Integrating mechanistic models and climate change projections to predict invasion of the mussel, Mytilopsis sallei, along the southern China coast

Abstract

Species invasion is an important cause of global biodiversity decline and is often mediated by shifts in environmental conditions such as climate change. To investigate this relationship, a mechanistic Dynamic Energy Budget model (DEB) approach was used to predict how climate change may affect spread of the invasive mussel Mytilopsis sallei, by predicting variation in the total reproductive output of the mussel under different scenarios. To achieve this, the DEB model was forced with present-day satellite data of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a), and SST under two warming RCP scenarios and decreasing current Chl-a levels, to predict future responses. Under both warming scenarios, the DEB model predicted the reproductive output of M. sallei would enhance range extension of the mussel, especially in regions south of the Yangtze River when future declines in Chl-a were reduced by less than 10%, whereas egg production was inhibited when Chl-a decreased by 20–30%. The decrease in SST in the Yangtze River may, however, be a natural barrier to the northward expansion of M. sallei, with colder temperatures resulting in a strong decrease in egg production. Although the invasion path of M. sallei may be inhibited northwards by the Yangtze River, larger geographic regions south of the Yangtze River run the risk of invasion, with subsequent negative impacts on aquaculture through competition for food with farmed bivalves and damaging aquaculture facilities. Using a DEB model approach to characterise the life history traits of M. sallei, therefore, revealed the importance of food availability and temperature on the reproductive output of this mussel and allowed evaluation of the invasion risk for specific regions. DEB is, therefore, a powerful predictive tool for risk management of already established invasive populations and to identify regions with a high potential invasion risk.