On the move: Influence of animal movements on count error during drone surveys.

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e.

Heating decoys to mimic thermal signatures of live animals for drones.

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.

Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.

Drones (unoccupied aircraft systems) have become effective tools for wildlife monitoring and conservation. Automated animal detection and classification using artificial intelligence (AI) can substantially reduce logistical and financial costs and improve drone surveys. However, the lack of annotated animal imagery for training AI is a critical bottleneck in achieving accurate performance of AI algorithms compared to other fields.

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).

Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map.

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.

Considering the influence of land use/land cover on estuarine biotic richness with Bayesian hierarchical models.

The composition of land use/land cover (LULC) in coastal watersheds has many implications for estuarine system ecological function. Land use/land cover can influence allochthonous inputs and can enhance or degrade the physical characteristics of estuaries, which in turn affects estuaries' ability to support local biota. However, these implications for estuaries are often poorly considered when assessing the value of lands for conservation.

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.

Relationships between survival and habitat suitability of semi-aquatic mammals.

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations.

Assessing multiregion avian benefits from strategically targeted agricultural buffers.

Mounting evidence of wildlife population gains from targeted conservation practices has prompted the need to develop and evaluate practices that are integrated into production agriculture systems and targeted toward specific habitat objectives. However, effectiveness of targeted conservation actions across broader landscapes is poorly understood. We evaluated multiregion, multispecies avian densities on row-crop fields with native grass field margins (i.