I am a community ecologist with a strong interest in data analysis and computational ecology. I use and develop tools and methods rooted in network theory to understand how different layers of information could contribute to the observed pattern of diversity.
I believe in a modern, open ecological science based on cooperation and the free use of data and methods.
Half Data Scientist Half Data Analyst, 100% ecologist!
Department of Ecological and Biological Sciences (DEB), Tuscia University of Viterbo, Italy.
email: bruno.bellisario@unitus.it
OrcID
Scholar
13 March 2025
In this paper we studied fish assemblages in three Mediterranean seagrasses (Posidonia oceanica, Cymodocea nodosa, Zostera marina) using trait-based approaches to assess their functional structure and habitat use. Assemblages showed non-random functional convergence, independent of taxonomy or diversity, with a core group of species exhibiting r-like reproductive strategies and low-to-intermediate trophic levels. Unique trait combinations were linked to species' presence during specific life stages, highlighting trade-offs between life-history and feeding strategies. These findings emphasize seagrasses' ecological importance and inform conservation efforts for marine biodiversity.
Data science is the art of programming code and combines it with statistical knowledge to create insights from data.
However, one of the biggest problem in data science is that it often replaces understanding of the mechanisms underlying a specific phenomenon we are studying.
Applying a tool is not the hardest part, but deciding which to apply based on specific hypotheses rooted in the deep knowledge of what we are managing surely it is.
That's why we need algorithmically-trained ecologists rather than ecologically-trained data scientists.
Focusing on a specific area doesn’t mean you can only do that one thing, but simply that you are best at doing it.
Data science can help modeling and analyzing animal movement and predict how different constraints (e.g., natural and/or anthropogenic barriers, climate change etc...) will affect animal fluxes within landscapes.
Merging metabarcoding data with ecological network analysis and machine learning may provide quicker, cheaper and more insightful bio-assessment and conservation strategies, opening the road for a new-generation of biomonitoring tools.
Infectious disease and pollution are, without doubts, major concerns in wildlife management and ecosystem conservation. Advanced statistics and machine learning may help identifying how populations respond to such treats, allowing to implement ad hoc conservation strategies.
Laura Cancellieri, Marta G. Sperandii, Leonardo Rosati, Bruno Bellisario, Cinzia Franceschini, Michele Aleffi, Fabrizio Bartolucci, Thomas Becker, Elena Belonovskaya, Asun Berastegi, Idoia Biurrun, Michele Brunetti, Christoph Bückle, Rongxiao Che, Fabio Conti, Iwona Dembicz, Stefania Fanni, Edy Fantinato, Dieter Frank, Anna Rita Frattaroli, Itziar Garcia-Mijangos, Adalgisa Guglielmino, Monika Janišová, Samuele Maestri, Martin Magnes, Giovanna Potenza, Riccardo Primi, Nikolay Sobolev, Nadezda Tsarevskaya, Andea Vacca, Jürgen Dengler, Goffredo Filibeck (2024)
Simonetta Mattiucci, Paolo Cipriani, Michela Paoletti, Valentina Nardi, Mario Santoro, Bruno Bellisario, Giuseppe Nascetti (2015)
Paolo Cipriani, Giorgio Smaldone, Virginia Acerra, Luisa D'Angelo, Aniello Anastasio, Bruno Bellisario et al. (2015)
Simonetta Mattiucci, Paolo Cipriani, Stephen C. Webb, Michela Paoletti, Federica Marcer, Bruno Bellisario, David I. Gibson, Giuseppe Nascetti (2014)
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