Para que servem os inventários de fauna?

Inventários de fauna acessam diretamente a diversidade de uma localidade, em um determinado espaço e tempo. Os dados primários gerados pelos inventários compõem uma das ferramentas mais importantes na tomada de decisões a respeito do manejo de áreas naturais. Entretanto, vários problemas têm sido observados em diversos níveis relacionados aos inventários de fauna no Brasil e vão desde a formação de recursos humanos até a ausência de padronização, de desenho experimental e de seleção de métodos inadequados. São apresentados estudos de caso com mamíferos, répteis, anfíbios e peixes, nos quais são discutidos problemas como variabilidade temporal e métodos para detecção de fauna terrestre, sugerindo que tanto os inventários quanto os programas de monitoramento devam se estender por prazos maiores e que os inventários devem incluir diferentes metodologias para que os seus objetivos sejam plenamente alcançados.Inventories of fauna directly access the diversity of a locality in a certain period of time. The primary data generated by these inventories comprise one of the most important steps in decisions making regarding the management of natural areas. However, several problems have been observed at different levels related to inventories of fauna in Brazil, and range from the training of humans to the lack of standardization of experimental design and selection of inappropriate methods. We present case studies of mammals, reptiles, amphibians and fishes, where they discussed issues such temporal variability and methods for detection of terrestrial fauna, suggesting that both inventories and monitoring programs should be extended for longer terms and that inventories should include different methodologies to ensure that their goals are fully achieved

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 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 or ganism 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 ne glected 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 lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 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,18,19 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