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

Relationships between methods of variety adaptability and stability in sugarcane

The identification and recommendation of superior genotypes is crucial for the growth of industrial crops, and sugarcane breeding performs a vital role by developing more productive cultivars. The study of genotype x environment interaction has been an essential tool in this process. Thereby, the purpose of this study was to investigate the relationship between methods of adaptability and stability in sugarcane. Data were collected from trials using a randomized block design with three repetitions and 15 clones of sugarcane in nine environments in the State of Minas Gerais, Brazil. Methodologies based on analysis of variance, linear regression, multivariate analysis, nonparametric statistics, and mixed model were used. The methods of Lin and Binns, Annicchiarico, and harmonic mean of relative performance of genotypic values (MHPRVG) were similar in their classification of genotypes. The additive main effect and multiplicative interactions (AMMI) and Wricke methods tended to select the most stable genotypes; however, genotypes were less productive, coinciding with the stability parameter of Eberhart and Russell. The MHPRVG method is preferred over the methods of Lin and Binns and Annicchiarico because it includes the concepts of productivity, adaptability, and stability, and it provide direct genetic values of individuals. The use of the MHPRVG and Eberhart and Russell methods is recommended because the combination of these methods is complementary and leads to greater accuracy in the identification of genotypes of sugarcane for different environments