scholarly journals A cross-scale assessment of productivity-diversity relationships

2019 ◽  
Author(s):  
Dylan Craven ◽  
Masha T. van der Sande ◽  
Carsten Meyer ◽  
Katharina Gerstner ◽  
Joanne M. Bennett ◽  
...  
Keyword(s):  
Structural Equation ◽  
Net Primary Productivity ◽  
Woody Species ◽  
Time Period ◽  
Forest Models ◽  
Random Forest Models ◽  
Spatial Grain ◽  
Median Area ◽  
Using Data ◽  
Underlying Mechanisms

AbstractAimBiodiversity and ecosystem productivity vary across the globe and considerable effort has been made to describe their relationships. Biodiversity-ecosystem functioning research has traditionally focused on how experimentally controlled species richness affects net primary productivity (S→NPP) at small spatial grains. In contrast, the influence of productivity on richness (NPP→S) has been explored at many grains in naturally assembled communities. Mismatches in spatial scale between approaches have fostered debate about the strength and direction of biodiversity-productivity relationships. Here we examine the direction and strength of productivity’s influence on diversity (NPP→S) and of diversity’s influence on productivity (S→NPP), and how this varies across spatial grains.Locationcontiguous USATime period1999 - 2015Major taxa studiedwoody species (angiosperms and gymnosperms)MethodsUsing data from North American forests at grains from local (672 m2) to coarse spatial units (median area = 35,677 km2), we assess relationships between diversity and productivity using structural equation and random forest models, while accounting for variation in climate, environmental heterogeneity, management, and forest age.ResultsWe show that relationships between S and NPP strengthen with spatial grain. Within each grain, S→NPP and NPP→S have similar magnitudes, meaning that processes underlying S→NPP and NPP→S either operate simultaneously, or that one of them is real and the other is an artifact. At all spatial grains, S was one of the weakest predictors of forest productivity, which was largely driven by biomass, temperature, and forest management and age.Main conclusionsWe conclude that spatial grain mediates relationships between biodiversity and productivity in real-world ecosystems and that results supporting predictions from each approach (NPP→S and S→NPP) serve as an impetus for future studies testing underlying mechanisms. Productivity-diversity relationships emerge at multiple spatial grains, which should widen the focus of national and global policy and research to larger spatial grains.

scholarly journals Deciphering organization of GOES-16 green cumulus through the empirical orthogonal function (EOF) lens

2021 ◽  
Vol 21 (16) ◽  
pp. 12261-12272
Author(s):  
Tom Dror ◽  
Mickaël D. Chekroun ◽  
Orit Altaratz ◽  
Ilan Koren
Keyword(s):  
Gravity Waves ◽  
Empirical Orthogonal Function ◽  
Data Driven ◽  
Time Variability ◽  
Spatial Properties ◽  
Orthogonal Function ◽  
Time Period ◽  
Dominant Period ◽  
Using Data ◽  
Underlying Mechanisms

Abstract. A subset of continental shallow convective cumulus (Cu) cloud fields has been shown to have distinct spatial properties and to form mostly over forests and vegetated areas, thus referred to as “green Cu” (Dror et al., 2020). Green Cu fields are known to form organized mesoscale patterns, yet the underlying mechanisms, as well as the time variability of these patterns, are still lacking understanding. Here, we characterize the organization of green Cu in space and time, by using data-driven organization metrics and by applying an empirical orthogonal function (EOF) analysis to a high-resolution GOES-16 dataset. We extract, quantify, and reveal modes of organization present in a green Cu field, during the course of a day. The EOF decomposition is able to show the field's key organization features such as cloud streets, and it also delineates the less visible ones, as the propagation of gravity waves (GWs) and the emergence of a highly organized grid on a spatial scale of hundreds of kilometers, over a time period that scales with the field's lifetime. Using cloud fields that were reconstructed from different subgroups of modes, we quantify the cloud street's wavelength and aspect ratio, as well as the GW-dominant period.

scholarly journals Deciphering Organization of GOES–16 Green Cumulus, through the EOF Lens

2021 ◽  
Author(s):  
Tom Dror ◽  
Mickaël D. Chekroun ◽  
Orit Altaratz ◽  
Ilan Koren
Keyword(s):  
Gravity Waves ◽  
Data Driven ◽  
Time Variability ◽  
Eof Analysis ◽  
Spatial Properties ◽  
Orthogonal Function ◽  
Time Period ◽  
Dominant Period ◽  
Using Data ◽  
Underlying Mechanisms

Abstract. A subset of continental shallow convective Cumulus (Cu) cloud fields were shown to have unique spatial properties and to form mostly over forests and vegetated areas, thus referred to as green Cu. Green Cu fields are known to form organized mesoscale patterns, yet the underlying mechanisms as well as the time variability of these patterns are still lacking understanding. Here, we characterize the organization of green Cu in space and time, by using data driven organization metrics, and by applying an Empirical Orthogonal Function (EOF) analysis to a high-resolution GOES–16 dataset. We extract, quantify and reveal modes of organization present in a green Cu field, during the course of a day. The EOF decomposition is able to show the field's key organization features such as cloud streets, and it also delineates the less visible ones, as the propagation of gravity waves (GW), and the emergence of a highly organized grid on a spatial scale of hundreds of kilometers, over a time period that scales with the field's lifetime. Using cloud fields that were reconstructed from different subgroups of modes, we quantify the cloud street's wavelength and aspect ratio, as well as the GW dominant period.

scholarly journals Longitudinal stability of urinary extracellular vesicle protein patterns within and between individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leyla A. Erozenci ◽  
Sander R. Piersma ◽  
Thang V. Pham ◽  
Irene V. Bijnsdorp ◽  
Connie R. Jimenez
Keyword(s):  
Expression Patterns ◽  
Interaction Network ◽  
Extracellular Vesicle ◽  
Protein Signaling ◽  
Protein Patterns ◽  
Specific Expression ◽  
Non Invasive ◽  
Data Independent Acquisition ◽  
Time Period ◽  
Using Data

AbstractThe protein content of urinary extracellular vesicles (EVs) is considered to be an attractive non-invasive biomarker source. However, little is known about the consistency and variability of urinary EV proteins within and between individuals over a longer time-period. Here, we evaluated the stability of the urinary EV proteomes of 8 healthy individuals at 9 timepoints over 6 months using data-independent-acquisition mass spectrometry. The 1802 identified proteins had a high correlation amongst all samples, with 40% of the proteome detected in every sample and 90% detected in more than 1 individual at all timepoints. Unsupervised analysis of top 10% most variable proteins yielded person-specific profiles. The core EV-protein-interaction network of 516 proteins detected in all measured samples revealed sub-clusters involved in the biological processes of G-protein signaling, cytoskeletal transport, cellular energy metabolism and immunity. Furthermore, gender-specific expression patterns were detected in the urinary EV proteome. Our findings indicate that the urinary EV proteome is stable in longitudinal samples of healthy subjects over a prolonged time-period, further underscoring its potential for reliable non-invasive diagnostic/prognostic biomarkers.

Comparing respondents who passed versus failed an Instructional Manipulation Check: A case study about support for climate change policies

2021 ◽  
Vol 63 (4) ◽  
pp. 408-415
Author(s):  
Maria Rubio Juan ◽  
Melanie Revilla
Keyword(s):  
Climate Change ◽  
External Validity ◽  
Structural Equation ◽  
Online Survey ◽  
Equation Modeling ◽  
Manipulation Check ◽  
Climate Change Policies ◽  
Instructional Manipulation ◽  
Using Data

The presence of satisficers among survey respondents threatens survey data quality. To identify such respondents, Oppenheimer et al. developed the Instructional Manipulation Check (IMC), which has been used as a tool to exclude observations from the analyses. However, this practice has raised concerns regarding its effects on the external validity and the substantive conclusions of studies excluding respondents who fail an IMC. Thus, more research on the differences between respondents who pass versus fail an IMC regarding sociodemographic and attitudinal variables is needed. This study compares respondents who passed versus failed an IMC both for descriptive and causal analyses based on structural equation modeling (SEM) using data from an online survey implemented in Spain in 2019. These data were analyzed by Rubio Juan and Revilla without taking into account the results of the IMC. We find that those who passed the IMC do differ significantly from those who failed for two sociodemographic and five attitudinal variables, out of 18 variables compared. Moreover, in terms of substantive conclusions, differences between those who passed and failed the IMC vary depending on the specific variables under study.

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

scholarly journals 33. Concerning Trends of Pediatric Leprosy in Minas Gerais, Brazil and Associations with Number of Municipality Medical Facilities

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S17-S17
Author(s):  
Taylor Landay ◽  
Julie A Clennon ◽  
José A Ferreira ◽  
Lucia A Fraga ◽  
Maria Aparecida F Grossi ◽  
...  
Keyword(s):  
Cross Sectional Study ◽  
Minas Gerais ◽  
Chi Square ◽  
Cross Sectional ◽  
Period 2 ◽  
Time Period ◽  
Medical Facilities ◽  
Age Of Diagnosis ◽  
Using Data ◽  
Incident Cases

Abstract Background Leprosy in children under 15 years of age, and in particular, the presence of leprosy grade 2 disability (G2D) in children, signifies ongoing transmission and the need for improved surveillance. Our objective was to describe the epidemiology of pediatric leprosy in Minas Gerais, Brazil and to explore associations with access to medical facilities. Methods A cross-sectional study was conducted using data from the Brazilian Notifiable Diseases Surveillance System (SINAN) from 2002–2017. Incident cases were included if they resided in a municipality with both adult and pediatric cases. Municipalities were divided by the number of medical facilities per municipality: < 5, 5–17, and 18 or higher. Analyses compared pediatric cases across two time periods (2002–2009 and 2010–2017) and number of medical facilities / municipality using chi-square, t-tests, and logistic regression. Results A total of 27,725 cases were reported with 1,611 under 15 years of age. Overall incidence declined from 34.8 per 100,000 to 13.6 per 100,000 during the study period with pediatric incidence declining from 2.6 per 100,000 to 0.8 per 100,000. Time period 2 (TP2) showed an increase in the proportion of pediatric G2D (2.58% vs 1.91%, p < 0.0001) when compared to time period 1 (TP1). Mean age of diagnosis in children was younger in TP2 then in TP1 (10.06 vs 10.43, p=0.02). In 2017, the pediatric incidence in municipalities with the fewest medical facilities was 0.95 per 100,000 compared to 0.23 per 100,000 in municipalities with > 5 facilities (p=0.009). There was significantly higher odds of disability at diagnosis (grades 1 and 2) in pediatric cases residing in municipalities with < 5 medical facilities (aOR 1.88; 95% CI 1.37–2.59), adjusted for age and sex. See map (Fig 1). Figure 1. Cases of Pediatric Disability By Number of Municipality Medical Facilities from 2002–2017 (White areas without reported pediatric leprosy) Conclusion The increasing proportion of G2D in children in the second half of the study period despite declining incidence suggest occult infections among children and adults alike in Minas Gerais. Furthermore, the average age of diagnosis in children should increase, not decrease, if M. leprae transmission was truly declining. Lastly, the association between fewer municipality health facilities and increased disability suggest barriers to timely diagnosis and a critical area of focus for research into access to healthcare and leprosy risk. Disclosures All Authors: No reported disclosures

A Psychological Process of Bureaucratic Whistleblowing: Applying the Theory of Planned Behavior

2021 ◽  
pp. 027507402110033
Author(s):  
Hongseok Lee ◽  
Minsung Michael Kang ◽  
Sun Young Kim
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Behavioral Control ◽  
Perceived Behavioral Control ◽  
Psychological Process ◽  
Public Employees ◽  
The Public ◽  
Using Data

Whistleblowing is a psychological process that involves the calculation of risks and benefits. While there exists a broad range of research on whistleblowing in the public sector, previous studies have not examined its entire process due to the limited focus on either whistleblowing intention or whistleblowing behavior. This study aims to fill this gap by applying the theory of planned behavior (TPB) to the whistleblowing context. Specifically, we examine how individual beliefs about the likely consequences of whistleblowing (attitude toward whistleblowing), others’ expectations about whistleblowing (subjective norm), and the capability of blowing the whistle (perceived behavioral control) influence public employees’ actual whistleblowing by way of their intention to report wrongdoings. A series of structural equation models are tested using data from the 2010 Merit Principles Survey. The findings show that the more the employees perceive that the consequences of whistleblowing are important, the more the key referents support whistleblowing, and the more the protections for whistleblowers are available, the more likely are their intentions to disclose wrongdoings and then actually engage in whistleblowing behavior. We conduct additional analyses for internal and external whistleblowers separately and find that there are both meaningful similarities and differences between the two groups. This study provides support for the validity of TPB as a theoretical framework for better understanding and explicating the psychological process of bureaucratic whistleblowing.

Suicidal Ideation Among International Students: The Role of Cultural, Academic, and Interpersonal Factors

2021 ◽  
pp. 001100002110024
Author(s):  
Andrés E. Pérez Rojas ◽  
Na-Yeun Choi ◽  
Minji Yang ◽  
Theodore T. Bartholomew ◽  
Giovanna M. Pérez
Keyword(s):  
International Students ◽  
Suicidal Ideation ◽  
Structural Equation ◽  
Structural Equation Models ◽  
The United States ◽  
Cross Cultural ◽  
Thwarted Belongingness ◽  
Perceived Burdensomeness ◽  
Using Data

We examined two structural equation models of international students’ suicidal ideation using data from 595 international students in two public universities in the United States. The models represented competing hypotheses about the relationships among discrimination, cross-cultural loss, academic distress, thwarted belongingness, perceived burdensomeness, and suicidal ideation. The findings indicated there were direct, positive links between discrimination, cross-cultural loss, and academic distress to perceived burdensomeness; a direct, positive link between perceived burdensomeness and suicidal ideation; and indirect, positive links between discrimination, cross-cultural loss, and academic distress to suicidal ideation via perceived burdensomeness. The only predictors that related to thwarted belongingness were cross-cultural loss and academic distress, and there were no indirect links to suicidal ideation via thwarted belongingness. In fact, with all other variables in the model, thwarted belongingness was unrelated to suicidal ideation. Finally, academic distress was directly related to suicidal ideation. We discuss implications of the findings.

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