Predictability of South Australian sardine habitat distribution

Sardines play a key ecological role in many marine food webs by transferring energy from plankton through to higher trophic levels. For example, sardines are known to be an important food source for a variety of predatory... [ view full abstract ]

Sardines play a key ecological role in many marine food webs by transferring energy from plankton through to higher trophic levels. For example, sardines are known to be an important food source for a variety of predatory animals such as squid, seabirds and marine mammals. The southern Australian sardine stock constitutes the largest component of the Australian sardine population and the South Australian Sardine Fishery (SASF) is Australia’s largest commercial fishery. However, over recent years, there have been reductions in the size and age of sardines caught from this region. To understand the ecology of sardines and hence provide better strategies for management of this fishery, it is important to understand the environmental conditions that shape sardine habitat distributions. In this study, we use generalized additive models (GAMs) to examine the relationships between environmental variables and the spatial distributions of spawning and juvenile sardines. We used seven years of daily egg production method (DEPM) surveys and measures of fish size obtained from commercial catch samples during the period 2004 to 2013. We examined six predictor variables: sea surface temperature, surface salinity, surface chlorophyll-a, depth of maximum chlorophyll, mixed layer depth and bottom depth. The resulting statistical models showed a good capability for predicting the potential habitat of juvenile and spawning sardines. Values of sea surface temperature, depth and chlorophyll concentration are sufficient to provide habitat predictions with a skill of up to 81% for eggs and 85% for adults: adding values of surface salinity and the depth of maximum chlorophyll provided smaller yet significant improvements to the model. The predictive ability of the developed model could be applied to optimize commercial harvest strategies and stock management areas as well as to further understand seasonal and climatic scale shifts in sardine habitats and their inferences for regional tropho-dynamic relationships.