Northern Bobwhite movement metrics in relation to rabbit hunting

Northern Bobwhite telemetry data gathered in the field via VHF-telemetry. Movement metrics described from the telemetry data were determined through analysis in ArcGIS and package "trajr" in R Statistical Software. Additional information on the attached files are described in the "README" text file

Data from: Nonconsumptive effects of hunting on a nontarget game bird

1. Human hunting activity and disturbance can significantly impact prey species through both consumptive and nonconsumptive effects. The nonconsumptive effects of rabbit hunting on Northern Bobwhite (Colinus virginianus; hereafter, bobwhite) are currently unknown. Increased perceived risk of predation by bobwhite during rabbit hunting events may elicit anti-predator responses among bobwhite that impact fitness via changes in behavior that ultimately impact population growth. 2. We estimated the nonconsumptive effects of rabbit hunting on bobwhite behavior using telemetry across varying rabbit hunting intensities. Movements were analyzed using Bayesian hierarchical modeling with a before-after-control-impact (BACI) design to determine the effect of rabbit hunting on bobwhite. 3. We observed an overall reduction in bobwhite movement in the presence of rabbit hunting, with a 38% (Posterior Overlap = 0.01) increase in bobwhite step length in the absence of rabbit hunting. We also observed bobwhite maintaining closer proximity to hardwood and escape cover under high rabbit hunting intensity, with a 59% (Posterior Overlap = 0.03) increase in distance from hardwood and a 28% (Posterior Overlap = 0.14) increase in distance from escape cover when rabbit hunting was removed. 4. Synthesis and applications. Heightened antipredator behavior through decreased movement may assist with bobwhite predator avoidance. However, decreased movement and increased use of poor habitats may also have negative effects as a result of reduced foraging time or increased susceptibility to other predators. Future research should attempt to quantify the effect of decreased movement on bobwhite fitness through the evaluation of foraging time and survival in order to continue to improve management efforts for the species

The development of a convolutional neural network for the automatic detection of Northern Bobwhite Colinus virginianus covey calls

Abstract Passive acoustic monitoring using Autonomous Recording Units (ARUs) is becoming a significant research tool for collecting large amounts of ecological data. Northern bobwhite Colinus virginianus is an economically important game bird whose declining populations are of conservation concern, so efforts to monitor bobwhite abundance using ARUs are being intensified. Yet, manual processing of ARU data is time consuming and often expensive, so developing automatic call detection methods is a key step in acoustic monitoring. We present here the first single species convolutional neural network (CNN) developed purely for automatic bobwhite covey call identification and classification. We demonstrate the value of meaningful data augmentation by including non‐target calls and background noise into our training dataset, as well as evaluating alternative CNN score thresholds and model extrapolation performance. We trained our CNN on 6,682 manually labeled covey calls across three groups of sites within the southeastern USA. Precision and AUC from both CNN classification and individual call detection was high (0.80–0.99), and our model showed strong extrapolation ability across site groups. However, extrapolation performance significantly decreased for sites that were more dissimilar to the training data set if our meaningful data augmentation process was omitted. Our CNN detected significantly more covey calls than manual labeling using Raven Pro software, and processing time was greatly reduced: a single one hour wav file can be now analyzed by the CNN in roughly eight seconds. We also demonstrate using a simple case study that extremely high variability in estimates of bobwhite site occupancy and detection are obtained depending on the method of acoustic data processing (manual versus CNN). Our results suggest that our CNN provides robust and time‐saving analysis of bobwhite covey call acoustic data and can be applied to future research and monitoring projects with high confidence in the performance of the model