Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

Consistent patterns of common species across tropical tree communities

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1,2,3,4,5,6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Curvas de lactação de vacas F1 Holandês-Gir ajustadas pela função gama incompleta Lactation curves adjusted by incomplete gamma function for crossbred F1 Holstein-Gyr cows

Estudaram-se o padrão das curvas de lactação de 5.368 vacas F1 Holandês-Gir pelo modelo gama incompleto e os efeitos da ordem de lactação e época de parição sobre os parâmetros da função e sobre a produção inicial (PI), produção no pico de lactação (PP), tempo ao pico de lactação (TP), persistência (PER) e produção total de leite estimada na lactação (PLTLE). As curvas de lactação apresentaram-se curvilíneas com queda da produção a partir do início da lactação. A diferença entre a produção de multíparas e primíparas foi de 48,9%, favorável às primeiras. As multíparas apresentaram maior queda na produção no primeiro mês de lactação e maior persistência, enquanto as primíparas apresentaram fortes quedas ao longo de toda lactação e menor persistência. A diferença da produção de leite entre lactações iniciadas na época seca e das águas foi de 1,6%, favorável à primeira. Os resultados para produção relativa mensal, queda percentual na produção referente ao mês anterior e queda percentual na produção referente ao primeiro mês de lactação indicam poucas diferenças no formato das curvas de lactação para vacas paridas nas épocas da seca e das águas. Baixos valores de R² encontrados indicam que a função não produziu bom ajuste para a curva de lactação de vacas desse grupo genético.<br>The lactation curves of 5,368 crossbred F1 Holstein-Gyr cows were studied. The gamma incomplete function was used as a model. The effects of parity and season of calving on the gamma incomplete parameters, the inicial milk production, the peak of production, the time to the peak of production, the lactation persistency and the total milk production were measured. The lactation curves showed curvilinear effect with decreasing in milk production since the beginning. The multiparous cows produced 48.9% more milk, more reduction in milk production during the first month of lactation and higher lactation persistency than primiparous cows. Although the lactation curves were similar, average milk production from dry season calving cows was 1.6% higher than those for rainy season calving. Low R² values suggested that the gamma incomplete function did not fit as a model for the lactation curves of this genetic group