Comparing Management Programs to Reduce Red–tailed Hawk Collisions with Aircraft

Washburn, Brian E.; Pullins, Craig K.; Guerrant, Travis L.; Martinelli, Gregory J.; Beckerman, Scott F.

doi:10.1002/wsb.1177

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…Further, a detailed analysis of return paths involving landscape searching patterns deserve to be explored to assess if owls use features like cities, roads, waterways to navigate when returning to the airport. In addition, we recommend building on previous work of translocation and management programs of other large raptors wintering at airports (Blackwell and Wright 2006, Pullins et al 2018, Washburn et al 2021), for developing translocation and mitigation protocols that are species‐specific and cater to the logistics of the program or geographic region.…”

Section: Discussionmentioning

confidence: 99%

Factors associated with returns of snowy owls to airports following translocation

et al. 2022

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Human‐dominated environments often include ecological traps for wildlife, such as airports that may be perceived as suitable habitat by grassland birds but reduce fitness because of collisions with aircraft. Birds of prey are often attracted to airports where collisions with aircraft (i.e., bird strikes) are usually fatal for the birds and are a significant threat to flight safety. The snowy owl (Bubo scandiacus) is known for its nomadism, exhibiting unpredictable and highly variable movements during the nonbreeding season, including being a common visitor to airports, which often have high small‐mammal populations and mimic flat, open habitats used naturally by owls. Since 2009, the Federal Aviation Administration reported an average of 22 snowy owl deaths annually due to aircraft collisions throughout 55 North American airports. To aid in active management of owls at airports, we assessed relocation data of 42 telemetry‐tracked snowy owls from 2000–2020 in the United States and Canada. Owls that returned to the airport after relocation (33%) frequently crisscrossed and perched near runways where they were at risk of strikes. Adult females and immature males were more likely to return than the other sex and age classes, and returns were less likely to occur as the distance between the release site and the airport increased. Owls relocated in open habitats with a greater proportion of wetland and cropland (including grasslands and pasture) land cover classes were also less likely to return. We conclude that inclusion of multiple factors to limit return rates of relocated snowy owls from airport facilities can unspring the ecological trap presented by airports to these owls.

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Section: Discussionmentioning

confidence: 99%

Factors associated with returns of snowy owls to airports following translocation

et al. 2022

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“…Here, we define risk in its basic form as the likelihood of a collision with the likelihood of predefined damage or negative effects [3]. Airport biologists and personnel attempt to mitigate these risks by deterring certain species from airports by habitat modification, fencing, translocation, auditory or visual deterrents, and population control, but identifying animal area use and prioritizing management actions can be difficult [4,5]. Animal monitoring is routinely conducted on many airports, but bias varies among human observers, and frequent monitoring is sometimes unattainable due to time and funding constraints and the amount of area needing to be covered [5,6].…”

Section: Introductionmentioning

confidence: 99%

Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks

Zhou

Elmore

Samiappan

et al. 2021

Sensors

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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 white-tailed 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.

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Evaluation of mitigation translocation as a management tool to reduce conflicts between humans and American kestrels

Washburn

2024

Wildlife Society Bulletin

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Raptor‐aircraft collisions (bird strikes) pose a serious safety risk to civil aircraft. Even smaller raptors, such as American kestrels (Falco sparverius), can be problematic within airport environments. Given widespread declines in kestrel populations, public interest, logistical and financial constraints, and other factors, managing kestrels at airports presents some unique challenges. I conducted a study to determine which biological (e.g., age and sex of the bird) and logistical factors (e.g., month and translocation distance) might influence the return of American kestrels live‐captured and translocated from 42 civil airports and military airbases within the contiguous USA during 2016–2021. American kestrels were live‐captured, banded, and translocated various distances from the airfields which were then monitored for returning kestrels. I developed a set of candidate generalized linear mixed models (binomial distribution) involving all possible subsets of 4 factors (age, sex, month, and distance translocated) as well as interactions, and used the location where a kestrel was live‐captured (i.e., airport) as a random factor in all mixed models. The return rate of American kestrels was very low (<4% of the individuals that were translocated). I found no evidence that the biological and logistical factors examined influenced return rates or the number of days it took returning birds to be resighted/recaptured. Management programs that use release sites 15 km from airfields would increase program efficacy and decrease implementation costs. There is no scientific information available regarding the efficacy of American kestrel management programs for reducing kestrel presence on airfields thus, these results provide a basis for informing such management decisions.

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Comparing Management Programs to Reduce Red–tailed Hawk Collisions with Aircraft

Cited by 9 publications

References 25 publications

Factors associated with returns of snowy owls to airports following translocation

Factors associated with returns of snowy owls to airports following translocation

Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks

Evaluation of mitigation translocation as a management tool to reduce conflicts between humans and American kestrels

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