权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Proposal: MSB-ECA: A multi-scale framework to quantify and forecast population changes and associated uncertainties

合作提案：MSB-ECA：量化和预测人口变化及相关不确定性的多尺度框架

基本信息

批准号：
1702635
负责人：
Elise Zipkin
金额：
$ 20.55万
依托单位：
Michigan State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1702635&HistoricalAwards=false
关键词：
Collaborative Proposal MSB ECA multi

项目摘要

Monarch butterflies (Danaus plexippus) have rapidly declined across North America over the last 19 years from environmental change, although the exact causes are under intensive study. Understanding and forecasting species changes in monarch butterflies at regional and continental scales is challenging because both local- and broad-scale environmental dynamics affect their changes. This project will develop a modeling framework which recognizes that butterfly abundance patterns reflect not only butterfly responses to their environments at fine scales (e.g., local weather), but also to broad-scale factors (e.g., distributions of suitable habitat). The specific goal is to determine the relative effects of climate and land-use on a model migratory species, the monarch butterfly and then forecast population changes at multiple scales under future climate and resource availability scenarios. This project will integrate continental-scale data from multiple regional and national butterfly monitoring programs and climate and land-use databases, spanning their entire migratory cycle, across all seasons, over 20 years. The modeling approach will explicitly quantify uncertainties from both population-environment relationships and future climate projections to evaluate how conservation actions may reverse population declines. This work meets a significant challenge that has long-hindered large-scale ecological research: the integration of biological processes at multi-scales using different data types coupled with predictive environmental models. The data integration modeling framework incorporates mechanism into broad-scale models, representing the best possible approach for many real-world macrosystems in which the development of wholly mechanistic models is not tractable due to data limitations and/or difficulties describing the details of every relevant process. Additionally, predications about the size and distribution of future populations can be made with a quantifiable level of confidence in those estimates. The modeling framework developed in this project will help determine how different regions and periods of a species annual cycle contribute to population trends, leading to more accurate estimations at broad spatial scales, and targeted data collection, modeling, and conservation efforts. The results will contribute broadly to scientific advancement in this field because of the transferability of the modeling framework to other cross-continental species where it is similarly difficult to track and predict population abundance and trends. The project will also offer a rigorous analysis of the factors causing declines of a high profile species and how effective specific conservation and restoration acts could be under changing climate conditions. The research team will collaborate with the Monarch Joint Venture, a multi-institutional partnership, to promote science and conservation of monarchs and disseminate results to the general public through their existing online resources and newsletters.

在过去的19年里，由于环境的变化，帝王蝶（Danaus plexippus）在北美迅速减少，尽管确切的原因正在深入研究中。了解和预测帝王蝶在区域和大陆尺度上的物种变化是具有挑战性的，因为当地和大规模的环境动态影响他们的变化。该项目将开发一个建模框架，该框架认识到蝴蝶丰度模式不仅反映了蝴蝶对环境的精细反应（例如，当地天气），而且还与大尺度因素（例如，适合的栖息地）。具体目标是确定气候和土地利用对一种模式迁徙物种帝王蝶的相对影响，然后在未来气候和资源可用性情景下预测多尺度的种群变化。该项目将整合来自多个区域和国家蝴蝶监测项目以及气候和土地利用数据库的大陆规模数据，涵盖20多年来所有季节的整个迁徙周期。该建模方法将明确量化来自人口-环境关系和未来气候预测的不确定性，以评估保护行动如何扭转人口下降。这项工作遇到了一个长期阻碍大规模生态研究的重大挑战：使用不同的数据类型结合预测环境模型，在多尺度上整合生物过程。数据集成建模框架将机制纳入大规模模型中，代表了许多现实世界宏观系统的最佳可能方法，其中由于数据限制和/或难以描述每个相关过程的细节，因此开发完全机械模型是不容易的。此外，可以对未来人口的规模和分布进行预测，并对这些估计值具有可量化的置信度。该项目开发的建模框架将有助于确定物种年周期的不同区域和时期如何影响种群趋势，从而在广泛的空间尺度上进行更准确的估计，并进行有针对性的数据收集，建模和保护工作。这些结果将广泛地促进这一领域的科学进步，因为建模框架可转移到其他跨大陆物种，在这些物种中，同样难以跟踪和预测种群丰度和趋势。该项目还将对导致高知名度物种减少的因素进行严格的分析，以及在不断变化的气候条件下，具体的保护和恢复行为如何有效。该研究团队将与多机构合作伙伴关系Monarch Joint Venture合作，以促进科学和保护帝王蝶，并通过现有的在线资源和通讯向公众传播成果。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Evaluating population viability and efficacy of conservation management using integrated population models

使用综合种群模型评估种群生存能力和保护管理的有效性

DOI：
10.1111/1365-2664.13080
发表时间：
2018
期刊：
Journal of Applied Ecology
影响因子：
5.7
作者：
Saunders, Sarah P.;Cuthbert, Francesca J.;Zipkin, Elise F.;Rhodes, ed., Jonathan
通讯作者：
Rhodes, ed., Jonathan

Innovations in data integration for modeling populations

用于人口建模的数据集成创新

DOI：
10.1002/ecy.2713
发表时间：
2019
期刊：
Ecology
影响因子：
4.8
作者：
Zipkin, Elise F.;Inouye, Brian D.;Beissinger, Steven R.
通讯作者：
Beissinger, Steven R.

Tracking trends in monarch abundance over the 20th century is currently impossible using museum records

目前无法利用博物馆记录追踪 20 世纪帝王蝶丰度的趋势

DOI：
10.1073/pnas.1904807116
发表时间：
2019
期刊：
Proceedings of the National Academy of Sciences
影响因子：
0
作者：
Ries, Leslie;Zipkin, Elise F.;Guralnick, Robert P.
通讯作者：
Guralnick, Robert P.

Multiscale seasonal factors drive the size of winter monarch colonies

DOI：
10.1073/pnas.1805114116
发表时间：
2019-04-23
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子：
11.1
作者：
Saunders, Sarah P.;Ries, Leslie;Zipkin, Elise F.
通讯作者：
Zipkin, Elise F.

Flying through hurricane central: impacts of hurricanes on migrants with a focus on monarch butterflies

飞越飓风中心：飓风对移民的影响，重点是帝王蝶