The Prairie State: Using Ecological Niche Modeling to Predict Distributions of Early Land Plants

Bryophytes are environmentally and ecologically significant biological indicators, as their distribution is largely determined by the climate and the land features that shape these factors. Yet it is a challenge to track the ranges of these plants and even more so to predict their future distribution patterns due to their small size and sensitivity to environmental change. This study aims to model the potential distribution of selected bryophyte species in Illinois to investigate the potential impact of global warming and determine what environmental factors affect distribution patterns. Bryophyte occurrences post 1970 of some of the most common epiphytic species and genera were investigated. Over 12,000 georeferenced occurrence records were downloaded from a public biodiversity aggregator, cleaned, and validated. The environmental variables consisted of the WorldClim Bioclim variables and National Land Cover Database land use variables, filtered to remove correlation. The occurrences and environmental variables were run through a MaxEnt model in R to generate heat maps of potential distribution. Statistical evaluation metrics and validation techniques were used to test model accuracy. Overall, current species models showed a higher level of confidence than the genera models, and all models were primarily reliant on the land use variables over the climate variables. Future models only showed minor distribution changes across climate scenarios, suggesting the use of more local indicators and further research on microclimate data. Attempting to quantify bryophyte-environment relationships and ecological niche modeling potentially provides a means of predicting how bryophytes might respond to environmental changes over time. Using such techniques enables us to test for significant differences in the characterization of niches between taxa. Successful models will not only show support for utilizing bryophytes as climate change indicators, but also for this open-source methodology in the niche modeling for other organisms. Overall, these results will possibly have important implications for species distribution patterns, conservation, land management and our understanding of ecological niche modeling using a poorly studied and overlooked group of plants.