Cover Picture and Issue Information

Abstract

Methods in Ecology and EvolutionVolume 10, Issue 11 p. 1829-1831 COVER PICTURE AND ISSUE INFORMATIONFree Access Cover Picture and Issue Information First published: 04 November 2019 https://doi.org/10.1111/2041-210X.13054AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract This month’s cover image was taken during the 2015 aerial animal survey in the Laikipia-Samburu Ecosystem (Kenya). Traditional aerial animal counts require a human observer to count all the animals from an airplane between two horizontally aligned bars, while the pilot tries to keep flying in a straight line at the same altitude. To verify that the number of giraffe in this group has been counted correctly, the observer also took pictures of large animal groups to double check the number of counted individuals back on the ground. In the related article, Eikelboom et al. used these images to train a Convolutional Neural Network to automatically detect the elephants, giraffes and zebras in the images. Their algorithm counted between 90% and 95% of the number of these species that were counted by the human observers, of which it correctly detected an extra 2.8 to 4.0% of animals that were missed by all human observers. Their results indicate that the accuracy and precision of animal population estimates can be improved when traditional aerial animal counts are replaced by more cost-efficient semi-automatic aerial counts. Furthermore, when the aerial images are taken at a fixed rate, Eikelboom et al. posit that their animal detection algorithm will outperform human observers in the counting of animals. Photo credit: © L.M. Kenana, Kenya Wildlife Service Volume10, Issue11November 2019Pages 1829-1831 RelatedInformation