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A Bayesian Geostatistical Moran Curve Model for Estimating Net Changes of Tsetse Populations in Zambia

For the first time a Bayesian geostatistical version of the Moran Curve, a logarithmic form of the Ricker stock recruitment curve, is proposed that is able to give an estimate of net change in population demographic rates considering components such as fertility and density dependent and density ind...

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Autores principales: Sedda, Luigi, Mweempwa, Cornelius, Ducheyne, Els, De Pus, Claudia, Hendrickx, Guy, Rogers, David J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3995969/
https://www.ncbi.nlm.nih.gov/pubmed/24755848
http://dx.doi.org/10.1371/journal.pone.0096002
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author Sedda, Luigi
Mweempwa, Cornelius
Ducheyne, Els
De Pus, Claudia
Hendrickx, Guy
Rogers, David J.
author_facet Sedda, Luigi
Mweempwa, Cornelius
Ducheyne, Els
De Pus, Claudia
Hendrickx, Guy
Rogers, David J.
author_sort Sedda, Luigi
collection PubMed
description For the first time a Bayesian geostatistical version of the Moran Curve, a logarithmic form of the Ricker stock recruitment curve, is proposed that is able to give an estimate of net change in population demographic rates considering components such as fertility and density dependent and density independent mortalities. The method is applied to spatio-temporally referenced count data of tsetse flies obtained from fly-rounds. The model is a linear regression with three components: population rate of change estimated from the Moran curve, an explicit spatio-temporal covariance, and the observation error optimised within a Bayesian framework. The model was applied to the three main climate seasons of Zambia (rainy – January to April, cold-dry – May to August, and hot-dry – September to December) taking into account land surface temperature and (seasonally changing) cattle distribution. The model shows a maximum positive net change during the hot-dry season and a minimum between the rainy and cold-dry seasons. Density independent losses are correlated positively with day-time land surface temperature and negatively with night-time land surface temperature and cattle distribution. The inclusion of density dependent mortality increases considerably the goodness of fit of the model. Cross validation with an independent dataset taken from the same area resulted in a very accurate estimate of tsetse catches. In general, the overall framework provides an important tool for vector control and eradication by identifying vector population concentrations and local vector demographic rates. It can also be applied to the case of sustainable harvesting of natural populations.
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spelling pubmed-39959692014-04-25 A Bayesian Geostatistical Moran Curve Model for Estimating Net Changes of Tsetse Populations in Zambia Sedda, Luigi Mweempwa, Cornelius Ducheyne, Els De Pus, Claudia Hendrickx, Guy Rogers, David J. PLoS One Research Article For the first time a Bayesian geostatistical version of the Moran Curve, a logarithmic form of the Ricker stock recruitment curve, is proposed that is able to give an estimate of net change in population demographic rates considering components such as fertility and density dependent and density independent mortalities. The method is applied to spatio-temporally referenced count data of tsetse flies obtained from fly-rounds. The model is a linear regression with three components: population rate of change estimated from the Moran curve, an explicit spatio-temporal covariance, and the observation error optimised within a Bayesian framework. The model was applied to the three main climate seasons of Zambia (rainy – January to April, cold-dry – May to August, and hot-dry – September to December) taking into account land surface temperature and (seasonally changing) cattle distribution. The model shows a maximum positive net change during the hot-dry season and a minimum between the rainy and cold-dry seasons. Density independent losses are correlated positively with day-time land surface temperature and negatively with night-time land surface temperature and cattle distribution. The inclusion of density dependent mortality increases considerably the goodness of fit of the model. Cross validation with an independent dataset taken from the same area resulted in a very accurate estimate of tsetse catches. In general, the overall framework provides an important tool for vector control and eradication by identifying vector population concentrations and local vector demographic rates. It can also be applied to the case of sustainable harvesting of natural populations. Public Library of Science 2014-04-22 /pmc/articles/PMC3995969/ /pubmed/24755848 http://dx.doi.org/10.1371/journal.pone.0096002 Text en © 2014 Sedda et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Sedda, Luigi
Mweempwa, Cornelius
Ducheyne, Els
De Pus, Claudia
Hendrickx, Guy
Rogers, David J.
A Bayesian Geostatistical Moran Curve Model for Estimating Net Changes of Tsetse Populations in Zambia
title A Bayesian Geostatistical Moran Curve Model for Estimating Net Changes of Tsetse Populations in Zambia
title_full A Bayesian Geostatistical Moran Curve Model for Estimating Net Changes of Tsetse Populations in Zambia
title_fullStr A Bayesian Geostatistical Moran Curve Model for Estimating Net Changes of Tsetse Populations in Zambia
title_full_unstemmed A Bayesian Geostatistical Moran Curve Model for Estimating Net Changes of Tsetse Populations in Zambia
title_short A Bayesian Geostatistical Moran Curve Model for Estimating Net Changes of Tsetse Populations in Zambia
title_sort bayesian geostatistical moran curve model for estimating net changes of tsetse populations in zambia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3995969/
https://www.ncbi.nlm.nih.gov/pubmed/24755848
http://dx.doi.org/10.1371/journal.pone.0096002
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