Environmental Drivers and Spatial Prediction of the Critically Endangered Species Thuja sutchuenensis in Sichuan-Chongqing,China

Identifying the ecological environment suitable for the growth of Thuja sutchuenensis and predicting other potential distribution areas are essential to protect this endangered species. After selecting 24 environmental factors thatcould affect the distribution of T. sutchuenensis, including climate, topography, soil and Normalized DifferenceVegetation Index (NDVI), we adopted the Random Forest-MaxEnt integrated model to analyze our data. Basedon the Random Forest study, the contribution of the mean temperature of the warmest quarter, mean temperatureof the coldest quarter, annual mean temperature and mean temperature of the driest quarter was large. Based onMaxEnt model prediction outputs, the potential distribution map not only identified areas that originallyrecorded T. sutchuenensis, such as Xuanhan County, Kai County and Chengkou County, but also identified highlysuitable distribution areas where T. sutchuenensis may exist, including Wanyuan County, Sichuan Province, andthe junction of Chongqing and Hubei Province. This provides a more explicit geographic range for ex situ conservation and reintroduction of T. sutchuenensis. Our results also indicate that, in addition to climate factors,topography and soil factors are also important environmental factors that affect distribution. This provides a theoretical basis for subsequent laboratory construction to simulate the indoor growth of T. sutchuenensis.

Exploring atmospheric environmental drivers of asthma among children in Shanghai, China: Evidence-informed policies are required

It is important to identify atmospheric environmental drivers of childhood asthma because asthma is the most common chronic disease in children globally.We provided an overview of the impacts of exposure to atmospheric environmental factors(e.g.,meteorological factors and air pollutants)on childhood asthma,based on a series of studies with the cutting-edge environmental epidemiological methodology in Shanghai,China.Overall,we found that the prevalence of asthma among the children aged 3–7 years had increased by approximately 7 fold,from 2.1%in 1990 to 14.6%in 2019.Most meteorological factors(i.e.daily mean temperature,temperature difference,air pressure,air pressure difference,precipitation,relative humidity,sunshine and wind speed)and air pollutants(PM10,PM2.5,NO2,SO2 and O3)were significantly associated with childhood asthma(p<0.05),but meteorological factors appeared to play more important roles than air pollutants in the occurrence of asthma exacerbations.We also found that temperature variability was significantly associated with the elevated relative risk of daily outpatient visits for childhood asthma(p<0.05).Furthermore,we conducted a systematic review on climate change adaptation measures and childhood asthma and found that there were a number of adaptation measures proposed for childhood asthma in response to climate change,including vulnerability assessment,improving ventilation and heating,enhancing community education,developing forecast models,and early warning systems.However,the effectiveness of most adaptation measures,except for improving ventilation and heating in winter,have not been explored and quantified.Since asthma is sensitive to the variation of atmospheric environmental conditions,it is critical to develop and implement appropriate climate change mitigation and adaptation strategies as global heating becomes apparent.

Spatial pattern of macrobenthic communities along a shelf-slope-basin transect across the Bering Sea

Due to its unique geological location, the Bering Sea is an ideal place to investigate the water exchange and ecosystem connectivity of the Pacific Ocean–Arctic Ocean and subarctic–Arctic region. Based on a number of summer surveys（July to September, 2010, 2012 and 2014）, macrobenthic communities and their spatial-temporal patterns are exhibited for the majority of the Bering Sea（53°59′–64°36′N）. The results show that the macrobenthic communities were dominated by northern cold-water species and immigrant eurythermic species, and the communities assumed a dispersed and patchy distribution pattern. Polychaetes（Scoloplos armiger）, crustaceans（Ceradocus capensis） and sea urchins（Echinarachnius parma） were the main dominant groups in the shallow shelves; the sea star（Ctenodiscus crispatus） and the brittle star（Ophiura sarsii） were the main dominant groups in the continental slope; whereas small polychaetes（Prionospio malmgreni） dominated the basin area. Sediment type, water depth, and currents were the major factors affecting the structure and spatial distribution of the macrobenthic communities. Compared with other seas, the shallow areas of the Bering Sea showed an extremely high-standing biomass. In particular, the northern shelf area（north of St. Lawrence Islands and west of 170°W）,which is primarily controlled by Anadyr Water, is an undersea oasis. In contrast, a deficiency in the downward transport of particulate organic carbon has resulted in a desert-like seabed in the basin area. By comparing our results to previous studies, we found that macrobenthic communities of the Bering Sea have undergone significant structural changes in recent decades, resulting in a decrease in abundance and an increase in biomass.In addition, populations of amphipods and bivalves in the northern shelves have decreased significantly and have been gradually replaced by other species. These changes might be associated with advanced seasonal ice melting,changes in organic carbon input, and global warming, indicating that large-scale ecosystem changes have been occurring in the Bering Sea.

Cladophora as ecological engineer:A new test from the largest lake of Qinghai-Tibet plateau with filamentous algal blooms

Filamentous algae blooms(FABs)have been increasing globally in recent years,and their presence can have both harmful and beneficial effects on aquatic ecosystems.As one of the most common FABs,Cladophora blooms have been reported in the lakes of the Qinghai-Tibet Plateau during the past few years.However,there have been few studies focused on how FABs impact other aquatic organisms,especially in alpine lakes since these are at the forefront of responding to global climate change.In this study,the phytoplankton communities in different regions of Qinghai Lake were profiled in different seasons using meta-barcode sequencing.The phytoplankton assemblages in areas with Cladophora blooms were compared to those without Cladophora.The phytoplankton community structure correlated with physicochemical properties including water temperature,electrical conductivity,nitrate,and the presence or absence of Cladophora blooms.The relative abundance of Bacillariophytes was found to be higher in zones with Cladophora blooms than in other regions.Significant seasonal changes in phytoplankton biomass andβdiversity were observed in zones with Cladophora blooms.Growth and microbial degradation of Cladophora can change the pH,dissolved oxygen,secchi depth,and nitrate.Together with seasonal temperature and electrical conductivity changes,Cladophora growth can significantly impact the phytoplankton biomass,community dissimilarity and assembly process.These results showed that Cladophora plays a key role in littoral aquatic ecosystem ecology.

Temporal variation and its drivers in the elemental traits of four boreal plant species

Aims Intraspecific variation in plant traits has important consequences for individual fitness and herbivore foraging.For plants,trait variability across spatial dimensions is well documented.However,temporal dimensions of trait variability are less well known,and may be influenced by seasonal differences in growing degree days(GDD),temperature and precipitation.Here,we aim to quantify intraspecific temporal variation in traits and the underlying drivers for four commonly occurring boreal plant species.Methods We sampled the elemental and stoichiometric traits(%C,%N,%P,C:N,C:P,N:P)of four common browse species'foliage across 2 years.Using a two-step approach,we first fitted generalized linear models(GzLM,n=24)to the species'elemental and stoichiometric traits,and tested if they varied across years.When we observed evidence for temporal variability,we fitted a second set of GzLMs(n=8)with temperature,productivity and moisture as explanatory variables.Important Findings We found no evidence of temporal variation for most of the elemental and stoichiometric traits of our four boreal plants,with two exceptions.Year was an important predictor for percent carbon across all four species(R^(2)=0.47-0.67)and for multiple elemental and stoichiometric traits in balsam fir(5/8,R2=0.29-0.67).Thus,variation in percent carbon was related to interannual differences,more so than nitrogen and phosphorus,which are limiting nutrients in the boreal forest.These results also indicate that year may explain more variation in conifers'stoichiometry than for deciduous plants due to life history differences.GDD was the most frequently occurring variable in the second round of models(8/8 times,R^(2)=0.21-0.41),suggesting that temperature is an important driver of temporal variation in these traits.

Metagenomic analysis to determine the characteristics of antibiotic resistance genes in typical antibiotic-contaminated sediments

Comprehensive studies of the effects of various physical and chemical variables(including heavymetals),antibiotics,and microorganisms in the environment on antibiotic resistance genes are rare.We collected sediment samples from the Shatian Lake aquaculture area and surrounding lakes and rivers located in Shanghai,China.The spatial distribution of sediment ARGs was assessed by metagenomic analysis that revealed 26 ARG types(510 subtypes),dominated by Multidrug,β-lactam,Aminoglycoside,Glycopeptides,Fluoroquinolone,and Tetracyline.Redundancy discriminant analysis indicated that antibiotics(SAs and MLs)in the aqueous environment and sediment along with water TN and TP were the key variables affecting the abundance distribution of total ARGs.However,the main environmental drivers and key influences differed among the different ARGs.For total ARGs,the environmental subtypes affecting their structural composition and distribution characteristics were mainly antibiotic residues.Procrustes analysis showed a significant correlation between ARGs and microbial communities in the sediment in the survey area.Network analysis revealed that most of the target ARGs were significantly and positively correlated with microorganisms,and a small number of ARGs(e.g.,rpoB,mdtC,and efpA)were highly significantly and positively correlated with microorganisms(e.g.,Knoellia,Tetrasphaera,and Gem-matirosa).Potential hosts for the major ARGs included Actinobacteria,Proteobacteria,and Gemmatimonadetes.Our study provides new insight and a comprehensive assessment of the distribution and abundance of ARGs and the drivers of ARG occurrence and transmission.

Algal community structure prediction by machine learning

The algal community structure is vital for aquatic management.However,the complicated environmental and biological processes make modeling challenging.To cope with this difficulty,we investigated using random forests(RF)to predict phytoplankton community shifting based on multi-source environmental factors(including physicochemical,hydrological,and meteorological variables).The RF models robustly predicted the algal communities composed by 13 major classes(Bray-Curtis dissimilarity=9.2±7.0%,validation NRMSE mostly<10%),with accurate simulations to the total biomass(validation R^(2)>0.74)in Norway's largest lake,Lake Mjosa.The importance analysis showed that the hydro-meteorological variables(Standardized MSE and Node Purity mostly>0.5)were the most influential factors in regulating the phytoplankton.Furthermore,an in-depth ecological interpretation uncovered the interactive stress-response effect on the algal community learned by the RF models.The interpretation results disclosed that the environmental drivers(i.e.,temperature,lake inflow,and nutrients)can jointly pose strong influence on the algal community shifts.This study highlighted the power of machine learning in predicting complex algal community structures and provided insights into the model interpretability.

Phylogenetic and climatic constraints drive flowering phenological patterns in a subtropical nature reserve

Aims Exploring flowering patterns and detecting processes are essential when probing into the nature of reproductive traits during the life history and the interactions among different evolutionary clades.such patterns are believed to be influenced by many factors,but quantifying these impacts at the community-level remains poorly understood.Methods We investigated the flowering patterns based on long-term her-barium records in a given area from subtropical forest regions in southern China.We obtained 5258 herbarium voucher specimens collected from the Dinghushan National Nature reserve(DNNr)belonging to 166 families,943 genera and 2059 species and exam-ined the month when each species was flowering during the period 1920-2007.Important Findings The results showed that plants flowered sequentially almost through-out the whole year,showing the characteristics of subtropical evergreen broad-leaved forests.Flowering spectrums of the entire flora and the four life forms exhibited a clear unimodality that is probably typical of subtropical forest communities.Flowering pat-terns of the DNNr were positively correlated with mean rainfall,mean air temperature and mean sunshine duration.median flowering dates of the 38 large species-rich families ranged from early april to late august;25 families exhibited significant unimodal distribution patterns,whereas the remaining families were unclear or bimodal.median flowering dates of the 10 most species-rich genera ranged from middle may to later July.While the results are consist-ent with climatic factors playing a general role in flowering patterns,median flowering dates varied significantly among species-rich families and genera,suggesting that phylogenies could provide spe-cific constraints in subtropical forests.