Preprints
Filtering by Subject: Physical Sciences and Mathematics
A continental measure of urbanness predicts avian response to local urbanization
Published: 2019-03-27
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 [...]
Complexity revealed in the greening of the Arctic
Published: 2019-02-04
Subjects: Ecology and Evolutionary Biology, Life Sciences, Other Life Sciences, Other Physical Sciences and Mathematics, Physical Sciences and Mathematics
As the Arctic warms, vegetation is responding and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most significant large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, [...]
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 [...]