Pietro Landi
Stellenbosch University
36 Papers
150 Citations
Pietro Landi is an academic researcher from Stellenbosch University. The author has contributed to research in topics: Ecological network & Biology. The author has an hindex of 12, co-authored 33 publications. Previous affiliations of Pietro Landi include Polytechnic University of Milan & International Institute for Applied Systems Analysis.
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Papers
•Journal Article
Trait evolution within bipartite ecological networks
Cang Hui,Henintsoa O. Minoarivelo,Andriamihaja Ramanantoanina,Savannah Nuwagaba,Feng Zhang,Pietro Landi +5 more
TL;DR: This model yielded a signicantly better t to 21% of a set of plant-frugivore mutualistic networks, and highlights the importance, in a substantial minority of cases, of inheritance of interaction patterns without excluding the potential role of ecological novelties in forming the current network architecture.
•Journal Article
Adaptive dynamics and evolutionary branching: Theory and applications
TL;DR: Current work is addressing the emergence of sympatric diversity in life-history strategies and its role in the emergence of intransitive competitive interactions, as well as the concurrent evolution of dispersal and self-fertilization in plant communities.
Prejudice, privilege, and power: Conflicts and cooperation between recognizable groups.
TL;DR: A model of socio-economic power between two prejudiced groups is formulated, and the conditions for their cooperative coexistence under two social scenarios in a well-mixed environment are explored.
Complexity and Stability of Adaptive Ecological Networks: A Survey of the Theory in Community Ecology
Pietro Landi,Henintsoa O. Minoarivelo,Åke Brännström,Cang Hui,Ulf Dieckmann +4 more
- 01 Jan 2018
TL;DR: In this paper, the relationship between complexity and stability of natural ecosystems is studied using the concept of ecological networks and their characteristics, followed by central and occasionally contrasting definitions of complexity and stabilisation.
Deep learning approaches to landmark detection in tsetse wing images
TL;DR: In this paper , a two-tier method using deep learning architectures to classify images and make accurate landmark predictions was developed for identifying and controlling isolated populations of tsetse (Glossina spp), vectors of human and animal trypanosomiasis in Africa.