Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

Vegetation, Soil, and Animal Indicators of Rangeland Health

We studied indicators of rangeland health on benchmark sites with long, well documented records of protection from stress by domestic livestock or histories of environmental stress and vegetation change. We measured ecosystem properties (metrics) that were clearly linked to ecosystem processes. We focused on conservation of soil and water as key processes in healthy ecosystems, and on maintenance of biodiversity and productivity as important functions of healthy ecosystems. Measurements from which indicators of rangeland health were derived included: sizes of unvegetated patches, cover and species composition of perennial grasses, cover and species composition of shrubs and herbaceous perennials, soil slaking, and abundance and species composition of the bird fauna. Indicators that provided an interpretable range of values over the gradient from irreversibly degraded sites to healthy sites included: bare patch index, cover of long-lived grasses, palatability index, and weighted soil surface stability index. Indicators for which values above a threshold may serve as an indicator of rangeland health include: cover of plant species toxic to livestock, cover of exotic species, and cover of increaser species. Several other indicator metrics were judged not sensitive nor interpretable. Examples of application of rangeland health indicators to evaluate the success of various restoration efforts supported the contention that a suite of indicators are required to assess rangeland health. Bird species diversity and ant species diversity were not related to the status of the sample site and were judged inadequate as indicators of maintenance of biodiversity.     

Comparative analysis of forest canopy mapping methods for the Sundarban biosphere reserve,West Bengal,India

The canopy density of forests is highly influenced by population pressure which cause forest fragmentations, deforestations, forest degradation and also land reclamation for infrastructure and agriculture. This study was envisaged with the objective of mapping the forest canopy density with two different methods by using Landsat 8 OLI dataset of the year 2016 after mapping the vegetation indices. One of the two methods is the semi-expert FCD mapper model, while the other model is newly developed by us and consists of eight vegetation indices. After running the models, the results of both the models were compared. It was found that for the semi-expert FCD model, the three classes viz. high canopy density, moderate canopy and low canopy covered an area of 81,615.51 ha (40%), 84,474.72 ha (41%) and 38,844.18 ha (19%), respectively. And for the modified FCD model, the same classes covered 69,134.670 ha (37%), 84,062.250 ha (45%) and 32,529.150 ha (18%), respectively. It was observed that the difference between semi-expert FCD model and the modified FCD model’s accuracy is about 1.75% and difference in Kappa statistics is 0.0362. Thus, the modified model is more accurate than the semi-expert FCD model and gives us more detailed canopy density map than the semi-expert FCD map.

     

Increasing food production and mitigating agricultural greenhouse gas emissions in the European Union: impacts of carbon pricing and calorie production targeting

Environmental Economics and Policy Studies - This study focuses on the links between food production and greenhouse gas emissions in the European Union. The analysis relies on two sets of...     

Illegal landfill in Italy (EU)—a multidisciplinary approach

Illegal dumping of solid waste is a matter of recent concern because of its numerous impacts. The regulatory framework of the European Union and Italy is disclosed. Degradation of air, bodies of water, groundwater, and soil can occur in the illegal landfill and surrounding sites causing e.g. acidification, eutrophication global warming and photochemical smog. Animals and plants are subjected to landfill borne pollutants that can damage them seriously. Landfill pollutants have also harmful effects on human beings raising the risk of illness such as cancer and cardiorespiratory disease. Finally illegal landfilling practices lead to economical, aesthetic impacts, and sociological discomfort that lead to multiple collective actions, blockages, and information campaigns, rising sense of belonging.     

基于风量-风压复合特征的通风系统阻变型故障诊断

为了避免风量单一特征进行故障位置诊断的不适定性,提出基于风量-风压复合特征的故障位置诊断方法,实现特征信息的多维互补,提高故障位置诊断的准确度。利用蒙特卡洛方法生成大致满足实际故障风阻值分布的故障仿真样本,为了避免不同变量之间不同量纲、不同数量级造成的数据损失,对原始风量、风压数据进行标准化处理,并分别以风量单一特征、风压单一特征、风量-风压复合特征作为支持向量机（SVM）的输入,构建通风系统阻变型故障位置诊断模型。通过故障模拟实验研究表明:风量、风压单一特征进行故障位置诊断的准确度分别为89.80%,90.34%,风量-风压复合特征进行故障位置诊断的准确度为98.23%,说明风量-风压复合特征进行故障诊断可以消除风量、风压单一特征进行故障诊断的不适定性,提高故障诊断的准确度。     

Heavy metals detection in river water with cantilever nanobiosensor

Abstract

Heavy metals can be highly toxic depending on the dose and the chemical form. In this context, sensing devices such as nanobiosensors have been presented as a promising tool to monitor contaminants at micro and nanoscale. In this work, cantilever nanobiosensors with phosphatase alkaline were developed and applied to detect heavy metals (Pb, Ni, Cd, Zn, Co, and Al) in river water. The nanobiosensor surface was functionalized by the self-assembled monolayers (SAM) technique using 16-mercaptohexadecanoic acid, N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide (EDC) and N- hydroxysuccinimide (NHS), and phosphatase alkaline enzyme. The sensing layer deposited on the cantilever surface presented a uniform morphology, at nanoscale, with 80?nm of thickness. The nanobiosensor showed a detection limit in the ppb range and high sensitivity, with a stability of fifteen days. The developed cantilever nanobiosensor is a simple tool, suitable for the direct detection of contaminants in river water.     

Effects of air filtration on spring wheat grown in open-top field chambers at a rural site. II. Effects on mineral partitioning, sulphur and nitrogen metabolism and on grain quality

In 1988 the effect of ambient levels of air pollutants on the nutrients status and grain quality of spring wheat (Triticum aestivum cv. Pelican) was investigated by comparing plants grown in open-top chambers (OTC) ventilated with ambient air (NF treatments) and charcoal-filtered air (CF treatments) at a rural site (Tervuren, Belgium). Spring wheat cultivated in NF OTC showed only minor differences in the P, K, Ca, Mg, Mn and Na concentrations of the different plant parts at final harvest, as well as organic and inorganic S fractions, compared to those of the plants grown in CF air. The plants' total P content was reduced, as well as the P and K concentration of the flour. The total S concentration of the flour was increased by 4%. Effects on N concentrations and grain quality were much more pronounced. At final harvest the N concentrations of straw and flour of the NF air treated plants were much higher compared to CF air. However, the N content of the aerial biomass and the grain N yield were not significantly affected, implying a reduction of other structual compounds. Nitrogen harvest index (NHI) and the ratio of NHI over grain harvest index (GHI), indicated a significant reduction of N translocation from the above-ground biomass to the grain. Changes in the N status and partitioning of spring wheat had an effect on the baking quality of wheat flour. Several parameters that are commonly used as an indication of baking quality have been significantly increased in the NF treatment: total protein concentration, Zeleny value, dry and wet gluten concentration. A slightly increased Hagberg value indicated a reduced alpha-amylase activity. The possibility of foliar N uptake as an additional N source, especially after anthesis and implications of increased protein production instead of carbohydrate synthesis are discussed.     

A Mathematical Framework for Resilience: Dynamics,Uncertainties, Strategies,and Recovery Regimes

Resilience is a rehashed concept in natural hazard management—resilience of cities to earthquakes, to floods, to fire, etc. In a word, a system is said to be resilient if there exists a strategy that can drive the system state back to “normal” after any perturbation. What formal flesh can we put on such a malleable notion? We propose to frame the concept of resilience in the mathematical garbs of control theory under uncertainty. Our setting covers dynamical systems both in discrete or continuous time, deterministic or subject to uncertainties. We will say that a system state is resilient if there exists an adaptive strategy such that the generated state and control paths, contingent on uncertainties, lay within an acceptable domain of random processes, called recovery regimes. We point out how such recovery regimes can be delineated thanks to so-called risk measures, making the connection with resilience indicators. Our definition of resilience extends others, be they “à la Holling” or rooted in viability theory. Indeed, our definition of resilience is a form of controllability for whole random processes (regimes), whereas others require that the state values must belong to an acceptable subset of the state set.     

整流幕对香溪河库湾水温特性影响数值模拟

为了研究整流幕的作用机理并解决三峡水库支流库湾的水华问题,基于香溪河库湾2010年1~12月的水环境监测数据,利用CE QUAL W2模型,分析比较了整流幕设置前后香溪河库湾水温的时空变化规律。结果表明：整流幕的作用效果因季节、位置、高度的差异而有所不同。整流幕可改变其上游侧水温,尤其是表层水温,夏秋季对水温的影响较大,降温时段也集中在夏秋季;整流幕高度越大,越靠近库湾上游,对水温的影响越明显;整流幕拦截水流的同时,也会促进上下层水体的掺混,减弱水温分层。