Fatores determinantes da distribuição de aves no interflúvio Purus-Madeira

Studies addressing deterministic and stochastic processes that effect changes in species composition among sites (beta diversity) have focused, for the most part, on sessile organisms. These are highly susceptible to random dispersal processes, confirming neutral theory assumptions. At first glance, birds would appear to have high dispersal ability. However, in Amazonian forests most birds are extremely sedentary, with restricted distributions, often limited by large rivers. In this study, we evaluated the environmental effects (palm species composition) relative to geographical distances between sites, as factors related differences in forest bird species composition. We sampled 11 sites in upland forest, separated from one another by 60 km, covering a 670 km extension in the Purus-Madeira interfluve of Western Amazon, Brazil. Similarity in bird assemblage was significantly correlated with palm species composition. Understory and canopy birds assemblages showed similar correlation with palm species composition. When the effect of palm species composition was controlled, distance was not a good indicator of changes in the bird community. Our results suggest that, in this region and at this spatial scale, birds are not limited by geographical distance and can disperse throughout the region studied. Nevertheless, they are not uniformly distributed which can best be explained by environmental variation, represented here by palm species composition. Although our results indicate that geographic distance has no effect on changes in bird composition, we emphasize that studies on a larger spatial scale could help to understand dispersal limitation effects in tropical Amazonian forest bird composition.Até o presente, estudos abordando fatores determinísticos e estocásticos que afetam mudanças na composição de espécies entre locais enfocaram, em geral, organismos sésseis. Esses organismos são especialmente suscetíveis a processos aleatórios de dispersão, corroborando premissas da teoria neutra. As aves, aparentemente, apresentam boa capacidade de dispersão. No entanto, em florestas amazônicas, muitas espécies de aves são extremamente sedentárias e apresentam distribuição restrita, tipicamente limitada por grandes rios. Neste estudo avaliamos a importância de efeitos ambientais, medidos pela composição de espécies de palmeiras, relativos aos efeitos da distância geográfica, como fatores geradores de diferenças na composição de aves amazônicas. Nós amostramos 11 localidades em uma floresta de terra firme, distantes aproximadamente 60 km entre si, cobrindo uma extensão de 670 km, no interflúvio Purus-Madeira, Amazônia brasileira. A similaridade das assembleias de aves foi significativamente correlacionada com a composição de palmeiras. Aves de sub-bosque e aves de dossel, analisadas separadamente, também apresentaram forte correlação com a composição de palmeiras. Com o efeito ambiental controlado, a distância geográfica não foi um bom indicador de mudanças na avifauna na área de estudo, não explicando a ocorrência de espécies de nenhum dos grupos de aves. Os resultados sugerem que, na escala espacial e na região deste estudo, as aves não são limitadas pela distância geográfica entre as localidades, podendo se dispersar por toda extensão da área estudada. No entanto, a composição de aves muda entre as localidades amostradas e essas mudanças podem ser melhor explicadas pela variação ambiental, representada pela composição de palmeiras. Embora nossos resultados indiquem que a distância geográfica não tenha efeito sobre as mudanças na composição de aves, enfatizamos que estudos em uma escala maior poderão ajudar a entender os efeitos da limitação de dispersão sobre a composição de aves florestais amazônicas

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

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