A general deep learning model for bird detection in high-resolution airborne imagery.

Autor: Weinstein BG; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA., Garner L; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA., Saccomanno VR; California Oceans Program, The Nature Conservancy, Sacramento, California, USA., Steinkraus A; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA., Ortega A; Geomatics Program, University of Florida, Gainesville, Florida, USA., Brush K; Montana State University, Bozeman, Montana, USA., Yenni G; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA., McKellar AE; Environment and Climate Change Canada, Saskatoon, Saskatchewan, Canada., Converse R; Center for the Advancement of Spatial Informatics Research and Education, University of New Mexico, Albuquerque, New Mexico, USA., Lippitt CD; Center for the Advancement of Spatial Informatics Research and Education, University of New Mexico, Albuquerque, New Mexico, USA., Wegmann A; California Oceans Program, The Nature Conservancy, Sacramento, California, USA., Holmes ND; California Oceans Program, The Nature Conservancy, Sacramento, California, USA., Edney AJ; Department of Zoology, University of Oxford, Oxford, UK., Hart T; Department of Zoology, University of Oxford, Oxford, UK., Jessopp MJ; School of Biological, Earth and Environmental Sciences, University College Cork, Cork, Ireland., Clarke RH; School of Biological Sciences, Monash University, Melbourne, Victoria, Australia., Marchowski D; Ornithological Station, Museum and Institute of Zoology, Polish Academy of Sciences, Gdańsk, Poland., Senyondo H; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA., Dotson R; Quantaero, Nevada, Reno, USA., White EP; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA., Frederick P; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA., Ernest SKM; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA.

Publikováno v: Ecological applications : a publication of the Ecological Society of America [Ecol Appl] 2022 Dec; Vol. 32 (8), pp. e2694. Date of Electronic Publication: 2022 Aug 10.

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

Autor: Ke, Tsung-Wei¹ (AUTHOR) twke@berkeley.edu, Yu, Stella X.^1,2 (AUTHOR) twke@berkeley.edu, Koneff, Mark D.³ (AUTHOR) twke@berkeley.edu, Fronczak, David L.⁴ (AUTHOR), Fara, Luke J.⁵ (AUTHOR), Harrison, Travis J.⁵ (AUTHOR), Landolt, Kyle L.⁵ (AUTHOR), Hlavacek, Enrika J.⁵ (AUTHOR), Lubinski, Brian R.⁴ (AUTHOR), White, Timothy P.⁶ (AUTHOR)

Publikováno v: PLoS ONE. 4/3/2024, Vol. 19 Issue 4, p1-19. 19p.

High-resolution density assessment assisted by deep learning of Dendrophyllia cornigera (Lamarck, 1816) and Phakellia ventilabrum (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay.

Autor: Gayá-Vilar, Alberto, Cobo, Adolfo, Abad-Uribarren, Alberto, Rodríguez, Augusto, Sierra, Sergio, Clemente, Sabrina, Prado, Elena

Publikováno v: PeerJ; Mar2024, p1-23, 23p

Understanding the biology of species' ranges: when and how does evolution change the rules of ecological engagement?

Autor: Bridle, Jon, Hoffmann, Ary

Publikováno v: Philosophical Transactions of the Royal Society B: Biological Sciences; 4/11/2022, Vol. 377 Issue 1848, p1-15, 15p