Dianchi Research Articles

Environmental Factors Shaping the Culturable Freshwater Fungi Diversity of Four Lakes in Yunnan Province, China

Our study focused on freshwater fungal diversity, an important aspect in assessing the ecology of aquatic ecosystems. We carefully explored the diversity and influencing factors of culturable fungi across Dianchi Lake, Fuxian Lake, Xingyun Lake, and Yangzonghai Lake in Yunnan Province, China. Through fungi culture, morphological characterization, and ITS sequence analysis, we identified a total of 565 isolates belonging to 405 species across 133 genera. The diversity indices viz. H′, D, and J were evaluated for fungal diversity across the lakes. Interestingly, although diversity indices were highest during summer and at Yangzonghai Lake, no significant differences in fungal community diversity were observed between seasons and regions. Water variables were analyzed and indicated that changes with rapid fluctuations in temperature, pH, and dissolved oxygen likely influence fungal diversity. These findings significantly contribute to our understanding of fungal communities within plateau lake ecosystems, thereby aiding in managing and conserving vital aquatic resources.

Towards the development of a ‘land-river-lake’ two-stage deep learning model for water quality prediction and its application in a large plateau lake

Accurate prediction of water quality that is essential for effective lake management requires a comprehensive understanding of influential factors in the ’land-river-lake’ continuum. We develop a novel deep learning model employing a Long Short-Term Memory (LSTM) model for predicting water quality, and apply it for a large plateau lake, Lake Dianchi. The model comprises two stages: the first establishes a correlation between land and rivers by predicting nutrient loads, incorporating socioeconomic and meteorological features. The second establishes a correlation between feeding rivers and the lake by predicting nutrient concentrations, taking water quality, meteorological conditions, riverine import, and agricultural Non-Point Source (NPS) loads into consideration. Compared to single-stage deep learning models developed using classical methods, the two-stage model reduces Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), indicating enhanced prediction accuracy. Shapley Additive Explanation (SHAP) analysis is further employed to identify key contributing features. Precipitation emerges as the primary feature in the first stage, while in the second stage, disparities in the most contributing features for two zones of the lake are revealed due to dam isolation and water diversion projects. In the main zone, the primary predicting features are temperature and agricultural NPS features, while in the dam-isolated zone, Secchi depth is the key predictor, highlighting spatial–temporal variations in driving features. Our model contributes a novel deep learning framework and valuable insights for water quality management in large lakes, emphasizing the importance of considering the dynamic interplay of features or factors in the ’land-river-lake’ system.

Water conservation for plateau lakeside cities under the new development philosophy

Urban progression influences water conservation by altering surface characteristics and local climate regimes, potentially posing significant risks to water sustainability and ecological integrity. Since 2015, the Chinese government has championed a new development philosophy aimed at promoting harmonious coexistence between human and nature. Based on the green connotation of the new development philosophy, this study simulated the spatial and temporal distribution of water conservation in five plateau lakeside cities (Plateau Lakeside City-PLC: Dianchi Lake Basin-DCB, Fuxian Lake Basin-FXB, Erhai Lake Basin-EHB, Qilu Lake Basin-QLB and Xingyun Lake Basin-XYB), established a comprehensive framework of indicators affecting water conservation, and quantified the changes in the drivers of water conservation. The results indicated that, except for DCB, the spatial distribution of water conservation in PLC exhibits similarity, but overall demonstrates a declining trend over time. The average importance of the factors in the PLC was weighed using Geodetector’s q value. The most critical factors affecting water conservation in PLC included climate change (precipitation: q_PRE=0.66; evaporation: q_ET=0.57) and land cover (q_LC=0.45). In addition, the topography (slope: q_SLOPE=0.39; elevation: q_DEM=0.43), vegetation coverage (q_NDVI=0.30), and soil (plant available water content: q_PAWC=0.38; root restricting layer depth: q_ROOT=0.18) were influential factors followed climate change and land cover. Among the social factors, population density (q_POP=0.37) has a small impact on water conservation, while economic activities and human-related land activities (q_CROP,q_NTL, and q_CITY are all less than 0.1) do not show a significant impact, due to the government’s implementation of the new development philosophy, which balances development and conservation.

Dynamics of the risk of algal blooms induced by surface water temperature in an alpine eutrophic lake under climate warming: Insights from Lake Dianchi

Climate change-induced higher water temperature is increasing the risk of algal blooms (ABs) in eutrophic water bodies. However, it is not well known how and to what extent warming affects ABs risk. In this work, with simple data input (satellite product, lake surface observation, and climate) and method structure (ensemble learning, statistical downscaling, and hierarchical clustering), a systematic lake surface water temperature (LSWT) and ABs risk evaluation system are constructed and applied in a typical eutrophic freshwater lake. The main results are as follows: 1. The warming rate of LSWT and air temperature from 2000 to 2023 reached 0.21 °C/10a and 0.30 °C/10a respectively, resulting in an extended ABs risk in the historical period. 2. The Nash–Sutcliffe efficiency between climate and LSWT reached 0.89, and LSWT showed a significant (p < 0.05) positive correlation with algal density. 3. The escalation of ABs risks mainly spread to spring and autumn in the forthcoming future, the adverse effect of high emission scenarios will evidently emerge in the mid-21st century. Policymakers and catchment managers should be cautious of lake warming and develop countermeasures in advance.

Shallow groundwater table fluctuations weaken nitrogen accumulation in the thin layer vadose zone of cropland around plateau lakes, Southwest China

Excessive accumulation of nitrogen (N) in the soil profile in the intensive agricultural region will seriously threaten groundwater quality and safety. However, the impact of shallow groundwater table (SGWT) fluctuations driven by seasonal variations on the N accumulation characterizations in the soil profiles has not been well quantified, particularly in the regions with thin layer vadose zone. Through in-situ monitoring and simulation experiments, the changes in the SGWT and N accumulation of soil profile in intensive cropland around 7 plateau lakes in Yunnan were studied during the rainy season (RS) and dry season (DS), and the N loss in soil profile of cropland driven by SGWT fluctuations was estimated. The results showed that the SGWT and N accumulation in soil profile of cropland around the plateau lakes had obvious seasonal variation characteristics. The proportion of N storage in different forms in 60–100 cm soil layer in the RS was greater than that in the DS, particularly the proportion of NH4+-N storage was as high as 55 %, while N accumulation in surface soil was obvious in the DS. Compared with the DS, due to the rising SGWT in the RS, the maximum storages of TN and NO3−-N in the 0–100 cm soil layer decreased by17% and 36 %, respectively. The TN loss intensities from the 0–100 cm soil profiles of cropland around Fuxian Lake, Yilong Lake, Qilu Lake, Dianchi Lake, Yangzong Lake, Erhai Lake, and Xingyun Lake were 74, 54, 127, 105, 93, 72 and 207 kg/ha, respectively. Moreover, if the SGWT was <30 cm, the average TN loss intensity and amount could reach 177 kg/ha and 1250 t, respectively. Therefore, the SGWT regulation was one of the key measures to reducing soil N loss from the thin layer vadose zone of cropland around plateau lakes and improving groundwater quality.

Understanding Zinc Transport in Estuarine Environments: Insights from Sediment Composition

Sediments are sources and sinks of heavy metals in water, and estuaries are heavily influenced by human production and life. Therefore, it is of great significance to study the composition of estuarine sediments and the relationship between their components to understand the transport and transformation pathways of heavy metals in the environment. In this research, we investigated the characteristics and patterns of Zn adsorption by organic–inorganic composites, organic–clay mineral composites, and iron oxide–clay mineral composites in eight estuarine sediment samples from Dianchi Lake. The results show that both Langmuir and Freundlich isothermal models can describe the adsorption behaviour of the adsorbent better. The order of the adsorption capacity of the three groups of samples for zinc was organic–inorganic composites &gt; organic–clay mineral composites &gt; iron oxide–clay mineral composites. Through FTIR and XRD analyses, the adsorption of Zn2+ on the three groups of samples was dominated by electrostatic attraction and coordination adsorption, accompanied by the occurrence of ion exchange and co-precipitation. After FTIR semi-quantitative analysis, it was found that the source of the differences in the high and low Zn adsorption of the three types of samples may be mainly due to the content of phenolic functional groups in the organic matter. This may be related to the low redox site of the phenolic hydroxyl group, which, as an electron donor, is susceptible to electrostatic attraction and complexation with heavy metal cations. The organic–inorganic composite has a higher adsorption capacity for Zn when the ratio of the active fraction of organic matter to the free iron oxide content is 0.65–0.70. In this range, the organic matter can provide enough negative charge without making the sample surface too dense. Iron oxides can also activate the sample by providing sufficient contact between the clay minerals and the organic matter. When this ratio is too high or too low, it will be unfavourable for Zn adsorption.

Appendopyricularia guttulata sp. nov., a lignicolous freshwater fungus from Dianchi Lake in Yunnan, China

During an investigation into species diversity of lignicolous freshwater fungi from the plateau lakes in Yunnan Province, China, a hyphomycetous fungus was collected from Dianchi Lake. The new isolate was placed in Appendopyricularia as a sister taxon of Appendopyricularia juncicola within Barbatosphaeriaceae based on the phylogenetic analysis of combined ITS and LSU sequence data. The fungus is characterized by acrogenous, fusoid, guttulate conidia that are 3–4 septate and have a flexuous central apical appendage. It exhibits more pronounced guttulate appendages and more prominent denticle sporulation structure. Therefore, Appendopyricularia guttulata is introduced as a new species based on morphological characteristics and phylogenetic evidence. The detailed description and illustrations of the new species are provided, as well as the morphological comparison with similar taxa.

The Recycling Characteristics of Different Silicon Forms and Biogenic Silicon in the Surface Sediments of Dianchi Lake, Southwest China

Silicon (Si) is one of the main biogenic elements in the aquatic ecosystem of lakes, significantly affecting the primary productivity of lakes. Lake sediment is an important sink of Si, which exists in different Si forms and will be released and participate in the recycling of Si when the sediment environment changes. Compared to carbon (C), nitrogen (N) and phosphorus (P), the understanding of different Si forms in sediments and their biogeochemical cycling is currently insufficient. Dianchi Lake, a typical eutrophic lake in southwest China, was selected as an example, and the contents of different Si forms and biogenic silicon (BSi), as well as their correlations with total organic carbon (TOC), total nitrogen (TN), and chlorophyll a in the surface sediments, were systematically investigated to explore Si’s recycling characteristics. The results showed that the coupling relationship of the four different Si forms in the surface sediments of Dianchi Lake was poor (p &gt; 0.05), indicating that their sources were relatively independent. Moreover, their formation may be greatly influenced by the adsorption, fixation and redistribution of dissolved silicon by different lake substances. The contents of different Si forms in the surface sediments of Dianchi Lake were ranked as iron-manganese-oxide-bonded silicon (IMOF-Si) &gt; organic sulfide-bonded silicon (OSF-Si) &gt; ion-exchangeable silicon (IEF-Si) &gt; carbonate-bound silicon (CF-Si). In particular, the contents of IMOF-Si and OSF-Si reached 2983.7~3434.7 mg/kg and 1067.6~1324.3 mg/kg, respectively, suggesting that the release and recycling of Si in surface sediments may be more sensitive to changes in redox conditions at the sediment–water interface, which become the main pathway for Si recycling, and the slow degradation of organic matter rich in OSF-Si may lead to long-term and continuous endogenous Si recycling. The low proportion (0.3~0.6%) and spatial differences of biogenic silicon (BSi) in the surface sediments of Dianchi Lake, as well as the poor correlation between BSi and TOC, TN, and chlorophyll a, indicated that the primary productivity of Dianchi Lake was still dominated by cyanobacteria and other algal blooms, while the relative abundance of siliceous organisms such as diatoms was low and closer to the central area of Dianchi Lake. Additionally, BSi may have a faster release capability relative to TOC and may participate in Si recycling.

Exploring spatiotemporal patterns of algal cell density in lake Dianchi with explainable machine learning

The escalating global occurrence of algal blooms poses a growing threat to ecosystem services. In this study, the spatiotemporal heterogeneity of water quality parameters was leveraged to partition Lake Dianchi into three clusters. Considering water quality parameters and both the delayed and instantaneous effects of meteorological factors, ensemble learning, and quasi-Monte Carlo methods were employed to predict daily algal cell density (AD) between January 2021 and January 2024. Consistently, superior predictive accuracy across all three clusters was exhibited by the Stacking-Elastic-Net regularization model. Furthermore, the minimum combination of drivers that achieved near-optimal accuracy for each cluster was identified, striking a balance between accuracy and cost. The ranking of the effect of drivers on AD varied by cluster, while the delayed effect of meteorological factors on AD generally outweighed their instantaneous effect for all clusters. Additionally, the heterogeneous or homogeneous thresholds and responses between drivers and AD were explored. These findings could serve as a scientific and cost-effective basis for government agencies to develop regional sustainable strategies for managing water quality.

A hybrid algorithm for estimating total nitrogen from a large eutrophic plateau lake using Orbita hyperspectral (OHS) satellite imagery

Total nitrogen concentration (CTN) enrichment is the primary cause of natural water eutrophication. Accurately estimating CTN and its spatiotemporal dynamics is crucial for formulating monitoring and control measures to alleviate lake eutrophication. A hybrid model was proposed for estimating CTN in optically complex inland waters by incorporating the relationship between CTN and water optical active components for Zhuhai-1 Orbita hyperspectral (OHS) imagery. Compared with other semi-analytical algorithms, the re-adjusted reference wavelength QAA716 shows the best performance in aph(λ) retrieval. The hybrid model for CTN estimation achieves an root mean square deviation (RMSD) of 0.20 mg/L, a mean absolute percentage deviation (MAPD) of 6.96 %, and a unbiased mean absolute percentage deviation (UMAPD) of 6.96 %, with aph(569) accuracy exerting the greatest influence. Ground-satellite synchronous validation demonstrates robust performance, with an RMSD of 0.28 mg/L, a MAPD of 14.49 %, and a UMAPD of 14.92 %. The hybrid model was applied to OHS observations of Lake Dianchi from April 2019 to September 2021. The analysis revealed a generally decreasing trend in CTN during this timeframe. The above results demonstrate that the robustness and applicability of the proposed CTN hybrid model for inland waters with complex optical properties. Furthermore, satellite-based data products provide valuable information for formulating lake management strategies.

Satellite Tracking Reveals the Speed Up of the Lacustrine Algal Bloom Drift in Response to Climate Change.

Satellite evidence indicates a global increase in lacustrine algal blooms. These blooms can drift with winds, resulting in significant changes of the algal biomass spatial distribution, which is crucial in bloom formation. However, the lack of long-term, large-scale observational data has limited our understanding of bloom drift. Here, we have developed a novel method to track the drift using multi-source remote sensing satellites and presented a comprehensive bloom drift data set for four typical lakes: Lake Taihu (China, 2011-2021), Lake Chaohu (China, 2011-2020), Lake Dianchi (China, 2003-2021), and Lake Erie (North America, 2003-2021). We found that blooms closer to the water surface tend to drift faster. Higher temperatures and lower wind speeds bring blooms closer to the water surface, therefore accelerating drift and increasing biomass transportation. Under ongoing climate change, algal blooms are increasingly likely to spread over larger areas and accumulate in downwind waters, thereby posing a heightened risk to water resources. Our research greatly improves the understanding of algal bloom dynamics and provides new insights into the driving factors behind the global expansion of algal blooms. Our bloom-drift-tracking methodology also paves the way for the development of high-precision algal bloom prediction models.

Long-term water quality variations and influencing factors in Dianchi Lake, China – Insights from 30-year data

ABSTRACT Changes in water quality in Dianchi Lake over a long temporal scale have been significantly influenced by climate change and policy issuance. This study utilized the Mann -Kendall test and Theil -Sen estimation to examine water quality trends at 10 monitoring sites within Dianchi Lake from 1989 to 2018. The corresponding impacts of policies, including point source (PS) and non-point source (NPS) pollution control, ecological restoration (ER), and enforcement and supervision (ES), along with climatic conditions, were evaluated. Analysis of regression curves of water quality indexes indicates a slight increase in ammonia (NH4+-N) and total nitrogen (TN), while total phosphorus (TP), biochemical oxygen demand (BOD5), permanganate index (CODMn), and chlorophyll-a (Chl-a) decreased from the baseline time (year 1989). During the mid-phase (2001 -2006) of the study period, the impacts of policies on water quality were inconspicuous, with water temperature affecting Chl-a concentration variations. During the 11th Five-Year Plan, pollutants like NH4+-N, TN, TP, and CODMn peaked and then significantly declined between 2009 and 2015, likely due to comprehensive issuance of NPS and ER policies. This study can provide a reference for water quality management in eutrophic lakes.

Seeing and Thinking about Urban Blue–Green Space: Monitoring Public Landscape Preferences Using Bimodal Data

Urban blue–green spaces (UBGSs) are a significant avenue for addressing the worldwide mental health crisis. To effectively optimise landscape design and management for the promotion of health benefits from UBGS, it is crucial to objectively understand public preferences. This paper proposes a method to evaluate public landscape preference from the perspective of seeing and thinking, takes the examples of seven parks around the Dianchi Lake in Kunming, China, and analyses the social media data by using natural language processing technology and image semantic segmentation technology. The conclusions are as follows: (1) The public exhibits significantly high positive sentiments towards various UBGSs, with over 93% of comments expressed positive sentiments. (2) Differences exist in the frequency and perception of landscape features between image and text modalities. Landscape elements related to stability are perceived more in images than in text, while dynamic and experiential elements are perceived more in text than in images. (3) In both modalities, the distinctive landscape features of parks are more frequently perceived and preferred by the public. In the end, the intrinsic links between landscape elements and public sentiment and preferences are discussed, and suggestions for design and management improvements are made to consolidate their health benefits to the public.

Shell Mounds of the Dian Lake

This article provides data on the search and study of the finds obtained during archaeological excavations of shell mounds in the region of Dian Lake in Yunnan Province, China. These objects represent a common, but at the same time, quite specific category of archaeological sources containing important information about the material and spiritual life of the population that left them. From the considerable number of Yunnan køkkenmøddings, the ancient fortified settlement of Guchengcun attracted the most attention, during the excavations of which it was possible for the first time to reveal the direct sequence of the Pre-Dian and Early Dian stages, dating back to the period of 3500– 2500 years BC, which are characterized by a unique set of ceramic and bronze implements. These finds give us the opportunity to express preliminary considerations regarding the origin of the Dian culture and state. Some part of the ceramics is similar to the vessels of the local Neolithic culture of Baiyangcun, which had contacts with the Neolithic painted pottery of the Yellow River valley; another part of it, as well as jade half-rings and sea shells, indicating a connection with the coastal cultures of Eastern China. Bronze knives and daggers are probably related in origin to the population of Northern China and adjacent territories. The interaction of these factors formed the basis of the Dian culture, while the contacts with the northern peoples outlined a line of interaction, following which the “horseback culture” later penetrated into Yunnan, shaping the formation of the Dian state.

A non-microcystin-producing Microcystis wesenbergii strain alters fish food intake by disturbing neuro-endocrine appetite regulation

Cyanobacterial harmful algal blooms (cHABs) are pervasive sources of stress resulting in neurotoxicity in fish. A member of the widely distributed Microcystis genus of bloom-forming cyanobacteria, Microcystis wesenbergii can be found in many freshwater lakes, including Dianchi Lake (China), where it has become one of the dominant contributors to the lake's recurrent blooms. However, unlike its more well-known counterpart M. aeruginosa, the effects of dense non-microcystin-containing M. wesenbergii blooms are seldom studied. The disturbance of appetite regulation and feeding behaviour can have downstream effects on the growth of teleost fish, posing a significant challenge to aquaculture and conservation efforts. Here we examined the effects of M. wesenbergii blooms on the food intake of Acrossocheilus yunnanensis, a native cyprinid in southern China. This fish species has disappeared in Dianchi Lake, and its reintroduction might be negatively affected by the presence of this newly-dominant Microcystis species. We co-cultured juvenile A. yunnanensis with a non-microcystin-producing strain of M. wesenbergii at initial densities between 5 × 104 and 1 × 106 cells/mL and monitored fish feeding behaviour and changes in neurotransmitter and hormone protein levels. High-density M. wesenbergii cultures increased the feeding rate of co-cultured fish, elevating concentrations of appetite-stimulating signalling molecules (Agouti-related protein and γ-aminobutyric acid), while decreasing inhibitory ones (POMC). These changes coincided with histopathological alterations and reduced somatic indices in brain and intestinal tissues. Given this potential for detrimental effects and dysregulation of food intake, further studies are necessary to determine the impacts of chronic exposure of M. wesenbergii in wild fish.

Pollution Characteristics and Risk Assessment of Heavy Metals in the Sediments of the Inflow Rivers of Dianchi Lake, China.

To explore the contamination status and identify the source of the heavy metals in the sediments in the major inflow rivers of Dianchi Lake in China, sediment samples were collected and analyzed. Specifically, the distribution, source, water quality, and health risk assessment of the heavy metals were analyzed using correlation analysis (CA), principal component analysis (PCA), the heavy metal contamination factor (Cf), the pollution load index (PLI), and the potential ecological risk index (PERI). Additionally, the chemical fractions were analyzed for mobility characteristics. The results indicate that the average concentration of the heavy metals in the sediment ranked in the descending order of Zn > Cr > Cu > Pb > As > Ni > Cd > Hg, and most of the elements existed in less-mobile forms. The Cfwas in the order of Hg > Zn > Cd > As > Pb > Cr > Ni; the accumulation of Hg, Zn, Cd, and As was obvious. Although the spatial variability of the heavy metal contents was pronounced, the synthetical evaluation index of the PLI and PERI both reached a high pollution level. The PCA and CA results indicate that industrial, transportation, and agricultural emissions were the dominant factors causing heavy metal pollution. These results provide important data for improving water resource management efficiency and heavy metal pollution prevention in Dianchi Lake.

Study of the Spatiotemporal Distribution Characteristics of Rainfall Using Hybrid Dimensionality Reduction-Clustering Model: A Case Study of Kunming City, China

In recent years, the frequency and intensity of global extreme weather events have gradually increased, leading to significant changes in urban rainfall patterns. The uneven distribution of rainfall has caused varying degrees of water security issues in different regions. Accurately grasping the spatiotemporal distribution patterns of rainfall is crucial for understanding the hydrological cycle and predicting the availability of water resources. This study collected rainfall data every five minutes from 62 rain gauge stations in the main urban area of Kunming City from 2019 to 2021, constructing an unsupervised hybrid dimensionality reduction-clustering (HDRC) model. The model employs the Locally Linear Embedding (LLE) algorithm from manifold learning for dimensionality reduction of the data samples and uses the dynamic clustering K-Means algorithm for cluster analysis. The results show that the model categorizes the rainfall in the Kunming area into three types: The first type has its rainfall center distributed on the north shore of Dian Lake and the southern part of Kunming’s main urban area, with spatial dynamics showing the rainfall distribution gradually developing from the Dian Lake water body towards the land. The second type’s rainfall center is located in the northern mountainous area of Kunming, with a smaller spatial dynamic change trend. The water vapor has a relatively fixed and concentrated rainfall center due to the orographic uplift effect of the mountains. The third type’s rainfall center is located in the main urban area of Kunming, with this type of rainfall showing smaller variations in all indicators, mainly occurring in May and September when the temperature is lower, related to the urban heat island effect. This research provides a general workflow for spatial rainfall classification, capable of mining the spatiotemporal distribution patterns of regional rainfall based on extensive data and generating typical samples of rainfall types.

Sphingomonas lacusdianchii sp. nov., an attached bacterium inhibited by metabolites from its symbiotic cyanobacterium

An alpha-proteobacterial strain JXJ CY 53 T was isolated from the cyanosphere of Microcystis sp. FACHB-905 (MF-905) collected from Lake Dianchi, China. JXJ CY 53 T was observed to be an aerobic, Gram-stain-negative, oval shaped, and mucus-secreting bacterium. It had C18:1ω7c and C16:0 as the major cellular fatty acids, Q-10 as the predominant ubiquinone, and sphingoglycolipid, diphosphatidylglycerol, phosphatidylcholine, and phosphatidylmethylethanolamine as the polar lipids. The G + C content of DNA was 65.85%. The bacterium had 16S rRNA gene sequence identities of 98.9% and 98.7% with Sphingomonas panni DSM 15761 T and Sphingomonas hankookensis KCTC 22579 T, respectively, while less than 97.4% identities with other members of the genus. Further taxonomic analysis indicated that JXJ CY 53 T represented a new member of Sphingomonas, and the species epithet was proposed as Sphingomonas lacusdianchii sp. nov. (type strain JXJ CY 53 T = KCTC 72813 T = CGMCC 1.17657 T). JXJ CY 53 T promoted the growth of MF-905 by providing bio-available phosphorus and nitrogen, plant hormones, vitamins, and carotenoids. It could modulate the relative abundances of nonculturable bacteria associated with MF-905 and influence the interactions of MF-905 and other bacteria isolated from the cyanobacterium, in addition to microcystin production characteristics. Meanwhile, MF-905 could provide JXJ CY 53 T dissolved organic carbon for growth, and control the growth of JXJ CY 53 T by secreting specific chemicals other than microcystins. Overall, these results suggest that the interactions between Microcystis and its attached bacteria are complex and dynamic, and may influence the growth characteristics of the cyanobacterium. This study provided new ideas to understand the interactions between Microcystis and its attached bacteria.Key points• A novel bacterium (JXJCY 53T) was isolated from the cyanosphere of Microcystis sp. FACHB-905 (MF-905)• JXJCY 53Tmodulated the growth and microcystin production of MF-905• MF-905 could control the attached bacteria by specific chemicals other than microcystins (MCs)

A novel algorithm for estimating phytoplankton algal density in inland eutrophic lakes based on Sentinel-3 OLCI images

As one of the optically active components, phytoplankton are common photosynthetic organisms in oceans, nearshore, and inland water bodies. The variations in phytoplankton algal density play a crucial role in understanding primary productivity, carbon cycling, and early warning of algal blooms. In this study, three typical eutrophic lakes in China, Lake Taihu, Lake Chaohu, and Lake Dianchi, were taken as the research area. Algorithms for estimating algal density of cyanobacteria-dominated and non-cyanobacteria-dominated water types were developed based on Mie theory. The results demonstrated that the developedalgorithm had favorable estimation performance for inland eutrophic lakes, with a determination coefficient (R2) of 0.88, a mean absolute percentage error (MAPE) of 51%, an unbiased mean absolute percentage error (UMAPE) of 39%, and a root mean square error (RMSE) of 23.99 × 106 cells/L. Furthermore, comparison with other algorithms for estimating algal density showed that the developed algorithm had the lowest MAPE of 60% and UMAPE of 43%, with the RMSE of 23.42 × 106cells/L. Extensive evaluation based on satellite-ground synchronous data demonstrated the applicability of the developed algorithm to the Sentinel-3 OLCI sensor, enabling the determination of spatial and temporal distribution characteristics of algal density in the three lakes from 2016 to 2022 using Sentinel-3 OLCI images. The results of algal density inversion revealed a continuous decreasing trend in algal density in Lake Dianchi from 2016 to 2022, while the algal density in Lake Taihu and Lake Chaohu both decreased after 2019.

The Origin and Dispersal of Austroasiatic Languages from the Perspectives of Linguistics, Archeology, and Genetics

The Austroasiatic (AA) languages comprise a large language family in mainland Southeast Asia (MSEA) and South Asia. Theoretical, methodological, and material constraints have limited research on the origin and dispersal of AA-speaking populations within historical-comparative linguistics. With the deepening of archaeological and genetic studies, interdisciplinary collaboration has become key to solving this problem. We review the latest hypotheses in linguistics, archaeology, and molecular anthropology and propose insights on the origin and dispersal of AA languages. The ancestors of the AA-speaking populations were suggested to be rice farmers living in the Neolithic Age in southern China. Between 3,000 and 4,500 BP, some of these ancestors who spoke Proto-AA migrated from southern China to northern Vietnam, together with shouldered stone tools and domesticated rice. They mixed with local hunter-gatherers and expanded to the south of MSEA, giving rise to the Mon-Khmer, Aslian, and Nicobarese populations. They also spread to the northeast of India to form the Munda-speaking populations. Another group arrived near Dian Lake in Yunnan about 2,500 BP, where they created the Bronze Drum culture with the Proto- Tai-Kadai (TK)–speaking populations and later spread eastward to northern Vietnam via Guangxi. Finally, the Proto-AA–speaking people who remained in southern China mixed with the Proto-TK–speaking groups from Fujian and Guangdong, leading to a language shift, which we hypothesize was one of the main reasons for the “disappearance” of AA in southern China.