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Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data
BACKGROUND: West Nile virus (WNV) is a mosquito-transmitted disease of birds that has caused bird population declines and can spill over into human populations. Previous research has identified bird species that infect a large fraction of the total pool of infected mosquitoes and correlate with huma...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686473/ https://www.ncbi.nlm.nih.gov/pubmed/31395085 http://dx.doi.org/10.1186/s13071-019-3656-8 |
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author | Kain, Morgan P. Bolker, Benjamin M. |
author_facet | Kain, Morgan P. Bolker, Benjamin M. |
author_sort | Kain, Morgan P. |
collection | PubMed |
description | BACKGROUND: West Nile virus (WNV) is a mosquito-transmitted disease of birds that has caused bird population declines and can spill over into human populations. Previous research has identified bird species that infect a large fraction of the total pool of infected mosquitoes and correlate with human infection risk; however, these analyses cover small spatial regions and cannot be used to predict transmission in bird communities in which these species are rare or absent. Here we present a mechanistic model for WNV transmission that predicts WNV spread (R(0)) in any bird community in North America by scaling up from the physiological responses of individual birds to transmission at the level of the community. We predict unmeasured bird species’ responses to infection using phylogenetic imputation, based on these species’ phylogenetic relationships with bird species with measured responses. RESULTS: We focused our analysis on Texas, USA, because it is among the states with the highest total incidence of WNV in humans and is well sampled by birders in the eBird database. Spatio-temporal patterns: WNV transmission is primarily driven by temperature variation across time and space, and secondarily by bird community composition. In Texas, we predicted WNV R(0) to be highest in the spring and fall when temperatures maximize the product of mosquito transmission and survival probabilities. In the most favorable months for WNV transmission (April, May, September and October), we predicted R(0) to be highest in the “Piney Woods” and “Oak Woods & Prairies” ecoregions of Texas, and lowest in the “High Plains” and “South Texas Brush County” ecoregions. Dilution effect: More abundant bird species are more competent hosts for WNV, and predicted WNV R(0) decreases with increasing species richness. Keystone species: We predicted that northern cardinals (Cardinalis cardinalis) are the most important hosts for amplifying WNV and that mourning doves (Zenaida macroura) are the most important sinks of infection across Texas. CONCLUSIONS: Despite some data limitations, we demonstrate the power of phylogenetic imputation in predicting disease transmission in heterogeneous host communities. Our mechanistic modeling framework shows promise both for assisting future analyses on transmission and spillover in heterogeneous multispecies pathogen systems and for improving model transparency by clarifying assumptions, choices and shortcomings in complex ecological analyses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-019-3656-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6686473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66864732019-08-12 Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data Kain, Morgan P. Bolker, Benjamin M. Parasit Vectors Research BACKGROUND: West Nile virus (WNV) is a mosquito-transmitted disease of birds that has caused bird population declines and can spill over into human populations. Previous research has identified bird species that infect a large fraction of the total pool of infected mosquitoes and correlate with human infection risk; however, these analyses cover small spatial regions and cannot be used to predict transmission in bird communities in which these species are rare or absent. Here we present a mechanistic model for WNV transmission that predicts WNV spread (R(0)) in any bird community in North America by scaling up from the physiological responses of individual birds to transmission at the level of the community. We predict unmeasured bird species’ responses to infection using phylogenetic imputation, based on these species’ phylogenetic relationships with bird species with measured responses. RESULTS: We focused our analysis on Texas, USA, because it is among the states with the highest total incidence of WNV in humans and is well sampled by birders in the eBird database. Spatio-temporal patterns: WNV transmission is primarily driven by temperature variation across time and space, and secondarily by bird community composition. In Texas, we predicted WNV R(0) to be highest in the spring and fall when temperatures maximize the product of mosquito transmission and survival probabilities. In the most favorable months for WNV transmission (April, May, September and October), we predicted R(0) to be highest in the “Piney Woods” and “Oak Woods & Prairies” ecoregions of Texas, and lowest in the “High Plains” and “South Texas Brush County” ecoregions. Dilution effect: More abundant bird species are more competent hosts for WNV, and predicted WNV R(0) decreases with increasing species richness. Keystone species: We predicted that northern cardinals (Cardinalis cardinalis) are the most important hosts for amplifying WNV and that mourning doves (Zenaida macroura) are the most important sinks of infection across Texas. CONCLUSIONS: Despite some data limitations, we demonstrate the power of phylogenetic imputation in predicting disease transmission in heterogeneous host communities. Our mechanistic modeling framework shows promise both for assisting future analyses on transmission and spillover in heterogeneous multispecies pathogen systems and for improving model transparency by clarifying assumptions, choices and shortcomings in complex ecological analyses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-019-3656-8) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-08 /pmc/articles/PMC6686473/ /pubmed/31395085 http://dx.doi.org/10.1186/s13071-019-3656-8 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Kain, Morgan P. Bolker, Benjamin M. Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data |
title | Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data |
title_full | Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data |
title_fullStr | Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data |
title_full_unstemmed | Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data |
title_short | Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data |
title_sort | predicting west nile virus transmission in north american bird communities using phylogenetic mixed effects models and ebird citizen science data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686473/ https://www.ncbi.nlm.nih.gov/pubmed/31395085 http://dx.doi.org/10.1186/s13071-019-3656-8 |
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