三种稻飞虱体内类酵母共生菌18S rDNA和 ITS 5.8S rDNA序列克隆及进化分析

为研究水稻3种主要害虫灰飞虱Laodelphax striatellus、 褐飞虱Nilaparvata lugens和白背飞虱Sogatella furcifera体内类酵母共生菌（yeast-like symbiotes, YLS）的种属地位及与寄主的进化关系, 测定了其体内YLS的18S rDNA及ITS-5.8S rDNA的全长序列。基于3种稻飞虱体内YLS的18S rDNA序列比对表明, 褐飞虱YLS和白背飞虱YLS的一致性比其与灰飞虱YLS的高（褐飞虱YLS和白背飞虱YLS为98.91%, 灰飞虱YLS和褐飞虱YLS为95.74%, 灰飞虱YLS和白背飞虱YLS为96.02%）, 而基于ITS-5.8S rDNA序列比对, 灰飞虱YLS和白背飞虱YLS的一致性比其与褐飞虱YLS的要高（白背飞虱YLS和灰飞虱YLS为99.57%, 灰飞虱YLS和褐飞虱YLS为91.91%, 白背飞虱YLS和褐飞虱YLS为90.46%）。基于真菌18S rDNA和ITS-5.8S rDNA的系统发育树均表明, 3种稻飞虱体内YLS与其他已知真菌进化关系较远。本研究证实了昆虫真菌类共生菌与寄主形成了长期的进化关系, 从而形成了不同于已知真菌的分类地位。     

褐飞虱发生的一维时间序列相空间重构及混沌吸引子维数的确定

利用我国长江流域的吴县1979－1990年及太湖地区农科所1986－1998年6－11月份褐飞虱田间发生时间序列资料,将褐飞虱发生的一维时间序列拓展到多维相空间中去,研究结果表明：（1）我国长江流域短期褐飞虱发生演化,在相空间中存在吸引子,并具有分维结构,其维数分别是1．6和0．68,为混沌吸引子（或奇异吸子）;（2）就我国长江流域褐飞虱发生的短期变化而言,为了能在维相空间支撑上述奇怪吸引了,最好选取4个变量或建立最低为4阶的动力学模型式来进行描述。     

褐飞虱发生系统的混沌诊断

褐飞虱在我国常年发生面积约为1．3×10^7～2×10^7hm^2,年均损失稻谷10×10^8kg．为更深入地研究褐飞虱发生系统的特性与规律,并为预测和防治提供理论依据,以我国长江流域太湖地区1986～1998年6～11月褐飞虱田间发生时间序列资料为例,运用混沌理论中的自相关函数、功率谱图、相轨迹图、Poincare截面、返回映象图等方法,研究了褐飞虱发生系统的特性．结果表明,即使有随机性噪声的影响,仍可判明我国长江流域褐飞虱发生系统具有混沌特性．     

Phylogenetic position of yeast-like symbiotes from three rice planthoppers (Homoptera: Delphacidae ) based on 18S rDNA and ITS-5.8S rDNA sequences

为研究水稻3种主要害虫灰飞虱Laodelphax striatellus、褐飞虱Nilaparvata lugens和白背飞虱Sogatella furcifera体内类酵母共生菌( yeast-like symbiotes,YLS)的种属地位及与寄主的进化关系,测定了其体内YIS的18SrDNA及ITS-5.8S rDNA的全长序列.基于3种稻飞虱体内YLS的18S rDNA序列比对表明,褐飞虱YLS和白背飞虱YLS的一致性比其与灰飞虱YLs的高(褐飞虱YLS和白背飞虱YLS为98.91％,灰飞虱YLS和褐飞虱YLS为95.74％,灰飞虱YLS和白背飞虱YLS为96.02％),而基于ITS-5.8S rDNA序列比对,灰飞虱YLS和白背飞虱YLS的一致性比其与褐飞虱YLS的要高(白背飞虱YLS和灰飞虱YLS为99.57％,灰飞虱YLS和褐飞虱YIS为91.91％,白背飞虱YLS和褐飞虱YLS为90.46％).基于真菌18S rDNA和ITS-5.8S rDNA的系统发育树均表明,3种稻飞虱体内YLS与其他已知真菌进化关系较远.本研究证实了昆虫真菌类共生菌与寄主形成了长期的进化关系,从而形成了不同于已知真菌的分类地位.     

大气低温胁迫对中国褐飞虱年内初始迁入的影响

为了揭示大气低温胁迫对中国褐飞虱年内初始迁入的影响,更好地预警来自境外的褐飞虱早期迁入,通过统计2000-2017年中国华南、西南两个稻区褐飞虱年内的始见期和首次迁入峰日,逆推其迁飞轨迹和虫源区,分析大气温度场对迁飞过程产生的作用,比较了褐飞虱在我国不同稻区、不同年内初始迁入期受大气低温胁迫产生的作用差异。结果表明：（1）近年来褐飞虱初始迁入中国的时间提前,初始迁入华南稻区的时间比西南稻区早,华南稻区始见期提早可能与褐飞虱种群越冬北界北移有关。（2）西南稻区褐飞虱年内初始迁入的境外虫源主要来自缅甸,华南稻区年内初始迁入的境外虫源主要来自越南和老挝的中北部。（3）对盛行迁飞层的温场分布研究表明,在褐飞虱年内初始迁入过程中低温屏障发生的概率约为54.4%,迁入当晚降虫地的平均低温强度为13.45℃,平均降温幅度为1.88℃。其中低温胁迫在华南稻区表现更为显著（发生概率为58.3%,平均强度为13.18℃）,在始见期表现得更明显（发生概率为70.6%,平均强度为12.53℃）。     

《褐飞虱研究与防治》

《褐飞虱研究与防治》一书 ,由陈遐年等教授主编 ,已于 2 0 0 3年 7月由中国农业出版社正式出版发行。本书全面、系统地论述了褐飞虱研究与治理领域的最新进展、研究方法和实际应用。全书分 :国内外褐飞虱研究进展、褐飞虱灾害的地理分布及其时空变化规律、褐飞虱的形态特征和稻田内近似种类的识别、褐飞虱的种群生物学特性、褐飞虱大发生的种群特征、褐飞虱大发生与稻田生物群落关系、诱发褐飞虱再猖獗的生理生态因素、褐飞虱的远距离迁飞行为及其生理生态机制、褐飞虱发生系统的混沌特性及预测预报和褐飞虱防治技术的研究与应用等 1 2章。…     

褐飞虱Nl15基因的克隆及功能分析

【目的】植食性刺吸式口器昆虫的唾液蛋白参与调控植物抗虫防御反应,影响其对寄主植物的适应性。本研究旨在通过克隆褐飞虱Nilaparvata lugens重要唾液蛋白基因Nl15,调查其时空表达模式,明确其在褐飞虱致害性中的作用。【方法】基于褐飞虱IR56种群转录组数据,用RT-PCR克隆褐飞虱基因Nl15 cDNA序列,并进行生物信息学分析。利用qPCR检测其在褐飞虱TN1和IR56种群不同发育阶段(卵、1-5龄若虫和雌雄成虫)和雌成虫不同组织(头、胸、腹和足)中的表达模式。通过显微注射dsRNA对褐飞虱TN1和IR56种群的4龄若虫进行Nl15的RNAi,利用qPCR检测Nl15 RNAi后褐飞虱若虫中Nl15的相对表达量以及Nl15 RNAi后褐飞虱若虫取食3 d时水稻植株中防御相关基因(OsLecRK4, OsMPK10, OsWRKY24, OsLox, OsNPR1和OsGns5)的相对表达量,并生物测定Nl15 RNAi后褐飞虱的存活率以及成虫蜜露量和体质量增量。【结果】克隆了褐飞虱Nl15 cDNA序列(GenBank登录号：OK181113),其开放阅读框长1 008 bp;预测编码335个氨基酸,理论等电点为7.54,分子量为38.7 kD,含有23个氨基酸的信号肽序列和一个糖基化修饰位点,不存在跨膜结构域和其他已知的功能域;Nl15与灰飞虱Laodelphax striatellus同源蛋白氨基酸序列一致性为45%。发育表达谱结果表明,Nl15在褐飞虱各个发育阶段均表达,在3-4龄若虫中的表达量最高;组织表达谱结果表明,Nl15在褐飞虱雌成虫头部中的表达量最高,且在IR56种群头部中的表达量高于在TN1种群头部中的。RNAi实验结果表明,与注射dsGFP的对照组相比,注射dsNl15的处理组中Nl15的表达量显著降低了89.5%,褐飞虱的存活率以及成虫蜜露量和体质量增量均显著降低,上述6个水稻防御相关基因的表达量显著上调。【结论】褐飞虱IR56种群中的Nl15参与褐飞虱与水稻的防御与反防御分子互作。本研究为进一步阐述褐飞虱克服抗虫基因的机制及揭示昆虫与植物互作的分子网络提供了思路。     

近30年气候异常对江苏省褐飞虱灾变性迁入的影响

近30年来,中国褐飞虱的灾变性迁入因受气候变化的影响而出现了明显的时空变异,为了揭示气候异常对褐飞虱区域性灾变的影响,为我国褐飞虱灾变预警及其区域危害的防控提供理论依据,在收集1983—2008年江苏省32个植保站褐飞虱灯诱资料、1981—2008年太平洋海表温度和南方涛动指数数据及1983—2008年江苏省70个气象台站降水和气温观测资料的基础上,选取高邮、通州和宜兴作为江苏省不同水稻生长区的代表性测站(其中高邮代表江淮稻区,通州代表苏北沿海稻区,宜兴代表苏南稻区),通过对近30年来太平洋海温场、南方涛动、迁入区降水和温度异常与江苏省褐飞虱发生程度之间关系的分析,探讨了气候异常对该省褐飞虱灾变性迁入的影响。结果表明:(1)赤道东太平洋海表水温持续偏高的厄尔尼诺事件多引发江苏省褐飞虱偏重以上程度的发生,且其首次迁入峰的出现时间与厄尔尼诺事件开始期之间有1—14个月的滞后期。(2)南方涛动指数(SOI)持续出现负值的厄尔尼诺事件发生的当年或次年,褐飞虱为偏重以上的发生程度,且其首次迁入峰的出现时间滞后于SOI负值开始期1—14个月。(3)登陆并影响江苏的强热带气旋偏多的拉尼娜年也会出现褐飞虱偏重以上的发生。(4)降水对褐飞虱迁入的影响明显,但有限制条件;降水量大、降水日数多的年份褐飞虱迁入量大、发生程度重;以6—10月降水量、降水日数和降水强度为预报因子,建立了褐飞虱发生等级的回归方程,方程拟合和试报效果好。(5)夏秋季6—10月地面气温的异常对江苏省褐飞虱迁入的发生有一定的影响,秋温偏高的年份常导致江苏省褐飞虱偏重以上的发生。     

应用马尔可夫链理论对褐飞虱和白背飞虱发生程度的预测

褐飞虱Nilaparvata lugens (Stál)和白背飞虱Sogatella furcifera (Horváth) 是我国稻区的主要迁飞性害虫,给水稻生产带来严重危害.以重庆市秀山县1983～2007年田间褐飞虱和白背飞虱发生程度的时间序列资料,运用基于马尔可夫链理论的转移概率预测法,构建了1～5阶转移概率矩阵,根据每一阶概率矩阵单独进行回报的历史符合率计算各阶转移概率矩阵的权重,以预报年前5a褐飞虱和白背飞虱的连续发生状态预测第6年的发生级别.回报20a的结果历史符合率均达到90%,对2008年的预测也符合当年田间实际发生情况.该方法构建的5阶转移概率矩阵及其权重对于秀山县及其邻近地区褐飞虱和白背飞虱发生程度的长期预报具有重要指导意义,为害虫的长期可预测性提供了一种快速、有效的方法.     

褐飞虱EST资源的微卫星信息分析

表达序列标签（expressed sequence tags,ESTs）是开发微卫星标记的一个重要的资源。褐飞虱Nilaparvata lugens （Stål） EST序列的公布为开发EST-SSRs提供了宝贵的数据资源,本研究利用生物信息学对NCBI公共数据库中的37 398条褐飞虱ESTs序列进行EST-SSRs特征分析,得到全长为7 619 324 kb的无冗余EST 9 852条。按照3个不同的查找标准在这些序列中搜索SSR。查找结果显示：褐飞虱EST-SSRs主要重复基元以1～3碱基为主,占总EST-SSR的95％以上。在单碱基重复基元中,A/T是占优势的重复基元,在二相重复类型中,AG/CT重复基元出现的频率最多,而AAG/CTT是三相重复中占绝对优势的重复基元。在褐飞虱EST-SSRs中未查找到GC重复基元。以100 bp为参照,在3种查找标准下含有SSR的EST序列中两端侧翼序列均≥100 bp的序列分别为738,89和42个。通过分析褐飞虱EST-SSRs标记可以为褐飞虱和近缘种的SSR标记的开发提供信息,同时通过分析褐飞虱EST-SSRs的分布频率和分布特征可以为昆虫EST-SSRs的研究提供借鉴和参考。     

我国林火发生预测模型研究进展

通过文献回顾,总结了国内林火发生预测模型的研究现状,并从林火发生驱动因子、林火发生概率预测模型、林火发生频次预测模型和模型检验方法等方面进行归纳分析。得出以下结论: 1)气象、地形、植被、可燃物、人类活动等因素是影响林火发生及模型预测精度的主要驱动因子;2)林火发生概率模型中,地理加权逻辑斯蒂回归模型考虑了变量之间的空间相关性,Gompit回归模型适宜非对称结构的林火数据,随机森林模型不需要多重共线性检验,在避免过度拟合的同时提高了预测精度,是林火发生概率预测模型的优选方法之一;3)林火发生频次模型中,负二项回归模型更适合对过度离散数据进行模拟,零膨胀模型和栅栏模型可以处理林火数据中包含大量零值的问题;4)ROC检验、AIC检验、似然比检验和Wald检验方法是林火概率和频次模型的常用检验方法。林火发生预测模型研究仍是我国当前林火管理工作的重点,预测模型的选择需要依据不同地区林火数据特点。此外,构建林火预测模型时需要考虑更多的影响因素,以提高模型预测精度;未来,需要进一步探索其他数学模型在林火发生预测中的应用,不断提高林火发生预测模型的准确度。     

Use of simulated data from a process‐based habitat model to evaluate methods for predicting species occurrence

Much research has centered on determining which habitat model best predicts species occurrence. However, previous work typically used data sets that are inherently biased for evaluation. The use of simulated data provides a way of testing model performance using un‐biased data where the true relationships between species occurrence and population processes are predefined using sound ecological theory. We used a process‐based habitat model to generate simulated occurrence data to evaluate presence–absence and presence–only methods: generalized linear and generalized additive models (GLM, GAM), maximum entropy model (Maxent), and discrete choice models (DCM). This is the first study to use a DCM for predicting species distributions. We varied the effect that habitat quality had on fecundity and reported the model responses to these changes. When the effect of habitat quality on fecundity was weak, model performance was no better than random for all methods, however, performance increased as the habitat/fecundity relationship became stronger. For each level of habitat quality effect, there was little variation in performance between the presence–absence and presence–only methods. The use of a process‐based habitat model to generate occurrence data for evaluating model performance has a distinct advantage over other testing methods, because no errors are made during sampling and the true ecological relationships between population process and species occurrence are known. This leads to un‐biased results and increased confidence in assessing model performance and making management recommendations.     

Spatial and temporal variations in species occurrence rate affect the accuracy of occurrence models

Aim Predictive models of species occurrence have potential for prioritizing areas for competing land uses. Before widespread application, however, it is necessary to evaluate performance using independent data and effective accuracy measures. The objectives of this study were to (1) compare the effects of species occurrence rate on model accuracy, (2) assess the effects of spatial and temporal variation in occurrence rate on model accuracy, and (3) determine if the number of predictor variables affected model accuracy. Location We predicted the distributions of breeding birds in three adjacent mountain ranges in the Great Basin (Nevada, USA). Methods For each of 18 species, we developed separate models using five different data sets — one set for each of 2 years (to address the effects of temporal variation), and one set for each of three possible pairs of mountain ranges (to address the effects of spatial variation). We evaluated each model with an independent data set using four accuracy measures: discrimination ability [area under a receiver operating characteristic curve (AUC)], correct classification rate (CCR), proportion of presences correctly classified (sensitivity), and proportion of absences correctly classified (specificity). Results Discrimination ability was not affected by occurrence rate, whereas the other three accuracy measures were significantly affected. CCR, sensitivity and specificity were affected by species occurrence rate in the evaluation data sets to a greater extent than in the model‐building data sets. Discrimination ability was the only accuracy measure affected by the number of variables in a model. Main conclusions Temporal variation in species occurrence appeared to have a greater impact than did spatial variation. When temporal variation in species distributions is great, the relative costs of omission and commission errors should be assessed and long‐term census data should be examined before using predictive models of occurrence in a management setting.     

Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

As the source and main producing area of tea in the world, China has formed unique tea culture, and achieved remarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frost damage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden is very important for tea plantation management and economic values. Aiming at the problems existing in current meteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, large amount of calculation for predicted models and incomplete information on frost damage occurrence, this paper proposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmental data, and temperature trend results from a nearest local weather station for accurate frost damage occurrence level determination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Time-series meteorological data collected from a small weather station was used for testing and parameterization of a two-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was further used to validate the capability of a two-fold model for frost damage forecasting. Results showed that compared with the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR), the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model (K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage, and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed method accurately predicted three times frost damage occurrences, including onset time, duration and occurrence level. These results suggested the newly-proposed two-fold method was suitable for tea plantation frost damage occurrence forecasting.     

A model to predict the presence of longfin eels in some New Zealand streams, with particular reference to riparian vegetation and elevation

Two logistic regression models were developed from a database of 27 biotic and physicochemical variables for 99 sites in the Taieri River, New Zealand, to predict the probability of occurrence of longfin eels. Average depth was associated positively with eels while woody debris and oxygen concentration were negatively associated. At a macro-scale probability of eel occurrence declined with increasing elevation and, for a given elevation, was higher in tussock and pasture catchments and lower in pine and native forest settings. Using a separate fish database for the Taieri River this macro-scale model predicted eel presence 95·4% in agreement with observation. A map was generated from the model showing areas of predicted high, moderate and low probabilities of eel occurrence. The model also estimated the minimum total number of eels present in the Taieri River catchment (excluding lakes, and streams below 100 m and above 1000 m) as 20 865 (95% CL: 10 560-36 350).     

Geographic selection bias of occurrence data influences transferability of invasive Hydrilla verticillata distribution models

Due to socioeconomic differences, the accuracy and extent of reporting on the occurrence of native species differs among countries, which can impact the performance of species distribution models. We assessed the importance of geographical biases in occurrence data on model performance using Hydrilla verticillata as a case study. We used Maxent to predict potential North American distribution of the aquatic invasive macrophyte based upon training data from its native range. We produced a model using all available native range occurrence data, then explored the change in model performance produced by omitting subsets of training data based on political boundaries. We also compared those results with models trained on data from which a random sample of occurrence data was omitted from across the native range. Although most models accurately predicted the occurrence of H. verticillata in North America (AUC > 0.7600), data omissions influenced model predictions. Omitting data based on political boundaries resulted in larger shifts in model accuracy than omitting randomly selected occurrence data. For well‐documented species like H. verticillata, missing records from single countries or ecoregions may minimally influence model predictions, but for species with fewer documented occurrences or poorly understood ranges, geographic biases could misguide predictions. Regardless of focal species, we recommend that future species distribution modeling efforts begin with a reflection on potential spatial biases of available occurrence data. Improved biodiversity surveillance and reporting will provide benefit not only in invaded ranges but also within under‐reported and unexplored native ranges.     

A stochastic simulation study on using different models for prediction of breeding values while changing the breeding goal

In a stochastic simulation study the effect of simultaneously changing the model for prediction of breeding values and changing the breeding goal was studied. A population of 100 000 cows with registrations on seven traits was simulated in two steps. In the first step of 15 years the population was selected for production and mastitis occurrence using a univariate model for prediction of breeding values for production and a trivariate model using information on mastitis treatments, udder depth and somatic cell score for prediction of breeding values for mastitis occurrence. In the second step six different scenarios were set up and simulated for 15 years combining two different breeding goals and three different models for prediction of breeding values in 20 replicates. Breeding goal 1 had relative economic value per genetic standard deviation on production (19.4) and mastitis occurrence ( − 50) whereas breeding goal 2 had a economic value on production (19.4), udder depth (4.2), mastitis occurrence ( − 50), non return rate (13.0) and days open ( − 16.75). Model 1 was a model similar to the one used in the first 15 years. Model 2 was an approximate multitrait model where solutions for fixed effects from a model corresponding to model 1 were subtracted from the phenotypes and a multitrait model with an overall mean, a year effect, an additive genetic and a residual effect were applied. Model 3 was a full multitrait model. Average genetic trends for total merit and each individual trait over 20 replicates were compared for each scenario. With the number of replicates the genetic responses using model 2 and 3 were not significant different. With a broad breeding goal using, model 2 or model 3 gave a significantly higher response in total merit than using model 1. Using a narrow breeding goal there was no significant difference between models used for prediction of breeding values. Results showed that with a breeding goal with a lot of emphasis on low heritable traits with a high economic value using a multitrait methodology for prediction of breeding values will redistribute the genetic progress in the total merit index. More gain will come from the low heritable traits in the breeding goal and less from traits with higher heritability. With a broad breeding goal and exploiting the available information in the data the inbreeding coefficient increased though not significantly.     

Predicting habitat distribution and frequency from plant species co-occurrence data

Aim Species frequency data have been widely used in nature conservation to aid management decisions. To determine species frequencies, information on habitat occurrence is important: a species with a low frequency is not necessarily rare if it occupies all suitable habitats. Often, information on habitat distribution is available for small geographic areas only. We aim to predict grid‐based habitat occurrence from grid‐based plant species distribution data in a meso‐scale analysis. Location The study was carried out over two spatial extents: Germany and Bavaria. Methods Two simple models were set up to examine the number of characteristic plant species needed per grid cell to predict the occurrence of four selected habitats (species data from FlorKart, http://www.floraweb.de ). Both models were calibrated in Bavaria using available information on habitat distribution, validated for other federal states, and applied to Germany. First, a spatially explicit regression model (generalized linear model (GLM) with assumed binomial error distribution of response variable) was obtained. Second, a spatially independent optimization model was derived that estimated species numbers without using spatial information on habitat distribution. Finally, an additional uncalibrated model was derived that calculated the frequencies of 24 habitats. It was validated using NATURA2000 habitat maps. Results Using the Bavarian models it was possible to predict habitat distribution and frequency from the co‐occurrence of habitat‐specific species per grid cell. As the model validations for other German federal states were successful, the models were applied to all of Germany, and habitat distribution and frequencies could be retrieved for the national scale on the basis of habitat‐specific species co‐occurrences per grid cell. Using the third, uncalibrated model, which includes species distribution data only, it was possible to predict the frequencies of 24 habitats based on the co‐occurrence of 24% of formation‐specific species per grid cell. Predicted habitat frequencies deduced from this third model were strongly related to frequencies of NATURA2000 habitat maps. Main conclusions It was concluded that it is possible to deduce habitat distributions and frequencies from the co‐occurrence of habitat‐specific species. For areas partly covered by habitat mappings, calibrated models can be developed and extrapolated to larger areas. If information on habitat distribution is completely lacking, uncalibrated models can still be applied, providing coarse information on habitat frequencies. Predicted habitat distributions and frequencies can be used as a tool in nature conservation, for example as correction factors for species frequencies, as long as the species of interest is not included in the model set‐up.     

When phylogeny and ecology meet: Modeling the occurrence of Trichoptera with environmental and phylogenetic data

Ecological studies are increasingly considering phylogenetic relationships among species. The phylogeny is used as a proxy or filter to improve statistical tests and retain evolutionary elements, such as niche conservation. We used the phylogenetic topology to improve the model for occurrence of Trichoptera genera in Cerrado (Brazilian Savanna) streams. We tested whether parameters generated by logistic models of occurrence, using phylogenetic signals, are better than models generated without phylogenetic information. We used a model with Bayesian updating to examine the influence of stream water pH and phylogenetic relationship among genera on the occurrence of Trichoptera genera. Then, we compared this model with the logistic model for each Trichoptera genus. The probability of occurrence of most genera increased with water pH, and the phylogeny‐based explicit logistic model improved the parameters estimated for observed genera. The inferred relationship between genera occurrence and stream pH improved, indicating that phylogeny adds relevant information when estimating ecological responses of organisms. Water with elevated acidity (low pH values) may be restrictive for the occurrence of Trichoptera larvae, especially if the regional streams exhibit neutral to alkaline water, as is observed in the Cerrado region. Using phylogeny‐based modeling to predict species occurrence is a prominent opportunity to extend our current statistical framework based on environmental conditions, as it enables a more precise estimation of ecological parameters.     

ARIMA与SVM组合模型在害虫预测中的应用

害虫发生是一种复杂、 动态时间序列数据, 单一预测模型都是基于线性或非线性数据, 不能同时捕捉害虫发生的线性和非线性规律, 很难达到理想的预测精度。本研究首先采用差分自回归移动平均模型对昆虫发生时间序列进行线性建模, 然后采用支持向量机对非线性部分进行建模, 最后得到两种模型的组合预测结果。将组合模型应用到松毛虫Dendrolimus punctatus发生面积的预测, 实验结果表明组合模型的预测精度明显优于单一模型, 发挥了两种模型各自的优势。组合模型是一种切实可行的害虫预测预报方法。