Reconstructing subsistence strategies from the Bronze to Iron age in the Tianshan Mountains: Integrating GIS spatial interpolation, MaxEnt and MixSIAR models

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e., Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR)) models for predicting monthly minimum, mean, and maximum NSAT in China, a domain with a large area, complex topography, and highly variable station density. This was conducted for a period of 12 months of 2010. The accuracy of the GWR model is better than the MLR model with an improvement of about 3 °C in the Root Mean Squared Error (RMSE), which indicates that the GWR model is more suitable for predicting monthly NSAT than the MLR model over a large scale. For three spatial interpolation models, the RMSEs of the predicted monthly NSAT are greater in the warmer months, and the mean RMSEs of the predicted monthly mean NSAT for 12 months in 2010 are 1.56 °C for the Kriging model, 1.74 °C for the IDW model, and 2.39 °C for the Spline model, respectively. The GWR model is better than the Kriging model in the warmer months, while the Kriging model is superior to the GWR model in the colder months. The total precision of the GWR model is slightly higher than the Kriging model. The assessment result indicated that the higher standard deviation and the lower mean of NSAT from sample data would be associated with a better performance of predicting monthly NSAT using spatial interpolation models.

To discuss the spatial interpolation based on panel data with spatial autocorrelation, the first-order spatial autoregressive interpolation model and the Kriging algorithm interpolation model are established from the perspective of the cross-sectional data. Genetic algorithm back-propagation neural network interpolation model is established from the perspective of the time-series data. A spatial combination interpolation model is established by the results of these models. The weights of the combination model is calculated by a new method of spatial drift. An empirical study is carried out with interpolation some areaspsila GDP per capita in Fujian 2007, China. The result shows that the most effective one is the spatial combination interpolation model.

The Canadian forest fire weather index (FWI) system requires spatially continuous, gridded weather data for temperature, relative humidity, wind speed, and precipitation. Reliable estimates of the Canadian FWI system components are needed to ensure the safety of communities, resources, and ecosystems. The quality of the interpolated input weather variables are typically evaluated using error estimates from cross‐validation. These error estimates are used for selecting between spatial interpolation methods for generating the continuous weather surfaces. Leave‐one‐out cross‐validation (LOOCV) is the most commonly used method, but it is biased in spatially clustered weather station networks. Accurate error estimation is important for selecting the optimal interpolation method and evaluating how well an interpolated surface represents true patterns in a weather variable. Other cross‐validation methods may better account for bias relating to clustered weather station networks. We present a comparison of cross‐validation methods for evaluating spatial interpolation models of weather variables for generating the inputs to the Canadian FWI system with the objective of determining whether they identify the same spatial interpolation model as having the lowest error. We found that LOOCV, shuffle‐split, stratified shuffle‐split, and a modified buffered leave‐one‐out procedure generally identified the same spatial interpolation models as having the lowest error. Spatial k‐fold favored spatial interpolation models with extrapolation ability. Our findings indicate that the most computationally efficient cross‐validation approach can be used for automatically selecting spatial interpolation models for weather surface generation, which will improve the quality of historical daily FWI maps.

Betula tianschanica Rupr. is distributed in regions such as China, Kyrgyzstan, and Tajikistan. Owing to the impacts of climate change, it is increasingly threatened by habitat fragmentation, resulting in a precipitous decline in its population. Currently listed as endangered on the Red List of Trees of Central Asia, this species is predominantly found in the Tianshan Mountains. Examining the influence of climate change on the geographical distribution pattern of Betula tianschanica is crucial for the management and conservation of its wild resources. This study employed two models, maximum entropy (MaxEnt) and random forest (RF), combined with 116 distribution points of Betula tianschanica and 27 environmental factor variables, to investigate the environmental determinants of the distribution of Betula tianschanica and project its potential geographical distribution areas. The MaxEnt model and the RF model determined the primary environmental factors influencing the potential distribution of Betula tianschanica. The MaxEnt model showed that the percentage of gravel volume in the lower soil layer and elevation are the most significant, while the RF model considered elevation and precipitation of the wettest quarter to be the most crucial. Both models unanimously asserted that elevation is the pivotal environmental element affecting the distribution of Betula tianschanica.The mean area under the curve (AUC) scores for the MaxEnt model and RF were 0.970 and 0.873, respectively, revealing that the MaxEnt model outperformed the RF model in predictive accuracy. Consequently, the present study employed the estimated geographical area for Betula tianschanica modeled by the MaxEnt model as a reference. Following the MaxEnt model's projected outcomes, Betula tianschanica is mainly located in territories such as the Tianshan Mountains, Ili River Basin, Lake Issyk-Kul, Turpan Basin, Irtysh River, Ulungur River, Bogda Mountains, Kazakh Hills, Lake Balkhash, Amu River, and the middle reaches of the Syr River.Within the MaxEnt model, the total suitable habitat area exhibits growth across all scenarios, with the exception of a decline observed during the 2041-2060 period under the SSP2-4.5 scenario. Remarkably, under the SSP58.5 scenario for the same timeframe, this area expands significantly by 42.7%. In contrast, the RF model demonstrated relatively minor fluctuations in the total suitable habitat area, with the highest recorded increase being 12.81%. This paper recommends establishing protected areas in the Tianshan Mountains, conducting long-term monitoring of its population dynamics, and enhancing international cooperation. In response to future climate change, climate refuges should be established and adaptive management implemented to ensure the survival and reproduction of Betula tianschanica.

Spatial interpolation has been applied for mapping various variables in a wide range of environmental disciplines. This study aims to develop novel tools for examining the relative performance of different interpolation methods. We shall quantify and compare the quality of interpolation models by applying, among others, some inequality indices of error distributions. Such indices can generally be classified as non-dimensional and global. The performance measures explored here provide a valuable supplement to the conventional accuracy assessment, and have so far received only scant attention in the relevant literature. Given a wide range of potential applications for the methods discussed here, the main focus of the paper will be on empirical research concerning variability of soil properties. The eight interpolation methods, i.e., Inverse Distance Weighting (IDW), Modified Shepard’s Method (MS), Radial Basis Function (RBF), Natural Neighbour (NaN), Nearest Neighbour (NeN), Triangulation with Linear Interpolation (TIN), Local Polynomial (LP) and Ordinary Kriging (OK) were applied to estimate spatial distribution of soil pH, nitrogen, potassium and phosphorus content. Biplot methods were applied to visually examine the numerical results on the assessment of prediction quality. The ordinary kriging showed superior performance compared to the competing methods in majority of the cases. Significantly, predictions by kriging approaches revealed substantial improvement by considering data transformations. As concerns the other tested methods, the IDW and the LP algorithms tend to share similar characteristics. In turn, the NeN, RBF and MS algorithms scored relatively small inequality indices, when compared to the other methods. The use of new proposed measures will enable practitioners to gain more insightful and comprehensive evaluations of spatial interpolation techniques.

A large amount of data for inconspicuous taxa is stored in natural history collections; however, this information is often neglected for biodiversity patterns studies. Here, we evaluate the performance of direct interpolation of museum collections data, equivalent to the traditional approach used in bryophyte conservation planning, and stacked species distribution models (S‐SDMs) to produce reliable reconstructions of species richness patterns, given that differences between these methods have been insufficiently evaluated for inconspicuous taxa. Our objective was to contrast if species distribution models produce better inferences of diversity richness than simply selecting areas with the higher species numbers. As model species, we selected Iberian species of the genus Grimmia (Bryophyta), and we used four well‐collected areas to compare and validate the following models: 1) four Maxent richness models, each generated without the data from one of the four areas, and a reference model created using all of the data and 2) four richness models obtained through direct spatial interpolation, each generated without the data from one area, and a reference model created with all of the data. The correlations between the partial and reference Maxent models were higher in all cases (0.45 to 0.99), whereas the correlations between the spatial interpolation models were negative and weak (−0.3 to −0.06). Our results demonstrate for the first time that S‐SDMs offer a useful tool for identifying detailed richness patterns for inconspicuous taxa such as bryophytes and improving incomplete distributions by assessing the potential richness of under‐surveyed areas, filling major gaps in the available data. In addition, the proposed strategy would enhance the value of the vast number of specimens housed in biological collections.

Soil fertility plays a crucial role in crop growth, so it is important to study the spatial distribution and variation of soil fertility for agricultural management and decision-making. However, traditional methods for assessing soil fertility are time-consuming and economically burdensome. Moreover, it is hard to capture the spatial variation of soil properties across continuous geographic space using the conventional methods. As key techniques of digital soil mapping (DSM), spatial interpolation techniques have been widely applied in soil surveys and analysis in recent years, since they can predict soil properties at unknown points in continuous space based on limited sample points. However, further research is needed on spatial interpolation models for DSM in regions with variable climates and complex terrains, which are characterized by strong spatial variation in both environmental variables and soil fertility. In this study, taking a typical hilly area in a subtropical monsoon climate, i.e., Gaozhou, Guangdong Province, China, as an example, the performances of four popular spatial interpolation models (Random Forest (RF), Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function) for digital soil mapping on available phosphorus (AP) are compared. Based on RF, the spatial variation and its driving factors of the AP of Gaozhou are then analyzed. Furthermore, by selecting three typical truncation lines from different directions, the correlations between environmental variables and AP in different spatial positions are demonstrated. The root mean square error (RMSE) results of the above four models are 32.01, 32.08, 32.74, and 33.08, respectively, which indicate that the RF has a higher interpolation accuracy. Based on the mapping results of RF, the minimum, maximum, and mean values of AP in the study area are 38.90, 95.24, and 64.96 mg/kg, respectively. The high-value areas of AP are mainly distributed in forested and orchard areas, while the low-value areas are primarily found in urban and cultivated areas in the eastern and western regions. Vegetation and topography are identified as the key factors shaping the spatial variations of AP in the study area. Furthermore, the spatial heterogeneity of the influence strength of altitude and EVI is revealed, providing a new direction for further research on DSM in the future, i.e., spatial interpolation models considering the spatial heterogeneity of the influence of environmental variables.

Soil-landscape modeling and land suitability evaluation: the case of rainwater harvesting in a dry rangeland environment

气候变化是影响物种分布的决定性因素之一, 研究高山植物天山花楸的适宜分布及其对未来气候变化的可能响应, 为了解西北地区高山植物类群的生态适宜性及其对未来气候情景的响应提供参考案例。利用天山花楸51个自然分布点和10个环境因子, 整合GIS空间分析和MAXENT模型, 分析基准气候(1970-2000)及未来气候下(2050时段, 基于RPC4.5情景)其在西北地区的适宜分布范围与空间分布特征。利用多元环境相似度面和最不相似变量分析研究区未来气候相比基准气候的波动情况, 利用环境变量贡献值、置换重要性值及刀切法明晰影响天山花楸分布的关键环境因子。利用GIS工具和R软件ggplot2程序包分析基准和未来气候下天山花楸适宜分布区内关键因子变化的数值范围。研究结果表明：(1)基准气候下, 天山花楸的适宜面积占研究区总面积的13%, 主要集中在阿尔泰山西段、准噶尔西部山地、天山西段及祁连山中段的高海拔山地等区域；(2)加入归一化植被指数显著提高了天山花楸模型模拟的准确性, 最干月降水量(0-18mm)、最湿月降水量(6-127mm)和平均气温日较差(8.2-16.3℃)主要限制了天山花楸的适宜分布；(3)相对基准气候, 2050时段下受降水因子影响, 天山花楸的适宜分布在阿尔泰山、准噶尔西部山地、天山及祁连山区域略有扩增, 适宜分布区的质心将向北迁移。本研究的结果表明天山花楸在西北地区的适宜生境面积较小而且破碎, 在未来气候情景下仅在局部适宜山地呈破碎化扩张, 而且将预测的该植物的适宜分布区与其适宜的海拔数值范围叠合分析后, 仅占研究区面积的4%, 但对该植物尚未建立自然保护区, 本研究结果建议将阿尔泰山西段、准噶尔西部山地、天山西段及祁连山中段作为天山花楸中风险保护区域, 将河西走廊东部、青海南山作为高风险保护区域。;Climate change is one of the key factors affecting species distribution. In order to understand the ecological suitability of alpine plant in taxa Northwest China and their responses to future climate scenarios, the suitable distribution of Sorbus tianschanica and its possible response to future climate change were studied. In this study, 51 natural distribution sites and 10 environmental factors of Sorbus tianschanica were used to integrate GIS spatial analysis and MAXENT model to analyze the suitable distribution area and spatial characteristics of Sorbus tianschanica in Northwest China under baseline climate (1970-2000) and future climate (2050 period, based on RCP 4.5 scenario). Based on the multivariate environmental similarity surface and the least similar variables, the future climate fluctuations in the study area compared with the baseline climate were analyzed. The key environmental factors affecting the distribution of Sorbus tianschanica were identified by integrating the contribution value of environmental variables, the replacement importance value and the knife cutting method. GIS tools and R software ggplot2 package were integrated to analyze the numerical range of the changes of key factors in the suitable distribution area of Sorbus tianschanica in the future climate. The results show that: (1) under the baseline climate, the suitable area of Sorbus tianschanica accounts for 13% of the total area of the study area, which is concentrated in the western part of Altay, the western part of Junggar, the middle and western part of Tianshan, Bogda Mountain, Qilian Mountain, South Mountain of Qinghai, and parts of Pamir Plateau and north part of Karakoram Mountain; (2) The accuracy of the model simulation is significantly improved by adding the Normalized Vegetation Index. The distribution of Sorbus tianschanica is mainly limited by the driest monthly precipitation, the wettest monthly precipitation, and the daily range of average temperature. Regarding the suitable distribution range, the driest monthly precipitation is 0-18 mm, the wettest monthly precipitation is 6-127 mm, and the daily range of average temperature is 8.2-16.3 ℃; (3) Compared with the baseline climate, influenced by precipitation factors in 2050, the suitable distribution of Sorbus tianschanica in Altai Mountain, western Junggar Mountain, Tianshan Mountain, and Qilian Mountain will expand slightly, and the center of mass of suitable distribution area will move northward. The results of the study show that the suitable habitat area of Sorbus mandshurica in Northwest China is small and fragmented. In the future climate scenario, only the local suitable mountains will be fragmented and expanded. The predicted suitable distribution area of Sorbus tianschanica and its suitable altitude range only account for 4% of the study area. However, there is no nature reserve for this plant. It is suggested that the western part of Altay, the western part of Junggar, the western part of Tianshan Mountain and the middle part of Qilian Mountain should be taken as the risk protection areas of Sorbus tianschanica, and the eastern part of Hexi Corridor and the southern mountain of Qinghai should be taken as the high risk protection areas. The suitable and most suitable distribution area of the plant predicted in this study is helpful to guide the protection of the Sorbus tianschanica.

High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method

Innovation of flux chamber network design for surface methane emission from landfills using spatial interpolation models

Continuously recording noise monitoring stations provide feedback of the noise environment at monitor locations. While this feedback is useful, it only provides information at a few point locations, and in many cases it is of interest to know the noise level(s) at the locations between and beyond noise monitoring locations. In this study, we test the accuracy of several spatial interpolation models with experimental data collected during the Strategic Environmental Research and Development Program (SERDP) Community Attitudes Towards Military Blast Noise study. These datasets include 9 months of blast noise events captured at two different study locations. In both cases, a small number of monitors (e.g., 3–9) were located over a large region of interest (e.g., 1–8 km2), thus providing realistic operational conditions. The utility of deterministic (e.g., nearest neighbor, Delaunay triangulation, thin plate splines, etc.) and stochastic (e.g., geostistical or kriging) interpolation models for estimating single-event and cumulative noise levels is examined using leave-one-out cross validation. The accuracy of each approach is assessed with the root-mean-square-error (RMSE), and we discuss the practical implications of implementing such approaches in real-time systems.

Rainfall erosivity is an essential factor to describe the potential soil loss caused by rain, which can be expected to change in correspondence to climate changes. Spatial distribution pattern of rainfall erosivity is a guideline to erosion regional difference revelation and soil conservation regionalization. The focus of this article is to search an optimum spatial interpolation model for the mapping of rainfall erosivity on a large scale. In this research, average annual rainfall erosivity on China mainland is calculated through daily precipitation dataset from 711 weather stations over a period of 58 years (1951-2008). Precisions of 29 spatial interpolation models are compared including inverse distance weighting (IDW), radial basis function (RBF), kriging, cokriging (CK) and thin plate smoothing spline (TPS). Results indicate that three variables cubic TPS is the optimum model for spatial interpolation to rainfall erosivity on a large scale. Spatial characteristic of rainfall erosivity on mainland was analysed with the model employed. A increasing from northwest to southeast is identified and the highest annual rainfall erosivity occurs in the southwest area of Guangxi on the value of 25885 J· mm / (m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> · h).

Desert plants in arid regions are facing escalating challenges from global warming, underscoring the urgent need to predict shifts in the distribution and habitats of dominant species under future climate scenarios. This study employed the Maximum Entropy (MaxEnt) model to project changes in the potential suitable habitats of three keystone desert species in Xinjiang—Halostachys capsica (M. Bieb.) C. A. Mey (Caryophyllales: Amaranthaceae), Haloxylon ammodendron (C. A. Mey.) Bunge (Caryophyllales: Amaranthaceae), and Karelinia caspia (Pall.) Less (Asterales: Asteraceae)—under varying climatic conditions. The area under the Receiver Operating Characteristic curve (AUC) exceeded 0.9 for all three species training datasets, indicating high predictive accuracy. Currently, Halos. caspica predominantly occupies mid-to-low elevation alluvial plains along the Tarim Basin and Tianshan Mountains, with a suitable area of 145.88 × 104 km2, while Halox. ammodendrum is primarily distributed across the Junggar Basin, Tarim Basin, and mid-elevation alluvial plains and aeolian landforms at the convergence zones of the Altai, Tianshan, and Kunlun Mountains, covering 109.55 × 104 km2. K. caspia thrives in mid-to-low elevation alluvial plains and low-elevation alluvial fans in the Tarim Basin, western Taklamakan Desert, and Junggar–Tianshan transition regions, with a suitable area of 95.75 × 104 km2. Among the key bioclimatic drivers, annual mean temperature was the most critical factor for Halos. caspica, precipitation of the coldest quarter for Halox. ammodendrum, and precipitation of the wettest month for K. caspia. Future projections revealed that under climate warming and increased humidity, suitable habitats for Halos. caspica would expand in all of the 2050s scenarios but decline by the 2070s, whereas Halox. ammodendrum habitats would decrease consistently across all scenarios over the next 40 years. In contrast, the suitable habitat area of K. caspia would remain nearly stable. These projections provide critical insights for formulating climate adaptation strategies to enhance soil–water conservation and sustainable desertification control in Xinjiang.

The outbreak of highly pathogenic avian influenza (HPAI) of the H5N1 subtypes in wild birds and poultry have caught worldwide attention. To explore the association between wild bird migration and avian influenza virus transmission, we monitored potential geographical distribution of four wild bird species that might carry the avian influenza viruses in China. They are bar-headed geese (Anser indicus), ruddy shelducks (Tadorna ferruginea), whooper swans (Cygnus cygnus) and black-headed gulls (Larus ridibundus). They served as major reservoir of the avian influenza viruses. We used bird watching records with the precise latitude/longitude coordinates from January 2002 to August 2014, and environmental variables with a pixel resolution of 5 km × 5 km from 2002 to 2014. The study utilized maximum entropy (MaxEnt) model based on ecological niche model approaches, and got the following results: (1) MaxEnt model have good discriminatory ability with the area under the curve (AUC) of the receiver operating curve (ROC) of 0.86–0.97; (2) the four wild bird species were estimated to concentrate in the North China Plain, the middle and lower region of the Yangtze River, Qinghai Lake, Tianshan Mountain and Tarim Basin, part of Tibet Plateau, and Hengduan Mountains; (3) radiation and temperature were found to provide the most significant information. Our findings will help to understand the spread of avian influenza viruses by wild bird migration in China, which benefits for effective monitoring strategies and prevention measures.