scholarly journals Assessing the Spatial and Spatio-Temporal Distribution of Forest Species via Bayesian Hierarchical Modeling

Forests ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 573 ◽  
Author(s):  
Óscar Rodríguez de Rivera ◽  
Antonio López-Quílez ◽  
Marta Blangiardo
Keyword(s):  
Hierarchical Modeling ◽  
Temporal Distribution ◽  
Short Term ◽  
Distribution Models ◽  
Address Climate Change ◽  
Bayesian Hierarchical ◽  
Temporal Models ◽  
Spatio Temporal ◽  
The Relationship

Climatic change is expected to affect forest development in the short term, as well as the spatial distribution of species in the long term. Species distribution models are potentially useful tools for guiding species choices in reforestation and forest management prescriptions to address climate change. The aim of this study is to build spatial and spatio-temporal models to predict the distribution of four different species present in the Spanish Forest Inventory. We have compared the different models and showed how accounting for dependencies in space and time affect the relationship between species and environmental variables.

scholarly journals Spatiotemporal mapping and detection of mortality cluster due to cardiovascular disease with Bayesian hierarchical framework using integrated nested Laplace approximation: A discussion of suitable statistic applications in Kersa, Oromia, Ethiopia

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Melkamu Dedefo ◽  
Henry Mwambi ◽  
Sileshi Fanta ◽  
Nega Assefa
Keyword(s):  
Cause Of Death ◽  
Linear Models ◽  
Temporal Distribution ◽  
Mortality Data ◽  
Contributing Factors ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Non Parametric

Cardiovascular diseases (CVDs) are the leading cause of death globally and the number one cause of death globally. Over 75% of CVD deaths take place in low- and middle-income countries. Hence, comprehensive information about the spatio-temporal distribution of mortality due to cardio vascular disease is of interest. We fitted different spatio-temporal models within Bayesian hierarchical framework allowing different space-time interaction for mortality mapping with integrated nested Laplace approximations to analyze mortality data extracted from the health and demographic surveillance system in Kersa District in Hararege, Oromia Region, Ethiopia. The result indicates that non-parametric time trends models perform better than linear models. Among proposed models, one with non-parametric trend, type II interaction and second order random walk but without unstructured time effect was found to perform best according to our experience and. simulation study. An application based on real data revealed that, mortality due to CVD increased during the study period, while administrative regions in northern and south-eastern part of the study area showed a significantly elevated risk. The study highlighted distinct spatiotemporal clusters of mortality due to CVD within the study area. The study is a preliminary assessment step in prioritizing areas for further and more comprehensive research raising questions to be addressed by detailed investigation. Underlying contributing factors need to be identified and accurately quantified.

scholarly journals Spatio-Temporal Distribution of Landslides in Java and the Triggering Factors

2017 ◽  
Vol 31 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Danang Sri Hadmoko ◽  
Franck Lavigne ◽  
Junun Sartohadi ◽  
Christopher Gomez ◽  
D Daryono
Keyword(s):  
Temporal Distribution ◽  
Rainfall Threshold ◽  
Short Term ◽  
Rainfall Events ◽  
Landslide Occurrence ◽  
Central Java ◽  
Causative Factors ◽  
Spatio Temporal ◽  
Gis Environment

Java Island, the most populated island of Indonesia, is prone to landslide disasters. Their occurrence and impact have increased mainly as the result of natural factors, aggravated by human imprint. This paper is intended to analyse: (1) the spatio-temporal variation of landslides in Java during short term and long-term periods, and (2) their causative factors such as rainfall, topography, geology, earthquakes, and land-use. The evaluation spatially and temporally of historical landslides and consequences were based on the landslide database covering the period of 1981 – 2007 in the GIS environment. Database showed that landslides distributed unevenly between West Java (67 %), Central Java (29 %) and East Java (4 %). Slope failures were most abundant on the very intensively weathered zone of old volcanic materials on slope angles of 30O – 40O. Rainfall threshold analysis showed that shallow landslides and deep-seated landslides were triggered by rainfall events of 300 – 600 mm and > 600 mm respectively of antecedent rainfall during 30 consecutive days, and many cases showed that the landslides were not always initiated by intense rainfall during the landslide day. Human interference plays an important role in landslide occurrence through land conversion from natural forest to dryland agriculture which was the host of most of landslides in Java. These results and methods can be used as valuable information on the spatio-temporal characteristics of landslides in Java and their relationship with causative factors, thereby providing a sound basis for landslide investigation in more detail.

scholarly journals Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

2020 ◽  
Author(s):  
Francois Rerolle ◽  
Emily Dantzer ◽  
Andrew A. Lover ◽  
John M. Marshall ◽  
Bouasy Hongvanthong ◽  
...  
Keyword(s):  
Forest Cover ◽  
Malaria Incidence ◽  
Lao Pdr ◽  
Short Term ◽  
Modeling Framework ◽  
Temporal Modeling ◽  
Forested Areas ◽  
Spatio Temporal ◽  
The Relationship

AbstractAs countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on forest-going populations, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases in confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest-going populations on malaria transmission in the GMS.

scholarly journals Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Francois Rerolle ◽  
Emily Dantzer ◽  
Andrew A Lover ◽  
John M Marshall ◽  
Bouasy Hongvanthong ◽  
...  
Keyword(s):  
Forest Cover ◽  
Malaria Incidence ◽  
Lao Pdr ◽  
Short Term ◽  
Modeling Framework ◽  
Temporal Modeling ◽  
Forested Areas ◽  
Spatio Temporal ◽  
The Relationship

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest activities on malaria transmission in the GMS.

scholarly journals Spatio-temporal associations of air pollutant concentrations, GP respiratory consultations and respiratory inhaler prescriptions: a 5-year study of primary care in the borough of Lambeth, South London

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Mark Ashworth ◽  
◽  
Antonis Analitis ◽  
David Whitney ◽  
Evangelia Samoli ◽  
...  
Keyword(s):  
Primary Care ◽  
Negative Binomial ◽  
Hospital Admissions ◽  
Air Pollutant ◽  
Respiratory Morbidity ◽  
Short Term ◽  
Short Term Exposure ◽  
Short And Long Term ◽  
Spatio Temporal

Abstract Background Although the associations of outdoor air pollution exposure with mortality and hospital admissions are well established, few previous studies have reported on primary care clinical and prescribing data. We assessed the associations of short and long-term pollutant exposures with General Practitioner respiratory consultations and inhaler prescriptions. Methods Daily primary care data, for 2009–2013, were obtained from Lambeth DataNet (LDN), an anonymised dataset containing coded data from all patients (1.2 million) registered at general practices in Lambeth, an inner-city south London borough. Counts of respiratory consultations and inhaler prescriptions by day and Lower Super Output Area (LSOA) of residence were constructed. We developed models for predicting daily PM2.5, PM10, NO2 and O3 per LSOA. We used spatio-temporal mixed effects zero inflated negative binomial models to investigate the simultaneous short- and long-term effects of exposure to pollutants on the number of events. Results The mean concentrations of NO2, PM10, PM2.5 and O3 over the study period were 50.7, 21.2, 15.6, and 49.9 μg/m3 respectively, with all pollutants except NO2 having much larger temporal rather than spatial variability. Following short-term exposure increases to PM10, NO2 and PM2.5 the number of consultations and inhaler prescriptions were found to increase, especially for PM10 exposure in children which was associated with increases in daily respiratory consultations of 3.4% and inhaler prescriptions of 0.8%, per PM10 interquartile range (IQR) increase. Associations further increased after adjustment for weekly average exposures, rising to 6.1 and 1.2%, respectively, for weekly average PM10 exposure. In contrast, a short-term increase in O3 exposure was associated with decreased number of respiratory consultations. No association was found between long-term exposures to PM10, PM2.5 and NO2 and number of respiratory consultations. Long-term exposure to NO2 was associated with an increase (8%) in preventer inhaler prescriptions only. Conclusions We found increases in the daily number of GP respiratory consultations and inhaler prescriptions following short-term increases in exposure to NO2, PM10 and PM2.5. These associations are more pronounced in children and persist for at least a week. The association with long term exposure to NO2 and preventer inhaler prescriptions indicates likely increased chronic respiratory morbidity.

Molecular biology of enzyme adaptations in higher eukaryotes

Genome ◽  
1989 ◽  
Vol 31 (1) ◽  
pp. 272-283 ◽  
Author(s):  
Donal A. Hickey ◽  
Bernhard F. Benkel ◽  
Charalambos Magoulas
Keyword(s):  
Molecular Biology ◽  
Tissue Specificity ◽  
Enzyme System ◽  
Direct Interaction ◽  
Evolutionary Genetics ◽  
Short Term ◽  
Eukaryotic Genes ◽  
Higher Eukaryotes ◽  
The Relationship

Multicellular eukaryotes have evolved complex homeostatic mechanisms that buffer the majority of their cells from direct interaction with the external environment. Thus, in these organisms long-term adaptations are generally achieved by modulating the developmental profile and tissue specificity of gene expression. Nevertheless, a subset of eukaryotic genes are still involved in direct responses to environmental fluctuations. It is the adaptative responses in the expression of these genes that buffers many other genes from direct environmental effects. Both microevolutionary and macroevolutionary patterns of change in the structure and regulation of such genes are illustrated by the sequences encoding α-amylases. The molecular biology and evolution of α-amylases in Drosophila and other higher eukaryotes are presented. The amylase system illustrates the effects of both long-term and short-term natural selection, acting on both the structural and regulatory components of a gene–enzyme system. This system offers an opportunity for linking evolutionary genetics to molecular biology, and it allows us to explore the relationship between short-term microevolutionary changes and long-term adaptations.Key words: gene regulation, molecular evolution, eukaryotes, Drosophila, amylase.

scholarly journals BANK PROFITABILITY AND MACROECONOMY: EVIDENCE FROM LITHUANIA

2017 ◽  
Vol 24 (2) ◽  
pp. 383-405 ◽  
Author(s):  
Laurynas NARUŠEVIČIUS
Keyword(s):  
Banking Sector ◽  
Short Term ◽  
Empirical Examination ◽  
Bank Profitability ◽  
Pooled Mean Group ◽  
Real Gdp ◽  
Income Statement ◽  
Main Determinants ◽  
The Relationship

The purpose of this paper is to investigate the relationship between profitability of the Lithuanian banking sector and its internal and external determinants. We use the panel error correc­tion model to assess long-term and short-term determinants of items from bank income statements (net interest income, net fee and commission income and operating expenses). The results of the pooled mean group estimator show that bank size and real GDP are the main determinants in the long-term. Meanwhile, empirical examination suggests various variables as short-term determinants of income statement items. The pooled mean group estimation technique and the analysis of sepa­rate income statement items enable us to have a better insight into the Lithuanian banking sector and determinants of its revenue and expenses.

scholarly journals Bootstrap ARDL Test on the Relationship among Trade, FDI and CO2 Emissions: Based on the Experience of BRICS Countries

2019 ◽  
Author(s):  
Fumei He ◽  
Ke-Chiun Chang ◽  
Min Li ◽  
Xueping Li ◽  
Fangjhy Li
Keyword(s):  
Foreign Direct Investment ◽  
Causal Relationship ◽  
Co2 Emissions ◽  
Direct Investment ◽  
Export Trade ◽  
Short Term ◽  
Brics Countries ◽  
The Relationship ◽  
Cointegration Relationship

We used the Bootstrap ARDL method to test the relationship between the export trades, FDI and CO2 emissions of the BRICS countries. We found that China's foreign direct investment and the lag one period of CO2 emissions have a cointegration on exports. South Africa's foreign direct investment and CO2 emissions have a cointegration relationship with the lag one period of exports, and South Africa's the lag one period of exports and foreign direct investment have a cointegration relationship with the lag one period of CO2 emissions. But whether it is China or South Africa, these three variables have no causal relationship in the long-term. Among the variables of other BRICS countries, Russia is the only country showed degenerate case #1 in McNown et al. mentioned in their paper. When we examined short-term causality, we found that CO2 emissions and export trade showed a reverse causal relationship, while FDI and carbon emissions were not so obvious. Export trade has a positive causal relationship with FDI. Those variables are different from different situations and different countries.

scholarly journals Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers´ Cohort

2021 ◽  
Author(s):  
KARLA CERVANTES-MARTINEZ ◽  
HORACIO RIOJAS-RODRÍGUEZ ◽  
CARLOS DÍAZ-AVALOS ◽  
HORTENSIA MORENO-MACÍAS ◽  
RUY LÓPEZ-RIDAURA ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Human Mobility ◽  
Epidemiological Studies ◽  
Additive Models ◽  
Secondary Source ◽  
Source Information ◽  
Promising Alternative ◽  
Temporal Models ◽  
Spatio Temporal

Epidemiological studies on air pollution in Mexico often use the environmental concentrations of pollutants as measured by monitors closest to the home of participants as exposure proxies, yet this approach does not account for the space gradients of pollutants and ignores intra-city human mobility. This study aimed to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in ~16,500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants, and used secondary source information on geographical and meteorological variables as well as other pollutants to fit two generalized additive models capable of predicting monthly PM2.5 and NO2 concentrations during the 2004-2019 period. Both models were evaluated through 10-fold cross-validation, and showed high predictive accuracy with out-of-sample data and no overfitting (CV-RMSE=0.102 for PM2.5 and CV-RMSE=4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) mg/m3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a promising alternative for estimating PM2.5 and NO2 exposure with high spatio-temporal resolution for epidemiological studies in the Mexico City Metropolitan Area.

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