Implications of different population model structures for management of threatened plants

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species‐specific threat. Long‐term demographic data sets were used to construct an individual‐based model (IBM), a complex stage‐based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species’ life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species’ responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output.     

Using empirical models of species colonization under multiple threatening processes to identify complementary threat‐mitigation strategies

Approaches to prioritize conservation actions are gaining popularity. However, limited empirical evidence exists on which species might benefit most from threat mitigation and on what combination of threats, if mitigated simultaneously, would result in the best outcomes for biodiversity. We devised a way to prioritize threat mitigation at a regional scale with empirical evidence based on predicted changes to population dynamics—information that is lacking in most threat‐management prioritization frameworks that rely on expert elicitation. We used dynamic occupancy models to investigate the effects of multiple threats (tree cover, grazing, and presence of an hyperaggressive competitor, the Noisy Miner (Manorina melanocephala) on bird‐population dynamics in an endangered woodland community in southeastern Australia. The 3 threatening processes had different effects on different species. We used predicted patch‐colonization probabilities to estimate the benefit to each species of removing one or more threats. We then determined the complementary set of threat‐mitigation strategies that maximized colonization of all species while ensuring that redundant actions with little benefit were avoided. The single action that resulted in the highest colonization was increasing tree cover, which increased patch colonization by 5% and 11% on average across all species and for declining species, respectively. Combining Noisy Miner control with increasing tree cover increased species colonization by 10% and 19% on average for all species and for declining species respectively, and was a higher priority than changing grazing regimes. Guidance for prioritizing threat mitigation is critical in the face of cumulative threatening processes. By incorporating population dynamics in prioritization of threat management, our approach helps ensure funding is not wasted on ineffective management programs that target the wrong threats or species.     

平原河网地区非点源污染风险差异化分区防控研究

土地利用优化和空间防控策略对非点源污染风险控制及水环境质量的改善具有重要意义。本文以太湖流域典型平原河网地区-上海市青浦区为研究对象,将灰色线性规划模型与最小累积阻力模型相结合,以控制非点源污染风险和增加经济效益、生态效益为目标,进行土地利用结构优化与空间分区防控研究,在空间上划设了水资源保育区、水资源重点防护区、非点源污染一般阻控区、非点源污染中等阻控区及非点源污染重点阻控区,并针对不同分区提出具有针对性的防控措施。与2012年相比,预测2020年优化防控方案下,可减少总氮、总磷的输出10.96%和41.33%。由此表明,优化土地利用结构和构建空间差异化防控机制是有效调控非点源污染风险,实现区域可持续土地利用,促进经济发展和保证生态环境安全的有效途径。     

The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple‐use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders’ willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders’ decisions to engage in management. To test the utility of our best‐supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost‐effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost‐effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost‐efficient and effective manner.     

夏、冬季降雨中溶解性有机质(DOM)光谱特征及来源辨析

利用紫外-可见光谱与三维荧光光谱,结合拉格朗日混合单粒子轨道模型及火点图,研究了重庆2013年夏、冬两季雨水DOM光谱特征,并对其来源进行解析.结果表明,雨水DOM与水体、土壤DOM具有类似性质光谱特征,证明降雨DOM也是陆地及水环境中DOM地化特征的重要贡献者.雨水DOM中DOC含量为0.88~12.80 mg·L-1,CDOM含量在3.17~21.11m-1之间,夏、冬两季降雨DOM差异明显(P0.05).与夏季相比,冬季降雨DOM分子量较小,芳香性程度较低,腐殖化程度也更低,输入主要以本地和短距离输送为主;而夏季DOM来源较分散.尽管吸收和荧光光谱可用于解析雨水DOM组成和来源,但在光谱特征的解析和来源识别上与其他来源DOM有所区别,传统"内、外源区分"并不适用于雨水DOM.     

How effective are River Basin Management Plans in reaching the nutrient load reduction targets?

Riverine nutrient loads are among the major causes of eutrophication of the Baltic Sea. This study applied the Soil & Water Assessment Tool (SWAT) in three catchments flowing to the Baltic Sea, namely Vantaanjoki (Finland), Fyrisån (Sweden), and Słupia (Poland), to simulate the effectiveness of nutrient control measures included in the EU’s Water Framework Directive River Basin Management Plans (RBMPs). Moreover, we identified similar, coastal, middle-sized catchments to which conclusions from this study could be applicable. The first modelling scenario based on extrapolation of the existing trends affected the modelled nutrient loads by less than 5%. In the second scenario, measures included in RBMPs showed variable effectiveness, ranging from negligible for Słupia to 28% total P load reduction in Vantaanjoki. Adding spatially targeted measures to RBMPs (third scenario) would considerably improve their effectiveness in all three catchments for both total N and P, suggesting a need to adopt targeting more widely in the Baltic Sea countries.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01393-x) contains supplementary material, which is available to authorized users.     

我国土壤修复行业面临的问题及商业模式分析

基于我国土壤污染的严峻形势,分析了我国土壤修复行业面临的主要问题。针对我国土壤修复资金需求量大,资金来源困难、责任界定情况复杂等现状,探讨了"谁污染,谁治理"、"谁使用,谁治理"、"政府出资"、RT、ROT、ROO、TRT等几种适合我国国情的土壤修复商业模式。同时,结合我国土壤修复行业正处于成长期初级阶段的现实情况,展望了我国土壤修复行业的发展,并给出了一些有益建议。     

北京市大气重污染过程中AQI的时空扩散特征及其预测模型

通过对北京市大气重度污染时段AQI与气象因子分析,探讨AQI的时间和空间变化特征,并建立了AQI的空间预测模型。结果表明:1)风速>2.5 m/s,且持续时间长(超过5 h)时,AQI曲线特征呈现倒"V"型。若其在1.7 m/s中部>北部,4 d后则中部>南部>北部。3)各方向上中部和北部的AQI预测模型中幂函数模型精度最高,且两者受西南方向的影响最大。     

我国城镇化进程中碳排放影响因素的实证研究

利用1980—2013年间的城镇人口与碳排放数据,选取城镇化进程中影响碳排放的城镇化水平、城镇建设用地面积、第三产业增加值、人均可支配收入、城镇人均绿化面积等因素,实证分析了城镇化对我国碳排放的影响程度,并利用格兰杰夫因果关系与误差修正模型分析了二者的因果关系与时间效应。最后,从降低工业碳排放、加快低碳城镇化试点建设、推进低碳社区发展,促进新能源发展等方面提出了相关建议。     

关于渗透通风条件建筑结构对室内PM2.5浓度水平影响评价模型探讨

我国京津冀地区近年频遭大气PM_2.5污染侵扰,相关研究表明,既使关闭建筑外窗,大气中PM_2.5仍可以通过渗透通风方式进入室内污染环境.为定量评价建筑渗透通风及无室内污染源条件下建筑结构（如外窗缝隙结构、房间建筑结构等）对室内ρ（PM_2.5）的影响规律,基于北京市东城区、朝阳区6个不同建筑结构的房间室内外ρ（PM_2.5）实时监测数据,重点比较分析了建筑结构对室内外ρ（PM_2.5）关联特性的影响规律.此外,根据颗粒物穿透特性及沉降特性机理,提出了反映建筑外窗缝隙结构（如缝高、缝深）的无量纲特征参数A_P与反映房间建筑结构（如层高、开间、进深）的无量纲特征参数A_k.在前期提出的室内ρ（PM_2.5）预测模型基础上,进一步构建了二者（A_P与A_k）对室内ρ（PM_2.5）影响的评价模型,并通过实测数据验证了模型的正确性.结果表明:当室外PM_2.5污染程度与气象条件一定时,建筑结构对I/O[室内外ρ（PM_2.5）之比]影响较大,其范围在0.4~0.7之间;随着建筑外窗气密性等级的提高,对应室内ρ（PM_2.5）呈显著的下降趋势.建筑外窗缝隙结构对室内ρ（PM_2.5）影响程度远大于房间建筑结构,敏感性分析结果表明,当建筑外窗缝高每降低50%或缝深每提高50%,对应室内ρ（PM_2.5）约可下降33.6%与31.9%.研究显示,气密性等级较高的建筑外窗缝隙缝高往往较小、缝深较长,渗透通风条件下对控制室内ρ（PM_2.5）水平作用更显著。