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The Way They Move: Towards a General Framework for Understanding Animal Movement in Changing Environments

它们的移动方式：建立一个理解不断变化的环境中动物运动的总体框架

基本信息

批准号：
EP/F069766/1
负责人：
Ruth King
金额：
$ 55.9万
依托单位：
University of St Andrews
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2009
资助国家：
英国
起止时间：
2009 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FF069766%2F1
关键词：
Way They Move Towards General

项目摘要

Almost all animals need to move in order to find food, mates, shelter and other necessities of life. Yet in moving they are expending energy, and exposing themselves to dangers such as competitors and predators. Moving animals may also unwittingly expose others to danger / for example they may carry infections diseases (think of avian flu or bovine TB in badgers, for example). Think of a whole population of animals, all making decisions about where to move next: it is clear that the decision strategies that individual animals use has a fundamental effect on the dynamics of the population. Similarly, when multiple species interact in communities, individual animal movement strategies will fundamentally affect the community dynamics. Animal movements are therefore the glue that keeps spatial wildlife systems together.One of the best ways to find out about animal movement in a natural environment is to attach a lightweight radio or satellite transmitter to an animal. This technology has been developing very rapidly and it is now possible to track individuals over long distances and time periods. In some species the locations can be measured with amazing accuracy, thanks to tags with built-in GPSs (Geographic Positioning Systems / like those in car navigation systems). The rapid development in tracking technology has not yet been matched by similar developments in analysis methods. Firstly, most current analyses focus on describing the data, in terms of distances moved in different habitats, home range sizes, etc / rather than attempting to understand the processes that generate the movements. Secondly, most current methods ignore issues that arise due to the way the data were collected, such as measurement error, timing of measurements and the fact that often many repeated measurements are made on few individuals. Lastly, current methods that do attempt to deal with these issues often break down when faced with the enormous datasets that are currently available.The goals of our work are :1) To develop new ways to model and understand animal movements. Our premises are that i) the complexities of animal movement can be dissected into a few general movement strategies; and ii) animals switch among these strategies as they are affected by changes in the internal and external environment. One advantage of thinking of movement in this way is that we can predict changes in movement behaviour as a result of changes in environment, for example through climate change.2) To develop advanced statistical tools that allow these models to be applied to real data. We propose to harness and extend advanced computer simulation techniques known generally as Monte Carlo methods . These methods can cope well with the analysis of complex models and large datasets. They are an intense focus of statistical research due to the rapid increase in available computing power, and we intend to build upon the latest developments, such as methods that allow multiple computer processors to be applied in parallel to the same problem.Our ideas and methods will be tested in three case studies. The first is a long-term study of movement of 120 radio collared elk, who spend variable amounts of time in groups or alone. This allows us to study how movement, and ultimately population dynamics, is affected by social forces. The second is a high-resolution dataset showing movements of individual elk and predatory wolf. We think of this as a landscape of fear where the predators are adjusting their movements and landscape use to maximise their encounter with prey which in turn tries to become unpredictable. Lastly, we have movement data for migrating Serengeti wilderbeest, as well as corresponding forage and rainfall data that will allow us to study how these animals determine which migration route to use / a question with important conservation implications.

几乎所有的动物都需要移动，以寻找食物，配偶，住所和其他生活必需品。然而，在移动中，它们消耗了能量，并将自己暴露在竞争对手和捕食者等危险中。移动的动物也可能无意中使其他动物暴露于危险之中，例如它们可能携带传染病（例如禽流感或獾的牛结核病）。想想整个动物种群，所有动物都在决定下一步去哪里：很明显，个体动物使用的决策策略对种群的动态有着根本性的影响。同样，当多个物种在群落中相互作用时，个体动物的运动策略将从根本上影响群落的动态。因此，动物的运动是将空间野生动物系统保持在一起的粘合剂。在自然环境中了解动物运动的最佳方法之一是在动物身上安装轻型无线电或卫星发射器。这项技术发展非常迅速，现在可以在长距离和长时间内跟踪个人。在某些物种中，由于内置GPS（地理定位系统/如汽车导航系统）的标签，位置可以以惊人的精度测量。跟踪技术的迅速发展还没有与分析方法的类似发展相匹配。首先，目前大多数分析集中在描述数据，在不同的栖息地，家域大小等移动的距离方面，而不是试图了解产生移动的过程。其次，目前大多数方法忽略了由于数据收集方式而产生的问题，例如测量误差、测量时间以及经常对少数人进行多次重复测量的事实。最后，目前试图处理这些问题的方法往往在面对目前可用的庞大数据集时失败。我们工作的目标是：1）开发新的方法来建模和理解动物运动。我们的前提是：i）动物运动的复杂性可以被分解为几个一般的运动策略; ii）动物在这些策略之间切换，因为它们受到内外环境变化的影响。以这种方式思考运动的一个优点是，我们可以预测由于环境变化（例如气候变化）而导致的运动行为的变化。2）开发先进的统计工具，使这些模型能够应用于真实的数据。我们建议利用和扩展先进的计算机模拟技术，一般称为蒙特卡罗方法。这些方法可以很好地科普复杂模型和大型数据集的分析。由于可用计算能力的快速增长，它们是统计研究的一个强烈焦点，我们打算建立在最新的发展基础上，例如允许多个计算机处理器并行应用于同一问题的方法。我们的想法和方法将在三个案例研究中进行测试。第一项是对120只无线电项圈麋鹿的运动进行长期研究，这些麋鹿在群体或单独中花费的时间不同。这使我们能够研究社会力量如何影响人口流动，最终影响人口动态。第二个是一个高分辨率的数据集，显示了个体麋鹿和掠食性狼的运动。我们认为这是一个恐惧的景观，捕食者正在调整他们的运动和景观使用，以最大限度地与猎物相遇，这反过来又试图变得不可预测。最后，我们有迁移塞伦盖蒂野生动物的运动数据，以及相应的饲料和降雨数据，这将使我们能够研究这些动物如何确定使用哪条迁移路线/一个具有重要保护意义的问题。