Calibrating the data logger…
I conducted an inventory of vascular flora on public and private property in southwestern Gunnison County, Colorado. The study area consisted of 3,004 acres of private land and 1,850 acres of adjacent public land managed by the Bureau of Land Management (BLM). While a small part of the BLM area was surveyed in the late 1990s, the majority of the study area represented a gap in the existing floristic research to date in the Rocky Mountains. Fieldwork was conducted in the growing seasons of 2016 and 2017. Six hundred five herbarium specimens were generated, representing 315 species and infraspecific taxa. Combining my findings with reliable observations and existing collections from the area, a checklist of 330 species and infraspecific taxa was compiled, representing ten percent of the known Colorado flora. The variable landscape of the area includes submontane to subalpine forests, wetlands, grasslands, sagebrush shrublands, xeric mesa tops, and rocky cliffs. Taken together these areas provide habitat for two species previously unknown in the county (Trifolium kingii, Pyrola picta), two further species that are considered vulnerable or imperiled in Colorado (Iliamna rivularis, Draba rectifructa), and 30 vulnerable or imperiled plant community types. The presence of species and communities of conservation concern not previously documented in the region emphasizes the need for continued floristic study of private lands and other undersampled areas. In order to efficiently make labels for the aforementioned herbarium specimens, I developed a Python program to generate formatted, print-ready labels from a comma-separated value (CSV) file. Efficiently generating specimen labels can be challenging for newcomers to the field, or for those unable to use existing tools. Several software tools exist for automatic specimen label generation, but these either require a significant learning curve to use, are not consistently maintained, are not compatible with multiple operating systems, or rely on proprietary software. My program generates labels in HTML, and these are formatted for print using Cascading Style Sheets (CSS) and the Mustache template system. The program does not rely on any proprietary software, is compatible with any operating system on which Python 3.0 or later can be installed, and is easily customizable to suit the user's needs. The minimal nature of the tool makes it easy and efficient to use. The tool will be especially useful to students learning to manage their collection data and produce their own specimen labels. As such, it could be a convenient resource for plant-taxonomy and related courses in which students make collections.
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