Salp distribution in coastal, subtropical, and subantarctic waters off the Southeast coast of New Zealand: linking hydrology with ecosystem function

Within only 65km from the Southeastern New Zealand coastline, we find four distinct water masses: neritic, subtropical, frontal, and subantarctic. Each of these water masses have unique oceanographic properties that have been well studied, but our knowledge of zooplankton biogeography in this region is nearly non-existent. Because salps can dramatically alter trophic pathways and carbon export during bloom events, we characterized the distribution of salps and other associated zooplankton in this region over one year. Every two months from Nov. 2017 to Sept. 2018 we collected surface zooplankton (<200m) at each of eight stations along an established transect (Munida Transect). We counted and identified 13 major zooplankton taxonomic groups and identified all salps to species-level. Salps were present in November, January, March, and September, but not May or July. In the months when salps were present, we identified solitary and aggregate individuals of salp species Thalia democratica, Salpa fusiformis, and Soestia zonaria in our samples. Maximum salp biomass occurred in March 2018, in which T. democratica contributed >2 mg C/m³ and comprised nearly half the total zooplankton biomass in neritic water samples. Multivariate analyses associated T. democratica with neritic waters, S. fusiformis with subtropical waters, and S. zonaria with subantarctic waters. Community composition analyses also revealed the presence of T. democratica strongly correlated with the presence of small Calanoid copepods (<1mm) and the absence of larger ones. These data are the first to provide quantitative estimates of salp distribution in this unique region in over forty years. Our results provide a link between the physical environment and the ecological impacts of salps and their associated zooplankton, a necessary step to not only better understand current ecosystem functioning, but also to predict and recognize ecosystem shifts induced by future environmental changes.