枣实蝇蛹发育起点温度和有效积温

室内设定21,24,27,30和33℃共5个温度梯度,分别测定不同恒温条件下枣实蝇越冬蛹的发育历期,利用最小二乘法计算出蛹的发育起点温度为13.63℃,有效积温为807.55日.度。根据吐鲁番20年(1988～2007)月均地表温度数据,使用小样本方法估算出月均地表温度的置信区间,利用有效积温法则,推算得出2008年枣实蝇越冬代成虫初次出现日期范围为5月13日至22日,与2008年野外实测越冬成虫初次出现日期5月15日接近,测报结果较准确。     

黄杨绒蚧的生物学特性及卵期预测预报

黄杨绒蚧Eriococcus abeliceae Kuwana是危害园林树木的重要害虫之一。该蚧在西宁地区1年发生1代,以2龄若虫在树干裂皮缝中越冬。翌年3月下旬2龄若虫蜕皮变为3龄若虫,开始为害,雌虫5月上旬开始产卵,卵期18~23d,每雌虫产卵量134~424粒,平均287粒。6月上旬卵开始孵化,孵化率100%。雌虫3龄,雄虫2龄。在室内自然变温条件下,对黄杨绒蚧卵的发育起点温度和有效积温进行测定。结果表明:卵发育起点温度12.98℃,有效积温39.03日.度;预测式为N=(39.03±2.66)/T-(12.98±0.18)。     

比较逻辑斯蒂与地理加权逻辑斯蒂回归模型在福建林火发生的适用性

林火预测预报是科学有效进行林火管理的前提,是林业管理部门和科研工作者的广泛关注的领域。逻辑斯蒂回归(Logistic Regression,LR)是目前国内外广泛应用于森林火灾预测的模型方法,然而近年来有学者发现该方法没有充分考虑林火影响因子的空间相关性和异质性,从而导致模型拟合结果偏差。地理加权逻辑斯蒂回归(Geographically weighted logistic regression,GWR)模型考虑到了模型变量之间的空间相关性,有效提高的模型的拟合能力。为探讨GWLR模型在福建林火预测上的适用性,本研究应用LR和GWLR两种方法分别建立福建省森林火灾与气象因子的预测模型,通过模型拟合能力对比,判断在GWLR的适用性。研究以2000—2005年福建地区森林火灾卫星火点数据和每日气象因子为基础,将全样本分为60%的建模数据和40%的校验数据,并重复5次,建立5个样本组。选择在5个样本组中3个及以上表现显著的变量进入最终模型。研究结果表明GWLR在模型拟合度、模型残差、空间自相关性以及预测准确率等方面均优于LR模型,说明充分考虑模型变量的空间异质性有助于提高模型的预测精度,同时也验证了GWLR在福建地区林火预测上的适应性。此外,模型参数结果显示,"日最高地表气温"、"日最低地表气温"、"日平均风速"、"24小时降水量"、"日最高本站气压"、"日照时数"、"日最高气温"和"日最小相对湿度"8个因子对福建省林火发生有显著影响,研究结论为福建地区林火预测预报提供了新的方法。     

Climatic impacts on winter wheat in Oklahoma and potential applications to climatic and crop yield prediction

Climatic anomalies can pose severe challenges for farmers and resource managers. This is particularly significant with respect to gradually developing anomalies such as droughts. The impact of the 1995-1996 drought on the Oklahoma wheat crop, and the possibility that predictive information might have reduced some of the losses, is examined through a combined modeling approach using climatological data and a crop growth model that takes into account an extensive range of soil, climatic, and plant variables. The results show potential outcomes and also illustrate the point at which all possible climatic outcomes were predicting a significantly low wheat yield. Based on anecdotal evidence of the 1995-1996 drought, which suggested that farmers who planted at different times experienced different yields, the model was run assuming a variety of different planting dates. Results indicate that there is indeed a noticeable difference in the modeled wheat yields given different planting dates. The information regarding effectiveness of planting date can be used in conjunction with current long-range forecasts to develop improved predictions for the current growing season. This approach produces information regarding the likelihood of extreme precipitation events and the impact on crop yield, which can provide a powerful tool to farmers and others during periods of drought or other climatic extremes.     

肿瘤免疫治疗专题报告 ——遇上最好的时代

肿瘤免疫治疗被称为继手术、放疗、化疗后第 4 种疗法,其可激活特异性、重要的免疫细胞,直接靶向性攻击癌症细胞,具有较高 的疗效和安全性,是目前全球肿瘤治疗研究的热点。报告采用文献调研、数据库检索、数据统计与分析等定性定量研究方法,从市场预测、 发展历程、国内外企业布局情况等方面对肿瘤免疫治疗药物领域进行多角度、多层次的分析,旨在为相关企业明确发展方向、确定产品研 发思路及制定市场策略提供线索和参考。     

Integrated modeling predicts shifts in waterbird population dynamics under climate change

Climate change has been identified as one of the most important drivers of wildlife population dynamics. The in‐depth knowledge of the complex relationships between climate and population sizes through density dependent demographic processes is important for understanding and predicting population shifts under climate change, which requires integrated population models (IPMs) that unify the analyses of demography and abundance data. In this study we developed an IPM based on Gaussian approximation to dynamic N‐mixture models for large scale population data. We then analyzed four decades (1972–2013) of mallard Anas platyrhynchos breeding population survey, band‐recovery and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. We aimed to test the hypothesis that climate change will cause shifts in population dynamics if climatic effects on demographic parameters that have substantial contribution to population growth vary spatially. More specifically, we examined the spatial variation of climatic effects on density dependent population demography, identified the key demographic parameters that are influential to population growth, and forecasted population responses to climate change. Our results revealed that recruitment, which explained more variance of population growth than survival, was sensitive to the temporal variation of precipitation in the southern portion of the study area but not in the north. Survival, by contrast, was insensitive to climatic variation. We then forecasted a decrease in mallard breeding population density in the south and an increase in the northwestern portion of the study area, indicating potential shifts in population dynamics under future climate change. Our results implied that different strategies need to be considered across regions to conserve waterfowl populations in the face of climate change. Our modelling approach can be adapted for other species and thus has wide application to understanding and predicting population dynamics in the presence of global change.     

基于SVR和CAR的多维时间序列分析及其在生态学中的应用

基于支持向量回归（SVR）并融合带受控项的自回归模型（CAR）,建立了一种既反映样本集动态特征又体现环境因子影响的非线性多维时间序列分析预测方法（SVR-CAR）。用一步预测法对两个生态学样本集的预测结果表明,SVR-CAR在所有参比模型中预测精度最高,并具结构风险最小、非线性、避免过拟合、泛化推广能力优异等诸多优点。SVR-CAR在生态学、农业科学、经济学等多维时间序列预测领域有较广泛的应用前景。     

沙地云杉种群增长预测模型研究

 本文以沙地云杉种群的静态生命表、生殖力表为基础,运用Leslie种群增长模型,预测了小腾格里沙地白音敖包地区有代表性的藓类云杉林中云杉种群的动态增长过程,揭示了沙地云杉各龄级植株的动态规律。结果表明：沙地云杉为缓慢增长型种群;种群的净增殖率、内禀增长率及周限增长率较高,而种群的世代周期及加倍时间偏低;在50年内种群1年生幼苗的数量及种群的总数量表现出持续递增的趋势,且种群总数目的变化幅度较小。     

爻纹细蛾的测报与防治技术研究

爻纹细蛾(Conopomorpha sinensis Bradley)是龙眼、荔枝的主要害虫,以幼虫为害幼梢和果宴,在厦门市同安区一年发生九代,主害代为7—8月份的第五、六代。本报道了该虫的形态特征和生活习性等,对卵、幼虫、蛹、成虫在不同温度下的发育历期和蛹的分级方法进行了研究,重点探讨了该虫的测报方法和防治技术。     

Evidence of peak-to-peak dynamics in ecology

Remarkably irregular peaks characterize the dynamics of many plant and animal populations. As such peaks are often associated with undesirable consequences (e.g. pest outbreaks, epidemics, forest fires), the forecast of the forthcoming peak is a problem of major concern. Here we show, through the analysis of a number of models and of some of the longest and most celebrated ecological time-series, that the intensity of the forthcoming peak can often be predicted simply from the previous peaks. When this is possible, one can also predict the time of occurrence of the forthcoming peak.