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Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy

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  • Vincenzi, Simone
  • Zucchetta, Matteo
  • Franzoi, Piero
  • Pellizzato, Michele
  • Pranovi, Fabio
  • De Leo, Giulio A.
  • Torricelli, Patrizia

Abstract

We present a modelling framework that combines machine learning techniques and Geographic Information Systems to support the management of an important aquaculture species, Manila clam (Ruditapes philippinarum). We use the Venice lagoon (Italy), the first site in Europe for the production of R. philippinarum, to illustrate the potential of this modelling approach. To investigate the relationship between the yield of R. philippinarum and a set of environmental factors, we used a Random Forest (RF) algorithm. The RF model was tuned with a large data set (n=1698) and validated by an independent data set (n=841). Overall, the model provided good predictions of site-specific yields and the analysis of marginal effect of predictors showed substantial agreement among the modelled responses and available ecological knowledge for R. philippinarum. The most influent environmental factors for yield estimation were percentage of sand in the sediment, salinity, and water depth. Our results agree with findings from other North Adriatic lagoons. The application of the fitted RF model to continuous maps of all the environmental variables allowed estimates of the potential yield for the whole basin. Such a spatial representation enabled site-specific estimates of yield in different farming areas within the lagoon. We present a possible management application of our model by estimating the potential yield under the current farming distribution and comparing it to a proposed re-organization of the farming areas. Our analysis suggests a reduction of total yield is likely to result from the proposed re-organization.

Suggested Citation

  • Vincenzi, Simone & Zucchetta, Matteo & Franzoi, Piero & Pellizzato, Michele & Pranovi, Fabio & De Leo, Giulio A. & Torricelli, Patrizia, 2011. "Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy," Ecological Modelling, Elsevier, vol. 222(8), pages 1471-1478.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:8:p:1471-1478
    DOI: 10.1016/j.ecolmodel.2011.02.007
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    1. Cucco, Andrea & Umgiesser, Georg & Ferrarin, Cristian & Perilli, Angelo & Canu, Donata Melaku & Solidoro, Cosimo, 2009. "Eulerian and lagrangian transport time scales of a tidal active coastal basin," Ecological Modelling, Elsevier, vol. 220(7), pages 913-922.
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    1. So Young Woo & Chung Gil Jung & Ji Wan Lee & Seong Joon Kim, 2019. "Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique," Sustainability, MDPI, vol. 11(12), pages 1-15, June.
    2. V. Kohestani & M. Hassanlourad & A. Ardakani, 2015. "Evaluation of liquefaction potential based on CPT data using random forest," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(2), pages 1079-1089, November.
    3. Wiltshire, Kathryn H & Tanner, Jason E, 2020. "Comparing maximum entropy modelling methods to inform aquaculture site selection for novel seaweed species," Ecological Modelling, Elsevier, vol. 429(C).
    4. das Neves, Patricia Bittencourt Tavares & Blanco, Claudio José Cavalcante & Montenegro Duarte, André Augusto Azevedo & das Neves, Filipe Bittencourt Souza & das Neves, Isabela Bittencourt Souza & de P, 2021. "Amazon rainforest deforestation influenced by clandestine and regular roadway network," Land Use Policy, Elsevier, vol. 108(C).
    5. Netrananda Sahu & Pritiranjan Das & Atul Saini & Ayush Varun & Suraj Kumar Mallick & Rajiv Nayan & S. P. Aggarwal & Balaram Pani & Ravi Kesharwani & Anil Kumar, 2023. "Analysis of Tea Plantation Suitability Using Geostatistical and Machine Learning Techniques: A Case of Darjeeling Himalaya, India," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    6. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
    7. Kosicki, Jakub Z., 2017. "Should topographic metrics be considered when predicting species density of birds on a large geographical scale? A case of Random Forest approach," Ecological Modelling, Elsevier, vol. 349(C), pages 76-85.
    8. Dandan Zhao & Hong S. He & Wen J. Wang & Lei Wang & Haibo Du & Kai Liu & Shengwei Zong, 2018. "Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region," Sustainability, MDPI, vol. 10(3), pages 1-14, March.
    9. Dandan Zhao & Hong S. He & Wen J. Wang & Jiping Liu & Haibo Du & Miaomiao Wu & Xinyuan Tan, 2018. "Distribution and Driving Factors of Forest Swamp Conversions in a Cold Temperate Region," IJERPH, MDPI, vol. 15(10), pages 1-14, September.
    10. Ewa Wilk & Małgorzata Krówczyńska & Bogdan Zagajewski, 2019. "Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm," Sustainability, MDPI, vol. 11(16), pages 1-13, August.
    11. Grimmett, Liam & Whitsed, Rachel & Horta, Ana, 2020. "Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics," Ecological Modelling, Elsevier, vol. 431(C).
    12. Fukuda, Shinji & Spreer, Wolfram & Yasunaga, Eriko & Yuge, Kozue & Sardsud, Vicha & Müller, Joachim, 2013. "Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 116(C), pages 142-150.

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