The importance of considering multiple interacting species for conservation of species at risk

Conservation of species at risk of extinction is complex and multifaceted. However, mitigation strategies are typically narrow in scope, an artifact of conservation research that is often limited to a single species or stressor. Knowledge of an entire community of strongly interacting species would greatly enhance the comprehensiveness and effectiveness of conservation decisions. We investigated how camera trapping and spatial count models, an extension of spatial-recapture models for unmarked populations, can accomplish this through a case study of threatened boreal woodland caribou (Rangifer tarandus caribou). Population declines in caribou are precipitous and well documented, but recovery strategies focus heavily on control of wolves (Canis lupus) and pay less attention to other known predators and apparent competitors. Obtaining necessary data on multispecies densities has been difficult. We used spatial count models to concurrently estimate densities of caribou, their predators (wolf, black bear [Ursus americanus], and coyote [Canis latrans]), and alternative prey (moose [Alces alces] and white-tailed deer [Odocoileus virginianus]) from a camera-trap array in a highly disturbed landscape within northern Alberta's Oil Sands Region. Median densities were 0.22 caribous (95% Bayesian credible interval [BCI] = 0.08–0.65), 0.77 wolves (95% BCI = 0.26–2.67), 2.39 moose (95% BCI = 0.56–7.00), 2.64 coyotes (95% BCI = 0.45–6.68), and 3.63 black bears (95% BCI = 1.25–8.52) per 100 km². (The white-tailed deer model did not converge.) Although wolf densities were higher than densities recommended for caribou conservation, we suggest the markedly higher black bear and coyote densities may be of greater concern, especially if government wolf control further releases these species. Caribou conservation with a singular focus on wolf control may leave caribou vulnerable to other predators. We recommend a broader focus on the interacting species within a community when conserving species.     

Linkages between measures of biodiversity and community resilience in Pacific Island agroforests

Designing agroecosystems that are compatible with the conservation of biodiversity is a top conservation priority. However, the social variables that drive native biodiversity conservation in these systems are poorly understood. We devised a new approach to identify social–ecological linkages that affect conservation outcomes in agroecosystems and in social‐ecological systems more broadly. We focused on coastal agroforests in Fiji, which, like agroforests across other small Pacific Islands, are critical to food security, contain much of the country's remaining lowland forests, and have rapidly declining levels of native biodiversity. We tested the relationships among social variables and native tree species richness in agroforests with structural equation models. The models were built with data from ecological and social surveys in 100 agroforests and associated households. The agroforests hosted 95 native tree species of which almost one‐third were endemic. Fifty‐eight percent of farms had at least one species considered threatened at the national or international level. The best‐fit structural equation model (R² = 47.8%) showed that social variables important for community resilience—local ecological knowledge, social network connectivity, and livelihood diversity—had direct and indirect positive effects on native tree species richness. Cash‐crop intensification, a driver of biodiversity loss elsewhere, did not negatively affect native tree richness within parcels. Joining efforts to build community resilience, specifically by increasing livelihood diversity, local ecological knowledge, and social network connectivity, may help conservation agencies conserve the rapidly declining biodiversity in the region.     

Impact of authentic leadership on performance: Role of followers' positive psychological capital and relational processes

Authentic leadership has received considerable attention and research support over the past decade. Now the time has come to refine and better understand how it impacts performance. This study investigates the moderating role followers' positive psychological capital (PsyCap) and the mediating role that leader–member exchange (LMX) may play in influencing the relationship between authentic leadership and followers' performance. Specifically, we tested this mediated moderation model with matched data from 794 followers and their immediate leaders. We found that authentic leadership is positively related to LMX and consequently followers' performance, and to a larger degree, among followers who have low rather than high levels of PsyCap. Our discussion highlights the benefits of understanding the roles of relational processes and followers' positive psychological resources involved in the effectiveness of authentic leadership and how they can be practically implemented. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

·OH自由基介导2,4-二氯酚光降解机理及产物毒性变化特征的理论研究

由于2,4-二氯酚(2,4-dichlorophenol,简称2,4-DCP)具有"三致"作用及潜在的生态风险,其光化学降解机理及产物的生态毒理效应目前成为人们关注的焦点。本研究采用理论化学计算手段,系统阐明了2,4-DCP在水环境中·OH介导的间接光化学转化机理、动力学和转化产物的生态毒性变化特征。结果表明2,4-DCP很容易被·OH氧化降解,其降解主要通过·OH-加成和H-迁移路径进行。在低温条件下·OH-加成路径将占主导,主要形成4,6-二氯苯-1,3-二酚;而当温度超过313 K时,·OH提取酚羟基上的氢原子为主要降解途径,主要形成2-氯苯-对苯醌。尤其在活性物种浓度较低的高温环境中,H-迁移路径有可能生成二噁英。计算毒理学结果表明:H-迁移产物的水生毒性超过·OH-加成产物,甚至超出母体2,4-DCP一个毒性等级。因此,我们建议在以后的环境监测以及风险评估过程中,关注2,4-DCP及其转化产物特别是H-迁移路径的转化产物的生态毒理学问题。     

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.     

Nitro polycyclic aromatic hydrocarbons in atmospheric particulate matter of Athens,Greece

Polycyclic aromatic hydrocarbons (PAHs) and nitro polycyclic aromatic hydrocarbons (N-PAHs) are chemical species of proven mutant and carcinogenic activity. In this study, the concentrations of seven different N-PAHs [2-nitronaphthalene (2N-NAP), 2-nitroflourene (2N-FLU), 2-nitroflouranthene (2N-FLA), 3-nitroflouranthene (3N-FLA), 1-nitropyrene (1N-PYR) and 2-nitropyrene (2N-PYR)] were determined in two fractions of atmospheric particulate matter from the atmosphere of Athens: coarse (2.4 μm?10 μm ) and fine (<2.4 μm ). 3N-FLA was not detected, whereas 1N-PYR, mostly originating from emissions from burning fuel, showed the maximum observed concentrations for both fractions and for the whole experimental period (especially during winter). In addition, 2N-FLA, a secondary nitro-PAH produced by photochemical reactions, showed relatively high values. Analysis of statistical data for N-PAH concentrations, using clustering technique, showed that: (1) 1N-NAP, 2N-FLU and 1N-PYR are mainly produced by direct burning; and (2) photochemical reactions are the dominant sources of 2N-NAP, 2N-PYR and 2N-FLA.