Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social–ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.     

近15a江苏省水源涵养功能时空变化与影响因素探析

定量评估生态系统服务功能是合理利用自然资源、促进人地关系与可持续发展的基础。以江苏省为研究区，基于InVEST模型产水模块，定量分析了江苏省2000~2015年产水功能和水源涵养功能的时空变化特征，并采用回归分析和主成分分析法评估了水源涵养功能与社会经济要素之间的关系。结果表明：（1）江苏省多年平均产水量为571×108 m³/a，水源涵养总量78.39×108 m³/a；（2）2000~2010年江苏省水源涵养功能呈下降趋势，2010~2015年有所回升，考虑到降水因素，江苏省实际水源涵养功能持续降低；（3）土地利用变化使15a来水源涵养功能下降15.2×108 m³，降低幅度为16.9%；（4）流域尺度上水源涵养功能与江苏社会经济因素相关关系不显著。研究结果可以为土地利用优化、流域水资源管理提供科学参考和支持。     

Microtargeting for conservation

Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high-resolution land-cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.     

2013年北京市PM2.5重污染日时空分布特征研究

根据2013年北京市环境保护监测中心监测的PM2.5数据,系统分析了北京市重污染日PM2.5污染的时空分布特征,并利用克里格插值初步统计了全年和重污染日PM2.5不同浓度区间的国土面积。2013年全市PM2.5年均浓度为89.5μg/m3,重污染日平均浓度为218μg/m3,重污染日主要集中在冬季;PM2.5年均浓度呈现明显的南高北低梯度分布特征,而重污染日空间分布较均匀,南部及城六区存在明显的高污染区,平均浓度在180μg/m3以上;2013年北京市重污染日PM2.5平均浓度为150~250μg/m3,其对应的国土面积约为12 656 km2,PM2.5平均浓度在250μg/m3以上的国土面积约为883 km2,而全年无PM2.5平均浓度在150μg/m3以上所对应的国土面积。     

汉丹江(陕西段)水质变化特征分析

基于2008—2012年陕西省境内汉江、丹江干流14个断面的水质监测数据,采用单因子评价法、平均综合污染指数法和秩相关系数法等对该段水体的水质变化特征进行研究。结果表明,汉丹江(陕西段)断面水质以Ⅱ类、Ⅲ类为主,水质总体为优;水质综合污染状况呈下降趋势,其中汉江下降趋势显著;水质综合污染状况空间差异和区域分布特征明显,城区段污染大于郊区,各行政区段河流下游污染大于上游。流域水质主要受有机污染和营养盐因子影响,水体污染源主要来自城镇生活源和农业面源,工业源占比不大且排放行业较为集中。     

乌鲁木齐市近年来大气降尘变化规律及空间分布特征

基于乌鲁木齐市多年监测的降尘数据,对乌鲁木齐市城区的降尘量进行了统计分析,得出了乌鲁木齐市近10年来降尘的总体变化规律;利用方差分析方法检验了不同季节降尘的显著性差异,发现采暖期的降尘量明显大于非采暖期;按行政区分析降尘量的分布变化,结果为不同行政区存在差别;利用秩相关系数法对其进行了趋势分析。结果表明,乌鲁木齐市的降尘呈下降趋势。利用Arc GIS9.3中的普通克里格插值方法绘制了乌鲁木齐市城市自然降尘的空间分布图。     

火灾发生率空间分异及影响因素研究

为了探究我国火灾空间聚集特征与影响因素的空间异质性，采用全局莫兰指数、局部莫兰指数、逐步回归模型、地理加权回归模型、地理探测器方法对我国地级市单元进行研究。研究结果表明:我国火灾发生率具有显著的聚集性；我国火灾发生率较低的“冷点”区域有1个，火灾发生率较高的“热点”区域有4个；人均GDP、城镇居民人均可支配收入、人口密度、年平均气温4 个因素的影响效应具有空间异质性。人均GDP与火灾发生率为正相关，另外3影响因素对火灾发生率的作用表现出正负2种相关关系；2因素交互作用要比单因素作用于火灾发生率时影响力更显著，各影响因素的交互作用类型有非线性增强型和双因子增强型2种。     

Minimizing opportunity costs to aquatic connectivity restoration while controlling an invasive species

Controlling invasive species is critical for conservation but can have unintended consequences for native species and divert resources away from other efforts. This dilemma occurs on a grand scale in the North American Great Lakes, where dams and culverts block tributary access to habitat of desirable fish species and are a lynchpin of long‐standing efforts to limit ecological damage inflicted by the invasive, parasitic sea lamprey (Petromyzon marinus). Habitat restoration and sea‐lamprey control create conflicting goals for managing aging infrastructure. We used optimization to minimize opportunity costs of habitat gains for 37 desirable migratory fishes that arose from restricting sea lamprey access (0–25% increase) when selecting barriers for removal under a limited budget (US$1–105 million). Imposing limits on sea lamprey habitat reduced gains in tributary access for desirable species by 15–50% relative to an unconstrained scenario. Additional investment to offset the effect of limiting sea‐lamprey access resulted in high opportunity costs for 30 of 37 species (e.g., an additional US$20–80 million for lake sturgeon [Acipenser fulvescens]) and often required ≥5% increase in sea‐lamprey access to identify barrier‐removal solutions adhering to the budget and limiting access. Narrowly distributed species exhibited the highest opportunity costs but benefited more at less cost when small increases in sea‐lamprey access were allowed. Our results illustrate the value of optimization in limiting opportunity costs when balancing invasion control against restoration benefits for diverse desirable species. Such trade‐off analyses are essential to the restoration of connectivity within fragmented rivers without unleashing invaders.     

2014年北京市CO浓度水平和时空分布

对北京市地面监测站点的CO浓度进行分析，探讨其浓度水平、变化趋势和时空分布特征。2014年春、夏、秋、冬四季北京市CO平均浓度分别为1.06、0.87、1.34、2.17 mg/m³。CO浓度均呈双峰型变化，第一个峰值出现在07：00-09：00，主要由交通早高峰的排放引起；第二个峰值出现在23：00左右，主要受交通晚高峰排放和夜间边界层高度降低的挤压效应的共同影响。从空间分布来看，全年整体呈现南高北低的分布特征，尤其是秋、冬季较为明显，体现了工业布局和区域传输对CO的影响。从全年来看，湿度对CO浓度的影响最大。对2014年冬季北京市的一次高CO浓度分析结果表明，此次过程是由本地排放和区域传输共同造成的，气象要素中地面气压对CO浓度影响最大。     

A new approach to spatial source apportionment of haze pollution in large scale and its application in China

Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention and control of air pollution in key area. Air quality models can identify and quantify the regional contribution of haze pollution and its key components with the help of numerical simulation, but it is difficult to be applied to larger spatial scale due to the complexity of model parameters. The time series analysis can recognize the existence of spatial interaction of haze pollution between cities, but it has not yet been used to further identify the spatial sources of haze pollution in large scale. Using econometric framework of time series analysis, this paper developed a new approach to perform spatial source apportionment. We applied this approach to calculate the contribution from spatial sources of haze pollution in China, using the monitoring data of particulate matter (PM_2.5) across 161 Chinese cities. This approach overcame the limitation of numerical simulation that the model complexity increases at excess with the expansion of sample range, and could effectively deal with severe large-scale haze episodes.