Collaborative Research: Modeling Animal Dispersal: Linking the Ideal to the Real

合作研究：模拟动物扩散：将理想与现实联系起来

基本信息

批准号：
1853465
负责人：
William Fagan
金额：
$ 18万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1853465&HistoricalAwards=false
关键词：
Collaborative Research Modeling Animal Dispersal

项目摘要

Animal movement behaviors fundamentally influence the dynamics of populations and the interactions of species. Consequently, understanding animal movement is crucial to understanding ecological processes such as the growth and decline of wildlife populations and the spread of disease. Recent advances in remote sensing, GIS, and other technologies and in methods of analyzing data have greatly increased scientists' biological understanding of animal movement behavior. Similarly, recent advances in mathematical modeling and analysis have greatly increased scientists' theoretical insight about what movement strategies would optimize the fitness of ideal animals in variable landscapes. However, those two directions of research have mostly developed independently. This applied mathematics project will help build a bridge between mathematical and biological aspects of animal movement in dynamic landscapes. This bridge will strengthen understanding of how different types of animal movement behavior influence the performance and persistence of species and the outcomes of species interactions. The mathematical models will be informed by empirical data from animal tracking studies (especially those involving movement by deer, caribou, and similar animals). This project will draw on a variety of modeling frameworks (e.g., partial differential equations, partial integro-differential equations, integro-difference equations) to build mathematical representations of animal movements in dynamic landscapes. The research will use models and mathematical analysis to explore why many species seem to use similar movement strategies to search for resources and how those search strategies depend on landscape types. These efforts will provide insight into how landscape dynamics such as the spatial and temporal distributions of resources drive individual movement behavior, and how movement then produces the population level distribution patterns characteristic of different species. By focusing on the question of how animals use perceptual information to inform movement behaviors, these studies will provide insights into how population patterns such as home range residency, migration, or nomadism might evolve. The project will feature the development of new kinds of models and of the new mathematics required for their analysis, such as movement models that incorporate cognition (e.g., switching between dispersal modes based on information that might be nonlocal in origin). The analysis of the new models will lead to advances in the theory of partial differential equations and integro-difference equations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

动物的运动行为从根本上影响种群动态和物种相互作用。因此，了解动物的运动对于了解生态过程（例如野生动物种群的增长和减少以及疾病的传播）至关重要。遥感、地理信息系统和其他技术以及数据分析方法的最新进展极大地增加了科学家对动物运动行为的生物学理解。同样，数学建模和分析的最新进展极大地提高了科学家对哪些运动策略可以优化不同景观中理想动物的适应性的理论洞察力。然而，这两个研究方向大多是独立发展的。该应用数学项目将有助于在动态景观中动物运动的数学和生物学方面建立一座桥梁。这座桥梁将加强对不同类型的动物运动行为如何影响物种的表现和持久性以及物种相互作用的结果的理解。数学模型将基于动物追踪研究的经验数据（特别是涉及鹿、驯鹿和类似动物运动的研究）。该项目将利用各种建模框架（例如偏微分方程、偏积分微分方程、积分差分方程）来构建动态景观中动物运动的数学表示。该研究将使用模型和数学分析来探索为什么许多物种似乎使用相似的运动策略来寻找资源，以及这些搜索策略如何依赖于景观类型。这些努力将深入了解景观动态（例如资源的时空分布）如何驱动个体运动行为，以及运动如何产生不同物种特征的种群水平分布模式。通过关注动物如何利用感知信息来告知运动行为的问题，这些研究将深入了解诸如家庭居住、迁徙或游牧等种群模式可能如何演变。该项目将重点开发新型模型和分析所需的新数学，例如包含认知的运动模型（例如，基于可能来自非本地的信息在分散模式之间切换）。新模型的分析将导致偏微分方程和积分差分方程理论的进步。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

For everything there is a season: Analysing periodic mortality patterns with the cyclomort r package

DOI：
10.1111/2041-210x.13305
发表时间：
2019-10-28
期刊：
METHODS IN ECOLOGY AND EVOLUTION
影响因子：
6.6
作者：
Gurarie, Eliezer;Thompson, Peter R.;Joly, Kyle
通讯作者：
Joly, Kyle

Tactical departures and strategic arrivals: Divergent effects of climate and weather on caribou spring migrations

DOI：
10.1002/ecs2.2971
发表时间：
2019-12-01
期刊：
ECOSPHERE
影响因子：
2.7
作者：
Gurarie, Eliezer;Hebblewhite, Mark;Boelman, Natalie
通讯作者：
Boelman, Natalie

Improved foraging by switching between diffusion and advection: benefits from movement that depends on spatial context

通过在扩散和平流之间切换来改善觅食：取决于空间背景的运动的好处

DOI：
10.1007/s12080-019-00434-w
发表时间：
2020
期刊：
Theoretical Ecology
影响因子：
1.6
作者：
Fagan, William F.;Hoffman, Tyler;Dahiya, Daisy;Gurarie, Eliezer;Cantrell, Robert Stephen;Cosner, Chris
通讯作者：
Cosner, Chris

Spatial Ecology: Herbivores and Green Waves — To Surf or Hang Loose?

空间生态学：食草动物和绿浪 — 冲浪还是逍遥法外？