TY - JOUR

T1 - Pervasive gaps in Amazonian ecological research

AU - Synergize Consortium

AU - Carvalho, Raquel L

AU - Resende, Angelica F

AU - Barlow, Jos

AU - França, Filipe M

AU - Moura, Mario R

AU - Maciel, Rafaella

AU - Alves-Martins, Fernanda

AU - Shutt, Jack

AU - Nunes, Cassio A

AU - Elias, Fernando

AU - Silveira, Juliana M

AU - Stegmann, Lis

AU - Baccaro, Fabricio B

AU - Juen, Leandro

AU - Schietti, Juliana

AU - Aragão, Luiz

AU - Berenguer, Erika

AU - Castello, Leandro

AU - Costa, Flavia R C

AU - Guedes, Matheus L

AU - Leal, Cecilia G

AU - Lees, Alexander C

AU - Isaac, Victoria

AU - Nascimento, Rodrigo O

AU - Phillips, Oliver L

AU - Schmidt, Fernando Augusto

AU - Ter Steege, Hans

AU - Vaz-de-Mello, Fernando

AU - Venticinque, Eduardo M

AU - Vieira, Ima Célia Guimarães

AU - Zuanon, Jansen

AU - Ferreira, Joice

PY - 2023/8/21

Y1 - 2023/8/21

N2 - Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%-18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost.

AB - Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%-18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost.

KW - spatial bias

KW - knowledge gap

KW - conservation science

KW - biological diversity

KW - community assessment

KW - Brazil

KW - biodiversity

KW - information deficits

U2 - 10.1016/j.cub.2023.06.077

DO - 10.1016/j.cub.2023.06.077

M3 - Journal article

C2 - 37473761

VL - 33

SP - 3495

EP - 3504

JO - Current biology : CB

JF - Current biology : CB

SN - 0960-9822

IS - 16

ER -