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美国北卡罗来纳州容器蚊(双翅目:蚊科)的短期大面积调查:其存在与丰度与精细尺度景观因素相关

Short-Term, Large-Area Survey of Container (Diptera: Culicidae): Presence and Abundance is Associated with Fine-scale Landscape Factors in North Carolina, USA.

作者信息

Reiskind Michael H, Styers Diane M, Hayes Isaac, Richards Stephanie L, Doyle Michael S, Reed Emily Mx, Hollingsworth Brandon, Byrd Brian D

机构信息

Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA.

Department of Geosciences and Natural Resources, Western Carolina University, Cullowhee, NC, USA.

出版信息

Environ Health Insights. 2020 Sep 21;14:1178630220952806. doi: 10.1177/1178630220952806. eCollection 2020.

Abstract

Container mosquitoes are responsible for the transmission of anthroponotic and zoonotic viruses to people. The surveillance and control of these mosquitoes is an important part of public health protection and prevention of mosquito-borne disease. In this study, we surveyed 327 sites over 2 weeks in late June and early July in 2017 in North Carolina, USA for the presence and abundance of spp. eggs in an effort to better target potential Ae. aegypti collections. We examined the ability of 2 types of landscape data, Light Detection And Ranging (LIDAR) and National Land Cover Database (NLCD) to explain the presence and abundance of eggs using principal component analysis to deal with collinearity, followed by generalized linear regression. We explained variation of both egg presence and abundance for (Skuse) and (Say) using both NLCD and LIDAR data. However, the ability to make robust predictions was limited by variation in the data. Increased sampling time and better landscape data would likely improve the predictive ability of our models, as would a better understanding of oviposition behavior.

摘要

容器蚊类会将人类传染病原体和人畜共患病病毒传播给人类。对这些蚊子的监测和控制是公共卫生保护及预防蚊媒疾病的重要组成部分。在本研究中,我们于2017年6月下旬和7月初的两周时间里,在美国北卡罗来纳州对327个地点进行了调查,以了解埃及伊蚊卵的存在情况和数量,从而更精准地确定潜在的埃及伊蚊采集点。我们研究了两种景观数据,即光探测与测距(LIDAR)数据和国家土地覆盖数据库(NLCD)数据,利用主成分分析来处理共线性问题,随后进行广义线性回归,以解释蚊卵的存在情况和数量。我们利用NLCD和LIDAR数据解释了斯氏伊蚊(Skuse)和赛氏伊蚊(Say)的蚊卵存在情况及数量变化。然而,数据的变异性限制了做出可靠预测的能力。增加采样时间、获取更好的景观数据以及更深入地了解产卵行为,可能会提高我们模型的预测能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1955/7513404/2472d3647f6b/10.1177_1178630220952806-fig1.jpg

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