2022
DOI: 10.3390/diseases10020032
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Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples

Abstract: Ensembles of Species Distribution Models (SDMs) represent the geographic ranges of pathogen vectors by combining alternative analytical approaches and merging information on vector occurrences with more extensive environmental data. Biased collection data impact SDMs, regardless of the target species, but no studies have compared the differences in the distributions predicted by the ensemble models when different sampling frameworks are used for the same species. We compared Ensemble SDMs for two important Ixo… Show more

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Cited by 2 publications
(6 citation statements)
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“…We were unable to use detection rates to improve model accuracy, as spatial covariates (specifically time or distance) were not recorded for the game counts. The data from the aerial surveys, despite the discrepancies between sampling regimes, represent an improvement in the widely practiced use of museum data to build models of ecological niches (see for example Elith et al 2006 ; Schoeman et al 2013 ; Bloom et al 2018 ; Kessler et al 2022 ). The data used in our study arise from systematic surveys over Gorongosa and are less prone to gaps in data that arise from the haphazard and opportunistic sampling that characterize museum collections.…”
Section: Discussionmentioning
confidence: 99%
“…We were unable to use detection rates to improve model accuracy, as spatial covariates (specifically time or distance) were not recorded for the game counts. The data from the aerial surveys, despite the discrepancies between sampling regimes, represent an improvement in the widely practiced use of museum data to build models of ecological niches (see for example Elith et al 2006 ; Schoeman et al 2013 ; Bloom et al 2018 ; Kessler et al 2022 ). The data used in our study arise from systematic surveys over Gorongosa and are less prone to gaps in data that arise from the haphazard and opportunistic sampling that characterize museum collections.…”
Section: Discussionmentioning
confidence: 99%
“…The results produce an overall superior product compared to any one single approach [ 87 ]. Once the various models are developed, they are merged into an Ensemble model to identify areas of agreement, as well as the range of disagreement among the methods [ 91 , 92 ]. In principle, these areas of disagreement create ancillary hypotheses to further explore the host/virus biology.…”
Section: Forecasting Orthohantavirus Riskmentioning
confidence: 99%
“…Sampling of the host/virus in the environment is the critical step for calibrating Ensemble forecasts by generating the monitored data sets that are fed into the analyses [ 90 , 92 ]. In some circumstances, such as during a case investigation or local outbreak, the goal is to implicate the agent and host in a restricted region.…”
Section: Host/virus Sampling Designmentioning
confidence: 99%
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