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1.
Risto K. Heikkinen 《Plant Ecology》1996,126(2):151-165
This paper is an attempt, using statistical modelling techniques, to understand the patterns of vascular plant species richness at the poorly studied meso-scale within a relatively unexplored subarctic zone. Species richness is related to floristic-environmental composite variables, using occurrence data of vascular plants and environmental and spatial predictor variables in 362 1 km2 grid squares in the Kevo Nature Reserve. Species richness is modelled in two different way. First, by detecting the major floristic-environmental gradients with the ordination procedure of canonical correspondence analysis, and subsequently relating these ordination axes to species richness by generalized linear modelling. Second, species richness is directly related to the composite environmental factors of explanatory variables, using partial least squares regression. The most important explanatory variables, as suggested by both approaches, are relatively similar, and largely reflect the influence of altitude or altitudinally related variables in the models. The most prominent floristic gradient in the data runs from alpine habitats to river valleys, and this gradient is the main source of variation in species richness. Some local environmental variables are also relatively important predictors; the grid squares rich in vascular plant taxa are mainly located in the lowlands of the reserve and are characterised by rivers and brooks, as well as by abundant cliff walls. The two statistical models account for approximately the same amount of variation in the species richness, with more than half of the variation unexplained. Potential reasons for the relatively modest fit are discussed, and the results are compared to the characteristics of the diversity-environment relationships at both broader- and finer-scales. 相似文献
2.
In regression with a vector of quantitative predictors, sufficientdimension reduction methods can effectively reduce the predictordimension, while preserving full regression information andassuming no parametric model. However, all current reductionmethods require the sample size n to be greater than the numberof predictors p. It is well known that partial least squarescan deal with problems with n < p. We first establisha link between partial least squares and sufficient dimensionreduction. Motivated by this link, we then propose a new dimensionreduction method, entitled partial inverse regression. We showthat its sample estimator is consistent, and that its performanceis similar to or superior to partial least squares when n < p,especially when the regression model is nonlinear or heteroscedastic.An example involving the spectroscopy analysis of biscuit doughis also given. 相似文献
3.
探讨环渤海湾和黄土高原两大苹果产区土壤养分对‘富士’苹果品质的影响,筛选不同产区影响果实品质特性的主要土壤养分因子,明确优质‘富士’苹果的土壤养分含量指标等,为两大苹果产区果园合理施肥、提高果实品质等提供理论依据.于2010—2011年分别在我国环渤海湾和黄土高原两大苹果产区各选择22个县,每个县3片果园,共计132个乔砧‘富士’苹果园,对每个果园的土壤养分含量和果实品质指标进行调查和分析,应用偏最小二乘回归方法筛选不同产区影响果实品质的主要土壤养分因子,并建立果实品质因素与土壤养分含量关系的回归方程,线性规划求解不同产区优质‘富士’苹果的土壤养分含量优化方案.结果表明: 环渤海湾产区的土壤碱解氮、有效磷、钙、铁和锌含量极显著高于黄土高原产区,而土壤pH,有效钾含量显著低于黄土高原产区;黄土高原产区的果实可溶性固形物含量显著高于环渤海湾产区,而固酸比显著低于环渤海湾产区.土壤有效硼含量对两产区果实单果质量影响的正效应最大,而土壤总氮与两产区果实硬度呈负效应;环渤海湾产区的果实可溶性固形物含量主要受土壤总氮和有效硼的负、正效应影响,黄土高原产区主要受土壤有效钙和碱解氮正、负效应的影响.环渤海湾产区优质乔砧‘富士’苹果的土壤养分含量需求为高的土壤有效硼和pH,适宜的土壤有效钾;黄土高原产区为低的土壤全氮,高的碱解氮、有效钾和有效铁,适宜的土壤有效锌和硼.环渤海湾产区优质乔砧‘富士’苹果的土壤养分管理技术措施为增加土壤有效硼含量和调高土壤pH、调整土壤有效钾含量;而黄土高原产区为提高土壤碱解氮、有效钾和有效铁含量,降低果园土壤pH,适当调整土壤有效锌和有效硼含量. 相似文献
4.
Cámara MS Mastandrea C Goicoechea HC 《Journal of biochemical and biophysical methods》2005,64(3):153-166
In the present report, carbamazepine is determined on serum samples of real patients by a procedure completely assisted by chemometric tools. First, a response surface methodology based on a mixture design was applied in order to select the best conditions for the extraction step. Finally, partial least squares multivariate calibration (PLS-1) was applied to second-derivative UV spectra, eliminating a shift baseline effect that originated in the extraction procedure. The performance assessment included: (a) a three-level precision study, (b) a recovery study analyzing spiked samples, and (c) a method comparison with high-performance liquid chromatography (HPLC) and fluorescence polarization immunoassay (FPIA) applied on real patient samples. The obtained results show the potentiality of the presently studied methodology for the monitoring of patients treated with this anticonvulsant. 相似文献
5.
分别于2012—2013、2013—2014年度越冬期候鸟越冬前(10月份)与越冬后(4月份)采用样方法调查沙湖沉水植物冬芽的种类、密度及生物量,分析不同水位条件的2个年度鄱阳湖碟形子湖沉水植物冬芽的分布及其对食块茎水鸟食物贡献的差异性,探讨越冬水鸟取食与水位变化对沉水植物冬芽分布的影响。结果表明:刺苦草(Vallisneria spinulosa)和罗氏轮叶黑藻(Hydrilla verticillata var.rosburghii)2种沉水植物的冬芽同域分布。2013年10月2种植物冬芽的密度与生物量均显著低于2012年同期,主要原因是鄱阳湖水位年际间变化剧烈,并对水质有显著影响:与2012年相比,2013年丰水期(4—9月)沙湖与主湖区连通的时间和日平均水深显著减小,但水体浊度显著增加,不利于沉水植物生长发育。2012—2013年度越冬水鸟迁出后2种冬芽的密度和生物量均明显下降,而2013—2014年度越冬期水鸟迁出后与迁入前相比两种植物冬芽的密度和生物量均无显著变化,很可能与食块茎水鸟的取食活动和高水位对食物可利用性的负面影响有密切关系。湖泊剧烈的水位变化导致越冬水鸟的食源具有年际波动的特征,而食块茎水鸟对鄱阳湖子湖的食物利用率受越冬季冬芽丰富度和食物可及性(accessibility)的共同影响。研究结果对鄱阳湖乃至长江中下游流域沉水植被恢复、越冬水鸟保护以及生态系统功能评估具有指导意义。 相似文献
6.
Summary . Motivated by an analysis of a real data set in ecology, we consider a class of partially nonlinear models where both a nonparametric component and a parametric component are present. We develop two new estimation procedures to estimate the parameters in the parametric component. Consistency and asymptotic normality of the resulting estimators are established. We further propose an estimation procedure and a generalized F -test procedure for the nonparametric component in the partially nonlinear models. Asymptotic properties of the newly proposed estimation procedure and the test statistic are derived. Finite sample performance of the proposed inference procedures are assessed by Monte Carlo simulation studies. An application in ecology is used to illustrate the proposed methods. 相似文献
7.
Richard J. Smith 《American journal of physical anthropology》2009,140(3):476-486
Many investigators use the reduced major axis (RMA) instead of ordinary least squares (OLS) to define a line of best fit for a bivariate relationship when the variable represented on the X‐axis is measured with error. OLS frequently is described as requiring the assumption that X is measured without error while RMA incorporates an assumption that there is error in X. Although an RMA fit actually involves a very specific pattern of error variance, investigators have prioritized the presence versus the absence of error rather than the pattern of error in selecting between the two methods. Another difference between RMA and OLS is that RMA is symmetric, meaning that a single line defines the bivariate relationship, regardless of which variable is X and which is Y, while OLS is asymmetric, so that the slope and resulting interpretation of the data are changed when the variables assigned to X and Y are reversed. The concept of error is reviewed and expanded from previous discussions, and it is argued that the symmetry‐asymmetry issue should be the criterion by which investigators choose between RMA and OLS. This is a biological question about the relationship between variables. It is determined by the investigator, not dictated by the pattern of error in the data. If X is measured with error but OLS should be used because the biological question is asymmetric, there are several methods available for adjusting the OLS slope to reflect the bias due to error. RMA is being used in many analyses for which OLS would be more appropriate. Am J Phys Anthropol, 2009. © 2009 Wiley‐Liss, Inc. 相似文献
8.
Jan‐Patrick Voss Nina E. Mittelheuser Roman Lemke Reiner Luttmann 《Engineering in Life Science》2017,17(12):1281-1294
This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with Pichia pastoris. In a feasibility study, Partial Least Squares Regression (PLSR) models were created off‐line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSEcvrel range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on‐line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSEPrel for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed‐forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L. 相似文献
9.
Ghosh D 《Biometrics》2003,59(4):992-1000
Due to the advent of high-throughput microarray technology, it has become possible to develop molecular classification systems for various types of cancer. In this article, we propose a methodology using regularized regression models for the classification of tumors in microarray experiments. The performances of principal components, partial least squares, and ridge regression models are studied; these regression procedures are adapted to the classification setting using the optimal scoring algorithm. We also develop a procedure for ranking genes based on the fitted regression models. The proposed methodologies are applied to two microarray studies in cancer. 相似文献
10.
Fernando T. Maestre 《Diversity & distributions》2004,10(1):21-29
Richness and diversity of perennial plant species were evaluated in 17 Stipa tenacissima steppes along a degradation gradient in semiarid SE Spain. The main objective of the study was to evaluate the relative importance of historical human impacts, small‐scale patch attributes and environmental factors as determinants of perennial plant species richness and diversity in S. tenacissima steppes, where vegetation is arranged as discrete plant patches inserted on a bare ground matrix. Partial least squares regression was used to determine the amount of variation in species richness and diversity that could be significantly explained by historical human impacts, patch attributes, and environmental factors together and separately. They explained up to 89% and 69% of the variation in species richness and diversity, respectively. In both cases, the predictive power of patch attributes models was higher than that of models consisting of abiotic characteristics and variables related to human impact, suggesting that patch attributes are the major determinants of species richness and diversity in semiarid S. tenacissima steppes. However, patch attributes alone are not enough to explain the observed variation in species richness and diversity. The area covered by late‐successional sprouting shrubs and the distance between consecutive patches were the most influencing individual variables on species richness and diversity, respectively. The implications of these results for the management of S. tenacissima steppes are discussed. 相似文献
11.
Esen Sokullu İsmail Murat Palabıyık Feyyaz Onur İsmail Hakkı Boyacı 《Engineering in Life Science》2010,10(4):297-303
Lactic, fumaric and malic acids are commonly used in food and pharmaceutical industries. During microbial production of these compounds, it is important to determine their concentrations in the fermentation broth with a rapid and sensitive method. Spectrophotometry is commonly used. However, UV‐spectral overlap between these organic acids makes it difficult to determine each of them individually from the mixture. In order to overcome this problem, statistical methods, namely principal component regression (PCR) and partial least squares‐1 methods, were tested and compared with conventional HPLC techniques. The absorbance data matrix was obtained by measuring the absorbances of 21 ternary mixtures of lactic, fumaric and malic acids in a wavelength range of 210–260 nm. Calibration and validation were performed by using the data obtained in a mixture of these organic acids. The prediction abilities of the methods were tested by applying them to fermentation broths. The precision of the PCR method was better than that of the partial least squares‐1 method. In the PCR method, the correlation coefficients between actual and predicted concentrations of the organic acids were calculated as 0.970 for lactic acid and 0.996 for fumaric acid in fermentation broths. The concentration of malic acid was not detected due to its low concentration in samples. These results show that the PCR method can be applied for simultaneous determination of lactic, fumaric and malic acids in fermentation broths. 相似文献
12.
Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R2 values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape. 相似文献
13.
Mohamed A. Korany Azza A. Gazy Essam F. Khamis Marwa A. A. Ragab Miranda F. Kamal 《Luminescence》2018,33(4):742-750
This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re‐weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (?F and F‐ratio) under ideal or non‐ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non‐ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. 相似文献
14.
José Alexandre Felizola Diniz-Filho Luis Mauricio Bini Bradford A. Hawkins† 《Global Ecology and Biogeography》2003,12(1):53-64
Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro‐scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least‐squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non‐detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de‐emphasized predictors with strong autocorrelation and long‐distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation. 相似文献
15.
Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R2 = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R2 by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy. 相似文献
16.
Yaoyang Xu Andrew W. Schroth Peter D. F. Isles Donna M. Rizzo 《Freshwater Biology》2015,60(9):1841-1853
- Although commonly used by those tasked with lake management, the statistical approach of data averaging (DA) followed by ordinary least‐squares regression (OLSR) to generate nutrient limitation models is outdated and may impede the understanding and successful management of lake eutrophication.
- Using a 21‐year data set from Lake Champlain as a case study, the traditional DA‐OLSR‐coupled approach was re‐evaluated and improved to quantify the cause–effect relationships between chlorophyll (Chl) and total nitrogen (TN) or total phosphorus (TP).
- We confirmed that the commonly used DA‐OLSR approach results in misleading cause–effect nutrient limitation inferences by illustrating how the process of DA reduces the range of data distribution considered and masks meaningful temporal variation observed within a given period.
- Our model comparisons demonstrate that using quantile regression (QR) to fit the upper boundary of the response distribution (99th quantile model) is more robust than the OLSR analysis for generating eutrophication models and developing nutrient management targets, as this method reduces the effects of unmeasured factors that plague the OLSR‐derived model. Because our approach is statistically in line with the ecological ‘law of the minimum’, it is particularly powerful for inferring resource limitation with broad potential utility to the ecological research community.
- By integrating percentile selection (PS) with QR‐derived model output, we developed a PS‐QR‐coupled approach to quantify the relative importance of TN and TP reductions in a eutrophic system. Utilising this approach, we determined that the reduction in TP to meet a specific Chl target should be the first priority to mitigate eutrophication in Lake Champlain. The structure of this statistically robust and straightforward approach for developing nutrient reduction targets can be easily adopted as an individual lake‐specific tool for the research and management of other lakes and reservoirs with similar water quality data sets.
- Moreover, the PS‐QR‐coupled approach developed here is also of theoretical importance to understanding and modelling the interacting effects of multiple limiting factors on ecological processes (e.g. eutrophication) with broad application to aquatic research.
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Two lanthanide complexes, namely 5-aminosalicylic acid ethylenediaminetetraacetate europium(III) (5As-EDTA-Eu3+) and 4-aminosalicylic acid ethylenediaminetetraacetate terbium(III), were evaluated for the analysis of carbonic anhydrase, human serum albumin (HSA), and gamma-globulin. Quantitative analysis is based on their luminescence enhancement upon protein binding and qualitative analysis on their lifetime capability to recognize the binding protein. Analytical figures of merit are presented for the three proteins. The limits of detection with 5As-EDTA-Eu3+ are at the parts per billion level. Partial least square regression analysis is used to determine HSA and gamma-globulin in binary mixtures without previous separation at the concentration ranges typically found in clinical tests of human blood serum. 相似文献