Citizen science aids the quantification of the distribution and prediction of present and future temporal variation in habitat suitability at species’ range edges

Abstract

Citizen science programs are effective methods to collect large volumes of data to assist researchers in monitoring ecological environments. As species shift their distributions globally due to climate change, the use of citizen science data to detect these shifts is increasing. Using targeted citizen science programs to collect data on these species could provide information on range edges to inform species distribution modelling. Currently, species distribution models (SDMs) often rely on large data repositories that may lack observations, and hence ability, to detect changes at the range edge. Here, we developed a SDM to compare traditional data repository observations with targeted citizen science data at the southern distribution limit of two recreationally important marine fish in Tasmania, Australia to investigate the potential change in spatial predictions at their range edge. The SDM using the targeted citizen science data in addition to traditional observation data improved the representation of species by 2.3 and 52.7% and increased the southern distribution of the species by 277 and 438 km, for snapper and King George whiting, respectively. Future (centred around 2050 under IPCC RCP 8.5) habitat suitability was predicted to increase more over the winter season, with implications for species overwintering and persistence of populations. The use of citizen science data allowed for the modelling of historical and future change for two range-extending species, an outcome possible due to the collaboration of two citizen science programs that collected observational data on the target species. Species range shifts will require ongoing monitoring and we have demonstrated that complimentary citizen science initiatives are effective in capturing occurrences of species at their range edge. Increasing collaboration between programs may further increase data collection efforts and provide the knowledge to create a hub for these data to be used more efficiently in the future.