Spatiotemporal Evolution of Ecological Quality in Alpine Freshwater Lake Nature Reserves in the Past 20 Years

The Caohai Nature Reserve is one of the three major plateau freshwater lakes in China.Since the 1950s,human activities such as land reclamation and population relocation have greatly damaged Caohai.A rapid evaluation of the spatiotemporal evolution trend of the ecological quality of the Caohai Nature Reserve is significant for the maintenance and construction of the ecosystem in this area.The research is based on the Google Earth Engine(GEE)remote sensing cloud computing platform.Landsat TM/OLI images from May to October in five time periods:2000-2002,2004-2006,2009-2011,2014-2016,and 2019-2021 were obtained to reconstruct the optimal cloud image set by averaging the images in each time period.By constructing four ecological indicators:Greenness(NDVI),Wetness(Wet),Hotness(LST),and Dryness(NDBSI),and using Principal Component Analysis(PCA)method to obtain the Remote Sensing Ecological Index(RSEI)for the corresponding years,the spatiotemporal variation of ecological quality in the Caohai Nature Reserve over 20 years was analyzed.The results indicate:①the mean value of RSEI increased from 0.460 in 2000-2002 to 0.772 in 2019-2021,a 67.83%increase,indicating a significant improvement in the ecological quality of the reserve over the 20 years;②from the perspective of functional zoning of the Caohai Nature Reserve,the ecological quality of the core area showed a degrading trend,while the ecological quality of the buffer zone and experimental zone significantly improved;③with the implementation of ecological restoration projects,the ecological quality of the reserve gradually recovered and improved from 2014 to 2021.The trend of RSEI value changes is well correlated with human interventions,indicating that the PCA-based RSEI model can be effectively used for ecological quality assessment in lake areas.

The Process of Tiger Disappearance in Guizhou

In this paper,the authors collected officially published literature on the South China tiger(Panthera tigris amoyensis)in Guizhou from 1900 to 1980,from which we extracted information on its historical distribution and population size,and collected data on the tiger skin trade after 1950,the change in subtropical broad-leaved evergreen forest cover,and demographic data in the relevant databases.GIS mapping was used to visualize the distribution range of the South China tiger in Guizhou Province during the period 1900–1980 and to discuss the history of its disappearance in Guizhou and its driving factors.The results show that in 1900,the South China tiger was distributed throughout 82 cities and counties in nine prefectures and municipalities in the province;the number of documented South China tiger distribution sites in 1900–1950 decreased to 48 compared to 1900;the number of counties with South China tigers in 1950–1980 further decreased and became extinct in some areas;and in the 1990s,the South China tiger became extinct in the wild in Guizhou.The main reasons for the extinction of the South China tiger in the wild in Guizhou are:on the one hand,with the socio-economic development of Guizhou Province,the population has increased dramatically,the magnitude of the demand for natural resources has increased,and in order to satisfy this demand,human activities,such as coal mining and clearing of mountains for planting,have been intensified,resulting in the reduction of the coverage rate of the subtropical broad-leaved evergreen forests,which has resulted in the extreme loss of the habitat of the South China tiger;on the other hand,the insufficient protection efforts and protection measures for this species in the country before the 1980s,which were subjected to anthropogenic hunting,were also factors leading to the extinction of this species in the wild in Guizhou Province.As a big cat at the top of the food chain,the distribution of the South China tiger can reflect the history of the natural environment in the region.By analyzing and discussing the distribution history of the South China tiger population in Guizhou Province,the significance of this case is to provide a scientific basis for the future conservation of biodiversity and the development of ecological restoration measures in the karst mountains of southern China.

Climate and fire drivers of forest composition and openness in the Changbai Mountains since the Late Glacial

Ongoing climate changes have a direct impact on forest growth;they also affect natural fire regimes,with further implications for forest composition.Understanding of how these will affect forests on decadal-to-centennial timescales is limited.Here we use reconstructions of past vegetation,fire regimes and climate during the Holocene to examine the relative importance of changes in climate and fire regimes for the abundance of key tree species in northeastern China.We reconstructed vegetation changes and fire regimes based on pollen and charcoal records from Gushantun peatland.We then used generalized linear modelling to investigate the impact of reconstructed changes in summer temperature,annual precipitation,background levels of fire,fire frequency and fire magnitude to identify the drivers of decadal-to-centennial changes in forest openness and composition.Changes in climate and fire regimes have independent impacts on the abundance of the key tree taxa.Climate variables are generally more important than fire variables in determining the abundance of individual taxa.Precipitation is the only determinant of forest openness,but summer temperature is more important than precipitation for individual tree taxa with warmer summers causing a decrease in cold-tolerant conifers and an increase in warmth-demanding broadleaved trees.Both background level and fire frequency have negative relationships with the abundance of most tree taxa;only Pinus increases as fire frequency increases.The magnitude of individual fires does not have a significant impact on species abundance on this timescale.Both climate and fire regime characteristics must be considered to understand changes in forest composition on the decadal-to-centennial timescale.There are differences,both in sign and magnitude,in the response of individual tree species to individual drivers.

基于高光谱反射率的喀斯特地区土壤重金属锌元素含量反演

针对传统土壤重金属锌元素含量测定效率低下和喀斯特地区山高坡陡土壤样品采集难度大,亟需先进手段获取土壤重金属锌元素含量的要求,以喀斯特流域为研究区,利用电感耦合等离子质谱测定土壤样品的锌元素含量和分光光广度计采集土壤光谱数据。将所测定的原始光谱,经过连续统去除、一阶、二阶微分、倒数、倒数对数、倒数对数一阶、倒数对数二阶微分7种数学变换,基于高光谱吸收重金属元素的特征吸收带初步判断光谱特征变量,利用相关分析进一步筛选特征变量,运用逐步回归最终确定有效建模光谱变量。采用非线性和线性算法,揭示光谱敏感波段反射率与重金锌元素含量之间的映射关系,进行土壤重金属含量估测。结果表明:基于耦合的光谱特征变量甄选方式,锌元素的特征波段580,810,1410,1910,2160,2260,2270,2350,2430 nm与铁氧化物、有机质、粘土矿物吸收带关联,表明一定程度上捕捉到喀斯特地区土壤重金属锌元素的光谱吸收特性;运用随机森林、支持向量机、偏最小二乘3种算法进行元素含量与光谱变量建模后,采用决定系数和均方根误差评价模型精度。从光谱变换形式和模型性能二个维度综合判断,基于二阶微分变换的随机森林算法准确度最高,为最佳估算模型。通过高光谱反射率估测重金属锌元素含量,实现了喀斯特地区土壤重金属锌元素含量的高效快速反演,为喀斯特地区重金属元素含量动态监测提供了可靠的技术支撑。

Hydro-climatic Characteristics of Yarlung Zangbo River Basin since the Last Glacial Maximum

Global climate changes significantly impact the water condition of big rivers in glacierized high mountains. However,there is a lack of studies on hydrological changes within river basins caused by climate changes over a geological timescale due to the impossibility of direct observations. In this study, we examine the hydro-climatic variation of the Yarlung Zangbo River Basin in the Tibet Plateau since the Last Glacial Maximum(LGM) by combining δ18 O proxy records in Indian and Omani caves with the simulated Indian summer monsoon, surface temperature, precipitation, evapotranspiration and runoff via the Community Climate System Model and the reconstructed glacier coverage via the Parallel Ice Sheet Model. The mean river runoff was kept at a low level of 145 billion cubic meters per year until an abrupt increase at a rate of 8.7 million cubic meters per year in the B?lling-Aller?d interval(BA). The annual runoff reached a maximum of 250 billion cubic meters in the early Holocene and then reduced to the current value of 180 billion cubic meters at a rate of 6.4 million cubic meters per year. The low runoff in the LGM and Heinrich Stadial 1(HS1) is likely attributed to such a small contribution of precipitation to runoff and the large glacier cover. The percentage of precipitation to runoff was only 20%during the LGM and HS1. Comparison of glacier area among different periods indicates that the fastest deglaciation occurred during the late HS1, when nearly 60% of glacier area disappeared in the middle reach, 50% in the upper reach,and 30% in the lower reach. The rapid deglaciation and increasing runoff between the late HS1 and BA may have accelerated widespread ice-dam breaches and led to extreme outburst flood events. Combining local geological proxy records and regional simulations could be a useful approach for the study of paleo-hydrologic variations in big river basins.

中亚热带红壤不同粒级有机质的化学特征及其对有机培肥的响应

为了解红壤区不同粒级土壤有机质的化学特征及其对有机培肥的响应,以湖南省祁阳县旱地红壤为研究对象,进行秸秆还田和施加有机肥的实验.采用湿筛法得到粗颗粒(>250μm)、细颗粒(53~250μm)和矿质结合组分(<53μm)共3种土壤粒级组分,利用元素分析仪和热裂解-气相色谱/质谱(Py-GC/MS)技术分别进行碳、氮含量和土壤化合物化学组成的测定和分析.结果表明:①27 a的有机培肥处理大幅增加了土壤中有机碳的含量,同秸秆还田相比,施加有机肥的增碳效果更显著.②全土中,施加有机肥使有机碳与总氮的含量分别显著提高了35.1%和32.0%,而未显著影响土壤的C/N(P>0.05).③粗颗粒中含有较多的芳香烃和木质素(主要指示植物来源);细颗粒中含有较多的脂肪族化合物;矿质结合组分中含有较多的酚类化合物和含氮化合物,表明它们具有较高的化学亲和力.同粒级组分相比,有机培肥对土壤有机质化学组成的影响较小,主要体现在对木质素和氨基化合物的影响上.综上,粒级组分对土壤的碳、氮含量与有机质的化学组成起主要调控作用,有机培肥方式对其影响相对较小.

Modelling stemflow production by juvenile lodgepole pine(Pinus contorta var.latifolia)trees

Stemflow is a focused point source input of precipitation and nutrients at the base of a tree or plant and can have a significant impact on site hydrology. To date, no known studies have modelled stemflow production for juvenile lodgepole pine （Pinus contorta vat. latifolia）. Meteorological conditions, tree characteristics, and stemflow were sampled for two juvenile lodgepole pine stands over the course of the 2009 growing season. Step-wise multiple regression was used to assess which meteorological and tree architecture variables influenced stemflow production for each research plot. Once predictor variables were identified, models were produced for each stand and a generic model was produced that applied to both plots. A model employing precipitation depth and crown projection area successfully explained 71.3% of the variation in stemflow production from sampled trees. Stemflow was found to represent 1.8% of the study period rainfall and, although not a large component of the plot-scale canopy water balance, it is an order of magnitude greater than the fractioning of stemflow from mature lodgepole and lodgepole pine dominated forest. Additionally, stemflow funnelling ratios were found to average 22.2 and 24.3 from the two sample plots over the study period with a single tree, single event maximum of 111.7 recorded for a tree with a 3.3 cm bole diameter and a rain depth of 17.4 mm.

Sediment generation and soil mound denudation in areas of high-density tree throw along a river valley in the Jura Mountains, Switzerland

A preliminary field-based investigation was undertaken in a small(<10 km^(2))river valley located in the mountainous Jura region of northwest Switzerland.The aims of the work were to assess sediment generation and annual sediment transport rates by tree throw on forested hillslopes,and to document surface hydrology characteristics on four fresh soil mounds associated with recent tree throws over a 24-day monitoring period.For the soil mounds,average sediment recovery ranged from 7.7-28.2 g(dry weight),equivalent to a suspended sediment concentration of 145.2-327.8 g L^(-1),and runoff coefficients ranged from 1.0%-4.2%.Based on a soil bulk density value of 1,044 kg m^(-3),upslope runoff generation areas were denuded by an average 0.14 mm by the end of the 24-day monitoring period,representing an erosion rate equivalent to 2.1 mm yr^(-1).A ca.50 cm high soil mound could therefore feasibly persist for around 200-250 years.For tree throw work,the dimensions of 215 individual tree throws were measured and their locations mapped in 12 separate locations along the river valley representing a cumulative area equivalent to 5.3 ha(av.density,43 per ha).Tree throws generated a total of 20.1 m^(3) of fine-sediment(<2 mm diameter),or the equivalent of 3.8×10^(-4) m^(3) m^(-2).The process of tree throw was originally attributed to two extreme weather events that occurred in west and central Europe in late December 1999.Taking the 18-year period since both storms,this represents an annual sediment transport rate of 2.7×10^(-5) m^(3) m^(-1) yr^(-1).Exploring the relationship with wind on fall direction,65.5%of tree throws(143)generally fell in a downslope direction irrespective of hillslope aspect on which they were located.This infers that individual storms may not have been responsible for the majority of tree throws,but instead,could be associated with root failure.Given the high density of tree throws and their relative maturity(average age 41 years),we hypothesise that once trees attain a certain age in this river valley,their physiognomy(i.e.height,mass and centre of gravity)compromises their ability to remain securely anchored.We tentatively attribute this possibility to the presence of bedrock close to the surface,and to the shallow soil profile overlaying steep hillslopes.

Towards understanding the environmental and climatic changes and its contribution to the spread of wildfires in Ghana using remote sensing tools and machine learning (Google Earth Engine)

Data processing and climate characterisation to study its impact is becoming difficult due to insufficient and unavailable data,especially in developing countries.Understanding climate’s impact on burnt areas in Ghana(Guinea-savannah(GSZ)and Forest-savannah Mosaic zones(FSZ))leads us to opt for machine learning.Through Google Earth Engine(GEE),rainfall(PR),maximum temperature(Tmax),minimum temperature(Tmin),average temperature(Tmean),Palmer Drought Severity Index(PDSI),relative humidity(RH),wind speed(WS),soil moisture(SM),actual evapotranspiration(ETA)and reference evapotranspiration(ETR)have been acquired through CHIRPS(Climate Hazards group Infrared Precipitation with Stations),FLDAS dataset(Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System)and TerraClimate platform from 1991 to 2021.The objective is to analyse the link and the contribution of climatic and environmental parameters on wildfire spread in GSZ and FSZ in Ghana.Variables were analysed(area burnt and the number of activefires)through Spearman correlation and the cross-correlation function(CCF)(2001 to 2021).The tests(Mann-Kendall and Sens’s slope trend test,Pettitt test and the Lee and Heghinian test)showed the overall decrease in rainfall and increase in temperature respectively(-0.1 mm;+0.8℃)in GSZ and(-0.9 mm;+0.3℃)in FSZ.In terms of impact,PR,ETR,FDI,Tmean,Tmax,Tmin,RH,ETA and SM contribute tofire spread.Through the codes developed,researchers and decision-makers could update them at different times easily to monitor climate variability and its impact onfires.

Zircon classification from cathodoluminescence images using deep learning

Zircon is a widely-used heavy mineral in geochronological and geochemical research because it can extract important information to understand the history and genesis of rocks. Zircon has various types,and an accurate examination of zircon type is a prerequisite procedure before further analysis.Cathodoluminescence(CL) imaging is one of the most reliable ways to classify zircons. However, current CL image examination is conducted by manual work, which is time-consuming, bias-prone, and requires expertise. An automated and bias-free method for zircon classification is absent but necessary. To this end, deep convolutional neural networks(DCNNs) and transfer learning are applied in this study to classify the common types of zircons, i.e., igneous, metamorphic, and hydrothermal zircons. An atlas with over 4000 CL images of these three types of zircons is created, and three DCNNs are trained using these images. The results of this study indicate that the DCNNs can distinguish hydrothermal zircons from other zircons, as indicated by the highest accuracy of 100%. Although similar textures in igneous and metamorphic zircons pose great challenges for zircon classification, the DCNNs successfully classify 95% igneous and 92% metamorphic zircons. This study demonstrates the high accuracy of DCNNs in zircon classification and presents the great potentiality of deep learning techniques in numerous geoscientific disciplines.

Assessment of Stakeholders’ Perceptions of Landuse/Landcover Change Drivers in Abuja, Nigeria

Landuse/Landcover(LULC)changes are recognised as some of the major causes of environmental problems like land degradation and climate change.To achieve sustainability,we need to properly understand such changes in order to have adequate information that will enable us to design and implementing measures to mitigate their negative impacts.Doing this particularly requires a proper understanding of how stakeholders perceive the changes in general and their drivers in particular.Unfortunately,not much is known for many areas about the perspective of landuse stakeholders on drivers of LULC changes.This paper reports the results of a study conducted to examine the perceptions of different landuse stakeholders on drivers of LULC changes in Abuja Federal Capital Territory,Nigeria.Questionnaire survey was utilised,involving 514 households across four settlements,2 rural(Karshi and Orozo)and 2 urban(Nyanya and Karu)towns in the territory,which were complimented with Focus Group Discussions were conducted.The results obtained showed that urban dwellers are largely aware of drivers of changes in socio-economic drivers(physical development on lands,more commercial complex development and more institutional development).Rural dwellers are largely aware of environmental drivers of LULC changes(bush burning,livestock overgrazing,collections of wood and medicinal plants,and agricultural expansion).It was concluded that there is the need to bring about a harmonisation of the perceptions of LULC change drivers by the rural and urban dwellers so as to bring about a common front understanding and responding to LULC changes in the study area.

Spatiotemporal Evolution of Ecological Quality in Typical Karst Ecologically Fragile Areas Based on Remote Sensing Ecological Indexes

Fast and effective remote sensing monitoring is an important means for analyzing the spatio-temporal changes in ecological quality in fragile karst regions.This study focuses on Guanling Autonomous County,a national-level demonstration county for comprehensive desertification control.Based on Landsat TM/OLI remote sensing image data from 2005,2010,2015,and 2020,remote sensing ecological indices were used to analyze the spatio-temporal changes in ecological quality in Guanling Autonomous County from 2005 to 2020.The results show that:①the variance contribution rates of the first principal component for the four periods were 66.31%,71.59%,63.18%,and 75.24%,indicating that PC1 integrated most of the characteristics of the four indices,making the RSEI suitable for evaluating ecological quality in karst mountain areas;②the remote sensing ecological index grades have been increasing year by year,with an overall trend of improving ecological quality.The area of higher-grade ecological quality has increased spatially,while fragmented patches have gradually decreased,becoming more concentrated in the low-altitude areas in the northwest and east,and there is a trend of expansion towards higher-altitude areas;③the ecological environment quality in most areas has improved,with the improvement in RSEI spatio-temporal variation becoming more noticeable with increasing slope.Areas of higher-grade quality appeared in 2010,and the range of higher-grade quality expanded with increasing slope.

Multi-step ahead soil temperature forecasting at different depths based on meteorological data:Integrating resampling algorithms and machine learning models

Direct soil temperature(ST)measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning(ML)tools is helpful.In this study,ML approaches,including KStar,instance-based K-nearest learning(IBK),and locally weighted learning(LWL),coupled with resampling algorithms of bagging(BA)and dagging(DA)(BA-IBK,BA-KStar,BA-LWL,DA-IBK,DA-KStar,and DA-LWL)were developed and tested for multi-step ahead(3,6,and 9 d ahead)ST forecasting.In addition,a linear regression(LR)model was used as a benchmark to evaluate the results.A dataset was established,with daily ST time-series at 5 and 50 cm soil depths in a farmland as models’output and meteorological data as models’input,including mean(T_(mean)),minimum(Tmin),and maximum(T_(max))air temperatures,evaporation(Eva),sunshine hours(SSH),and solar radiation(SR),which were collected at Isfahan Synoptic Station(Iran)for 13 years(1992–2005).Six different input combination scenarios were selected based on Pearson’s correlation coefficients between inputs and outputs and fed into the models.We used 70%of the data to train the models,with the remaining 30%used for model evaluation via multiple visual and quantitative metrics.Our?ndings showed that T_(mean)was the most effective input variable for ST forecasting in most of the developed models,while in some cases the combinations of variables,including T_(mean)and T_(max)and T_(mean),T_(max),Tmin,Eva,and SSH proved to be the best input combinations.Among the evaluated models,BA-KStar showed greater compatibility,while in most cases,BA-IBK and-LWL provided more accurate results,depending on soil depth.For the 5 cm soil depth,BA-KStar had superior performance(i.e.,Nash-Sutcliffe efficiency(NSE)=0.90,0.87,and 0.85 for 3,6,and 9 d ahead forecasting,respectively);for the 50 cm soil depth,DA-KStar outperformed the other models(i.e.,NSE=0.88,0.89,and 0.89 for 3,6,and 9 d ahead forecasting,respectively).The results con?rmed that all hybrid models had higher prediction capabilities than the LR model.

改进的南极洲陆地卫星影像镶嵌图

研究改进LIMA数据处理方法,使用1073景Landsat7-ETM+数据制作了改进的南极洲陆地卫星影像镶嵌图.改进的镶嵌图较LIMA的优势体现在影像处理流程的3个方面:(1)DN值饱和溢出调整采用更高精度的线性回归方法;(2)利用每个像元的经纬度和影像中心点的拍摄时间逐像元计算太阳高度角,提高了太阳高度角的计算精度;(3)选择了更有效的GS融合方法,将更多的波段(波段5和7)加入融合产品的制作,在南极地类识别上优势更加明显.另外,以16bit进行存储的行星反射率产品很好的保持了冰雪表面的高辐射分辨率.从目视效果、信息熵以及分类等方面与LIMA数据进行比较,结果表明本文数据具有明显的优势.

Crop type mapping using LiDAR,Sentinel-2 and aerial imagery with machine learning algorithms

LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and in precision agriculture.The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field-often informed by data collected during ground and aerial surveys.However,manual digitizing and labeling is time-consuming,expensive and subject to human error.Automated remote sensing methods is a cost-effective alternative,with machine learning gaining popularity for classifying crop types.This study evaluated the use of LiDAR data,Sentinel-2 imagery,aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area.Different combinations of the three datasets were evaluated along with ten machine learning.The classification results were interpreted by comparing overall accuracies,kappa,standard deviation and f-score.It was found that LiDAR data successfully differentiated between different crop types,with XGBoost providing the highest overall accuracy of 87.8%.Furthermore,the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data,with LiDAR obtaining a mean overall accuracy of 84.3%and Sentinel-2 a mean overall accuracy of 83.6%.However,the combination of all three datasets proved to be the most effective at differentiating between the crop types,with RF providing the highest overall accuracy of 94.4%.These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.

Determinants of farmers’perception to invest in soil and water conservation technologies in the North-Western Highlands of Ethiopia

Soil erosion by water is a severe and continuous ecological problem in the north-western Highlands of Ethiopia.Limited perception of farmers to practice soil and water conservation(SWC)technologies is one of the major causes that have resulted accelerated soil erosion.Therefore,this paper examines the major determinants of farmers’perception to use and invest in SWC technologies in Ankasha District,north-western highlands of Ethiopia.A detailed field survey was carried out among 338 households,randomly selected from two rural sample kebeles(called villages here after).Descriptive statistics and logistic regression model were used to analyse the effects of multiple variables on farmers’perception.The results indicate that educational level of the respondents and their access to trainings were found to have a positive and very significant association(P<0.01)with farmers’perception.Likewise,land ownership,plot size,slope type,and extension contact positively and significantly influenced farmers’perception at 5%level of significance.On the other hand,the influence of respondents’age and plot distance from the homestead was found to be negative and significant(P<0.05).The overall results of this study indicate that the perception of farmers to invest in SWC technologies was highly determined by socioeconomic,institutional,attitudinal and biophysical factors.Thus,a better understanding of constrains that influence farmers'perception is very important while designing and implementing SWC technologies.Frequent contacts between farmers and extension agents and continues agricultural trainings are also needed to increase awareness of the impacts of SWC benefits.

Democratization of ecosystem services--a radical approach for assessing nature's benefits in the face of urbanization

Objectives:(1)To evaluate how ecosystem services may be utilized to either reinforce or fracture the planning and development practices that emerged from segregation and eco-nomic exclusion;(2)To survey the current state of ecosystem service assessments and synthesize a growing number of recommendations from the literature for renovating ecosys-tem service analyses.Methods:Utilizing current maps of ecosystem service distribution in Bushbuckridge Local Municipality,South Africa,we considered how a democratized process of assessing ecosys-tem services will produce a more nuanced representation of diverse values in society and capture heterogeneity in ecosystem structure and function.Results:We propose interventions for assessing ecosystem services that are inclusive of a broad range of stakeholders'values and result in actual quantification of social and ecological processes.We demonstrate how to operationalize a pluralistic framework for ecosystem service assessments.Conclusion:A democratized approach to ecosystem service assessments is a reimagined path to rescuing a poorly implemented concept and designing and managing future social-ecological systems that benefit people and support ecosystem integrity.It is the responsi-bility of scientists who do ecosystem services research to embrace more complex,pluralistic frameworks so that sound and inclusive scientific information is utilized in decision-making.

Spatial-temporal modeling of initial COVID-19 diffusion: The cases of the Chinese Mainland and Conterminous United States

COVID-19 outbreaks in China in late December 2019,then in the United States(US)in early 2020.In the initial wave of diffusion,the virus respectively took 14 and 33 days to spread across the provinces/states in the Chinese mainland and the coterminous US,during which there are 43%and 70%zero entries in the space-time series for China and US respectively,indicating a zero-inflated count process.A logistic growth curve as a function of the number of days since the first case appeared in each of these countries accurately portrays the national aggregate per capita rates of infection for both.This paper presents two space-time model specifications,one based upon the generalized linear mixed model,and the other upon Moran eigenvector space-time filtering,to describe the spread of COVID-19 in the initial 19 and 58 days across the Chinese mainland and the coterminous US,respectively.Results from these case studies show both models shed new light on the role of spatial structures in COVID-19 diffusion,models that can forecast new cases in subsequent days.A principal finding is that describing the spatiotemporal diffusion of COVID-19 benefits from including a hierarchical structural component to supplement the commonly employed contagion component.

Buffering functions of mangroves in the 2004 tsunami

The complex nature of coastal ecosystems and their protection require a deeper understanding of land cover change and dynamics.Although a number of ecological studies have been conducted to realise this important objective,little information is available regarding the quantification of this land cover change.The role of mangroves as living barriers was under appreciated prior to the 2004 tsunami event.In this paper,we investigate the buffering functions of mangroves in the 2004 tsunami by employing the methodology developed in our companion paper.We focus more on mangrove distribution patterns in different buffer zones before and after the 2004 tsunami.The presence of mangroves before and after the event was statistically significant for the North(χ^(2)=154.08,p50.001)and Upper South(χ^(2)=62.25,p50.001).We observed positive linear relationships suggesting a loss of mangrove resulted into a gain of barren and sand land cover as a result of a devastating impact from the 2004 tsunami event.There are pockets of inland tsunami inundations and penetrations in the North and Upper-South in part owing to the river mouth profiles and dense mangrove trees.Although the North and Upper South Regions of the study area with large mangrove forest areas suffered slight damages,these regions put up a strong buffer against the tsunami suggesting that mangrove forests play a significant role in shoreline protection.