Preprints
Filtering by Subject: Artificial Intelligence and Robotics
Japanese mayfly family classification with a vision transformer model
Published: 2024-02-10
Subjects: Aquaculture and Fisheries Life Sciences, Artificial Intelligence and Robotics, Biodiversity, Civil and Environmental Engineering, Computer Sciences, Databases and Information Systems, Engineering, Environmental Health and Protection, Environmental Indicators and Impact Assessment, Environmental Monitoring, Environmental Sciences, Terrestrial and Aquatic Ecology
Benthic macroinvertebrates are a frequently used indicator group for biomonitoring and biological assessment of river ecosystems. However, their taxonomic identification is laborious and requires special expertise. In this study, we aimed to assess the capability of a vision transformer (ViT) model for family-level identification of mayflies (order Ephemeroptera). Specifically, we focused on [...]
Improving ecological connectivity assessments with transfer learning and function approximation
Published: 2023-05-04
Subjects: Artificial Intelligence and Robotics, Biodiversity, Environmental Monitoring, Numerical Analysis and Computation, Other Ecology and Evolutionary Biology, Statistical Models, Sustainability
This is a conference paper presented at the ICLR 2023 "Machine Learning for Remote Sensing" workshop. Protecting and restoring ecological connectivity is essential to climate change adaptation, and necessary if species are to shift their geographic distributions to track their suitable climatic conditions over the coming century. Despite the increasing availability of near real-time and high [...]
[Final version available] Explainable Artificial Intelligence enhances the ecological interpretability of black-box species distribution models
Published: 2020-04-17
Subjects: Artificial Intelligence and Robotics, Biodiversity, Computer Sciences, Ecology and Evolutionary Biology, Life Sciences, Physical Sciences and Mathematics, Research Methods in Life Sciences, Terrestrial and Aquatic Ecology
Species distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. During the past two decades, the field has increasingly made use of machine learning approaches for constructing and validating SDMs. Model [...]