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
01 September 2023
From September I'll join the Department of Ecological and Biological Sciences (DEB) @ Tuscia University of Viterbo as Data Scientist/Analyst & Computational Ecology Technician, covering all aspects in biological and ecological data science.
19 September 2023
Altmetric has tracked 24,397,980 research outputs across all sources so far. Compared to these, our paper has done particularly well and is in the 95th percentile. With more than 600 download in less than one month it's in the top 5% of all research outputs ever tracked by Altmetric!
25 September 2023
We performed a systematic review by implementing a semi-automated, threshold-based filtering pipeline that allowed building up a dataset concerning all fish species reported
in native Mediterranean seagrasses, including specific functional traits known to be involved with the potential
use of seagrasses by fish.
16 July 2024
It took a while, but finally our paper A review of fish diversity in Mediterranean seagrass habitats, with a focus on functional traits, has been accepted for publication in Reviews in Fish Biology and Fisheries. In this paper we provide support to several assumptions repeatedly stated in literature but so far sustained mainly by local and fragmented data, suggesting the onset of a general pattern in the occurrence of seagrass-associated fish, mostly based on the life history and driven by body size and feeding habits. We also provide one of the most up-to-date database covering all fish species reported in native Mediterranean seagrasses, including specific functional traits known to be involved with the potential use of seagrasses by fish. We hope our findings and dataset could provide a sound basis for scientists and managers across many fields, from fisheries to biodiversity assessment and conservation.
A link to the published paper will be available as soon as possible!
01 October 2024
We used Boosted Regression Trees to investigate how the
richness levels of semi-natural grasslands in Southern Europe (biodiversity hotspots) compare to other
Palaearctic grasslands and how precipitation and other environmental parameters influence different taxonomic groups.
Drivers of vascular plant, bryophyte and lichen richness in grasslands along a precipitation gradient (central Apennines, Italy)
01 October 2024
Introducing MedSeaFishE 1.0 Mediterranean Seagrass Fish Data Explorer a web app entirely written in R and built on SHINY to speed up and facilitate the consultation and download of the data associated with the recent publication in Reviews in Fish Biology and Fisheries. Future implementations will follow with the aim of making MedSeaFishE a well-structured web-repository about fish diversity on seagrasses worldwide.
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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