Predictive mechanistic bioenergetics to model habitat suitability of shellfish culture in coastal lakes

• We apply a fully mechanistic functional trait based model.
• DEB models generate spatially-explicit predictions on fitness of M. galloprovincialis.
• We estimate faeces released by a theoretical amount of bivalves reared in each lake.
• DEB model simulations depict well data observed in the field (Sicilian lakes).

Quantitative tools based on mechanistic modelling of functional traits able to enhance the sustainability of aquaculture and most other human activities (i.e. reducing the likelihood of detrimental impacts optimising productions), are especially important factors in the decision to site aquaculture facilities in coastal lakes, ponds and lagoons and, in the case of detrimental impact, to adopt mitigation measures. We tested the ability of mechanistic functional trait based models to predict life history traits of cultivable shellfish in shallow coastal lakes. Dynamic Energy Budget (DEB) models were run to generate spatially explicit predictions of Mytilus galloprovincialis life history (LH) traits (e.g. body size and fecundity). Using fortnightly data of food supply and hourly data of body temperatures, and exploiting the power of mechanistic rules, we estimated the amount of faeces ejected by a fixed quantity of organisms cultivated in two shallow Southern Mediterranean (Sicily) lakes. These differed in terms of temperature and food density, implying large differences in life history traits of mussels in the two study areas. This information could help facilitate the selection of sites where environmental conditions are more suitable for aquaculture and contextually compatible with sustainability. The validation exercise obtained by comparing the predicted and observed data was nearly consistent. Therefore, a mechanistic functional traits-based model seems able to capture the link between habitat characteristics and functional traits of organisms, delineating the fundamental portion of an ecological niche, the possibility of predicting LH traits and potential ecological applications in the management of natural coastal resources.