More realistic statistical models for stage-structured time-series data

针对阶段结构时间序列数据的更真实的统计模型

• 批准号: 1021553
• 负责人: Perry de Valpine
• 金额: $ 36.39万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1021553&HistoricalAwards=false
• 关键词: More; realistic; statistical; models; stage

Biologists often need to make predictions about how quickly animal or plant populations will grow or decline. For example, predictions of insect populations in agriculture are needed for pest management, and predictions of small fish and plankton species that are vital for marine food webs are important for marine conservation and management. Predicting population change is notoriously difficult because biologists know relatively little about the life cycle of most organisms and because they are subject to many influences of weather, predators, food resources, and habitat conditions. One important approach is to use past records of populations to estimate how quickly organisms develop, reproduce, and die. Accomplishing this is particularly difficult for the many kinds of organisms that can be counted only by their life stages, such as the eggs, larvae, or adults of insects. This project will improve methodology for estimating patterns of population change when only data on organism stages is available. The approach will be to adapt state-of-the-art computer algorithms to the context of such data. These algorithms will determine the range of plausible population growth patterns from the kind of rough data that can typically be collected. An important step in validating new algorithms for data analysis is to evaluate their performance in a controlled setting. Laboratory experiments with Pacific spider mites, an important agricultural pest, will be used for this purpose. The new analytical methodology to be developed in this project will be made available to the public as open-source software. In addition, training workshops will be conducted at major national conferences to facilitate the broad dissemination and application of this software. This project will result in the training of undergraduate and graduate students and a post-doctoral researcher in mathematical and statistical methods for population ecology.

生物学家经常需要预测动物或植物种群的增长或下降速度。 例如，害虫管理需要预测农业昆虫种群，对海洋食物网至关重要的小鱼和浮游生物物种的预测对海洋养护和管理很重要。 预测种群变化是出了名的困难，因为生物学家对大多数生物的生命周期知之甚少，而且它们受到天气、捕食者、食物资源和栖息地条件的诸多影响。 一个重要的方法是利用过去的种群记录来估计生物体发育、繁殖和死亡的速度。 要做到这一点，对于许多只能按生命阶段计数的生物来说尤其困难，例如昆虫的卵、幼虫或成虫。 该项目将改进在只有生物体阶段数据的情况下估计种群变化模式的方法。 方法将是使最先进的计算机算法适应这种数据的背景。 这些算法将从通常可以收集的粗略数据中确定合理的人口增长模式范围。 验证用于数据分析的新算法的一个重要步骤是评估它们在受控环境中的性能。 为此目的，将利用太平洋蜘蛛螨这种重要的农业害虫进行实验室试验。 该项目拟开发的新分析方法将作为开放源码软件向公众提供。 此外，将在主要的国家会议上举办培训讲习班，以促进这一软件的广泛传播和应用。 该项目将培养本科生和研究生以及一名人口生态学数学和统计方法的博士后研究员。