On the evolution of dispersal strategies under the costs of acquisition of private and social information - Antoine Sion; Matteo Marcantonio; Stefano Masier; Elio Tuci (Ecological Modelling, 2025)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy - Duccio Rocchini; Matteo Marcantonio; Daniele Da Re; Giovanni Bacaro; Enrico Feoli; Giles M. Foody; Reinhard Furrer; Ryan J. Harrigan; David Kleijn; Martina Iannacito; Jonathan Lenoir; Meixi Lin; et al. (Global Ecology and Biogeography, 2021)
Aim The majority of work done to gather information on the Earth's biodiversity has been carried out using in‐situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed diversity of ecosystems are based on Shannon’s information theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, that is they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time. Innovation The parametric Rao’s quadratic entropy, coded in R, (a) allows the representation of the whole continuum of potential diversity indices in one formula, and (b) starting from the Rao’s quadratic entropy, allows the explicit use of distances among pixel reflectance values, together with relative abundances. Main conclusions The proposed unifying measure is an integration between abundance‐ and distance‐based algorithms to map the continuum of diversity given a satellite image at any spatial scale. Being part of the rasterdiv R package, the proposed method is expected to ensure high robustness and reproducibility.

Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective - Elisa Thouverai; Matteo Marcantonio; Giovanni Bacaro; Daniele Da Re; Martina Iannacito; Elisa Marchetto; Carlo Ricotta; Clara Tattoni; Saverio Vicario; Duccio Rocchini (Community Ecology, 2021)
The variation of species diversity over space and time has been widely recognised as a key challenge in ecology. However, measuring species diversity over large areas might be difficult for logistic reasons related to both time and cost savings for sampling, as well as accessibility of remote ecosystems. In this paper, we present a new package - - to calculate diversity indices based on remotely sensed data, by discussing the theory behind the developed algorithms. Obviously, measures of diversity from space should not be viewed as a replacement of in situ data on biological diversity, but they are rather complementary to existing data and approaches. In practice, they integrate available information of Earth surface properties, including aspects of functional (structural, biophysical and biochemical), taxonomic, phylogenetic and genetic diversity. Making use of the package can result useful in making multiple calculations based on reproducible open source algorithms, robustly rooted in Information Theory.

The impact of road disturbance on vegetation and soil properties in a beech stand, Hyrcanian forest - Azade Deljouei; Seyed Mohammad Moein Sadeghi; Ehsan Abdi; Markus Bernhardt-Römermann; Emily Louise Pascoe; Matteo Marcantonio (European Journal of Forest Research, 2018)

The impact of forest roads on understory plant diversity in temperate hornbeam-beech forests of Northern Iran - Azade Deljouei; Ehsan Abdi; Matteo Marcantonio; Baris Majnounian; Valerio Amici; Hormoz Sohrabi (Environmental Monitoring and Assessment, 2017)

Impact of alien species on dune systems: a multifaceted approach - Matteo Marcantonio; Duccio Rocchini; Gianluigi Ottaviani (Biodiversity and Conservation, 2014)