RESUMO
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.
Assuntos
Biodiversidade , Florestas , Humanos , Floresta Úmida , Brasil , Clima Tropical , Conservação dos Recursos Naturais , EcossistemaRESUMO
Decision-makers increasingly seek scientific guidance on investing in nature, but biodiversity remains difficult to estimate across diverse landscapes. Here, we develop empirically based models for quantifying biodiversity across space. We focus on agricultural lands in the tropical forest biome, wherein lies the greatest potential to conserve or lose biodiversity. We explore two questions, drawing from empirical research oriented toward pioneering policies in Costa Rica. First, can remotely sensed tree cover serve as a reliable basis for improved estimation of biodiversity, from plots to regions? Second, how does tropical biodiversity change across the land-use gradient from native forest to deforested cropland and pasture? We report on understory plants, nonflying mammals, bats, birds, reptiles, and amphibians. Using data from 67,737 observations of 908 species, we test how tree cover influences biodiversity across space. First, we find that fine-scale mapping of tree cover predicts biodiversity within a taxon-specific radius (of 30-70 m) about a point in the landscape. Second, nearly 50% of the tree cover in our study region is embedded in countryside forest elements, small (typically 0.05-100 ha) clusters or strips of trees on private property. Third, most species use multiple habitat types, including crop fields and pastures (to which 15% of species are restricted), although some taxa depend on forest (57% of species are restricted to forest elements). Our findings are supported by comparisons of 90 studies across Latin America. They provide a basis for a planning tool that guides investments in tropical forest biodiversity similar to those for securing ecosystem services.
Assuntos
Agricultura/métodos , Biodiversidade , Conservação dos Recursos Naturais , Anfíbios , Animais , Aves , Quirópteros , Costa Rica , Produtos Agrícolas , Ecologia , Florestas , Geografia , Humanos , Mamíferos , Répteis , Especificidade da Espécie , Árvores , Clima TropicalRESUMO
Environmental governance is more effective when the scales of ecological processes are well matched with the human institutions charged with managing human-environment interactions. The social-ecological systems (SESs) framework provides guidance on how to assess the social and ecological dimensions that contribute to sustainable resource use and management, but rarely if ever has been operationalized for multiple localities in a spatially explicit, quantitative manner. Here, we use the case of small-scale fisheries in Baja California Sur, Mexico, to identify distinct SES regions and test key aspects of coupled SESs theory. Regions that exhibit greater potential for social-ecological sustainability in one dimension do not necessarily exhibit it in others, highlighting the importance of integrative, coupled system analyses when implementing spatial planning and other ecosystem-based strategies.