Michele Scardi

Full Professor of Ecology

Department of Biology
Laboratory of Experimental Ecology and Aquaculture
Tel. 06-7259-5991
e-mail mscardi@mclink.it
Web: www.michele.scardi.name








Latest publications

  • 2020 – Chemical signatures of femoral pore secretions in two syntopic but reproductively isolated species of Galápagos land iguanas (Conolophus marthae and C. subcristatus) – Scientific Reports10.1038/s41598-020-71176-7
  • 2020 – Modelling matter and energy flows in the biosphere and human economy – Ecological Modelling10.1016/j.ecolmodel.2020.108984
  • 2020 – Structure and environmental drivers of phytoplanktonic resting stage assemblages in the central Mediterranean Sea – Marine Ecology Progress Series10.3354/meps13244
  • 2020 – Embedding ecological knowledge into artificial neural network training: A marine phytoplankton primary production model case study – Ecological Modelling10.1016/j.ecolmodel.2020.108985
  • 2020 – Modeling macroalgal forest distribution at mediterranean scale: Present status, drivers of changes and insights for conservation and management – Frontiers in Marine Science10.3389/fmars.2020.00020
  • 2020 – A Machine Learning approach to the assessment of the vulnerability of Posidonia oceanica meadows – Ecological Indicators10.1016/j.ecolind.2019.105744
  • 2019 – A model predicting the PSP toxic dinoflagellate Alexandrium minutum occurrence in the coastal waters of the NW Adriatic Sea – Scientific Reports10.1038/s41598-019-40664-w
  • 2019 – Rummaging through the bin: Modelling marine litter distribution using Artificial Neural Networks – Marine Pollution Bulletin10.1016/j.marpolbul.2019.110580
  • 2019 – Predicting Fishing Footprint of Trawlers From Environmental and Fleet Data: An Application of Artificial Neural Networks – Frontiers in Marine Science10.3389/fmars.2019.00670
  • 2019 – Quantifying the impact of linear regression model in deriving bio-optical relationships: The implications on ocean carbon estimations – Sensors (Switzerland)10.3390/s19133032
  • 2019 – An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models – PLoS ONE10.1371/journal.pone.0211445
  • 2019 – Artificial reproduction of Holothuria polii: A new candidate for aquaculture – Aquaculture10.1016/j.aquaculture.2018.08.060
  • 2019 – Trends in effort and yield of trawl fisheries: A case study from the Mediterranean Sea – Frontiers in Marine Science10.3389/fmars.2019.00153
  • 2018 – Cascaded neural networks improving fish species prediction accuracy: The role of the biotic information – Scientific Reports10.1038/s41598-018-22761-4
  • 2018 – Modelling the vertical distribution of phytoplankton biomass in the mediterranean sea from satellite data: A neural network approach – Remote Sensing10.3390/rs10101666
  • 2018 – A depth-resolved artificial neural network model of marine phytoplankton primary production – Ecological Modelling10.1016/j.ecolmodel.2018.05.003
  • 2018 – Spawning and rearing of Holothuria tubulosa: A new candidate for aquaculture in the Mediterranean region – Aquaculture Research10.1111/are.13487
  • 2017 – Analysis of phytoplankton assemblage structure in the Mediterranean Sea based on high-throughput sequencing of partial 18S rRNA sequences – Marine Genomics10.1016/j.margen.2017.06.001
  • 2017 – Shoot micro-distribution patterns in the Mediterranean seagrass Posidonia oceanica – Marine Biology10.1007/s00227-017-3121-1
  • 2017 – Bight of Benin: a Maternal Perspective of Four Beninese Populations and their Genetic Implications on the American Populations of African Ancestry – Annals of Human Genetics10.1111/ahg.12186
  • 2016 – Dataset exploited for the development and validation of automated cyanobacteria quantification algorithm, ACQUA – Data in Brief10.1016/j.dib.2016.06.042
  • 2016 – Modeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data – Fisheries Research10.1016/j.fishres.2016.04.005
  • 2016 – ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning – Journal of Microbiological Methods10.1016/j.mimet.2016.03.007