MSB-FRA: Testing abiotic drivers of activity, abundance, and diversity of ground-dwelling arthropod communities at a continental scale

MSB-FRA：测试大陆范围内地面节肢动物群落的活动、丰度和多样性的非生物驱动因素

• 批准号: 1702426
• 负责人: Michael Kaspari
• 金额: $ 120.74万
• 依托单位: University of Oklahoma Norman Campus
• 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: 2017-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1702426&HistoricalAwards=false
• 关键词: MSB; FRA; Testing; abiotic; drivers

Insects are among the most abundant and ecologically important animals in the biosphere. They include serious crop pests and invasive species that cause millions in damage. A key goal of this project is to understand how and when the number, and activity of insects change as one moves from place to place across the U.S., and why those numbers fluctuate from year to year. Such an understanding can help predict insect pest outbreaks, the spread of invasive species, and changes in an ecosystems ability to provide food and fiber and conserve soil nutrients. This project will use insect samples collected by the National Ecological Observatory Network (NEON) at 47 locations spanning the U.S.'s major ecosystems to determine how abundance, activity, and diversity of soil insects vary across the U.S. The project will use environmental barcoding (analyzing insect DNA from the samples' preservative) and image analysis techniques to train computer algorithms to count and identify insects preserved in the samples. Together, these technologies will automate and streamline NEON's monitoring network, providing the first such nationwide dataset on abundance, activity, and diversity of the U.S.'s soil insects. In doing so they will serve a variety of stakeholders: ecologists testing and refining models that predict future insect communities; land managers who want to know the likelihood of a pest eruption; conservation biologists and urban planners hoping to anticipate spread of invasive ants and beetles. A key aim of this project is to quantify and predict how Earth's great abiotic drivers--temperature, precipitation, and biogeochemistry--govern how ecological communities of soil insects vary from place to place. Community data at continental extents vastly underrepresent the terrestrial arthropods in part due to the immense effort required to count, size, and identify taxa ranging from mites to ants to beetles to spiders. Yet the few existing arthropod datasets suggest that as one travels from deserts to rainforests, terrestrial arthropod communities vary by orders of magnitudes in abundance (the number of individuals), size (mass per individual), activity (the rate at which individuals do work on the system), and diversity (the number of species/forms). Combined, these four variables help predict how arthropods regulate ecosystem processes like decomposition, herbivory, and seed dispersal. This knowledge gap will be filled by the analysis of samples from the NEON pitfall network (arrays of traps, sunk in the soil, that capture and store biweekly samples of arthropods in ethanol). It will develop two complementary methods to do so. Environmental Bar Coding samples and identifies pitfall taxa from extracts of ethanol. Image Analysis uses machine learning to count, size, and classify arthropods in a sample. Pitfall samples containing key orders of Earth's arthropods will be analyzed from NEON's 47 sites, and Environmental Bar Coding and Image Analysis pipelines that count, size, and identify taxa from these samples will be developed, tested, honed, and validated, and then used to analyze two years of NEON samples.

昆虫是生物圈中数量最多、生态最重要的动物之一。它们包括严重的农作物害虫和入侵物种，造成数百万人的损失。这个项目的一个关键目标是了解昆虫的数量和活动在美国各地迁徙时如何以及何时发生变化，以及为什么这些数量每年都会波动。这样的理解可以帮助预测虫害的爆发、入侵物种的传播以及生态系统提供食物和纤维以及保护土壤养分的能力的变化。该项目将使用美国国家生态观测网络(NEON)在美国主要生态系统的47个地点收集的昆虫样本，以确定美国各地土壤昆虫的丰度、活动和多样性的差异。该项目将使用环境条形码(分析样本防腐剂中的昆虫DNA)和图像分析技术来训练计算机算法，以计数和识别保存在样本中的昆虫。总而言之，这些技术将使霓虹灯的监测网络自动化和简化，提供首个有关美国S土壤昆虫丰度、活动和多样性的全国性数据集。在这样做的过程中，他们将服务于各种利益相关者：生态学家测试和改进预测未来昆虫群落的模型；土地管理者想知道虫害爆发的可能性；保护生物学家和城市规划者希望预测入侵蚂蚁和甲虫的传播。该项目的一个关键目标是量化和预测地球上最大的非生物驱动因素--温度、降水和生物地球化学--如何控制土壤昆虫的生态群落在不同地方的变化。在大陆范围内的群落数据极大地低估了陆地节肢动物的数量，部分原因是需要巨大的努力来计算、调整和识别从螨类到蚂蚁、从甲虫到蜘蛛的各种分类群。然而，现有的为数不多的节肢动物数据集表明，随着一个人从沙漠旅行到热带雨林，陆地节肢动物群落在丰度(个体数量)、大小(个体质量)、活动(个体在系统中工作的速度)和多样性(物种/形式的数量)方面存在着数量级的差异。这四个变量加在一起，有助于预测节肢动物如何调节生态系统过程，如分解、草食和种子传播。这一知识空白将通过对霓虹陷阱网络(埋在土壤中的陷阱阵列，每两周捕获和存储乙醇中的节肢动物样本)样本的分析来填补。它将开发两种互补的方法来做到这一点。环境条形码对乙醇提取物中的陷阱类群进行采样和鉴定。图像分析使用机器学习来计算样本中节肢动物的数量、大小和分类。包含地球节肢动物关键顺序的陷阱样本将从霓虹灯的47个地点进行分析，环境条形码和图像分析管道将根据这些样本进行计数、大小和识别分类群的开发、测试、磨练和验证，然后用于分析两年的霓虹灯样本。

项目成果

Activity density at a continental scale: What drives invertebrate biomass moving across the soil surface?

大陆尺度的活动密度：是什么驱动无脊椎动物生物量在土壤表面移动？

• DOI: 10.1002/ecy.3542
• 发表时间: 2021
• 期刊: Ecology
• 影响因子: 4.8
• 作者: Kaspari, Michael;Weiser, Michael D.;Marshall, Katie E.;Miller, Matthew;Siler, Cameron;de Beurs, Kirsten
• 通讯作者: de Beurs, Kirsten

Embracing imperfection: Machine-assisted invertebrate classification in real-world datasets

拥抱缺陷：现实世界数据集中的机器辅助无脊椎动物分类

• DOI: 10.1016/j.ecoinf.2022.101896
• 发表时间: 2022
• 期刊: Ecological Informatics
• 影响因子: 5.1
• 作者: Blair, Jarrett;Weiser, Michael D.;de Beurs, Kirsten;Kaspari, Michael;Siler, Cameron;Marshall, Katie E.
• 通讯作者: Marshall, Katie E.

Temperature–habitat interactions constrain seasonal activity in a continental array of pitfall traps

温度与生境的相互作用限制了一系列大陆陷阱的季节性活动

• DOI: 10.1002/ecy.3855
• 发表时间: 2022
• 期刊: Ecology
• 影响因子: 4.8
• 作者: Kaspari, Michael;Weiser, Michael D.;Marshall, Katie E.;Siler, Cameron D.;de Beurs, Kirsten
• 通讯作者: de Beurs, Kirsten

Robust metagenomic evidence that local assemblage richness increases with latitude in ground‐active invertebrates of North America

强有力的宏基因组证据表明，北美地面活跃无脊椎动物的局部组合丰富度随着纬度的增加而增加

• DOI: 10.1111/oik.08791
• 发表时间: 2022
• 期刊: Oikos
• 影响因子: 3.4
• 作者: Weiser, Michael D.;Siler, Cameron D.;Smith, Sierra N.;Marshall, Katie E.;McLaughlin, Jessica F.;Miller, Matthew J.;Kaspari, Michael
• 通讯作者: Kaspari, Michael