• Insights into the quantification and reporting of model-related uncertainty across different disciplines 

      Simmonds, Emily Grace; Dunn-Sigouin, Etienne; Adjei, Kwaku Peprah; Andersen, Christoffer Wold; Aspheim, Janne Cathrin Hetle; Battistin, Claudia; Bulso, Nicola; Christensen, Hannah M.; Cretois, Benjamin; Cubero, Ryan John Abat; Davidovich, Ivan Andres; Dickel, Lisa; Dunn, Benjamin Adric; Dyrstad, Karin; Einum, Sigurd; Giglio, Donata; Gjerløw, Haakon; Godefroidt, Amélie; González-Gil, Ricardo; Gonzalo Cogno, Soledad; Große, Fabian; Halloran, Paul; Jensen, Mari Fjalstad; Kennedy, John James; Langsæther, Peter Egge; Laverick, Jack H; Lederberger, Debora; Li, Camille; Mandeville, Elizabeth G; Mandeville, Caitlin; Moe, Espen; Schröder, Tobias Navarro; Nunan, David; Sicacha-Parada, Jorge; Simpson, Melanie Rae; Skarstein, Emma Sofie; Spensberger, Clemens; Stevens, Richard; Subramanian, Aneesh C.; Svendsen, Lea; Theisen, Ole Magnus; Watret, Connor; O'Hara, Robert B. (Peer reviewed; Journal article, 2022)
      Quantifying uncertainty associated with our models is the only way we can ex- press how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with ...
    • Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld’s deer 

      Bowler, DIana E.; Nilsen, Erlend B.; Bischof, Richard; O'Hara, Robert B.; Yu, Thin Thin; Oo, Tun; Aug, Myint; Linnell, John D.C. (Peer reviewed; Journal article, 2019)
      Despite its value for conservation decision-making, we lack information on population abundances for most species. Because establishing large-scale monitoring schemes is rarely feasible, statistical methods that combine ...