Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples

Version 1 : Received: 8 March 2022 / Approved: 14 March 2022 / Online: 14 March 2022 (08:55:50 CET)

How to cite: Kessler, W.H.; De Jesus, C.; Wisely, S.M.; Glass, G.E.E. Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples. Preprints 2022, 2022030177 (doi: 10.20944/preprints202203.0177.v1). Kessler, W.H.; De Jesus, C.; Wisely, S.M.; Glass, G.E.E. Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples. Preprints 2022, 2022030177 (doi: 10.20944/preprints202203.0177.v1).

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 Ixodid tick vectors, Amblyomma americanum and Ixodes scapularis in mainland Florida, USA, when inputs were either convenience samples of ticks, or collections obtained using the standard protocols promulgated by the U.S. Centers for Disease Control and Prevention. The Ensemble SDMs for the convenience samples and standard surveys showed only a slight agreement (Kappa = 0.060, A. americanum; 0.053, I. scapularis). Convenience sample SDMs indicated A. americanum and I. scapularis should be absent from 34.5% and 30.9% of the state where standard surveys predicted the highest likelihood of occurrence of the respective vectors. Ensemble models from standard surveys predicted 81.4% and 72.5% (A. americanum and I. scapularis) of convenience sample sites. Omission errors by standard survey SDMs, of the convenience collections, frequently were associated with adjacency to at least one SDM or errors in geocoding algorithms that failed to correctly locate convenience samples. These geocoding errors emphasize commonly overlooked needs to explicitly evaluate and improve data quality for vector survey data used in spatial models.

Keywords

ensemble models; species distribution models (SDMs); ticks; Amblyomma americanum; Ixodes scapularis; Florida; biased sampling; study design

Subject

BIOLOGY, Ecology

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