Maria João Feio

Maria João Feio's picture
Email: 
mjf@ci.uc.pt
URI: 
MARE - UCoimbra
Role: 
Researcher
Qualifications: 
PhD
Research domain: 
River Basins
Research lines: 
Biodiversity and Ecosystem Functioning
Environmental Risk
Technological Tools for Exploration and Monitoring
ORCID ID: 
http://orcid.org/0000-0003-0362-6802
ResearchGate: 
https://www.researchgate.net/profile/Maria_Feio2

Activity summary

She studies freshwater ecosystems, their communities and functioning, and the effect of global changes on rivers, at local and global scales. She has developed methods for the ecological assessment of rivers. She has been involved in the various stages of implementation of the Water Framework Directive in Portugal and Mediterranean countries. She investigates urban aquatic ecosystems and their biodiversity, and their importance for the sustainability of cities and human health.

Highlights

Feio MJ, Hughes RM, Serra SRQ, Nichols SJ, Kefford BJ et al. 2023. Fish and macroinvertebrate assemblages reveal extensive degradation of the world's rivers. Global Change Biology, 29: 355-374 Rivaes RP, Feio MJ, Almeida SFP, Calapez AR, Mortágua A, Gebler D, Lozanovska I, FC (2022) River ecosystem endangerment from climate change-driven regulated flow regimes. Science of the Total Environment 818; 151857 Rios de Portugal. Comunidades, processos e alterações. Urban stream Assessment system (UsAs): an integrative tool to assess biodiversity, ecosystem functions and services. Feio MJ, Mantas AI, Serra SRQ, Calapez AR, Almeida SFP, Sales MC, Montenegro M, Moreira F (2022) Effect of environmental education on the knowledge of aquatic ecosystems and reconnection with nature in early childhood. PLoS ONE 17: e0266776 Ranta E, Vidal-Abarca MR, Calapez AR, Feio MJ (2021) Urban stream Assessment system (UsAs): an integrative tool to assess biodiversity, ecosystem functions and services. Ecological Indicators 121: 106980 Jovem-Azevêdo D, Bezerra-Neto JF, Molozzi J, Feio MJ (2020) Rehabilitation scenarios for reservoirs: predicting their effect on invertebrate communities through machine learning. River Research and Applications 36:1109–112 Feio MJ, Serra SQR, Mortágua A, Bouchez A, Rimet F, Vasselon V, Almeida SFP (2020) A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms. Science of the Total Environment 722: 137900.