Meta-analysis of susceptibility of woody plants to loss of genetic diversity through habitat fragmentation

Shrubs and trees are assumed less likely to lose genetic variation in response to habitat fragmentation because they have certain life-history characteristics such as long lifespans and extensive pollen flow. To test this assumption, we conducted a meta-analysis with data on 97 woody plant species derived from 98 studies of habitat fragmentation. We measured the weighted response of four different measures of population-level genetic diversity to habitat fragmentation with Hedge's d and Spearman rank correlation. We tested whether the genetic response to habitat fragmentation was mediated by life-history traits (longevity, pollination mode, and seed dispersal vector) and study characteristics (genetic marker and plant material used). For both tests of effect size habitat fragmentation was associated with a substantial decrease in expected heterozygosity, number of alleles, and percentage of polymorphic loci, whereas the population inbreeding coefficient was not associated with these measures. The largest proportion of variation among effect sizes was explained by pollination mechanism and by the age of the tissue (progeny or adult) that was genotyped. Our primary finding was that wind-pollinated trees and shrubs appeared to be as likely to lose genetic variation as insect-pollinated species, indicating that severe habitat fragmentation may lead to pollen limitation and limited gene flow. In comparison with results of previous meta-analyses on mainly herbaceous species, we found trees and shrubs were as likely to have negative genetic responses to habitat fragmentation as herbaceous species. We also found that the genetic variation in offspring was generally less than that of adult trees, which is evidence of a genetic extinction debt and probably reflects the genetic diversity of the historical, less-fragmented landscape.     

Varying responses of northeastern North American amphibians to the chytrid pathogen Batrachochytrium dendrobatidis

Chytridiomycosis, caused by Batrachochytrium dendrobatidis (Bd), is widespread among amphibians in northeastern North America. It is unknown, however, whether Bd has the potential to cause extensive amphibian mortalities in northeastern North America as have occurred elsewhere. In the laboratory, we exposed seven common northeastern North American amphibian species to Bd to assess the likelihood of population-level effects from the disease. We exposed larval wood frogs (Lithobates sylvaticus) and postmetamorphic frogs of six other species to two different strains of Bd, a northeastern strain (JEL404) and a strain that caused die-offs of amphibians in Panama (JEL423), under ideal in vitro growth conditions for Bd. Exposed American toads (Anaxyrus americanus) all died; thus, this species may be the most likely to die from Bd-caused disease in the wild. Both Bd strains were associated with mortalities of wood frogs, although half the metamorphs survived. The Bd strain from Panama killed metamorphic green frogs (L. clamitans), whereas the northeastern strain did not, which means novel strains of Bd may lead to death even when local strains may not. No mortality was observed in four species (bullfrogs [L. catesbeianus], northern leopard frogs [L. pipiens], spring peepers [Pseudacris crucifer], and blue-spotted salamanders [Ambystoma laterale]) and in some individuals of green frogs and wood frogs that we exposed. This finding suggests these six species may be Bd vectors. Our results show that systematic exposures of amphibian species to Bd in the laboratory may be a good first step in the identification of species susceptible to Bd-caused declines and in directing regional conservation efforts aimed at susceptible species.     

基于高光谱技术的土壤镉污染分级评价研究

提出一种利用高光谱技术进行土壤镉污染分级评价的方法。以FieldSpec 3地物光谱仪采集厂矿区土壤光谱反射率175份,随机分成校正集（135份）和检验集（40份）。光谱经小波去噪和标准归一化（SNV）处理后,以主成分分析法（PCA）降维。将降维所得的前5个主成分数据为输入变量,分别采用Fisher线性判别、Byes逐步判别、模糊模式识别、BP-ANN判别以及SVM 5种方法建立了土壤镉污染分级评价模型,并利用40个未知样对模型进行检验。结果表明：Fisher线性判别准确率为77.5%,Byes逐步判别与模糊模式识别预测为80.0%,BP-ANN模型预测精度为82.5%,SVM模型预测精度最高,达85.0%。说明采用高光谱技术进行土壤镉污染分级评价是可行,其中SVM是建模的优选算法。     

基于多类支持向量机的化学物质生态危害分类研究

应用多类支持向量机(M-SVMs)方法研究了化学物质生态危害程度的分类,以提高分类的准确性和效率.对采集到的61种环境优先污染物的环境行为和生物毒性方面的7项指标进行相关性分析,建立了M-SVMs分类模型并对数据集进行10折交叉验证以评价模型的分类能力,运用所建模型对7种化学物质的生态危害进行预测.结果表明,去除与鱼毒有信息重叠的溞毒指标,选取鱼毒、藻毒、降解性、蓄积性、分配系数和吸附系数6项指标用于构建M-SVMs分类模型;M-SVMs模型识别率较高,交叉验证平均分类正确率达86.89％;对7种化学物质生态危害的预测结果与实际情况基本相符.     

县域粮食单产及其生产投入因素的空间异质性——以河南省为例

在当前耕地总量很难增加的总体形势下,粮食单产及其生产投入因素的关系研究对国家粮食安全具有重要意义。Moran’s I指数及散点图显示,粮食单产及其OLS(Ordinary Least Squares)估计的残差存在显著的空间自相关性,因而不满足经典线性回归分析的建模条件,而GWR(Geographically Weighted Regression)模型能够克服这一缺陷,并能进行参数的局部估计。河南省108个县的实践表明,GWR模型有效地降低了残差的空间自相关,各项检验指标均好于OLS估计。4个生产投入影响因素的空间分异规律存在显著差异。灌溉因素在OLS和GWR模型中均为正效应,单位播种面积机械动力、单位播种面积化肥施用量(折纯)、单位播种面积用电量3个变量的系数估计值在OLS模型中为负,而在GWR模型中则有正有负,说明存在空间异质性。黄河以北粮食高产区主要受机械动力、灌溉和用电量3个因素的正向影响,化肥投入的再增加反而引致单产降低。淮河沿岸和信阳的粮食高产区主要受化肥施用量和用电量的正向影响。为提高粮食单产,西部山地、丘陵县的粮食低产区应加强机械动力投入并提高用电效率,最西部的山地县还应适度增加化肥的用量。     

基于MFOA-SVR露天矿边坡变形量预测研究

为实现边坡危险性及时预警预报,以露天矿边坡变形量为研究对象,提出采用七项影响指标作为边坡位移变形量的响应参数,建立支持向量机回归预测模型(SVR)。引入修正的果蝇优化算法(MFOA)对模型参数进行优化,构建基于MFOA-SVR露天矿边坡变形量协同预测模型,并以实际监测数据进行模型仿真预测。结果表明:该模型平均绝对误差为0.9167mm,平均相对误差为4.2737%,较其他模型预测精度高,综合性能好,将其运用于露天矿边坡变形量预测研究具有较好的适用性和可靠性。     

基于加权线性回归模型组的湖北省工矿事故死亡人数分析预测

以2005-2014年湖北省工矿企业事故死亡人数为基础,通过对时间序列图分析可知,该省工矿企业事故具有明显的周期性、季节性。另外,依据安全系统特征属性,系统近期的状况相对远期的状况对未来预测更具有影响力。因此,各个季节数据的权重应有所不同。综合考虑以上两种情况,提出对4个季节建立不同的线性加权线性回归模型,并进行组合,形成加权线性回归模型组,进而对2015-2016年各个季度进行预测。最后,与传统的时间序列分析方法比较可知,加权线性回归模型组的方法建立的模型显著性、拟合度更高,并且模型的可读性更强、更简单。     

基于粗糙集和支持向量机的标准农田地力等级评价

标准农田是耕地的精华,是确保国家粮食安全的关键。科学评价标准农田地力等级对标准农田培肥和土壤改良有着重要意义。将粗糙集(Rough Set,RS)理论和支持向量机(Support Vector Machine,SVM)相结合,提出了基于RS和SVM的标准农田地力等级评价方法,同时,利用遗传算法的并行搜索结构和模拟退火的概率突跳特性,提出了GASA优化SVM参数算法。该方法首先在确定标准农田地力等级评价指标的基础上,利用地力调查样本数据及传统的指数和法评价结果构建RS决策表,应用RS穷尽算法对决策表进行约简,剔除冗余的评价指标,然后用约简后的评价指标作为SVM的输入,运用GASA优化SVM参数算法对SVM进行训练,建立标准农田地力等级的RS-SVM评价模型。应用该方法对温州市鹿城区标准农田地力等级进行评价,与未用RS约简的SVM模型和BP神经网络模型评价结果进行对比,SVM模型和BP神经网络模型的输入指标数均为15个,其评价正确率分别为100%和90%;RS-SVM模型的输入指标数为14个,其评价正确率分别为100%,结果表明,该方法通过RS约简评价指标后,SVM评价精度并没有降低,但降低了SVM输入向量维数和计算复杂度,提高了训练效率;SVM 用于标准农田地力等级评价,具有比BP神经网络更高的评价精度,可有效用于标准农田地力等级评价,为耕地地力评价提供了新方法。     

A discrete model for estimating the development time from egg to infecting larva of Ostertagia ostertagi parametrized using a fuzzy rule-based system

Ostertagia ostertagi is a nematode, predominantly affecting cattle in the Pampean region of Argentina. A mathematical model parametrized using fuzzy rule-based systems of the Takagi-Sugeno-Kant type (FTSK) for estimating the development time from egg to infecting larval stage L3 of the gastrointestinal parasite O. ostertagi is here proposed. The estimation of development time of O. ostertagi is essential for the generation of appropriate control mechanisms, since this provides information about the time when parasites are ready to migrate to pastures. For the purpose of reflecting the natural environmental conditions, the mean daily temperature is taken as the main and only regulator of the development time. Humidity conditions are considered to be sufficient for the normal development of the larvae. Hence the individual's daily growth is a function of its length and the mean temperature recorded on the previous day. It is expressed in terms of a difference equation with fuzzy parameters, which are defined using laboratory data. Model outputs are tested against results of field experiments. Simulation results are very satisfactory, yielding a mean estimation error (MEE) of 0.64 weeks, with variance 0.34, and a determination coefficient R² = 0.74. The model clearly exhibits an inverse relationship between development time and temperature both in controlled and in field conditions. It also exhibits a very sensitive response both to the order in which the temperature sequence occurs, - reproducing the differences observed between spring and autumn - and to the amplitude of the temperature range.     

区域农业生态环境质量综合评价方法与模型研究

为了客观准确的评价区域农业生态环境质量,引入灰色系统理论中的关联度分析法,提出了区域生态环境质量的多级模糊综合评价——灰色关联优势分析复合模型,并以安徽省芜湖市区域农业生态环境质量的综合评价为例,根据芜湖市区域农业生态环境调查与监测资料,进行了实例研究。结果表明:安徽省芜湖市区域农业生态环境已经受到了明显的污染,并且各区域污染水平具有一定的差异;芜湖市区域农业生态环境质量优劣的关联序依次为:南陵县、芜湖县、繁昌县和城市郊区。