Effects of climate and land-cover change on the conservation status of gibbons

Species shift their distribution in response to climate and land-cover change, which may result in a spatial mismatch between currently protected areas (PAs) and priority conservation areas (PCAs). We examined the effects of climate and land-cover change on potential range of gibbons and sought to identify PCAs that would conserve them effectively. We collected global gibbon occurrence points and modeled (ecological niche model) their current and potential 2050s ranges under climate-change and different land-cover-change scenarios. We examined change in range and PA coverage between the current and future ranges of each gibbon species. We applied spatial conservation prioritization to identify the top 30% PCAs for each species. We then determined how much of the PCAs are conserved in each country within the global range of gibbons. On average, 31% (SD 22) of each species’ current range was covered in PAs. PA coverage of the current range of 9 species was <30%. Nine species lost on average 46% (SD 29) of their potential range due to climate change. Under climate-change with an optimistic land-cover-change scenario (B1), 12 species lost 39% (SD 28) of their range. In a pessimistic land-cover-change scenario (A2), 15 species lost 36% (SD 28) of their range. Five species lost significantly more range under the A2 scenario than the B1 scenario (p = 0.01, SD 0.01), suggesting that gibbons will benefit from effective management of land cover. PA coverage of future range was <30% for 11 species. On average, 32% (SD 25) of PCAs were covered by PAs. Indonesia contained more species and PCAs and thus has the greatest responsibility for gibbon conservation. Indonesia, India, and Myanmar need to expand their PAs to fulfill their responsibility to gibbon conservation. Our results provide a baseline for global gibbon conservation, particularly for countries lacking gibbon research capacity.     

Identifying socioeconomic and biophysical factors driving forest loss in protected areas

Protected areas (PAs) are a commonly used strategy to confront forest conversion and biodiversity loss. Although determining drivers of forest loss is central to conservation success, understanding of them is limited by conventional modeling assumptions. We used random forest regression to evaluate potential drivers of deforestation in PAs in Mexico, while accounting for nonlinear relationships and higher order interactions underlying deforestation processes. Socioeconomic drivers (e.g., road density, human population density) and underlying biophysical conditions (e.g., precipitation, distance to water, elevation, slope) were stronger predictors of forest loss than PA characteristics, such as age, type, and management effectiveness. Within PA characteristics, variables reflecting collaborative and equitable management and PA size were the strongest predictors of forest loss, albeit with less explanatory power than socioeconomic and biophysical variables. In contrast to previously used methods, which typically have been based on the assumption of linear relationships, we found that the associations between most predictors and forest loss are nonlinear. Our results can inform decisions on the allocation of PA resources by strengthening management in PAs with the highest risk of deforestation and help preemptively protect key biodiversity areas that may be vulnerable to deforestation in the future.     

长江江豚监测现状及展望

长江江豚(Neophocaena asiaeorientalis)是唯一淡水生活的鼠海豚类,近年来种群数量严重下降,2013年被世界自然保护联盟红色名录列为“极度濒危”,2021年升级为我国国家一级重点保护野生动物,2022年种群有所恢复。长江江豚是长江生态系统健康的指示物种,我国科学家自20世纪50年代开始监测至今,监测方法不断更新完善,对长江江豚现状的了解也越来越充分。该文回顾了截线抽样法、水下被动声学及自动实时监测系统、无人机、环境DNA等监测方法的应用及取得的成效,分析监测方法存在的不足,预测技术发展趋势,提出改进建议,为长江江豚的监测及保护提供基本参考。     

节能环保产业政策工具评述与展望

节能环保产业是打赢污染防治攻坚战的重要支撑,也是推动经济发展的新兴绿色动能。本文利用瓦当设计的政策工具三分法,对当前节能环保产业政策工具进行了分类研究。当前,影响节能环保产业政策的工具主要包括管制型工具、经济型工具、信息型工具三个类型。管制型工具包括指标控制、强制性标准和监督考核等;经济型工具包括财政支持、税收优惠、价格政策、金融政策等;信息型工具包括技术推广机制、产品推广机制等。尽管我国已经初步建立了政策支持体系,但当前政策工具仍然存在一系列不足,管制型工具、经济型工具、信息型工具都有较大的改进调整空间,应当进一步形成规范、合理的管制型制度,以普惠为主而非补助为主的经济型制度,以提供信息服务为主的信息型制度,并以此促进节能环保产业健康、有序发展。     

河北省自然灾害危险性分析及其在主体功能区划评价中的应用

自然灾害危险性是主体功能区划评判的基础指标之一,用于反映特定区域自然灾害发生的可能性和灾害损失的严重性,是评价区域开发支撑条件的一个重要指标。在分析自然灾害危险性指标评价方法、流程及其在主体功能区划评价中的作用的基础上,依据河北省的自然环境特点及主要自然灾害情况,选取其主要灾害要素为评价对象,利用G IS技术对河北省自然灾害危险性进行了综合分析,并在此基础上综合评价了河北省自然灾害对区域发展的影响。     

Using a zeolite medium biofilter to remove organic pollutant and ammonia simultaneously

IntroductionWatershortageproblemisquiteseriousinmanycitiesofChina .Thereuseofmunicipalwastewaterisoneofthekeymethodstoreleasethisproblem .Makeupwaterforrecirculatingcoolingsystemisthelargestwaterusageinmanyfactories .Thewaterqualitystandardsforreclaimedwa…     

湖北省榉树自然种群分布研究

对湖北省天然榉树的自然种群分布现状、种群特征及种群分布格局进行了研究。结果表明：湖北榉树资源主要分布在海拔500~1 000 m地带,不同程度地分割包围在谷底和山腰,地理分布点呈收缩集群分布,种群规模较小;采用方差/均值法对榉树地理分布格局类型进行分析,其值=1.66>1,表明其分布点的地理分布趋于集群分布。湖北榉树种群星散间隔的地理分布形式,使地理分布点间产生空间间隔,成为相互间基因交错的障碍,而较小的种群规模,增加了遗传漂变的机率,导致遗传上的不稳定性增加,影响种群的生存能力,这些是导致物种趋向濒危的重要原因。湖北榉树种群这种分布现状产生的原因,一方面是物种自身的生物学特性及对生境的特定要求所致,另一方面是由于人类对榉树资源的过度开发利用以及人类经济活动导致的生境片段化。应借助于人为帮助,改善生态环境,扩大其种群规模,促进不同种群之间的基因交流,以利保护。     

基于信息融合的自然灾害等级评估方法研究

为对自然灾害灾情等级进行准确评估,在BP神经网络模型的基础上,结合DS证据理论建立基于信息融合的自然灾害灾情等级评估模型。该模型通过对输入的灾害评估指标数据进行分类,建立网络组,对网络组的输出,建立对于各类信任度的基本概率分配函数,最后利用DS证据理论融合,从而实现灾害的最终等级评估。在MATLAB环境下,以我国45个自然灾害的灾情历史资料数据为训练样本进行模型训练,并对2009年自然灾害灾情进行评估测试。结果表明,该模型能改善单一BP神经网络不稳定、误差大的缺点,得到较优的结果。     

电流密度对BDD电极电化学矿化吲哚的影响与机制

掺硼金刚石膜(BDD)电极电化学氧化法是去除难降解有机污染物的有效手段.与总有机碳(TOC)等的测定相比,气态中间产物的逸出量能够更直观有效地反映有机物的矿化程度与去除效果.本研究以吲哚为代表性污染物,通过对比不同电流密度(10、20和30 m A·cm-2)下BDD电极对吲哚的去除率与矿化率,结合降解过程中碳和氮形态的变化与守恒情况,分析吲哚的降解机制.结果表明,BDD电极对吲哚有良好的去除效果,电流密度为10、20、30 m A·cm-2时,吲哚达到100%去除的时间分别为8、5和4 h;TOC去除率、CO_2产生量均随电流密度的增加而增大,证明矿化率与电流密度成正相关;电解产生的CO_2气体与TOC、无机碳(TIC)构成了碳守恒体系.4~5 h时,体系TOC、TON和CO_2产生量均没有变化,表明电解产生的靛红具有较高的稳定性,此时为中间产物积累阶段;XPS表征进一步证实了中间产物靛红、苯醌等在电极表面的吸附,随着电解时间的延长,这些吸附的中间产物可进一步被降解.本研究从气态产物检测及碳氮形态分析与守恒的角度阐释吲哚矿化过程,对于辅助揭示有机物的电解过程有重要意义.     

Toward sustainable use of natural resources: Nexus between resource rents,affluence, energy intensity and carbon emissions in developing and transition economies

Sustainable use of natural resources would entail ensuring that derived economic benefits today do not undermine the welfare of generations to come. On this basis, this study examines the nexus between natural resource rents and carbon dioxide (CO₂) emissions disaggregated into production and consumption-based (i.e., trade-adjusted) CO₂ emissions for a selected panel of 45 developing and transition economies over the period 1995–2017. The empirical model also incorporates the impacts of population, affluence, and energy intensity. The results show that affluence increases production-based CO₂ emissions by 1.407%, with the EKC's predicted inverted U-shaped curve only explaining consumption-based CO₂ emissions. Economic reliance on natural resource rents and energy intensification contribute 0.022% and 0.766%, respectively, to CO₂ emissions embedded in territorial production inventories and 0.035% and 0.583%, respectively, to CO₂ emissions embedded in consumption inventories. The bootstrap non-causality test shows that historical data on each variable has significant predictive power for future CO₂ emissions from both sources. The historical information about natural resource rents has significant predictive power over the future levels of affluence and energy intensity. Clearly, the results show that the environmental impact of natural resource rents is stronger when CO₂ emissions are adjusted for trade and varies among the countries, with Bangladesh, Guinea, India, Malaysia, Mexico, Nigeria, Pakistan, Saudi Arabia, Vietnam, and Zimbabwe among the most affected countries. Overall, this study provides motivation for policies to keep the use of natural resources within sustainable limits.