山西省化肥投入区域分异及环境风险评价

基于1985—2017年化肥施用量和作物播种面积, 采用化肥施用环境风险评价模型, 探讨了山西省化肥施用及环境风险的时空变化特征。结果表明, 山西省化肥施用总量在1985—2017年间整体呈增加趋势, 2017年山西省化肥施用强度为285.14 kg·hm-2, 大多数地区农田化肥投入过量; 其基本趋势是从晋北到晋南逐步增加, 晋西北投入少、晋东南投入高。总化肥施用强度极值比为2.4, 氮肥、磷肥和钾肥的极值比分别是1.9、2.7和3.4。全省11个地市氮磷钾投入比例是1:0.69:0.62, 不尽合理。山西省2017年化肥施用环境总风险指数为0.52, 呈低度环境风险; 氮、磷、钾肥施用的环境风险指数分别为0.50、0.58和0.55。氮肥施用的环境风险处于安全状态; 磷肥的环境风险区域集中在晋东南和晋中地区, 钾肥的环境风险区域集中在晋东南和运城地区, 均呈现中度、严重风险程度并有成片聚集特征。研究结果有利于调控山西农田施肥合理分布并进行分区指导, 制定不同施肥方案, 协调粮食增产和生态保护间的关系, 为防治农业面源污染提供决策参考。     

长期施肥下三类典型农田土壤小麦磷肥利用效率的差异

对我国北方长期施肥下三类典型农田土壤（塿土、潮土和褐潮土）的小麦产量、小麦磷肥农学利用效率、小麦磷肥利用率进行了研究.结果表明：长期施用磷肥处理（氮磷化肥配合施用、氮磷钾化肥配合施用、氮磷钾化肥和秸秆配合施用、氮磷钾化肥和有机肥配合施用）的小麦产量为2914～6219 kg·hm^-2,较不施磷肥处理（不施肥对照、单施化肥氮、氮钾化肥配合施用）提高了2～4倍,各施磷肥处理之间无显著差异.试验起始年施用氮磷钾化肥处理的塿土、潮土和褐潮土上的小麦磷肥农学利用效率分别为17.0、20.3和13.3 kg·kg^-1,小麦磷肥利用率分别为15.3%、31.2%和23.8%;施肥15年后,小麦磷肥农学利用效率每年分别增加3.9、2.5和2.8 kg·kg^-1,小麦磷肥利用率每年分别增加1.3%、0.9%和1.0%.同一类型土壤不同施磷处理间的磷肥农学利用效率和利用率差异不显著.在我国北方地区,长期施用磷肥可以显著提高小麦产量和磷肥利用效率;氮磷钾化肥和有机肥配施处理下,塿土平均每年增长的小麦磷肥农学利用效率和磷肥利用率较潮土和褐潮土高.     

烟草斑须蝽Dolycoris baccarum（L．）的研究

斑须蝽是一种世界性害虫．分布广．寄主种类较多．笔者于1987-1990年在江苏南京江浦,龙潭,苏北的新沂,盱眙地区进行了生物学及其防治的系统研究。表明此虫在长江流域中下游,苏南地区年发生4代,在苏北烟区年发生3代,均以成虫越冬．运用有效积温法则推算基本相符．烟田发生的种群动态与气温,湿度和栖息环境密切相关．第二,三代为害春烟和夏烟．已成为江苏烟草生产上一种重要害虫．     

江苏地区烟粉虱生物型演替研究初报

本文连续5年系统监测了江苏省13个地级市烟粉虱Bemisia tabaci(Gennadius)生物型的发生分布状况,探讨了它们的迁移扩散和演替规律。利用RAPD分子标记和mtDNA COI基因序列进行烟粉虱生物型鉴定,结果表明:江苏地区发生的烟粉虱生物型为B型和Q型。自2005年到2009年,B型烟粉虱在苏南和苏北地区的发生分布频率逐年下降,在苏北地区的发生分布频率由52.58%下降为22.22%,在苏南由56.52%下降为9.37%;而Q型烟粉虱在苏南和苏北的发生分布频率逐年升高,在苏北由47.42%上升至77.78%,在苏南由43.48%上升为90.63%。B型烟粉虱的发生分布范围由全省逐渐向苏北地区缩小,而Q型烟粉虱的发生分布范围逐渐扩大遍及全省;此外,Q型烟粉虱在江苏是由苏中和苏南地区向苏北地区扩散,并逐步取代B型烟粉虱成为江苏地区农作物的主要害虫。     

西南喀斯特典型石漠化生态系统土壤养分生态化学计量特征及其影响因素

喀斯特石漠化生态系统土壤养分元素生态化学计量特征及其对环境变异的生态响应是喀斯特退化森林生态系统恢复重建必需明确的关键科学问题。为探明喀斯特石漠化土壤C、N、P、K养分元素生态化学计量特征,探讨其对环境因子的响应,对西南喀斯特3个典型石漠化调查点(贵州毕节鸭池、清镇红枫湖和关岭-贞丰花江) 90个样方土壤及环境因子调查取样,研究了其土壤有机碳(C)、全氮(N)、全磷(P)及全钾(K)的化学计量特征及其影响因素。结果表明:西南喀斯特典型石漠化生态系统土壤C、N、P、K平均含量分别为45.61、2.54、0.79 g/kg和3.33 g/kg,计量比C∶N、C∶P、C∶K、N∶P、N∶K、P∶K平均值分别为19.56、65.07、23.65、3.45、1.32和0.39。4个土壤养分元素中,K元素表现明显高于其他元素的波动性。土壤养分含量及化学计量比在不同调查点、石漠化等级及植被覆盖率环境均有显著差异。无石漠化环境土壤养分C、N、P含量显著大于潜在、轻度、中度和强度石漠化,而强度石漠化环境土壤养分K含量却显著高于其他等级石漠化。土壤养分含量之间及其与化学计量比之间多具有显著的非线性相关关系。降水、温度、岩石裸露率和土地覆被是西南喀斯特石漠化生态系统土壤养分及其化学计量比最主要的影响因素。研究结果对丰富土壤生态化学计量学科学理论和我国西南喀斯特石漠化退化植被科学恢复具有重要意义。     

江苏省县域森林生态安全评价及空间计量分析

通过考察生态区位因素对森林生态安全的影响,建立评价指标体系,研究其空间相关性的内在效应机制,从而实现森林生态安全的评价与监测。以江苏省80个区县为研究对象,基于2000—2015年面板数据,运用熵权法、专家法及模糊物元法计算森林生态安全指数,然后结合气象类指标及地形类指标计算生态区位系数,再用该系数修正森林ESI,同时结合Arc GIS技术、空间相关性分析、SLM与SEM模型得出如下结论:(1)人口密度、单位面积能源消耗量、退耕还林面积占比等指标权重最大;(2)生态区位系数高值区主要分布在江苏南部少数地区,低值区主要分布在江苏东北部;(3)苏南地区森林生态安全状况整体好于苏北及中东部地区;(4) 2000—2015年,江苏省67.5%的区县森林ESI呈现出较明显下降趋势,反映出江苏省森林生态安全发展状况不容乐观;(5)江苏省县域森林ESI整体空间相关性显著(P≤0.01),但2000—2015年空间聚集程度有所下降,且Low-Low聚类显著性更强;(6)森林ESI在江苏省县域间为扩散效应与回流效应并存。     

东北地区森林资源生态风险评价研究

根据区域生态风险评价的理论和方法体系,对东北地区森林资源进行了生态风险评价．东北林区生态风险问题形成的主要胁迫包括物理胁迫、化学胁迫、生物胁迫和社会胁迫,其权重系数分别为0．30、0．22、0．11和0．37．东北93个林业局中,轻风险占64．4％,中风险占21．2％,重风险占13．3％。严重风险占1．1％,符合偏正态分布．东北区森林生态风险的地域分异明显,靠近长白山区的森林处于轻风险和中风险等级,向西生态风险越来越重,处于重风险和严重风险等级的林业局主要集中在小兴安岭地区．必须加强东北区风险管理和森林景观的修复与重建。     

江苏省县域森林生态安全评价及空间计量分析

通过考察生态区位因素对森林生态安全的影响,建立评价指标体系,研究其空间相关性的内在效应机制,从而实现森林生态安全的评价与监测。以江苏省80个区县为研究对象,基于2000—2015年面板数据,运用熵权法、专家法及模糊物元法计算森林生态安全指数,然后结合气象类指标及地形类指标计算生态区位系数,再用该系数修正森林ESI,同时结合ArcGIS技术、空间相关性分析、SLM与SEM模型得出如下结论:(1)人口密度、单位面积能源消耗量、退耕还林面积占比等指标权重最大;(2)生态区位系数高值区主要分布在江苏南部少数地区,低值区主要分布在江苏东北部;(3)苏南地区森林生态安全状况整体好于苏北及中东部地区;(4)2000—2015年,江苏省67.5%的区县森林ESI呈现出较明显下降趋势,反映出江苏省森林生态安全发展状况不容乐观;(5)江苏省县域森林ESI整体空间相关性显著(P≤0.01),但2000—2015年空间聚集程度有所下降,且Low—Low聚类显著性更强;(6)森林ESI在江苏省县域间为扩散效应与回流效应并存。     

基于RS和GIS的珠江三角洲生态环境脆弱性综合评价

使用空间主成分分析法构建评价指标体系,采用层次分析法确定指标权重,结合遥感数据和地理信息系统软件,对珠江三角洲2004-2008年生态环境脆弱性进行了综合评价并对脆弱性成因进行分析.结果表明:生态环境极度和重度脆弱区主要分布在珠江三角洲中部,占整个研究区面积的34.0％;生态环境中度脆弱区主要分布在珠江三角洲东部,占25.5％;生态环境轻度和微度脆弱区主要分布在珠江三角洲西部,分别占28.7％和l1.8％.中度和轻度脆弱区占整个研究区面积的54.2％,表明珠江三角洲大部分区域的生态环境属中度和轻度脆弱.影响珠江三角洲生态环境脆弱性的自然因素主要有海拔高度、大暴雨日数、水土流失比率、易涝耕地面积比率、归一化植被指数、景观多样性指数,人为因素主要有人口密度、单位面积废水排放量、单位面积废气排放量、土地利用变化、化肥施用强度、农药使用强度、万人机动车拥有量、环保投资指数.极度和重度脆弱区的主要特征是海拔低、洪涝灾害发生频率高、易涝耕地面积多、植被破坏严重、污染强度大和环保投资指数小等.     

施用猪粪对油麦菜产量、硝酸盐含量及土壤养分的影响

应用盆栽试验方法,研究了施用猪粪对西南地区黄壤和紫色土中油麦菜产量、硝酸盐含量及土壤养分的影响.结果表明:施用猪粪能显著提高油麦菜产量,且黄壤中油麦菜增产幅度大于紫色土;油麦菜中硝酸盐、氮磷钾含量与土壤类型及猪粪施用量密切相关,以中国农业科学院制定的蔬菜中硝酸盐污染程度评价标准为依据,在紫色土对照(CK)及1倍猪粪(相当于施纯N 200 kg·hm^-2)处理下油麦菜硝酸盐含量较低,符合一级标准(≤432 mg·kg^-1,轻度污染);其他处理多超过二级标准(≤758 mg·kg^-1,中度污染),但均未超过三级标准(≤1440 mg·kg-¹,重度污染);黄壤中除化肥和8倍猪粪(相当于施纯N 1600 kg·hm^-2)处理下油麦菜硝酸盐含量超过二级标准外,其他各处理均符合一级标准;黄壤和紫色土中表征磷素淋失风险的有效磷临界值分别为96.3和107.7 mg·kg^-1.黄壤的猪粪环境安全容量较紫色土高.施用猪粪能显著提高两种土壤的有机碳和全氮含量.     

Environmental migrants,structural injustice,and moral responsibility

Climate change and environmental problems will force or induce millions of people to migrate. In this article, I describe environmental migration and articulate some of the ethical issues. To begin, I give an account of these migrants that overcomes misleading dichotomies. Then, I focus attention on two important ethical issues: justice and responsibility. Although we are all at risk of becoming environmental migrants, we are not equally at risk. Our risk depends on our temporal position, geographical location, social position, and the kind of society in which we live. We all contribute to environmental problems, but we do not contribute equally. About 11% of the world population is responsible for 50% of carbon emissions. These inequalities raise issues of justice because many of the people who are at high risk have contributed little to the problems. Since the issues of justice are relatively clear and compelling, I focus more attention on issues of responsibility. I use Iris Marion Young’s account of responsibility for structural injustice to address four key questions about moral responsibility and environmental migration.     

生态效率方法研究进展与应用

生态效率同时考虑经济效益和环境效益,是将可持续发展的宏观目标融入中观（区域）和微观（企业）的发展规划与管理中的有效工具。回顾了生态效率的概念和发展过程,分析了其内涵和指标体系,探讨了几种典型计算方法与模型,并介绍了国内外在企业、行业和区域3个层次上的应用实践,讨论和提出了进一步开展生态效率研究的焦点问题和未来方向。     

Contemplating the future: Acting now on long‐term monitoring to answer 2050's questions

In 2050, which aspects of ecosystem change will we regret not having measured? Long‐term monitoring plays a crucial part in managing Australia's natural environment because time is a key factor underpinning changes in ecosystems. It is critical to start measuring key attributes of ecosystems – and the human and natural process affecting them – now, so that we can track the trajectory of change over time. This will facilitate informed choices about how to manage ecological changes (including interventions where they are required) and promote better understanding by 2050 of how particular ecosystems have been shaped over time. There will be considerable value in building on existing long‐term monitoring programmes because this can add significantly to the temporal depth of information. The economic and social processes driving change in ecosystems are not identical in all ecosystems, so much of what is monitored (and the means by which it is monitored) will most likely target specific ecosystems or groups of ecosystems. To best understand the effects of ecosystem‐specific threats and drivers, monitoring also will need to address the economic and social factors underpinning ecosystem‐specific change. Therefore, robust assessments of the state of Australia's environment will be best achieved by reporting on the ecological performance of a representative sample of ecosystems over time. Political, policy and financial support to implement appropriate ecosystem‐specific monitoring is a perennial problem. We suggest that the value of ecological monitoring will be demonstrable, when plot‐based monitoring data make a unique and crucial contribution to Australia's ability to produce environmental accounts, environmental reports (e.g. the State of the Environment, State of the Forests) and to fulfilling reporting obligations under international agreements, such as the Convention on Biological Diversity. This paper suggests what must be done to meet Australia's ecological information needs by 2050.     

EVOLUTION OF PHENOTYPE–ENVIRONMENT ASSOCIATIONS BY GENETIC RESPONSES TO SELECTION AND PHENOTYPIC PLASTICITY IN A TEMPORALLY AUTOCORRELATED ENVIRONMENT

Covariation between population‐mean phenotypes and environmental variables, sometimes termed a “phenotype–environment association” (PEA), can result from phenotypic plasticity, genetic responses to natural selection, or both. PEAs can potentially provide information on the evolutionary dynamics of a particular set of populations, but this requires a full theoretical characterization of PEAs and their evolution. Here, we derive formulas for the expected PEA in a temporally fluctuating environment for a quantitative trait with a linear reaction norm. We compare several biologically relevant scenarios, including constant versus evolving plasticity, and the situation in which an environment affects both development and selection but at different time periods. We find that PEAs are determined not only by biological factors (e.g., magnitude of plasticity, genetic variation), but also environmental factors, such as the association between the environments of development and of selection, and in some cases the level of temporal autocorrelation. We also describe how a PEA can be used to estimate the relationship between an optimum phenotype and an environmental variable (i.e., the environmental sensitivity of selection), an important parameter for determining the extinction risk of populations experiencing environmental change. We illustrate this ability using published data on the predator‐induced morphological responses of tadpoles to predation risk.     

关于我国国民环境的态度调查

2004年10月随机抽取了北京、上海、河北、河南、湖南、陕西六省市对5000余位国民做了环境态度问卷调查.了解我国民众的生态观念、制约因素、以及潜在的保护环境的动机.调查结果显示,91%的被访者感到我国环境已严重恶化,78%的被访者支持政府耗资3000多亿元人民币开展退耕还林项目.居民的环境意识同经济收入、受教育水平、年龄、职业、居住环境有着密切关系,其中经济收入和受教育水平是影响居民环境意识变化的首要因素.区域差异分析结果表明,我国现阶段的环境压力主要集中在贫困的边远山区和快速崛起的城市周边地区,把发展经济、改善教育、提高居民的生活质量与环境修复有机地结合起来,是生态政策管理的根本途径.     

Perception of Environmental Threat in the Shadow of War: The Effect of Future Orientation

People may express high concern for global warming or environmental degradation, but when forced to evaluate the severity of environmental threats in relation to other threats, environmental threats are pushed to the bottom of the list. The present study was based on the premise that prioritizing threats involves, inter alia, hierarchizing the threats according to their perceived temporal proximity and since environmental threats are perceived, relative to other threats, as more temporally distant, they rank low. Future orientation (FO) is an individual-difference variable that describes the tendency to take into account temporally distant considerations. We predicted that environmental threats would be evaluated as more severe by individuals who were more future-oriented. The strongest effect of FO was found to be on concern about global warming and environmental degradation. This indicated that environmental threats were perceived as temporally farthest and, not surprisingly, received the lowest priority. External security threat was a top-priority; its severity evaluation was uncorrelated to FO, indicating that it was perceived as temporally closest. These evaluations significantly affected environmental attitudes and behavior. The implications on promoting environmental behavior in the presence of ever-existing shorter-term existential threats are discussed.     

How stress selects for reversible phenotypic plasticity

Stress occurring in periods shorter than life span strongly selects for reversible phenotypic plasticity, for maximum reliability of stress indicating cues and for minimal response delays. The selective advantage of genotypes that are able to produce adaptive reversible plastic phenotypes is calculated by using the concept of environmental tolerance. Analytic expressions are given for optimal values of mode and breadth of tolerance functions for stress induced and non-induced phenotypes depending on (1) length of stress periods, (2) response delay for switching into the induced phenotype, (3) response delay for rebuilding the non-induced phenotype, (4) intensity of stress, i.e. mean value of the stress inducing environment, (5) coefficient of variation of the stress environment and (6) completeness of information available to the stressed organism. Adaptively reversible phenotypic plastic traits will most probably affect fitness in a way that can be described by simultaneous reversible plasticity in mode and breadth of tolerance functions.     

Environmental variability uncovers disruptive effects of species' interactions on population dynamics

How species respond to changes in environmental variability has been shown for single species, but the question remains whether these results are transferable to species when incorporated in ecological communities. Here, we address this issue by analysing the same species exposed to a range of environmental variabilities when (i) isolated or (ii) embedded in a food web. We find that all species in food webs exposed to temporally uncorrelated environments (white noise) show the same type of dynamics as isolated species, whereas species in food webs exposed to positively autocorrelated environments (red noise) can respond completely differently compared with isolated species. This is owing to species following their equilibrium densities in a positively autocorrelated environment that in turn enables species–species interactions to come into play. Our results give new insights into species'' response to environmental variation. They especially highlight the importance of considering both species'' interactions and environmental autocorrelation when studying population dynamics in a fluctuating environment.     

A Framework for Quantified Eco-efficiency Analysis

Eco-efficiency is an instrument for sustainability analysis, indicating an empirical relation in economic activities between environmental cost or value and environmental impact. This empirical relation can be matched against normative considerations as to how much environmental quality or improvement society would like to offer in exchange for economic welfare, or what the trade-off between the economy and the environment should be if society is to realize a certain level of environmental quality. Its relevance lies in the fact that relations between economy and environment are not self-evident, not at a micro level and not at the macro level resulting from micro-level decisions for society as a whole. Clarifying the why and what of eco-efficiency is a first step toward decision support on these two aspects of sustainability. With the main analytic framework established, filling in the actual economic and environmental relations requires further choices in modeling. Also, the integration of different environmental effects into a single score requires a clear definition of approach, because several partly overlapping methods exist. Some scaling problems accompany the specification of numerator and denominator, which need a solution and some standardization before eco-efficiency analysis can become more widely used. With a method established, the final decision is how to embed it in practical decision making. In getting the details of eco-efficiency better specified, its strengths, but also its weaknesses and limitations, need to be indicated more clearly.