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Climatic influences on Cryptoccoccus gattii populations, Vancouver Island, Canada, 2002-2004.

Opportunistic fungal infections, such as those caused by Cryptococcus neoformans, are common causes of death and illness among persons with compromised immune systems. C. gattii is a related fungus that can cause serious illness. Specific genotypes (AFLP4/VGI, AFLP6/ VGII) are isolated more commonly from immunocompetent persons, and other genotypes (AFLP5/VGIII, AFLP7/ VGIV and AFLP10/VGIV) are isolated more commonly from immunocompromised persons. In 1999, a C. gattii genotype that had previously been reported in Brazil and Colombia was first documented on Vancouver Island in the province of British Columbia, Canada (1,2). The environmental genotypes in British Columbia are primarily VGIIa (AFLP6A, serotype B), VGIIb (AFLP6B, serotype B), and more rarely VGI (AFLP4, serotype B). In 2004, the fungus was identified in the Pacific Northwest region of the United States, and subsequently, C. gattii infections have been detected in 8 additional US states (3,4). Globally, the highest rates of C. gattii cryptococcosis incidence among humans and animals and the highest rates of positive environmental samples are reported from Vancouver Island (5,6). The natural habitat of this fungus seems to be a broad range of native trees and the surrounding soil (2,7,8). The epidemiology, nomenclature, historical climate, and population dynamics of C. gattii are summarized in the online Technical Appendix ( and Table 1.

In general, previous studies examined seasonal versus short-term (e.g., monthly) C. gattii associations and primarily focused on C. gattii dynamics on trees versus in the air or soil. A limitation of studying seasonal C. gattii changes is that it is difficult to disentangle which biophysical conditions (temperature, sunlight, moisture, momentum) most strongly influence C. gattii concentrations. For example, which is the primary driver of airborne C. gattii levels in southern Australia: temperature, dryness, or both? More frequent C. gattii measurements and longitudinal statistics can help distinguish between competing processes. Most long-term studies documented C. gattii dynamics on trees; however, seasonality of C. gattii may differ in the soil and air (5). In particular, airborne C. gattii may have the most relevance for human health and deserves further attention. Furthermore, scrutinizing C. gattii dynamics in multiple media may provide additional support for conceptualizations of the C. gattii life cycle.

Our goal with this study was to determine the relative strength of associations between biophysical conditions and monthly C. gattii dynamics from the air, trees, and soil on Vancouver Island, Canada. The first research question examines specific plots from which repeated measurements were made during 2003-2004, and the second question examines only newly sampled C. gattii plots during 2002-2004. Based on environmental samples, these investigations were designed to provide insight into the periods with the greatest C. gattii area concentrations. This study expands on previous research in the area by studying changes over time, using representative weather stations, considering more biophysical conditions, and using statistics that control for autocorrelation.


Concentrations of C. gattii in the environmental soil, air, and trees were collected by previously described standardized methods (5; online Technical Appendix). We evaluated 2 datasets of C. gattii VGIIb (AFLP6B, serotype B) previously collected by different sampling strategies: repeatedly measured and newly sampled. The first strategy sporadically resampled a geographic plot after a positive C. gattii sample was obtained for this site during 2003-2004. This dataset is similar to the permanently colonized sites analyzed in an ecologic habitat study (15). The definition of a plot refers to a specific tree, soil sample 2 meters from the tree base, and the surrounding air. Plots were initially selected with [greater than or equal to] 4 more longitudinal samples. The second strategy analyzed only the first samples from a newly tested plot as analyzed by Kidd et al. (5). The sample plots were taken from 9 study areas (Figure). The study areas reported cases in humans, animals, or both or were in biogeoclimatic zones similar to areas with reported cases. Only plots from study areas visited on [greater than or equal to] 3 occasions and from which [greater than or equal to] 1 C. gattii-positive sample was obtained were included in the analysis. In each area, new plots were tested in 16%-41% of the study months. Newly sampled plots may reflect C. gattii dynamics across the broader study area.

The study examined a broad range of biophysical conditions that plausibly influence population dynamics of fungi in the phylum Basidiomycota. Environment Canada provided daily temperature and precipitation data from 15 weather stations in 7 study areas (http://climate.weatheroffice. (Figure). The second-generation North American Land Data Assimilation System (NLDAS) provided specific humidity, shortwave solar radiation (0.3-3 [micro]m), and wind speed across the domain. Wind speed and solar radiation were infrequently considered in previous studies. Shortwave radiation was converted into Z-scores (number of SDs away from the mean) to align the range of the independent variables and promote statistical convergence. NLDAS uses weather models to interpolate conditions between stations by using physical laws and processes. The spatial resolution of the gridded NLDAS dataset was [approximately equal to] 14 [km.sup.2]. Validation shows good agreement between the NLDAS variables used in this study and independent observations (16).

There is minimal research to support the selection of periods over which biophysical conditions most strongly influence C. gattii dynamics. This analysis broadly considered biophysical conditions over the previous and current day, previous week, and previous month (past 30 days). For each sampling date, C. gattii observations for each plot were aligned with the corresponding weather conditions of the surrounding study area.

Statistical Analyses

Long-term C. gattii studies may reanalyze data collected for different purposes, such as surveillance and detection. C. gattii was rarely sampled continuously from the same plots. More commonly, repeated measurements were sporadically taken from the same plots. For example, tree A might have been sampled in January-March and August-October, tree B in April-July and November-December, and tree C in April-October. Although no tree was continuously sampled throughout the year, standardizing and pooling the sporadic samples can collectively yield seasonal C. gattii information. The analysis maximized the information available from the sporadic samples by use of hierarchical generalized linear and mixed effect models (GLMMs) that control for repeated measurements and clustered sampling (17).


GLMMs were used to investigate association of weather conditions with monthly C. gattii CFU counts (soil, air) or C. gattii presence/absence (trees). Poisson GLMMs with a random effect for each study observation accounted for overdispersion for the soil and air samples. Logistic GLMMs were used to analyze the tree samples. The analysis was conducted in R version 2.15.3 with use of the LME4 package ( In the first analysis of longitudinal samples, hierarchical random effects controlled for repeated plot measurements and plots nested within study areas.

The random effect in the second analysis accounted for plots nested within study areas. Both analyses controlled for tree genus (cedar, fir, oak, maple, pine, and other). If C. gattii were observed <20 times in trees of a given genus, genera were further aggregated into families or lumped into the "other" category. To control for residual spatial autocorrelation, we considered latitude and longitude as candidate independent variables in the analysis. We also controlled for seasonality with fixed-effect indicator variables for winter (November-February), spring (MarchMay), summer (June-July), and fall (August-October). The GLMM results were reported when the post-variable selection model residuals were not significantly auto-correlated. Residual autocorrelation was tested by autocorrelation and partial autocorrelation functions that were adjusted for missing data.

Intuitively, the C. gattii levels for a given month may be strongly related to the previous month's values. Monthly C. gattii samples may exhibit a more complex temporal correlation structure. If the GLMM residuals were significantly autocorrelated, we conducted the analysis on a reduced dataset. For the first analysis, the reduced dataset included plots sampled in sequential months from the plots with [greater than or equal to] 4 longitudinal samples. For the second analysis, the reduced dataset included all first samples in a study area, provided that the study area was sampled in the previous month. Thus, the GLMM controlled for seasonality, plot, or study area-specific random effects, and first-order autoregressive terms for each plot (first analysis) or study area (second analysis). The autoregressive term was the natural logarithm of average C. gattii concentration plus 1 (soil, air) or proportion of positive C. gattii samples (tree) in the previous month. In the reduced dataset, study areas in which C. gattii were observed <20 times were lumped together in the "other area" category.

Because of the relationships among weather conditions, a forward stepwise variable selection procedure involving the Akaike information criterion was used to select the multiple variable models. After a weather variable entered the model, the selection procedure did not consider other temporal aggregations of the same variable. For example, if daily absolute humidity exhibited the most significant C. gattii association, weekly or monthly absolute humidity was not tested in the next stepwise iteration. There are minor to moderate differences in the magnitudes of weather conditions across the study area. The statistical results therefore reflect time periods and geographic areas in which weather systematically influences C. gattii levels. Weather conditions in the study area were not further standardized to retain the interpretability and biological plausibility of weather conditions for C. gattii population dynamics.


Plot Level

Table 2 summarizes the mean C. gattii concentrations and sample size for the soil and air samples and the proportion of positive tree swab samples. On a plot level (first analysis), weather systematically influenced soil and airborne C. gattii levels (Table 3). The soil results from the reduced dataset with plots sampled in sequential months that controlled autocorrelation are reported. The statistical model controlled for a west-to-east gradient of increasing C. gattii concentrations across Vancouver Island and for seasonality. Geographic areas and periods with cooler temperatures, lower wind speeds, or both corresponded to the highest C. gattii concentrations. Soil concentrations of C. gattii were often elevated in the study areas with the coolest temperatures (Parksville and Little Qualicum Falls Park). Average ind speeds were weakest in the study areas surrounding Courtenay and Errington. During October-April, areaaveraged ([approximately equal to] 14 [km.sup.2]) monthly wind speeds were <2 m/s.

Airborne C. gattii levels for a given month were not associated with those of the previous month. Therefore all plots sampled [greater than or equal to] 4 times were included in the analysis. Similar to the trend for the soil samples, there was an increasing eastward trend of C. gattii across the island. Solar radiation intensity was positively associated with airborne C. gattii concentrations. The most daily solar radiation is received in the southerly areas (Victoria, Parksville, Duncan) and during May-August. Wind speeds exhibited a more complex, nonlinear relationship to airborne propagules. Moderate daily wind speeds (1.5-3 m/s) may be more likely than less windy days (<1.5 m/s) to entrain C. gattii propagules into the air. However, C. gattii concentrations were lower on very windy days than on relatively tranquil days. Temperature was not associated with airborne concentrations.

A tree with a positive C. gattii sample in a given month was more likely to be positive in the following month. Thus, results are reported from the reduced dataset of trees sampled in sequential months (Table 3). Detection of C. gattii in tree samples was not significantly associated with weather conditions. Within the study area, northerly regions were less likely to host C. gattii-positive trees.

Study Area Level

Random sampling of new environmental samples during 2002-2004 showed that at the study area level, weather was systematically associated with C. gattii in soil and trees (Table 4). Most of the air samples were collected in sequential months, and the small number of air samples from newly sampled plots precluded formal statistical analysis. Consistent with the plot level, concentrations of C. gattii in soil were significantly associated with concentrations the previous month. The results of the subset of samples from sequential months are reported. In agreement with the plotlevel analyses, higher average temperatures were associated with lower C. gattii concentrations in a study area after controlling for seasonality. However, wind speed did not significantly influence concentrations in soil.

Of note, temperature, wind speed, and solar radiation strongly influenced C. gattii dynamics on trees at the study area but not the plot level. Across each study area, a higher proportion of positive tree swab samples from the previous month increased the chances of elucidating C. gattii in the current month. The weather relationships were largely consistent with the results from the other media (soil and air). As with the soil samples, geographic areas and periods with warmer temperatures were associated with reduced frequency of C. gattii isolation. Similar to the air samples, solar radiation and wind speed were positively associated with frequency of C. gattii isolation. C. gattii isolation was more likely in southern study areas and during May-August, which had the most solar radiation.


In British Columbia, Canada, C. gattii exhibits specialized habitat preferences. It thrives in the area of the Vancouver Island rain shadow (i.e., southeast coast of Vancouver Island and the southwest coast of mainland British Columbia), where winter temperatures are predominantly above freezing and summer temperatures are not too hot (15). In the analysis of resampled plots, weather conditions over the previous and current day most strongly influenced C. gattii concentrations. For the first C. gattii sample analysis, weekly and monthly weather exhibited the best-fitting associations with detection of C. gattii in tree swab samples. Granados and Castaneda suggested that conditions up to 15 days before sampling most strongly influence C. gattii concentrations (18).

Geographic areas and periods with elevated temperatures decreased isolation of C. gattii from tree samples and concentration in soil. The results are consistent with C. gattii serotype B in Colombia, where C. gattii was sampled from the detritus of trees of species with persistent and elevated C. gattii concentrations (Eucalyptus camaldulensis and Terminalia cattapa) (18). In that study, the greatest proportions of positive samples were also found during periods of lower temperatures. Similarly, an elevational transect study conducted at elevations of 300-3,000 m found that C. gattii concentrations were greater at high elevations with cold temperatures (12[degrees]C-18[degrees]C annual average temperatures) than in temperate and tropical regions (19). In the Vancouver Island study area, average annual temperatures in C. gattii-endemic areas were slightly cooler (9.8[degrees]C-11.4[degrees]C). Outbreaks of C. gattii infection in humans or animals in Western Australia, Mediterranean Europe, and North America have been characterized by dry summers or dry winters with warm but not hot monthly temperatures (<22[degrees]C) (20). Laboratory studies of the optimum growth rates for C. gattii and competitors have not been conducted. This knowledge might provide a stronger mechanistic interpretation of temperature associations. According to research of other Basidiomycota, temperature may influence the ecologic niche by regulating the rate of enzyme-catalyzed reactions (21).

The aversion of the C. gattii strain in British Columbia to higher temperatures may partially account for the difficulty detecting C. gattii in environmental samples in warmer neighboring regions. In general, the proportion of C. gattii-positive samples declines with increasing southerly distance from Vancouver Island and the Gulf Islands. Prevalence of C. gattii in new soil samples (9.6%) and trees (7.7%) on Vancouver Island is remarkably high (5). In Washington, USA, British Columbia's neighbor to the south, C. gattii was recovered in 3.0% of air, soil, and tree samples (5). This trend continues farther to the south in Oregon, USA, where C. gattii was detected in 0-0.6% of tree swab samples (3,22). The caveat to this trend is that Oregon is host to a different combination of C. gattii strains (AFLP6A/VGIIa, AFLP6C/VGIIc) than are British Columbia and Washington (AFLP6A/VGIIa, AFLP6B/VGIIb).

To adapt to biophysical stressors such as temperature, nutrient stress, and radiation, Cryptococcus spp. produce melanin. Melanin may increase the integrity of C. neoformans cells and make them less susceptible than non-melanized cells to temperature extremes (23). Nutrient stress (glucose and peptone) enhances the production of melanin in C. gattii VGI and VGII (24). In laboratory C. neoformans studies, melanin increases survival to UV-C but not UV-B radiation (25,26). In our study, periods with more solar radiation (sum of visible, UV, and near-infrared) seem to promote C. gattii in the air and trees. Research on C. gattii serotype C in Colombia documented a similar association with solar radiation (18). To further clarify the role of melanin for mediating environmental stressors, further laboratory studies of C. gattii genotypes are needed.

The association between windy days and airborne C. gattii concentrations may have >1 interpretation. Very windy conditions may be strong enough to transport C. gattii away from the local air monitor. It is also possible that these periods coincide with depressed soil C. gattii concentrations when there are fewer propagules that can be mobilized. Also, the accuracy of the isokinetic air sampler decreases during periods with stronger wind speeds (27).

Collectively, the study results support common conceptualizations of the life cycle of C. gattii. Trees and the surrounding soil are permanently colonized and seem to act as C. gattii reservoirs. Wind may provide a key process for transferring C. gattii from the soil into the air and onto trees in the wider study area. Concentrations of C. gattii near the soil surface (0 to <15 cm depth) are greater than concentrations deeper (15-30 cm) in the soil (3). Moderate wind speeds may mobilize surface soil and increase local airborne C. gattii concentrations. Higher wind speeds may transport C. gattii from the soil to trees across the broader area. It is also possible that wind is simply a proxy that coincides with life stages in which propagules are more likely to disperse. C. gattii colonization seems to be transitory on many of the recently colonized substrates. C. gattii flexibly inhabits and colonizes the soil and specific trees during different seasons, which may decrease intraspecific competition.

The primary route of human C. gattii exposure is probably the inhalation of infectious propagules. In the study area, the fungus causes [approximately equal to] 25 clinically diagnosed human illnesses and 4 deaths per year ( html). According to our results, the highest airborne C. gattii concentrations occur during August-October on sunny days with moderately windy conditions. The greatest risk for exposure to C. gattii from the soil is during relatively cool June and July summer days. Although these associations are consistent, until more research provides information about the infectious dose for humans, the study results characterize the risk for exposure associated with environmental factors, rather than disease risk. Weather and airborne concentrations of C. gattii should be associated with human cryptococcosis incidence; however, onset of documented cryptococcosis cases in British Columbia does not vary by season or month (28,29). The temporal discrepancy may be masked by the long and variable incubation period of this pathogen. Host factors may be stronger predictors of developing disease risk (30). Nonetheless, refined risk projections may benefit susceptible humans and animals living in areas of marginal C. gattii transmission.

Author affiliations: Florida State University, Tallahassee, Florida, USA (C.K. Uejio); British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada (S. Mak); Centers for Disease Control and Prevention, Atlanta, Georgia, USA (A. Manangan, G. Luber); The University of British Columbia, Vancouver (K.H. Bartlett)



We thank the British Columbia Cryptococcal Working Group for their environmental sampling, epidemiological, and laboratory diagnostic support. We acknowledge Micah B. Hahn and anonymous reviewers for constructive comments on previous manuscript drafts. We also thank Julie R. Harris and Benjamin J. Park for helping to build the interdisciplinary research team. This work was supported by the National Center for Atmospheric Research/Centers for Disease Control and Prevention Fellowship Program and the Florida State University First Year Assistant Professor Program.

Dr. Uejio is an assistant professor at Florida State University in the Department of Geography and Program in Public Health. He studies how the physical environment influences human health and well-being.


(1.) Meyer W, Castaneda A, Jackson S, Huynh M, Castaneda E. IberoAmerican Cryptococcal Study Group. Molecular typing of IberoAmerican Cryptococcus neoformans isolates. Emerg Infect Dis. 2003;9:189-95.

(2.) Kidd SE, Hagen F, Tscharke RL, Huynh M, Bartlett KH, Fyfe M, et al. A rare genotype of Cryptococcus gattii caused the cryptococcosis outbreak on Vancouver Island (British Columbia, Canada). Proc Natl Acad Sci U S A. 2004;101:17258-63.

(3.) MacDougall L, Kidd SE, Galanis E, Mak S, Leslie MJ, Cieslak PR, et al. Spread of Cryptococcus gattii in British Columbia, Canada, and detection in the Pacific Northwest, USA. Emerg Infect Dis. 2007;13:42-50. eid1301.060827

(4.) Harris JR, Lockhart SR, Sondermeyer G, Vugia DJ, Crist MB, D'Angelo MT, et al. Cryptococcus gattii infections in multiple states outside the US Pacific Northwest. Emerg Infect Dis. 2013; 19:1620-6.

(5.) Kidd SE, Chow Y, Mak S, Bach PJ, Chen H, Hingston AO, et al. Characterization of environmental sources of the human and animal pathogen Cryptococcus gattii in British Columbia, Canada, and the Pacific Northwest of the United States. Appl Environ Microbiol. 2007;73:1433-43.

(6.) Lester SJ, Malik R, Bartlett KH, Duncan CG. Cryptococcosis: update and emergence of Cryptococcus gattii. Vet Clin Pathol. 2011;40:4-17.

(7.) Lazera MS, Cavalcanti MA, Trilles L, Nishikawa MM, Wanke B. Cryptococcus neoformans var. gattii-evidence for a natural habitat related to decaying wood in a pottery tree hollow. Med Mycol. 1998;36:119-22.

(8.) Randhawa HS, Kowshik T, Chowdhary A, Preeti Sinha K, Khan ZU, Sun S, et al. The expanding host tree species spectrum of Cryptococcus gattii and Cryptococcus neoformans and their isolations from surrounding soil in India. Med Mycol. 2008;46:823-33.

(9.) Granados DP, Castaneda E. Isolation and characterization of Cryptococcus neoformans varieties recovered from natural sources in Bogota, Colombia, and study of ecological conditions in the area. Microb Ecol. 2005;49:282-90.

(10.) Granados DP, Castaneda E. Influence of climatic conditions on the isolation of members of the Cryptococcus neoformans species complex from trees in Colombia from 1992-2004. FEMS Yeast Res. 2006;6:636-44.

(11.) Randhawa HS, Kowshik T, Chowdhary A, Prakash A, Khan ZU, Xu J. Seasonal variations in the prevalence of Cryptococcus neoformans var. grubii and Cryptococcus gattii in decayed wood inside trunk hollows of diverse tree species in north-western India: A retrospective study. Med Mycol. 2011;49:320-3.

(12.) Bedi NG, Nawange SR, Singh SM, Naidu J, Kavishwar A. Seasonal prevalence of Cryptococcus neoformans var. grubii and Cryptococcus gattii inhabiting Eucalyptus terreticornis and Eucalyptus camaldulensis trees in Jabalpur City of Madhya Pradesh, Central India. J Mycol Med. 2012;22:341-7. http://dx.doi.Org/10.1016/j.mycmed.2012.09.001

(13.) Montenegro H, Paula CR. Environmental isolation of Cryptococcus neoformans var. gattii and C. neoformans var. neoformans in the city of Sao Paulo, Brazil. Med Mycol. 2000;38:385-90.

(14.) Ellis DH, Pfeiffer TJ. Natural habitat of Cryptococcus neoformans var. gattii. J Clin Microbiol. 1990;28:1642-4.

(15.) Mak S, Klinkenberg B, Bartlett K, Fyfe M. Ecological niche modeling of Cryptococcus gattii in British Columbia, Canada. Environ Health Perspect. 2010;118:653-8.

(16.) Luo L, Robock A, Mitchell KE, Houser PR, Wood EF, Schaake JC, et al. Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the Southern Great Plains. J Geophys Res. 2003;108:8843.

(17.) Brown H, Prescott R. Applied mixed models in medicine. 2nd ed. West Sussex (UK): John Wiley & Sons; 2006.

(18.) Granados DP, Castaneda E. Influence of climatic conditions on the isolation of members of the Cryptococcus neoformans species complex from trees in Colombia from 1992-2004. FEMS Yeast Res. 2006;6:636^4.

(19.) Quintero E, Castaneda E, Ruiz A. Distribution ambiental de Cryptococcus neoformans en el departamento de cundinamarcacolombia. Rev Iberoam Micol. 2005;22:93-8. 10.1016/S1130-1406(05)70015-2

(20.) Hagen F, Ceresini PC, Polacheck I, Ma H, van Nieuwerburgh F, Gabaldon T, et al. Ancient dispersal of the human fungal pathogen Cryptococcus gattii from the amazon rainforest. PLoS ONE. 2013;8:e71148.

(21.) Magan N. Ecophysiology: impact of environment on growth, synthesis of compatible solutes and enzyme production. In: Boddy L, Frankland JC, van West P, editors. Ecology of saprotrophic Basidiomycetes. London: Elsevier; 2008. p. 63-78.

(22.) Mortenson JA, Bartlett KH, Wilson RW, Lockhart SR. Detection of Cryptococcus gattii in selected urban parks of the Willamette Valley, Oregon. Mycopathologia. 2013;175:351-5.

(23.) Rosas AL, Casadevall A. Melanization affects susceptibility of Cryptococcus neoformans to heat and cold. FEMS Microbiol Lett. 1997;153:265-72.

(24.) Mistry D, Carter D, D'Souza Basseal J. Low nutrient eucalyptus wood chip agar: a semi-quantitative medium for assessing melanin production by Cryptococcus gattii. Aust Mycol. 2009;28:15-8 [cited 2015 Sep 2].

(25.) Wang Y, Casadevall A. Decreased susceptibility of melanized Cryptococcus neoformans to UV light. Appl Environ Microbiol. 1994;60:3864-6.

(26.) Schiave LA, Pedroso RS, Candido RC, Roberts DW, Braga GU. Variability in UVB tolerances of melanized and nonmelanized cells of Cryptococcus neoformans and C. laurentii. Photochem Photobiol. 2009;85:205-13.

(27.) Nicholson KW. Physical aspects of bioaerosol sampling and deposition. In: Cox CS, Wathes CM, editors. Bioaerosols handbook. 1st ed. Boca Raton (FL): CRC Press; 1995. p. 27-53.

(28.) Galanis E, Macdougall L, Kidd S, Morshed M, and the British Columbia Cryptococcus gattii Working Group. Epidemiology of Cryptococcus gattii, British Columbia, Canada, 1999-2007. Emerg Infect Dis. 2010;16:251-7.

(29.) Johannson KA, Huston SM, Mody CH, Davidson W. Cryptococcus gattii pneumonia. CMAJ. 2012;184:1387-90.

(30.) MacDougall L, Fyfe M. Emergence of Cryptococcus gattii in a novel environment provides clues to its incubation period. J Clin Microbiol. 2006;44:1851-2.

Address for correspondence: Christopher K. Uejio, Department of Geography, Rm 323, Bellamy Bldg, 113 Collegiate Loop, Florida State University, Tallahassee, FL 32306-2190, USA; email:
Table 1. Summary of findings from longitudinal
Cryptococcus gattii studies *

Location               Genotype,      Medium
(reference)             serotype

British Columbia,    VGIIa (AFLP6A,    Air
Canada (5)            serotype B),
                         VG IIb
                      serotype B)

Bogota,                    B           Tree
Colombia (9)

Bogota, Cucuta,            B           Tree
Medellin, Cali,            C           Tree
Colombia (10)

Punjab, Haryana,      VGIb (AFLP4)     Tree
Delhi, Chandigarh,
India (11)

Jabalpur,                  B           Tree
India (12)

Sao Paulo,                 B           Tree
Brazil (13)

Barroso Valley,            B           Air
Australia (14)

Location              Highest isolation       Lowest isolation
(reference)               frequency              frequency

British Columbia,       Summer: PPT 31      Winter: PPT 166 mm-
Canada (5)                 mm-mo, T         mo, T-1[degrees]C to
                        11[degrees]C-           6[degrees]C

Bogota,                Rainy season: RH      Dry season: Low RH
Colombia (9)         [approximately equal   [approximately equal
                     to] 85%, PPT 120 mm-    to] 67%, PPT <5 mm, T
                            mo, T              14.0[degrees]C

Bogota, Cucuta,      High RH, low T, low    Low RH, high T, high
Medellin, Cali,      EVAP Low RH, high T,   EVAP High RH, low T,
Colombia (10)             high EVAP               low EVAP

Punjab, Haryana,          Autumn: RH             Winter: RH
Delhi, Chandigarh,   [approximately equal   [approximately equal
India (11)            to] 54%, PPT 60 mm/    to] 55%, PPT 10 mm/
                     mo, T 25[degrees]C;    mo, T [approximately
                          summer: RH               equal
                     [approximately equal    to] 17[degrees]C;
                      to] 30%, PPT 20 mm/         Spring: RH
                     mo, T 32[degrees]C;    [approximately equal
                     rainy: RH /60%, PPT     to] 39%, PPT 11 mm/
                         150 mm/mo, T        mo, T 23[degrees]C

Jabalpur,                 Summer: T               Rainy: T
India (12)            32[degrees]C, PPT        6.6[degrees]C/
                        0.9/141 mm/mo       30.6[degrees]C, PPT
                                               141/589 mm/mo

Sao Paulo,            November: PPT 244      Other months: PPT
Brazil (13)                mm/mo, T           10-400 mm-mo, T
                         22[degrees]C          18[degrees]C-

Barroso Valley,      Eucalyptus flowering    Other months: PPT
Australia (14)        (Dec-Feb): PPT 0-      5.08-164 mm-mo, T
                        4.32 mm-mo, T           8[degrees]C-
                       20.4[degrees]C-          20[degrees]C

* Most studies identified the seasons with the greatest or
lowest C. gattii isolation frequency. Studies commonly
examined relative humidity (RH), temperature (T),
precipitation (PPT), or evaporation (EVAP).

Table 2. Mean Cryptococcus gattii concentrations for soil
and air samples or proportion of positive tree swab samples,
Vancouver Island, British Columbia Canada, 2002-2004 *

                          Mean C. gattii concentration ([dagger])

Medium            Level   Parksville     Duncan      Courtenay

Soil, CFU         Plot    2,006 (49)   80,139 (18)      --
  (no. samples)
Soil, CFU         Area     572 (12)      56 (43)     556 (17)
  (no. samples)
Air, CFU          Plot    100 (113)     202 (38)      2 (34)
  (no. samples)
Tree, %           Plot     26 (57)       95 (21)        --
  (no. samples)
Tree, %,          Area     55 (55)       10 (42)      15 (34)
  (no. samples)

                  Mean C. gattii concentration ([dagger])

Medium            Errington   LQFP    Nanaimo   Victoria     Other

Soil, CFU                      --                  --      1,635 (28)
  (no. samples)
Soil, CFU          4 (14)     0 (7)    0 (6)     0 (18)        --
  (no. samples)
Air, CFU
  (no. samples)
Tree, %                        --               60 (15)     50 (22)
  (no. samples)
Tree, %,                      13 (9)   0 (4)    5 (110)
  (no. samples)

* LQFP, Little Qualicum Falls Park.

([dagger]) Blank cells indicate areas not included in
the analysis. Dashes (-) indicate study areas with a
small sample size that were lumped into the column
entitled "other." This information is reported for the
plot and area analysis levels.

Table 3. Generalized linear and mixed effect model
result of the association between weather and
Cryptococcus gattii in resampled plots in Vancouver
Island, British Columbia Canada, 2002-2004

Medium and independent variable    Estimate     SE

Soil, CFU *
  Intercept                         567.16    167.21
  Mar-May vs. Nov-Feb                1.06      0.78
  Jun-Jul vs. Nov-Feb               15.75      2.38
  Aug-Oct vs. Nov-Feb               12.12      1.83
  Longitude ([degrees]W)             4.47      1.34
  Average daily temperature,        -1.25      0.19
  Average daily wind speed          -3.45      0.81
    1.5-3 m/s
  Average daily wind speed          -5.68      0.99
    >3 m/s
  Previous month's natural           0.51      0.11
    logarithm (C. gattii + 1)
  Garry oak vs. fir/cedar            1.19      1.82
  Maple vs. fir/cedar                1.61      1.43
  Other tree vs. fir/cedar          -2.82      1.50

Air, CFU ([dagger])
  Intercept                         484.28    94.69
  Mar-May vs. Nov-Feb                0.78      1.27
  Jun-Jul vs. Nov-Feb                0.89      1.56
  Aug-Oct vs. Nov-Feb                2.46      1.05
  Longitude, [degrees]W              3.91      0.76
  Daily shortwave solar              2.32      0.60
    radiation, watts/[m.sup.2],
  Average daily wind speed           1.53      0.64
    1.5-3 m/s
  Average daily wind speed          -3.97      1.37
    >3 m/s
  Garry oak vs. fir/cedar            0.35      0.84
  Maple vs. fir/cedar               -0.27      0.87
  Other vs. fir/cedar                0.99      0.73

Swab sample, proportion
  positive ([double dagger])
  Intercept                         145.29    49.42
  Mar-May vs. Nov-Feb                2.32      0.80
  Jun-Jul vs. Nov-Feb                2.22      0.88
  Aug-Oct vs. Nov-Feb                2.62      0.88
  Latitude, [degrees]N              -2.99      1.01
  Proportion of C. gattii-           2.38      0.58
    positive samples
    previous month
  Fir/cedar vs. alder                0.18      0.86
  Garry oak vs. alder               -0.23      0.97
  Other tree vs. alder              -0.80      1.03

Medium and independent variable        95% CI        p value

Soil, CFU *
  Intercept                        232.7 to 901.5     0.001
  Mar-May vs. Nov-Feb              -0.50 to 2.626     0.174
  Jun-Jul vs. Nov-Feb              10.98 to 20.50    <0.001
  Aug-Oct vs. Nov-Feb               8.45 to 15.78    <0.001
  Longitude ([degrees]W)            1.79 to 7.15     <0.001
  Average daily temperature,       -1.63 to -0.87    <0.001
  Average daily wind speed         -5.06 to -1.83    <0.001
    1.5-3 m/s
  Average daily wind speed         -7.66 to -3.69    <0.001
    >3 m/s
  Previous month's natural          0.30 to 0.73     <0.001
    logarithm (C. gattii + 1)
  Garry oak vs. fir/cedar           -2.45 to 4.84     0.514
  Maple vs. fir/cedar               -1.25 to 4.47     0.262
  Other tree vs. fir/cedar          -5.82 to 0.18     0.060

Air, CFU ([dagger])
  Intercept                        294.8 to 673.6    <0.001
  Mar-May vs. Nov-Feb               -1.74 to 3.31     0.537
  Jun-Jul vs. Nov-Feb               -2.23 to 4.01     0.570
  Aug-Oct vs. Nov-Feb               0.36 to 4.56      0.019
  Longitude, [degrees]W             2.39 to 5.44     <0.001
  Daily shortwave solar             1.11 to 3.52     <0.001
    radiation, watts/[m.sup.2],
  Average daily wind speed          0.24 to 2.82      0.017
    1.5-3 m/s
  Average daily wind speed         -6.71 to -1.21     0.004
    >3 m/s
  Garry oak vs. fir/cedar           -1.33 to 2.03     0.680
  Maple vs. fir/cedar               -1.99 to 1.47     0.760
  Other vs. fir/cedar               -0.46 to 2.44     0.174

Swab sample, proportion
  positive ([double dagger])
  Intercept                        46.44 to 244.10    0.003
  Mar-May vs. Nov-Feb               0.71 to 3.93      0.004
  Jun-Jul vs. Nov-Feb               0.46 to 3.98      0.012
  Aug-Oct vs. Nov-Feb               0.87 to 4.37      0.003
  Latitude, [degrees]N             -5.02 to -0.96     0.003
  Proportion of C. gattii-          1.22 to 3.53     <0.001
    positive samples
    previous month
  Fir/cedar vs. alder               -1.53 to 1.91     0.831
  Garry oak vs. alder               -2.17 to 1.72     0.817
  Other tree vs. alder              -2.85 to 1.26     0.437

* 95 samples, 45 plots, 3 study areas, Akaike
Information Criterion = 648.4.

([dagger]) 175 samples, 24 plots, 3 study areas,
Akaike Information Criterion = 615.4.

([double dagger]) 115samples, 44 plots, 4 study
areas, Akaike Information Criterion = 117.9.

Table 4. Association between weather and the first Cryptococcus
gattii sample in study areas, Vancouver Island, British Columbia
Canada, 2002-2004 *

Medium and independent variable      Estimate    SE

Soil ([dagger])
Intercept                             25.08     15.57
  Jun-Juy vs. Mar-May                 60.47     25.04
  Aug-Oct vs. Mar-May                 20.24     12.13
  Average daily temperature,          -4.66     2.15
  Cedar vs. alder                      1.24     6.56
  Fir vs. alder                       -2.34     7.44
  Oak vs. alder                       -1.28     9.49
  Maple vs. alder                     -0.63     7.07
  Other vs. alder                     -0.95     7.33
  Previous month's natural             0.65     1.52
    logarithm(C. gattii + 1)
Swab sample ([double dagger])
  Intercept                           10.31     2.45
  Weekly wind speed, m/s               0.76     0.26
  Average weekly temperature,         -1.23     0.20
  Monthly solar radiation,             6.25     1.34
    watts/[m.sup.2], centered
  Mar-May vs. Nov-Feb                  0.92     1.18
  Jun-Jul vs. Nov-Feb                  2.77     1.70
  Aug-Oct vs. Nov-Feb                  1.24     1.28
  Cedar (western red, yellow)         -1.00     1.12
    vs. alder
  Fir (Douglas, other) vs.            -0.55     0.72
  Garry Oak vs. alder                  0.94     0.90
  Maple vs. alder                       0       0.85
  Other vs. alder                     -0.61     0.98
  Pine vs. alder                      -1.70     1.46
  Proportion of C. gattii-positive     2.21     0.89
    samples previous mo

Medium and independent variable          95% CI        p value

Soil ([dagger])
Intercept                            -6.05 to 56.21     0.107
  Jun-Juy vs. Mar-May                10.39 to 110.50    0.016
  Aug-Oct vs. Mar-May                -4.02 to 44.49     0.095
  Average daily temperature,         -8.96 to -0.36     0.030
  Cedar vs. alder                    -11.88 to 14.35    0.850
  Fir vs. alder                      -17.22 to 12.52    0.753
  Oak vs. alder                      -20.27 to 17.70    0.893
  Maple vs. alder                    -14.78 to 13.51    0.929
  Other vs. alder                    -15.60 to 13.70    0.897
  Previous month's natural            -2.38 to 3.69     0.666
    logarithm(C. gattii + 1)
Swab sample ([double dagger])
  Intercept                           5.42 to 15.19    <0.001
  Weekly wind speed, m/s              0.24 to 1.28      0.003
  Average weekly temperature,        -1.63 to -0.82    <0.001
  Monthly solar radiation,            3.58 to 8.93     <0.001
    watts/[m.sup.2], centered
  Mar-May vs. Nov-Feb                 -1.43 to 3.27     0.435
  Jun-Jul vs. Nov-Feb                 -0.63 to 6.17     0.103
  Aug-Oct vs. Nov-Feb                 -1.31 to 3.80     0.332
  Cedar (western red, yellow)         -3.24 to 1.25     0.374
    vs. alder
  Fir (Douglas, other) vs.            -2.00 to 0.89     0.444
  Garry Oak vs. alder                 -0.85 to 2.73     0.296
  Maple vs. alder                     -1.69 to 1.71     0.997
  Other vs. alder                     -2.55 to 1.35     0.534
  Pine vs. alder                      -4.61 to 1.21     0.243
  Proportion of C. gattii-positive    0.43 to 3.98      0.013
    samples previous mo

* Determined by generalized linear and mixed effect model.

([dagger]) 116 samples, 7 study areas, Akaike Information
Criterion = 194.7.

([double dagger]) 254 samples, 6 study areas, Akaike
Information Criterion = 180.7.
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Article Details
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Title Annotation:RESEARCH
Author:Uejio, Christopher K.; Mak, Sunny; Manangan, Arie; Luber, George; Bartlett, Karen H.
Publication:Emerging Infectious Diseases
Article Type:Report
Geographic Code:1CANA
Date:Nov 1, 2015
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