林丹烟剂干扰下毛竹林节肢动物群落的时间动态

利用2001年11月到2002年9月期间对未防治、白僵菌防治和林丹烟剂防治毛竹林节肢动物群落的系统调查资料,分析了林丹烟剂干扰下群落多样性、天敌与害虫个体数比例和叶部主要害虫种群的时间动态。结果表明:以未防治和白僵菌防治为对照,林丹烟剂干扰明显降低了群落各阶段的丰富度、物种多样性,削弱了大多数阶段天敌对害虫的自然控制作用,增加了大多数阶段蠕须盾蚧和8~9月期间刚竹毒蛾的种群数量,有利于这两种害虫的暴发。     

赤眼蜂和平腹小蜂寄生率与放蜂量、卵块密度关系的研究

松毛虫赤眼蜂Trichogramma dendrolimi和白跗平腹小蜂Pseudanastatusalbitarsis是松毛虫卵期二种寄生性天敌。为了便于人工利用,根据害虫虫口密度提出了经济合理的放蜂量,编制了赤眼峰和平腹小蜂寄生率与放蜂量、虫口密度的相关模式:y=2.6864x_1-0.1631x_2+34.9363(赤眼蜂);y=173.790x_1+0.005969x_2-6.9538(平腹小蜂)。     

广东桐花树群落上的柑橘长卷蛾研究

柑橘长卷蛾是红树林桐花树群落的重要害虫,在广东省局部区域的红树林保护区,已经对桐花树造威严重危害、对秋茄造成危害。柑橘长卷蛾在桐花树1年完成6代,以中龄幼虫越冬,第1～5代虫历时38～58d,越冬代虫历时130～137d。在桐花树群落,幼虫危害嫩芽、叶片、花序和果实;在桐花树＋秋茄群落,幼虫危害秋茄的嫩芽和嫩叶片。在桐花树群落,5月上旬-5月下旬和9月上旬～10月上旬,受害嫩芽数量与受害叶片数量有两个高峰期,柑橘长卷蛾种群数量消长规律与受害嫩芽与受害叶片数量的变化规律成正相关,柑橘长卷蛾种群数量全年也对应有两个高峰期,但秋季害虫高峰期害虫的总数量只有春季暴发期害虫总数量的29％。柑橘长卷蛾数量与捕食性蜘蛛数量的比例为1：0．33,林间捕食性蜘蛛数量的比例偏低。     

訾短企共识

黄建东等同志撰写的《改革十年来我国科技期刊发展模型的研究》一文(以下简称《黄文》),将灰色Verhulst非动态模型引入文献计量学,它较好地解决了文献逻辑增长规律中参数不能很好拟合的矛盾,其模型作为文献增长模型比指数增长模型和逻辑增长模型更具有优越性。但是,笔者认为,《黄文》中有两个问题需要进行更正。1.数据处理问题《黄文》中将《全国报刊索引》所采得的数据(即表1)定为x_((i))~((0)),并对x_((i))~((0))进行累加得x_((i))~((1))(即表2),这是不妥的。在《黄文》的参考文献[1]中明确指出:x_((i))~((0))是指ⅰ年的净增长量,x_((i))~((1))是x_((i))~((0))累加处理后的量(即x_((i))~((1))=sum from i=1 to i x_((i))~((0)))。笔者认为,应该将所采集的数     

Bt转基因杨树对杨树昆虫群落结构的影响

通过种植转基因杨树纯林和混交林 ,研究了Bt转基因杨树对杨树昆虫群落结构的影响。研究表明 1hm2的小面积的新疆转基因杨树和非转基因杨树之间群落结构和多样性差别不大 ;而对 6 7hm2 以上的转基因杨树纯林和 1∶1的转基因和非转基因杨树混交林中昆虫和蜘蛛群落结构的比较研究发现 ,Bt转基因杨树会改变其昆虫群落结构 ,在转基因纯林中杨叶蜂为优势种 ,而在混交林中杨扇舟蛾为优势种。为了避免其它非鳞翅目食叶害虫的危害 ,在转基因时应采用对非鳞翅目食叶害虫有抗性的种类或品系。转基因纯林的多样性和均匀性都较混交林高 ,有利于系统的稳定性 ,但转基因混交林中瓢虫的数量明显比转基因纯林高 ,分别为 0 2 1头·枝- 1 和 0 0 2 1头·枝 - 1 ,相差 1 0倍。蜘蛛的数量是纯林中较多 ,分别为 0 1 2 5头·枝 - 1 和 0 0 6 2 5头·枝 - 1 ,相差 1倍。转基因杨树对非目标昆虫和天敌的影响还需进行进一步详细的研究。转基因纯林和混交林的叶片被害率分别为 1 1 2 6 %和1 8 4 8% ,转基因纯林抑制目标食叶害虫的作用较好 ,但与混交林之间的差异不显著 ,两种林分的被害率都在不成灾的水平。     

广东白骨壤群落上的广州小斑螟生物学特性及种群数量消长规律研究

广州小斑螟是白骨壤群落上的重要害虫。室内饲养1年可完成7代,第1代至第6代虫历时33～42d,越冬代虫历时122d。幼虫危害嫩芽、嫩叶、叶片和果实,严重受害的白骨壤林成片枯死。在白骨壤群落,各地受害嫩芽和受害叶片数量变化规律不相同,广州小斑螟种群数量消长规律与受害嫩芽和受害叶片数量变化规律成正相关,受害嫩芽和受害叶片全年平均数保持为10．8—21．5片叶（个嫩芽）／m2,害虫数量全年平均数保持在4．4～20．4头／30个1m2样方。捕食性蜘蛛是控制广州小斑螟种群的重要天敌,蜘蛛数量全年平均数保持在5．9—10．8头／30个1m2样方,广州小斑螟种群数量与捕食性蜘蛛种群数量的比例为1：0．29～2．45,捕食性蜘蛛的比例越高,对广州小斑螟种群的控制作用就越大,害虫暴发成灾的可能性就越小。     

害虫种群经济阀限水平的一般模式

一种害虫种群的经济阀限通常指的是将开始进行防治的水平。明断的开展杀虫剂防治、决定害虫综合管理的成功与否,这个水平的知识是一个基础。昆虫学家(自然科学院,1969,格莱斯1975)和生态学家都研究了影响这个水平的因子。赫得利(1972)研究了一种杀虫剂用于一个单一的害虫种群,这个因子包括作物价值和防治费用。哈尔加诺加德(1973)描述了杀虫剂应用时间和使用剂量这两个因子。博罗斯和塔尔帕兹     

拐棍竹种群动态的初步研究

拐棍竹是大熊猫的主食竹种之一。通过对拐棍竹年发笋规律、种群密度及种群年龄结构的研究,其结果是:①拐棍竹发笋总数与二年生竹数量关系,y_1=5.1266+0.3822x_2;②竹笋成活数与一、二年生竹数的关系,y_2=0.6450+0.3159x_1+0.2627X_2;③拐棍竹竹茎可存活10～13年左右,④拐棍竹种群处于稳定状态。     

杨树防护林蛀干害虫及其天敌的调查与分析

吉林省西部杨树防护林蛀干害虫的调查结果表明:白杨透翅蛾和青杨天牛的平均危害率分别为19.31%和11.98%,是杨树防护林的主要蛀干害虫。两种蛀干害虫的主要天敌均为啄木鸟和肿腿蜂,且种群数量很少,呈零星分布;害虫的种群变化与天敌的种群变化密切相关,据此可以对害虫的种群数量进行监测,并预测害虫大发生的可能性。对于没有实施过人为防治的杨树防护林,可以利用诱捕害虫数量与基础危害率的相关关系进行蛀干害虫的预测预报。     

落叶松落叶病预测预报技术的研究

我们通过对气象因子与落叶松落叶病发病关系的研究,和对入选的主要气象因子在各月份中作用的分析,将六七月份降水量、平均相对湿度和四五月份平均气温确定为病情测报因子。其当年值预测式为y=b_0+b_1x_1+b_2x_2……+b_(12)x_(12)(x_1、x_2……x_(12)为前1—12年测报因子观测值;b_0、b_1……b_(12)为相应回归系数);由23年全省历史资料建立的病情指数预测式为y=68.31-1.68x_1-0.5638x_2+0.36x_3(x_1、x_2、x_3分别为气温、湿度、降水预测值);全省发病面积预测式为y=(-0.13325+0.00533x)Sy(式中,x为病情指数预测值;Sy为全省幼龄落叶松人工林面积)。根据各地区发病面积比例系数和各地区轻、中、重发病面积的百分比,可预测各地区发病面积和轻、中、重发病面积。应防治面积为重、中级发病面积与20％轻病级面积之和。     

用一维序列方法研究昆虫种群时空结构

提出了研究种群空间结构的一维序列方法，包括一维序列抽样、单种群空间结构分析、时空相关分析以及时空分析与种群动态预测之间关系。对马尾松毛虫空间结构分析表明，本文的方法能解决分布型指数方法无法解释的问题；对马尾松毛虫幼虫的时空分析表明一维序列分析用于研究种群扩散规律是合适的。     

Methods for combining phenological time series: application to bud burst in birch (Betula pendula) in Central Finland for the period 1896-1955

We compared four methods for combining separate fragmentary phenological time series into a single long reliable series. The systematic linear effect of differences in observers, genotypes, geography and climate at the observation points produces disturbing variation in the observations and bias in the means of some time points. The three methods based on the adjustment of individual series eliminated the disturbing variation and bias. The methods were compared based on phenological observations of bud burst in birch (Betula pendula Roth). The method based on a linear mixed model of analysis of variance and the maximum likelihood estimation was considered preferable to the other methods.     

Quantitative Classification of Plant Functional Types Based on Photosynthesis

Classifying plant functional types by a general and quantitative technique is important for sustainable forest management. Here, the time series of diurnal net photosynthetic rate and other related external and internal factors of 11 tree species in a young mixed broadleaf and coniferous Korean pine forest in Northeast China were recorded during the growing season. All time series data were analyzed by a newly proposed model, by which the relative importance of external environmental factors and internal factors on leaf photosynthetic rate among tree species was evaluated. The analysis showed that this method can be useful to identify environmentally sensitive tree species and to quantitatively classify plant functional types. Photosynthetic rate in most pioneer tree species is less sensitive to variation in environmental conditions than in late successional species. The photosynthetic rate of all 11 tree species was mainly determined by their internal factors. The results may also have implications for tree species selection in forest plantations and forest restoration.     

基于DRNN和ARIMA模型的森林火灾面积时空综合预测方法

森林火灾是一个跨空间发展的动态过程,不易被传统的分析方法和静态神经网络有效处理.提出一种基于动态回归神经网络(DRNN)和自回归集成移动平均(ARIMA)组合模型的森林火灾时空综合预测方法.该方法先用ARIMA对时空数据的时序进行预测,再用DRNN捕获时空数据间隐藏的空间相关,最后用统计回归将时间和空间预测结果组合起来,得到时空综合预测结果.以广东省森林火灾面积预测为例,说明其原理和建模过程,并对预测结果的精度进行验证.结果表明:由于考虑了数据间的空间关系,该时空综合预测模型可以对森林火灾面积进行较准确有效的预测,比单纯应用ARIMA模型预测精度高,是预测森林火灾等跨空间动态变化问题的有效工具.     

Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation

Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests.     

Improving the reliability of a combined phenological time series by analyzing observation quality

Collecting phenological data is a slow process. Although such data have been collected by a number of organizations, the reliability of these data is not known because the data-generating process cannot be repeated. No further observations to improve the reliability can be obtained. However, the data usually consist of several overlapping observation series and this overlap can be utilized to construct a combined phenological time series and to improve its reliability. We have developed two techniques for selecting the most reliable observations or observation series and thereby improve the reliability of the combined time series. Both techniques require that the method used to combine the separate phenological time series adjusts the individual series to eliminate possible systematic differences between them. A data set of bud burst in Betula pendula Roth collected in Central Finland during 1896-1955 was adjusted and used to test both techiques. Both techniques considerably improved the reliability of the combined time series; the mean of the confidence intervals of the annual means decreased by 12%. Despite the improvement in reliability, the resulting changes in the annual values of the combined time series were small, the largest change being 2.5 days. Removing outliers was the most effective method of improving reliability, i.e., it resulted in the greatest improvement with the smallest number of discarded observations.     

基于子波变换的时间序列波动特征分析

本文运用子波变换的分析方法,研究股票时间序列多尺度局域结构的波动性。通过对具体的股指数据分析、比较表明:利用子波分析法,不仅能准确判定出序列的稳定周期大小和相对强弱,比较不同股指序列的周期相关性,而且能提取序列的局部波动和突发波动信息。     

社会经济因素对林业经济发展的影响分析:以江西为例

分析影响林业经济发展的主要因素, 有利于深入研究林业发展状况, 找出其中存在的问题并提出对策建议。文中以江西为例, 利用1978——2009年的时间序列数据, 采取逐步回归分析方法, 对影响林业总产值的社会经济因素进行量化分析。结果显示, 农村居民人均年消费、本年度营林完成投资、粮食播种面积、地区生产总值和粮食作物产量对林业总产值的影响显著。其中, 农村居民人均年消费与林业产值呈负相关, 其他均为正效应。建议通过增加投资、提高森林质量、加快社会转型来促进林业经济的发展。     

红锥种群结构和空间分布格局的研究

采用"空间序列代替时间变化"的方法,对刀石山红锥种群结构进行分析,并运用C、Lδ、M*、M*/X、GA5种聚焦度指标及Iwao M*-X回归测定种群空间分布格局和动态.结果表明红锥种群结构存在增长和下降两种类型;聚焦度指标测定结果一致表明红锥种群的空间分布为随机型IwaoM*-X回归分析表明分布的基本成分是红锥个体;在种群成长过程中,分布格局从聚集型向随机型转化.     

银杏落叶期叶片NDVI值2种拟合方法比较

【目的】对银杏Ginkgo biloba L.落叶期(9—12月)的叶片进行连续定期定点的高光谱测量,计算出能代表其生长状况和营养信息的高光谱参量NDVI值,通过选用两种不同的曲线拟合方法对其NDVI值进行宏观上的以时间为自变量的曲线拟合,选出最优拟合方法,更好地了解银杏落叶期叶片光谱特征参量NDVI值的变化趋势,从而更有效地对其进行决策和控制,为植被的大尺度遥感动态监测提供方法参考。【方法】利用SVC HR-1024I全波段地物光谱仪,选取三株健康、生长环境相同、长势相近的中龄银杏为叶片采集对象,对其落叶期冠层叶片进行定期定点定方位的高光谱观测。对获取的高光谱原始数据进行数据筛选与预处理后,通过计算得出叶片的NDVI值,分别采用二次函数拟合法和ARIMA时间序列拟合法对落叶期叶片的NDVI值进行曲线拟合,并对两种拟合方法的拟合结果进行比较,选出最适合银杏叶片落叶期NDVI值的拟合方法。【结果】二次函数拟合结果为NDVI=-0.0221T2+0.0547T+0.711,决定系数R2为0.926,但因拟合结果t值不显著,样本结果随机性大,不具广泛性;ARIMA时间序列拟合中ARIMA(2,1,2)模型估测结果与实际情况接近,R2为0.811,拟合效果较好。【结论】ARIMA时间序列拟合方法比二次行数拟合方法更适用于对银杏落叶期叶片的NDVI值进行拟合。