Radial Basis Function Network for Ore Grade Estimation

This paper highlights the performance of a radial basis function (RBF) network for ore grade estimation in an offshore placer gold deposit. Several pertinent issues including RBF model construction, data division for model training, calibration and validation, and efficacy of the RBF network over the kriging and the multilayer perceptron models have been addressed in this study. For the construction of the RBF model, an orthogonal least-square algorithm (OLS) was used. The efficacy of this algorithm was testified against the random selection algorithm. It was found that OLS algorithm performed substantially better than the random selection algorithm. The model was trained using training data set, calibrated using calibration data set, and finally validated on the validation data set. However, for accurate performance measurement of the model, these three data sets should have similar statistical properties. To achieve the statistical similarity properties, an approach utilizing data segmentation and genetic algorithm was applied. A comparative evaluation of the RBF model against the kriging and the multilayer perceptron was then performed. It was seen that the RBF model produced estimates with the R ² (coefficient of determination) value of 0.39 as against of 0.19 for the kriging and of 0.18 for the multilayer perceptron.     

Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques

Air over-pressure (AOp) is one of the products of blasting operations for rock fragmentation in open-pit mines. It can cause structural vibration, smash glass doors, adversely affect the surrounding environment, and even be fatal to humans. To assess its dangerous effects, seven artificial intelligence (AI) methods for predicting specific blast-induced AOp have been applied and compared in this study. The seven methods include random forest, support vector regression, Gaussian process, Bayesian additive regression trees, boosted regression trees, k-nearest neighbors, and artificial neural network (ANN). An empirical technique was also used to compare with AI models. The degree of complexity and the performance of the models were compared with each other to find the optimal model for predicting blast-induced AOp. The Deo Nai open-pit coal mine (Vietnam) was selected as a case study where 113 blasting events have been recorded. Indicators used for evaluating model performances include the root-mean-square error (RMSE), determination coefficient (R²), and mean absolute error (MAE). The results indicate that AI techniques provide better performance than the empirical method. Although the relevance of the empirical approach was acceptable (R²?=?0.930) in this study, its error (RMSE?=?7.514) is highly significant to guarantee the safety of the surrounding environment. In contrast, the AI models offer much higher accuracies. Of the seven AI models, ANN was the most dominant model based on RMSE, R², and MAE. This study demonstrated that AI techniques are excellent for predicting blast-induced AOp in open-pit mines. These techniques are useful for blasters and managers in controlling undesirable effects of blasting operations on the surrounding environment.

     

METHODOLOGY: MISUSE OF R2 VALUES FROM LINEAR REGRESSION IN FRACTAL ANALYSIS

Least squares linear regression (LSLR) analysis is often used for estimating fractal dimension from the slope of scatterplots produced by fractal analysis. Coefficients of determination close to unity are commonly accepted as sufficient evidence of linearity, and hence statistical self-similarity (or affinity). In this study, high R ² values derived from linear regression analyses are shown to increase the likelihood of detecting significant curvature in a relation. While this curvature is unlikely to hinder the use of LSLR in predicting y from x, it is demonstrated that large variations in the slope coefficient, and hence in estimates of fractal dimension, can exist in the presence of R ² values close to unity. Therefore, coefficients of determination close to unity should not be used as evidence of linearity. In light of this finding, it is strongly suggested that even in the presence of high coefficients of determination, residual analysis and/or other tests for linearity always be conducted when linear regression is employed in fractal analysis. [Key words: coefficient of determination, statistical self-similarity, fractal dimension.]     

Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO

Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R²) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R². Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R² compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO.

     

Benthic diatom communities as indicators of anthropogenic metal contamination at Casey Station,Antarctica

Prior to environmental legislation in the 1980s, anthropogenic waste in Antarctica was often deposited into landfill sites or into the sea. This resulted in metal contamination in terrestrial and near-shore marine environments. In this study, we assess the feasibility of using both past and present diatom assemblages to reconstruct and monitor past and future metal contamination. Our dataset included the analyses of both surface sediment samples and sediment cores from a contaminated site near Casey Station, Antarctica. Redundancy analyses indicated a strong relationship between metal concentrations and the composition of diatom communities. Within the surface sediment samples, tin and lead individually explained 43% of the variation observed in the diatom data; copper and iron explained 42% of this variation. In the sediment cores, tin and lead individually explained 53% of the variation in diatom community composition. In the same samples copper explained 47% of this variation, with iron explaining 46% of the observed variation. Once one metal had been selected, incorporating further metal data into the analyses added little extra information. Modern analog technique (MAT) analyses showed a strong correlation between actual and predicted values within one dataset (R²: Cu 0.75; Pb 0.86; Sn 0.89; p<0.05 for each). MAT reconstructions of metal concentrations closely followed measured concentrations, with both high and low concentrations recorded. MAT analyses performed favorably when compared to predictive techniques based on multivariate linear regression and multilayer perceptron neural networks. This study demonstrates that the composition of benthic diatom communities is a good indicator of anthropogenic metal contamination, and may be useful in monitoring the success of environmental remediation strategies in Antarctica and elsewhere.     

Comparison of Volterra Model and Artificial Neural Networks for Rainfall–Runoff Simulation

This study evaluates the performances of two distinct linear and non-linear models for simulating non-linear rainfall–runoff processes and their applications to flood forecasting in the Navrood River basin, Iran. Due to the excellent capacity of the artificial neural networks [multilayer perceptron (MLP)] and Volterra model, these models were used to approximate arbitrary non-linear rainfall–runoff processes. The MLP model was trained using two different training algorithms. The Volterra model was applied as a linear model [the first-order Volterra (FOV) model] and solved using the traditional ordinary least-square (OLS) method. Storm events within the Navrood River basin were used to verify the suitability of the two models. The models’ performances were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge, and error of time for peak to arrive. Results indicated that the non-linear MLP models outperform the linear FOV model. The latter was ineffective because of the non-linearity of the rainfall–runoff process. Moreover, the OLS method is inefficient when the FOV model has many parameters that must be estimated.     

Uncertainty due to DEM error in landslide susceptibility mapping

Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km²) and the other for model extrapolation (approximately 4600 km²). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.     

Chironomid-based paleosalinity records in southern British Columbia, Canada: a comparison of transfer functions

Chironomid remains from Big Lake, British Columbia were analysed and paleosalinities were estimated using a pre-existing transfer function and several developed using new regression methods. A two component partial-least-squares model (PLS-2) had the highest coefficient of determination (R² _(Jackknifed) = 0.75) and lowest root-mean-squared error-of-prediction (RMSEP). As compared to the pre-existing model, it was also less sensitive to the influence of rare taxa. Nevertheless, the marginally larger R² _(Jackknifed) and lower RMSEP do not clearly identify a single best model. The models were applied to Big, Mahoney and Kilpoola lakes, revealing the sensitivity of paleosalinity inferences to model selection. A synopsis of chironomid-based paleosalinities in British Columbia and their correspondence with other paleoclimatic data are presented and discussed.     

Diatom transfer-functions for quantifying past air temperature, pH and total organic carbon concentration from lakes in northern Sweden

The relationships between diatoms (Bacillariophyceae) in surface sediments of lakes and summer air temperature, pH and total organic carbon concentration (TOC) were explored along a steep climatic gradient in northern Sweden to provide a tool to infer past climate conditions from sediment cores. The study sites are in an area with low human impact and range from boreal forest to alpine tundra. Canonical correspondence analysis (CCA) constrained to mean July air temperature and pH clearly showed that diatom community composition was different between lakes situated in conifer-, mountain birch- and alpine-vegetation zones. As a consequence, diatoms and multivariate ordination methods can be used to infer past changes in treeline position and dominant forest type. Quantitative inference models were developed to estimate mean July air temperature, pH and TOC from sedimentary diatom assemblages using weighted averaging (WA) and weighted averaging partial least squares (WA-PLS) regression. Relationships between diatoms and mean July air temperature were independent of lake-water pH, TOC, alkalinity and maximum depth. The results demonstrated that diatoms in lake sediments can provide useful and independent quantitative information for estimating past changes in mean July air temperature (R² _jack = 0.62, RMSEP = 0.86 °C; R² and root mean squared error of prediction (RMSEP) based on jack-knifing), pH (R² _jack = 0.61, RMSEP = 0.30) and TOC (R² _jack = 0.49, RMSEP = 1.33 mg l^-1). The paper focuses mainly on the relationship between diatom community composition and mean July air temperature, but the relationships to pH and TOC are also discussed.     

Environmental variables related to wheat yields in the semiarid pampa region of Argentina

The current paper analyses various environmental parameters in relation to wheat yields in Bordenave, Province of Buenos Aires, Argentina. The variables used are: precipitation (ppt), maximum (Tx) and minimum temperature (Tmi) as well as those obtained by applying the Palmer model. Decadic and phenological scales are used for data corresponding to the period 1977–2000. The stepwise method is used to obtain a multi-variate equation to calculate yield taking into account environmental variables only. For a five variates model the coefficient of determination, R² equals 95.79% and the standard error of estimation is 129.0 kg ha⁻¹. In the sample yields, the incidence of total variability for thermal variables is 42.7% and for hydrological variables, 53%. The value and sign of the correlation coefficients were analysed throughout the cultivation cycle. The α coefficient is mainly responsible for yield variance during tillering and stem elongation. There is good correlation with the values of Palmer's Drought Severity Index (PDSI) for the flowering and grain filling stages.     

Diatom-based total phosphorus (TP) and pH transfer functions for the Irish Ecoregion

A 72-lake diatom training set was developed for the Irish Ecoregion to examine the response of surface sediment diatom assemblages to measured environmental variables. A variety of multivariate data analyses was used to investigate environmental and biological data structure and their inter-relationships. Of the variables used in determining a typology for lakes in the Irish Ecoregion, alkalinity was the only one found to have a significant effect on diatom assemblages. A total of 602 diatom taxa were identified, with 233 recorded at three or more sites with abundances ≥1%. Generally diatom data displayed a high degree of heterogeneity at the species level and non-linear ecological responses. Both pH and total phosphorus (TP) (in the ranges of 5.1–8.5 and 4.0–142.3 μg l⁻¹ respectively) were shown to be the most significant variables in determining the surface sediment diatom assemblages. The calibration models for pH and TP were developed using the weighted averaging (WA) method; data manipulation showed strong influences on model performances. The optima WA models based on 70 lakes produced a jack-knifed coefficient of determination (r ² _jack) of 0.89 with a root mean squared error (RMSEP) of 0.32 for pH and r ² _jack of 0.74 and RMSEP of 0.21 (log₁₀ μg l⁻¹) for TP. Both models showed strong performances in comparison with existing models for Ireland and elsewhere. Application of the pH and TP transfer functions developed here will enable the generation of quantitative water quality data from the expanding number of palaeolimnological records available for the Irish Ecoregion, and thus facilitate the use of palaeolimnological approaches in the reconstruction of past lake water quality, ecological assessment and restoration.     

高光谱指数法用于确定多枝柽柳(Tamarix ramosissima)蒸腾速率

蒸腾速率(Tr)是植物生理生态学研究中表征蒸腾耗水的常用指标,研究植物的蒸腾耗水有助于了解当地生态系统稳定性和水资源的可持续利用,但在遥感应用尤其在干旱区遥感应用中很少被使用。本文以古尔班通古特沙漠南缘的主要建群种多枝柽柳(Tamarix ramosissima)作为研究对象,应用高光谱指数法对其Tr日变化过程进行研究,寻找和确定最佳的Tr光谱指数。选择的6个光谱指数判定系数R2介于0.06~0.73,其中简单比值(SR)光谱指数有最高的判定系数(R2=0.73)、较低的均方根误差(RMSE=0.24)和较为简单的形式,光谱范围处于近红外波段(1 645~1 655nm)/(1 775~1 785nm)。SR作为Tr最佳光谱指数,对植被水分关系变化敏感,能够较好地记录和监测Tr日变化过程,有益于揭示光谱指数物理和生理机制。     

基于TM影像的荒漠绿洲交错带土壤有机质含量反演模型

选取Landsat TM影像的光谱反射率（R）、反射率之倒数（1/R）、反射率倒数之对数（lg（1/R））、反射率一阶导数（FDR）、波段深度（D）等5种光谱指标,分别建立了奇台县土壤有机质（SOM）含量的反演模型,并利用F检验来验证模型的显著性。结果表明：用各光谱指标建立的土壤各层和不同深度SOM含量的反演模型精度值由低到高的顺序均为lg（1/R）<R<1/R<FDR<D,以D反演SOM含量的模型效果最好,且对10～20 cm的SOM含量的反演精度最高,适用于对研究区SOM含量的反演,FDR的反演效果次之,1/R和R的模型精度一般,而lg（1/R）的模型精度最差;各层拟合模型的反演精准度由低到高的顺序为50～60 cm <40～50 cm <30～40 cm <20～30 cm <0～10 cm <10～20 cm,不同深度反演模型的优劣为0～60 cm <0～50 cm <0～40 cm <0～30 cm <0～10 cm <0～20 cm。     

How does taxonomic resolution affect chironomid-based temperature reconstruction?

The resolution achievable for chironomid identifications has increased in recent years because of significant improvements in taxonomic literature. However, high taxonomic resolution requires more training for analysts. Furthermore, with greater taxonomic resolution, misidentifications and the number of rare, poorly represented taxa in chironomid calibration datasets may increase. We assessed the effects of various levels of taxonomic resolution on the performance of chironomid-based temperature inference models (transfer functions) and temperature reconstruction. A calibration dataset consisting of chironomid assemblage and temperature data from 100 lakes was examined at four levels of taxonomic detail. The coarsest taxonomic resolution primarily represented identifications to genus or suprageneric level. At the highest level of taxonomic resolution, identification to genus level was possible for 37% of taxa, and identification below genus was possible for 60% of taxa. Transfer functions were obtained using Weighted Averaging (WA) and Weighted Averaging-Partial Least Squares (WA-PLS) regression. Cross-validated performance statistics, such as the root mean square error of prediction (RMSEP) and the coefficient of determination (r ²) between inferred and observed values improved considerably from the lowest taxonomic resolution level (WA: RMSEP 1.91°C, r ² 0.78; WA-PLS: RMSEP 1.59°C, r ² 0.86) to the highest taxonomic resolution level (WA: RMSEP 1.66°C, r ² 0.84; WA-PLS: RMSEP 1.41°C, r ² 0.89). Reconstructed July air temperatures during the Lateglacial period based on fossil chironomid assemblages from Hijkermeer (The Netherlands) were similar for all levels of taxonomic resolution, except the coarsest level. At the coarsest taxonomic level, reconstruction failed to infer one of the known Lateglacial cold episodes in the record. Also, the difference in reconstructed values based on lowest and highest taxonomic resolutions exceeded sample-specific estimated standard errors of prediction in several instances. Our results suggest that chironomid-based transfer functions at the highest taxonomic resolution outperform models based on lower-resolution calibration data. However, transfer functions of intermediate taxonomic resolution produced results very similar to models based on high-resolution taxonomic data. In studies that include analysts with different levels of expertise, inference models based on intermediate taxonomic resolution, therefore, might provide an alternative to transfer functions of maximum taxonomic detail in order to ensure taxonomic consistency between calibration datasets and down-core records produced by different analysts.     

Assessing vegetation cover and biomass in restored erosion areas in Iceland using SPOT satellite data

Due to highly erodible volcanic soils and a harsh climate, livestock grazing in Iceland has led to serious soil erosion on about 40% of the country's surface. Over the last 100 years, various revegetation and restoration measures were taken on large areas distributed all over Iceland in an attempt to counteract this problem. The present research aimed to develop models for estimating percent vegetation cover (VC) and aboveground biomass (AGB) based on satellite data, as this would make it possible to assess and monitor the effectiveness of restoration measures over large areas at a fairly low cost. Models were developed based on 203 vegetation cover samples and 114 aboveground biomass samples distributed over five SPOT satellite datasets. All satellite datasets were atmospherically corrected, and digital numbers were converted into ground reflectance. Then a selection of vegetation indices (VIs) was calculated, followed by simple and multiple linear regression analysis of the relations between the field data and the calculated VIs.Best results were achieved using multiple linear regression models for both %VC and AGB. The model calibration and validation results showed that R² and RMSE values for most VIs do not vary very much. For percent VC, R² values range between 0.789 and 0.822, leading to RMSEs ranging between 15.89% and 16.72%. For AGB, R² values for low-biomass areas (AGB < 800 g/m²) range between 0.607 and 0.650, leading to RMSEs ranging between 126.08 g/m² and 136.38 g/m². The AGB model developed for all areas, including those with high biomass coverage (AGB > 800 g/m²), achieved R² values between 0.487 and 0.510, resulting in RMSEs ranging from 234 g/m² to 259.20 g/m². The models predicting percent VC generally overestimate observed low percent VC and slightly underestimate observed high percent VC. The estimation models for AGB behave in a similar way, but over- and underestimation are much more pronounced.These results show that it is possible to estimate percent VC with high accuracy based on various VIs derived from SPOT satellite data. AGB of restoration areas with low-biomass values of up to 800 g/m² can likewise be estimated with high accuracy based on various VIs derived from SPOT satellite data, whereas in the case of high biomass coverage, estimation accuracy decreases with increasing biomass values. Accordingly, percent VC can be estimated with high accuracy anywhere in Iceland, whereas AGB is much more difficult to estimate, particularly for areas with high-AGB variability.     

A study of the contribution of mass elevation effect to the altitudinal distribution of timberline in the Northern Hemisphere简

Alpine timberline, as the ＂ecologica tion of scientists in many fields, especially in transition zone,＂ has long attracted the atten- recent years. Many unitary and dibasic fitting models have been developed to explore the relationship between timberline elevation and latitude or temperature. However, these models are usually on regional scale and could not be applied to other regions; on the other hand, hemispherical-scale and continental-scale models are usually based on about 100 timberline data and are necessarily low in precision. The present article collects 516 data sites of timberline, and takes latitude, continentality and mass elevation effect （MEE） as independent variables and timberline elevation as dependent variable to develop a ternary linear regression meteorological data released by WorldClim and model. Continentality is calculated using the mountain base elevation （as a proxy of mass elevation effect） is extracted on the basis of SRTM 90-meter resolution elevation data. The results show that the coefficient of determination （R2） of the linear model is as high as 0.904, and that the contribution rate of latitude, continentality and MEE to timberline elevation is 45.02% （p=0.000）, 6.04% （p=0.000） and 48.94% （p=0.000）, respectively. This means that MEE is simply the primary factor contributing to the elevation distribution of timberline on the continental and hemispherical scales. The contribution rate of MEE to timberline altitude dif- fers in different regions, e.g., 50.49% （p=0.000） in North America, 48.73% （p=0.000） in the eastern Eurasia, and 43.6% （p=0.000） in the western Eurasia, but it is usually very high.     

Quantitative relationship between water-depth and sub-fossil ostracod assemblages in Lake Donggi Cona,Qinghai Province,China

A calibration data set of 51 surface sediment samples from Lake Donggi Cona on the northeastern Tibetan Plateau was investigated to study the relationship between sub-fossil ostracod assemblages and water depth. Samples were collected over a depth range from 0.6 to 80 m. A total of 16 ostracod species was identified from the lake with about half of the species restricted to the Tibetan Plateau and its adjacent mountain ranges and poorly known in terms of ecological preferences, and the other half displaying a mainly Holarctic distribution. Living macrophytes and macroalgae were recorded in Lake Donggi Cona down to a depth of about 30 m, and bivalve (Pisidium cf. zugmayeri) and gastropod (Gyraulus, Radix) shells were found down to depths of 43 and 48 m, respectively. The ostracod-water-depth relationship was assessed by multivariate statistical analysis and ostracod-based transfer functions for water depth were constructed. Weighted averaging partial least squares (WA-PLS) regression provided the best model with a coefficient of determination r ² of 0.91 between measured and ostracod-inferred water depth, a root mean square error of prediction of 8% and a maximum bias of 10.6% of the gradient length, as assessed by leave-one-out cross-validation. Our results show the potential of ostracods as palaeo-depth indicators in appropriate settings. However, transfer-function applications using fossil ostracod assemblages for palaeo-depth estimations require a thorough understanding of the palaeolimnological conditions of lakes and therefore detailed multi-proxy analysis to avoid misinterpretation of ostracod-based inferences.     

山体基面高度对青藏高原及其周边地区雪线空间分布的影响简

山体效应是地理地带性之外,在大尺度上影响垂直带分布的主要因素,山体基面高度则是山体效应的第一影响因子。青藏高原及其周边地区,雪线呈现出中心高、周围低,与山体基面高度相一致的环状分布模式。为分析山体基面高度对雪线分布的影响,本文共收集青藏高原及周边地区雪线数据142个,采用纬度、经度和基面高度为自变量的三元一次方程拟合研究区雪线分布,计算各自的标准回归系数和相对贡献率,再将基面高度划分成5个子集（0~1000 m、1001~2000 m、2001~3000 m、3001~4000 m和4001~5000 m）,分析基面高度不同的山地对雪线的影响差异。结果表明：① 在青藏高原,纬度、经度和基面高度对雪线高度分布的相对贡献率分别为51.49%、16.31%和32.20%;② 随着基面高度的增高,各子集模型的决定系数虽有逐渐降低的趋势,但仍保持在较高的值域（R²=0.895~0.668）,说明模型的有效性;③ 随基面高度的抬升,纬度和山体基面高度对雪线分布高度的相对贡献率分别表现出降低（92.6%~48.99%,R²=0.855）和增大（3.33%~31.76%,R²=0.582）的趋势,表明基面高度越高,其对雪线分布高度的影响越大。     

Development of a chironomid-based air temperature inference model for the central Canadian Arctic

Subfossil midge remains were identified in surface sediment recovered from 88 lakes in the central Canadian Arctic. These lakes spanned five vegetation zones, with the southern-most lakes located in boreal forest and the northern-most lakes located in mid-Arctic tundra. The lakes in the calibration are characterized by ranges in depth, summer surface-water temperature (SSWT), average July air temperature (AJAT) and pH of 15.5 m, 10.60°C, 8.40°C and 3.69, respectively. Redundancy analysis (RDA) indicated that maximum depth, pH, AJAT, total nitrogen-unfiltered (TN-UF), Cl and Al capture a large and statistically significant fraction of the overall variance in the midge data. Inference models relating midge abundances and AJAT were developed using different approaches including: weighted averaging (WA), weighted averaging-partial least squares (WA-PLS) and partial least squares (PLS). A chironomid-based inference model, based on a two-component WA-PLS approach, provided robust performance statistics with a high coefficient of determination (r ² = 0.77) and low root mean square error of prediction (RMSEP = 1.03°C) and low maximum bias. The use of a high-resolution gridded climate data set facilitated the development of the midge-based inference model for AJAT in a region with a paucity of meteorological stations and where previously only the development of a SSWT inference model was possible. David Porinchu and Nicolas Rolland contributed equally to the work.     

Diatom–environment relationships and a transfer function for conductivity in lakes of the Badain Jaran Desert, Inner Mongolia, China

We describe a dataset of 26 modern diatom samples and associated environmental variables from the Badain Jaran Desert, northwest China. The influence of electrical conductivity (EC) and other variables on diatom distribution was explored using multivariate analyses and generalized additive modeling of species response curves. A transfer function was derived for EC, the variable with the largest unique effect on diatom variance, as shown by partial canonical correspondence analysis. Weighted-averaging partial least squares regression and calibration provided the best model, with a high coefficient of determination ( $ {\text{r}}_{\text{boot}}^{2} $  = 0.91) and low prediction error (RMSEP_boot = 0.136 log₁₀ μS cm^?1). To assess its potential for palaeosalinity and palaeoclimate reconstructions, the EC transfer function was applied to fossil diatom assemblages from ²¹⁰Pb-dated short sediment cores collected from two subsaline lakes of the Badain Jaran Desert. The diatom-inferred (DI) EC reconstructions were compared with meteorological data for the past 50 years and with remote sensing data for the period AD 1990–2012. Changes in DI–EC were small and their relationship with climate was weak. Moreover, remote sensing data indicate that the surface areas and water depths of these lakes did not change, which suggests that water loss by evaporation is compensated by groundwater inflow. These results suggest that the response of these lakes to climate change is mediated by non-climatic factors such as the hydrogeological setting, which control recharge from groundwater, and may be non-linear and non-stationary.