Centre for Research into Ecological & Environmental Modelling and School of Mathematics & Statistics, University of St Andrews, St Andrews, Fife, Scotland
Elizabeth A. Becker
Ocean Associates, Inc. under contract to Marine Mammal and Turtle Division, Southwest Fisheries Science Center National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, United States of America
Karin A. Forney
Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Moss Landing, CA, United States of America
Jason J. Roberts
Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America
Ana Cañadas
Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America
Robert S. Schick
Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America
Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem.
