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Open AccessArticle

Of Mice and Fungi: Coccidioides spp. Distribution Models

Department of Microbiology, Centro de Investigación Científica y Educación Superior de Ensenada (CICESE), Ctra. Ensenada-Tijuana No. 3918, Ensenada, Baja California 22860, Mexico
Academic Unit of Ensenada, Universidad Tecnológica de Tijuana, Ctra. a la Bufadora KM. 1, Maneadero Parte Alta, Ensenada, Baja California 22790, Mexico
Author to whom correspondence should be addressed.
J. Fungi 2020, 6(4), 320;
Received: 15 October 2020 / Revised: 18 November 2020 / Accepted: 25 November 2020 / Published: 27 November 2020
(This article belongs to the Special Issue Coccidioides and Coccidioidomycosis 2020)
The continuous increase of Coccidioidomycosis cases requires reliable detection methods of the causal agent, Coccidioides spp., in its natural environment. This has proven challenging because of our limited knowledge on the distribution of this soil-dwelling fungus. Knowing the pathogen’s geographic distribution and its relationship with the environment is crucial to identify potential areas of risk and to prevent disease outbreaks. The maximum entropy (Maxent) algorithm, Geographic Information System (GIS) and bioclimatic variables were combined to obtain current and future potential distribution models (DMs) of Coccidioides and its putative rodent reservoirs for Arizona, California and Baja California. We revealed that Coccidioides DMs constructed with presence records from one state are not well suited to predict distribution in another state, supporting the existence of distinct phylogeographic populations of Coccidioides. A great correlation between Coccidioides DMs and United States counties with high Coccidioidomycosis incidence was found. Remarkably, under future scenarios of climate change and high concentration of greenhouse gases, the probability of habitat suitability for Coccidioides increased. Overlap analysis between the DMs of rodents and Coccidioides, identified Neotoma lepida as one of the predominant co-occurring species in all three states. Considering rodents DMs would allow to implement better surveillance programs to monitor disease spread. View Full-Text
Keywords: Coccidioides spp.; distribution modeling; Maxent; GIS; biological variables Coccidioides spp.; distribution modeling; Maxent; GIS; biological variables
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MDPI and ACS Style

Ocampo-Chavira, P.; Eaton-Gonzalez, R.; Riquelme, M. Of Mice and Fungi: Coccidioides spp. Distribution Models. J. Fungi 2020, 6, 320.

AMA Style

Ocampo-Chavira P, Eaton-Gonzalez R, Riquelme M. Of Mice and Fungi: Coccidioides spp. Distribution Models. Journal of Fungi. 2020; 6(4):320.

Chicago/Turabian Style

Ocampo-Chavira, Pamela; Eaton-Gonzalez, Ricardo; Riquelme, Meritxell. 2020. "Of Mice and Fungi: Coccidioides spp. Distribution Models" J. Fungi 6, no. 4: 320.

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