scholarly journals Early Tetrapodomorph Biogeography: Controlling for Fossil Record Bias in Macroevolutionary Analyses

2019 ◽  
Author(s):  
Jacob D. Gardner ◽  
Kevin Surya ◽  
Chris L. Organ
Keyword(s):  
Fossil Record ◽  
Regression Models ◽  
Phylogenetic Analyses ◽  
Sampling Bias ◽  
Data Sampling ◽  
Species Dispersal ◽  
Geographic Context ◽  
Adaptive Radiations ◽  
Patterns And Processes

ABSTRACTThe fossil record provides direct empirical data for understanding macroevolutionary patterns and processes. Inherent biases in the fossil record are well known to confound analyses of this data. Sampling bias proxies have been used as covariates in regression models to test for such biases. Proxies, such as formation count, are associated with paleobiodiversity, but are insufficient for explaining species dispersal owing to a lack of geographic context. Here, we develop a sampling bias proxy that incorporates geographic information and test it with a case study on early tetrapodomorph biogeography. We use recently-developed Bayesian phylogeographic models and a new supertree of early tetrapodomorphs to estimate dispersal rates and ancestral habitat locations. We find strong evidence that geographic sampling bias explains supposed radiations in dispersal rate (potential adaptive radiations). Our study highlights the necessity of accounting for geographic sampling bias in macroevolutionary and phylogenetic analyses and provides an approach to test for its effect.

scholarly journals Phylogenetic taxonomy and classification of the Crinoidea (Echinodermata)

2017 ◽  
Vol 91 (4) ◽  
pp. 829-846 ◽  
Author(s):  
David F. Wright ◽  
William I. Ausich ◽  
Selina R. Cole ◽  
Mark E. Peter ◽  
Elizabeth C. Rhenberg
Keyword(s):  
Phylogenetic Relationships ◽  
Fossil Record ◽  
Phylogenetic Analyses ◽  
Major Goal ◽  
Evolutionary Patterns ◽  
Phylogenetic Taxonomy ◽  
Phylogenetic Perspective ◽  
Phylogenetic Hypotheses ◽  
Patterns And Processes

AbstractA major goal of biological classification is to provide a system that conveys phylogenetic relationships while facilitating lucid communication among researchers. Phylogenetic taxonomy is a useful framework for defining clades and delineating their taxonomic content according to well-supported phylogenetic hypotheses. The Crinoidea (Echinodermata) is one of the five major clades of living echinoderms and has a rich fossil record spanning nearly a half billion years. Using principles of phylogenetic taxonomy and recent phylogenetic analyses, we provide the first phylogeny-based definition for the Clade Crinoidea and its constituent subclades. A series of stem- and node-based definitions are provided for all major taxa traditionally recognized within the Crinoidea, including the Camerata, Disparida, Hybocrinida, Cladida, Flexibilia, and Articulata. Following recommendations proposed in recent revisions, we recognize several new clades, including the Eucamerata Cole 2017, Porocrinoidea Wright 2017, and Eucladida Wright 2017. In addition, recent phylogenetic analyses support the resurrection of two names previously abandoned in the crinoid taxonomic literature: the Pentacrinoidea Jaekel, 1918 and Inadunata Wachsmuth and Springer, 1885. Last, a phylogenetic perspective is used to inform a comprehensive revision of the traditional rank-based classification. Although an attempt was made to minimize changes to the rank-based system, numerous changes were necessary in some cases to achieve monophyly. These phylogeny-based classifications provide a useful template for paleontologists, biologists, and non-experts alike to better explore evolutionary patterns and processes with fossil and living crinoids.

Understanding the Sampling Bias: A Case Study on NBA Drafts

2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Polychronis Economou ◽  
Apostolos Batsidis ◽  
George Tzavelas ◽  
Sonia Malefaki
Keyword(s):  
Sampling Bias

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

scholarly journals A Method for Assessing Threats to the Economic Security of a Region: A Case Study of Public Procurement in Russia

Risks ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Valentina Kravchenko ◽  
Tatiana Kudryavtseva ◽  
Yuriy Kuporov
Keyword(s):  
Regression Models ◽  
Public Procurement ◽  
Ordinary Least Squares ◽  
Economic Security ◽  
Government Procurement ◽  
Security Monitoring ◽  
Significant Relationships ◽  
Monitoring Tools ◽  
Russian Region

The issue of economic security is becoming an increasingly urgent one. The purpose of this article is to develop a method for assessing threats to the economic security of the Russian region. This method is based on step-by-step actions: first of all, choosing an element of the region’s economic security system and collecting its descriptive indicators; then grouping indicators by admittance-process-result categories and building hypotheses about their influence; testing hypotheses using a statistical package and choosing the most significant connections, which can pose a threat to the economic security of the region; thereafter ranking regions by the level of threats and developing further recommendations. The importance of this method is that with the help of grouping regions (territory of a country) based on proposed method, it is possible to develop individual economic security monitoring tools. As a result, the efficiency of that country’s region can be higher. In this work, the proposed method was tested in the framework of public procurement in Russia. A total of 14 indicators of procurement activity were collected for each region of the Russian Federation for the period from 2014 to 2018. Regression models were built on the basis of the grouped indicators. Ordinary Least Squares (OLS) Estimation was used. As a result of pairwise regression models analysis, we have defined four significant relationships between public procurement indicators. There are positive connections between contracts that require collateral and the percentage of tolerances, between the number of bidders and the number of regular suppliers, between the number of bidders and the average price drop, and between the number of purchases made from a single supplier and the number of contracts concluded without reduction. It was determined that the greatest risks for the system were associated with the connection between competition and budget savings. It was proposed to rank analyzed regions into four groups: ineffective government procurement, effective government procurement, and government procurement that threatens the system of economic security of the region, that is, high competition with low savings and low competition with high savings. Based on these groups, individual economic security monitoring tools can be developed for each region.

scholarly journals Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

2021 ◽  
Vol 13 (13) ◽  
pp. 7504
Author(s):  
Jie Liu ◽  
Paul Schonfeld ◽  
Jinqu Chen ◽  
Yong Yin ◽  
Qiyuan Peng
Keyword(s):  
Regression Models ◽  
Influential Factors ◽  
Rail Transit ◽  
Linear Regression Models ◽  
Transit Network ◽  
Time Index ◽  
Trip Time ◽  
Buffer Time ◽  
Transfer Times

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

From Community to Public Familiarity: Neighborhood, Sociability, and Belonging in the Neoliberal City

2021 ◽  
pp. 107808742110215
Author(s):  
Felipe Link ◽  
Andrés Señoret ◽  
Felipe Valenzuela
Keyword(s):  
Social Relations ◽  
Sense Of Belonging ◽  
Regression Models ◽  
Secondary Data ◽  
Critical Factor ◽  
Urban Morphology ◽  
Demographic Characteristics ◽  
Logistic Regression Models ◽  
Pedestrian Space

Current urban neoliberalism processes have shaped and changed contemporary cities, including the local scale’s built environment and social relations. This article aims to study how such transformations affect local sociability by analyzing the effects of neighborhoods’ morphology and socio-demographic characteristics on different forms of interactions and how they affect the sense of belonging. Taking the Metropolitan Area of Santiago, Chile, as a case study, we gathered secondary data on urban morphology and surveyed ten neighborhoods to measure sociability patterns. The results obtained from multilevel logistic regression models show that time living in the neighborhood and public pedestrian space is the most critical factor affecting neighborhood sociability. Moreover, instead of local ties, public familiarity is the form of sociability with the most substantial effects on a sense of belonging. We conclude that recent neighborhoods, formed by neoliberal urbanization, tend to discourage neighborhood sociability and a sense of belonging.

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