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Professional Summary

 

 

Education

 

 

 

 

 

Memberships

 

 

Roles

 

 

Teaching activity

 

 

 

Research Projects and Grants

 

Selected Publications 

Sbrana, A. et al. (2022). Ask the shark: Blackmouth catshark (galeus melastomus) as a sentinel of plastic waste on the seabed. Marine Biology169(7). https://doi.org/10.

1007/s00227-022-04084-1

Maiello, G. et al. (2022). Little samplers, big fleet: eDNA metabarcoding from commercial trawlers enhances ocean monitoring. Fisheries Research249https://doi.org/10.1016/j.fishres.2022.106259

Russo, T. et al. (2022). Defend as you can, react quickly: The effects of the COVID-19 shock on a large fishery of the mediterranean sea. Frontiers in Marine Science9https://doi.org/10.3389/fmars.2022.824857

Russo, T. et al. (2021). All is fish that comes to the net: Metabarcoding for rapid fisheries catch assessment. Ecological Applications31(2). https://doi.org/10.1002/eap.2273 D’Andrea, L. et al. (2020). The MINOUWApp: A web-based tool in support of by- catch and discards management. Environmental Monitoring and Assessment192(12). https://doi.org/10.1007/s10661-020-08704-5

D’Andrea, L. et al.. (2020). smartR: An r package for spatial modelling of fisheries and scenario simulation of management strategies. Methods in Ecology and Evolution11(7),

859–868. https://doi.org/10.1111/2041-210X.13394 Mendo, T. et al. (2019). Effect of temporal and spatial resolution on identification of fishing activities in small-scale fisheries using pots and traps. ICES Journal of Marine Science76(6), 1601–1609. https://doi.org/10.1093/icesjms/fsz073

Russo, T. et al. (2019). Predicting fishing footprint of trawlers from environmental and fleet data: An application of artificial neural networks. Frontiers in Marine Science6https://doi.org/10.3389/fmars.2019.00670

Cianchetti-Benedetti, M. et al. (2018). Interactions between commercial fishing vessels and a pelagic seabird in the southern mediterranean sea. BMC Ecology18(1). https://doi.org/10.1186/s12898-018-0212-x

Amoroso, R. O. et al. (2018). Bottom trawl fishing footprints on the worlds continental shelves. Proceedings of the National Academy of Sciences of the United States of America115(43), E10275–E10282. https://doi.org/10.1073/pnas.1802379115

Russo, T. et al. (2018). A model combining landings and VMS data to estimate landings by fishing ground and harbor. Fisheries Research199, 218–230. https: //doi.org/10.1016/j.fishres.2017.11.002

Eigaard, O. R. et al.. (2017). The footprint of bottom trawling in european waters: Distribution, intensity, and seabed integrity. ICES Journal of Marine Science74(3), 847–865. https://doi.org/10.1093/icesjms/fsw194

Russo, T. et al. (2016). Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities. Ecological Indicators69, 818–827. https://doi.org/10.1016/j.ecolind.2016.04.043

Russo, T. et al.. (2016). Modeling landings profiles of fishing vessels: An application of self-organizing maps to VMS and logbook data. Fisheries Research181, 34–47. https://doi.org/10.1016/j.fishres.2016.04.005

Russo, T. et al. (2015). Modelling the strategy of mid-water trawlers targeting small pelagic fish in the adriatic sea and its drivers. Ecological Modelling300, 102–113.  https://doi.org/10.1016/j.ecolmodel.2014.12.001

Russo, T. et al. (2014). SMART: A spatially explicit bio-economic model for assessing and managing demersal fisheries, with an application to italian trawlers in the strait of sicily. PLoS ONE9(1). https://doi.org/10.1371/journal.pone.0086222

Russo, T. et al. (2013). Spatial indicators of fishing pressure: Preliminary analyses and possible developments. Ecological Indicators26, 141–153. https://doi.org/10.1016/j.ecolind.2012.11.002

Russo, T. et al. (2011). When behaviour reveals activity: Assigning fishing effort to me?tiers based on VMS data using artificial neural networks. Fisheries Research111 (1-2), 53–64. https://doi.org/10.1016/j.fishres.2011.06.011

Russo, T. et al. (2011). New insights in interpolating fishing tracks from VMS data for different me?tiers. Fisheries Research108(1), 184–194. https://doi.org/10.1016/j. fishres.2010.12.020

Russo, T. et al. (2009). Le?vy processes and stochastic von bertalanffy models of growth, with application to fish population analysis. Journal of Theoretical Biology258(4), 521–529. https://doi.org/10.1016/j.jtbi.2009.01.033

Professional Summary

Roles

Selected Publications 

Sbrana, A. et al. (2022). Ask the shark: Blackmouth catshark (galeus melastomus) as a sentinel of plastic waste on the seabed. Marine Biology169(7). https://doi.org/10.

1007/s00227-022-04084-1

Maiello, G. et al. (2022). Little samplers, big fleet: eDNA metabarcoding from commercial trawlers enhances ocean monitoring. Fisheries Research249https://doi.org/10.1016/j.fishres.2022.106259

Russo, T. et al. (2022). Defend as you can, react quickly: The effects of the COVID-19 shock on a large fishery of the mediterranean sea. Frontiers in Marine Science9https://doi.org/10.3389/fmars.2022.824857

Russo, T. et al. (2021). All is fish that comes to the net: Metabarcoding for rapid fisheries catch assessment. Ecological Applications31(2). https://doi.org/10.1002/eap.2273 D’Andrea, L. et al. (2020). The MINOUWApp: A web-based tool in support of by- catch and discards management. Environmental Monitoring and Assessment192(12). https://doi.org/10.1007/s10661-020-08704-5

D’Andrea, L. et al.. (2020). smartR: An r package for spatial modelling of fisheries and scenario simulation of management strategies. Methods in Ecology and Evolution11(7),

859–868. https://doi.org/10.1111/2041-210X.13394 Mendo, T. et al. (2019). Effect of temporal and spatial resolution on identification of fishing activities in small-scale fisheries using pots and traps. ICES Journal of Marine Science76(6), 1601–1609. https://doi.org/10.1093/icesjms/fsz073

Russo, T. et al. (2019). Predicting fishing footprint of trawlers from environmental and fleet data: An application of artificial neural networks. Frontiers in Marine Science6https://doi.org/10.3389/fmars.2019.00670

Cianchetti-Benedetti, M. et al. (2018). Interactions between commercial fishing vessels and a pelagic seabird in the southern mediterranean sea. BMC Ecology18(1). https://doi.org/10.1186/s12898-018-0212-x

Amoroso, R. O. et al. (2018). Bottom trawl fishing footprints on the worlds continental shelves. Proceedings of the National Academy of Sciences of the United States of America115(43), E10275–E10282. https://doi.org/10.1073/pnas.1802379115

Russo, T. et al. (2018). A model combining landings and VMS data to estimate landings by fishing ground and harbor. Fisheries Research199, 218–230. https: //doi.org/10.1016/j.fishres.2017.11.002

Eigaard, O. R. et al.. (2017). The footprint of bottom trawling in european waters: Distribution, intensity, and seabed integrity. ICES Journal of Marine Science74(3), 847–865. https://doi.org/10.1093/icesjms/fsw194

Russo, T. et al. (2016). Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities. Ecological Indicators69, 818–827. https://doi.org/10.1016/j.ecolind.2016.04.043

Russo, T. et al.. (2016). Modeling landings profiles of fishing vessels: An application of self-organizing maps to VMS and logbook data. Fisheries Research181, 34–47. https://doi.org/10.1016/j.fishres.2016.04.005

Russo, T. et al. (2015). Modelling the strategy of mid-water trawlers targeting small pelagic fish in the adriatic sea and its drivers. Ecological Modelling300, 102–113.  https://doi.org/10.1016/j.ecolmodel.2014.12.001

Russo, T. et al. (2014). SMART: A spatially explicit bio-economic model for assessing and managing demersal fisheries, with an application to italian trawlers in the strait of sicily. PLoS ONE9(1). https://doi.org/10.1371/journal.pone.0086222

Russo, T. et al. (2013). Spatial indicators of fishing pressure: Preliminary analyses and possible developments. Ecological Indicators26, 141–153. https://doi.org/10.1016/j.ecolind.2012.11.002

Russo, T. et al. (2011). When behaviour reveals activity: Assigning fishing effort to métiers based on VMS data using artificial neural networks. Fisheries Research111 (1-2), 53–64. https://doi.org/10.1016/j.fishres.2011.06.011

Russo, T. et al. (2011). New insights in interpolating fishing tracks from VMS data for different métiers. Fisheries Research108(1), 184–194. https://doi.org/10.1016/j. fishres.2010.12.020

Russo, T. et al. (2009). Lévy processes and stochastic von bertalanffy models of growth, with application to fish population analysis. Journal of Theoretical Biology258(4), 521–529. https://doi.org/10.1016/j.jtbi.2009.01.033

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