4 research outputs found
HABITAT USE BY BURNISHED-BUFF TANAGER (TANGARA CAYANA) AND GREAT ANTSHRIKE (TARABA MAJOR) IN A HUMAN-MODIFIED LANDSCAPE IN SOUTHEAST BRAZIL
Abstract · The agricultural expansion in tropical regions is causing loss and reductions habitat, as well as changes in biodiversity. Intrinsic ecological factors, such as trophic level, and habitat conditions, such as vegetation structure, can determine how a particular species uses the habitat. The Brazilian savanna is a world biodiversity hotspot and the most threatened in the country, with a higher deforestation rate than the Amazon. Therefore, it is important to understand how the presence/absence of forest-dependent birds is affected by local characteristics and by landscape features of habitat remnants. Here we study patterns of habitat use in two forest-dependent bird species, Burnishedbuff Tanager (Tangara cayana) and Great Antshrike (Taraba major), to learn how characteristics at the local and landscape scales can influence their occurrence in forest remnants. This work was carried out in a forest remnant area embedded in a human transformed landscape, belonging to the Cerrado biome, Brazilian Savanna. The study area is localized in the municipality of Assis, SĂŁo Paulo State. The selected area was delimited and divided into 120 quadrants of 22,500 m² each. In the center of each quadrant we positioned one observation point. The points were visited three times and presence/absence data for both species were collected using playback. For each point we recorded local characteristics – interior vs edge, canopy height, canopy cover, presence of dead standing trees, dead trees with arthropods, trees with fruits, and grasses; and landscape characteristics – distance to water bodies, distance to floodplain (várzea), distance to nearest farmland, highways, unpaved roads, railroads, and houses. Our results indicate that T. cayana was more likely to be present in points located at the forest edge, close to water bodies and with high canopy. In addition, the distance from farming activity was the variable with most influence on the occurrence of T. major. The final models for each species predicted patterns of presence/absence correctly in 73% of cases for T. cayana and 76% for T. major. The results have implications for the conservation of forest specialist species that occupy forest remnants in deeply modified landscapes and can contribute to designing proper management plans. Resumo · Variáveis ambientais relacionadas ao uso do habitat da SaĂra amarela (Tangara cayana) e do ChorĂł-boi (Taraba major) em uma paisagem antropizada no sudeste do Brasil A expansĂŁo agrĂcola nas regiões tropicais vem causando perdas e reduções de habitat que resultam em mudanças na biodiversidade. Fatores ecolĂłgicos intrĂnsecos, tal como nĂvel trĂłfico e condições de habitat, como a estrutura da vegetação, podem indicar como uma determinada espĂ©cie usa o habitat. A savana brasileira Ă© um dos principais pontos de biodiversidade do mundo e a mais ameaçada do paĂs, com uma taxa de desmatamento mais alta que a da AmazĂ´nia. Portanto, Ă© importante entender como a presença/ausĂŞncia de aves dependentes de florestas Ă© afetada pelas caracterĂsticas tanto locais quanto da paisagem na qual encontra-se um remanescentes de habitat. Neste trabalho foi estudaado os padrões de uso do habitat de duas espĂ©cies de aves dependentes da floresta, SaĂra amarela (Tangara cayana) e ChorĂł-boi (Taraba major). Somado a isso, conhecer como as caracterĂsticas ambientais nas escalas local e de paisagem podem influenciar a ocorrĂŞncia dessas espĂ©cies em remanescentes florestais. Este trabalho foi realizado em uma floresta remanescente inserida em uma paisagem humana transformada, pertencente ao bioma Cerrado. A área de estudo está localizada no municĂpio de Assis, Estado de SĂŁo Paulo. A área selecionada foi delimitada e dividida em 120 quadrantes de 22.500 m² cada. No centro de cada quadrante, posicionamos um ponto de observação. Os pontos foram visitados trĂŞs vezes e dados de presença/ausĂŞncia para ambas as espĂ©cies foram coletados usando playback. Inicialmente foram registradas caracterĂsticas locais em cada ponto - interior vs borda, altura do dossel, cobertura do dossel, presença de árvores mortas, árvores mortas com artrĂłpodes, árvores com frutos, e gramĂneas; e caracterĂsticas da paisagem - distância a corpos de água, distância a várzea, distância da atividade agropecuaria mais prĂłximas, rodovias, estradas nĂŁo pavimentadas, ferrovias e casas. Os resultados indicaram que T. cayana tem maior probabilidade de estar presente em pontos localizados na borda da floresta, prĂłximos a corpos de água e com copas mais alta. A distância da atividade agropecuária foi a variável com maior influĂŞncia na ocorrĂŞncia de T. major. Os modelos finais para cada espĂ©cie previram padrões de presença / ausĂŞncia corretamente em 73% dos casos para T. cayana e 76% para T. major. Os resultados, tĂŞm implicações para a conservação de espĂ©cies florestais especializadas que ocupam remanescentes florestais em paisagens profundamente modificadas e podem contribuir para o planejamento de manejo mais adequados para áreas de floresta remanecentes
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