Floodplain forests drive fruit-eating fish diversity at the Amazon Basin-scale.

Unlike most rivers globally, nearly all lowland Amazonian rivers have unregulated flow, supporting seasonally flooded floodplain forests. Floodplain forests harbor a unique tree species assemblage adapted to flooding and specialized fauna, including fruit-eating fish that migrate seasonally into floodplains, favoring expansive floodplain areas. Frugivorous fish are forest-dependent fauna critical to forest regeneration via seed dispersal and support commercial and artisanal fisheries.

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.

Warming waters lead to increased habitat suitability for juvenile bull sharks (Carcharhinus leucas).

Coastal ecosystems are highly vulnerable to the impacts of climate change and other stressors, including urbanization and overfishing. Consequently, distributions of coastal fish have begun to change, particularly in response to increasing temperatures linked to climate change. However, few studies have evaluated how natural and anthropogenic disturbances can alter species distributions in conjunction with geophysical habitat alterations, such as changes to land use and land cover (LU/LC).

Optimising verbal fluency analysis in neurological patients with dysarthria: examples from Parkinson's disease and hereditary ataxia.

Background: Verbal fluency tests (VFTs) are widely used to assess cognitive-linguistic performance in neurological diseases. However, the influence of dysarthria on performance in tests requiring oral responses is unclear in ataxia and Parkinson's disease.

Objectives: To determine the impact of dysarthria on VFT performance and evaluate the validity and reliability of alternative methods for analyzing VFT data.

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.