In this trail camera photo, provided by Oregon State University undergraduate student Owen S Okuley, bighorn sheep are seen at the Kerr Guzzler in the Mojave Desert. Oregon State University scientists have improved artificial intelligence's ability to identify wildlife species in photos taken by motion-activated cameras. Their study, which introduces a less-is-more approach to the data on which an AI model is trained, opens the door to wildlife image analysis that's more accurate and also more cost effective. Motion-activated cameras are an important wildlife monitoring tool, but reviewing thousands of images manually can be prohibitively time consuming, and current AI models are at times too inaccurate to be useful for scientists and wildlife managers. "One of the biggest problems in using AI in wildlife research is limited accuracy when we use the model to classify images at a novel location – one the model has never'seen' before," said study co-author Christina Aiello, a research associate in the Oregon State University College of Agricultural Sciences.

ECOLOGICAL INFORMATICS FOR SUSTAINABLE DEVELOPMENT GOALS Environmental Policy Analytics Scalable environmental start-up initiatives Communicating Science and Informing decision Futurecasting research and education in Ecological Informatics We envisage the three themes to be both independent as well as interdependent. Note: 1- The topics listed under each theme are only indicative. The organizers will be glad to accommodate relevant topics that are not indicated at present. In addition to expanding the scope by adding new themes, we look forward to building upon and take forward theme(s) initiated in Jena. Responsibilities of session chair: As a proposer of an accepted ICEI 2020 1 session, you will become the session chair.