Quantifying the efficiency and biases of forest Saccharomyces sampling strategies

Saccharomyces yeasts are emerging as model organisms for ecology and evolution, and researchers need environmental Saccharomyces isolates to test ecological and evolutionary hypotheses. However, methods for isolating Saccharomyces from nature have not been standardized and isolation methods may influence the genotypes and phenotypes of studied strains. We compared the effectiveness and potential biases of an established enrichment culturing method against a newly developed direct plating method for isolating forest floor Saccharomyces spp. In a European forest, enrichment culturing was both less successful at isolating S. paradoxus per sample collected and less labor intensive per isolated S. paradoxus colony than direct isolation. The two methods sampled similar S. paradoxus diversity: the number of unique genotypes sampled (i.e., genotypic diversity) per S. paradoxus isolate and average growth rates of S. paradoxus isolates did not differ between the two methods, and growth rate variances (i.e., phenotypic diversity) only differed in one of three tested environments. However, enrichment culturing did detect rare S. cerevisiae in the forest habitat, and also found two S. paradoxus isolates with outlier phenotypes. Our results validate the historically common method of using enrichment culturing to isolate representative collections of environmental Saccharomyces. We recommend that researchers choose a Saccharomyces sampling method based on resources available for sampling and isolate screening. Researchers interested in discovering new Saccharomyces phenotypes or rare Saccharomyces species from natural environments may also have more success using enrichment culturing. We include step-by-step sampling protocols in the supplemental materials

Towards a Taxonomy of the Model-Ladenness of Data

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data