Collaborative Research: MRA: Scale, Space, and Time: A Unifying Approach to Aquatic Invasions

合作研究：MRA：规模、空间和时间：水生入侵的统一方法

• 批准号: 2017858
• 负责人: Shweta Singh
• 金额: $ 9万
• 依托单位: Purdue University
• 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: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017858&HistoricalAwards=false
• 关键词: Collaborative; Research; MRA; Scale; Space

Invasive species pose a serious threat to biodiversity and economic security, with acute effects in river ecosystems. Yet despite decades of research, ecology still faces a glaring paradox: many invasion theories are at odds with one another, but each is supported by strong evidence. The paradox exists because most studies are conducted in only one of three key ecological dimensions: scale, space, and time. This project will take a cross-scale approach from small stream segments to whole watersheds across the landscape at numerous time steps. In doing so, this work will use a macrosystems framework to link invasion theories with empirical evidence across all three dimensions to further our understanding of how species invasions happen and how to prevent or manage them. This research will focus on riverine fishes—one of the most diverse yet imperiled groups of organisms in North America. In the process, this project will generate a large-extent, fine-resolution dataset of fish occurrences that can be used for numerous purposes beyond the lifetime of the project. This dataset can be linked to the National Ecological Observatory Network (NEON) and other federally-supported data to infer broad mechanistic patterns and processes. Results from this work will be used to develop an interactive spatial planning tool for predicting future fish invasions across the Mississippi River basin—the largest connected river network in North America. This tool will be shared via numerous communication outlets including webinars, websites, and social media. A diverse group of over 20 graduate and undergraduate students will be trained in the course of the project. The goal of this project is to understand how natural and human-created dispersal networks (e.g. rivers and roads) interact with invasion drivers (e.g. habitat and species traits) to determine cross-scale invasion patterns. This work will be conducted in the Mississippi River Basin; rivers provide a unique opportunity for studying scale-dependent invasion processes because watersheds create discrete boundaries of fish species distribution. Accordingly, many of the most prolific invaders arrive not from distant continents, but from nearby watersheds. Native status for riverine fishes is thus scale-dependent, which affects how invasions are conceptualized and modeled. Using rich datasets of historical and contemporary fish distribution, this project takes a three-pronged approach: 1) using Bayesian tools to quantify invasion drivers across spatial scales; 2) linking concepts from metacommunity and invasion ecology to investigate invasion network dynamics across space; and 3) predicting trajectories of species invasions through time. The project will integrate information from these three research objectives to forecast future riverine invasions across the landscape.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.

入侵物种对生物多样性和经济安全构成严重威胁，对河流生态系统造成严重影响。然而，尽管经过数十年的研究，生态学仍然面临着一个明显的悖论：许多入侵理论彼此矛盾，但每一种都有强有力的证据支持。这种悖论的存在是因为大多数研究仅在三个关键生态维度之一进行：规模、空间和时间。该项目将采用跨尺度方法，在多个时间步长内从小溪流段到整个景观流域。为此，这项工作将使用宏观系统框架将入侵理论与所有三个维度的经验证据联系起来，以进一步了解物种入侵如何发生以及如何预防或管理它们。这项研究将重点关注河流鱼类——北美最多样化但也面临危险的生物群体之一。在此过程中，该项目将生成大范围、高分辨率的鱼类出现数据集，该数据集可用于项目生命周期之外的多种用途。该数据集可以链接到国家生态观测站网络（NEON）和其他联邦政府支持的数据，以推断广泛的机械模式和过程。这项工作的结果将用于开发交互式空间规划工具，用于预测密西西比河流域（北美最大的连通河流网络）未来的鱼类入侵。该工具将通过众多通信渠道共享，包括网络研讨会、网站和社交媒体。超过 20 名研究生和本科生将在该项目过程中接受培训。该项目的目标是了解自然和人类创建的扩散网络（例如河流和道路）如何与入侵驱动因素（例如栖息地和物种特征）相互作用，以确定跨规模的入侵模式。这项工作将在密西西比河流域进行；河流为研究规模依赖性入侵过程提供了独特的机会，因为流域创造了鱼类分布的离散边界。因此，许多最多产的入侵者不是来自遥远的大陆，而是来自附近的流域。因此，河流鱼类的原生状态取决于规模，这会影响入侵的概念化和建模方式。该项目利用历史和当代鱼类分布的丰富数据集，采取三管齐下的方法：1）使用贝叶斯工具量化跨空间尺度的入侵驱动因素； 2）将元社区和入侵生态学的概念联系起来，研究跨空间的入侵网络动态； 3）预测物种入侵随时间的轨迹。该项目将整合这三个研究目标的信息，以预测未来整个地区的河流入侵。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。