Freshwater Biology. 2024;00:1–14. wileyonlinelibrary.com/journal/fwb
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1© 2024 John Wiley & Sons Ltd.
Received: 28 March 20 24
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Revised: 8 July 2024
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Accepted: 7 September 2024
DOI : 10.1111/f wb.14349
ORIGINAL ARTICLE
Temporal dynamics of alpha and beta diversity of lake
algae: Opposing patterns and influencing factors over
the past 200 years
Rui Ma1,2 | Janne Soininen2 | Aifeng Zhou1 | Panpan Ji1 | Tongzhuo Jiang1 |
Ramiro Martín- Devasa2 | Jianhui Chen1
1MOE Key Laboratory of Western China's
Environmental Systems, College of Eart h
and Environment al Sciences, L anzhou
University, Lan zhou, China
2Depar tment of Geosci ences and
Geography, Univer sity of H elsink i,
Helsinki, Finland
Correspondence
Jianhui C hen, MO E Key Laborator y of
Western China's Environmental Systems,
College of E arth and Envir onment al
Science s, Lanzhou Universit y, L anzhou
730000, China.
Email: jhchen@lzu.edu.cn
Funding information
Nationa l Natural Scien ce Foundation of
China, G rant/Award Numbe r: 41822102;
the 111 Project, Grant/Award Num ber:
BP06180 01; China S cholar ship Council
Abstract
1. Better understanding of the responses of algal biodiversity to multiple pressures,
such as climate warming and eutrophication, is a key issue in aquatic ecology.
Alpha and beta diversity may have various patterns over temporal scales, espe-
cially in the Anthropocene, when external pressures became more multifaceted.
However, the limited availability of historical data hampers the exploration of algal
biodiversity through time.
2. Recently, sediment DNA has emerged as a potential tool for elucidating temporal
patterns in algal communities. Here, we used sediment DNA to reconstruct tem-
poral turnover and diversity of algal communities in four remote lakes in north-
ern China over the past 200 years. Furthermore, to distinguish the contributions
of possible influencing environmental factors, we conducted structural equation
modelling.
3. Our results revealed that algal communities have experienced rapid shifts since
the Anthropocene, characterized by increased alpha diversity and decreased tem-
poral beta diversity. Warmer climate and eutrophication were associated with
changes in alpha diversity, while temporal environmental variation was associated
with temporal beta diversity.
4. This study revealed opposing patterns in alpha and beta diversity for algal commu-
nities, possibly caused by warming, eutrophication and lower temporal environ-
mental variation, respectively. While climatic factors played a major role in remote
lakes with a natural environment, lakes that are more human impacted may be
more structured by nutrient- related factors.
5. Under climate warming and intensified human activities, remote lakes may en-
counter complex pressures in the near future. Our findings offer valuable insights
into patterns in aquatic biodiversity and possible factors underlying multiple
pressures.
KEYWORDS
climate warming, diversity, environmental variation, eutrophication, remote lakes
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1 | INTRODUC TION
Climate warming and eutrophication have exerted pervasive im-
pacts on lake ecosystems on a global scale (Han et al., 2020; Reid
et al., 2019), as lakes have experienced substantial changes under
multiple pressures (Carey et al., 2012). These pressures may result in
a dec line in aqu atic biod iversit y an d po se a threat to ecological func-
tion of lakes (Jenny et al., 2020). Moreover, lakes have been impacted
by anthropogenic pressures for millennia, while such pressures have
largely increased since the mid- twentieth centur y, defined as ‘The
Great Acceleration’ (Steffen et al., 2007). Therefore, understanding
the responses of aquatic biodiversity to multiple pressures since
The Great Acceleration has become a pivotal question in ecological
research.
Lake sediment is a widely distributed archive and contains valu-
able ecological information. It provides continuous records and con-
tributes to a comprehensive understanding of biodiversity dynamics
(Capo et al., 2016). Even in some remote areas with limited human
impact , aquatic diversity has undergone unprecedented changes
(Qin et al., 2013; Saros et al., 2012), which underscores the impor-
tance of further investigation in remote areas. Different biodiversit y
components may show various patterns under complex pressures,
as, for example, warming may increase both alpha and beta diversit y
(Hautier et al., 2014). In contrast, decreased environmental variation,
which may stem from intensified human disturbance, can signifi-
cantly reduce beta diversity (Menezes et al., 2023). Moreover, even
if global diversity may be declining, in some regions, diversity may
increase or remain stable (Hooper et al., 2005; Sax & Gaines, 2003).
For instance, alpine and boreal lakes, which are remote and lack
human disturbance, typically harbour high biodiversit y. Such lakes,
characterized by cold temperature, low productivity and low nutri-
ent levels, may retain as suitable ecosystems for the species pre-
ferring cold and low- productivity environments (Smol et al., 2005;
Wang et al., 2016). These complex phenomena underscore the im-
portance of targeted biodiversity studies in remote lakes with fragile
ecosystems.
In recent years, advancements in high- throughput sequenc-
ing have revolutionized DNA studies for aquatic ecosystems.
Sediment DNA not only offers a new perspective at the genetic
level but also overcomes the time- consuming and professional
requirements of traditional species identification (Domaizon
et al., 2013; Pedersen et al., 2013; Tse et al., 2018). DNA tr ack s the
taxa that cannot be captured by fossils thus filling gaps in aquatic
research. This is especially true for algae, which play essential
ecological roles, such as primary production, nutrient and organic
matter storage, sediment accumulation and stabilization, and pro-
vide complex habitats and food for aquatic organisms (Carpenter
& Lodge, 1986; Pawlowski et al., 2018). Recently, plent y of stud-
ies have been carried out on lake algal communities by using
sediment DNA (Giguet- Covex et al., 20 14; Jia et al., 2021; Zhang
et al., 2021), but these studies have primarily focused on lakes in
densely populated regions, so there is a lack of understanding of
diversit y patterns in remote lakes.
Northern China spans a large longitudinal range and exhibits
not able gr ad ient s in many aspec ts, suc h as clima te and population
distribution (Yang et al., 2003). This area comprises diverse lake
ecosystems, valuable to be investigated. Among these lakes, some
remote lakes with naturally hydrologically closed watersheds are
considered sensitive and vulnerable to both climate change and
human activities (Liu et al., 2015; Yan et al., 2021). Remote lakes
are often located in arid and semi- arid, as well as alpine regions,
and they are ideal ecosystems to investigate community responses
to global warming under natural conditions (Catalan et al., 2014;
Wan et al., 2022). In this study, we examined four remote lakes
in northern China with relatively closed hydrogeological condi-
tions and high elevation, but with different environmental and
human influences. We reconstruc ted the variation of algae over
the past 20 0 years using sediment DNA and used structural equa-
tion modelling (SEM) to elucidate t he contributio ns of climate a nd
nutrients to biodiversity. Our specific aims were to reconstruct
variation in algal diversity and identify influencing factors. We
hypothesized that (1) in these remote lakes, characterized by a
cold environment and low productivity, alpha diversity may in-
crease with rising temperatures and eutrophication through time
while lower environmental variation may result in lower temporal
beta div er si t y. Secondly (2), in la ke s with st ro nger human im pa cts ,
nutrients have stronger effects than climate on algal diversit y,
whereas climate influences on diversity will be more profound in
more natural lakes.
2 | METHODS
2.1 | Study design
2.1.1 | Study sites
Northern China comprises Northeast, North and Northwest China.
It is a region with a diverse ecological environment. There are many
closed inland lakes in arid, semi- arid and alpine areas of northern
China (Ma et al., 2010 ). These lakes are far away from the sea and
mainly supplied by surface runoff, rainfall or snowmelt, and water
is lost mainly by evaporation, thus having relatively closed hydro-
logical conditions (Ni et al., 2023; Tang et al., 2018). These closed
lakes are not only located in underdeveloped regions with limited
human disturbance (e.g. some alpine regions and desert areas in
Northwest China) (Ji et al., 2024; Jiao et al., 2021) but also in re-
gions surrounded by densely populated areas but with high altitude
(e.g. some alpine regions in North China and Northeast China) (Ji
et al., 2023; Shen et al., 2022).
We collected sediment cores from four remote lakes lo-
cated in northern China, including Gonghai lake (GH), Daihai
lake (DH), Yueliang lake (YL) and Shuanghu lake (SH) (Figure 1).
In November 2019, we collected samples from GH, which is lo-
cated in the southwest of Ningwu Count y, Shanxi Province, China
(112°14′6.2″E, 38°54′36.35″N, 1860 m a.s.l). As an alpine lake with
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MA et al.
a hydrologically closed environment, we expected the lake to be
suitable for monitoring environmental changes (Chen et al., 2015).
It has a maximum water depth of c. 10 m and a water surface area
of 0.36 km2 (Chen et al., 2020). The water sources are mainly re-
gional precipit ation and underground water. The mean annual
precipitation is 468 mm, with more than 77% occurring between
June and September. The average annual temperature is 6.2°C.
The immediate vicinity of the lake is occupied by woodland and
agricultural land, the surrounding area is relatively densely popu-
lated with several towns (Ji et al., 2023).
In August 2020, we collected sediment cores from lake DH,
located 10 km east of Liangcheng County, Inner Mongolia, China
(112 °40′48″E, 40°34′48″N, 1210 m a.s.l). DH is a typical closed
inland lake on the Inner Mongolia Plateau, with a surface area of
68.67 km2 and a maximum water depth of c. 7 m (Shen et al., 2022).
The water source is mainly regional precipitation. The average
annual temperature (1959–1999) is about 3.5°C, and the mean an-
nual precipitation (1959–1999) is 400 mm (Jin et al., 2006).
In August 2020, we collected sediment cores from lake YL ,
located in Alxa League, Inner Mongolia, China (105°9′14.5 3″E,
38°27′46.92″N, 1295 m a.s.l). YL is located c. 100 km west of
Yinchuan City, situated in the centre of the Tengger Desert. It is a
hydrologically closed lake, with wetlands on the southwestern side
of the lake. The maximum lake depth is about 4 m, and the water
surface area is c. 4.2 km2 (Ji et al., 2023). The lake is surrounded by
desert, with no residential areas in the vicinity.
In December 2021, we collected sediments for lake SH
(87°1′48″E, 48°52′48″N, 1523 m a.s.l), which is located in the middle
of the Altai Mountains. The lake has an area of 0.8 km2 and a max-
imum water depth of 14.5 m. The annual precipitation in this area
ranges between 400 and 700 mm, and the mean annual tempera-
ture is −1°C. More than 50% of precipitation falls as snow in winter
FIGURE 1 Distribution of study lakes
in China, including Gonghai (GH), Daihai
(DH), Yueliang lake (YL) and Shuanghu
(SH). (a) Geographical location of lakes
(red plot) in northern China, the grey line
shows the territorial boundary of China.
(b) Location of lakes in relation to major
highways (orange line), railways (blue
line) and population density (data were
collected from Resource and Environment
Science and Data Center, w w w . r e s d c . c n ),
which revealed that the four lakes were
influenced by different extents of human
influences.
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MA et a l.
and early spring. The lake is remote and relatively undisturbed by
human activities (Li et al., 2022). Based on the distribution of pop-
ulation, highways and railways, we classified lakes into t wo groups
(lake s GH and DH are considered strongly disturbe d lakes, whereas
lakes YL and SH are considered weakly disturbed lakes).
2.1.2 | Sediment coring and dating
Sediment cores were retrieved by a UWITEC gravit y corer with an
internal core tube, and all cores were sectioned into 0.5 cm inter-
vals by a close- interval extruder. Sterile disposable materials were
used to obtain DNA samples, which were carefully taken from the
centre, and sterile disposable materials and RNase/DNase Away
(PHYGENE) to prevent contamination. These samples were care-
fully placed in sterile sample bags and transported to the laboratory
under low temperature and stored at −80 °C.
Samples for dating were freeze- dried to test water content, fol-
lowed by subsequent dating. Samples stored at −80°C were used
for molecular biology experiments. The activities were tested by an
acquisition time of 24 h (99% confidence level), using a EURISYS®
low background photon (γ- ray) multichannel (214) energy spec-
trometer with a high- purity germanium (HPGe) well- type probe (Ji
et al., 2023). The age models were constructed using a constant rate
of supply (CRS) model based on the results of 210 Pb (Appleby, 2001;
Appleby et al., 1979). The activities of 210Pb and 226Ra, together
with the CRS model, were used to build chronological frameworks
for the four cores, and the records of 137Cs activity were used as a
cross- check (Ji et al., 2023). The 137Cs peak value was used as the
control point of 1963 CE for adjustment, and the bottom age was
calculated using the average sediment rate which was determined
by the model.
2.1.3 | Sedimentary DNA extraction and PCR
amplification
The DNA was extracted using the MO- BIO PowerSoil DNA Isolation
Kit (Mo Bio Laboratories, Carlsbad, CA, USA) following the manu-
facturer's instructions. We ran the samples on 1.2% agarose gel.
The total DNA concentration and purity were measured using a mi-
crovolume UV spec trophotometer (NanoDrop 300, Thermo Fisher
Scientific). Each DNA sample was subjected to t wo parallel extrac-
tions and then pooled together. The extracted DNA was stored at
−20 ° C .
Then the extracted sediment DNA were used as a template,
and the specific primers p23SrV_f1- GGACAGAAAGACCCTATGAA
and p23SrV_r1- TCAGCCTGT TATCCCTAGAG were used to amplify
the plastid 23S rRNA gene. Previous studies have demonstrated
the feasibility of using these primers for tracking multiple algal lin-
eages, including cyanobacteria and eukaryotic algae (Sherwood &
Presting, 2010). We linked different barcodes to the 5′ end of the
reverse primer (p23SrV_r1) to amplify each sample. The initial PCR
reaction system was 25 μL, consisting of 1–2 μL DNA template,
200 μM dNTPs (GeneAmp™, USA), 0.2 μM of each primer, 10 μM
PCR buffer (TaKaRa, Japan) and 1 μL Phusion DNA Polymerase
(New England Biolabs, USA). The amplification conditions were
as follows: 1 cycle of 95°C for 2 min, 35 cycles of denaturation at
94°C for 30 s, 66–58°C (set temperature decreased 0.5°C for ever y
cycle until reached 58°C) for 30 s and 72°C for 30 s (Sherwood
et al., 2008). The barcod ed PCR pr od uct s were pu rifie d us in g a DNA
gel extraction kit (A xygen, USA) and quantified by F TC- 30 00 TM
real- time PCR (Funglyn, China). The PCR products from dif ferent
samples were mixed at equal ratios. After being purified, the DNA
was sequenced by 2 × 250 bp paired- end sequencing on the Illumina
platform, that is Novaseq 6000 SP 50 0 Cycle Reagent Kit (Illumina,
USA). The sequencing processes were conducted at TinyGen Bio-
Tech (Shanghai) Co., Ltd. We took strict measures in laboratory
protocols to prevent possible contamination. All experimental work
was carried out in a sterile workspace. We used materials that have
been cleaned with 75% ethanol and exposed to a laminar flow hood
un der a UV lamp . DNA ex tra cti o n and am pli fic ation were pe r for m ed
on separate days in different laboratories. Moreover, we conducted
DNA extraction for only six samples in each round to avoid po-
tential contamination. We used nuclease- free water (UltraPure™,
USA) as a blank control for DNA ex traction. We opened a tube with
2 mL of nuclease- free water to detect contamination in each work
round. Then we took samples from the blank control for amplifica-
tion to test potential contamination. During the amplification, we
conducted PCR for six samples per round and added blank controls
(used nuclease- free water). Additionally, we conducted regular ster-
ilization for the workspace with UV light and RNase/DNase Away
(PHYGENE) every hour.
2.2 | Data analysis
2.2.1 | Bioinformatic analysis
All purified amplicons were paired- end sequenced on the Illumina
MiSeq platform by TinyGene Bio- Tech (Shanghai, China) Co., Ltd.
The raw fastq files were demultiplexed and PE reads were run by
Trimmomatic (version 0.36). Then the trimmed reads were merged,
with a minimum overlap of 15 and a minimum merging length of
300 nucleotides. Then we removed low- quality contigs and ampli-
fied primers. Sequences were analysed by Mothur (version 1.33.3),
UPARSE and R. The reads were clustered at 97% to formulate op-
erational taxonomic unit s (OTUs), and the singleton OTUs were de-
leted using UPARSE pipeline (h t t p s : / / d r i v e 5 . c o m / u s e a r c h / m a n u a l / ).
All representative sequences for OTU were compared with tax-
onomy information in NCBI (https:// www. ncbi. nlm. nih. gov/ ). Then
we selected sequences which matched with the species and the
similarity was higher than 0.9 and deleted plants and bac teria. We
removed OTUs with <10 reads in individual samples (Nikodemova
et al., 2023), as wel l as se que nc es wi th ≥10 re ads exis te d in ne gativ e
controls. These sequence data have been submitted to the database
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MA et al.
under accession number PRJNA1086235. The address is as follows:
h t t p s : / / w w w . n c b i . n l m . n i h . g o v / b i o p r o j e c t / P R J N A 1 0 8 6 2 3 5 .
2.2.2 | Diversity analysis and SEM model
After obtaining the OTU abundance table for algal OTUs, we used
non- metric multidimensional scaling (NMDS) to ordinate the commu-
nity in each lake. Then, we calculated Shannon diversity as a primar y
index of alpha diversity, as it indicates both richness and evenness
and reflect s different aspects of diversity, Shannon diversity and, for
example, species richness typically correlate quite strongly based on
earlier literature (Stirling & Wilsey, 2001; Thukral, 2017). Bet a diver-
sity was calculated by Bray–Curtis dissimilarity, which is appropriate
for abundance data (Cook et al., 2018). To calculate temporal beta
diversit y, we compared the adjacent samples in time with a mov-
ing window approach using Bray- Cur tis dissimilarities, that is, the
temporal bet a diversity index (TBI). Temporal beta diversity ( TBI) is
an ideal index to record temporal variability of species communities
(Magurran et al., 2019). We conducted the calculation followed by
Legendre, 2019. These steps were all conducted by ‘vegan’ package
on the R plat form (Oksanen et al., 2015).
We gathered data for lakes from published literature on vari-
ables that are widely recognized to impact algal communities and
also have continuous records spanning the past 200 years (including
temper at ure, prec ip it ation , nu trien ts and po ll ution levels) ( Table S1).
Among them, nitrogen and phosphorus are widely used to reflect
lake trophic status and have been considered the most influential el-
ements on algal communities (Smith, 1982, 2003). For these remote
and enclosed lakes with low productivit y, nitrogen and phosphorus
are mai nly tr an spor te d vi a soil erosio n, sur face run- of f, at mo spheric
deposition and then entering the sediments (Liu et al., 2017 ). Thus,
th ey co uld re fle c t the nu tri e nt s that the lake has re cei ved an d s to r ed
and could be considered comprehensive indicators of nutrient levels
(Ji et al., 2023). Then, we conducted redundancy analysis (RDA) to
verify significant factors for algal communities using the rda function
in the vegan R library. Environmental data were set as explanatory
variables, and abundance tables with the top nine genera were set
as respo ns e va riables becaus e we obt ained the best res ul ts fo r DN A
sequence alignment at the genus level. Data sets were checked and
adjusted to normal distributions before conducting RDA. We also
performed detrended correspondence analysis (DCA) to measure
total variation in the data and used forward selection (with ordistep
R function in the vegan package) for environmental variables. We
used the env fit function to selec t environmental variables with sig-
nificant influences (p < 0.05) and to build the common environmen-
tal space by a principal component analysis (PCA) (Broennimann
et al., 2012). Before this, we conduc ted interpolation to standardize
data and normalized environmental data to eliminate dimensional
differences. The temporal environmental variation was computed
by the average dissimilarit y between adjacent samples, based on the
Euclidean distance matrix of environmental variables. These steps
were also conducted by ‘vegan’ package. To reveal the significant
transition of the community, Bayesian change point (bcp) analysis
was used to calculate posterior probabilities for the presence of a
change point to the first axis of NMDS (Capo et al., 2016). This step
was conducted in the ‘bcp’ package on the R platform (Erdman &
Emerson, 2008).
To identify different contributions to algal diversity, we used the
data collected above to construct SEM for each lake by AMOS 21.0
(Amos Development Corporation) (Grace, 2006). We set two latent
va ria b le s: clim ate and nu t rie nt fa c tor s into the mo d el. Th en, we ide n-
tified explanatory variables by RDA and selected significant factors
that are vital to algal communities, as indicated by the low p- values
in Table S2. Alpha diversity was represented by the Shannon index,
and beta diversity was represented by TBI. We calculated the aver-
age Shannon index, climate and nutrient factors for the pair of ad-
jacent samples to match with temporal beta diversit y and temporal
environmental variation for the pair of samples. We established pair-
wise covariance paths in our model based on previously documented
correlations in the literature. For instance, since temperature and
precipitation are known to influence nitrogen and phosphorus lev-
els, we included a path between climate and nutrient factors. We
transformed data prior to analysis to better approximate normality
and eliminate the dimensions. Overall, several statistical indices
were chosen to assess models, including the chi- square test (χ2/d f ),
goodness–of- fit index (GFI), comparative fit index (CFI), normed fit
index (NFI) and Akaike information criterion (AIC) (Akaike, 1974; Fan
et al., 2016; Hu & Bentler, 1999). Non- significant variables and paths
were removed until an optimal model was achieved.
3 | RESULTS
3.1 | Chronology
In the CRS models (Figure 2), the initial appearance of 137Cs in
sedimentary records was considered to represent the initiation of
atmospheric nuclear weapons testing (1954 CE), and the peak activ-
ity could represent the 1963 CE (Pennington et al., 1973). For lake
GH, the sediment core spanned from 1842 to 2019 CE, showing a
uniform sedimentation rate with an average accumulation rate of
0.294 cm/year. In lake DH, the sediment accumulation rate was con-
sistent (0.836 cm/year), covering the period from 1910 to 2020 CE.
Lake YL also exhibited a uniform accumulation rate (0.350 cm/year),
spanning from 1840 to 2020 CE. Lake SH had the lowest sedimen-
tation rate of 0.145 cm/year, with the sediment core ranging from
1796 to 2020 CE. The detailed chronological results have been re-
ported in our previous study (Ji et al., 2024).
3.2 | Community composition and turnover in
four lakes
The observed plateau in rarefaction curves indicates that the se-
quencing data volume is sufficient and encompasses a substantial
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MA et a l.
portion of the biological information (Figure S1). Based on NMDS
and bcp, we identified break points in community composition
around the past 20 0 years (Figures S2 and S3).
The algae identif ied in lake GH were classified into six phyla, in-
cluding Cyanobacteria, Bacillariophyta, Chlorophyta, Streptophyta,
Euglenida and Xanthophyceae (Figure S4). Among these, the
dominant groups were Synechococcus, Choricystis, Cyanobium,
Cyclotella, Lobosphaera, Synechocystis, Dinophysis, Ankistrodesmus
and Chlorella. Notably, bcp analysis revealed the distinct shift in
the community composition was in the 1960s: before the 1960s,
the community was primarily dominated by Cyanobacteria and
Chlorophyta. However, af ter the 1960s, there was a marked de-
crease in Cyanobacteria, accompanied by a significant increase in
Bacillariophyta.
In lake DH, the community was composed of Cyanobacteria,
Chlorophyta, Streptophyta, Bacillariophyta, Euglenida and
Xanthophyceae (Figure S5). The dominant taxa were Synechococcus,
Neglectella, Cyanobium, Nodularia, Choricystis, Tisochrysis, Nostoc and
Chlamydomonas. Community experienced a significant transition
in the 1930s: prior to the 1930s, Cyanobacteria, Chlorophyta and
Streptophyta were dominant taxa, and Chlorophyta maintained a
stable level while Cyanobacteria exhibited some fluctuations. After
the 1930s, Streptophyta nearly disappeared while Cyanobacteria
showed an increasing trend. Some dominant species, such as
Chlamydomonas and Nostoc, decreased to a low level.
In lake YL, the community was composed of Cyanobacteria,
Bacillariophyta, Chlorophyta, Streptophyta and Euglenida
(Figure S6). The dominant taxa were Synechococcus, Picocystis,
Dunaliella, Cyanobium, Nodosilinea, Entomoneis, Synechocystis,
Cyanobacterium and Tisochrysis. The bcp analysis revealed that the
communit y composition had a significant transition in the 1970s:
prior to the 1970s, Cyanobacteria and Chlorophyta dominated the
communit y, with a decrease in Chlorophyta and a gradual increase in
Cyanobacteria. However, after the 1970s, Cyanobacteria exhibited
a slight decrease, while Bacillariophyta significantly increased. Some
dominant species, such as Dunaliella and Picocystis, disappeared
after the 1970s.
In lake SH, the community was composed of Bacillariophyta,
Chlorophyta, Cyanobacteria, Streptophyta, Haptophyte and
Rhodophyta (Figure S7). The dominant genera included Melosira,
Chromulina, Mallomonas, Lithodesmium, Synechococcus, Entomoneis,
Choricystis, Melanothamnus and Merismopedia. The results show a
remarkable tr ansition in the 1980 s. Prior to the 1980s, the dominant
taxa were Bacillariophyta, Chlorophyta and Cyanobacteria. However,
after the 198 0s , there was a subs tantial increase in the abu ndance of
Bacillariophyta, while others decreased to a lower level.
FIGURE 2 Results of radiometric
dating and age- depth models for the four
lakes (a–d), 210Pbex (green line) and 137Cs
radioactive activit y (black line) for each
lake, and CRS model results were shown
in the graph, detailed chronological results
have been reported in our previous study
(Ji et al., 2024).
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MA et al.
3.3 | Diversity and environment variation for
four lakes
Alpha (Shannon index) and beta diversity (TBI index) observed
in four lakes exhibited opposing trends especially in the past
100 years, characterized by increasing alpha diversit y and decreas-
ing beta diversity (Figure 3). In lake GH, alpha diversity experi-
enced a decreasing trend before the 1880s, and then consistently
increased after the 1880s; after the 1950s, it exhibited a sharply
increasing trend. TBI had an opposite trend as alpha diversity, it
ha d a sig nif icant de cre asi ng tre nd af ter the 195 0s. In lake DH , alp ha
diversit y had a low level before the 1920s and then increased re-
markably to a stable level after the 1920s; TBI exhibited a signifi-
cant increasing trend during the 1920s–1930s and then decreased,
especially after the 1950s. In lake YL, alpha diversity had a slightly
increasing trend before the 1880s and then decreased until the
1960s, after which it increased. TBI increased gradually before
the 1880s after which it experienced multiple fluctuations. In lake
SH, alpha diversity experienced repeated fluctuations before the
1880s , and th e n exh ibi ted a cons ist ent rise af ter the 197 0s. TB I had
an opposite trend with alpha diversity, characterized by a notable
decreasing trend after the 1970s.
For four lakes, temperature records showed the common in-
creasing trend since the 1960s, while precipitation indices showed
variable trends (Figure 4). In lake GH and DH, the precipitation
showed a gradual decrease after the 1960s. Similarly, lake YL also
experienced a drought trend after the 1960s, whereas lake SH had
more rains during the same period. TN showed a significant increase
in four lakes over the past 60 years, whereas TP exhibited variable
trends. Lake GH and lake DH all showed a significant increase in TP
since the 1940s. In lakes YL and SH, TP decreased to a low level be-
fore gradually increasing af ter the 1950s.
Based on PCA analysis, temporal environmental variation of GH
was at a high level until the 1920s, decreased rapidly from the 1920s
to the 1960s, then decreased after the 1960s and finally increased
after the 2000s (Figure 4). Temporal environmental variation of lake
DH increased sharply in the 1970s and decreased sharply after the
1980s. Temporal environmental variation of lake YL exhibited an in-
creasing trend before the 1940s, while after the 1940s, it decreased
gradually. For lake SH, the temporal environmental variation exhib-
ited a high level until the 1860s and showed a decreased trend after
the 1860s.
3.4 | RDA and SEM models
The RDAs provided overall insight into the relationships between
algal communities and environmental variables. In GH, DH, YL and
SH, lengths of the first axis for DCA were 2.76, 2.16, 1.24 and 1.39,
respectively. The gradient lengths (<3) suggested that a linear model
(RDA) was suitable. In lake GH, RDA1 explained 60.1% of the total
variance and was especially associated with temperature and nutri-
ent factors (Figure S8). RDA2 explained 34.6% of the total variance
and was correl ated strong ly with precipi ta tion. In la ke DH, RDA1 ex-
plained 69.9% of the total variance and was associated mostly with
nutrient fac to rs. RDA 2 ex pl ai ne d 15 .4% of the variance and was cor-
related with temperature and lake level. In lake YL, RDA1 explained
79.6% of the community variance and was correlated strongly with
precipitation and temperature, whereas RDA2 only explained 9.1%
of the algal variance, being correlated with nutrients. In lake SH,
RDA1 explained 91.9% of the variance and was primarily associated
with temperature, precipitation and TP. RDA2 explained 7.6% of the
communit y variance, being correlated with TN and BC. Significance
levels of environmental variables are shown in Table S2.
FIGURE 3 Alpha diversity (Shannon
index) and temporal beta diversit y (TBI)
for four lakes (a–d). The yellow area
highlight s a significant transition for
NMDS1, as identified by Bayesian change
point analysis (bcp). Additionally, this
transition also coincided with remarkably
opposing diversit y patterns across
different periods.
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8
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MA et a l.
FIGURE 4 Temporal patterns of environmental variables and diversity for the four study lakes (GH, DH, YL, SH, respectively). For
each lake, we compared (a) temperature records from sediments (solid black lines) and monitoring data (solid grey lines), (b) mean annual
precipitation or water level records (solid blue lines) from sediments and monitoring data (solid purple lines), (c, d) total nitrogen (TN)
and total phosphorus (TP) from sediments, (e) Shannon index (alpha diversit y), (f) the temporal environmental variation calculated by the
Euclidean distance of PCA matrix bet ween adjacent samples and (g) temporal beta diversity (TBI) index. For the detailed information about
environment records and references, see Table S1.
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9
MA et al.
Overall, SEM explained 73%, 70%, 34% and 35% of alpha diver-
sity for GH, DH, YL and SH, respectively. It also explained 38%, 39%,
79% and 62% of beta diversity for these lakes (Figure 5). For lake
GH, nutrient factors were the most significant explanatory variables
for both alpha and beta diversity (0.53**, p < 0.01; −0.33*, p < 0.05).
Temporal environmental variation had direct effects on both alpha
and beta diversity (−0.44**, p < 0.01; 0.43*, p < 0.05), while climate
factors had a weak impact. In lake DH, nutrient factors explained
the largest proportion of alpha diversit y (0.31***, p < 0.001), while
climate factors had a weak and non- significant relationship with
both alpha and beta diversity. Temporal environmental variation ac-
counted for a large proportion of alpha diversit y (−0.30**, p < 0.01).
For lake YL, climate factors accounted for the largest proportion of
beta diversity (0.57*, p < 0.05), whereas nutrient factors contributed
minimally to both alpha and beta diversity. Temporal environmen-
tal variation had insignificant effects on both diversity components.
In lake SH, climate factors significantly contributed to alpha diver-
sity (0.57**, p < 0.01), while nutrient factors had a weak ef fect on
both alpha and beta diversity. Temporal environmental variation
accounted for a small but significant proportion of beta diversit y
(−0.14***, p < 0.001). Detailed information about models is shown in
Tables S3 and S4.
4 | DISCUSSION
4.1 | Temporal variation of communities across the
past 200 years
Lakes worldwide have experienced unprecedented climate warm-
ing and intensive anthropogenic pressures. Even in some remote
lakes without direct human impact, aquatic community composition
and diversity have been influenced by human activities (Grabherr
et al., 2010 ; Hotaling et al., 2017). Our result s revealed a notable
transition in algal community composition since the Anthropocene,
which confirmed sensitive responses for aquatic communities in
remote lakes. These transitions may relate to the history of envi-
ronmental changes and regional human activities. Interestingly, GH
and DH, which are loc ated in densely populated areas with intensive
human activities, had earlier transformation than YL and SH, which
have more natural environments. It is reasonable to consider that
GH and DH have experienced prolonged external pressures due to
their earlier socio- economic development in surrounding areas. This
could also be shown by black carbon records which represent re-
gional human activities (Han et al., 2010; Ji et al., 2023). Compared
to Northwest China, North China underwent earlier socio- economic
and industrial development. Moreover, in Northwest China, socio-
economic activities are primarily concentrated around provincial
capitals and their outskirts, while alpine and desert areas are un-
derdeveloped. Therefore, GH and DH may be vulnerable to external
changes by atmospheric deposition and water cycle.
The four lakes shared some species but showed different tem-
poral variations. For inst ance, in GH, Synechococcus and Cyanobium
were dominant species before the 1960s. However, after the 1960s,
Cyclotella increased to replace Synechococcus as a dominant species.
DH was dominated by Synechococcus, while the gradual increase
of Synechococcus since the 1920s had an opposite trend compared
with GH. In YL , Picocystis and Dunaliella were the dominant species
before the 1970s, and Synechococcus increased significantly after
the 1970s. Synechococcus are widely recognized as a pivotal genus
of cyanobacteria, which could distribute ubiquitously across diverse
environments, ranging from marine to freshwater systems and some
extreme habitats (Callieri, 2017; Domaizon et al., 2013; Huisman
et al., 2018). In addition to Synechococcus, Choricystis and Cyanobium
could also be observed in the four study lakes. The presence of these
species in various ecosystems suggests their strong environmen-
tal adaptation and resilience (Botkin et al., 2007; Doré et al., 2023;
Farrant et al., 2016). Such community variation also reveals that eco-
sy ste ms wi t h differ ent ge ograp hic al lo cat ion s may pos ses s si mil ar en -
vironmental conditions, and environmental filtering dominates over
FIGURE 5 Structural equation
modelling result s for four lakes (a–d),
illustrating the effects of dif ferent
factors on algal diversit y. Pink and blue
arrows indicate positive and negative
path coefficients. Path widths are scaled
propor tionally to path coefficients.
Numbers on arrows represent significant
standardized path coefficients. Asterisks
indicate the statistical significance
(***p < 0.001, **p < 0.01, *p < 0.05).
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10
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MA et a l.
dispersal processes in their communit y assembly processes, thus
leading to similar species co- occurrence patterns (Wang et al., 2016).
4.2 | Diversity patterns and influencing factors
Although community composition exhibited different patterns of
variation among the study lakes, their diversity patterns exhibited
remarkable consistency in the Anthropocene. Firstly, we obser ved
a significant increase in alpha diversity at different periods. These
lakes, characterized by cold temperature and low productivity all
experienced a gradual rise in temperature and nutrient levels over
the last 100 years, while precipitation had variable patterns. In GH,
DH and YL, moisture records all showed a gradual decrease after the
1960s, while SH exhibited a gradually wetter trend after the 1960s.
This is consistent with previous studies showing that the climate in
North China has become more arid, whereas Northwest China has
become wetter (Gao et al., 2012). Climate warming may enhance
lake productivity through increasing water temperature and produc-
tivi ty in the catchmen t, and wa rm ing may possibl y re sult in eut rophi-
cation (Soininen et al., 2015; Wan et al., 2022; Wang et al., 2016).
The enriched resources could facilitate diversity and biomass pro-
duction, which may relate to community adaptation and niche differ-
entiation (Brucet et al., 2013). Increasing resources and productivity
coul d sup por t mor e spe cie s and als o may lead to hi ghe r niche dime n-
sionalit y so that the species complement each other in niche space
(Hautier et al., 2014). Conversely, lakes in southern China, initially in
a relatively temperate state, have experienced significant impacts
from climate warming and human activities. This may lead to eu-
trophication and lower alpha diversity (Qin et al., 2013, 2021).
In contrast to increasing alpha diversity, our results revealed a sig-
nificant decline in temporal beta diversity. Algal communities became
more homogeneo us ove r tim e, as shown by the de crease in sam ple dis-
persion between different groups in the NMDS and lower TBI values.
This trend aligns with the decreased temporal environmental variation
of lakes. As environmental and habitat heterogeneity play a crucial role
in maintaining beta diversity (Veech & Crist, 20 07), the loss of beta di-
versity may be strongly influenced by environmental filtering (Zhang
et al., 2022), associated with climate change and human activities (Keil
et al., 2012). In these lakes, decreased temporal environmental variation
may account for the loss of temporal beta diversity. Similarly, research
conducted on 10 alpine lakes in the Alps demonstrated that eutrophi-
cation led to a widespread community homogenization, accompanied
by more similar environment al conditions (Monchamp et al., 2018).
4.3 | Different contributions of influencing factors
on diversity patterns
As community ecology aims to understand what factors determine
community composition and diversity at different spatiotemporal
scales, such fac tors can be effectively revealed by SEM (Arhondit sis
et al., 20 06; Fan et al., 2016). We us e d SEM to ident i f y the co n t ribu tio n s
of climate and nutrient factors on algal diversit y. Our results revealed
that opposing patterns of different diversity components could most
likely be explained by a milder environment and the loss of temporal
environmental variation. Climate and nutrient factors accounted for
major contributions to alpha diversity in four lakes, highlighting their
strong relationship with rising alpha diversity. In comparison, temporal
environmental variation played a significant role in shaping temporal
beta diversity in GH and SH, indicating the robust relationship be-
tween environment al variation and beta diversity in these lakes.
Moreover, SEM models showed that mechanisms varied depend-
ing on lake conditions and how lakes were af fected by human activ-
ities. In the more human- impacted lakes GH and DH, nutrients had
more significant influences on diversity. In contrast, diversity in the
more pristine lakes YL and SH were controlled more by climate fac-
tors. Although differences among lakes were relatively modest, we
proposed a potential underlying explanation for the reason why the
degree of human influences may explain such findings. GH is located
in Ningwu Count y, Shanxi Province, which is densely populated in
North China. DH is located in Liangcheng County, Inner Mongolia,
a densely populated and economically developed area. These two
lakes are relatively strongly affec ted by human disturbance and thus
may be more susceptible to remarkable fluctuations in nutrient levels
in the Anthropocene. In contrast, YL is located in Alxa League, Inner
Mongolia, in the interior of deserts with enclosed hydrogeological
conditions. Moreover, SH is situated in Burqin County, Xinjiang,
being a remote and pristine alpine lake, characterized by minimal
human presence and natural ecological environment. Thus, YL and
SH are far from human disturbance, so more strongly controlled by
climate factors with lower temporal environmental variation.
5 | CONCLUSIONS
Overall, this study investigated sediment DNA from four remote
lakes in northern China to reconstruct the diversity patterns of algal
communities over the past two centuries. By integrating climate and
environmental records, we explored influencing factors for diversity
patterns. Our main findings are as follows:
1. Alpha diversity of algal communities increased rapidly in the
Anthropocene, which may relate to warming and eutrophication.
Conversely, the overall decline in temporal beta diversity can
be attributed to decreased temporal environmental variation,
which may lead to the loss of dissimilarity among communities.
2. Diversity patterns of lakes that suffer higher human impact are
mo r e sig n ifi c a ntl y in f lue n ced by nutr ien t fac tor s . In co n tras t , lake s
with more natural environment s are mainly controlled by climate
variables. This is because human activities could exert strong
pressures on nutrient fluctuations and environmental variation,
which are known to be important factors for algal communities.
DNA from lake sed iment s is a powe rful tool to provide a co mpre-
hensive perspective of aquatic diversity. In remote lake ecosystems,
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11
MA et al.
algal communities may face more complex threats under global
climate change and intensified human activities. Warming and eu-
trophication may shape such lakes, which have been originally cold
and oligotrophic acting as a refuge for alpha diversity. Moreover, de-
creased temporal environmental variation also may pose a threat to
temporal beta diversity. When external pressures exceed the self-
regulat ing capacity of the co mmunity, the loss of key species and th e
occurrence of new spe cie s may lead to the disrup tion of biodiversity,
and trigger further ecological responses, thus affecting ecosystem
function. For future studies, sediment DNA may provide new in-
sights for diversit y patterns and mechanisms at different scales.
AUTHOR CONTRIBUTIONS
Conceptualization: JC, RM, JS and AZ. Developing methods: RM, JS,
JC and AZ. Conducting the research: RM, JS, JC, AZ, PJ, TJ and RMD.
Data analysis: RM, JS, RMD and TJ. Data interpretation: RM, JS, JC
and PJ. Preparation of figures and tables: RM, JS and PJ. Writing: RM,
JS, JC, AZ, PJ, TJ and RMD.
ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation
of China (41822102), the 111 Project (BP0618001) and the China
Scholarship Council. We thank Guoqiang Ding, Ruijin Chen and Shuo
Wang for their help with field sampling and lab work, and we thank
Caio Graco Roza, Javier Pérez and Wenqian Zhao for technical as-
sistance and analysis.
FUNDING INFORMATION
The funding resources were supported by the National Natural
Science Foundation of China (41822102), the Ministry of Science
and Technolog y of the People's Republic of China (BP0618001) and
the China Scholarship Council.
CONFLICT OF INTEREST STATEMENT
The authors declare that they have no conflicts of interest .
DATA AVA ILAB ILITY STATE MEN T
The data that we have used in this research are openly available at
h t t p s : / / z e n o d o . o r g / r e c o r d s / 1 2 6 4 1 8 6 4 .
ETHICS STATEMENT
We declare that we obey the ethics and integrit y policies of
Freshwater Biology. The data that we have used in this research are
openly available at h t t p s : / / d o i . o r g / 1 0 . 5 2 8 1 / z e n o d o . 1 0 8 6 2 6 1 4 .
ORCID
Rui Ma https://orcid.org/0000-0002-9125-2566
Janne Soininen https://orcid.org/0000-0002-8583-3137
Aifeng Zhou https://orcid.org/0000-0001-8349-8585
Panpan Ji https://orcid.org/0000-0003-4647-3703
Tongzhuo Jiang https://orcid.org/0009-0001-0125-5019
Ramiro Martín- Devasa https://orcid.org/0000-0002-3890-5003
Jianhui Chen https://orcid.org/0000-0002-6768-1619
REFERENCES
Akaike, H. (1974). A new look at the statistical identific ation model. IEEE
Transactions on Automatic Control, 19, 716–723. h t t p s : / / d o i . o r g / 1 0 .
1 0 0 7 / 9 7 8 - 1 - 4 6 1 2 - 1 6 9 4 - 0 _ 1 6
Appleby, P., Oldfield, F., Thompson, R., Huttunen, P., & Tolonen, K. (1979).
210pb dating of annually laminated lake sediments from Finland.
Nature, 280(5717), 53–55. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / 2 8 0 0 5 3 a 0
Apple by, P. G. (2001). Chronostr atigr aphic techniques in recent sediment s.
In W. M . Last & J. P. Smol (Eds.), Tracking environmental change using
lake sediments: Basin analysis, coring, and chronological techniques (pp.
171–203). Springer. h t t p s : // d o i . o r g / 1 0 . 1 0 0 7 / 0 - 3 0 6 - 4 7 6 6 9 - x _ 9
Arhonditsis, G., Stow, C., Steinberg, L ., Kenney, M., Lat hrop, R., McBride,
S., & Reckhow, K. H. (2006). Exploring ecologic al patterns with
struc tural equation modeling and Bayesian analysis. Ecological
Modelling, 192, 385–409.
Botkin, D. B., Saxe, H., A raújo, M. B., Betts, R., Bradshaw, R. H., Cedhagen,
T., Che sson, P., Dawson, T. P., Etterson , J. R., Faith, D., Ferrier, S.,
Guisan, A., Hansen, A., Hilbert, D. W., Loehle, C., Margule s, C.,
New, M. G., Sobel, M. J., & Stockwell, D. R. (2007). Forecasting the
effec ts of global warming on biodiversity. Bioscience, 57(3), 227–
236. h t t p s : / / d o i . o r g / 1 0 . 1 6 4 1 / B 5 7 0 3 0 6
Broennimann, O., Fit zpatrick, M. C., Pearman, P. B., Petitpierre, B.,
Pellissier, L., Yoccoz, N. G., Thuiller, W., Fortin, M.- J., Randin, C.,
Zimmermann, N . E., Graham, C. H., & Guisan, A . (2012). Measuring
ecological niche overlap from occurrence and spatial environmen-
tal data. Global Ecology and Biogeography, 21(4), 481–497. h tt p s : //
d o i . o r g / 1 0 . 1 1 1 1 / j . 1 4 6 6 - 8 2 3 8 . 2 0 1 1 . 0 0 6 9 8 . x
Brucet , S., Pédron, S., Mehner, T., Lauridsen, T. L., Argillier, C., Winfield, I.
J., Volta, P., Emmrich, M., Hesthagen, T., Holmgren, K., Benejam, L.,
Kelly, F., Kr ause, T., Palm, A ., R ask, M., & Jeppesen, E. (2013). Fish
diversity in European lakes: Geographical factors dominate over
anthropogenic pressures. Freshwater Biology, 58(9), 1779–1793 .
h t t p s : / / d o i . o r g / 1 0 . 1 1 1 1 / f w b . 1 2 1 6 7
Callieri, C. (2017). Synechococcus plasticity under environmental
changes. FEMS Microbiology Letters, 364(23), fnx229. h t t p s : / / d o i .
o r g / 1 0 . 1 0 9 3 / f e m s l e / f n x 2 2 9
Capo, E., Debroas, D., Arnaud, F., Guillemot, T., Bichet, V., Millet, L., Gauthier,
E., Massa, C., Develle, A.- L., Pignol, C., Lejzerowicz, F., & Domaizon,
I. (2016). Long- term dynamics in microbial eukaryotes communities:
A palaeolimnological view based on sedimentary DNA. Molecular
Ecology, 25(23), 5925–5943. https:// doi. org/ 10. 1111/ mec. 13893
Carey, C. C., Ibelings, B. W., Hoffmann, E. P., Hamilton, D. P., & Brookes,
J. D. (2012). Eco- physiological adaptations that favour freshwater
cyanobacteria in a changing climate. Water Research, 46 (5), 1394–
14 07. h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / J . W A T R E S . 2 0 1 1 . 1 2 . 0 1 6
Carpenter, S. R ., & Lo dge, D. M . (1986). Effects of submersed macro-
phytes on ecosystem processes. Aquatic Botany, 26, 341–370.
h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / 0 3 0 4 - 3 7 7 0 ( 8 6 ) 9 0 0 3 1 - 8
Catalan, J., Pla- Rabés, S., García, J., & Camarero, L. (2014). Air
temperature- driven CO2 consumption by rock weathering at
short timescales: Evidence from a Holocene lake sediment record.
Geochimica et Cosmochimica Acta, 136, 67–79. h t t p s : / / d o i . o r g / 1 0 .
1 0 1 6 / j . g c a . 2 0 1 4 . 0 4 . 0 0 5
Chen, F., Chen, S., Zhang, X., Chen, J., & Liu, J. (2020). A sian dust- storm
activity dominated by Chinese dynasty changes since 2000 BP.
Nature Communications, 11, 992. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 4 6 7 -
0 2 0 - 1 4 7 6 5 - 4
Chen, F., Xu, Q., Chen, J., Birks, H. J. B., Liu, J., Zhang, S., Jin, L., An, C .,
Telford, R . J., Cao, X ., Wang, Z., Zhang, X., Selvaraj, K., Lu, H., Li, Y.,
Zheng, Z., Wang, H., Zhou, A., Dong, G., … Rao, Z. (2015). East Asian
summer monsoon precipitation variability since the last deglaciation.
Scientific Reports, 5(1), 1–11. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s r e p 1 1 1 8 6
Cook, S. C ., Housley, L ., Back, J. A., & King, R. S. (2018). Freshwater eu-
trophication drives sharp reductions in tempor al beta diversity.
Ecology, 99(1), 47–56. h t t p s : / / d o i . o r g / 1 0 . 1 0 0 2 / e c y . 2 0 6 9
13652427, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/fwb.14349 by RUI MA - Lanzhou University , Wiley Online Library on [07/10/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
12
|
MA et a l.
Domaizon, I., Savichtcheva, O., Debroas, D., Arnaud, F., Villar, C., Pignol,
C., Alric , B., & Perga, M. E. (2013). DNA from lake sediments reveals
the long- term dynamic s and diversit y of Synechococcus assem-
blages. Biogeosciences, 10 (6), 3817–3838. h t t p s : / / d o i . o r g / 1 0 . 5 1 9 4 /
b g d - 1 0 - 2 5 1 5 - 2 0 1 3
Doré, H., Guyet, U., Leconte, J., Farrant , G. K ., Alric, B., Ratin, M.,
Ostrowski, M., Ferrieux , M., Brillet- Guéguen, L., Hoebeke, M.,
Siltanen, J., Gildas, L . C., Corre, E., Wincker, P., Scanlan, D. J.,
Eveillard, D., Partensky, F., & Garczarek, L. (2023). Differential
global distribution of marine picocyanobacteria gene clusters re-
veals distinct niche- related adaptive strategies. The ISME Journal,
17(5), 720–732. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 3 9 6 - 0 2 3 - 0 1 3 8 6 - 0
Erdman, C., & Emerson, J. W. (2008). A fast Bayesian change point anal-
ysis for the segmentation of microarray data. Bioinformatics, 24(19),
2143–2148 . h t t p s : / / d o i . o r g / 1 0 . 1 0 9 3 / b i o i n f o r m a t i c s / b t n 4 0 4
Fan, Y., Chen, J., Shir key, G., John, R., Wu, S. R., Park, H., & Shao, C.
(2016). Applications of structural equation modeling (SEM) in eco-
logical studies: An updated review. Ecological Processes, 5, 1–12.
h t t p s : / / d o i . o r g / 1 0 . 1 1 8 6 / s 1 3 7 1 7 - 0 1 6 - 0 0 6 3 - 3
Farrant, G. K., Doré, H., Cornejo- Castillo, F. M., Partensky, F., Ratin, M.,
Ostrowski, M., Pitt, F. D., Wincker, P., Scanlan, D. J., Iudicone, D.,
Acinas, S. G., & Garczarek, L. (2016). Delineating ecologically sig-
nificant taxonomic units from global patterns of marine picocya-
nobacteria. Proceedings of the National Academy of Sciences of the
United States of A merica, 113(24), E3365–E3374. https://doi.org/10.
1073/ pnas.1524865113
Gao, X., Shi, Y., Zhang, D., & Giorgi, F. (2012). Climate change in China in
the 21st centur y as simulated by a high resolution regional climate
model. Chinese Science Bulletin, 57, 1188–1195. h t t p s : / / d o i . o r g / 1 0 .
1 0 0 7 / s 1 1 4 3 4 - 0 1 1 - 4 9 3 5 - 8
Giguet- Covex, C., Pansu , J., Arnaud, F., Rey, P. J., Griggo, C., Gielly, L .,
Domaizon , I., Coissac, E., David, F., Choler, P., Poulenard, J., &
Taberlet, P. (2014). Long livestock farming histor y and human
landscape shaping revealed by lake sediment DNA. Nature
Communications, 5(1), 3211. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / n c o m m s 4 2 1 1
Gra bhe rr, G., Gott fri ed, M., & Pauli, H. (2010). Climat e cha nge impa ct s in
alpine environments. Geography Compass, 4(8) , 1133–1153. h t tps://
d o i . o r g / 1 0 . 1 1 1 1 / j . 1 7 4 9 - 8 1 9 8 . 2 0 1 0 . 0 0 3 5 6 . x
Grace, J. B. (2006). Structural equation modeling and natural sys tems.
Cambridge University Press.
Han, X., Schubert, C. J., Fiskal, A., Dubois, N., & Lever, M. A. (2020).
Eutrophication as a driver of microbial community structure in lake
sediments. Environmental Microbiology, 22(8), 344 6–3462. h t tps://
d o i . o r g / 1 0 . 1 1 1 1 / 1 4 6 2 - 2 9 2 0 . 1 5 1 1 5
Han, Y., Cao, J., Jin, Z., Liu, S., & An, Z. (2010). Comparison of char and
soot variations in sediments from lakes Daihai and Taihu. Quaternary
Sciences, 30(3), 550–558.
Hautier, Y., Seabloom, E. W., Borer, E. T., Adler, P. B., Harpole, W. S.,
Hillebr and, H., Lind, E. M., MacDougall, A. S., Stevens, C. J., Bakker,
J. D., Buckley, Y. M., Chu, C., Collins, S. L., Daleo, P., Damschen , E.
I., Davies, K. F., Fay, P. A., Firn, J., Gruner, D. S., … Hector, A. (2014).
Eutrophication weakens stabilizing effects of diversity in natur al
grasslands. Nature, 508(7497), 521–525. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 /
n a t u r e 1 3 0 1 4
Hooper, D. U., Chapin, F. S., III, Ewel, J. J., Hector, A., Inchausti, P., Lavorel,
S., Lawton, J. H ., Lodge, D. M., Loreau, M., Naeem, S., Schmid , B.,
Setälä, H., Symstad, A. J., Vandermeer, J., & Wardle, D. (2005).
Effects of biodiversity on ecosystem functioning: A consensus of
current knowledge. Ecological Monographs, 75(1), 3–35. h t t p s : / / d o i .
o r g / 1 0 . 1 8 9 0 / 0 4 - 0 9 2 2
Hotaling, S., Finn, D. S., Joseph Giersch, J., Weisrock, D. W., & Jacobsen,
D. (2017). Climate change and alpine stream biolog y: Progress,
challenges, and opportunities for the future. Biological Reviews,
92(4), 2024–2045. h t t p s : / / d o i . o r g / 1 0 . 1 1 1 1 / b r v . 1 2 3 1 9
Hu, L. T., & Bentler, P. M. (1999). Cutof f criteria for fit indexes in co-
variance structure analysis: Conventional criteria versus new
alternatives. Structural Equation Modeling: A Multidisciplinary
Journal, 6(1), 1–55. h t t p s : / / d o i . o r g / 1 0 . 1 0 8 0 / 1 0 7 0 5 5 1 9 9 0 9 5 4 0 1 1 8
Huisman, J., Codd, G. A., Paerl, H. W., Ibelings, B. W., Verspagen, J.
M., & Visser, P. M. (2018). Cyanobacterial blooms. Nature Reviews
Microbiology, 16 (8), 471–483. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 7
9- 018- 0040- 1
Jenny, J. P., Anneville, O., Arnaud, F., Baulaz, Y., & Weyhenmeyer, G. A.
(2020). Scientists' warning to humanity: Rapid degradation of the
world's large lakes. Journal of Great Lakes Research, 46 (4), 686–702.
h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j . j g l r . 2 0 2 0 . 0 5 . 0 0 6
Ji, P., Chen, J., Chen, R., Liu, J., Yu, C., & Chen , F. (2024). Nitrogen and
phosphorus trends in lake sediments of China may diverge. Nature
Communications, 15(1), 2644. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 4 6 7 -
0 2 4 - 4 6 9 6 8 - 4
Ji, P., Chen, J., Zhou, A ., Chen, R ., Ding, G., Wang, H., Chen, S., & Chen, F.
(2023). Anthropogenic atmospheric deposition caused the nutrient
and toxic metal enrichment of the enclosed lakes in North China.
Journal of Hazardous Materials, 448(130), 972. h t t p s : / / d o i . o r g / 1 0 .
1 0 1 6 / j . j h a z m a t . 2 0 2 3 . 1 3 0 9 7 2
Jia, W., Liu, X., Stoof- Leichsenring, K. R., Liu, S., Li, K., & Her zschuh, U.
(2021). Preservation of sedimentar y plant DNA is related to lake
water chemistr y. Environmental DNA, 4(1), 6–439. h t t p s : / / d o i . o r g /
10. 1002/ edn3. 259
Jiao, L ., Ma, L., Zhang, T., & Wang, S. (2021). Changes of mean minimum
temperature in June–July since 1798 in central Alt ay Mountain re-
corded by tree rings. Acta Ecologica Sinica, 41, 1944–1958. h t tps://
d o i . o r g / 1 0 . 5 8 4 6 / s t x b 2 0 1 9 1 2 3 1 2 8 3 5
Jin, Z., Cao, J., Wu, J., & Wang, S. (2006). A Rb/Sr. record of catch-
ment weathering response to Holocene climate change in Inner
Mongolia. Earth Surface Processes and Landforms, 31(3), 285–291.
h t t p s : / / d o i . o r g / 1 0 . 1 0 0 2 / e s p . 1 2 4 3
Ke i l , P., S chw eig er, O. , Küh n , I. , Ku nin , W. E. , Kuu ssa ari, M . , Set tele, J., H e nle ,
K., Brotons, L., Pe’er, G., Lengyel, S., Moustakas, A ., & Steinicke, H.
(2012). Patter ns of beta diversity in Europe: The role of climate,
land cover and distance across sc ales. Journal of Biogeography, 39(8),
1473–1486. h t t p s : / / d o i . o r g / 1 0 . 1 1 11 / j . 1 3 6 5 - 2 6 9 9 . 2 0 1 2 . 0 2 7 0 1 . x
Legendre, P. (2019). A temporal beta- diversit y index to identify sites that
have changed in exceptional ways in space–time surveys. Ecology
and Evolutio n, 9(6), 3500–3514. h t t p s : / / d o i . o r g / 1 0 . 1 0 0 2 / e c e 3 . 4 9 8 4
Li, Q., Zhao, J., Xie, M., Dong, H., Darin, A ., Darin, F., Rakshun, Y., Su, Y.,
Wang, S., Sun, Q., & Chu, G. (2022). Distribution and carbon isoto-
pic composition of long- chain leaf wax n- alkanes from Holocene
Lake sediment s in the Altai Mountains. Quaternary International,
625, 29–37. h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j . q u a i n t . 2 0 2 2 . 0 4 . 0 0 8
Liu, J., Rühland, K. M., Chen, J., Xu, Y., Chen, S., Chen, Q., … Smol, J. P.
(2017). Aerosol- weakened summer monsoons decrease lake fertil-
ization on the Chinese loess plateau. Nature Climate Change, 7(3),
190–194. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / N C L I M A T E 3 2 2 0
Liu, S., Deng, C., Xiao, J., Li, J., Paterson, G. A., Chang, L., Yi, L., Qin, H.,
Pan, Y., & Zhu, R. (2015). Insolation driven biomagnetic response to
the Holocene warm period in semi- arid East Asia. Science Reporter,
5, 8001. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s r e p 0 8 0 0 1
Ma, R., Duan, H., Hu, C., Feng, X., Li, A., Ju, W., Jiang, J., & Yang, G.
(2010). A half- century of changes in China's lakes: Global war ming
or human influence? Geophysical Research Letters, 37(24), L24106.
h t t p s : / / d o i . o r g / 1 0 . 1 0 2 9 / 2 0 1 0 G L 0 4 5 5 1 4
Magurran, A. E., Dornelas, M., Moyes, F., & Henderson, P. A. (2019).
Temporal β diversity—A macroecological perspective. Global
Ecology and Biogeography, 28(12), 1949–1960. h t t p s : / / d o i . o r g / 1 0 .
1111/ geb. 13 026
Menezes , R. F., Svenning, J.- C., Fu, H., De Meester, L ., Lauridsen, T. L.,
Søndergaard, M., Conde- Porcuna, J. M., & Jeppesen, E. (2023).
Fish Beta diversit y patterns across environmental gradients in 63
European Shallow Lakes: Ef fect s of turbidity, nutrient enrichment,
and exotic species. Watermark, 15(10), 1831. h t t p s : / / d o i . o r g / 1 0 .
33 90 / w 1510 183 1
13652427, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/fwb.14349 by RUI MA - Lanzhou University , Wiley Online Library on [07/10/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
|
13
MA et al.
Monchamp, M. E., Spaak, P., Domaizon, I., Dubois , N., Bouffard, D., &
Pomati, F. (2018). Homogenization of lake cyanobacterial commu-
nities over a centur y of climate change and eutrophication. Nature
Ecology & Evolution, 2(2), 317–324. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 5
9 - 0 1 7 - 0 4 0 7 - 0
Ni, Z., Zhang, E., Meng, X., Sun, W., & Ning, D. (2023). Chironomid- based
reconstruction of 500- year water- level changes in Daihai Lake,
northern China. Catena, 227(107), 122. h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j .
catena. 2023. 107122
Nikodemova, M., Holzhausen, E. A., Deblois, C. L., Barnet, J. H., Peppard,
P. E., Suen, G., & Malecki, K. M. (2023). The effect of low- abundance
OTU filtering methods on the reliability and variability of microbial
composition assessed by 16S rRNA amplicon sequencing. Frontiers
in Cellular and Infection Microbiology, 13, 1165295. h t t p s : / / d o i . o r g /
1 0 . 3 3 8 9 / f c i m b . 2 0 2 3 . 1 1 6 5 2 9 5
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'Hara,
R., Simpson, G. L., Sólymos, P., Stevens, M. H. H., & Wagner, H.
(2015). Vegan: Community Ecolog y Package. R package ver. 2. 2–1.
h t t p s : / / C R A N . R - p r o j e c t . o r g / p a c k a g e = v e g a n
Pawlowski, J., Kelly- Quinn, M., Altermat t, F., Apothéloz- Perret- Gentil,
L., & Domaizon, I. (2018). The future of biotic indices in the ecog-
enomic era: Integrating (e)DNA metabarcoding in biological assess-
ment of a quatic ecosystems. Science of the Total Environment, 637,
1295–1310. h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j . s c i t o t e n v . 2 0 1 8 . 0 5 . 0 0 2
Pedersen, M. W., Ginolhac, A., Orlando, L., Olsen, J., Andersen, K.,
Ho lm , J., Fu nder, S. , Wille r sl ev, E., & Kj ær, K. H. (2013). A co mpa r a-
tive study of ancient environmental DNA to pollen and macrofos-
sils from lake sediment s reveals t axonomic overlap and additional
plant taxa . Quaternary Science Reviews, 75 (1), 161–16 8. h t t p s : / / d o i .
o r g / 1 0 . 1 0 1 6 / j . q u a s c i r e v . 2 0 1 3 . 0 6 . 0 0 6
Penning ton, W., Tutin, T., Cambray, R ., & Fisher, E. (1973). Obser vations
on lake sediments using fallout 137 Cs as a tracer. Nature, 242(5396),
324–326. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / 2 4 2 3 2 4 a 0
Qin, B., Deng, J., Shi, K., Wang, J., Brookes, J., Zhou, J., Zhang, Y., Zhu, G.,
Paerl, H. W., & Wu, L. (2021). Ex treme climate anomalies enhanc-
ing cyanobacterial blooms in eutrophic Lake Taihu, China. Water
Resources Research, 57(7), e2020WR029371. h t t p s : / / d o i . o r g / 1 0 .
1029/ 2020W R029371
Qin, B., Gao, G., Zhu, G., Zhang, Y., Song , Y., Tang, X., Xu, H., & Deng, J.
(2013). Lake eutrophication and its ecosystem response. Chinese
Science Bulletin, 58(9), 961–970. h t t p s : / / d o i . o r g / 1 0 . 1 3 6 0 / c s b 2 0
13- 58- 10- 855
Reid, A. J., Carlson, A. K., Creed, I. F., Eliason, E. J., Gell, P. A., Johnson,
P. T., Kidd, K. A ., Maccormack, T. J., Olden, J. D., Ormerod, S. J.,
Smol, J. P., Taylor, W. W., Tockner, K., Vermaire, J. C., Dudgeon, D.,
& Cooke, S. (2019). Emerging threats and persistent conservation
challenges for freshwater biodiver sity. Biological Reviews, 94 (3),
849–873. h t t p s : / / d o i . o r g / 1 0 . 1 1 1 1 / b r v . 1 2 4 8 0
Saros, J. E., Stone, J. R., Peder son, G. T., & Slemmons, K. E. H. (2012).
Climate- induced changes in lake ecosystem structure inferred
from coupled neo- and paleoecological approaches. Ecology, 93(10),
2155–2164. h t t p s : / / d o i . o r g / 1 0 . 2 3 0 7 / 4 1 7 3 9 2 8 0
Sax, D. F., & Gaines, S. D. (2003). Species diversit y: From global decreases
to local increases. Trends in Ecology & Evolution, 18(11), 561–566.
h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / S 0 1 6 9 - 5 3 4 7 ( 0 3 ) 0 0 2 2 4 - 6
Shen, Z., Zhang, Z., Chen, J., Chen, L., Pang, X., Chen, R., Liu, J., & Chen,
S. (2022). Investigation of modern n- alkanes in Daihai Lake Basin,
northern China: Implications for the interpretation of paleoclimate
research. Frontiers in Earth Science, 10(915), 500. h t t p s : / / d o i . o r g /
1 0 . 3 3 8 9 / f e a r t . 2 0 2 2 . 9 1 5 5 0 0
Sher wood, A. R., Ch an, Y. L., & Presting , G. G. (2008). Application of uni-
versally amplifying plastid primers to environmental sampling of a
stream periphyton community. Molecular Ecology Resources, 8(5),
1011–1014. h t t p s : / / d o i . o r g / 1 0 . 1 1 1 1 / j . 1 7 5 5 - 0 9 9 8 . 2 0 0 8 . 0 2 1 3 8 . x
Sherwood, A. R ., & Presting, G. G. (2010). Universal primers amplify a
23S rDNA plastid marker in eukaryotic algae and cyanobacteria.
Journal of Phycolog y, 43(3), 605–608. h t t p s : / / d o i . o r g / 1 0 . 1 1 1 1 / j .
1 5 2 9 - 8 8 1 7 . 2 0 0 7 . 0 0 3 4 1 . x
Smith, V. H. (1982). The nitrogen and phosphorus dependence of algal
biomass in lakes: An empirical and theoretical analysis 1. Limnology
and Oceanography, 27(6), 1101–1111. h t t p s : / / d o i . o r g / 1 0 . 4 3 1 9 / l o .
1 9 8 2 . 2 7 . 6 . 1 1 0 1
Smith, V. H. (200 3). Eutrophic ation of freshwater and coastal marine
ecosystems a global problem. Environmental Science and Pollution
Research, 10, 126–139. h t t p s : / / d o i . o r g / 1 0 . 1 0 6 5 / e s p r 2 0 0 2 . 1 2 . 1 4 2
Smol, J. P., Wolfe, A. P., Birks, H. J. B., Douglas, M. S. V., Jones, V. J.,
Korhola, A., Pienitz, R., Rühland, K., Sorvari, S., Antoniades, D.,
Brooks, S. J., Fallu, M. A., Hughes, M., Keatley, B. E., Laing, T.
E., Michelutti, N., Nazarova, L., Nyman, M., Paterson, A. M., …
Weckström, J. (2005). Climate- driven regime shift s in the biologi-
cal communities of arctic lakes. Proce edings of the Nationa l Academy
of Science s of the United States of America, 102(12), 4397–4402.
h t t p s : / / d o i . o r g / 1 0 . 1 0 7 3 / p n a s . 0 5 0 0 2 4 5 1 0 2
Soininen, J., Bartels, P., Heino, J., Luoto, M., & Hillebrand, H. (2015).
Toward more integrated ecosystem research in aquatic and terres-
trial environments. Bioscience, 65(2), 174–182. h t t p s : / / d o i . o r g / 1 0 .
1093/ biosci/ biu216
Steffen , W., Crutzen, P. J., & McNeill, J. R. (2007). The Anthropocene: Are
humans now overwhelming the great forces of nature. AMBIO: A
Journal of the Human Environment, 36(8), 614–621. h t t p s : / / d o i . o r g /
1 0 . 1 5 2 5 / 9 7 8 0 5 2 0 9 6 4 2 9 7 - 0 5 1
Stirling, G., & Wilsey, B. (2001). Empirical relationships between spe-
cies richness, evenness, and proportional diversity. The American
Naturalist, 158(3), 286–299. h t t p s : / / d o i . o r g / 1 0 . 1 0 8 6 / 3 2 1 3 1 7
Tang, L., Duan, X., Kong, F., Zhang, F., Zheng, Y., Li, Z., Mei, Y., Zhao,
Y., & Hu, S. (2018). Influences of climate change on area variation
of Qinghai Lake on Qinghai- Tibetan plateau since 1980s. Scientific
Reports, 8(1), 7331. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 9 8 - 0 1 8 - 2 5 6 8 3 - 3
Thukral, A . K. (2017). A review on measurement of alpha diversit y in bi-
olog y. A gricultural Research Journal, 54(1), 1–10. h t t p s : / / d o i . o r g / 1 0 .
5 9 5 8 / 2 3 9 5 - 1 4 6 X . 2 0 1 7 . 0 0 0 0 1 . 1
Tse, T. J., Doig, L. E., Tang, S., Zhang, X., Sun, W., Wiseman , S. B., Feng, C.
X., Liu, H., Giesy, J. P., Hecker, M., & Jones, P. D. (2018). Combining
high- throughput sequencing of sedaDNA and traditional paleo-
limnological techniques to infer historical trends in cyanobacterial
communities. Environmental Science & Technology, 52(12), 684 2–
6853. h t t p s : / / d o i . o r g / 1 0 . 1 0 2 1 / a c s . e s t . 7 b 0 6 3 8 6
Veech, J. A., & Crist, T. O. (2007). Habitat and climate heterogeneity
maintain beta- diversity of birds among landscapes within ecore-
gions. Global Ecology an d Biogeography, 16(5), 650–656. h t t p s : / / d o i .
o r g / 1 0 . 1 1 1 1 / j . 1 4 6 6 - 8 2 3 8 . 2 0 0 7 . 0 0 3 1 5 . x
Wan, N., Zhang, R., Kong, X., Yang, Y., & Ran, Y. (2022). Effect of aquatic
organic matter and global warming on accumulation of PAHs in
lakes, East China. Journal of Geophysical Research: Biogeosciences,
127(11), e2022JG007167. h t t p s : / / d o i . o r g / 1 0 . 1 0 2 9 / 2 0 2 2 J G 0 0 7 1 6 7
Wang , J. , Pan , F., So in ine n, J. , He ino , J. , & She n, J. (20 16). Nut rie nt en ri ch-
ment modifies temperature- biodiversity relationships in large- scale
field experiments. Nature Communications, 7(1), 13960. h t t p s : / / d o i .
o r g / 1 0 . 1 0 3 8 / n c o m m s 1 3 9 6 0
Yan, Z., Wang, Q., Li, Y., Wu, L., Wang, J., Xing, B., Yu, D., Wang, L., &
Liu, C. (2021). Combined effects of warming and nutrient enrich-
ment on water properties, growth, reproductive strategies and
nutrient stoichiometry of Potamogeton crispus. Environmental
and Experimental Botany, 190 (104), 572. h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j .
e n v e x p b o t . 2 0 2 1 . 1 0 4 5 7 2
Yang, J., Ding , Y., Chen, R., & Liu, L . (2003). Variations of precipitation
and evaporation in North China in recent 40 years. Journal of A rid
Land Resources and Environment, 17, 6–11. h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 /
S 0 9 5 5 - 2 2 1 9 ( 0 2 ) 0 0 0 7 3 - 0
Zhang, J., Liu, J., Yuan, Y., Zhou, A., Chen, J., Shen, Z., Chen, S., Zhang,
Z., & Zhang, K. (2021). Pre- industrial cyanobacterial dominance
in Lake moon (NE China) revealed by sedimentar y ancient DNA .
13652427, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/fwb.14349 by RUI MA - Lanzhou University , Wiley Online Library on [07/10/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
14
|
MA et a l.
Quaternary Science Reviews, 261(106), 966. h t t p s : / / d o i . o r g / 1 0 .
1 0 1 6 / j . q u a s c i r e v . 2 0 2 1 . 1 0 6 9 6 6
Zhang, Y., Zhang, L ., Kang, Y., Li, Y., Chen, Z ., Li, R., Tian, C ., Wang, W.,
& Wang, M. (2022). Biotic homogenization increases with human
intervention: Implications for mangrove wetland restoration.
Ecography, 4, ecog.05835. h t t p s : / / d o i . o r g / 1 0 . 1 1 1 1 / e c o g . 0 5 8 3 5
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Jiang, T., Martín- Devasa, R ., & Chen, J. (2024). Temporal
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patterns and influencing factors over the past 200 years.
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