Data collection and manipulation
We deployed ARUs (Song Meter Model SM2 and SM4, Wildlife Acoustics, Concord, MA, USA) to collect ambient sound from 1 March – 31 May. We deployed 15 ARUs on the Webb WMA Complex during 2015 – 2018, 10 on CWMA during 2014 – 2018, 20 on SRS during 2014 – 2018, 16 on CCWMA in 2017, and 8 on BFG in 2017. We increased sampling efforts during 2018 in Georgia by deploying 20 additional ARUs on CCWMA and 10 on BFG. We placed ARUs >2 km apart to prevent multiple units from detecting the same call (Wightman et al. 2019, Wakefield et al. 2020). We attached ARUs to tree trunks approximately 3m off the ground and placed an external microphone between 6m and 10m above the ground on the same tree (Wightman et al. 2019). We placed ARUs at locations observed to have turkey activity based on field observations and global positioning system (GPS) locations of wild turkeys collected during previous research (Wightman et al. 2019). We used ambient sound recorded from 30 minutes prior to sunrise until 150 minutes post sunrise as this is when > 75% of vocalizations occurred (Wightman et al. 2019, Wakefield et al. 2020).
We used a Convolutional Neural Network (CNN) developed to autonomously search for turkey gobbles (Wightman et al. 2021). We implemented the CNN in Python (Python Software Foundation, Wilmington, DE, USA) with the Keras library (Chollet 2015) using a backend of the open-source TensorFlow software developed by Google (Abadi et al. 2015). For each potential gobble selected by the CNN, a record was created containing call location in the spectrogram, date and time stamp, and a 3 second sound file of the potential gobble. We auditorily verified all selections and classified each as a true or false gobble, producing daily counts of gobbles on all sites.
We collected weather data for SRS and CWMA from 2 weather stations located on SRS maintained by the U.S. Department of Energy and U.S. Department of Agriculture Forest Service. We used the most centrally located weather station on SRS to describe weather metrics associated with gobbling activity onsite. The second weather station was on the southern border of SRS, approximately 10.5 km from the center of CWMA, and was used for gobbling evaluation on CWMA. For the Webb WMA Complex, CCWMA, and BFG, we collected weather metrics from the closest National Oceanic and Atmospheric Administration (NOAA) weather station. The closest weather station to the Webb WMA Complex was located in Varnville, SC (35 km), whereas the closest station to CCWMA (25 km) and BFG (35 km) was near Eatonton, GA. Although previous authors have suggested the potential for placing weather stations at each ARU (Palumbo et al. 2019, Wightman et al. 2019) such a study design was not logistically feasible. We offer that using weather data collected on the same study site or within the distances detailed above is sufficient for detailing how daily changes in local weather conditions influence gobbling activity. We calculated mean daily values from 15-minute weather recordings from 30 minutes prior to 150 minutes after sunrise for temperature (C°), relative humidity percentage, and wind speed (kph). For barometric pressure (mb) we calculated the mean for each morning and then subtracted it from the prior morning to get a change in barometric pressure. For precipitation, we classified whether rain occurred (Yes = 1, No = 0) from 30 minutes before to 150 minutes after sunrise.