Long-term change in drivers of forest cover expansion: an analysis for Switzerland (1850-2000)

Regional Environmental Change - The spatial distribution of forests in Europe represents the legacy of centuries of human land use decisions. Due to the limited availability of historical data,...     

印染行业清洁生产技术

论述了目前印染行业水污染的现状及原因，指出调整产业结构、应用新工艺、大力提倡节约用水、清沽生产将是印染行业实现可持续发展的必由之路。     

Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting,generalized additive models,and tree-based methods

Many efforts are underway to produce broad-scale forest attribute maps by modelling forest class and structure variables collected in forest inventories as functions of satellite-based and biophysical information. Typically, variants of classification and regression trees implemented in Rulequest's^© See5 and Cubist (for binary and continuous responses, respectively) are the tools of choice in many of these applications. These tools are widely used in large remote sensing applications, but are not easily interpretable, do not have ties with survey estimation methods, and use proprietary unpublished algorithms. Consequently, three alternative modelling techniques were compared for mapping presence and basal area of 13 species located in the mountain ranges of Utah, USA. The modelling techniques compared included the widely used See5/Cubist, generalized additive models (GAMs), and stochastic gradient boosting (SGB). Model performance was evaluated using independent test data sets. Evaluation criteria for mapping species presence included specificity, sensitivity, Kappa, and area under the curve (AUC). Evaluation criteria for the continuous basal area variables included correlation and relative mean squared error. For predicting species presence (setting thresholds to maximize Kappa), SGB had higher values for the majority of the species for specificity and Kappa, while GAMs had higher values for the majority of the species for sensitivity. In evaluating resultant AUC values, GAM and/or SGB models had significantly better results than the See5 models where significant differences could be detected between models. For nine out of 13 species, basal area prediction results for all modelling techniques were poor (correlations less than 0.5 and relative mean squared errors greater than 0.8), but SGB provided the most stable predictions in these instances. SGB and Cubist performed equally well for modelling basal area for three species with moderate prediction success, while all three modelling tools produced comparably good predictions (correlation of 0.68 and relative mean squared error of 0.56) for one species.     

建筑环境的生命周期分析

生命周期思想是解决环境与社会问题的一种整体性方法.这种方法是可持续建筑概念的关键.LCA(生命周期分析,或者生命周期评价)是将生命周期思想应用于建筑和建造业的一种重要工具.LCA可以提供有关物质和能源流动的重要信息.由于它很难应用于建筑物本身,所以关注的焦点越业越集中在对建筑环境实施更全面的分析上.通过LCA获得的知识最好是作为综合设计方法的一部分进行应用.在大多数项目中,如果不把LCA与数量调查或者能源模拟等其他手段结合起来就无法获得完整的LCA.将LCA应用于政策制定和需要优先考虑的问题依地区和经济条件而有所不同.     

Natural forest regrowth as a proxy variable for agricultural land abandonment in the Swiss mountains: a spatial statistical model based on geophysical and socio-economic variables

In many European mountain regions, natural forest regrowth on abandoned agricultural land and the related consequences for the environment are issues of increasing concern. We developed a spatial statistical model based on multiple geophysical and socio-economic variables to investigate the pattern of natural forest regrowth in the Swiss mountain area between the 1980s and 1990s. Results show that forest regrowth occurred primarily in areas with low temperature sum, intermediate steepness and soil stoniness as well as close to forest edges and relatively close to roads. Model results suggest that regions with weak labor markets are favored in terms of land abandonment and forest regrowth. We could not find an effect of population change on land abandonment and forest regrowth. Therefore, we conclude that decision makers should consider non-linearities in the pattern of forest regrowth and the fact that labor markets have an effect on land abandonment and forest regrowth when designing measures to prevent agricultural land abandonment and natural forest regrowth in the Swiss mountains.     

Interactive effects of plant species diversity and elevated CO2 on soil biota and nutrient cycling

Terrestrial ecosystems consist of mutually dependent producer and decomposer subsystems, but not much is known on how their interactions are modified by plant diversity and elevated atmospheric CO2 concentrations. Factorially manipulating grassland plant species diversity and atmospheric CO2 concentrations for five years, we tested whether high diversity or elevated CO2 sustain larger or more active soil communities, affect soil aggregation, water dynamics, or nutrient cycling, and whether plant diversity and elevated CO2 interact. Nitrogen (N) and phosphorus (P) pools, symbiotic N2 fixation, plant litter quality, soil moisture, soil physical structure, soil nematode, collembola and acari communities, soil microbial biomass and microflora community structure (phospholipid fatty acid [PLFA] profiles), soil enzyme activities, and rates of C fluxes to soils were measured. No increases in soil C fluxes or the biomass, number, or activity of soil organisms were detected at high plant diversity; soil H2O and aggregation remained unaltered. Elevated CO2 affected the ecosystem primarily by improving plant and soil water status by reducing leaf conductance, whereas changes in C cycling appeared to be of subordinate importance. Slowed-down soil drying cycles resulted in lower soil aggregation under elevated CO2. Collembola benefited from extra soil moisture under elevated CO2, whereas other faunal groups did not respond. Diversity effects and interactions with elevated CO2 may have been absent because soil responses were mainly driven by community-level processes such as rates of organic C input and water use; these drivers were not changed by plant diversity manipulations, possibly because our species diversity gradient did not extend below five species and because functional type composition remained unaltered. Our findings demonstrate that global change can affect soil aggregation, and we advocate that soil aggregation should be considered as a dynamic property that may respond to environmental changes and feed back on other ecosystem functions.     

建筑环境的生命周期分析

生命周期思想是解决环境与社会问题的一种整体性方法.这种方法是可持续建筑概念的关键.LCA(生命周期分析,或者生命周期评价)是将生命周期思想应用于建筑和建造业的一种重要工具.LCA可以提供有关物质和能源流动的重要信息.由于它很难应用于建筑物本身,所以关注的焦点越业越集中在对建筑环境实施更全面的分析上.通过LCA获得的知识最好是作为综合设计方法的一部分进行应用.在大多数项目中,如果不把LCA与数量调查或者能源模拟等其他手段结合起来就无法获得完整的LCA.将LCA应用于政策制定和需要优先考虑的问题依地区和经济条件而有所不同.     

Above- and below-ground methane fluxes and methanotrophic activity in a landfill-cover soil

Landfills are a major anthropogenic source of the greenhouse gas methane (CH(4)). However, much of the CH(4) produced during the anaerobic degradation of organic waste is consumed by methanotrophic microorganisms during passage through the landfill-cover soil. On a section of a closed landfill near Liestal, Switzerland, we performed experiments to compare CH(4) fluxes obtained by different methods at or above the cover-soil surface with below-ground fluxes, and to link methanotrophic activity to estimates of CH(4) ingress (loading) from the waste body at selected locations. Fluxes of CH(4) into or out of the cover soil were quantified by eddy-covariance and static flux-chamber measurements. In addition, CH(4) concentrations at the soil surface were monitored using a field-portable FID detector. Near-surface CH(4) fluxes and CH(4) loading were estimated from soil-gas concentration profiles in conjunction with radon measurements, and gas push-pull tests (GPPTs) were performed to quantify rates of microbial CH(4) oxidation. Eddy-covariance measurements yielded by far the largest and probably most representative estimates of overall CH(4) emissions from the test section (daily mean up to ～91,500μmolm(-2)d(-1)), whereas flux-chamber measurements and CH(4) concentration profiles indicated that at the majority of locations the cover soil was a net sink for atmospheric CH(4) (uptake up to -380μmolm(-2)d(-1)) during the experimental period. Methane concentration profiles also indicated strong variability in CH(4) loading over short distances in the cover soil, while potential methanotrophic activity derived from GPPTs was high (v(max)～13mmolL(-1)(soil air)h(-1)) at a location with substantial CH(4) loading. Our results provide a basis to assess spatial and temporal variability of CH(4) dynamics in the complex terrain of a landfill-cover soil.     

Effects of sample survey design on the accuracy of classification tree models in species distribution models

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys.