国家公园规划制度功能定位与空间属性

我国将构建统一的国土空间规划体系, 整体谋划国土空间开发保护格局, 强化对各类专项规划的指导约束, 这必将对国家公园保护制度产生重大影响。本文分析了国土空间规划视角下国家公园规划制度的定位, 阐述了国家公园规划承载空间规划与发展规划两大体系的基本属性, 应在国土空间规划体系中具有优先权和前置权; 重点分析了各国家公园总体规划在国家规划体系中的功能与属性特征, 作为空间规划应承接好上位空间规划对国家公园的控制指标和管控要求, 作为建设规划应承接好上位或同域国民经济发展规划等对国家公园的目标任务要求, 作为管理规划要明确国家公园顺畅运行的要求。国家公园应在分区规划、控制性详细规划等专项规划方面寻求创新, 探索国家公园用途管制的实现模式。     

基于综合评价法的洞庭湖区绿地生态网络构建

绿地生态网络对于改善区域生态空间破碎化、生物多样性降低、生态系统服务供需不平衡及保障区域生态安全具有重要意义。本研究以洞庭湖区为例,在3S技术支持下,从生态系统服务功能、潜在生物多样性、形态空间格局的角度综合评价和识别生态源地及计算栅格单元的基本生态阻力;利用夜间灯光指数修正基本生态阻力;运用最小累积阻力模型识别生态廊道;构建加权评价模型,对源地的聚合度、离散度及廊道的生态连接贡献度进行评价;利用结构特征指数对综合网络、“源-汇”潜在网络及规划网络进行对比分析和评价。结果表明: 源地空间分布不均衡,林地、灌丛与水域三者面积之和占源地总面积的95.9%,位于研究区中部的洞庭湖湿地生态风险较高;源地离生态网络系统中心位置越近及到其他源地的平均最小累积阻力越小,聚合及离散的优势越强;高生态质量源地周围的中、高生态质量源地分布越密集,其聚合度、离散度越高;廊道离高生态质量的源地越近,表现为生态连接贡献度越大;林地、灌丛,尤其是河道在自然生态系统与人类社会系统之间起着非常重要的生态连接作用;“源-汇”规划绿道对“源-汇”潜在生态廊道形成了良好补充,与“源-汇”潜在网络相比,综合网络的α、β、γ、ρ指数分别提高123.1%、25.8%、26.2%、74.6%;与“源-汇”规划网络相比,α、β、γ、ρ指数分别提高了190.0%、31.1%、32.5%、114.6%。本研究结果能为洞庭湖区绿地生态网络构建、国土空间规划提供参考。     

IPBES框架下的生物多样性和生态系统服务区域评估及政策经验

生物多样性和生态系统服务为人类的生计和良好的生活质量奠定了重要基础。然而, 越来越多的研究表明, 生物多样性和生态系统服务在全球范围内的持续下降使自然对人类的贡献大幅降低。多尺度评估能够说明不同尺度下生物多样性的现状, 有利于制定适合区域特点、符合国情的决策建议。2013年12月, 生物多样性和生态系统服务政府间科学政策平台(Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, IPBES)通过第一轮工作方案, 决定开展“区域/次区域生物多样性和生态系统服务评估”(简称“区域评估”), 即评估亚洲-太平洋(简称亚太)、美洲、非洲以及欧洲-中亚四大地理区域的生物多样性和生态系统服务。区域评估报告及其决策者摘要已在IPBES第六次全体会议上(2018年3月, 哥伦比亚麦德林)审议通过。本文概述了四大地理区域的生物多样性的重要性、生物多样性保护领域取得的进展、面临的主要危机和机遇, 探讨了评估对其他国际进程的影响, 综合分析了各区域生物多样性和生态系统服务的特点以及各区域评估结果的差别, 总结了评估的政策经验, 以期为中国的生物多样性保护提供科学参考。     

New Caledonian crows plan for specific future tool use

The ability to plan for future events is one of the defining features of human intelligence. Whether non-human animals can plan for specific future situations remains contentious: despite a sustained research effort over the last two decades, there is still no consensus on this question. Here, we show that New Caledonian crows can use tools to plan for specific future events. Crows learned a temporal sequence where they were (a) shown a baited apparatus, (b) 5 min later given a choice of five objects and (c) 10 min later given access to the apparatus. At test, these crows were presented with one of two tool–apparatus combinations. For each combination, the crows chose the right tool for the right future task, while ignoring previously useful tools and a low-value food item. This study establishes that planning for specific future tool use can evolve via convergent evolution, given that corvids and humans shared a common ancestor over 300 million years ago, and offers a route to mapping the planning capacities of animals.     

基于外溢生态系统服务价值的区域生态补偿机制研究

由于不同的区域存在着资源分配、经济发展的不平等性,区域间的生态补偿日益受到关注。但区域生态补偿的依据一直存在争议,中国尚没有完善的区域综合生态补偿机制。提出了一种生态系统服务外溢理论,以扣除当地生态服务使用量后外溢的生态价值作为生态补偿依据,建立了区域综合生态补偿框架,同时考虑区域生态服务外溢占用情况和经济发展水平,有针对性地制定了9种补偿方案。由于生态系统服务具有流动性,生态补偿的受偿方和补偿方并非一一对应的关系,所以各行政区域之间进行补偿资金的。横向转移需要在全省或全国层面进行统筹和保障,最后统筹结果的少量盈缺再由中央财政或地方政府通过纵向转移支付辅助调平。构建了生态补偿模型和方案,针对现有生态系统服务价值评估结果,利用综合生态补偿机制分别分析了2015年长江流域上海、江西、四川等6个典型地区的生态补偿支付方案。结果表示需要接受生态补偿的地区有青海、四川、湖南和江西,应分别得到横向转移支付资金32.26亿元、153.3亿元、59.08亿元、67.1亿元;需要提供生态补偿的地区有浙江、上海,应该分别支付横向转移支付资金1.97亿元和47.31亿元。建立的补偿框架提高了各区域的参与度,对促进生态建设、统筹区域的协调发展、体现社会公平性有重要意义。     

基于增强回归树与区域增温敏感性指数的城市升温效应空间分异

热岛效应已成为城市化进程中备受关注的城市生态环境问题。有关城市热岛效应的研究对区域在地表升温过程中的空间分异关注尚不足。通过设计区域增温敏感性指数,借助增强回归树（boosted regression tree,BRT）机器学习算法,量化城市不同区域在地表升温过程中的敏感性差异。结果表明：（1）上海市各辖区增温敏感性从高至低依次是：黄浦区、杨浦区、虹口区、静安区、普陀区、徐汇区、长宁区、宝山区、闵行区、浦东新区、嘉定区、奉贤区、松江区、青浦区、金山区、崇明区;（2）区域增温敏感性在不同的温度梯度下具有明显差异,在高温梯度下区域间指数值的差异最大;（3）地表覆被的增温敏感性存在明显的区域分异特性;（4）基于BRT的增温敏感性指数具有较好的指示意义和较高的灵敏度。在城市热环境持续恶化背景下,探讨城市异质空间中地表温度变化的敏感性可为城市热环境调控提供科学依据。     

Modeling Cumulative Effects of Climate and Development on Moose,Wolf, and Caribou Populations

Wildlife models focused solely on a single strong influence (e.g., habitat components, wildlife harvest) are limited in their ability to detect key mechanisms influencing population change. Instead, we propose integrated modeling in the context of cumulative effects assessment using multispecies population dynamics models linked to landscape-climate simulation at large spatial and temporal scales. We developed an integrated landscape and population simulation model using ALCES Online as the model-building platform, and the model accounted for key ecological components and relationships among moose (Alces alces), grey wolves (Canis lupus nubilus), and woodland caribou (Rangifer tarandus caribou) in northern Ontario, Canada. We simulated multiple scenarios over 5 decades (beginning 2020) to explore sensitivity to climate change and land use and assessed effects at multiple scales. The magnitude of effect and the relative importance of key factors (climate change, roads, and habitat) differed depending on the scale of assessment. Across the full extent of the study area (654,311km² [ecozonal scale]), the caribou population declined by 26% largely because of climate change and associated predator-prey response, which led to caribou range recession in the southern part of the study area. At the caribou range scale (108,378 km²), which focused on 2 herds in the northern part of the study area, climate change led to a 10% decline in the population and development led to an additional 7% decline. At the project scale (8,331 km²), which was focused more narrowly on the landscape surrounding 4 proposed mines, the caribou population declined by 29% largely in response to simulated development. Given that observed caribou population dynamics were sensitive to the cumulative effects of climate change, land use, interspecific interactions, and scale, insights from the analysis might not emerge under a less complex model. Our integrated modeling framework provides valuable support for broader regional assessments, including estimation of risk to caribou and Indigenous food security, and for developing and evaluating potential caribou recovery strategies. © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.     

大尺度区域生态环境治理及国家生态安全格局构建的技术途径和战略布局

陆地生态系统是地球生物圈系统的核心组分,是人类生活、生产及社会经济活动的场所。然而,人类文明发展和科学技术进步在不断地扩大对资源的利用规模和强度,也导致了严重威胁社会可持续发展的全球气候变化、生物多样性丧失、环境污染、资源枯竭、生态系统退化等环境问题。社会公众期望生态学研究能够为区域、大陆及全球尺度的生态系统的利用和保护、维持人类社会可持续发展提供科学理论及系统性的解决方案。本研究以服务中国生态文明建设、生态安全格局构建、区域生态环境治理,以及宏观生态系统科学发展为目标,回顾了中国的区域生态环境治理成效及经验;分析了我国生态文明建设对大尺度生态系统科学研究的科技需求与时代特征,提出了利用基于自然的宏生态系统途径的新思路提升区域生态环境治理及安全格局构建的设想。在此基础上,讨论了中国的大尺度区域安全格局构建及生态环境治理的新思路、战略布局、技术途径与科技支撑体系,为中国安全、健康和美丽的国土空间利用及生态文明建设进步提供理论参考。     

Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML)

PurposeTo predict the impact of optimization parameter changes on dosimetric plan quality criteria in multi-criteria optimized volumetric-modulated-arc therapy (VMAT) planning prior to optimization using machine learning (ML).MethodsA data base comprising a total of 21,266 VMAT treatment plans for 44 cranial and 18 spinal patient geometries was generated. The underlying optimization algorithm is governed by three highly composite parameters which model a combination of important aspects of the solution. Patient geometries were parametrized via volume- and shape properties of the voxel objects and overlap-volume histograms (OVH) of the planning-target-volume (PTV) and a relevant organ-at-risk (OAR). The impact of changes in one of the three optimization parameters on the maximally achievable value range of five dosimetric properties of the resulting dose distributions was studied. To predict the extent of this impact based on patient geometry, treatment site, and current parameter settings prior to optimization, three different ML-models were trained and tested. Precision-recall curves, as well as the area-under-curve (AUC) of the resulting receiver-operator-characteristic (ROC) curves were analyzed for model assessment.ResultsSuccessful identification of parameter regions resulting in a high variability of dosimetric plan properties depended on the choice of geometry features, the treatment indication and the plan property under investigation. AUC values between 0.82 and 0.99 could be achieved. The best average-precision (AP) values obtained from the corresponding precision/recall curves ranged from 0.71 to 0.99.ConclusionsMachine learning models trained on a database of pre-optimized treatment plans can help finding relevant optimization parameter ranges prior to optimization.