Examining songbird migration along the Delaware Bay: a comprehensive approach
University of Delaware
There are several remote-sensing tools readily available for the study of nocturnally flying animals (e.g., migrating birds); each possessing unique measurement biases. I used multiple techniques to measure migratory bird passage (weather surveillance radar, thermal-infrared camera, acoustic recorder) and bird stopover (daily transect counts) to better understand relationships among methods during the spring and fall migrations of 2011 and 2012 in Lewes, DE. When comparing mean-nightly traffic rates across nights, I found methods to be positively correlated. I also found the strength of correlations generally increased throughout the night, peaking 2-3 hours before morning twilight. For radar and thermal imaging, the greatest observed traffic rate tended to occur at or shortly after evening twilight, while for the acoustic recorder peak bird flight-calling activity was observed just prior to morning twilight. Radar and thermal imaging tended to show positive within night relationships, while acoustic detections were inversely related to other methods, although all comparisons showed a high degree of variability. I determined that height of nocturnal migrants strongly influenced thermal imaging and flight call detections, positively for thermal imaging and negatively for acoustic detections. Thermal imaging detections decreased with increased mean ground speed and cloud cover presence; acoustic detections decreased with increased mean ground speed but showed no influence with the presence of clouds. My comparisons of radar target flow with acoustic and thermal imaging data to assess detection probabilities are novel and provide insight into the biases of these tools for detecting flying animals. With my assessment of nocturnal passage estimates (radar reflectivity and thermal-infrared detections) and daily transect counts I found relationships between nocturnal traffic and stopover densities to be strongest during the spring, and between stopover densities and weather surveillance radar data from the following evening. Although we found relationships to be positive, they tended to be weak and likely due to the complexity of these comparisons. I identified several probable sources of bias, including study-site placement, our inability to accurately estimate migrant turnover, and determine nocturnal migrant composition.