Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Characterization of Pathogen Air-Borne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

Version 1 : Received: 10 October 2020 / Approved: 12 October 2020 / Online: 12 October 2020 (16:12:14 CEST)

How to cite: Choudhury, R.A.; McRoberts, N. Characterization of Pathogen Air-Borne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data. Preprints 2020, 2020100255 (doi: 10.20944/preprints202010.0255.v1). Choudhury, R.A.; McRoberts, N. Characterization of Pathogen Air-Borne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data. Preprints 2020, 2020100255 (doi: 10.20944/preprints202010.0255.v1).

Abstract

Air sampling using vortex air samplers combined with species specific amplification of pathogen DNA, was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic dynamics. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of the pathogen abundance data, but also indicated that practicality may limit the capacity for definitively classifying the dynamics of air borne plant pathogen inoculum. Over the two years of the study five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating the pathogen abundance data were increasing or not, revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves, and also (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence is positive or not.

Subject Areas

time series; entropy; average mutual information; stochastic process; deterministic dynamics

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