2 μL of BSA and 10 ng of template DNA. The
resulting PCR products were extracted from a 2% agarose gel, further
purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences,
Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, USA)
according to the manufacturer’s protocol.
Purified amplicons were pooled in equimolar amounts and subjected to
paired-end sequencing on an Illumina MiSeq platform (Illumina, San
Diego, USA) according to standard protocols by Majorbio Bio-Pharm
Technology Co., Ltd. (Shanghai, China). Raw fastq files were
demultiplexed, quality-filtered by Trimmomatic and merged by FLASH.
Operational taxonomic units (OTUs) were clustered with a 97% similarity
cutoff using UPARSE, and chimeric sequences were identified and removed
using UCHIME. The taxonomy of each 16S rRNA gene sequence was analyzed
by the RDP classifier Bayesian algorithm against the
Silva132/16S_Bacteria database using a confidence threshold of 70%.
The data were analyzed on the Majorbio Cloud Platform
(www.majorbio.com ). The raw data have been deposited in the CNSA
(https://db.cngb.org/cnsa/) of CNGBdb with accession number CNP0001046.
Statistical Analysis
The OTU numbers, types and taxonomic features of the samples of the
three groups, i.e., those sampled on April 8 and 9, June 25 and 26 and
September 19 and 20, 2019, were analyzed. Community richness (Chao
richness index) was examined to reveal the variation among the three
groups. The WBIF (including the WBIF from the upstream to downstream
regions and the WBIF from land to river) of each group was assessed to
reveal the effectiveness of monitoring the biodiversity information of
the upstream and riparian zones using riverine water eDNA. The analysis
of the WBIF follows the processing approach proposed by Yang et al.
(2019). In the analysis of the WBIF, all statistics used the types of
OTUs of each sample rather than the numbers of OTUs of each sample. The
processing approach can be described simply as follows.
As the WBIF driven by watershed ecosystem processes was transported from
land to river and from upstream to downstream regions, the
transportation effectiveness of the WBIF could be estimated by comparing
the OTU assemblages between adjacent soil eDNA samples and water eDNA
samples and by comparing the OTU assemblages between two adjacent water
eDNA samples. The transportation effectiveness of the WBIF was indicated
by the proportion of input OTU types (i.e., the common types between the
source site sample and the pool site sample) to output OTU types (the
total types of source site sample) (Eq. 1).
e = Num(SOTU ∩POTU )/Num(SOTU ) (Eq. 1)
where e denotes the transportation effectiveness of the WBIF;SOTU denotes the OTU assemblage of the source
site sample (i.e., the adjacent soil eDNA sample in the land-river WBIF
or the adjacent upstream water eDNA sample in the upstream-downstream
WBIF); and POTU denotes the OTU assemblage of the
pool site sample (i.e., the adjacent water eDNA sample in the land-river
WBIF or the adjacent downstream water eDNA sample in the
upstream-downstream WBIF).
As the transportation effectiveness of the WBIF relied on transport
capacity, degradation rate and environmental filtration and the distance
of the land-to-river WBIF was less than 5 m, the transportation
effectiveness of the land-to-river WBIF was assumed to be constructed by
transport capacity and environmental filtration. The transportation
effectiveness of the land-to-river WBIF could be indicated by the
proportion of the common types shared between adjacent soil eDNA samples
and water eDNA samples to the total types of soil eDNA samples (Eq. 1).
The transport capacity of the land-to-river WBIF could be indicated by
the proportion of the common types shared between adjacent soil eDNA
samples and water eDNA samples to the common types shared between the
soil eDNA sample and all water eDNA samples in the corresponding group
(Eq. 2). The environmental filtration of the land-to-river WBIF could be
indicated by the proportion of the types included in the soil eDNA
sample but not in any water eDNA sample to the total types in the soil
eDNA sample (Eq. 3).
t = Num(SOTU ∩POTU )/Num(SOTU ∩WOTU ) (Eq. 2)
f = 1 - Num(SOTU ∩WOTU )/Num(SOTU ) (Eq. 3)
where t denotes the transport capacity; f denotes the
environmental filtration; SOTU denotes the OTU
assemblage of the source site sample (i.e., the soil eDNA sample); andWOTU denotes the OTU assemblage of all water eDNA
samples.
The upstream-to-downstream WBIF that is indicated by environmental
microbes includes the effective WBIF (i.e., the flow of living
organisms) and the noneffective WBIF (i.e., the flow of dead organisms).
The effective WBIF was impacted by transport capacity and environmental
filtration. The noneffective WBIF was impacted by transport capacity and
degradation rate. We established the following assumptions: the
transport capacity was consistent in a defined runoff condition; the
proportion of noneffective WBIF at each site was consistent; the
noneffective WBIF degraded over time (i.e., distance) in a logistic
manner; and the environmental filtration was consistent in a definite
environmental change. The transportation effectiveness of the
upstream-to-downstream WBIF could be constructed by the transport
capacity of the WBIF, the environmental filtration of the effective WBIF
and the degradation rate of the noneffective WBIF (Eq. 4). In practice,
as watershed ecosystem processes are impacted by varied influencing
factors at any site and time, the analytical solution of Eq. 4 is
impossible. Therefore, Eq. 4 will be programming-solved according to the
evolutionary algorithm.
e = (t ^d )[(1-k )(1-f ) +k (1/2)^(d /D )] (Eq. 4)
where e denotes the transportation effectiveness of the WBIF;t denotes the transport capacity; d denotes the distance
of the WBIF; k denotes the proportion of the noneffective WBIF;f denotes the environmental filtration; and D denotes the
half-life distance.
Results
Biological Information Features of the Samples of the
Three
Groups
A total of 1,030,826, 968,122 and 842,317 clean sequences were obtained,
respectively, from the 18 samples of the spring group, summer group and
autumn group (more details in Fig. 2a), and the average lengths of these
sequences were 447.54 bp, 416.06 bp, and 416.32 bp, respectively. A
total of 10,602, 13,766 and 16,500 types of bacterial OTUs were detected
(UPARSE, 97% cutoff), respectively, from the 18 samples of the three
groups (more details in Fig. 2b and Fig. 3), which belonged to 58 phyla,
141 classes, 424 orders, 782 families, 1,895 genera and 4,537 species.
The OTU counts and compositions of each group were highly variable (Fig.
2b and Fig. 2c). The rarefaction curves of each sample showed that the
number of clean sequences of each sample in five groups (except for the
spring water eDNA samples) was lower than it should be, especially for
the water eDNA samples in summer and autumn (Fig. 3).
The total types of OTUs detected in the soil eDNA samples estimated
using the species accumulation curve were similar among the spring,
summer and autumn groups (Fig. 2d), although there were some spatially
and temporally heterogeneous OTU compositions (Fig. 2c and Fig. 4). The
total types of OTUs detected in the water eDNA samples estimated using
the species accumulation curve showed that the richest OTU types
occurred in autumn (Fig. 2d). Moreover, the temporal heterogeneity of
OTU composition among the three groups was obvious (Fig. 4). The common
OTUs shared between the soil and water eDNA samples totaled 2,834, 5,651
and 5,279 in the spring group, summer group and autumn group,
respectively, which accounted for 36.30%, 71.98% and 67.58% of the
total types of OTUs detected in the soil eDNA samples of each group,
respectively.
Land-river WBIF Features of the Three
Groups
The transportation effectiveness of the WBIF from the riparian zone to
the river was 16.62% (0.1662±0.1254, 95%) in the frozen spring,
62.76% (0.6276±0.0873, 95%) in the rainy summer, and 48.09%
(0.4809±0.0522, 95%) in the cloudy autumn. The transport capacity of
the WBIF from the riparian zone to the river was 26.88% (0.2688±0.2024,
95%) in the frozen spring, 68.49% (0.6849±0.0913, 95%) in the rainy
summer, and 57.36% (0.573579±0.052897, 95%) in the cloudy autumn. The
environmental filtration of the WBIF from the riparian zone to the river
was 38.54% (0.3854±0.0293, 95%) in the frozen spring, 8.38%
(0.0838±0.0206, 95%) in the rainy summer, and 16.18% (0.1618±0.0451,
95%) in the cloudy autumn. More details are shown in Table 1.
During the spring sampling period, the ice water in the estuary
(transect SL1) covered the riparian grassland. Because the water sample
was likely to be the eluate from the soil at SL1, the transportation
effectiveness and transport capacity of the WBIF from the riparian zone
to the river were high. As Qinghai Lake is saline, the environmental
filtration of the WBIF from the riparian zone to the river in the
estuary (SL1) was high. During the summer sampling period, because the
samples from SL1 and SL2 were sampled on sunny days and the other
samples were sampled on rainy days, the transport capacity of the WBIF
from the riparian zone was low, the environmental filtration of the WBIF
from the riparian zone was high, and the transportation effectiveness of
the WBIF from the riparian zone was low at SL1 and SL2. During the
autumn sampling period, because the samples from SL1 and SL2 were
sampled on rainy days and the other samples were sampled on cloudy days,
the transport capacity of the WBIF from the riparian zone was high at
SL2. Because of the environmental stress caused by estuary saline water,
the environmental filtration of the WBIF from the riparian zone was high
at SL1.
Upstream-downstream WBIF Features of the Three
Groups
Along with the flow from upstream to downstream, there were 4 chains
that connected the sampling transects. Their cumulative transportation
effectiveness of the upstream-to-downstream WBIF varied with cumulative
distance (Table 2). The transportation effectiveness of the
upstream-to-downstream WBIF relied on transport capacity, degradation
rate and environmental filtration. In spring, the transport capacity of
the upstream-to-downstream WBIF was 99.97% (0.9997±0.0003, 95%) per
km, of which there was 66.85% (0.6685±0.0034, 95%) noneffective WBIF,
the half-life distance of the noneffective WBIF was 1.55 (1.5490±0.1269,
95%) km, and the environmental filtration from SL2 to SL1 (from the
freshwater ecosystem to saline water ecosystem) was 16.04%
(0.1604±0.0082, 95%). In summer, the transport capacity of the
upstream-to-downstream WBIF was 99.42% (0.9942±0.0009, 95%) per km, of
which there was 43.46% (0.4346±0.0417, 95%) noneffective WBIF, the
half-life distance of the noneffective WBIF was 14.52 (14.5234±1.4405,
95%) km, the environmental filtration from SL3 to SL2 (from rainy
sampling conditions to sunny sampling conditions) was 0.57%
(0.0057±0.0055, 95%) and the environmental filtration from SL2 to SL1
(from the freshwater ecosystem to saline water ecosystem) was 54.42%
(0.5442±0.0100, 95%). In autumn, the transport capacity of the
upstream-to-downstream WBIF was 99.23% (0.9923±0.0016, 95%) per km, of
which there was 49.35% (0.4935±0.0410, 95%) noneffective WBIF, the
half-life distance of the noneffective WBIF was 10.40 (10.3981±0.7111,
95%) km, and the environmental filtration from SL2 to SL1 (from the
freshwater ecosystem to saline water ecosystem) was 12.87%
(0.1287±0.0171, 95%). The transportation effectiveness of the
upstream-to-downstream WBIF was 75.86%, 97.41% and 96.07% in spring,
summer and autumn, respectively, without regard to environmental
filtration in defined areas and times, with environmental change.
Discussion
Effectiveness of Monitoring Riparian Biodiversity
Information Using Riverine Water
eDNA
Because riverine water eDNA incorporates terrestrial biodiversity
information (Deiner, et al.,2016; Yang, et al.,2019), by analyzing
riverine water eDNA, information can be obtained on terrestrial
biological compositions (Deiner, et al.,2016; Matsuoka, et al.,2019).
The effectiveness of monitoring riparian biodiversity information
depends on the transportation effectiveness of the WBIF from the
riparian zone to the river. Following the WBIF framework (Yang, et
al.,2019), the transportation effectiveness of the WBIF from the
riparian zone to the river is mainly constructed by the capacity of
transporting eDNA in the riparian zone into the river (driven by runoff,
animals, wind, etc.) and environmental filtration (driven by soil/water
environmental shifts, soil aggregates, etc.). Therefore, the
effectiveness of monitoring riparian biodiversity information is also
impacted by these factors.
In the frozen spring period, the maximum effectiveness of monitoring
riparian biodiversity information was 16.62%, as the transportation
effectiveness of the WBIF from the riparian zone to the river was
16.62% (estimated based on the OTU compositions detected from the soil
eDNA sample and the water eDNA sample at the same sampling transect). In
other words, a water eDNA sample could include information on as much as
16.62% of the biodiversity information from the adjacent riparian zone.
Moreover, nearly 36.30% of the types of OTUs detected from the soil
eDNA samples could be detected in water eDNA samples. This result
suggested that as much as 36.30% of the riparian zone biodiversity
information could be monitored using water eDNA samples. This
effectiveness was mainly controlled by the low transport capacity of the
WBIF (26.88%) and by the high environmental filtration (38.54%).
In the rainy summer period, the maximum effectiveness of monitoring the
riparian biodiversity information was 62.76%, as the transportation
effectiveness of the WBIF from the riparian zone to the river was
62.76% (estimated based on the OTU compositions detected from the soil
eDNA sample and the water eDNA sample at the same sampling transect). In
other words, a water eDNA sample could monitor as much as 62.76% of
biodiversity information from the adjacent riparian zone. Moreover,
nearly 71.98% of the types of OTUs detected from the soil eDNA samples
could be detected in the water eDNA samples. This result suggested that
as much as 71.98% of the biodiversity information in the riparian zone
could be monitored using water eDNA samples. This effectiveness was
mainly controlled by the transport capacity of the WBIF (68.49%).
In the cloudy autumn period, the maximum effectiveness of monitoring
riparian biodiversity information was 48.09%, as the transportation
effectiveness of the WBIF from the riparian zone to the river was
48.09% (estimated based on the OTU compositions detected from the soil
eDNA sample and the water eDNA sample at the same sampling transect). In
other words, a water eDNA sample could monitor as much as 48.09% of the
biodiversity information from the adjacent riparian zone. Moreover,
nearly 67.58% of the types of OTUs detected from the soil eDNA samples
could be detected in the water eDNA samples. This result suggested that
as much as 67.58% of the biodiversity information in the riparian zone
could be monitored using water eDNA samples. This effectiveness was
mainly controlled by the transport capacity of the WBIF (57.36%) and by
environmental filtration (16.18%).
The transport capacity of the WBIF from the riparian zone to the river
was driven by animal movement, wind erosion and transport, rainfall and
surface runoff erosion, and surface runoff transport (Yang, et
al.,2019). In the frozen spring period, there was little animal
movement, as most animals were hibernating during Qinghai-Tibet
Plateau’s long winter. Although there was strong wind, the frozen soil
limited the amount of wind erosion and transport. Moreover, the
precipitation form was snow, and there was little surface runoff, which
provided suboptimal conditions for significant erosion and transport. In
summer and autumn, animal movement was frequent, wind erosion and
transport were permitted, and rainfall and surface runoff erosion and
surface runoff transport were significant. Therefore, the transport
capacity of the WBIF from the riparian zone to the river in spring was
obviously lower than that in summer and autumn and that on sunny or
cloudy days was obviously lower than that on rainy days.
The environmental filtration of the WBIF was impacted by soil-to-water
environmental shifts and soil aggregates (Wilpiszeski, et al.,2019;
Yang, et al.,2019). In the frozen spring period, the microbial
activities were low in both soil and water, and most microbes (input to
rivers from soil) could not be effectively preserved in water. Because
there was no significant surface runoff, there was no soil aggregate to
conserve the microbial input to the river. In summer and autumn, the
microbial activities were high in both soil and water, and most microbes
(input to rivers from soil) could be effectively preserved in water.
Rainfall and surface runoff promoted the input of soil aggregates into
rivers and promoted the conservation of the microbes living in them.
Therefore, the environmental filtration of the WBIF in spring was
obviously higher than that in summer and autumn and that on sunny or
cloudy days was obviously higher than that on rainy days.
Sales et al. (2020) indicated that although the detection probability of
riverine water eDNA was 40%~67, it provided comparable
results to conventional survey methods per unit of survey effort for
three species (water vole, field vole and red deer); in other words, the
results from 3~6 water replicates would be equivalent to
the results from 3~5 latrine surveys and
5~30 weeks of single camera deployment (Sales, et
al.,2020). Considering that the number of clean sequences of each summer
water eDNA sample and autumn water eDNA sample is obviously lower than
it should be (Fig. 3), it is probable that the water eDNA samples
contain more types of OTUs than the current results indicate. In other
words, the transportation effectiveness of the WBIF from the riparian
zone to the river in summer and autumn was obviously underestimated;
therefore, the effectiveness of monitoring riparian biodiversity
information in summer and autumn was obviously underestimated. However,
the monitoring effectiveness was still 62%~72%. This
result suggests that riverine water eDNA is viable for monitoring
riparian biodiversity information on rainy days in summer or autumn.
Effectiveness of Monitoring Upstream Biodiversity
Information Using Downstream Water eDNA
The fact that species information at some distance upstream could be
detected is the key for eDNA application in lotic systems (Stoeckle, et
al.,2016; Carraro, et al.,2018; Pont, et al.,2018). The effectiveness of
monitoring upstream biodiversity information depends on the
transportation effectiveness of the upstream-to-downstream WBIF (Deiner
& Altermatt,2014; Sansom & Sassoubre,2017; Pont, et al.,2018;
Seymour,2019; Yang, et al.,2019). eDNA could originate from living and
dead organisms and could be detected at distances downstream, which
determined the eDNA transport and degradation processes (Stoeckle, et
al.,2016; Nukazawa, et al.,2018; Tillotson, et al.,2018; Yang, et
al.,2019). Following the framework of the WBIF (Yang, et al.,2019), the
transportation effectiveness of the upstream-to-downstream WBIF was
mainly constructed by the transport capacity of the
upstream-to-downstream WBIF (impacted by eDNA evenness dispersed in
water) and the degradation rate (constructed by the proportion and the
half-life distance of noneffective WBIF). Therefore, the effectiveness
of monitoring upstream biodiversity information was also impacted by
these factors.
In spring, the maximum effectiveness of biodiversity information
monitoring 1 km upstream was 75.86%, as the transportation
effectiveness of the upstream-to-downstream WBIF was 75.86% per km.
This result suggested that a water eDNA sample could monitor as much as
75.86% of the biodiversity information 1 km upstream. This
effectiveness was mainly controlled by the high proportion of the
noneffective WBIF (66.85%) and the small half-life distance of the
noneffective WBIF (1.55 km).
In summer, the maximum effectiveness of biodiversity information
monitoring 1 km upstream is 97.41%, as the transportation effectiveness
of the upstream-to-downstream WBIF is 97.41% per km. This result
suggested that a water eDNA sample could monitor as much as 97.41% of
the biodiversity information 1 km upstream. This effectiveness was
mainly controlled by the proportion of the noneffective WBIF (43.46%)
and its half-life distance (14.52 km).
In autumn, the maximum effectiveness of biodiversity information
monitoring 1 km upstream was 96.07%, as the transportation
effectiveness of the upstream-to-downstream WBIF was 96.07% per km.
This result suggested that a water eDNA sample could monitor as much as
96.07% of the biodiversity information 1 km upstream. This
effectiveness was mainly controlled by the proportion of the
noneffective WBIF (49.35%) and its half-life distance (10.40 km).
The transport capacity of the upstream-to-downstream WBIF was impacted
by the even dispersal of eDNA in water. When the community richness is
too high to spread each type of OTU in every liter of water, the
transport capacity of the upstream-to-downstream WBIF declines.
Therefore, along with the increase in the types of bacterial OTUs from
spring to summer to autumn (Fig. 2d), the transport capacity declined
from spring to summer to autumn. Fortunately, in the Shaliu River basin,
the transport capacity was high (more than 99%) in the three seasons.
The degradation rate in the present study was constructed by the
proportion and the half-life distance of the noneffective WBIF. The
proportion of noneffective WBIF relies on microbial activity, which is
impacted by temperature (Lin, et al.,2016). Therefore, the highest
proportion of noneffective WBIF was detected in the frozen spring
period, and the lowest proportion as detected in summer. The half-life
distance of noneffective WBIF was mainly impacted by the flow rate of
river runoff and the half-life period of eDNA degradation (Yang, et
al.,2019). The half-life period of eDNA degradation is impacted by many
environmental conditions, such as biochemical oxygen demand,
temperature, pH, and organic matter (Barnes, et al.,2014; Eichmiller, et
al.,2016; Nukazawa, et al.,2018; Seymour, et al.,2018; van Bochove, et
al.,2020). However, the highest flow rate and the largest half-life
distance of noneffective WBIF are found in summer. In other words, the
half-life distance of the noneffective WBIF was mainly controlled by the
flow rate in the Shaliu River.
The number of clean sequences of each water eDNA sample in summer and
autumn was obviously lower than it should be (Fig. 3), and this result
indicated that the water eDNA samples contained more types of OTUs than
the current results presented; in other words, the transportation
effectiveness of the upstream-to-downstream WBIFs in summer and autumn
were obviously underestimated. Therefore, the effectiveness of
monitoring upstream biodiversity information in summer and autumn was
also obviously underestimated. However, the monitoring effectiveness was
greater than 96% 1 km downstream in summer and autumn. This result
suggested that riverine water eDNA was viable for monitoring upstream
biodiversity information in summer and autumn.
Cost-effective Proposal for Monitoring the Biodiversity
Information of Upstream and Riparian Zones Using Water eDNA
Because eDNA can be used to assess diversity across the tree of life
with a single-field sampling protocol (Deiner, et al.,2016), eDNA
sampling is more cost-effective (including cost and time) than
conventional biodiversity monitoring methods (Mächler, et al.,2014;
Valentini, et al.,2016; Seymour,2019). Moreover, simultaneously
monitoring aquatic and terrestrial biodiversity using riverine water
eDNA represents one step toward obtaining a more cost-effective
biodiversity monitoring method.
As the highest effectiveness of monitoring riparian biodiversity
information (62.76% for adjacent sites, or 71.98% for overall sites)
appeared on rainy days in summer, the cost-effective sampling time for
monitoring riparian biodiversity information using water eDNA is a rainy
day in summer. Considering that the effectiveness was mainly impacted by
rainfall and surface runoff, a rainy day in autumn would also be a
cost-effective sampling time. Considering the temporal heterogeneity of
biodiversity information, although there was variation in the OTU
composition (Fig. 4), we do not suggest monitoring riparian biodiversity
information using water eDNA during the frozen spring period because the
monitoring effectiveness would be too low to detect the different
biodiversity information in spring from that in summer and autumn. If
sampling is required in the frozen spring period, it would be better to
monitor riparian biodiversity information using soil eDNA directly.
As the effectiveness of biodiversity information monitoring 1 km
upstream was 97.41% in summer, 96.07% in autumn, and higher than
75.86% in spring, the cost-effective sampling time for monitoring
riparian biodiversity information using water eDNA is summer or autumn.
In term of the temporal heterogeneity of biodiversity information, the
water samples in autumn had the highest community richness, followed by
those collected in summer and spring (Figs. 2, 3). Considering the
degradation of noneffective WBIF, the flow rate mainly controlled the
half-life distance of noneffective WBIF. We suggest that, to monitor
microbial biodiversity information, autumn is the first choice for
sampling, followed by summer because of the relatively high flow rate.
Because there were low monitoring effectiveness and high OTU composition
differences with other seasons in spring, the microbial biodiversity in
spring should be monitored with a high sampling site density, if spring
sampling is required.
After examining the rarefaction curves of each sample, it was obvious
that the number of clean sequences of each sample in the five groups
(except for the spring water eDNA samples) was lower than it should be
(Fig. 3). Perhaps more than 60,000 clean sequences of each sample or
more than one duplicate sample is needed, especially for the water eDNA
samples in summer and autumn. Therefore, to monitor the biodiversity
information of upstream and riparian zones using riverine water eDNA, a
rainy day in autumn or summer would be the most cost-effective sampling
time, and more than one duplicate sample would be a better sampling
choice for water eDNA samples in summer and autumn. If sampling is
needed in spring, riparian biodiversity information should be monitored
by soil eDNA rather than by water eDNA, and aquatic biodiversity
information should be monitored using a high-density sampling method.
All biodiversity assessments based on the monitoring results using
riverine water eDNA should be revalued based on the monitoring
effectiveness.
Moreover, the monitoring effectiveness in this study is indicated by
environmental microbes, and whether the effectiveness of monitoring
other taxonomies using riverine water eDNA is higher or lower is still
unknown, and more research is required. Although the seasonal variation
in monitoring effectiveness was delineated based on the results
indicated by environmental microbes, it would be effective for other
taxonomic groups. In other words, a rainy day in autumn or summer is the
most cost-effective sampling time for monitoring the biodiversity
information of upstream and riparian zones using riverine water eDNA for
other taxonomic groups. Therefore, a study on monitoring the
effectiveness assessment of other taxonomic groups would be useful for
implementation on rainy days in autumn or summer to assess a
cost-effective monitoring proposal. If the monitoring effectiveness in
all taxonomic groups was verified to be higher than that obtained using
conventional methods, the claim of revolutionizing biodiversity science
(Thomsen & Willerslev,2015; Cristescu & Hebert,2018; Altermatt, et
al.,2020) would come true.
The results showed that the effectiveness of monitoring the biodiversity
information of the upstream and riparian zones was
96%~97% (1 km upstream) and 62%~72%
(5 m distance from the river) on rainy days in summer or autumn,
respectively, which was investigated in the Shaliu River basin (a
typical watershed on the Qinghai-Tibet Plateau), and this information is
likely a useful reference for other watersheds on the Qinghai-Tibet
Plateau. Moreover, the following points may be applicable to watersheds
in other regions: (1) rainfall, surface runoff and animal movements
increased the effectiveness of monitoring riparian biodiversity
information using riverine water eDNA; (2) frozen soil limited the
effectiveness of monitoring riparian biodiversity information using
riverine water eDNA; and (3) a high flow rate increased the
effectiveness of monitoring upstream biodiversity information using
downstream water eDNA. We believe that our framework for assessing
monitoring effectiveness is reasonable and general. We encourage more
research on monitoring effectiveness in other watersheds with different
climatic conditions to support simultaneous aquatic and terrestrial
biodiversity assessments.
Acknowledgements
This work was supported by the Central Public-Interest Scientific
Institution Basal Research Fund (Grant No. 2019HY-XKQ02, 2020TD08), the
Basic Research Program of Science and Technology Department Qinghai
Provence (Grant No. 2018-ZJ-703), the Provincial Natural Science
Foundation of Qinghai (Grant No. 2018-ZJ-908).
References
Altermatt
F, Little CJ, Mächler E, et al. (2020) Uncovering the complete
biodiversity structure in spatial networks: the example of riverine
systems. Oikos .
Anderson
CB (2018) Biodiversity monitoring, earth observations and the ecology of
scale. Ecology Letters 21 , 1572-1585.
Barnes
MA, Turner CR (2016) The ecology of environmental DNA and implications
for conservation genetics. Conservation Genetics 17 ,
1-17.
Barnes
MA, Turner CR, Jerde CL, et al. (2014) Environmental Conditions
Influence eDNA Persistence in Aquatic Systems. Environmental
Science & Technology 48 , 1819-1827.
Cardinale
BJ, Duffy JE, Gonzalez A, et al. (2012) Biodiversity loss and its
impact on humanity. Nature 486 , 59-67.
Carraro
L, Hartikainen H, Jokela J, Bertuzzo E, Rinaldo A (2018) Estimating
species distribution and abundance in river networks using environmental
DNA. Proceedings of the National Academy of Sciences of the United
States of America 115 , 11724-11729.
Carrizo
SF, Lengyel S, Kapusi F, et al. (2017) Critical catchments for
freshwater biodiversity conservation in Europe: identification,
prioritisation and gap analysis. Journal of Applied Ecology54 , 1209-1218.
Chase
JM, McGill BJ, McGlinn DJ, et al. (2018) Embracing
scale-dependence to achieve a deeper understanding of biodiversity and
its change across communities. Ecology Letters 21 ,
1737-1751.
Cristescu
ME, Hebert PDN (2018) Uses and Misuses of Environmental DNA in
Biodiversity Science and Conservation. Annual Review of Ecology,
Evolution, and Systematics 49 , 209-230.
Deiner
K, Altermatt F (2014) Transport Distance of Invertebrate Environmental
DNA in a Natural River. PLoS ONE 9 , e88786.
Deiner
K, Fronhofer EA, Mächler E, Walser J, Altermatt F (2016) Environmental
DNA reveals that rivers are conveyer belts of biodiversity information.Nature Communications 7 , 12544.
Eichmiller
JJ, Best SE, Sorensen PW (2016) Effects of Temperature and Trophic State
on Degradation of Environmental DNA in Lake Water. Environmental
Science & Technology 50 , 1859-1867.
Hooper
DU, Adair EC, Cardinale BJ, et al. (2012) A global synthesis
reveals biodiversity loss as a major driver of ecosystem change.Nature 486 , 105-108.
Jetz
W, Cavender-Bares J, Pavlick R, et al. (2016) Monitoring plant
functional diversity from space. Nature Plants 2 , 16024.
Lin Q,
De Vrieze J, Li J, Li X (2016) Temperature affects microbial abundance,
activity and interactions in anaerobic digestion. Bioresource
Technology 209 , 228-236.
Lugg
WH, Griffiths J, van Rooyen AR, Weeks AR, Tingley R (2018) Optimal
survey designs for environmental DNA sampling. Methods in Ecology
and Evolution 9 , 1049-1059.
Luo Y,
Xu L, Rysz M, et al. (2011) Occurrence and Transport of
Tetracycline, Sulfonamide, Quinolone, and Macrolide Antibiotics in the
Haihe River Basin, China. Environmental Science & Technology45 , 1827-1833.
M L,
XJ S, WJ W, et al. (2020) Studying the retention time of
Fenneropenaeus chinensis eDNA in water. Progress in Fishery
Sciences 41 , 51-57.
Mächler
E, Deiner K, Steinmann P, Altermatt F (2014) Utility of environmental
DNA for monitoring rare and indicator macroinvertebrate species.Freshwater Science 33 , 1174-1183.
Mallick
PH, Chakraborty SK (2018) Forest, wetland and biodiversity: Revealing
multi-faceted ecological services from ecorestoration of a degraded
tropical landscape. Ecohydrology & Hydrobiology 18 ,
278-296.
Matsuoka
S, Sugiyama Y, Sato H, et al. (2019) Spatial structures of fungal
DNA assemblages revealed with eDNA metabarcoding in a forest river
network in western Japan. bioRxiv , 637686.
McGlinn
DJ, Xiao X, May F, et al. (2019) Measurement of Biodiversity
(MoB): A method to separate the scale-dependent effects of species
abundance distribution, density, and aggregation on diversity change.Methods in Ecology and Evolution 10 , 258-269.
Nukazawa
K, Hamasuna Y, Suzuki Y (2018) Simulating the Advection and Degradation
of the Environmental DNA of Common Carp along a River.Environmental Science & Technology 52 , 10562-10570.
Pont
D, Rocle M, Valentini A, et al. (2018) Environmental DNA reveals
quantitative patterns of fish biodiversity in large rivers despite its
downstream transportation. Scientific Reports 8 ,
10313-10361.
Sales
NG, McKenzie MB, Drake J, et al. (2020) Fishing for mammals:
Landscape-level monitoring of terrestrial and semi-aquatic communities
using eDNA from riverine systems. Journal of Applied Ecology57 , 707-716.
Sansom
BJ, Sassoubre LM (2017) Environmental DNA (eDNA) Shedding and Decay
Rates to Model Freshwater Mussel eDNA Transport in a River.Environmental Science & Technology 51 , 14244-14253.
Seymour
M (2019) Rapid progression and future of environmental DNA research.Communications Biology 2 , 80.
Seymour
M, Durance I, Cosby BJ, et al. (2018) Acidity promotes
degradation of multi-species environmental DNA in lotic mesocosms.Communications Biology 1 , 4.
Shogren
AJ, Tank JL, Andruszkiewicz E, et al. (2017) Controls on eDNA
movement in streams: Transport, Retention, and Resuspension.Scientific Reports 7 , 5065.
Stat
M, Huggett MJ, Bernasconi R, et al. (2017) Ecosystem
biomonitoring with eDNA: metabarcoding across the tree of life in a
tropical marine environment. Scientific Reports 7 ,
12240.
Stoeckle
BC, Kuehn R, Geist J (2016) Environmental DNA as a monitoring tool for
the endangered freshwater pearl mussel (Margaritifera margaritifera L.):
a substitute for classical monitoring approaches? Aquatic
Conservation: Marine and Freshwater Ecosystems 26 , 1120-1129.
Thomsen
PF, Willerslev E (2015) Environmental DNA – An emerging tool in
conservation for monitoring past and present biodiversity.Biological Conservation 183 , 4-18.
Tillotson
MD, Kelly RP, Duda JJ, et al. (2018) Concentrations of
environmental DNA (eDNA) reflect spawning salmon abundance at fine
spatial and temporal scales. Biological Conservation220 , 1-11.
Valentini
A, Taberlet P, Miaud C, et al. (2016) Next‐generation monitoring
of aquatic biodiversity using environmental DNA metabarcoding.Molecular Ecology 25 , 929-942.
van
Bochove K, Bakker FT, Beentjes KK, et al. (2020) Organic matter
reduces the amount of detectable environmental DNA in freshwater.Ecology and Evolution 10 , 3647-3654.
Vina
A, Chen X, Yang W, et al. (2013) Improving the efficiency of
conservation policies with the use of surrogates derived from remotely
sensed and ancillary data. Ecological Indicators 26 ,
103-111.
Wang
S, Fu B, Piao S, et al. (2016) Reduced sediment transport in the
Yellow River due to anthropogenic changes. Nature Geoscience9 , 38-41.
Wilpiszeski
RL, Aufrecht JA, Retterer ST, et al. (2019) Soil aggregate
microbial communities: towards understanding microbiome interactions at
biologically relevant scales. Applied and Environmental
Microbiology 85 , e319-e324.
Yang
H, Du H, Qi H, Yu L, Wei Q (2019) Quantifying the watershed biological
information flow driven by runoff based on the indicator of
environmental microbes. ChinaXiv 201911 , 75.
Data Accessibility
The datasets generated for this study can be found in the CNSA
(https://db.cngb.org/cnsa/) of CNGBdb with accession number CNP0001046.
Author Contributions
HY acquired fund, designed and performed research, analyzed data, wrote
and edited the paper.
HD acquired fund, designed and supervised research, reviewed and edited
the paper.
HQ acquired fund, designed research, reviewed and edited the paper.
LY performed field sampling.
XH performed laboratory experiment.
HZ, JL, JW and CW reviewed and edited the paper.
QZ administrated project.
QW supervised and validated research.
Tables and Figures (with
captions)