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The occurrence of potentially pathogenic fungi and protists in Canadian lakes predicted using geomatics, in situ and satellite-derived variables: Towards a tele-epidemiological approach

Authors: Oliva AGarner REWalsh DHuot Y


Affiliations

1 Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; CARTEL - Centre d'applications et de recherche en télédétection, Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; GRIL - Groupement de Recherche Interuniversitaire en Limnologie, Département de Sciences Biologiques, Université de Montréal, Campus MIL, Montréal, QC H3C 3J7, Canada. Electronic address: anais.oliva@usherbrooke.ca.
2 GRIL - Groupement de Recherche Interuniversitaire en Limnologie, Département de Sciences Biologiques, Université de Montréal, Campus MIL, Montréal, QC H3C 3J7, Canada; Department of Biology, Concordia University, Montréal QC H4B 1R6, Canada.
3 Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; CARTEL - Centre d'applications et de recherche en télédétection, Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada

Description

Eukaryotic pathogens including fungi and enteroparasites infect humans, animals and plants. As integrators of landscape catchment, lakes can reflect and record biological and geochemical events or anthropogenic changes and provide useful knowledge to formulate public health, food security and water policies to manage and prevent diseases. In this context, potentially pathogenic fungi and parasites were sampled using 18S rRNA gene amplicon sequencing in 382 lakes displaying a broad range of sizes and human impact on the watershed in 10 ecozones across Canada. Based on pathogen classifications from the ePATHogen database published by the Public Health Agency of Canada, we identified 23 health-relevant genera for human and animal hosts, including Cryptococcus and Cryptosporidium. Our study investigated the potential of remote sensing and geomatics to predict microbial contamination in a tele-epidemiological approach. We used boosted regression tree modeling to evaluate the probability of occurrence of the most common genera found in our dataset based on 10 satellite-derivable, geomatics and field survey variables which could be potential sources or transport mechanisms through the watershed or survival factors in the water. We found that southern ecozones that possess the highest agricultural and pasture activities tend to contain lakes with the largest number of potential pathogens including several fungi associated with plant diseases. Bio-optical factors, such as colored dissolved organic matter, were highly related to the occurrence of the genera, potentially by protecting against damage from ultraviolet light. Our results demonstrate the capability of tele-epidemiology to provide useful information to develop government policies for recreational and drinking water regulations as well as for food security.


Keywords: Boosted regression treeEukaryotic pathogenFreshwaterPublic healthTele-epidemiology


Links

PubMed: https://pubmed.ncbi.nlm.nih.gov/34915335/

DOI: 10.1016/j.watres.2021.117935