高森林火险天气形势及其前期气候特征与预报

利用1980-2006年27年间黑龙江省林区发生的37次特大森林火灾资料,针对高空500hPa温压场的结构,将着火前3天短期时段内的天气形势分为4类8型,在此基础上对相对湿度与气温等因子的变化进行了分析,并对高火险日数及前期气候与大气环流特征进行了预报.     

黑龙江省森林火灾年际变化的小波分析

小波分析是一种新的时一频分析工具，广泛适用于非线性科学领域。选用Daubechies小波系对黑龙江省森林火灾的特征进行了分解和重构，结果表明，黑龙江省1953—2002年受害森林面积有近15～20a的周期波动性，森林火灾次数则有近30a的周期波动性。未来几年内，该省森林火灾受害面积和火灾次数会在较低的水平上波动，但森林火灾可能要比2000年严重。     

基于O/E模型的浅水湖泊生态系统健康评价

以长江中下游阻隔型浅水湖泊为研究对象,分析了1998—2018年116湖次底栖动物野外调查数据,构建了基于预测模型的观测物种组成/期望物种组成(O/E)指数健康评价指标体系,评价了长江中下游浅水湖泊的底栖动物物种组成完整性.O/E指数模型评价结果显示,在物种采集概率(Pc)>0阈值条件下的O/E0模型预测得到的监测湖泊...     

南京市典型森林群落枯枝落叶层的生态功能研究

对南京市5种典型森林群落枯枝落叶层现存量、营养元素氮磷贮存量、持水量以及吸纳重金属等生态功能的研究结果表明,5种典型森林群落枯枝落叶层现存量、对氮的贮存能力以及最大拦蓄量均以麻栎-栓皮栎群落最大;对磷和5种重金属的贮存量以银杏群落最大,麻栎-栓皮栎群落次之;枫香-桂花和三角枫群落的各项生态功能相对较弱。建议在南京市的城市森林规划和建设中,应保护好现存的地带性植物群落———以麻栎-栓皮栎为优势种的落叶阔叶林群落或营建类地带性植物群落,维护林地枯枝落叶层的完整性,将有助于改善城市生态环境。     

试论资源可持续利用的评价指标

本文给出了判断资源是否可持续发展的四个条件，据此，并分析了1993年我国森林资源可持续利用的状况。     

三峡库区森林植被恢复与可持续经营研究

系统的野外调查数据和历史资料分析结果表明,三峡库区森林植被在空间上发展不均匀,无明显垂直分带,且分布分散,人为干扰严重。目前,在70多个植被类型中,森林类型占25个,江岸两侧海拔800 m以下地区森林已经很少。森林类型中,马尾松、柏木林的分布面积最大,并在许多疏林中成为主要树种,主要是飞播或人工种植。库区大于25°的坡耕地占耕地面积的17.5％。库区薪炭林仅能满足农村用能总需求量的10.78％。不合理的土地利用方式和对森林的不合理砍伐导致脆弱的土壤系统和森林生态系统的严重退化、生产力下降和严重的水土流失。库区土壤年侵蚀量达到2.9亿t。三峡工程建设和移民安置对库区森林生态系统的压力和影响将是长期的。调查结果同时表明,库区植物生物多样性丰富,植物种类在5 032种以上,而且乡土树种多。分析认为,只要抓住三峡工程建设的契机,紧密结合国家天然林保护和退耕还林工程建设,充分利用良好的生物学基础,科学地封山育林,分带逐步恢复,同时,因地制宜,退耕还林,发展森林能源,建设多目标复合和多种模式并存的林业经营体系,发展经济,就能从根本上改善森林经营环境,实现森林恢复,形成合理布局的水库森林防护系统,促进三峡库区生态环境建设和可持续发展。     

庐山不同森林植被类型土壤碳库管理指数评价

土壤碳库管理指数(CPMI)是表征土壤碳库变化的一个重要量化指标,能够反映土壤的碳库变化和碳库质量。选取庐山8种森林植被类型土壤为研究对象,对其土壤有机碳库特征及碳库管理指数进行系统研究。结论表明:(1)土壤有机碳(SOC)主要分布于0~20 cm土层中,随着土层深度增加,不同森林植被类型下SOC含量急剧下降;在0~60 cm土层中,不同森林植被类型下SOC含量的平均值排序为:马尾松林常绿阔叶林灌丛针阔混交林常绿-落叶混交林黄山松林落叶阔叶林竹林。(2)不同森林植被类型下活性有机碳(ASOC)含量为0.24~0.57 g·kg–1,总有机碳(TOC)含量为9.72~14.74 g·kg–1,土壤碳库指数(CPI)为1.63~2.48,碳库活度(A)为0.019~0.062,碳库活度指数(AI)为0.388~1.265。不同森林植被类型下ASOC含量排序:落叶阔叶林黄山松林常绿-落叶阔叶混交林灌丛针阔混交林常绿阔叶林竹林马尾松林;不同森林植被类型下ASOC/TOC(%)排序:落叶阔叶林黄山松林常绿-落叶阔叶混交林竹林灌丛针阔混交林常绿阔叶林马尾松林;不同森林植被类型下CPMI排序为:落叶阔叶林黄山松林常绿-落叶阔叶混交林灌丛针阔混交林常绿阔叶林竹林马尾松林。     

秦岭火地塘森林水质的季节性变化特征

在秦岭火地塘林区选择2个闭合小流域及2个支沟集水区,对流域出口径流及支沟集水区溪流水pH值及水化学成分进行了8 a的测试,采用机理分析方法,就森林水质的季节性变化特征进行了研究.结果表明,林区径流pH值在7.1～8.4之间,冬、春季较高,夏、秋季较低,总体上水呈弱碱性;NO-3浓度春季和初夏较高,NH 4冬季和初春较高,PO3-4冬季与夏季较高;K 浓度春、秋季较高,Na 浓度则9月最大,8月最小;Ca2 浓度以6月和10月较高,年内变化范围19.4～44.3 mg·L-1,Mg2 则3月较高,年内变化范围2.18～5.25 mg·L-1;Cd浓度随季节的变化以1～4月较高,Pb浓度12、1月较高,但Cd、Pb均以秋季较低,年内变化范围分别为0.019～0.326 5 μg稬-1和0.217～3.886 μh稬-1;Mn浓度5、12月较高,Fe浓度春季较高,Zn则以3、8月较高.林区径流水质属Ⅰ类,相对而言,水质随季节的变化以秋季较好,冬、春季较差.     

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures—ways of generating matching pairs at different scales—in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelation and using independent matching for each subset of data is needed to infer the effectiveness of protection in reducing deforestation. Not accounting for spatial autocorrelation can distort the assessment of protection effectiveness, increasing type I and II errors and inflating effect size. Our method allowed improved estimates of protection effectiveness across scales and under different conditions and can be applied to other regions to effectively assess PA performance.     

Efficacy of extracting indices from large‐scale acoustic recordings to monitor biodiversity

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic‐index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random‐forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R² ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random‐forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R² ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.