, Volume 724, Issue 1, pp 267–277 | Cite as

Rapid estimation of potential yield for data-poor Tapes philippinarum fisheries in North Adriatic coastal lagoons

  • Simone Vincenzi
  • Giulio A. De Leo
  • Cristina Munari
  • Michele Mistri
Primary Research Paper


We show how a simple species distribution model can be used for the rapid estimation of potential yield and for the identification of suitable sites for farming of Tapes philippinarum in two North Adriatic lagoons (Caleri and Marinetta-Vallona, Italy) in the face of limited data. We used a two-part species distribution model with sediment type, hydrodynamism, dissolved oxygen, and salinity as predictors of T. philippinarum potential yield. The first model component uses logistic regression to identify the areas in which clams occur, while the second component uses a weighted geometric mean of suitability values to estimate the potential annual yield (kg m−2 year−1) for the sites where T. philippinarum is predicted to be present. We used site-specific yield data from Caleri and Marinetta-Vallona to estimate the weights of the geometric mean by constrained linear regression. We validated the two-part model on an independent set of yield data (R adj 2  = 0.82), and we then estimated the spatial distribution of potential yield in the two lagoons. The calibration and application of a simple species distribution model are useful tools for objectively identifying the most suitable sites for farming of T. philippinarum in North Adriatic lagoons.


Data-poor system Aquaculture Two-part model Clam farming Species distribution model 



Simone Vincenzi is supported by an IOF Marie Curie Fellowship FP7-PEOPLE-2011-IOF for the project “RAPIDEVO” on rapid evolutionary responses to climate change in natural populations.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Simone Vincenzi
    • 1
    • 2
  • Giulio A. De Leo
    • 3
  • Cristina Munari
    • 4
  • Michele Mistri
    • 4
  1. 1.Center for Stock Assessment ResearchNational Marine Fisheries ServiceSanta CruzUSA
  2. 2.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanItaly
  3. 3.Hopkins Marine StationStanford UniversityPacific GroveUSA
  4. 4.Department of Life Sciences and BiotechnologiesUniversity of FerraraFerraraItaly

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