Using species distribution models to assess the long-term impacts of changing oceanographic conditions on abalone density in south east Australia

Abstract

Warming from climate change and resulting increases in energy stored in the oceans is causing changes in the hydrodynamics and biogeochemistry of marine systems, exacerbating current challenges facing marine fisheries. Although studies have evaluated effects of rising temperatures on marine species, few have looked at these impacts along with other environmental drivers over long time periods. In this study, we associate long-term density of blacklip abalone to changing oceanographic conditions in a climate change ‘hot-spot’ off southeast Australia. We downscaled and hind-casted existing hydrodynamic models to provide information on waves and currents over 25 yr and used this information to run biophysical connectivity models. We combined the connectivity models with 21 yr of data on abalone density, temperature, seafloor habitat, and the effects of a disease outbreak in an machine learning modeling approach to develop a spatio-temporal model of abalone density. We found that the combination of temperature, connectivity, current speed, wave orbital velocity, fishery catch, depth, reef structure and a disease outbreak explain 70% of variation in abalone density and allowed us to create 30 m resolution predictive grids with 75% accuracy. An emerging hotspot analysis run on the individual predictive grids from each year detected a predominance of low-density grids across the region, with 49.5% of cells classified as cold spots, 14.3% as hotspots and 36.2% with no significant patterns observed. This type of spatio-temporal analysis provides important insights into how changing environmental conditions are impacting density in an important fishery species, allowing for better adaptive management in the face of future climate change.