Estimating clam yield potential in the Sacca di Goro lagoon (Italy) by using a two-part conditional model

The assessment of clam yield potential and the identification of suitable sites for clam rearing are a necessary step to improve the economic and environmental sustainability of the exploitation activities in a regulated fishery. We discuss the development, validation and application of a method for estimating the clam yield potential of a northern Adriatic lagoon (Sacca di Goro, Italy) by combining logistic and ordinary regression. Clam yield potential was derived on the basis of six environmental parameters, namely sediment type, dissolved oxygen, salinity, hydrodynamism, water depth and chlorophyll "a". Density data were positively skewed and contained a substantial proportion of zero values due to the patchy-distribution of Tapes philippinarum. The original data set was divided in two parts: one indicating if T. philippinarum was present or not and the other indicating the abundance of the species when it was present. The presence data was modelled by using logistic regression and the abundance data was separately modelled by using ordinary regression. The two models were then combined to identify suitable sites, to compute the expected clam yield potential in the Goro lagoon and to define the role of each environmental parameter in determining clam presence and abundance. The two-part model was then validated on a further data set (R2 = 0.87). Data on environmental parameters gathered in 15 sampling sites were interpolated via a nearest neighbour algorithm so as to derive maps of each environmental parameter for the whole lagoon. Finally, the two-part model was applied to derive the thematic maps of suitable sites for clam rearing and the corresponding yield potential. We claim that this reasonably rapid and cost-effective approach provides a rigorous framework for a fair partition of harvesting concessions among competitive users and for a substantial improvement of transparency in the decision-making process.