Nitrogen concentrations in mosses indicate the spatial distribution of atmospheric nitrogen deposition in Europe

In 2005/6, nearly 3000 moss samples from (semi-)natural location across 16 European countries were collected for nitrogen analysis. The lowest total nitrogen concentrations in mosses (<0.8%) were observed in northern Finland and northern UK. The highest concentrations (≥1.6%) were found in parts of Belgium, France, Germany, Slovakia, Slovenia and Bulgaria. The asymptotic relationship between the nitrogen concentrations in mosses and EMEP modelled nitrogen deposition (averaged per 50 km × 50 km grid) across Europe showed less scatter when there were at least five moss sampling sites per grid. Factors potentially contributing to the scatter are discussed. In Switzerland, a strong (r² = 0.91) linear relationship was found between the total nitrogen concentration in mosses and measured site-specific bulk nitrogen deposition rates. The total nitrogen concentrations in mosses complement deposition measurements, helping to identify areas in Europe at risk from high nitrogen deposition at a high spatial resolution.     

Land Management in the American Southwest: A State-and-Transition Approach to Ecosystem Complexity

State-and-transition models are increasingly being used to guide rangeland management. These models provide a relatively simple, management-oriented way to classify land condition (state) and to describe the factors that might cause a shift to another state (a transition). There are many formulations of state-and-transition models in the literature. The version we endorse does not adhere to any particular generalities about ecosystem dynamics, but it includes consideration of several kinds of dynamics and management response to them. In contrast to previous uses of state-and-transition models, we propose that models can, at present, be most effectively used to specify and qualitatively compare the relative benefits and potential risks of different management actions (e.g., fire and grazing) and other factors (e.g., invasive species and climate change) on specified areas of land. High spatial and temporal variability and complex interactions preclude the meaningful use of general quantitative models. Forecasts can be made on a case-by-case basis by interpreting qualitative and quantitative indicators, historical data, and spatially structured monitoring data based on conceptual models. We illustrate how science- based conceptual models are created using several rangeland examples that vary in complexity. In doing so, we illustrate the implications of designating plant communities and states in models, accounting for varying scales of pattern in vegetation and soils, interpreting the presence of plant communities on different soils and dealing with our uncertainty about how those communities were assembled and how they will change in the future. We conclude with observations about how models have helped to improve management decision-making.     

NEAR REAL-TIME MAPPING OF THE 1975 MISSISSIPPI RIVER FLOOD IN LOUISIANA USING LANDSAT IMAGERY1

ABSTRACT: The project described in this report was undertaken by the Louisiana State Planning Office to establish the extent of backwater flooding in Louisiana in April 1975. Band 7 Landsat imagery, enlarged to a scale of 1:250,000 was used to visually identify flooded areas. Inundated areas were delineated on overlays keyed to 1:250,000 U.S. Geological Survey topographic quadrangles. Tabular data identifying acres flooded, according to land use type, were derived by merging the flood map overlays with computerized 1972 land use data. Approximately 1.12 million acres of the state were inundated by flood waters. The total acreage and land use types affected by flooding were determined within 72 hours from the time the flood areas were imaged. Flooded maps were prepared for 26 parishes. Field observations were made by Louisiana Cooperative Extension Service county agents in order to determine the accuracy of parish flood maps and flood acreage figures by land use type. Results indicated that this was a fast, accurate, and relatively inexpensive method of compiling flood data for disaster planning and postflood analysis.     

PROBLEMS IN WEIGHTING OF HYETOGRAPHS1

ABSTRACT: It was found that the conventional weighting factor application to hyetograph ordinates results in artificially attenuated storm patterns. A modified weighting procedure is suggested which allows adjustments in the storm timing, peak intensity, and volume but conserves the storm pattern observed at the raingage nearest to the watershed point of interest. The systematic underestimation of peak flood flows, which result from conventional hyetograph weighting, can be avoided by conserving the hyetograph shape from the raingage nearest to any subarea of a modeled watershed and merely applying weighting factors to the rainfall volumes and temporal center of gravity of several hyetographs.     

荧光溶解性有机质EEMs的新旧自组织映射图解析方法比较研究

目前,对地表水体荧光溶解性有机质(Fluorescent Dissolved Organic Matter,FDOM)三维激发发射矩阵(Excitation-Emission Matrices,EEMs)数据的解析仍旧存在挑战.本文结合原始EEMs和从中分离的荧光平行因子分析(Parallel Factor Analysis,PARAFAC)组分,利用自组织映射图(Self-Organizing Maps,SOM),开展了基于传统EEMs-SOM和新型PARAFAC-SOM神经网络模型的复杂荧光数据解析能力对比研究,以此探索和改进EEMs多元解析方法技术体系.模型以已发表论文原始数据为基础构建,研究对象为南太湖重要入湖河流东苕溪水系.结果显示:EEMsSOM模型需依赖常规\"摘峰法\"对各荧光峰做出主观判断,不能有效识别重叠荧光峰,且人为割裂多激发共发射荧光物质峰之间的联系,从而弱化分析结果的实际环境意义,但神经元的模式荧光光谱可视化效果较佳,且操作简便,耗时较少;PARAFAC-SOM模型克服了上述缺陷,可清除EEMs中的噪声或无用信息,大幅降低输入变量数(从1742个降至4个),缩减运算时间,较大地改善了输出结果的准确性,获得无损可靠且实际环境意义较强的结果,但该法的PARAFAC预处理要求较高,工作量较大;两类模型解析结论大体一致,对前期研究进行了全新的阐述和补充,且生动揭示了FDOM在多级河流水体中的空间赋存变化规律及河流的动态污染过程.综上,推荐在今后研究中综合使用两类优势互补的模型以实现对EEMs的深度解析.     

A new approach to the analysis of adjacencies: Potentials for landscape insights

Spatial adjacencies are a key-issue in environmental studies. Adjacency effects have been amply observed for biotic (plants and animals) and abiotic components of ecosystems. Particularly well-documented are the effects from human manufactures onto the contiguous vegetation mosaics of natural and semi-natural areas.In this work we first propose and reformulate association rules analysis (ARA), a relatively new data mining algorithm with very limited scientific applications so far, in the form of an in-depth investigation method of the spatial pattern of landcover and vegetation maps. We applied ARA to two very different study areas in Northern Italy, the first (Ceno valley) having a substantial human footprint and mapped at 1:25,000 scale, the second (Foses valley) being almost natural and mapped at 1:5000 scale.We were able to: (a) detect the entire network of spatial adjacencies among landcover types and (b) quantify the frequency and strength of detected adjacencies. Based on our spatial analysis, we also advanced hypotheses on both natural and man-driven vegetation dynamics. In addition, ARA allowed us to propose an index of naturality based on the discovered contiguities.Results show the skill of the proposed approach to characterize landcover spatial patterns for both mid-resolution and high-resolution maps. Furthermore the proposed approach bears a general interest, since it can be applied to the analysis of any landcover map.     

Can machines recognise stress in plants?

In this paper we show that chlorophyll a fluorescence signals analysed with the self-organizing map (SOM) can be used as a routine tool for the monitoring and classification of pea varieties (Pisum sativum) according to their degree of resistance against drought stress. Fluorescence kinetics measurements were obtained from non-stressed plants. The aim of this study is to evaluate the applicability of artificial intelligence techniques in eco-physiological research. Our goal is to provide a fast tool that will contribute to the knowledge needed to develop strategies that would help to decrease the impact of environmental stress in agriculture and forestry.     

Farmer models of socio-ecologic systems: Application of causal mapping across multiple locations

Modelling ecological or environmental problems has potential to provide understanding of the causes of such problems and to indicate how to better manage them. Özesmi and Özesmi (2004) showed that cognitive or causal mapping can be used to develop maps of socio-ecological systems but these maps were based on stakeholders concerned with one ecosystem. This article shows how maps from a number of different dairy farmers in different locations, but each considering his or her own farm, can be used in meta analysis to make maps that represent how farmers think their farm ecosystem works. It also shows that the combination of causal mapping with the additional technique of Q method provides a useful solution to the practical problem of selecting from a sufficiently broad range of factors with potential to use in a map. Causal mapping in single or multiple locations contributes to the goal of using peoples’ knowledge of ecosystems to improve our understanding of socio-ecological systems.