Dear colleagues, My co-authors and I are pleased to announce our recent publication in Ecological Modelling: Pavanato HJ, Wedekin LL, Guilherme-Silveira FR, Engel MH, Kinas PG. 2017. Estimating humpback whale abundance using hierarchical distance sampling. Ecological Modelling, 358: 10-18. https://doi.org/10.1016/j. ecolmodel.2017.05.003 Highlights - A hierarchical Bayesian framework is proposed to model distance sampling data. - The method accounts for covariate effects to variation on density. - Approach useful for evaluating whether concentration areas change across years. - Humpback whale density was higher at 65–86 km from the shore. - Results suggest population is reaching its carrying capacity. Abstract We developed a Bayesian distance sampling analysis using a hierarchically structured model parameterization to estimate humpback whale abundance in the Southwest Atlantic Ocean (Breeding Stock A). We included covariates that affect detection (altitude and sighting cue) and occurrence probability (year and distance from shore). Population sizes for 2008, 2011 and 2015 were estimated to be 7,689 (P.I.95% = 6,585–8,931), 8,652 (P.I.95% = 7,696–9,682), and 12,123 (P.I.95% = 10,811–13,531), respectively. The results indicate an aggregation of humpback whales in an intermediate distance from shoreline, an increasing in density from 2008 to 2001 and a substantial overlap between posterior distributions of density for 2011 and 2015, which suggests a stabilization of population growth over the last year. Our parameterization provided a clear view of observational and ecological processes and illustrates that the Bayesian hierarchical line transect approach provides a flexible tool to account for and evaluate various sources of uncertainty. The article can be downloaded here: https://authors.elsevier.com/a /1V56115DJ~pjqM or you can email hpavanato@maths.otago.ac.nz to request a copy or further inquiries. Best regards, Heloise