Regional ecological risk assessment using a relative risk model: A case study of the Darwin Harbour,Darwin, Australia

There are many proposed and ongoing commercial, industrial, and residential developments within the Darwin Harbour catchment in Northern Australia, to accommodate the projected population growth over the next 20 years. Hence, it is necessary to ensure the balance between these developments and ecosystem conservation. We evaluated ecological risk for the Darwin Harbour using a relative risk model (RRM). The catchment was divided into 22 risk regions based on small catchment boundaries and their homogeneity. Through the RRM, we ranked and summed the stressors and habitats within regions. The interaction between stressors and habitats were modeled through exposure and effect filters. The ecological assessment endpoints were maintenance of the mangrove health and the maintenance of water quality. The risk regions—Myrmidon Creek, Blackmore River, Bleesers Creek, and Elizabeth River—showed the highest total relative risk for ecological assets. These risk regions had a high percentage cover of industrial, commercial, and residential areas; diffuse entry points; and climate change effects. Creek A, Sandy Creek, West Arm, and Pioneer Creek were the risk regions with lowest total relative risk scores. The RRM is a robust application that is suitable for a large geographic area where multiple stressors are of concern.     

唐古拉山以北地区生态资产核算

生态系统核算可以为生态文明建设提供定量性的决策依据,包括生态资产核算和生态系统服务核算两个方面,生态资产指生产和提供生态系统产品和服务的生态系统。以唐古拉山以北地区(简称唐北地区)为研究对象对其生态资产进行了核算,建立生态资产实物量及变化核算表、损益表,提出了生态资产综合指数。2015年唐北地区草地生态资产面积为21800.01 km~2,其中良级比重最高达68.46%,湿地生态资产面积为4763.01 km~2,其中优级比例最高为59.72%,野生动植物共有138种,其中重点保护动物10种。2015年唐北地区生态资产综合指数为79.77,比2000年降低了3.60%。2000—2015年,湿地、草地生态资产分别增加了164.23、2.82 km~2。2000—2015年湿地生态资产存量增加202.90 km~2,其中由湿地恢复导致面积增加最大为200.50 km~2,存量减少38.63 km~2,其中湿地退化是导致存量减少的主要原因,面积为36.23 km~2,草地存量增加了39.18 km~2,主要是由于湿地退化导致的草地扩张,存量减少36.26 km~2,主要由湿地恢复和荒漠化引起。研究中不同生态资产质量等级的核算以及生态资产综合指数的提出利于生态资产的全面核算和比较,对于建立离任责任制、生态文明建设意义重大。     

退化高寒草甸关键生态属性对多途径恢复措施的响应特征

高寒草甸是青藏高原的主体植被类型，但退化态势较为严峻，严重威胁青藏高原生态屏障的战略地位。退化高寒草甸的复健是世界性难题，治理效果也因退化状态、恢复措施及气候环境而异。以春季休牧、秋季休牧、畜群结构优化、减畜轮牧、围栏封育及翻耕改建等典型多途径恢复措施下的退化高寒草甸为对象，系统探讨主要生态要素和生态功能的响应特征及潜在过程。结果表明，典型恢复措施下退化高寒草甸的植被生产力、土壤有机碳密度及土壤饱和持水量等生态要素都得到一定程度的提升，而恢复效果与实施年限及恢复措施密切相关。围栏封育和翻耕改建下土壤有机碳密度及饱和持水量随恢复年限均表现为对数饱和型的响应特征，退化高寒草甸固碳持水功能的基本恢复年限约为6—10年。春季休牧、秋季休牧、畜群结构优化、减畜轮牧、围栏封育等放牧管理恢复措施应适用于轻度退化至重度退化的高寒草甸，而翻耕改建则是极度退化高寒草甸的适宜治理措施。由于多途径恢复措施的关注目标不同，今后研究应集中在恢复措施的组合优化和综合评价等方面。     

基于MSPA-连接度-空间句法的生态保护空间及优先级识别——以苏锡常地区为例

随着城市化进程的加快,对生态保护空间(Ecological Protect Area,EPA)进行优先级识别已经成为保护生物多样性,缓解城市生态压力,提高土地利用效率的重要方法之一。以苏锡常地区为例,通过形态空间格局分析(Morphological Spatial Pattern Analysis,MSPA)确定EPA,进而采用景观连接度方法对其优先级进行识别,以优先级较高的林地和水域为"源地",构建EPA网络;同时由于景观连接度方法在识别优先级时,未能体现EPA对物种决策的非等权重影响,因此本研究引入空间句法进一步对EPA进行优先级识别。结果显示:1)苏锡常地区EPA中编号1、2的林地以及太湖的优先级最高,是区域网络的3个中心,需要优先保护;2)引入空间句法的优先级识别结果与基于景观连接度的优先级识别结果存在明显差异,后者优先级较高的8个EPA,在引入空间句法后优先级降低1—2个等级;后者优先级较低的4个EPA的优先级则上升了2个等级。同时也表明:基于连接度-空间句法的EPA优先级识别方法能够反映不同景观要素对物种决策行为的非等权重影响,强化了生态要素的空间配置的生态效应,为明确关键性生态空间,提供了有效的方法补充,对保护生物多样性和维系区域生态安全具有重要意义。     

A comparison of the wood anatomy of 11 species from two cerrado habitats (cerrado s.s. and adjacent gallery forest)

A comparative study of the secondary xylem (wood) anatomy of 11 species (38 specimens) occurring in cerrado s.s. and the adjacent gallery forest (both cerrado s.l. habitat) was made with the aim of identifying the anatomical characteristics of ecological value and correlating them with the environmental conditions. The anatomical features that vary, in general, between the two habitats are: growth ring distinctness (well or poorly defined); tyloses and deposits (more abundant in cerrado specimens); gelatinous fibres (more evident in cerrado specimens and in different patterns between habitats); variation in paratracheal and banded parenchyma (more abundant in cerrado); and more cells per parenchyma strand in cerrado. In general, gallery forest specimens have wider vessels, fewer vessels per square millimetre and larger intervessel pits, indicating more efficient water conduction, whereas cerrado s.s. specimens are the opposite, with low vulnerability and mesomorphy indices, demonstrating greater safety under conditions of water stress. © 2012 The Linnean Society of London, Botanical Journal of the Linnean Society, 2012, ?? , ??–??.     

The common patterns of nature

We typically observe large‐scale outcomes that arise from the interactions of many hidden, small‐scale processes. Examples include age of disease onset, rates of amino acid substitutions and composition of ecological communities. The macroscopic patterns in each problem often vary around a characteristic shape that can be generated by neutral processes. A neutral generative model assumes that each microscopic process follows unbiased or random stochastic fluctuations: random connections of network nodes; amino acid substitutions with no effect on fitness; species that arise or disappear from communities randomly. These neutral generative models often match common patterns of nature. In this paper, I present the theoretical background by which we can understand why these neutral generative models are so successful. I show where the classic patterns come from, such as the Poisson pattern, the normal or Gaussian pattern and many others. Each classic pattern was often discovered by a simple neutral generative model. The neutral patterns share a special characteristic: they describe the patterns of nature that follow from simple constraints on information. For example, any aggregation of processes that preserves information only about the mean and variance attracts to the Gaussian pattern; any aggregation that preserves information only about the mean attracts to the exponential pattern; any aggregation that preserves information only about the geometric mean attracts to the power law pattern. I present a simple and consistent informational framework of the common patterns of nature based on the method of maximum entropy. This framework shows that each neutral generative model is a special case that helps to discover a particular set of informational constraints; those informational constraints define a much wider domain of non‐neutral generative processes that attract to the same neutral pattern.