modeling the habitat range of phototrophic microorganisms in yellowstone national park: toward the development of a comprehensive fitness landscape

The extent to which geochemical variation constrains the distribution of phototrophic metabolisms was modeled based on 439 observations in geothermal springs in Yellowstone National Park (YNP), Wyoming. Generalized additive models (GAMs) were developed to predict the distribution of photosynthesis as a function of spring temperature, pH, and total sulfide. GAMs comprised of temperature explained 42.7% of the variation in the distribution of phototrophic metabolisms whereas GAMs comprised of sulfide and pH explained 20.7% and 11.7% of the variation, respectively. These results suggest that of the measured variables, temperature is the primary constraint on the distribution of phototrophic metabolism in YNP. GAMs comprised of multiple variables explained a larger percentage of the variation in the distribution of phototrophic metabolism, indicating additive interactions among variables. A GAM that combined temperature and sulfide explained the greatest variation in the dataset (54.8%) while minimizing the introduction of degrees of freedom. In an effort to verify the extent to which phototroph distribution reflects constraints on activity, we examined the influence of sulfide and temperature on dissolved inorganic carbon (DIC) uptake rates under both light and dark conditions. Light-driven DIC uptake decreased systematically with increasing concentrations of sulfide in acidic, algal-dominated systems, but was unaffected in alkaline, bacterial-dominated systems. In both alkaline and acidic systems, light-driven DIC uptake was suppressed in cultures incubated at temperatures 10°C greater than their in situ temperature. Collectively, these results suggest that the habitat range of phototrophs in YNP springs, specifically that of cyanobacteria and algae, largely results from constraints imposed by temperature and sulfide on the activity and fitness of these populations, a finding that is consistent with the predictions from GAMs.