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

Free Software and SMEs, A Perspective of the Future

What is Free Software, Why Free Software , Disadvantages of Free Software, What's Missing in Free Software Today , The Metasys Solutio

SME Metasys: A Business Management System for SMEs

SME Metasys: A Business Management System for SME

Free Software and SMEs, A Perspective of the Future

What is Free Software, Why Free Software , Disadvantages of Free Software, What's Missing in Free Software Today , The Metasys Solutione-Business, Science & Technology, Innovation, Telecommunications, ICT, ITC, Software solutions

The Verus Language: Representing Time Efficiently with BDDs

There have been significant advances on formal methods to verify complex systems recently. Nevertheless, these methods have not yet been accepted as a realistic alternative to the verification of industrial systems. One reason for this is that formal methods are still difficult to apply efficiently. Another reason is that current verification algorithms are still not efficient enough to handle many complex systems. This work addresses the problem by presenting a language designed especially to simplify writing timecritical programs. It is an imperative language with a syntax similar to C. Special constructs are provided to allow the straightforward expression of timing properties. The familiar syntax makes it easier for non-experts to use the tool. The special constructs make it possible to model the timing characteristics of the system naturally and accurately. A symbolic representation using BDDs, model checking and quantitative algorithms are used to check system timing properties