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Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea

  • Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. & Kent, J. M. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Cuttelod, A., García, V., Abdul Malak, D., Temple, H. & Katariya, V. The Mediterranean: A biodiversity hotspot under threat. In Wildl. a Chang. World an Anal. 2008 IUCN Red List Threat. Species 89–101 (2008).

  • Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS ONE 5, e11842–e11842 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Coll, M. et al. The Mediterranean Sea under siege: Spatial overlap between marine biodiversity, cumulative threats and marine reserves. Glob. Ecol. Biogeogr. 21, 465–480 (2012).

    Article 

    Google Scholar 

  • Micheli, F. et al. Cumulative human impacts on mediterranean and black sea marine ecosystems: Assessing current pressures and opportunities. PLoS ONE 8, e79889 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lejeusne, C., Chevaldonné, P., Pergent-Martini, C., Boudouresque, C. F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25, 250–260 (2010).

    Article 
    PubMed 

    Google Scholar 

  • Tsirintanis, K. et al. Bioinvasion impacts on biodiversity, ecosystem services, and human health in the Mediterranean Sea. Aquatic Invasions, 17(3), 308–352 (2022).

    Article 

    Google Scholar 

  • Sanderson, C. E. & Alexander, K. A. Unchartered waters: Climate change likely to intensify infectious disease outbreaks causing mass mortality events in marine mammals. Glob. Chang. Biol. 26, 4284–4301 (2020).

    Article 
    PubMed 

    Google Scholar 

  • EEC, 1992. European Commission. In EU Council Directive 92/43/EEC on the Conservationof Natural Habitats and of Wild Fauna and Flora. Orkesterjournalen L 7–50 206 (1992).

  • Bearzi, G. Interactions between cetacean and fisheries in the Mediterranean Sea. In: G. Notarbartolo di Sciara (Ed.), Cetaceans of the Mediterranean and Black Seas: state of knowledge and conservation strategies. A report to the ACCOBAMS Secretariat, Monaco, 9, 20 (2002).

  • Reeves, R. R., Smith, B. D., Crespo, E. A. & Notarbartolo di Sciara, G. Dolphins, Whales and Porpoises : 2002–2010 Conservation Action Plan for the world’s Cetaceans (2003).

  • Dolman, S., Evans, P., Ritter, F., Simmonds, M. & Swabe, J. Implications of new technical measures regulation for cetacean bycatch in European waters. Mar. Policy 124, 1043 (2020).

    Google Scholar 

  • Carlucci, R. et al. Managing multiple pressures for cetaceans’ conservation with an Ecosystem-Based Marine Spatial Planning approach. J. Environ. Manage. 287, 112240 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Carlucci, R. et al. Assessment of cetacean–fishery interactions in the marine food web of the Gulf of Taranto (Northern Ionian Sea, Central Mediterranean Sea). Rev. Fish Biol. Fish. 31, 135–156 (2020).

    Article 

    Google Scholar 

  • Fossi, C. & Lauriano, G. Impacts of shipping on the biodiversity of large marine vertebrates: Persistent organic pollutants, sewage and debris. Marit. Traffic Eff. Biodivers. Mediterr. Sea Rev Impacts Prior. Areas Mitig. Meas. 3, 65–73 (2008).

    Google Scholar 

  • Cardellicchio, N. Persistent contaminants in dolphins: An indication of chemical pollution in the mediterranean sea. Water Sci. Technol. 32, 331–340 (1995).

    Article 
    CAS 

    Google Scholar 

  • Fossi, M. C., Panti, C., Baini, M. & Lavers, J. L. A review of plastic-associated pressures: Cetaceans of the Mediterranean Sea and Eastern Australian Shearwaters as case studies. Front. Mar. Sci. 5, 125 (2018).

    Article 

    Google Scholar 

  • Marsili, L., Jiménez, B. & Borrell, A. Persistent Organic Pollutants in Cetaceans Living in a Hotspot Area (Elsevier, 2018).

    Book 

    Google Scholar 

  • Dolman, S. J., Evans, P. G. H., Notarbartolo-di-Sciara, G. & Frisch, H. Active sonar, beaked whales and European regional policy. Mar. Pollut. Bull. 63, 27–34 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • di Sciara, G. N. et al. Place-based approaches to marine mammal conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 85–100 (2016).

    Article 

    Google Scholar 

  • Holcer, D., Fortuna, C. M., Mackelworth, P., Cebrian, D. & Requena Moreno, S. Adriatic Sea: Important Areas for Conservation of Cetaceans, Sea Turtles and Giant Devil Rays (2015).

  • Carlucci, R. et al. Modeling the spatial distribution of the striped dolphin (Stenella coeruleoalba) and common bottlenose dolphin (Tursiops truncatus) in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). Ecol. Indic. 69, 707–721 (2016).

    Article 

    Google Scholar 

  • Carlucci, R., Ricci, P., Cipriano, G. & Fanizza, C. Abundance, activity and critical habitat of the striped dolphin Stenella coeruleoalba in the Gulf of Taranto (northern Ionian Sea, central Mediterranean Sea). Aquat. Conserv. Freshw. Ecosyst. 28, 324–336 (2018).

    Article 

    Google Scholar 

  • Carlucci, R. et al. Random Forest population modelling of striped and common-bottlenose dolphins in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). Estuar. Coast. Shelf Sci. 204, 177–192 (2018).

    Article 

    Google Scholar 

  • Arcangeli, A., Campana, I. & Bologna, M. A. Influence of seasonality on cetacean diversity, abundance, distribution and habitat use in the western Mediterranean Sea: Implications for conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 995–1010 (2017).

    Article 

    Google Scholar 

  • Panigada, S. et al. Estimating cetacean density and abundance in the Central and Western Mediterranean Sea through aerial surveys: Implications for Management. Deep. Res. Part II-Top. Stud. Oceanogr. 141, 41–58 (2017).

    Article 

    Google Scholar 

  • Mannocci, L. et al. Assessing cetacean surveys throughout the Mediterranean Sea: A gap analysis in environmental space. Sci. Rep. 8, 1 (2018).

    Article 
    CAS 

    Google Scholar 

  • Panigada, S. et al. Estimates of Abundance and Distribution of Cetaceans, Marine Mega-Fauna and Marine Litter in the Mediterranean Sea from 2018–2019 surveys. ACCOBAMS vol. ACCOBAMS S (2021).

  • Paiu, R.-M. et al. Estimates of abundance and distribution of cetaceans in the Black Sea from 2019 surveys. ACCOBAMS 54, 45 (2021).

    Google Scholar 

  • Azzolin, M. et al. Spatial distribution modelling of striped dolphin (Stenella coeruleoalba) at different geographical scales within the EU Adriatic and Ionian Sea Region, central-eastern Mediterranean Sea. Aquat. Conserv. Freshw. Ecosyst. 30, 1194–1207 (2020).

    Article 

    Google Scholar 

  • Renò, V. et al. A SIFT-based software system for the photo-identification of the Risso’s dolphin. Ecol. Inform. 50, 95–101 (2019).

    Article 

    Google Scholar 

  • Maglietta, R. et al. DolFin: an innovative digital platform for studying Risso’s dolphins in the Northern Ionian Sea (North-eastern Central Mediterranean). Sci. Rep. 8, 17185 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hammond, P. S. et al. Estimating the abundance of marine mammal populations. Front. Mar. Sci. 8, 96 (2021).

    Article 

    Google Scholar 

  • Fontaine, M. C. et al. History of expansion and anthropogenic collapse in a top marine predator of the Black Sea estimated from genetic data. Proc. Natl. Acad. Sci. 109, E2569–E2576 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Alter, S. E., Rynes, E. & Palumbi, S. R. DNA evidence for historic population size and past ecosystem impacts of gray whales. Proc. Natl. Acad. Sci. 104, 15162–15167 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chavez-Rosales, S., Palka, D. L., Garrison, L. P. & Josephson, E. A. Environmental predictors of habitat suitability and occurrence of cetaceans in the western North Atlantic Ocean. Sci. Rep. 9, 5833 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Buckland, S. et al. Introduction to Distance Sampling: Estimating Abundance of Biological Populations (Oxford University Press, 2001).

    MATH 

    Google Scholar 

  • Buckland, S. T. et al. Advanced Distance Sampling: Estimating Abundance of Biological Populations (OUP Oxford, 2004).

    MATH 

    Google Scholar 

  • Laake, J. S. T., Buckland, E. A., Rexstad, T. A., Marques, C. S. & Oedekoven, F. Distance sampling: Methods and applications. Biometrics 72, 1389–1390 (2016).

    Article 

    Google Scholar 

  • Hammond, P. S., Mizroch, S. A. & Donovan, G. P. Individual recognition of cetaceans: Use of photo-identification and other techniques to estimate population parameters. In Incorporating the Proceedings of the Symposium and Workshop on Individual Recognition and the Estimation of Cetacean Population Parameters (1990).

  • Sandercock, B. K. Handbook of capture-recapture analysis. Biometrics 62, 1276–1277 (2006).

    Article 

    Google Scholar 

  • Hammond, P. S. Mark-Recapture. In Encyclopedia of Marine Mammals (Third Edition) (eds Würsig, B. et al.) 580–584 (Academic Press, 2018).

  • Pless, E., Saarman, N. P., Powell, J. R., Caccone, A. & Amatulli, G. A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data. Proc. Natl. Acad. Sci. 118, 9 (2021).

    Article 

    Google Scholar 

  • Belanger, C. L. et al. Global environmental predictors of benthic marine biogeographic structure. Proc. Natl. Acad. Sci. 109, 14046–14051 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Frainer, A. et al. Climate-driven changes in functional biogeography of Arctic marine fish communities. Proc. Natl. Acad. Sci. USA 114, 12202–12207 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Miller, D. L., Burt, M. L., Rexstad, E. A. & Thomas, L. Spatial models for distance sampling data: Recent developments and future directions. Methods Ecol. Evol. 4, 1001–1010 (2013).

    Article 

    Google Scholar 

  • Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography (Cop.) 43, 1261–1277 (2020).

    Article 

    Google Scholar 

  • Redfern, J. V. et al. Techniques for cetacean-habitat modeling. Mar. Ecol. Prog. Ser. 310, 271–295 (2006).

    Article 

    Google Scholar 

  • Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Taylor & Francis, 1990).

    MATH 

    Google Scholar 

  • Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).

    MATH 

    Google Scholar 

  • Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

    Article 
    MathSciNet 
    MATH 

    Google Scholar 

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article 
    MATH 

    Google Scholar 

  • Vapnik, N.V. Statistical Learning Theory (1998).

  • Culley, C., Vijayakumar, S., Zampieri, G. & Angione, C. A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. Proc. Natl. Acad. Sci. 117, 18869–18879 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Moore, B. M. et al. Robust predictions of specialized metabolism genes through machine learning. Proc. Natl. Acad. Sci. 116, 2344–2353 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Renò, V. et al. Combined color semantics and deep learning for the automatic detection of dolphin dorsal fins. Electronics 9, 75 (2020).

    Article 

    Google Scholar 

  • Maglietta, R., Milella, A., Caccia, M. & Bruzzone, G. A vision-based system for robotic inspection of marine vessels. Signal Image Video Process. 12, 471–478 (2018).

    Article 

    Google Scholar 

  • Maglietta, R. et al. Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm. Pattern Anal. Appl. 19, 579–591 (2016).

    Article 
    MathSciNet 
    PubMed 

    Google Scholar 

  • Ancona, N., Maglietta, R. & Stella, E. Data representations and generalization error in kernel based learning machines. Pattern Recognit. 39, 1588–1603 (2006).

    Article 
    MATH 

    Google Scholar 

  • Martín, B., González-Arias, J. & Vicente-Virseda, J. A. Machine learning as a successful approach for predicting complex spatial temporal patterns in animal species abundance. Anim. Biodivers. Conserv. 2021, 25 (2021).

    Google Scholar 

  • Dimauro, G. et al. A novel approach for biofilm detection based on a convolutional neural network. Electronics 9, 88 (2020).

    Article 

    Google Scholar 

  • Inglese, P. et al. Multiple RF classifier for the hippocampus segmentation: Method and validation on EADC-ADNI Harmonized Hippocampal Protocol. Phys. Med. 31(8), 1085–1091 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Maglietta, R. et al. Convolutional neural networks for Risso’s Dolphins identification. IEEE Access 8, 80195–80206 (2020).

    Article 

    Google Scholar 

  • Conference on Biological Diversity—Nagoya 2010 European Parliament resolution of 7 October 2010 on the EU strategic objectives for the 10th Meeting of the Conference of the Parties to the Convention on Biological Diversity (CBD), to be held in Nagoya (2010).

  • EU. In Commission Decision (EU) 2017/848 of 17 May 2017 Laying Down Criteria and Methodological Standards on Good Environmental Status of Marine Waters and Specifications and Standardised Methods for Monitoring and Assessment, and Repealing Decision 2 (2017).

  • European Commission. Directive 2014/89/EU of the European Parliament and of the Council of 23 July 2014 establishing a framework for maritime spatial planning. In Off. J. Eur. Union 2014, L 257, 135; MSFD (2008/56/EC) (2014).

  • Muckenhirn, A., Baş, A. A. & Richard, F.-J. Assessing the influence of environmental and physiographic parameters on common bottlenose dolphin (Tusiops truncatus) distribution in the southern Adriatic Sea. In Proc. 1st Int. Electron. Conf. Biol. Divers. Ecol. Evol. (2021).

  • Correia, A. et al. Predicting Cetacean Distributions in the Eastern North Atlantic to Support Marine Management. Front. Mar. Sci. 8, 256 (2021).

    Article 

    Google Scholar 

  • Redfern, J. V., Barlow, J., Ballance, L. T., Gerrodette, T. & Becker, E. A. Absence of scale dependence in dolphin-habitat models for the eastern tropical Pacific Ocean. Mar. Ecol. Prog. Ser. 363, 1–14 (2008).

    Article 

    Google Scholar 

  • Kruse, S. L. Aspects of the Biology, Ecology, and Behavior of Risso’s dolphins (Grampus griseus) off the California Coast (University of California, Santa Cruz, 1989).

  • Kruse, S., Caldwell, D. K., Caldwell, M. C., Ridgway, S. H. & Harrison, R. Risso’s dolphin Grampus griseus (G. Cuvier, 1812). Handb. Mar. Mamm. Sec. B Dolphins Porpoises 6, 12 (1999).

    Google Scholar 

  • Gómez-de-Segura, A., Hammond, P. S. & Raga, J. A. Influence of environmental factors on small cetacean distribution in the Spanish Mediterranean. J. Mar. Biol. Assoc. UK 88, 1185–1192 (2008).

    Article 

    Google Scholar 

  • Pitchford, J. et al. Predictive spatial modelling of seasonal bottlenose dolphin (Tursiops truncatus) distributions in the Mississippi Sound: Seasonal spatial distributions of bottlenose dolphins. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 289–306 (2015).

    Article 

    Google Scholar 

  • La Manna, G., Ronchetti, F. & Sarà, G. Predicting common bottlenose dolphin habitat preference to dynamically adapt management measures from a Marine Spatial Planning perspective. Ocean Coast. Manag. 130, 317–327 (2016).

    Article 

    Google Scholar 

  • Becker, E. A. et al. Predicting cetacean abundance and distribution in a changing climate. Divers. Distrib. 25, 626–643 (2019).

    Article 

    Google Scholar 

  • Cañadas, A. & Hammond, P. S. Abundance and habitat preferences of the short-beaked common dolphin Delphinus delphis in the southwestern Mediterranean: Implications for conservation. Endanger. Spec. Res. 4, 309–331 (2008).

    Article 

    Google Scholar 

  • Mannocci, L. et al. Predicting cetacean and seabird habitats across a productivity gradient in the South Pacific gyre. Prog. Oceanogr. 120, 383–398 (2014).

    Article 

    Google Scholar 

  • Carretta, J. V. Estimates of Marine Mammal, Sea Turtle, and Seabird Bycatch in the California Large-Mesh Drift Gillnet Fishery: 1990–2019 U.S. Department of Commerce, NOAA Technical Memorandum NMFS-SWFSC-654.
    https://doi.org/10.25923/7emj-za90 (2021).

  • Rustam, F. et al. A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. PLoS ONE 16, e0245909 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • D’Addabbo, A. & Maglietta, R. Parallel selective sampling method for imbalanced and large data classification. Pattern Recognit. Lett. 62, 61–67 (2015).

    Article 

    Google Scholar 

  • Dimauro, G. et al. An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset. Artif. Intell. Med. 136, 102477 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Spooner, A. et al. A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction. Sci. Rep. 10, 20410 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Becker, E. A. et al. Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees. Ecol. Evol. 10, 5759–5784 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kosicki, J. Z. Generalised additive models and random forest approach as effective methods for predictive species density and functional species richness. Environ. Ecol. Stat. 27, 273–292 (2020).

    Article 
    CAS 

    Google Scholar 

  • Barreto, J. et al. Drone-monitoring: Improving the detectability of threatened marine megafauna. Drones 5, 14 (2021).

    Article 

    Google Scholar 

  • Sarr, J.-M.A. et al. Complex data labeling with deep learning methods: Lessons from fisheries acoustics. ISA Trans. 109, 113–125 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Capezzuto, F. et al. The bathyal benthopelagic fauna in the north-western Ionian Sea: Structure, patterns and interactions. Chem. Ecol. 26, 199–217 (2010).

    Article 

    Google Scholar 

  • Harris, P. & Whiteway, T. Global distribution of large submarine canyons: Geomorphic differences between active and passive continental margins. Mar. Geol. 285, 69–86 (2011).

    Article 

    Google Scholar 

  • Pescatore, T. & Senatore, M. R. A comparison between a present.day (Taranto Gulf) and a Miocene (Irpinian Basin) foredeep of the Southern Apennine (Italy). Spec. Publ. 1986, 169–182 (1986).

    Google Scholar 

  • Rossi, S. & Gabbianelli, G. Geomorfologia del Golfo di Taranto. Ital. J. Geosci. 97, 423–437 (1978).

    Google Scholar 

  • Federico, I. et al. Observational evidence of the basin-wide gyre reversal in the Gulf of Taranto. Geophys. Res. Lett. 47, 1030 (2020).

    Article 

    Google Scholar 

  • Carlucci, R., Battista-Capezzuto, F., Serena, F. & Sion, L. Occurrence of the basking shark Cetorhinus maximus (Gunnerus, 1765) (Lamniformes: Cetorhinidae) in the central-eastern Mediterranean Sea. Ital. J. Zool. 81, 280–286 (2014).

    Article 

    Google Scholar 

  • Matarrese, R., Chiaradia, M. T., Tijani, K., Morea, A. & Carlucci, R. Chlorophyll A multi-temporal analysis in coastal waters with MODIS data. Eur. J. Remote Sens. 2011, 39–48 (2011).

    Google Scholar 

  • Civitarese, G., Gačić, M., Lipizer, M. & Eusebi-Borzelli, G. L. On the impact of the Bimodal Oscillating System (BiOS) on the biogeochemistry and biology of the Adriatic and Ionian Seas (Eastern Mediterranean). Biogeosciences 7, 3987–3997 (2010).

    Article 
    CAS 

    Google Scholar 

  • Pinardi, N. et al. Marine rapid environmental assessment in the hack{newline} Gulf of Taranto: A multiscale approach. Nat. Hazards Earth Syst. Sci. 16, 2623–2639 (2016).

    Article 

    Google Scholar 

  • Ciancia, E. et al. Investigating the chlorophyll-a variability in the Gulf of Taranto (North-western Ionian Sea) by a multi-temporal analysis of MODIS-Aqua Level 3/Level 2 data. Cont. Shelf Res. 155, 34–44 (2018).

    Article 

    Google Scholar 

  • Trotta, F., Pinardi, N., Fenu, E., Grandi, A. & Lyubartsev, V. Multi-nest high-resolution model of submesoscale circulation features in the Gulf of Taranto. Ocean Dyn. 67, 1609–1625 (2017).

    Article 

    Google Scholar 

  • Federico, I. et al. Coastal ocean forecasting with an unstructured grid model in the southern Adriatic and northern Ionian seas. Nat. Hazards Earth Syst. Sci. 17, 45–59 (2017).

    Article 

    Google Scholar 

  • Trotta, F. et al. A relocatable ocean modeling platform for downscaling to shelf-coastal areas to support disaster risk reduction. Front. Mar. Sci. 8, 103 (2021).

    Article 

    Google Scholar 

  • Artegiani, A. et al. The Adriatic Sea general circulation. Part I: Air-sea interactions and water mass structure. J. Phys. Oceanogr. 27, 1492–1514 (1997).

    <a data-track="click" rel="nofollow noopener" data-track-label="10.1175/1520-0485(1997)0272.0.CO;2″ data-track-action=”article reference” href=”https://doi.org/10.1175%2F1520-0485%281997%29027%3C1492%3ATASGCP%3E2.0.CO%3B2″ aria-label=”Article reference 100″ data-doi=”10.1175/1520-0485(1997)0272.0.CO;2″>Article 

    Google Scholar 

  • Artegiani, A. et al. The Adriatic Sea general circulation. Part II: Baroclinic circulation structure. J. Phys. Oceanogr. 27, 1515–1532 (1997).

    <a data-track="click" rel="nofollow noopener" data-track-label="10.1175/1520-0485(1997)0272.0.CO;2″ data-track-action=”article reference” href=”https://doi.org/10.1175%2F1520-0485%281997%29027%3C1515%3ATASGCP%3E2.0.CO%3B2″ aria-label=”Article reference 101″ data-doi=”10.1175/1520-0485(1997)0272.0.CO;2″>Article 

    Google Scholar 

  • Cushman-Roisin, B., Gacić, M., Poulain, P. M. & Artegiani, A. Physical Oceanography of the Adriatic Sea (2001).

  • Escudier, R. et al. Mediterranean sea production centre MEDSEA_MULTIYEAR_PHY_006_004 (2021).

  • Clementi, E. et al. Mediterranean sea physical analysis and forecast (CMEMS MED-Currents, EAS6 system) (Version 1) set. In Copernicus Monitoring Environment Marine Service (CMEMS) (2021).

  • Madec, G. NEMO Ocean Engine (2008).

  • Dobricic, S. & Nadia, P. An oceanographic three-dimensional variational data assimilation scheme. Ocean Model 22, 89–105 (2008).

    Article 

    Google Scholar 

  • Roquet, F., Madec, G., McDougall, T. J. & Barker, P. M. Accurate polynomial expressions for the density and specific volume of seawater using the TEOS-10 standard. Ocean Model 90, 29–43 (2015).

    Article 

    Google Scholar 

  • IOC, SCOR & IAPSO. In The International Thermodynamic Equation of Seawater—2010: Calculation and Use of Thermodynamic Properties 196 (2010).

  • MEDSEA_MULTIYEAR_BGC_006_008 (2020).

  • Mediterranean Sea Monthly and Daily Reprocessed Surface Chlorophyll Concentration from Multi Satellite observations + SeaWiFS daily climatology (2020).

  • Volpe, G. et al. Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Sci. 25, 1527–1532 (2019).

    Google Scholar 

  • Berthon, J.-F. & Zibordi, G. Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens. 25, 1527–1532 (2004).

    Article 

    Google Scholar 

  • De Dominicis, M. et al. A relocatable ocean model in support of environmental emergencies. Ocean Dyn. 64, 667–688 (2014).

    Article 

    Google Scholar 

  • Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997).

    Article 
    MathSciNet 
    MATH 

    Google Scholar 

  • Wu, J. et al. Hyperparameter optimization for machine learning models based on bayesian optimizationb. J. Electron. Sci. Technol. 17, 26–40 (2019).

    Google Scholar 

  • Yang, L. & Shami, A. On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing 415, 295–316 (2020).

    Article 

    Google Scholar 


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