Identification of spatial and temporal parameters to support monitoring of Lepidoptera in urban areas

Steady declines in pollinators worldwide have led to strong interest in monitoring their populations and developing plans to protect and enhance critical habitat areas to support their stabilization and recovery. For butterfly populations, long-term data suggest a 35% decline on a global scale over the past 40 years. Identifying appropriate butterfly monitoring strategies is key to understanding the current population status of different species and their presence in potential habitat types. Although a limited number of butterfly monitoring protocols exist, most have been developed to survey relatively large natural areas and reserves, and may not be applicable to other habitat types (smaller areas) or locations (such as urban settings). Further, given phenological shifts in life-cycles of both plants and animals in response to climate change, there are new questions about the timing of monitoring activities to better coincide with species’ presence. In the first part of this study, I examined butterfly use of two habitat types within urban settings (public gardens and restored/reconstructed prairies) and compared effectiveness of three survey methods (Pollard transects, purposive point counts, and random point counts) in each. I determined that although there were no significant differences in the number of butterfly sightings between habitat types (1,076 in public gardens, 1,151 in prairie areas), purposive point counts consistently resulted in more sightings overall (798) and in each habitat type than did Pollard transects (297)or random point counts (266; p \u3c 0.0001). Occupancy modeling also indicated that purposive point counts enhanced detection of certain species of butterflies, most notably those of the Pieridae and Papilionidae. For the second part of this study, I used a geographically and temporally extensive butterfly survey database collected by the Ohio Lepidopterists to determine whether climatic parameters could be used to predict phenology of three species of butterflies (great spangled fritillary, clouded sulphur, and little wood satyr). I used two methods, single-variable and combined models, to determine if accumulated growing degree days (GDD) and/or shortwave radiation flux densities (SRAD) could enhance predictions compared to day of year based on the historical record of first sightings. For the single-variable analyses, GDD best predicted butterfly sightings at a greater number of sites, with lower error rates compared against day of year or SRAD. Models that combined GDD and SRAD increased the misclassification rate (the proportion of predictions that did not align with actual observations) for the great spangled fritillary and the GDD model for the little wood satyr, but decreased it for the clouded sulphur and the SRAD model for the little wood satyr. From a practical standpoint, however, the difference in predicted dates for first sightings of all three species was within one day for all three models, indicating that the additional effort required to develop climate-based models may not lead to commensurate improvements in predictive ability. Taken together, these studies suggest that changing landscapes and climate-driven phenological shifts translate into a need to refocus surveying efforts to include methods that accommodate urban habitats and timing of surveys to reflect potential life-cycle shifts. This is especially true in the Midwest where extensive agricultural land use and intensive agricultural practices threaten butterfly population abundance and species richness