Dinâmica espacial e formação de clusters significativos no setor agropecuário de Minas Gerais

A produção do setor agropecuário brasileiro passou recentemente por mudanças profundas, com rebatimentos na competitividade dos estados e, em particular, do Estado de Minas Gerais. O objetivo do trabalho é testar a hipótese de que  existe no estado de Minas Gerais uma natureza multidirecional do padrão de interação intermunicipal, que produz notórios efeitos espaciais. A análise espacial dos dados é categórica, existe certa dependência espacial na produção agropecuária, o que implica em dizer que o padrão de interação intermunicipal produz externalidades espaciais positivas, que formam e ampliam os clusters significativos, explicando o maior dinamismo setorial nas regiões mais produtivas

Physical training improves physical activity levels but is associated with amplification of sedentary behavior in older women

Physical activity level (PAL) and sedentary behavior (SB) are independent predictors of mortality. It is unclear how these predictors interact with each other and health variables. Investigate the bidirectional relationship between PAL and SB, and their impact and health variables of women aged 60 to 70 years. One hundred forty-two older adults women (66.3 ± 2.9 years) considered insufficiently active were submitted to 14 weeks of multicomponent training (MT), multicomponent training with flexibility (TMF), or the control group (CG). PAL variables were analyzed by accelerometry and QBMI questionnaire, physical activity (PA) light, moderate, vigorous and CS by accelerometry, 6 min walk (CAM), SBP, BMI, LDL, HDL, uric acid, triglycerides, glucose and cholesterol total. In linear regressions, CS was associated with glucose (B:12.80; CI:9.31/20.50; p < 0.001; R2:0.45), light PA (B:3.10; CI:2, 41/4.76; p < 0.001; R2:0.57), NAF by accelerometer (B:8.21; CI:6.74/10.02; p < 0.001; R2:0.62), vigorous PA (B:794.03; CI:682.11/908.2; p < 0.001; R2:0.70), LDL (B:13.28; CI:7.45/16.75; p < 0.002; R2:0.71) and 6 min walk (B:3.39; CI:2.96/8.75; p < 0.004; R2:0.73). NAF was associated with mild PA (B:0.246; CI:0.130/0.275; p < 0.001; R2:0.624), moderate PA (B:0.763; CI:0.567/0.924; p < 0.001; R2:0.745), glucose (B:−0.437; CI:−0.789/−0.124; p < 0.001; R2:0.782), CAM (B:2.223; CI:1.872/4.985; p < 0.002; R2:0.989) and CS (B:0.253; CI: 0.189/0.512; p < 0.001; R2:1.94). The NAF can enhance CS. Build a new look at how these variables are independent but dependent simultaneously, being able to influence the quality of health when this dependence is denied

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

Factors influencing terrestriality in primates of the Americas and Madagascar

Among mammals, the order Primates is exceptional in having a high taxonomic richness in which the taxa are arboreal, semiterrestrial, or terrestrial. Although habitual terrestriality is pervasive among the apes and African and Asian monkeys (catarrhines), it is largely absent among monkeys of the Americas (platyrrhines), as well as galagos, lemurs, and lorises (strepsirrhines), which are mostly arboreal. Numerous ecological drivers and species-specific factors are suggested to set the conditions for an evolutionary shift from arboreality to terrestriality, and current environmental conditions may provide analogous scenarios to those transitional periods. Therefore, we investigated predominantly arboreal, diurnal primate genera from the Americas and Madagascar that lack fully terrestrial taxa, to determine whether ecological drivers (habitat canopy cover, predation risk, maximum temperature, precipitation, primate species richness, human population density, and distance to roads) or species-specific traits (bodymass, group size, and degree of frugivory) associate with increased terrestriality. We collated 150,961 observation hours across 2,227 months from 47 species at 20 sites in Madagascar and 48 sites in the Americas. Multiple factors were associated with ground use in these otherwise arboreal species, including increased temperature, a decrease in canopy cover, a dietary shift away from frugivory, and larger group size. These factors mostly explain intraspecific differences in terrestriality. As humanity modifies habitats and causes climate change, our results suggest that species already inhabiting hot, sparsely canopied sites, and exhibiting more generalized diets, are more likely to shift toward greater ground use

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