Estimating the accuracy of psychological scales using longitudinal data

This article presents a method for estimating the accuracy of psychological screening scales using receiver operating characteristic curves and associated statistics. Screening scales are typically semicontinuous within a known range with distributions that are nearly symmetric when the target condition is present and highly skewed when the condition is absent. We model screening scale outcomes using truncated normal distributions that accommodate these different distributional shapes and use subject-specific random effects to adjust for multiple assessments within individuals. Using the proposed model, we estimate the accuracy of the Symptom Checklist as a measure of major depression from a repeatedly screened sample of patients.     

Construction and Comparison of Two Receiver Operating Characteristic Curves Derived from the Same Samples

Receiver operating characteristic (ROC) curves are used to describe the performance of diagnostic procedures. This paper proposes a simple method for the statistical comparison of two ROC curves derived from the same set of patients and the same set of healthy subjects. Generalization to studies involving more than two screening factors is straightforward. This method does not require the calculation of variances of the areas or difference of areas under the curves.     

A comparison of methods for predicting vegetation type

Predictive modeling of vegetation patterns has wide application in vegetation science. In this paper I discuss three methods of predictive modeling using data from the alpine treeline ecotone as a case study. The study area is a portion of Glacier National Park, Montana. Parametric general linear models (GLM), artificial neural networks (ANN) and classification tree (CT) methods of predicting vegetation type are compared to determine the relative strength of each predictive approach and how they may be used in concert to increase understanding of important vegetation – environment relations. For each predictive method, vegetation type within the alpine treeline ecotone is predicted using a suite of environmental indicator variables including elevation, moisture potential, solar radiation potential, snow potential index, and disturbance history. Results from each of the predictive methods are compared against the real vegetation types to determine the relative accuracy of the methods.When the entire data field is examined (i.e., not evaluated by smaller spatial aggregates of data) the ANN procedure produces the most accurate predictions (=0.571); the CT predictions are the least accurate (=0.351). The predicted patterns of vegetation on the landscape are considerably different using the three methods. The GLM and CT methods produce large contiguous swaths of vegetation types throughout the study area, whereas the ANN method produces patterns with much more heterogeneity and smaller patches.When predictions are compared to reality at catchment scale, it becomes evident that the accuracy of each method varies depending upon the specific situation. The ANN procedure remains the most accurate method in the majority of the catchments, but both the GLM and PCT produce the most accurate classifications in at least one basin each.The variability in predictive ability of the three methods tested here indicates that there may not be a single best predictive method. Rather it may be important to use a suite of predictive models to help understand the environment – vegetation relationships. The ability to use multiple predictive methods to determine which spatial subunits of a landscape are outliers is important when identifying locations useful for climate change monitoring studies.     

Prediction of plant species distribution in lowland river valleys in Belgium: modelling species response to site conditions

In ecological modelling, limitations in data and their applicability for predictive modelling are more rule than exception. Often modelling has to be performed on sub-optimal data, as explicit and controlled collection of (more) appropriate data would not be feasible. An example of predictive ecological modelling is given with application of generalized additive and generalized linear models fitted to presence–absence records of plant species and site condition data from four nutrient-poor Flemish lowland valleys. Standard regression procedures are used for modelling, although explanatory and response data do not meet all the assumptions implicit in these procedures. Data were non-randomly collected and are spatially autocorrelated; model residuals retain part of that correlation. The scale of most site-condition records does not match the scale of the response variable (species distribution). Hence, interpolated and up-scaled explanatory variables are used. Data are aggregated from distinct phytogeographical regions to allow for generalized models, applicable to a wider population of river valleys in the same region. Nevertheless, ecologically sound models are obtained, which predict well the distribution of most plant species for the Flemish river valleys considered.     

中国北方5种栎属树木多度分布及其对未来气候变化的响应

植物分布与气候之间的关系是预估未来气候变化对生态系统影响的实现基础。以往的物种分布模型通常以物种的分布区或者分布点的物种存在数据作为物种分布的响应变量。相较于物种存在数据, 多度反映了一个物种占用资源并把资源分配给个体的能力, 更能衡量物种对区域生态系统的影响。该研究通过野外调查获取了华北及周边地区1 045个样方的栎属树木多度, 利用广义线性模型、广义加性模型和随机森林模型模拟栓皮栎(Quercus variabilis)、麻栎(Q. acutissima)、槲栎(Q. aliena)、锐齿槲栎(Q. aliena var. acuteserrata)和蒙古栎(Q. mongolica) 5个树种多度的地理分布及未来2个不同时期(2050年和2070年)的潜在分布。结果表明: 随机森林模型对5个栎属树种的多度的拟合结果要优于广义线性模型和广义加性模型; 典型浓度路径(RCP) 8.5下的5个栎属树种在未来两个时期的多度变化幅度都要大于RCP 2.6下的变化, 在超过一半面积的区域中麻栎、槲栎、锐齿槲栎和蒙古栎的多度减少, 其中内蒙古东北部和黑龙江北部地区是5种栎属植物多度减少的集中分布地区。未来气候变化背景下, 需要加强对这几个区域的监测与物种保护。     

Evaluating the effects of climate change on tree species abundance and distribution in the Italian peninsula

Question: What is the effect of climate change on tree species abundance and distribution in the Italian peninsula? Location: Italian peninsula. Methods: Regression tree analysis, Random Forest, generalized additive model and geostatistical methods were compared to identify the best model for quantifying the effect of climate change on tree species distribution and abundance. Future potential species distribution, richness, local colonization, local extinction and species turnover were modelled according to two scenarios (A2 and B1) for 2050 and 2080. Results: Robust Random Forest proved to be the best statistical model to predict the potential distribution of tree species abundance. Climate change could lead to a shift in tree species distribution towards higher altitudes and a reduction of forest cover. Pinus sylvestris and Tilia cordata may be considered at risk of local extinction, while the other species could find potential suitable areas at the cost of a rearrangement of forest community composition and increasing competition. Conclusions: Geographical and topographical regional characteristics can have a noticeable influence on the impact of predicted climate change on forest ecosystems within the Mediterranean basin. It would be highly beneficial to create a standardized and harmonized European forest inventory in order to evaluate, at high resolution, the effect of climate change on forest ecosystems, identify regional differences and develop specific adaptive management strategies and plans.     

广义模型及分类回归树在物种分布模拟中的应用与比较

比较3个应用较广的模拟物种地理分布模型:广义线性模型(GLM)、广义加法模型(GAM)与分类回归树(CART)对中国树种地理分布模拟的优劣,以提出更为合适的模拟物种地理分布模型,并用于预测气候变化对物种地理分布的影响。3个模型对中国15种树种地理分布的模拟研究表明:除对油松、辽东栎分布的模拟精度稍差外,对其余树种分布的模拟精度均较高,其中以GAM模型最好。结合地理信息系统(GIS),比较分析了这3个模型对青冈、木荷、红松和油松4种树种的地理分布模拟效果,结果亦表明:这3个模型均能很好模拟青冈和木荷的地理分布,而GLM模型对红松分布的模拟结果不太理想,3个模型对油松分布的模拟结果均不甚理想,其中以GLM模型最差。基于3个模型对未来气候变化下青冈与蒙古栎地理分布的预测表明:GLM模型与GAM模型对青冈分布的预测结果较为接近,青冈在未来气候变化情景下向西和向北扩展,而CART模型预测青冈在未来气候变化情景下除有向西、向北扩展趋势外,广东和广西南部的青冈分布区将消失;3个模型均预测蒙古栎在未来气候变化情景下向西扩展,扩展面积的大小为:模型的模拟面积>模型>模型。