Of Mice and Fungi: Coccidioides spp. Distribution Models

Abstract

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.

A Review of Coccidioides Research, Outstanding Questions in the Field, and Contributions by Women Scientists

“…It is possible, if not likely, that C. immitis and C. posadasii may be specialized for different host reservoirs, which could vary by geographical region. One species of interest for further investigation in the southwestern US is the desert woodrat ( Neotoma lepida ), whose presence significantly overlaps with human coccidioidomycosis cases [ 32 ].…”

“…This is imperative to understanding the risk of coccidioidomycosis, especially if the geographical distribution shifts in response to climate change [ 49 ]. Soil samples positive for Coccidioides , human coccidioidomycosis case data, and important environmental drivers paired with ecological niche models have made it possible to create high-resolution estimates of the Coccidioides endemic region [ 32 , 50 , 51 ••]. Using human coccidioidomycosis data as a proxy for Coccidioides presence, Dr. Morgan Gorris and colleagues predict that by the end of the twenty-first century, warming temperatures across the dry, western US may cause the endemic region to expand north [ 51 ••].…”

“…This may cause a large increase in the number of people at risk for contracting coccidioidomycosis, the subsequent number of disease cases, and the financial burden of this disease [ 52 ]. In contrast, a separate analysis using Coccidioides presence data in a niche model found that by 2070, the geographical distribution of Coccidioides may contract, but the habitat suitability within already suitable locations will increase [ 32 ]. Continued disease surveillance and soil sampling will help to further resolve the ecological niche of Coccidioides and discrepancies between estimates, especially as we identify new endemic areas, such as Washington State [ 53 , 54 ].…”

Humidity and low pH boost occurrence of Onygenales fungi in soil at global scale

“…Recently, species distribution models (SDM), which associate occurrence records with environmental variables that are expected to affect the species probability of persistence, have been increasingly applied to soil-dwelling fungi (Hao et al 2020) and Onygenales in particular (Ocampo-Chavira et al 2020) to estimate the environmental conditions that are most suitable for this particular group of fungi. However, despite these sound advances, our knowledge remain limited to local and regional scale (e.g.…”

No Consistent Link Between Dust Storms and Valley Fever (Coccidioidomycosis)

“…Coccidioidomycosis became nationally notifiable in 1995 (Benedict et al., 2019 ) and studies since then have confirmed and added to historical analyses by including many more atmospheric variables, geospatial techniques, and statistical modeling. This work has refined the general characterization of atmospheric controls as follows: (i) the hot and semi‐arid conditions that define the current endemic areas and potential shifts under climate change (Gorris et al., 2018 , 2019 ; Ocampo‐Chavira et al., 2020 ; Weaver et al., 2020 ); and (ii) the basic seasonality of the disease and its association with the annual lag between previous fall/winter precipitation (via soil moisture) and subsequent cases along with concurrent dry conditions (Coopersmith et al., 2017 ; Khan, 2020 ; Kolivras & Comrie, 2003 ; Park et al., 2005 ; Shriber et al., 2017 ; Stacy et al., 2012 ; Talamantes et al., 2007 ; Tamerius & Comrie, 2011 ; Weaver & Kolivras, 2018 ; Zender & Talamantes, 2006 ).…”