Ecological Niche Modeling of Cryptococcus gattii in British Columbia, Canada
Sunny Mak, MSc
August 2007, Department of Geography, The University of British Columbia
Introduction:
Cryptococcus gattii unexpectedly emerged on Vancouver Island, British
Columbia (BC), Canada in 1999 causing human and animal illness1. To date over 170
human cases and 330 animal cases have been reported to public health in BC. This
represents the highest rate of C. gattii infection2 and the only documented multi-species
outbreak of cryptococcal disease in the world11. Infection can occur simply through
environmental exposure, inhalation and successful colonization of the microscopic-sized
fungal spores (approximately 1-2 µm in size), which may lead to a potentially fatal
infection of the lungs and/or central nervous system. However, it is believed that the
majority of people who are exposed to C. gattii do not experience any adverse health
effects as their immune systems are able to ward off infection.
Environmental sampling for C. gattii in BC has isolated it from native vegetation,
soil, air and water on the south and central eastern coast of Vancouver Island, and more
recently on the Gulf Islands, Vancouver Lower Mainland and Whatcom County of
Washington State1,2. Before its discovery in this temperate rainforest region of
northwestern North America, C. gattii had been typically associated with the red gum
group of eucalyptus tree species in tropical and sub-tropical regions of the world such as
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Australia, Africa, India, Italy, Papua New Guinea, South America and southern
California3. Natural dispersal of C. gattii by wind and insect vectors3, and anthropogenic
transport of C. gattii via wood material (commercial trade of eucalypts in particular),
vehicle and foot travel have been documented2,4.
The epidemiology, genetic characterization, geographical distribution and
environmental sampling for C. gattii in BC have been well described elsewhere1,2,4,5. The
purpose of this study was to help public health officials delineate the geographical areas
where C. gattii is currently established and forecast areas that could support C. gattii in
the future as the organism continues to spread on Vancouver Island and the BC mainland.
Residents of and visitors to these areas could also be specifically informed about the
public health risk of cryptococcal disease. Since it is not feasible to sample every location
in the province for the presence or absence of C. gattii, ecological niche modeling (ENM)
was used to predict the spatial distribution of C. gattii by relating the environmental
characteristics of field observations to the predictor variables. Geographic Information
Systems (GIS) and the Genetic Algorithm for Rule-set Prediction (GARP) were used to
model the ecological niche of C. gattii in BC. The utility of ecological niche modeling
based on human and animal C. gattii disease surveillance data was also examined.
Methods and Data:
ENM was used in this study to forecast the ecological niche (the geographical
space where physical and biological conditions under which the species can maintain its
population without immigration) of C. gattii to identify the “landscape” of human and
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animal cryptococcocal disease in BC. Landscape epidemiology, which provides the basis
for using ENM to identify geographic areas of risk to C. gattii exposure, is an emerging
and evolving field of research which explores the relationship between the ecology and
epidemiology of infectious diseases to identify geographical areas where disease
transmission occurs6. The theory behind landscape epidemiology is that by knowing the
environmental conditions (e.g. climate, geology, vegetation) necessary for the
maintenance of specific pathogens in nature, one can use the landscape to identify the
spatial and temporal distribution of disease risk7.
A number of sophisticated ecological niche models have been developed and are
available to researchers8. GARP was selected for this study because it has a proven
record of accuracy and performance for predicting the ecological niches of a variety of
species9-11, the specialized software is freely available12 and is relatively easy to operate
on standard desktop computers, software documentation and user support exists12, and
datasets in GIS format can easily be imported in and exported out of the software
program. GARP has been used extensively to predict the ecological niches of a variety of
plant and animal species for conservation biology and applied ecology purposes.
Peterson6 has recently encouraged the public health community to use ENM to forecast
the distribution of vectorborne and environmental diseases such as Lyme disease13 and
cryptococcosis due to C. gattii14 based on the distribution of vectors and environmental
reservoirs.
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GARP uses an iterative, artificial intelligence based approach to ENM9. It
employs a “superset” of rules to identify the ecological niche of a species based on the
environmental characteristics of known occurrence locations, to search for non-random
correlations between species presence, species absence and environmental parameter
values12. GARP divides the species occurrence data (model input points) into training and
testing data subsets, and then environmental data layers relevant to the ecology of the
species in question are added to construct the model.
In this study, human and animal cases of cryptococcal disease and C. gattii
environmental sampling data were used as input data points to build and test the
ecological niche models. Fifteen environmental data layers believed to be relevant to C.
gattii biogeography were used in the ENM: elevation, aspect, slope, biogeoclimatic zone,
January average, maximum and minimum temperature, July average, maximum and
minimum temperature, annual, January and July total precipitation, and soil drainage and
development. These environmental data themes have been routinely used to model the
ecological niche of a variety of species including plants, animals, insects and fungi.
Results and Discussion:
“Optimal” ecological niche areas for C. gattii in BC were identified along the
central and south eastern coast of Vancouver Island, the Gulf Islands, Sunshine Coast and
Vancouver Lower Mainland (Figure 1). Small, isolated geographical areas on the Queen
Charlotte Islands, BC central coast, west coast of Vancouver Island and southern BC
interior have environmental conditions that could potentially support the establishment of
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C. gattii in these areas. These forecasted ecological niche areas for C. gattii in BC are
characterized by low lying elevations (below 770 m and averaging 100 m above sea
level), above freezing daily January average temperatures, and presence within the
Coastal Douglas-fir and Coastal Western Hemlock very dry biogeoclimatic zones.
The predictive accuracy of the C. gattii ecological niche models was >98% (pvalue <0.001) based on the distribution of human cases, animal cases, and positive
environmental sampling locations from permanently established sites. Furthermore,
validation of the models using human (8) and animal (11) C. gattii cases without travel to
Vancouver Island (the endemic area for C. gattii in BC), and transiently positive C. gattii
environmental samples (from 4 unique geographic sites) found that all of these
observations were located within or directly adjacent (within 2.5 km proximity) to the
forecasted C. gattii ecological niche areas on the BC mainland, thereby validating the
usefulness and predictive power of ENM employed in this study.
The forecasted ecological niche of C. gattii based on the distribution of human
and animal cases corresponded well to the forecasted ecological niche of C. gattii based
on the distribution of positive environmental sampling locations from permanently
established sites. Similarly, the use of animal surveillance data and C. gattii
environmental sampling data were useful and effective in forecasting the geographic
distribution of human cryptococcal disease. In particular, animal surveillance data proved
to be good indicators for C. gattii presence in an area. This suggests that surveillance for
animal cryptococcosis may be useful as an early human disease alert system in BC
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because new reports of cryptococcal disease in previously non-endemic C. gattii
geographical areas have been identified in animals before humans and the number of
reported animal cases is approximately 2 times greater than the number of reported
human cases. The creation of a comprehensive, province wide animal cryptococcal
disease surveillance system to track laboratory confirmed and clinically diagnosed cases
of cryptococcal disease is recommended.
Conclusion:
This study was the first attempt to describe the ecological niche and forecasted
geographic range of C. gattii in BC. The investigation into the detection of C. gattii in the
environment and identification of human and animal infections has been a model of
cooperation between public health, veterinary health and academia15. This study
highlights the value and strength of using a multi-disciplinary, landscape epidemiology
approach to communicable disease surveillance and research since the health of human
and animal populations are in large part determined by their interaction with the
environment around them.
The findings of this study provide opportunities for possible future studies. The
role of climate change on the future geographic expansion of C. gattii in BC should be
explored as a warming trend will likely produce larger geographical areas with suitable
ecological habitat for C. gattii in BC (e.g. more areas with above freezing daily January
average temperatures), and increase the potential for human illness as the landscape of C.
gattii in the environment expands. Additional environmental sampling for C. gattii in
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forecasted “optimal” and “potential” ecological niches should be performed to identify
whether C. gattii is present in these areas to further validate the model, and to warn
residents of the presence and potential health risks to C. gattii in the local environment.
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Figure 1. Cryptococcus gattii ecological niche prediction maps based on the distribution
of human (A) and animal cases (B) and positive environmental sampling locations from
permanently established sites (C).
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12. University of Kansas Center for Research. 2002. Desktop GARP. Accessed 12
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