Investigating Accuracy of Climate vs. Yearly Weather for Predicting the Spread of White-Nose Syndrome in the United States

Yaw Kumi-Ansu, a Biology major from Emory University, worked with Dr. Andrew Kramer on an ongoing project to model White-Nose syndrome in bats.

Abstract: White-Nose Syndrome is an epizootic fungal disease caused by Pseudogymnoascus destructans which has caused a significant decline in Vespertilionid bat populations in the United States. It is a psychrophilic fungus and produces infectious conidia during the hibernation season of most cave-dwelling bat species. Studies have found strong ties between the rate and pattern of spread and factors such as the density of caves within an area and temperature. In our foundational paper (Maher et al. 2012), studies based on  models designed to predict the spatial spread of WNS showed that the model based on average length of winter (number of days under 10°C) and density of caves within and between counties (Gravity(caves)+Winter) provided the best fit for projections and observed spread of the disease. In this project, we wanted to know whether yearly variations in temperature (weather) was a better environmental factor than climate (average length of winter) in predicting the spread of White-Nose Syndrome in the contiguous United States. We modified code for the Gravity(caves)+Winter model to run yearly maximum and minimum temperature in place of average length of winter and we also calculated average temperature from 2006 to 2014. Our results showed that Average length of winter remained the best environmental factor in predicting the spatial spread of WNS based on NLL and AIC values which were obtained from the MLE parameter sets. Spatial spread in both the climate and yearly weather models were similar but climate models projected faster spread to counties with caves. In the future, we hope to improve upon our study of spatial spread by using yearly variations in length of winter as well as data on co-occurring species to get a better understanding of how inter-specific differences in hibernation patterns and length of hibernation could contribute to the spread of the disease.


Download (PDF, 3.13MB)