Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

A review of recent developments in lake modelling

This paper reviews the lake models published the last five years, mainly in Ecological Modelling. The review shows that structurally dynamic modelling and coupling between hydrodynamic and ecological models are applied increasingly. A number of processes that have not been included in lake models before have been proposed. It has been shown that these additional processes in specific case studies are significant, for instance the competition between phytoplankton and macrophytes or cyanobacteria growth and growth of mussels. It is recommended to study these models for the development of models for case studies where these processes are relevant.     

基于GLM模糊评价在煤矿安全中的应用

煤矿安全评价是现代绿色矿山建设的主要指标之一。为对煤矿安全进行有效的评价,综合分析影响煤矿安全的人员因素、地质因素、技术设备因素、环境因素等各项指标。应用广义线性理论和SAS统计分析方法,建立基于GLM模糊评价模型。通过计算各项指标对应等级的煤矿安全影响权重〖AKω- ij,结合模糊综合评价方法得出煤矿的安全生产状况,同时通过优势比分析各指标的敏感性因子,得出防范指标危险性的有效措施。研究表明:该模型依据标准整理数据,操作性强,较准确地得出煤矿安全的状况,为煤矿的安全评价和管理决策提供依据。     

国际河流管理合作模式的分类及演进规律探讨

论文对国际河流管理合作模式的概念、分类及演进规律进行了探讨。在从合作主体、目标、途径、制度等方面对合作模式进行单维度分类的基础上,借鉴霍尔三维结构图系统分析方法,构建了合作模式三维度分类模型,较全面、准确地概括了当前国际河流管理合作实践的模式类型。最后,综合世界各种典型国际河流管理合作模式的形成和发展过程,从模式要素的发展演变及模式演进的动力机制两个方面入手,详细阐述了国际河流管理合作模式的演进规律。     

Do voluntary pollution reduction programs (VPRs) spur or deter environmental innovation? Evidence from 33/50

We study whether a government-sponsored voluntary pollution reduction program (VPR) promotes or deters the development of new environmental technologies that yield future emission reduction benefits. Using a panel of 127 U.S. manufacturing industries defined by 3-digit SIC classifications over the 1989–2004 period, we estimate impacts of industry-level participation in the 33/50 program, a VPR initiated by government regulators in 1991, on industry-level rates of environmental patenting. We find that higher rates of 33/50 program participation are associated with significant reductions in the number of successful environmental patent applications five to nine years after the program ended.     

Combining heat-transfer and energy budget models to predict thermal stress in Mediterranean intertidal mussels

Recent studies have emphasised that organisms can experience physiological stress well within their geographic range limits. Developing methods for mechanistically predicting the presence, absence and physiological performance of organisms is therefore important because of the ongoing effects of climate change. In this study, we merged a biophysical–ecological (BE) model that estimates the aquatic (high tide) and aerial (low tide) body temperatures of Mytilus galloprovincialis with a Dynamic Energy Budget (DEB) model to predict growth, reproduction and mortality of this Mediterranean mussel in both intertidal and subtidal environments. Using weather and chlorophyll-a data from three Mediterranean sites along the Italian coasts, we show that predictions of sublethal and lethal (acute) stress can potentially explain the observed distribution (both presence and absence) of mussels in the intertidal and subtidal zones, and the maximum size of animals in the subtidal zones. Importantly, our results suggest that different mechanisms limit the intertidal distribution of mussels, and that these mechanisms do not follow a simple latitudinal gradient. At the northernmost site (Palermo), M. galloprovincialis appears to be excluded from the intertidal zone due to persistent exposure to lethal aerial temperatures, whereas at the southernmost sites (Porto Empedocle and Lampedusa) sublethal stress is the most important driver of mussel intertidal distribution. Our predictions provide a set of hypotheses for future work on the role of climate change in limiting intertidal distribution of mussels in the Mediterranean.     

Regression models for some solute distribution equilibria in the terrestrial environment

Sorption coefficients (K_p) of several organochlorine insecticides (OCs) in volcanic ash silt from Central Java are presented.

Selected experimental and estimated octanol‐water partition coefficient (P) values are used to study log‐log regressions with K_p data collected from the literature (PAHs, chlorophenols, phenylureas, chloro‐s‐triazines, carbamates and organophosphorus insecticides) and those of the OCs determined in the present study. Leaching distances and bioactivities in soil are correlated with the K_p values of the pesticides, and with the organic matter and the water content of the soil.     

A critique of the official report on the evacuation of the World Trade Center: continued doubts

This paper criticises the conclusions and the unanswered questions in the National Institute of Standards and Technology (NIST)'s official report on the evacuation of the World Trade Center in New York City, United States, on 11 September 2001. It reviews the extent to which the report disregards several conventional statistical methods and comments on the NIST's refusal to share the machine‐readable data file with the scientific community for replication and further analysis. Problems lie in the sampling methods employed, the treatment of missing data, the use of ordinary least squares (OLS) with binary dependent variables, the failure to document the scalability of the scales used, the lack of tests to check for constant error variance, and the absence of overall fit tests of the model. There are also conceptual and theoretical issues, such as the absence in the report of considerations of the influence of group‐level processes and their impact on the collective behaviour of evacuating collectivities.     

An integrated approach for predicting fates of reintroductions with demographic data from multiple populations

We devised a novel approach to model reintroduced populations whereby demographic data collected from multiple sites are integrated into a Bayesian hierarchical model. Integrating data from multiple reintroductions allows more precise population-growth projections to be made, especially for populations for which data are sparse, and allows projections that account for random site-to-site variation to be made before new reintroductions are attempted. We used data from reintroductions of the North Island Robin (Petroica longipes), an endemic New Zealand passerine, to 10 sites where non-native mammalian predators are controlled. A comparison of candidate models that we based on deviance information criterion showed that rat-tracking rate (an index of rat density) was a useful predictor of robin fecundity and adult female survival, that landscape connectivity and a binary measure of whether sites were on a peninsula were useful predictors of apparent juvenile survival (probably due to differential dispersal away from reintroduction sites), and that there was unexplained random variation among sites in all demographic rates. We used the two best supported models to estimate the finite rate of increase (λ) for populations at each of the 10 sites, and for a proposed reintroduction site, under different levels of rat control. Only three of the reintroduction sites had λ distributions completely >1 for either model. At two sites, λ was expected to be >1 if rat-tracking rates were <5%. At the other five reintroduction sites, λ was predicted to be close to 1, and it was unclear whether growth was expected. Predictions of λ for the proposed reintroduction site were less precise than for other sites because distributions incorporated the full range of site-to-site random variation in vital rates. Our methods can be applied to any species for which postrelease data on demographic rates are available and potentially can be extended to model multiple species simultaneously.     

Predicting species distributions from samples collected along roadsides

Predictive models of species distributions are typically developed with data collected along roads. Roadside sampling may provide a biased (nonrandom) sample; however, it is currently unknown whether roadside sampling limits the accuracy of predictions generated by species distribution models. We tested whether roadside sampling affects the accuracy of predictions generated by species distribution models by using a prospective sampling strategy designed specifically to address this issue. We built models from roadside data and validated model predictions at paired locations on unpaved roads and 200 m away from roads (off road), spatially and temporally independent from the data used for model building. We predicted species distributions of 15 bird species on the basis of point-count data from a landbird monitoring program in Montana and Idaho (U.S.A.). We used hierarchical occupancy models to account for imperfect detection. We expected predictions of species distributions derived from roadside-sampling data would be less accurate when validated with data from off-road sampling than when it was validated with data from roadside sampling and that model accuracy would be differentially affected by whether species were generalists, associated with edges, or associated with interior forest. Model performance measures (kappa, area under the curve of a receiver operating characteristic plot, and true skill statistic) did not differ between model predictions of roadside and off-road distributions of species. Furthermore, performance measures did not differ among edge, generalist, and interior species, despite a difference in vegetation structure along roadsides and off road and that 2 of the 15 species were more likely to occur along roadsides. If the range of environmental gradients is surveyed in roadside-sampling efforts, our results suggest that surveys along unpaved roads can be a valuable, unbiased source of information for species distribution models.