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Thermal sensors mounted on drones (unoccupied aircraft systems) are popular and effective tools for monitoring cryptic animal species, although few studies have quantified sampling error of animal counts from thermal images. Using decoys is one effective strategy to quantify bias and count accuracy; however, plastic decoys do not mimic thermal signatures of representative species. Our objective was to produce heat signatures in animal decoys to realistically match thermal images of live animals obtained from a drone-based sensor. We tested commercially available methods to heat plastic decoys of three different size classes, including chemical foot warmers, manually heated water, electric socks, pad, or blanket, and mini and small electric space heaters. We used criteria in two categories, 1) external temperature differences from ambient temperatures (ambient difference) and 2) color bins from a palette in thermal images obtained from a drone near the ground and in the air, to determine if heated decoys adequately matched respective live animals in four body regions. Three methods achieved similar thermal signatures to live animals for three to four body regions in external temperatures and predominantly matched the corresponding yellow color bins in thermal drone images from the ground and in the air. Pigeon decoys were best and most consistently heated with three-foot warmers. Goose and deer decoys were best heated by mini and small space heaters, respectively, in their body cavities, with a heated sock in the head of the goose decoy. The materials and equipment for our best heating methods were relatively inexpensive, commercially available items that provide sustained heat and could be adapted to various shapes and sizes for a wide range of avian and mammalian species. Our heating methods could be used in future studies to quantify bias and validate methodologies for drone surveys of animals with thermal sensors.•We determined optimal heating methods for plastic animal decoys with inexpensive and commercially available equipment to mimic thermal signatures of live animals.•Methods could be used to quantify bias and improve thermal surveys of animals with drones in future studies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Collisions between birds and aircraft cause bird mortality, economic damage, and aviation safety hazards. One proposed solution to increasing the distance at which birds detect and move away from an approaching aircraft, ultimately mitigating the probability of collision, is through onboard lighting systems. Lights in vehicles have been shown to lead to earlier reactions in some bird species but they could also generate attraction, potentially increasing the probability of collision. Using information on the visual system of the Canada goose ( Branta canadensis ), we developed light stimuli of high chromatic contrast to their eyes. We then conducted a controlled behavioral experiment ( i.e. , single-choice test) to assess the avoidance or attraction responses of Canada geese to LED lights of different wavelengths (blue, 483 nm; red, 631 nm) and pulsing frequencies (steady, pulsing at 2 Hz). Overall, Canada geese tended to avoid the blue light and move towards the red light; however, these responses depended heavily on light exposure order. At the beginning of the experiment, geese tended to avoid the red light. After further exposure the birds developed an attraction to the red light, consistent with the mere exposure effect. The response to the blue light generally followed a U-shape relationship (avoidance, attraction, avoidance) with increasing number of exposures, again consistent with the mere exposure effect, but followed by the satiation effect. Lights pulsing at 2 Hz enhanced avoidance responses under high ambient light conditions; whereas steady lights enhanced avoidance responses under dim ambient light conditions. Our results have implications for the design of lighting systems aimed at mitigating collisions between birds and human objects. LED lights in the blue portion of the spectrum are good candidates for deterrents and lights in the red portion of the spectrum may be counterproductive given the attraction effects with increasing exposure. Additionally, consideration should be given to systems that automatically modify pulsing of the light depending on ambient light intensity to enhance avoidance., Competing Interests: The authors declare there are no competing interests., (©2023 Lunn et al.)

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., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Small unoccupied aircraft systems (UAS) are replacing or supplementing occupied aircraft and ground-based surveys in animal monitoring due to improved sensors, efficiency, costs, and logistical benefits. Numerous UAS and sensors are available and have been used in various methods. However, justification for selection or methods used are not typically offered in published literature. Furthermore, existing reviews do not adequately cover past and current UAS applications for animal monitoring, nor their associated UAS/sensor characteristics and environmental considerations. We present a systematic map that collects and consolidates evidence pertaining to UAS monitoring of animals., Methods: We investigated the current state of knowledge on UAS applications in terrestrial animal monitoring by using an accurate, comprehensive, and repeatable systematic map approach. We searched 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 used a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted metadata concerning UAS, sensors, animals, methodology, and results were recorded in Microsoft Access. We queried and catalogued evidence in the final database to produce tables, figures, and geographic maps to accompany this full narrative review, answering our primary and secondary questions., Review Findings: We found 5539 articles from our literature searches of which 216 were included with extracted metadata categories in our database and narrative review. Studies exhibited exponential growth over time but have levelled off between 2019 and 2021 and were primarily conducted in North America, Australia, and Antarctica. Each metadata category had major clusters and gaps, which are described in the narrative review., Conclusions: Our systematic map provides a useful synthesis of current applications of UAS-animal related studies and identifies 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 guide future research directions and UAS applications. The literature for the use of UAS to conduct animal surveys has expanded intensely since its inception in 2006 but is still in its infancy. Since 2015, technological improvements and subsequent cost reductions facilitated widespread research, often to validate UAS technology to survey single species with application of descriptive statistics over limited spatial and temporal scales. Studies since the 2015 expansion have still generally focused on large birds or mammals in open landscapes of 4 countries, but regulations, such as maximum altitude and line-of-sight limitations, remain barriers to improved animal surveys with UAS. Critical knowledge gaps include the lack of (1) best practices for using UAS to conduct standardized surveys in general, (2) best practices to survey whole wildlife communities in delineated areas, and (3) data on factors affecting bias in counting animals from UAS images. Promising advances include the use of thermal sensors in forested environments or nocturnal surveys and the development of automated or semi-automated machine-learning algorithms to accurately detect, identify, and count animals from UAS images., (© 2023. The Author(s).)

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein's economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade., Competing Interests: The authors have declared that no competing interests exist.

A challenge that conservation practitioners face is manipulating behavior of nuisance species. The turkey vulture (Cathartes aura) can cause substantial damage to aircraft if struck. The goal of this study was to assess vulture responses to unmanned aircraft systems (UAS) for use as a possible dispersal tool. Our treatments included three platforms (fixed-wing, multirotor, and a predator-like ornithopter [powered by flapping flight]) and two approach types (30 m overhead or targeted towards a vulture) in an operational context. We evaluated perceived risk as probability of reaction, reaction time, flight-initiation distance (FID), vulture remaining index, and latency to return. Vultures escaped sooner in response to the fixed-wing; however, fewer remained after multirotor treatments. Targeted approaches were perceived as riskier than overhead. Vulture perceived risk was enhanced by flying the multirotor in a targeted approach. We found no effect of our treatments on FID or latency to return. Latency was negatively correlated with UAS speed, perhaps because slower UAS spent more time over the area. Greatest visual saliency followed as: ornithopter, fixed-wing, and multirotor. Despite its appearance, the ornithopter was not effective at dispersing vultures. Because effectiveness varied, multirotor/fixed-wing UAS use should be informed by management goals (immediate dispersal versus latency)., (© 2021. The Author(s).)

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.

Background: Animal-vehicle collisions represent substantial sources of mortality for a variety of taxa and can pose hazards to property and human health. But there is comparatively little information available on escape responses by free-ranging animals to vehicle approach versus predators/humans., Methods: We examined responses (alert distance and flight-initiation distance) of focal Canada geese ( Branta canadensis maxima ) to vehicle approach (15.6 m·s -1 ) in a semi-natural setting and given full opportunity to escape. We manipulated the direction of the vehicle approach (direct versus tangential) and availability of social information about the vehicle approach (companion group visually exposed or not to the vehicle)., Results: We found that both categorical factors interacted to affect alert and escape behaviors. Focal geese used mostly personal information to become alert to the vehicle under high risk scenarios (direct approach), but they combined personal and social information to become alert in low risk scenarios (tangential approach). Additionally, when social information was not available from the companion group, focal birds escaped at greater distances under direct compared to tangential approaches. However, when the companion group could see the vehicle approaching, focal birds escaped at similar distances irrespective of vehicle direction. Finally, geese showed a greater tendency to take flight when the vehicle approached directly, as opposed to a side step or walking away from the vehicle., Conclusions: We suggest that the perception of risk to vehicle approach (likely versus unlikely collision) is weighted by the availability of social information in the group; a phenomenon not described before in the context of animal-vehicle interactions. Notably, when social information is available, the effects of heightened risk associated with a direct approach might be reduced, leading to the animal delaying the escape, which could ultimately increase the chances of a collision. Also, information on a priori escape distances required for surviving a vehicle approach (based on species behavior and vehicle approach speeds) can inform planning, such as location of designated cover or safe areas. Future studies should assess how information from vehicle approach flows within a flock, including aspects of vehicle speed and size, metrics that affect escape decision-making., Competing Interests: The authors declare that they have no competing interests.

Collisions between birds and military aircraft are common and can have catastrophic effects. Knowledge of relative wildlife hazards to aircraft (the likelihood of aircraft damage when a species is struck) is needed before estimating wildlife strike risk (combined frequency and severity component) at military airfields. Despite annual reviews of wildlife strike trends with civil aviation since the 1990s, little is known about wildlife strike trends for military aircraft. We hypothesized that species relative hazard scores would correlate positively with aircraft type and avian body mass. Only strike records identified to species that occurred within the U.S. (n = 36,979) and involved United States Navy or United States Air Force aircraft were used to calculate relative hazard scores. The most hazardous species to military aircraft was the snow goose (Anser caerulescens), followed by the common loon (Gavia immer), and a tie between Canada goose (Branta canadensis) and black vulture (Coragyps atratus). We found an association between avian body mass and relative hazard score (r2 = 0.76) for all military airframes. In general, relative hazard scores per species were higher for military than civil airframes. An important consideration is that hazard scores can vary depending on aircraft type. We found that avian body mass affected the probability of damage differentially per airframe. In the development of an airfield wildlife management plan, and absent estimates of species strike risk, airport wildlife biologists should prioritize management of species with high relative hazard scores., Competing Interests: The authors have declared that no competing interests exist.

Background: Avian collisions with man-made objects and vehicles (e.g., buildings, cars, airplanes, power lines) have increased recently. Lights have been proposed to alert birds and minimize the chances of collisions, but it is challenging to choose lights that are tuned to the avian eye and can also lead to avoidance given the differences between human and avian vision. We propose a choice test to address this problem by first identifying wavelengths of light that would over-stimulate the retina using species-specific perceptual models and by then assessing the avoidance/attraction responses of brown-headed cowbirds to these lights during daytime using a behavioral assay., Methods: We used perceptual models to estimate wavelength-specific light emitting diode (LED) lights with high chromatic contrast. The behavioral assay consisted of an arena where the bird moved in a single direction and was forced to make a choice (right/left) using a single-choice design (one side with the light on, the other with the light off) under diurnal light conditions., Results: First, we identified lights with high saliency from the cowbird visual perspective: LED lights with peaks at 380 nm (ultraviolet), 470 nm (blue), 525 nm (green), 630 nm (red), and broad-spectrum (white) LED lights. Second, we found that cowbirds significantly avoided LED lights with peaks at 470 and 630 nm, but did not avoid or prefer LED lights with peaks at 380 and 525 nm or white lights., Discussion: The two lights avoided had the highest chromatic contrast but relatively lower levels of achromatic contrast. Our approach can optimize limited resources to narrow down wavelengths of light with high visual saliency for a target species leading to avoidance. These lights can be used as candidates for visual deterrents to reduce collisions with man-made objects and vehicles., Competing Interests: The authors declare that they have no competing interests.

Poor communication between academic researchers and wildlife managers limits conservation progress and innovation. As a result, input from overlapping fields, such as animal behaviour, is underused in conservation management despite its demonstrated utility as a conservation tool and countless papers advocating its use. Communication and collaboration across these two disciplines are unlikely to improve without clearly identified management needs and demonstrable impacts of behavioural-based conservation management. To facilitate this process, a team of wildlife managers and animal behaviour researchers conducted a research prioritisation exercise, identifying 50 key questions that have great potential to resolve critical conservation and management problems. The resulting agenda highlights the diversity and extent of advances that both fields could achieve through collaboration., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Animal-vehicle collisions cause high levels of vertebrate mortality worldwide, and what goes wrong when animals fail to escape and ultimately collide with vehicles is not well understood. We investigated alert and escape behaviours of captive brown-headed cowbirds (Molothrus ater) in response to virtual vehicle approaches of different sizes and at speeds ranging from 60 to 360 km h(-1). Alert and flight initiation distances remained similar across vehicle speeds, and accordingly, alert and flight initiation times decreased at higher vehicle speeds. Thus, avoidance behaviours in cowbirds appeared to be based on distance rather than time available for escape, particularly at 60-150 km h(-1); however, at higher speeds (more than or equal to 180 km h(-1)) no trend in response behaviour was discernible. As vehicle speed increased, cowbirds did not have enough time to assess the approaching vehicle, and cowbirds generally did not initiate flight with enough time to avoid collision when vehicle speed exceeded 120 km h(-1). Although potentially effective for evading predators, the decision-making process used by cowbirds in our study appears maladaptive in the context of avoiding fast-moving vehicles. Our methodological approach and findings provide a framework to assess how novel management strategies could affect escape rules, and the sensory and cognitive abilities animals use to avoid vehicle collisions., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

Animal-vehicle collisions (AVCs) are a substantial problem in a human-dominated world, but little is known about what goes wrong, from the animal's perspective, when a collision occurs with an automobile, boat, or aircraft. Our goal is to provide insight into reactions of animals to oncoming vehicles when collisions might be imminent. Avoiding a collision requires successful vehicle detection, threat assessment, and evasive behaviour; failures can occur at any of these stages. Vehicle detection seems fairly straightforward in many cases, but depends critically on the sensory capabilities of a given species. Sensory mechanisms for detection of collisions (looming detectors) may be overwhelmed by vehicle speed. Distractions are a likely problem in vehicle detection, but have not been clearly demonstrated in any system beyond human pedestrians. Many animals likely perceive moving vehicles as non-threatening, and may generally be habituated to their presence. Slow or minimal threat assessment is thus a likely failure point in many AVCs, but this is not uniformly evident. Animals generally initiate evasive behaviour when a collision appears imminent, usually employing some aspect of native antipredator behaviour. Across taxa, animals exhibit a variety of behaviours when confronted with oncoming vehicles. Among marine mammals, right whales Eubalaena spp., manatees Trichechus spp., and dugongs Dugong dugon are fairly unresponsive to approaching vehicles, suggesting a problem in threat assessment. Others, such as dolphins Delphinidae, assess vehicle approach at distance. Little work has been conducted on the behavioural aspects of AVCs involving large mammals and automobiles, despite their prevalence. Available observations suggest that birds do not usually treat flying aircraft as a major threat, often allowing close approach before taking evasive action, as they might in response to natural predators. Inappropriate antipredator behaviour (often involving immobility) is a major source of AVCs in amphibians and terrestrial reptiles. Much behavioural work on AVCs remains to be done across a wide variety of taxa. Such work should provide broad phylogenetic generalizations regarding AVCs and insights into managing AVCs., (Published 2014. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

The fundamental causes of animal-vehicle collisions are unclear, particularly at the level of animal detection of approaching vehicles and decision-making. Deer-vehicle collisions (DVCs) are especially costly in terms of animal mortality, property damage, and safety. Over one year, we exposed free-ranging white-tailed deer (Odocoileus virginianus) to vehicle approach under low ambient light conditions, from varying start distances, and vehicle speeds from 20 km/h to approximately 90 km/h. We modeled flight response by deer to an approaching vehicle and tested four hypotheses: 1) flight-initiation distance (FID) would correlate positively with start distance (indicating a spatial margin of safety); 2) deer would react to vehicle speed using a temporal margin of safety; 3) individuals reacting at greater FIDs would be more likely to cross the path of the vehicle; and 4) crossings would correlate positively with start distance, approach speed, and distance to concealing/refuge cover. We examined deer responses by quantiles. Median FID was 40% of start distance, irrespective of start distance or approach speed. Converting FID to time-to-collision (TTC), median TTC was 4.6 s, but uncorrelated with start distance or approach speed. The likelihood of deer crossing in front of the vehicle was not associated with greater FIDs or other explanatory variables. Because deer flight response to vehicle approach was highly variable, DVCs should be more likely with increasing vehicle speeds because of lower TTCs for a given distance. For road sections characterized by frequent DVCs, we recommend estimating TTC relative to vehicle speed and candidate line-of-sight distances adjusted downward by (1-P), where P represents our findings for the proportion of start distance by which >75% of deer had initiated flight. Where road design or conservation goals limit effectiveness of line-of-sight maintenance, we suggest incorporation of roadway obstacles that force drivers to slow vehicles, in addition to posting advisory speed limits.

The avoidance of motorized vehicles is a common challenge for birds in the modern world. Birds appear to rely on antipredator behaviors to avoid vehicles, but modern vehicles (automobiles and aircraft) are faster than natural predators. Thus, birds may be relatively ill-equipped, in terms of sensory capabilities and behaviors, to avoid vehicles. We examined the idea that birds may be unable to accurately assess particularly high speeds of approaching vehicles, which could contribute to miscalculations in avoidance behaviors and ultimately cause collisions. We baited turkey vultures (Cathartes aura) to roads with animal carcasses and measured flight initiation distance and effective time-to-collision in response to a truck driving directly towards vultures from a starting distance of 1.13 km and at one of three speeds: 30, 60, or 90 kph (no vultures were struck). Flight initiation distance of vultures increased by a factor of 1.85 as speed increased from 30 to 90 kph. However, for 90-kph approaches there was no clear trend in flight initiation distance across replicates: birds appeared equally likely to initiate escape behavior at 40 m as at 220 m. Time-to-collision decreased by a factor of 0.62 with approach speeds from 30 to 90 kph. Also, at 90 kph, four vehicle approaches (17%) resulted in near collisions with vultures (time-to-collision ≤ 1.7 s), compared to none during 60 kph approaches and one during 30 kph approaches (4%). Our findings suggest that antipredator behaviors in turkey vultures, particularly stimulus processing and response, might not be well tuned to vehicles approaching at speeds ≥ 90 kph. The possible inability of turkey vultures to react appropriately to high-speed vehicles could be common among birds, and might represent an important determinant of bird-vehicle collisions.

Color vision is not uniform across the retina because of differences in photoreceptor density and distribution. Retinal areas with a high density of cone photoreceptors may overlap with those with a high density of ganglion cells, increasing hue discrimination. However, there are some exceptions to this cell distribution pattern, particularly in species with horizontal visual streaks (bands of high ganglion cell density across the retina) that live in open habitats. We studied the spectral sensitivity and distribution of cone photoreceptors involved in chromatic and achromatic vision in the Canada goose (Branta canadiensis), which possesses an oblique rather than horizontal visual streak at the ganglion cell layer. Using microspectrophotometry, we found that the Canada goose has a violet-sensitive visual system with four visual pigments with absorbance peaks at 409, 458, 509 and 580 nm. The density of most cones involved in chromatic and achromatic vision peaked along a band across the retina that matched the oblique orientation of the visual streak. With the information on visual sensitivity, we calculated chromatic and achromatic contrasts of different goose plumage regions. The regions with the highest visual saliency (cheek, crown, neck and upper tail coverts) were the ones involved in visual displays to maintain flock cohesion. The Canada goose oblique visual streak is the retinal center for chromatic and achromatic vision, allowing individuals to sample the sky and the ground simultaneously or the horizon depending on head position. Overall, our results show that the Canada goose visual system has features that make it rather different from that of other vertebrates living in open habitats.

Scaling up for alternative energy such as solar, wind, and biofuel raises a number of environmental issues, notably changes in land use and adverse effects on wildlife. Airports offer one of the few land uses where reductions in wildlife abundance and habitat quality are necessary and socially acceptable, due to risk of wildlife collisions with aircraft. There are several uncertainties and limitations to establishing alternative energy production at airports, such as ensuring these facilities do not create wildlife attractants or other hazards. However, with careful planning, locating alternative energy projects at airports could help mitigate many of the challenges currently facing policy makers, developers, and conservationists.

The distribution of ganglion cells in the retina determines the specific regions of the visual field with high visual acuity, and thus reflects the perception of a species' visual environment. The terrain hypothesis proposes that animals living in open areas should have a horizontal visual streak across the retina with high ganglion cell density to increase visual acuity along the horizon. We tested this hypothesis in Canada geese (Branta canadensis) by assessing retinal topography, visual field configuration, and scanning behavior. We found that geese have an oblique rather than a horizontal visual streak across the retina: from a dorsal-nasal to a ventral-temporal position. Geese showed narrow blind areas, which increased the range of their lateral vision, and a relatively large degree of eye movement. Canada geese have relatively wide binocular fields and can see their bill tips. Goose head movement rates were low compared to species with a single fovea, and head movement rates increased in visually obstructed habitats. Canada geese have high acuity across their retina, which would allow them to simultaneously scan the ground and the sky when the head is up and parallel to the ground, as well as align the visual streak with the horizon when the head is tilted downwards. Their visual streak, along with their large eye size, may reduce the need for large amplitude head movements during vigilance bouts in visually unobstructed habitats. Overall, the visual system of geese combines features related to the detection of predators/conspecifics in open areas (visual streak, large lateral field, reduced head movements) as well as visual specializations that would allow for monitoring both the ground and sky (oblique streak) and for extracting and handling of food items (wide binocular fields, visualization of the bill tip)., (Copyright © 2011 S. Karger AG, Basel.)

Ohio's oral rabies vaccination (ORV) program was established to prevent the westward spread of the raccoon (Procyon lotor) rabies virus (Lyssavirus, Rhabdoviridae) in Ohio, USA. The program, which targets raccoons, distributes vaccine-bait units (VBU) at a target density of 75 units/km2. Few studies have examined the relationship of VBU density and target population density to the prevalence of rabies virus-neutralizing antibodies (RVNA). We conducted experimental VBU distributions in August 2003 and August 2004, 150 km west of the ORV zone where there was no history of raccoon rabies. We measured change in RVNA titers in blood collected from live-trapped raccoons before and after VBU distributions. A closed population mark-recapture estimate of the size of the target population was 91 raccoons/km2, compared to the realized VBU distribution density of 70 units/km2. Surprisingly, 41% of 37 serum samples were RVNA-positive (>or=0.05 IU/ml) before VBU distribution in 2003, but all titers were <0.25 IU/ml. Although viable VBUs were distributed in August 2003, only 21% of 315 samples were RVNA-positive before VBU distribution in 2004, but 9% had titers>or=0.25 IU/ml. Tetracycline (biomarker in bait) prevalence in teeth indicated that 57% of raccoons ingested VBUs after distribution in 2003, and 54% ingested VBUs after distribution in 2004. However, only 8% and 11% of sera were positive for RVNA (>or=0.05 IU/ml) after VBU distribution in 2003 and 2004, respectively. Only 4-5% of sera collected after bait distribution had titers>or=0.25 IU/ml each year. The standard distribution density of 75 VBUs/km2 was insufficient to produce a population-wide immunoprotective response against rabies infection in our high-density target population. Presence of RVNA in a presumed naïve population before baiting demonstrates that estimating prevalence of RVNA after oral rabies vaccination can be problematic without knowledge of background titers and seasonal changes in prevalence of RVNA before and after baiting.

Sera from 936 mammalian mesopredators (Virginia opossums, gray foxes, striped skunks, hooded skunks, raccoons, a bobcat, and a red fox) were collected during 2003 and 2004 in California, Arizona, Texas, Louisiana, Ohio, and Wyoming and screened for flavivirus-specific antibodies by an epitope-blocking enzyme-linked immunosorbent assay (blocking ELISA). Serum samples positive for antibodies against flaviviruses were screened for West Nile virus (WNV)-specific antibodies by blocking ELISA and selectively confirmed with plaque-reduction neutralization tests. High prevalence rates were observed in raccoons (45.6%) and striped skunks (62.9%). The high WNV antibody prevalence noted in mesopredators, their peridomestic tendencies, and their overall pervasiveness make these species potentially useful sentinels for monitoring flaviviruses in defined areas.

Oral rabies vaccination (ORV) baiting programs for control of raccoon (Procyon lotor) rabies in the USA have been conducted or are in progress in eight states east of the Mississippi River. However, data specific to the relationship between raccoon population density and the minimum density of baits necessary to significantly elevate rabies immunity are few. We used the 22-km2 US National Aeronautics and Space Administration Plum Brook Station (PBS) in Erie County, Ohio, USA, to evaluate the period of exposure for placebo vaccine baits placed at a density of 75 baits/km2 relative to raccoon population density. Our objectives were to 1) estimate raccoon population density within the fragmented forest, old-field, and industrial landscape at PBS: and 2) quantify the time that placebo, Merial RABORAL V-RG vaccine baits were available to raccoons. From August through November 2002 we surveyed raccoon use of PBS along 19.3 km of paved-road transects by using a forward-looking infrared camera mounted inside a vehicle. We used Distance 3.5 software to calculate a probability of detection function by which we estimated raccoon population density from transect data. Estimated population density on PBS decreased from August (33.4 raccoons/km2) through November (13.6 raccoons/km2), yielding a monthly mean of 24.5 raccoons/km2. We also quantified exposure time for ORV baits placed by hand on five 1-km2 grids on PBS from September through October. An average 82.7% (SD = 4.6) of baits were removed within 1 wk of placement. Given raccoon population density, estimates of bait removal and sachet condition, and assuming 22.9% nontarget take, the baiting density of 75/ km2 yielded an average of 3.3 baits consumed per raccoon and the sachet perforated.