Applying network theory to prioritize multispecies habitat networks that are robust to climate and land‐use change

Designing connected landscapes is among the most widespread strategies for achieving biodiversity conservation targets. The challenge lies in simultaneously satisfying the connectivity needs of multiple species at multiple spatial scales under uncertain climate and land‐use change. To evaluate the contribution of remnant habitat fragments to the connectivity of regional habitat networks, we developed a method to integrate uncertainty in climate and land‐use change projections with the latest developments in network‐connectivity research and spatial, multipurpose conservation prioritization. We used land‐use change simulations to explore robustness of species’ habitat networks to alternative development scenarios. We applied our method to 14 vertebrate focal species of periurban Montreal, Canada. Accounting for connectivity in spatial prioritization strongly modified conservation priorities and the modified priorities were robust to uncertain climate change. Setting conservation priorities based on habitat quality and connectivity maintained a large proportion of the region's connectivity, despite anticipated habitat loss due to climate and land‐use change. The application of connectivity criteria alongside habitat‐quality criteria for protected‐area design was efficient with respect to the amount of area that needs protection and did not necessarily amplify trade‐offs among conservation criteria. Our approach and results are being applied in and around Montreal and are well suited to the design of ecological networks and green infrastructure for the conservation of biodiversity and ecosystem services in other regions, in particular regions around large cities, where connectivity is critically low.     

The representation of cyanobacteria life cycle processes in aquatic ecosystem models

Two simplified versions of a numerical life cycle model for diazotrophic cyanobacteria (of the order Nostocales) are developed and evaluated. Both consider two-life cycle stages, one growing, nitrogen-fixing stage and one stage that combines the resting, germinating and vegetative stages. The versions differ in the vertical resolution of the non-diazotrophic stage: version 1 collects the biomass in one layer at the bottom, version 2 considers sinking and rising of biomass explicitly. The results of the two versions are compared with a complex cyanobacteria life cycle model which describes four different life cycle stages each with two internal quotas for energy and nitrogen. The two simplified approaches show a good agreement with respect to the main characteristics of cyanobacteria dynamics (timing and duration of blooms, magnitude of nitrogen fixation, interannual variability). Our model study shows that both simplified approaches are suitable to be implemented into three-dimensional coastal or lake models.     

COMPARISON OF TWO AUTOREGRESSIVE MODELS FOR MONTHLY STREAM FLOW GENERATION1

ABSTRACT: Two autoregressive models for monthly stream flow generation are compared based on the reproduction of the historical record in terms of several important statistics such as the mean, standard deviation, skewness coefficient, correlation coefficient, and the reservoir storage components. In the comparison, both theoretical considerations and practical applications are employed to evaluate the performance of each model.     

我国林地土壤有效态镉的影响因素及拟合模型

选取全国13个具有代表性的不同气候带和植被类型的森林土壤,通过外源添加重金属镉(Cd),比较分析土壤Cd的固-液分配系数(Kd)和有效态Cd的分布特征,探讨了土壤/土壤溶液性质对Kd及土壤有效态Cd的影响,并建立了土壤Kd及有效态Cd的拟合模型.结果表明,在不同浓度Cd处理的土壤中,其Kd值的变化范围为0.91～623...     

Climatic Change, Wildfire, and Conservation

Abstract:  Climatic variability is a dominant factor affecting large wildfires in the western United States, an observation supported by palaeoecological data on charcoal in lake sediments and reconstructions from fire-scarred trees. Although current fire management focuses on fuel reductions to bring fuel loadings back to their historical ranges, at the regional scale extreme fire weather is still the dominant influence on area burned and fire severity. Current forecasting tools are limited to short-term predictions of fire weather, but increased understanding of large-scale oceanic and atmospheric patterns in the Pacific Ocean (e.g., El Niño Southern Oscillation, Pacific Decadal Oscillation) may improve our ability to predict climatic variability at seasonal to annual leads. Associations between these quasi-periodic patterns and fire occurrence, though evident in some regions, have been difficult to establish in others. Increased temperature in the future will likely extend fire seasons throughout the western United States, with more fires occurring earlier and later than is currently typical, and will increase the total area burned in some regions. If climatic change increases the amplitude and duration of extreme fire weather, we can expect significant changes in the distribution and abundance of dominant plant species in some ecosystems, which would thus affect habitat of some sensitive plant and animal species. Some species that are sensitive to fire may decline, whereas the distribution and abundance of species favored by fire may be enhanced. The effects of climatic change will partially depend on the extent to which resource management modifies vegetation structure and fuels.     

Translating habitat class to land cover to map area of habitat of terrestrial vertebrates

Area of habitat (AOH) is defined as the “habitat available to a species, that is, habitat within its range” and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.     

COMPARISON OF REGRESSION AND TIME-SERIES METHODS FOR SYNTHESIZING MISSING STREAMFLOW RECORDS1

ABSTRACT: Regression and time-series techniques have been used to synthesize and predict the stream flow at the Foresta Bridge gage from information at the upstream Pohono Bridge gage on the Merced River near Yosemite National Park. Using the available data from two time periods (calendar year 1979 and water year 1986), we evaluated the two techniques in their ability to model the variation in the observed flows and in their ability to predict stream flow at the Foresta Bridge gage for the 1979 time period with data from the 1986 time period. Both techniques produced reasonably good estimates and forecasts of the flow at the downstream gage. However, the regression model was found to have a significant amount of autocorrelation in the residuals, which the time-series model was able to eliminate. The time-series technique presented can be of great assistance in arriving at reasonable estimates of flow in data sets that have large missing portions of data.     

健康风险评估方法在中国重金属污染中的应用及暴露评估模型的研究进展

经济的快速发展导致中国环境质量日趋恶化.随着健康意识的增强,人们越来越重视污染物暴露人群的健康风险评估.与其他污染物相比,重金属污染区域广,重金属暴露人群多且集中.为了研究重金属暴露条件下人群的健康风险,USEPA 模型、统计模型、地理信息系统、可给性研究的方法已被中国不同学者应用.暴露评估模型作为污染物暴露人群健康风险评估的主要环节,国外的研究已经比较成熟,但相关研究在中国还处于空白阶段.对中国近年来在城市表层土壤（灰尘）、矿区土壤、膳食、地下水和饮用水、大气颗粒物进行重金属风险评估中应用的健康风险评估方法,进行了归纳和评述,并对欧美常用暴露评估模型：环境暴露评估模型、膳食暴露评估模型进行了介绍.中国健康风险评估工作起步晚,在评估的各环节均存在很大缺陷.随着新技术的发展以及人群对环境健康风险认识的深化,健康风险评估将成为中国热门研究领域之一.污染的环境行为、剂量一效应关系、模型、风险信息等方面,将是未来中国健康风险评估研究的重点.     

水华对沉积物中氮的转化和去除的影响

湖泊富营养化和水华暴发已经成为突出的环境问题之一,了解水华对湖泊氮素的转化和去除的影响,对于削减湖泊氮负荷至关重要.本文研究了水华对沉积物中氮转化和氮转化相关功能基因的影响,并采用结构方程模型分析了水华影响湖泊中氮转化和去除的途径.结果表明,与厌氧氨氧化相比,反硝化作用是太湖沉积物氮削减的主要途径,对沉积物中总溶解性氮去除率的解释度为42. 3%.水华可以直接造成沉积物中TDN和TOC量的增加,提高厌氧氨氧化菌、nir S和nir K的基因丰度,并且间接提高沉积物中氨氮和硝酸盐的浓度,通过增强厌氧氨氧化和反硝化过程加速沉积物中氮的去除.