Deciphering interactions between white-tailed deer and approaching vehicle

Deer-vehicle collisions are a major transportation hazard, but factors affecting deer escape decision-making in response to vehicle approach remain poorly characterized. We made opportunistic observations of deer response to vehicle approach during daylight hours on a restricted- access facility in Ohio, USA (vehicle speeds were ≤64 km/h). We hypothesized that animal proximity to the road, group size, vehicle approach, and ambient conditions would affect perceived risk by white-tailed deer (Odocoileus virginianus) to vehicle approach, as measured by flight-initiation distance (FID). We constructed a priori models for FID, as well as road-crossing behavior. Deer responses were variable and did not demonstrate spatial or temporal margins of safety. Road-crossing behavior was slightly and positively influenced by group size during winter. Deer showed greater FIDs and likelihood of crossing when approached in the road; directionality of approach likely increased the perceived risk. These findings are consistent with antipredator theory relative to predator approach direction

Forage or Biofuel: Assessing Native Warm-season Grass Production among Seed Mixes and Harvest Frequencies within a Wildlife Conservation Framework

Native warm-season grasses (NWSG) are gaining merit as biofuel feedstocks for ethanol production with potential for concomitant production of cattle forage and wildlife habitat provision. However, uncertainty continues regarding optimal production approaches for biofuel yield and forage quality within landscapes of competing wildlife conservation objectives. We used a randomized complete block design of 4 treatments to compare vegetation structure, forage and biomass nutrients, and biomass yield between Panicum virgatum (Switchgrass) monocultures and NWSG polycultures harvested once or multiple times near West Point, MS, 2011–2013. Despite taller vegetation and greater biomass in Switchgrass monocultures, NWSG polycultures had greater vegetation structure heterogeneity and plant diversity that could benefit wildlife. However, nutritional content from harvest timings optimal for wildlife conservation (i.e., late dormant season-collected biomass and mid-summer hay samples) demonstrated greater support for biofuel production than quality cattle forage. Future research should consider testing various seed mixes for maximizing biofuel or forage production among multiple site conditions with parallel observations of wildlife use

Improving animal monitoring using small unmanned aircraft systems (sUAS) and deep learning networks

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and whitetailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals

Detection Rates of Northern Bobwhite Coveys Using a Small Unmanned Aerial System-Mounted Thermal Camera

The northern bobwhite (Colinus virginianus; hereafter, bobwhite) requires intensive monitoring to evaluate management efforts and determine harvest rates. However, traditional monitoring techniques (e.g., covey-call surveys) are labor-intensive and imprecise. Small unmanned aerial systems (sUASs) mounted with thermal cameras have demonstrated promise for monitoring multiple avian species and could provide a less intensive and more effective approach to monitoring bobwhite coveys, assuming coveys produce a recognizable heat signature. To assess sUAS monitoring, we evaluated the influence of bobwhite covey size (3, 6, and 12) and cover type (grass, shrub, and forest) on covey detectability by a sUAS equipped with a thermal camera. We hypothesized that forest would have the lowest covey detection due to trees obstructing detection of the thermal signature and that larger covey size would improve covey detection due to the formation of larger, more visibly distinct thermal signatures. We placed groups of known-size, pen-reared bobwhites in steel mesh cages (3, 6, and 12 individuals/cage) in 3 vegetation types (grass, shrub, and forest) among predetermined locations on a private farm in Clay County, Mississippi, USA (3 replicates, 27 total cages). At civil twilight on 5 March 2020, the sUAS flew a systematic route over the cage area at 30 m above ground level, capturing thermal infrared photographs every 5 seconds. To assess detection, we distributed 57 photographs to 31 volunteers and asked them to assign a binary value for detection (1, 0) regarding covey presence in each photograph. Overall true positive rate was 0.551 but improved with increasing covey size. By vegetation type, simulated coveys in grass had the lowest true positive rate by photograph (0.403), followed by forest (0.562) and shrub (0.605). Results indicate that sUASs and thermal camera technology could be a viable method for surveying intact bobwhite coveys, especially if detection of smaller groups and those in denser vegetation improves. As this technology advances, we recommend that future research focus on evaluating the efficacy of this novel methodology through assessing the influence of weather conditions, camera specifications, flight speed, and altitude, as well as assessing machine learning for processing photos

Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach. Supplemental files attached below

Dataset for Controllable factors affecting accuracy and precision of human identification of animals from drone imagery

Dataset from the results of an experiment to determine how three controllable factors, flight altitude, camera angle, and time of day, affect human identification and counts of animals from drone images to inform best practices to survey animal communities with drones. We used a drone (unoccupied aircraft system, or UAS) to survey known numbers of eight animal decoy species, representing a range of body sizes and colors, at four GSD (ground sampling distance) values (0.35, 0.70, 1.06, 1.41 cm/pixel) representing equivalent flight altitudes (15.2, 30.5, 45.7, 61.0 m) at two camera angles (45° and 90°) and across a range of times of day (morning to late afternoon). Expert human observers identified and counted animals in drone images to determine how the three controllable factors affected accuracy and precision. Observer precision was high and unaffected by tested factors. However, results for observer accuracy revealed an interaction among all three controllable factors. Increasing flight altitude resulted in decreased accuracy in animal counts overall; however, accuracy was best at midday compared to morning and afternoon hours, when decoy and structure shadows were present or more pronounced. Surprisingly, the 45° camera enhanced accuracy compared to 90°, but only when animals were most difficult to identify and count, such as at higher flight altitudes or during the early morning and late afternoon. We provide recommendations based on our results to design future surveys to improve human accuracy in identifying and counting animals from drone images for monitoring animal populations and communities

Deciphering interactions between white-tailed deer and approaching vehicle

Deer-vehicle collisions are a major transportation hazard, but factors affecting deer escape decision-making in response to vehicle approach remain poorly characterized. We made opportunistic observations of deer response to vehicle approach during daylight hours on a restricted- access facility in Ohio, USA (vehicle speeds were ≤64 km/h). We hypothesized that animal proximity to the road, group size, vehicle approach, and ambient conditions would affect perceived risk by white-tailed deer (Odocoileus virginianus) to vehicle approach, as measured by flight-initiation distance (FID). We constructed a priori models for FID, as well as road-crossing behavior. Deer responses were variable and did not demonstrate spatial or temporal margins of safety. Road-crossing behavior was slightly and positively influenced by group size during winter. Deer showed greater FIDs and likelihood of crossing when approached in the road; directionality of approach likely increased the perceived risk. These findings are consistent with antipredator theory relative to predator approach direction

Forage or Biofuel: Assessing Native Warm-season Grass Production among Seed Mixes and Harvest Frequencies within a Wildlife Conservation Framework

Native warm-season grasses (NWSG) are gaining merit as biofuel feedstocks for ethanol production with potential for concomitant production of cattle forage and wildlife habitat provision. However, uncertainty continues regarding optimal production approaches for biofuel yield and forage quality within landscapes of competing wildlife conservation objectives. We used a randomized complete block design of 4 treatments to compare vegetation structure, forage and biomass nutrients, and biomass yield between Panicum virgatum (Switchgrass) monocultures and NWSG polycultures harvested once or multiple times near West Point, MS, 2011–2013. Despite taller vegetation and greater biomass in Switchgrass monocultures, NWSG polycultures had greater vegetation structure heterogeneity and plant diversity that could benefit wildlife. However, nutritional content from harvest timings optimal for wildlife conservation (i.e., late dormant season-collected biomass and mid-summer hay samples) demonstrated greater support for biofuel production than quality cattle forage. Future research should consider testing various seed mixes for maximizing biofuel or forage production among multiple site conditions with parallel observations of wildlife use

Northern Bobwhite ( Colinus virginianus ) breeding season roost site selection in a working agricultural landscape in Clay County, Mississippi

Appropriate habitat management may be one of the most important factors contributing to Northern Bobwhite ( Colinus virginianus ) population persistence, but biologists lack information on how individual bobwhite select roost sites during the breeding season. Therefore, we examined breeding season third-order roost site selection on B. Bryan Farms, Mississippi, from 2021 to 2022. We observed a quadratic relationship with average vegetation height, where roost site selection increased with increasing vegetation height to a point and then slightly decreased (β1 = 0.14084, 95% CI = 0.05, 0.24; β12 = -0.01005, 95% CI = -0.06, 0.04). However, uncertainty in the quadratic term was notable. Similarly, we observed a quadratic relationship with litter (β1 = 0.25479, 95% CI = 0.12, 0.39; β12 = -0.09606, 95% CI = -0.16, -0.04). We also found selection decreased linearly with increasing bare ground (β1 =-0.20938, 95% CI = -0.31, -0.11). Individual birds may require taller vegetation, greater visual obstruction, greater litter coverage, and lesser bare ground coverage for better concealment from nocturnal predators when they are roosting individually during the breeding season or are constrained by limited mobility (i.e., brooding). Understanding the vegetative composition, structure, and location of roost sites during the breeding season may provide land managers with a better understanding of the vegetative characteristics necessary during all phases of bobwhite life history. Our results provide the first information on bobwhite breeding season roost site selection, which will help to develop a more complete understanding of bobwhite habitat requirements and increase the effectiveness of habitat management and conservation efforts for this species of conservation concern

Evidence on the effectiveness of small unmanned aircraft systems (sUAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map protocol

Background: Small unmanned aircraft systems (sUAS) are replacing or supplementing manned aircraft and groundbased surveys in many animal monitoring situations due to better coverage at finer spatial and temporal resolutions, access, cost, bias, impacts, safety, efficiency, and logistical benefits. Various sUAS models and sensors are available with varying features and usefulness depending on survey goals. However, justification for selection of sUAS and sensors are not typically offered in published literature and existing reviews do not adequately cover past and current sUAS applications for animal monitoring nor their associated sUAS model and sensor technologies, taxonomic and geographic scope, flight conditions and considerations, spatial distributions of sUAS applications, and reported technical difficulties. We outline a systematic map protocol to collect and consolidate evidence pertaining to sUAS monitoring of animals. Our systematic map will provide a useful synthesis of current applications of sUAS-animal related studies and identify major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that may influence future research directions and sUAS applications. Methods: Our systematic map will investigate the current state of knowledge using an accurate, comprehensive, and repeatable search. We will find relevant peer-reviewed and grey literature as well as dissertations and theses using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We will use a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted data concerning sUAS, sensors, animals, methodology, and results will be recorded in Microsoft Access. We will query and catalogue evidence in the final database to produce tables, figures, and geographic maps to accompany a full narrative review that answers our primary and secondary questions