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

Multiple Response Optimization: Comparative Analysis Between Models Obtained by Ordinary Least Method and Genetic Programming

Purpose: This work aims to analyze and compare the performance between the Ordinary Least Squares (OLS) method executed in Minitab (v. 17) and the genetic programming performed in Eureqa Formulize (v. 1.24.0).   Theoretical reference: Obtaining a model that mathematically describes the relationship between the independent variable and the response variable is essential to optimizing the process. The model can be described as an approximate representation of the real system or process, while the modeling process is a balance between simplicity and accuracy (X. Chen et al., 2018; Gomes et al., 2019; Sampaio et al., 2022; A. R. S. Silva et al., 2021).   Method: An Evaluation of the best method for constructing mathematical models was performed using the Adjusted Coefficient of Determination (Radj2) and Akaike's Information Criterion   Results and conclusion: The comparison between the use of the methods showed the superiority of genetic programming over OLS in the construction of mathematical models.   Originality/Value: Genetic Programming produces mathematical models that are sometimes differentiated when several replicates are performed, but always with similar explanatory power and with biased characteristic that does not affect in any way the quality of prediction of the dependent variable being studied

Increasing students’ skills in operations management classes: Cumbuca Method as teaching-learning strategy

Abstract This paper analyses the use of the Cumbuca Method as a teaching strategy to develop reading skills in engineering students. Despite their importance, reading skills are little stimulated in engineering undergraduate courses. Teamwork, communication, organization and exposure of ideas, time management are also important skills to new employees. The Cumbuca Method was created to disseminate quality concepts among company employees, by discussing text related to a given topic of interest. This analysis is based on a qualitative approach involving a sample of 200 engineering undergraduates from Operations Management discipline at a public University in the State of Sao Paulo, Brazil. The results showed that the use of this teaching strategy inducted improvements to regular reading and debate among students

Endoparasites of horses from the Formiga city, located in center-west region of the state of Minas Gerais, Brazil

With the aim of studying the endoparasite fauna of horses from the Formiga city, located in center-west region of the state of Minas Gerais, 25 animals that were naturally infected with helminths were evaluated. By means of parasitological necropsies, different endoparasites were found. The subfamily Cyathostominae presented the highest incidence, followed by Trichostrongylus axei, Oxyuris equi, Triodontophorus serratus, Strongyloides westeri, Strongylus edentatus, Habronema muscae, Parascaris equorum, Probstmayria vivipara, Strongylus vulgaris, Gasterophilus nasalis, Anoplocephala magna and Anoplocephala perfoliata. In the present study, if the species Probstmayria vivipara was not considered in the prevalence, the frequency of Cyathostominae was equivalent to 94.85%. The results obtained in this study allowed us to detect and identify different species of helminths in horses, and confirmed the high incidence of nematodes belonging to the subfamily Cyathostominae in the center-west region of Minas Gerais.Com o objetivo de estudar a fauna de endoparasitas de equinos da Região Centro-Oeste do Estado de Minas Gerais, 25 animais naturalmente infectados por helmintos foram avaliados. Por meio de necropsias parasitológicas, diferentes endoparasitas foram identificados. A sub - família Cyathostominae apresentou maior incidência, seguido por Trichostrongylus axei, Oxyuris equi, Triodontophorus serratus, Strongyloides westeri, Strongylus edentatus,Habronema muscae, Parascaris equorum, Probstmayria vivipara, Strongylus vulgaris, Gasterophilus nasalis,Anoplocephala magnae Anoplocephala perfoliata. No presente estudo, se não for considerada a espécieProbstmayria vivipara na prevalência, a frequência de Cyathostominae é equivalente a 94,85%. Os resultados obtidos neste estudo, permitiu detectar e identificar diferentes espécies de helmintos em equinos, bem como confirmar a elevada incidência de nematódeos pertencentes à sub-família Cyathostominae na Região Centro-Oeste de Minas Gerais