Diagnóstico de clima organizacional : uma proposta para sistema de diagnóstico permanente

Mestrado em Gestão de Recursos HumanosO presente trabalho tem como principal objetivo desenvolver e validar um instrumento de investigação e diagnóstico do clima organizacional da Parque EXPO. A identificação das principais dimensões a considerar no modelo de clima, foram obtidas através da revisão da literatura, com posterior validação dos colaboradores da empresa. Com base no modelo, procurou-se compreender a relação existente entre a Satisfação dos colaboradores e cada uma das dimensões selecionadas: Avaliação, Bem Estar, Recompensa e Benefícios, Clareza, Comunicação, Orgulho, Liderança, Relacionamento e Formação. O diagnóstico de clima foi concretizado através da aplicação de um questionário aos 175 colaboradores da empresa. Da análise desenvolvida, destaca-se que os colaboradores da Parque EXPO revelam um clima muito favorável, apresentando resultados globais bastante positivos. Os dados obtidos permitiram ainda a reavaliação de diversos aspetos organizacionais, resultando num Plano de Ação de Melhorias definido para o curto e longo prazos. A maior preocupação centrou-se no sistema de Gestão de Desempenho, em relação ao qual foram delineadas estratégias graduais de atuação e otimização. O diagnóstico do clima organizacional representa um modo constante de obter e de fornecer feedback, orientando a participação dos colaboradores nos processos de decisão. Resulta assim, a proposta da realização de um diagnóstico bianual visando, por um lado, a monitorização das medidas corretivas anteriores e por outro, a proatividade da gestão antecipando e solucionando problemas relativos aos colaboradores e ao ambiente de trabalho, prevenindo situações críticas que podem influenciar negativamente o fluxo normal de atividade das empresas.This work has the main purpose to develop and validate an instrument of investigation and diagnosis of the organizational climate at Parque EXPO. The key dimensions to take into consideration in the climate model were identified through literature review, with subsequent validation of company employees. Based on the model, we sought to understand the relationship between employee satisfaction and each of the selected dimensions: Evaluation, Welfare, Reward and Benefits, Clarity, Communication, Pride, Leadership, Relationships and Training. The climate diagnosis has been achieved by applying a questionnaire to the 175 employees. The analysis emphasized the fact that the employees of Parque EXPO have developed a very favorable climate, with very positive results overall. The enquiry results also allowed the reassessment of various organizational aspects, and the result was an Action Plan for Improvements set for the short and long term. The main concern centered on the Performance Management System, for which gradual performance and optimization strategies were laid. The diagnosis of the organizational climate is a constant mode of obtaining and providing feedback, and off guiding employee participation in decision¬making. Hence the proposal of making a biannual diagnosis aiming at: monitoring the effects of previous corrective measures, on the one hand; and at a type of proactive management that anticipates and solves problems relating to employees and the working environment, and that prevents critical situations that might affect the regular functioning and workflow of the company

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

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data