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EAGER: Quantifying Great Basin butterfly-pollen networks across spatial and temporal disturbance gradients.

EAGER：跨空间和时间扰动梯度量化大盆地蝴蝶花粉网络。

基本信息

批准号：
2114942
负责人：
Lee Dyer
金额：
$ 28.34万
依托单位：
Board of Regents, NSHE, obo University of Nevada, Reno
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114942&HistoricalAwards=false
关键词：
EAGER Quantifying Great Basin butterfly

项目摘要

From the food on our table, to the flowers that enrich our lives, pollinators, which include a diverse group of bees, butterflies, moths, and other insects, are required for the persistence of thousands of plant species in the United States. However, the webs of interactions between flowering plants and their pollinators are not very well resolved. Much of what we know about plant-pollinator networks, even for some common and economically important plants, is based only on observations of insects visiting flowers. One way to be sure that insects are acting as pollinators of a particular plant species is to examine their bodies for pollen of that plant. Large collections of insects in museums provide a perfect resource for quantifying pollen on insect bodies. Using the insect collections at the University of Nevada Museum of Natural History (UNRMNH), researchers will identify large webs of pollen-butterfly interactions, with a focus on the Great Basin Desert in Nevada. These networks will also allow inferences about how plant-pollinator interactions have been affected by changes in climate in the Great Basin over the last century. Additionally, the research will provide insight into how pollinators may be threatened by a changing environment. The project will include training students in ecology and museum science, expanding UNRMNH collections with pollen samples collected from museum specimens and butterflies encountered in the field, and creating large interactive displays for the public that illustrate the roles of butterflies as pollinators in the Great Basin. The research will integrate new techniques, combining data gleaned from museum specimens, field observations, and artificial intelligence (AI) to characterize insect-pollen networks over the last 100 years. Data from the combined approaches will allow quantification of historic and contemporary pollen-butterfly interaction networks in the Great Basin. This information will be used to test hypotheses about changes in pollination networks in response to extreme weather events and other commonly measured global change parameters. The project will produce an associated pollen collection, an online pollen image database, and an AI model for identifying pollen species from images. The results from this work have the potential to transform what is known about how climate change will affect networks of interacting plants and insects.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.

从我们餐桌上的食物，到丰富我们生活的花朵，传粉者，其中包括各种各样的蜜蜂、蝴蝶、飞蛾和其他昆虫，是美国数千种植物物种生存所必需的。然而，开花植物与其传粉者之间的相互作用网络并没有很好地解决。我们对植物传粉者网络的了解，即使是对一些常见的和经济上重要的植物，也只是基于对昆虫访花的观察。确定昆虫是某种特定植物的传粉者的一种方法是检查它们的身体以寻找该植物的花粉。博物馆中大量的昆虫标本为昆虫体内花粉的定量研究提供了很好的资源。利用内华达大学自然历史博物馆（UNRMNH）的昆虫收藏，研究人员将确定花粉-蝴蝶相互作用的大型网络，重点关注内华达州的大盆地沙漠。这些网络还将有助于推断上个世纪大盆地气候变化如何影响植物与传粉者的相互作用。此外，这项研究将深入了解传粉媒介如何受到不断变化的环境的威胁。该项目将包括对学生进行生态学和博物馆科学方面的培训，扩大UNRMNH从博物馆标本和野外遇到的蝴蝶中收集的花粉样本的收藏，并为公众创建大型互动展示，说明蝴蝶在大盆地中作为传粉者的作用。该研究将整合新技术，结合从博物馆标本、实地观察和人工智能（AI）收集的数据，以表征过去100年的昆虫花粉网络。综合方法的数据将允许对大盆地历史和当代花粉-蝴蝶相互作用网络进行量化。这些信息将用于检验关于授粉网络因极端天气事件和其他通常测量的全球变化参数而发生变化的假设。该项目将产生一个相关的花粉收集，一个在线花粉图像数据库，以及一个从图像中识别花粉种类的人工智能模型。这项工作的结果有可能改变人们对气候变化如何影响相互作用的植物和昆虫网络的认识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Modern approaches for leveraging biodiversity collections to understand change in plant-insect interactions

利用生物多样性收集来了解植物与昆虫相互作用的变化的现代方法

DOI：
10.3389/fevo.2022.924941
发表时间：
2022
期刊：
Frontiers in Ecology and Evolution
影响因子：
3
作者：
Balmaki, Behnaz;Rostami, Masoud A.;Christensen, Tara;Leger, Elizabeth A.;Allen, Julie M.;Feldman, Chris R.;Forister, Matthew L.;Dyer, Lee A.
通讯作者：
Dyer, Lee A.

Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study