scispace - formally typeset
M

Matteo Adorisio

Researcher at International School for Advanced Studies

Publications -  7
Citations -  166

Matteo Adorisio is an academic researcher from International School for Advanced Studies. The author has contributed to research in topics: Random walk & Mutualism (biology). The author has an hindex of 4, co-authored 7 publications receiving 107 citations. Previous affiliations of Matteo Adorisio include International Centre for Theoretical Physics.

Papers
More filters
Journal ArticleDOI

Feasibility and coexistence of large ecological communities.

TL;DR: A geometrical framework to study the range of conditions necessary for feasible coexistence in mutualistic systems is developed and it is shown that feasibility is determined by few quantities describing the interactions, yielding a nontrivial complexity–feasibility relationship.
Journal ArticleDOI

Exact and Efficient Sampling of Conditioned Walks

TL;DR: In this article, a general stochastic approach was proposed to obtain equilibrated samples of conditioned walks with their correct statistical weight and without rejections, for a jump process conditioned to evolve within a cylindrical channel and forced to reach one of its ends.
Journal ArticleDOI

Chemotaxis emerges as the optimal solution to cooperative search games

TL;DR: A dictionary is established that maps notions from decision-making theory to biophysical observables in chemotaxis, and vice versa, to offer a fundamental explanation of why search algorithms that mimic microbial chemoattractant can be very effective and suggest how to optimize their performance.
Posted Content

The geometry of coexistence in large ecosystems

TL;DR: A geometrical framework to study the range of conditions necessary for feasible coexistence in both mutualistic and consumer-resource systems is developed and the geometric shape of the feasibility domain is characterized, thereby identifying the direction of perturbations that are more likely to cause extinctions.
Posted Content

Spatial maximum entropy modeling from presence/absence tropical forest data

TL;DR: This work proposes a spatially explicit maximum entropy model suitable to describe spatial patterns such as the species area relationship and the endemic area relationship, and uses the information at shorter spatial scales to infer the spatial organization at larger ones.