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，授粉媒介，包括多种蜜蜂，蝴蝶，飞蛾和其他昆虫，都是美国成千上万种植物物种的持续存在所必需的。但是，开花植物及其传粉媒介之间相互作用的网络并不是很好。我们对植物 - 托管网络的了解，即使对于某些常见和经济重要的植物，我们也仅基于观察到昆虫来访花的观察。确保昆虫充当特定植物物种的授粉媒介的一种方法是检查其植物花粉的身体。博物馆中的大量昆虫收集为量化昆虫体内花粉的理想资源。研究人员将使用内华达大学自然历史博物馆（UNRMNH）的昆虫收藏品，将确定大量的花粉蝇相互作用，重点关注内华达州的大盆地沙漠。这些网络还将允许推断上世纪大盆地气候变化的植物 - 授粉相互作用如何影响。此外，该研究将提供有关如何受到不断变化的环境威胁授粉媒介的洞察力。该项目将包括培训生态学和博物馆科学的学生，扩大来自博物馆标本和野外蝴蝶的花粉样本的临时收藏，并为公众创造了大型互动式展示，以说明蝴蝶作为大盆地的授粉者的作用。这项研究将整合新技术，结合从博物馆标本，现场观察和人工智能（AI）收集的数据，以表征过去100年的昆虫 - 纸质网络。联合方法的数据将允许量化大盆地中历史和当代的花粉相互作用网络。该信息将用于测试有关响应极端天气事件和其他常见的全球变化参数的授粉网络变化的假设。该项目将产生相关的花粉集，在线花粉图像数据库以及用于从图像中识别花粉物种的AI模型。这项工作的结果有可能改变有关气候变化将如何影响互动植物和昆虫网络的了解的可能性。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

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