Integrating images, sounds, citizen science and AI to assess the biodiversity of the Atlantic Forest of Brazil
- 1Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
- 2Colégio Brasileiro de Altos Estudos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
Abstract
The Atlantic Forest is an endangered biome in Brazil with only 15% of its area remaining. Although it is reported that it has a high biodiversity and many endemic species, the biological composition of its territory remains largely unknown. Citizen science can speed up the description of the living organisms present in this biome and at the same time increase citizens’ perception of the importance of the conservation of this endangered tropical ecosystem. Here, we used citizen science resources to evaluate the biodiversity of an Atlantic Forest area in southeast Brazil. During a 6 year-period, we photographed plants, animals and fungi specimens using different approaches depending on the behavior and characteristics of each organism. We used easy-to-use mobile phones, night vision cameras, high-quality photographic cameras equipped with tele-macro lenses, and citizen science AI applications, such as iNaturalist and Merlin. We analyzed amphibians, arachnids, birds, fungi, insects, mammals, mollusks, plants, reptiles and crustaceans. We identified 492 species, some of which are threatened (4 plants, 4 mammals, 3 birds, and 1 reptile) according to the IUCN Red List categories of threatened species. We also identified some endemics and introduced species. Insects and plants were by far the groups with the highest numbers of species registered, followed by birds, fungi and arachnids. Birds were the group with the highest numbers of observations, followed by insects and plants. Amphibians were the group with more endemic species, with 60% of their species endemic in Brazil. Lepidoptera (butterflies and moths) correspond to 50% of the insect species found in the area. The collection of our data demonstrates that it is easily feasible to use a technological approach to register and identify the species found in small properties and we provide an easy guide for beginners. To our knowledge this is the first description of the biodiversity across several taxonomic categories of the Brazilian Atlantic Forest using an integrated approach combining digital observations, AI-assisted species identification, and citizen-science community validation. Our data could have an impact on the engagement of citizens on the management and conservation of Atlantic Forest sustainability in Brazil and in other parts of the globe.
Related
- Who Eats Whom? A global food web derived from citizen science
- Americans’ multidimensional relationship with science: Is citizen science a path forward?
- Five Decades of Citizen Science Reveal Erosion of Hotspots of Multifaceted Plant Diversity
- The training–discovery paradox: Evidence from citizen science
- Citizen science data as a tool for monitoring moss diversity in Poland: potential and limitations
BibTeX
@article{costa2026integrating,
title = {{Integrating images, sounds, citizen science and AI to assess the biodiversity of the Atlantic Forest of Brazil}},
author = {Manoel Luis Costa and Claudia Mermelstein},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0360070},
url = {https://doi.org/10.1371/journal.pone.0360070},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrating images, sounds, citizen science and AI to assess the biodiversity of the Atlantic Forest of Brazil
AU - Manoel Luis Costa
AU - Claudia Mermelstein
PY - 2026
DA - 2026/10/07
JO - PLOS One
DO - 10.1371/journal.pone.0360070
UR - https://doi.org/10.1371/journal.pone.0360070
AB - The Atlantic Forest is an endangered biome in Brazil with only 15% of its area remaining. Although it is reported that it has a high biodiversity and many endemic species, the biological composition of its territory remains largely unknown. Citizen science can speed up the description of the living organisms present in this biome and at the same time increase citizens’ perception of the importance of the conservation of this endangered tropical ecosystem. Here, we used citizen science resources to evaluate the biodiversity of an Atlantic Forest area in southeast Brazil. During a 6 year-period, we photographed plants, animals and fungi specimens using different approaches depending on the behavior and characteristics of each organism. We used easy-to-use mobile phones, night vision cameras, high-quality photographic cameras equipped with tele-macro lenses, and citizen science AI applications, such as iNaturalist and Merlin. We analyzed amphibians, arachnids, birds, fungi, insects, mammals, mollusks, plants, reptiles and crustaceans. We identified 492 species, some of which are threatened (4 plants, 4 mammals, 3 birds, and 1 reptile) according to the IUCN Red List categories of threatened species. We also identified some endemics and introduced species. Insects and plants were by far the groups with the highest numbers of species registered, followed by birds, fungi and arachnids. Birds were the group with the highest numbers of observations, followed by insects and plants. Amphibians were the group with more endemic species, with 60% of their species endemic in Brazil. Lepidoptera (butterflies and moths) correspond to 50% of the insect species found in the area. The collection of our data demonstrates that it is easily feasible to use a technological approach to register and identify the species found in small properties and we provide an easy guide for beginners. To our knowledge this is the first description of the biodiversity across several taxonomic categories of the Brazilian Atlantic Forest using an integrated approach combining digital observations, AI-assisted species identification, and citizen-science community validation. Our data could have an impact on the engagement of citizens on the management and conservation of Atlantic Forest sustainability in Brazil and in other parts of the globe.
SN - 1932-6203
ER -