Advancing Marine Species Density Surface Modelling. Webinar – 1st June 1100-1330 EST Following on from two successful workshops held at the Society for Marine Mammalogy Conferences in Halifax and Barcelona, this 2.5 hour webinar will provide a final summary of advancements made by the LMR-funded DenMod project team over the last four years on analytical methods for density surface modelling. To register for the webinar please follow this link*: https://forms.office.com/r/2Th931Pa9n<https://urldefense.com/v3/__https:/forms.office.com/r/2Th931Pa9n__;!!OToaGQ!_hrISWBY2i0PJT3S1IzJnhh7CGcNlKsGbhPikppWWCZ3yhKGpNe0gkD_cK62mLY$> A link to join the webinar will be sent out to all registrants one week in advance of the event. Further information: Density surface models attempt to describe animal distribution as a function of spatially referenced variables. Data typically come from visual line transect surveys, though other effort-indexed methods (i.e., passive acoustics) are emerging. Outputs include modelled relationships between density and explanatory variables, maps of population density, and stratified estimates of population size with uncertainty. These are of practical use for marine spatial planning and impact assessment. Many statistical approaches are available for density surface modeling. This seminar is being hosted by those involved with the LMR-funded research project, DenMod, that has compared different density surface modelling approaches, identified common ground and outstanding issues over the last four years. This will be the final public dissemination event of the DenMod project and will follow workshops held in Halifax (2017) and Barcelona (2019). This webinar is relevant to researchers who build density surface models and those who use DSM-predicted abundance and uncertainty estimates in their work. Format: The webinar will be a MS Teams Live event with a series of presentations and time for a moderated Q&A session. *Participant information will not be held beyond the date of the webinar other than summary statistics on categories of participant affiliations (e.g. academic, NGO, government). All information will be held securely and not shared with other parties.