Suppr超能文献

SAFARIS:一种用于害虫预测系统的空间分析框架。

SAFARIS: a spatial analytic framework for pest forecast systems.

作者信息

Takeuchi Yu, Tripodi Amber, Montgomery Kellyn

机构信息

Center for Integrated Pest Management, North Carolina State University, Raleigh, NC, United States.

Plant Pest Risk Analysis, Science & Technology, Plant Protection and Quarantine, Animal and Plant Health Inspection Service, United States Department of Agriculture, Raleigh, NC, United States.

出版信息

Front Insect Sci. 2023 Jul 6;3:1198355. doi: 10.3389/finsc.2023.1198355. eCollection 2023.

Abstract

Non-native pests and diseases pose a risk of economic and environmental damage to managed and natural U.S. forests and agriculture. The U.S. Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) Plant Protection and Quarantine (PPQ) protects the health of U.S. agriculture and natural resources against invasive pests and diseases through efforts to prevent the entry, establishment, and spread of non-native pests and diseases. Because each pest or disease has its own idiosyncratic characteristics, analyzing risk is highly complex. To help PPQ better respond to pest and disease threats, we developed the Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS), an integrated system designed to provide a seamless environment for producing predictive models. SAFARIS integrates pest biology information, climate and non-climate data drivers, and predictive models to provide users with readily accessible and easily customizable tools to analyze pest and disease risks. The phenology prediction models, spread forecasting models, and other climate-based analytical tools in SAFARIS help users understand which areas are suitable for establishment, when surveys would be most fruitful, and aid in other analyses that inform decision-making, operational efforts, and rapid response. Here we introduce the components of SAFARIS and provide two use cases demonstrating how pest-specific models developed with SAFARIS tools support PPQ in its mission. Although SAFARIS is designed to address the needs of PPQ, the flexible, web-based framework is publicly available, allowing any user to leverage the available data and tools to model pest and disease risks.

摘要

外来有害生物和疾病对美国人工林及天然林以及农业的经济和环境构成破坏风险。美国农业部(USDA)动植物卫生检验局(APHIS)植物保护与检疫处(PPQ)通过努力防止外来有害生物和疾病的传入、定殖和传播,保护美国农业和自然资源的健康。由于每种有害生物或疾病都有其独特的特征,分析风险非常复杂。为帮助PPQ更好地应对有害生物和疾病威胁,我们开发了高级风险信息系统空间分析框架(SAFARIS),这是一个集成系统,旨在为生成预测模型提供无缝环境。SAFARIS整合了有害生物生物学信息、气候和非气候数据驱动因素以及预测模型,为用户提供易于获取且易于定制的工具,以分析有害生物和疾病风险。SAFARIS中的物候预测模型、扩散预测模型及其他基于气候的分析工具,帮助用户了解哪些区域适合定殖、何时进行调查最有成效,并有助于开展其他为决策、业务工作和快速反应提供依据的分析。在此,我们介绍SAFARIS的组成部分,并提供两个用例,展示如何使用SAFARIS工具开发的特定有害生物模型支持PPQ履行其使命。虽然SAFARIS旨在满足PPQ的需求,但这个灵活的基于网络的框架是公开可用的,任何用户都可以利用现有数据和工具来模拟有害生物和疾病风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59d0/10926409/b83b0d71a0f7/finsc-03-1198355-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验