Modelado de los factores ambientales que determinan la distribución de especies sinantrópicas de physalis

Modelado de la distribución de especies sinantrópicas de Physalis. Lugares con distribución potencial y real de especies de Physalis (tomatillos)El modelado de la distribución de especies sinantrópicas (malezas) y los factores ambientales que determinan dicha distribución han sido poco estudiados. Physalis tiene 90 especies distribuidas en las Américas, y algunas especies en el Viejo Mundo. México alberga cerca de 70 especies y aproximadamente 35 son endémicas. La sección Angulatae incluye diez especies, todas sinantrópicas en mayor o menor grado. Las especies se concentran en la Sierra Madre Occidental, la Sierra Madre del Sur y en la Faja Volcánica Transmexicana. El objetivo de este trabajo fue modelar e identifi car las variables ambientales que determinan la distribución potencial de las diez especies de Physalis sección Angulatae. Se emplearon 524 registros revisados por especialistas en la taxonomía del grupo y 20 variables ambientales; de éstas 12 fueron climáticas, tres edáfi cas, dos de cobertura de la vegetación y tres de atributos topográfi cos. Los modelos se calcularon con el algoritmo Maxent. Los resultados del modelado mostraron que el hábitat más adecuado para la persistencia de ocho especies se defi nió por el índice normalizado diferencial de vegetación en los meses secos del año, la materia orgánica del suelo, la altitud y el aspecto, las cuales en conjunto explicaron entre el 73 y el 91 % de la variación en su distribución. Otros factores como la precipitación total anual y la isotermalidad determinaron la distribución de P. crassifolia y de P. glabra, respectivamente. El índice normalizado diferencial de la vegetación y las propiedades de los suelos, son predictores determinantes en la distribución potencial de las especies de la sección Angulatae. Palabras clave: máxima entropía, predictores ambientales, sección Angulatae, tomatillos. Abstract: The modeling of the distribution of synanthropic (weedy) species and the environmental factors that determine their distribution is not well studied. Physalis has 90 species distributed in the Americas, and several in the Old World. Mexico harbors about 70 species and approximately 35 are endemic. The section Angulatae includes 10 species, all synanthropic to some degree. The species tended to concentrate in the Sierra Madre Occidental, the Sierra Madre del Sur, and the Transmexican Volcanic Belt. The aim of this work was to model and identify the environmental variables that determine the potential distribution of the ten species of Physalis sect. Angulatae. A total of 524 records that had been verifi ed by the taxonomic experts of the group and 20 environmental variables were used; 12 were climatic, and the other eight were novel and of different types: three soil properties, two normalized differential vegetation indexes and three topographic attributes. The models were obtained with the Maxent algorithm. The results of the modelling showed that the most suitable habitat for the persistence of eight species was delimited by the normalized differential vegetation index during the dry months of the year, the soil organic matter, the elevation and the aspect, which together explained between the 73 and 91 % of the variation in its distribution. Other groups of factors like total precipitation and isthermality determined the distribution of P. crassifolia and P. glabra, respectively. We show that the novel environmental factors such as the normalized differential vegetation index and the soil properties were decisive predictors in the potential distribution of the species in the section Angulatae.Conacy

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

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

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

Proyecto de Tesis II - CI189 - 202101

DESCRIPCIÓN Curso de especialidad en la carrera de ingeniería civil de carácter teórico-práctico dirigido a los estudiantes del 10mo ciclo. El curso Proyecto de Tesis II busca que los estudiantes de Ingeniería Civil apliquen sus capacidades adquiridas durante todos sus estudios, en completar una investigación, que plantea resolver una problemática en una de las líneas de la carrera. Con la ayuda de un docente asesor especialista en el tema lograran redactar el informe de tesis al 100%, este informe será revisado por otro docente especialista que proporciona sugerencias de mejoras a la investigación. Por último, los estudiantes exponen ante un jurado especialista sus resultados quienes evalúan y también hacen sugerencia de mejoras a la investigación. PROPÓSITO En el Perú actualmente existe un gran número de estudiantes de Ingeniería Civil que no cuentan con el título profesional, por no realizar la tesis de investigación, lo cual disminuye significativamente su desarrollo profesional y sus oportunidades laborales. Adicionalmente las leyes Peruanas exigen que para el obtener el bachillerato los estudiantes deben redactar un trabajo de investigación. El curso de proyecto de Tesis 2 permite que los estudiantes puedan desarrollar el 100% de la Tesis y un trabajo de investigación, siendo ambos certificados por un asesor y un jurado evaluador. Este curso contribuye con el desarrollo de las competencias generales de comunicación escrita, comunicación oral, manejo de la información y ciudadanía y las competencias específicas 2, 3, 5 y 6 de ABET, todas a nivel de logro 3. Cuenta con el prerrequisito de Proyecto de Tesis 1

Proyecto De Tesis I - CI186 - 202101

Descripción: Curso de especialidad en la carrera de ingeniería civil de carácter teórico-práctico dirigido a los estudiantes del 9no ciclo. El curso Proyecto de Tesis I busca que los estudiantes de Ingeniería Civil apliquen sus capacidades adquiridas durante todos sus estudios, en completar una investigación, que plantea resolver una problemática en una de las líneas de la carrera. Con la ayuda de un docente asesor especialista en el tema lograran redactar el informe de tesis al 50%, este informe será revisado por otro docente especialista que proporciona sugerencias de mejoras a la investigación. Por último, los estudiantes exponen ante un jurado especialista sus resultados quienes evalúan y también hacen sugerencia de mejoras a la investigación. Propósito: En el Perú actualmente existe un gran número de estudiantes de Ingeniería Civil que no cuentan con el título profesional, por no realizar la tesis de investigación, lo cual disminuye significativamente su desarrollo profesional y sus oportunidades laborales. El curso de proyecto de Tesis 1 permite que los estudiantes puedan desarrollar el 50% de la Tesis de investigación, siendo la misma certificada por un asesor y un jurado evaluador. Contribuye con el desarrollo de las competencias generales de Pensamiento Crítico, Razonamiento Cuantitativo, Pensamiento Innovador y las competencias específicas 1, 4 y 7 de ABET, todas a nivel de logro 3

Proyecto De Tesis I - CI186 - 202102

Descripción: Curso de especialidad en la carrera de ingeniería civil de carácter teórico-práctico dirigido a los estudiantes del 9no ciclo. El curso Proyecto de Tesis I busca que los estudiantes de Ingeniería Civil apliquen sus capacidades adquiridas durante todos sus estudios, en completar una investigación, que plantea resolver una problemática en una de las líneas de la carrera. Con la ayuda de un docente asesor especialista en el tema lograran redactar el informe de tesis al 50%, este informe será revisado por otro docente especialista que proporciona sugerencias de mejoras a la investigación. Por último, los estudiantes exponen ante un jurado especialista sus resultados quienes evalúan y también hacen sugerencia de mejoras a la investigación. Propósito: En el Perú actualmente existe un gran número de estudiantes de Ingeniería Civil que no cuentan con el título profesional, por no realizar la tesis de investigación, lo cual disminuye significativamente su desarrollo profesional y sus oportunidades laborales. El curso de proyecto de Tesis 1 permite que los estudiantes puedan desarrollar el 50% de la Tesis de investigación, siendo la misma certificada por un asesor y un jurado evaluador. Contribuye con el desarrollo de las competencias generales de Pensamiento Crítico, Razonamiento Cuantitativo, Pensamiento Innovador y las competencias específicas 1, 4 y 7 de ABET, todas a nivel de logro 3

Proyecto de Tesis II - CI189 - 202102

DESCRIPCIÓN Curso de especialidad en la carrera de ingeniería civil de carácter teórico-práctico dirigido a los estudiantes del 10mo ciclo. El curso Proyecto de Tesis II busca que los estudiantes de Ingeniería Civil apliquen sus capacidades adquiridas durante todos sus estudios, en completar una investigación, que plantea resolver una problemática en una de las líneas de la carrera. Con la ayuda de un docente asesor especialista en el tema lograran redactar el informe de tesis al 100%, este informe será revisado por otro docente especialista que proporciona sugerencias de mejoras a la investigación. Por último, los estudiantes exponen ante un jurado especialista sus resultados quienes evalúan y también hacen sugerencia de mejoras a la investigación. PROPÓSITO En el Perú actualmente existe un gran número de estudiantes de Ingeniería Civil que no cuentan con el título profesional, por no realizar la tesis de investigación, lo cual disminuye significativamente su desarrollo profesional y sus oportunidades laborales. Adicionalmente las leyes Peruanas exigen que para el obtener el bachillerato los estudiantes deben redactar un trabajo de investigación. El curso de proyecto de Tesis 2 permite que los estudiantes puedan desarrollar el 100% de la Tesis y un trabajo de investigación, siendo ambos certificados por un asesor y un jurado evaluador. Este curso contribuye con el desarrollo de las competencias generales de comunicación escrita, comunicación oral, manejo de la información y ciudadanía y las competencias específicas 2, 3, 5 y 6 de ABET, todas a nivel de logro 3. Cuenta con el prerrequisito de Proyecto de Tesis 1