Preprints
Filtering by Subject: Environmental Monitoring
A protocol for using drones to assist monitoring of large breeding bird colonies
Published: 2019-04-28
Subjects: Animal Sciences, Behavior and Ethology, Biology, Ecology and Evolutionary Biology, Environmental Monitoring, Environmental Sciences, Life Sciences, Ornithology, Physical Sciences and Mathematics, Research Methods in Life Sciences, Zoology
Drones are rapidly becoming part of environmental monitoring and management applications. They provide an opportunity to improve a number of activities related to monitoring population dynamics of aggregations of wildlife. Bird surveys using drones have attracted particular attention, with a range of potential metrics able to be derived from high resolution drone imagery. Whilst a number of [...]
A continental measure of urbanness predicts avian response to local urbanization
Published: 2019-03-26
Subjects: Animal Sciences, Biodiversity, Biology, Ecology and Evolutionary Biology, Environmental Monitoring, Environmental Sciences, Life Sciences, Ornithology, Physical Sciences and Mathematics, Social and Behavioral Sciences, Urban Studies and Planning
Understanding species-specific relationships with their environment is essential for ecology, biogeography, and conservation biology. Moreover, understanding how these relationships change with spatial scale is critical to mitigating potential threats to biodiversity. But methods which measure inter-specific variation in responses to environmental parameters, generalizable across multiple spatial [...]
Monitoring large and complex wildlife aggregations with drones
Published: 2019-01-02
Subjects: Animal Sciences, Ecology and Evolutionary Biology, Environmental Monitoring, Environmental Sciences, Life Sciences, Ornithology, Physical Sciences and Mathematics, Research Methods in Life Sciences, Terrestrial and Aquatic Ecology
• Recent advances in drone technology have rapidly led to their use for monitoring and managing wildlife populations but a broad and generalised framework for their application to complex wildlife aggregations is still lacking • We present a generalised semi-automated approach where machine learning can map targets of interest in drone imagery, supported by predictive modelling for estimating [...]