Next Article in Journal
Amphotericin B and Other Polyenes—Discovery, Clinical Use, Mode of Action and Drug Resistance
Next Article in Special Issue
Laser Capture Microdissection-Assisted Protein Biomarker Discovery from Coccidioides-Infected Lung Tissue
Previous Article in Journal
Detection of Histoplasma DNA from Tissue Blocks by a Specific and a Broad-Range Real-Time PCR: Tools to Elucidate the Epidemiology of Histoplasmosis
Previous Article in Special Issue
Comparative Study of Newer and Established Methods of Diagnosing Coccidioidal Meningitis
Open AccessArticle

Of Mice and Fungi: Coccidioides spp. Distribution Models

1
Department of Microbiology, Centro de Investigación Científica y Educación Superior de Ensenada (CICESE), Ctra. Ensenada-Tijuana No. 3918, Ensenada, Baja California 22860, Mexico
2
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; https://doi.org/10.3390/jof6040320
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
Show Figures

Figure 1

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. https://doi.org/10.3390/jof6040320

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. https://doi.org/10.3390/jof6040320

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. https://doi.org/10.3390/jof6040320

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop