Do Investments in Innovation and Quality Management Systems Ensure Superior Financial Performance? A Quantitative Study of Brazilian Publicly Traded Companies

Investments in innovation and quality management systems have long been presented as tools that can boost organizational performance. In Brazil, empirical, systematic, and rigorous research on such relations on such relations is still scarce. Given this context, this study sought to verify, using multiple linear regressions, how investments in innovation, and adoption of the six sigma methodology and ISO 9001 certification, impacted the financial performance, in terms of profitability, of the 101 Brazilian publicly traded companies comprising the study sample in fiscal year 2019. Regression results shows evidence that Brazilian publicly traded companies are having little success in regards to financial results by their six sigma efforts, and that R&D efforts and ISO 9001 certification exert positive and significant impact on profitability, via the ROA index. The interaction between ISO 9001 and R&D came close to significance, indicating a possible synergistic effect to be tested in future studies. Besides contributions to the entrepreneurial field, aiding companies to direct their efforts toward initiatives capable of positively impacting profitability, such findings also help advance academic knowledge

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

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

Rock n' Seeds: A database of seed functional traits and germination experiments from Brazilian rock outcrop vegetation

Advancing functional ecology depends fundamentally on the availability of data on reproductive traits, including those from tropical plants, which have been historically underrepresented in global trait databases. Although some valuable databases have been created recently, they are mainly restricted to temperate areas and vegetative traits such as leaf and wood traits. Here, we present Rock n' Seeds, a database of seed functional traits and germination experiments from Brazilian rock outcrop vegetation, recognized as outstanding centers of diversity and endemism. Data were compiled through a systematic literature search, resulting in 103 publications from which seed functional traits were extracted. The database includes information on 16 functional traits for 383 taxa from 148 genera, 50 families, and 25 orders. These 16 traits include two dispersal, six production, four morphological, two biophysical, and two germination traits-the major axes of the seed ecological spectrum. The database also provides raw data for 48 germination experiments, for a total of 10,187 records for 281 taxa. Germination experiments in the database assessed the effect of a wide range of abiotic and biotic factors on germination and different dormancy-breaking treatments. Notably, 8255 of these records include daily germination counts. This input will facilitate synthesizing germination data and using this database for a myriad of ecological questions. Given the variety of seed traits and the extensive germination information made available by this database, we expect it to be a valuable resource advancing comparative functional ecology and guiding seed-based restoration and biodiversity conservation in tropical megadiverse ecosystems. There are no copyright restrictions on the data; please cite this paper when using the current data in publications; also the authors would appreciate notification of how the data are used in publications