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

Thresholds of freshwater biodiversity in response to riparian vegetation loss in the Neotropical region

Protecting riparian vegetation around streams is vital in reducing the detrimental effects of environmental change on freshwater ecosystems and in maintaining aquatic biodiversity. Thus, identifying ecological thresholds is useful for defining regulatory limits and for guiding the management of riparian zones towards the conservation of freshwater biota. Using nationwide data on fish and invertebrates occurring in small Brazilian streams, we estimated thresholds of native vegetation loss in which there are abrupt changes in the occurrence and abundance of freshwater bioindicators and tested whether there are congruent responses among different biomes, biological groups and riparian buffer sizes. Mean thresholds of native vegetation cover loss varied widely among biomes, buffer sizes and biological groups: ranging from 0.5% to 77.4% for fish, from 2.9% to 37.0% for aquatic invertebrates and from 3.8% to 43.2% for a subset of aquatic invertebrates. Confidence intervals for thresholds were wide, but the minimum values of these intervals were lower for the smaller riparian buffers (50 and 100 m) than larger ones (200 and 500 m), indicating that land use should be kept away from the streams. Also, thresholds occurred at a lower percentage of riparian vegetation loss in the smaller buffers, and were critically lower for invertebrates: reducing only 6.5% of native vegetation cover within a 50-m riparian buffer is enough to cross thresholds for invertebrates. Synthesis and applications. The high variability in biodiversity responses to loss of native riparian vegetation suggests caution in the use of a single riparian width for conservation actions or policy definitions nationwide. The most sensitive bioindicators can be used as early warning signals of abrupt changes in freshwater biodiversity. In practice, maintaining at least 50-m wide riparian reserves on each side of streams would be more effective to protect freshwater biodiversity in Brazil. However, incentives and conservation strategies to protect even wider riparian reserves (~100 m) and also taking into consideration the regional context will promote a greater benefit. This information should be used to set conservation goals and to create complementary mechanisms and policies to protect wider riparian reserves than those currently required by the federal law. © 2020 British Ecological Societ