Mapping cetacean distribution using citizen science in the Western Cape South Africa

The Western Cape is a unique area to undertake research, due to the varying oceanographic conditions along the coast. These diverse environments create a hotspot for cetacean presence and diversity. This study aimed to collate and map distribution information of local whale and dolphin species in the Western Cape using citizen science. The first data chapter focused on obtaining opportunistic sightings from water users from scientific, platform of opportunity and sporadic sighting platforms including social media. The second data chapter focussed on obtaining local knowledge on cetacean species by interviewing experienced water users such as fisherman, divers and conservationists on local cetacean presence. Both of these methods were aimed at trying to answer the same question which is to understand where the species range boundaries are and what the potential drivers are. Results between the two chapters yielded congruent information which validated the use of citizen science in this context. Most of the results obtained supported existing knowledge of the species’ distribution range in South Africa. Results identified a seasonal pattern whereby common dolphin presence peaked during autumn in False Bay and is probably linked to bait fish availability. False Bay was highlighted as an important habitat area for dolphins and whales, but most notably for common dolphins and Bryde’s whales. Possible range shifts of humpback dolphins and bottlenose dolphins are also suggested. It is important to monitor these local cetacean populations and any current or potential anthropogenic threats. The abundance and distribution of prey species was also suggested as an important factor for cetacean distribution. The limited knowledge and research of cetaceans in South Africa clearly highlights the need for the on-going collection of opportunistic sightings data. Future research could focus on comparing cetacean distribution data to accurate environmental data to identify any potential patterns as well as investigate distribution patterns in data scarce areas.