The analysis of presence-only data is a problem in determining species distributions and accurately determining population sizes. The collection of such data is common from unequal or nonrandomised effort surveys, such as those surveys conducted by citizen scientists. However, causative regression-based methods have been less well examined using presence-only data. In this study, we examine a range of predictive factors which might influence Cetacean sightings (specifically minke whale sightings) from whale-watching vessels in Faxaflói Bay in Iceland. In this case, environmental variables were collected regularly regardless of whether sightings were recorded. Including absences as well as presence in the analysis resulted in a multiple-generalised linear regression model with significantly more explanatory power than when data were presence only. However, by including extra information on the sightings of the whales, in this case, their observed behaviour when the sighting occurred resulted in a significantly improved model over the presence-only data model. While there are limitations of conducting nonrandomised surveys for the use of predictive models such as regression, presence-only data should not be considered as worthless, and the scope of collection of these data by citizen scientists using modern technology should not be underestimated.