scholarly journals Assessing the Role of Sampling Uncertainty When Predicting Behavioral Responses of Tagged Cetaceans Exposed to Naval Sonar

2021 ◽  
Vol 8 ◽  
Author(s):  
Phil J. Bouchet ◽  
Catriona M. Harris ◽  
Len Thomas
Keyword(s):  
Dose Response ◽  
Hierarchical Modeling ◽  
Satellite Telemetry ◽  
Behavioral Responses ◽  
Management Decision ◽  
Modeling Framework ◽  
Cetacean Species ◽  
Bayesian Hierarchical ◽  
Management Decision Making ◽  
Strategic Monitoring

Concerns over cetacean mortality events coincident with maritime warfare exercises have motivated efforts to characterize the effects of anthropogenic noise on free-ranging whales and dolphins. By monitoring the movement, diving, and acoustic behaviors of individual whales before, during, and after sound exposure, behavioral response studies (BRSs) have supported significant progress in our understanding of the sensitivity of various cetacean species to high-powered naval sonar signals. However, differences in the designs and sampling capabilities of animal-borne tags typically used in BRS experiments prompt questions about the influence of data resolution in quantitative assessments of noise impacts. We conducted simulations to examine how uncertainty in the acoustic dose either measured on high-resolution multi-sensor biologging tags or modeled from position-transmitting satellite telemetry tags may affect predictions of behavioral responses in Cuvier’s beaked whales (Ziphius cavirostris) exposed to low- and mid-frequency active sonar. We considered an array of scenarios representative of real-world BRSs and used posterior estimates of dose-response functions obtained under an established Bayesian hierarchical modeling framework to explore the consequences of different tag choices for management decision-making. Our results indicate that (1) the zone of impact from a sonar source is under-estimated in most test conditions, (2) substantial reductions in the uncertainty surrounding dose-response relationships are possible at higher sample sizes, and (3) this largely holds true irrespective of tag choice under the scenarios considered, unless positional fixes from satellite tags are consistently poor. Strategic monitoring approaches that combine both archival biologging and satellite biotelemetry are essential for characterizing complex patterns of behavioral change in cetaceans exposed to increasing levels of acoustic disturbance. We suggest ways in which BRS protocols can be optimized to curtail the effects of uncertainty.

scholarly journals A Bayesian Hierarchical Modeling Framework for Correcting Reporting Bias in the U.S. Tornado Database

2018 ◽  
Vol 34 (1) ◽  
pp. 15-30 ◽  
Author(s):  
Corey K. Potvin ◽  
Chris Broyles ◽  
Patrick S. Skinner ◽  
Harold E. Brooks ◽  
Erik Rasmussen
Keyword(s):  
Rural Areas ◽  
Hierarchical Modeling ◽  
Reporting Rate ◽  
Bayesian Hierarchical Modeling ◽  
Road Density ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Adopted Model ◽  
Central United States ◽  
Prediction Systems

Abstract The Storm Prediction Center (SPC) tornado database, generated from NCEI’s Storm Data publication, is indispensable for assessing U.S. tornado risk and investigating tornado–climate connections. Maximizing the value of this database, however, requires accounting for systemically lower reported tornado counts in rural areas owing to a lack of observers. This study uses Bayesian hierarchical modeling to estimate tornado reporting rates and expected tornado counts over the central United States during 1975–2016. Our method addresses a serious solution nonuniqueness issue that may have affected previous studies. The adopted model explains 73% (>90%) of the variance in reported counts at scales of 50 km (>100 km). Population density explains more of the variance in reported tornado counts than other examined geographical covariates, including distance from nearest city, terrain ruggedness index, and road density. The model estimates that approximately 45% of tornadoes within the analysis domain were reported. The estimated tornado reporting rate decreases sharply away from population centers; for example, while >90% of tornadoes that occur within 5 km of a city with population > 100 000 are reported, this rate decreases to <70% at distances of 20–25 km. The method is directly extendable to other events subject to underreporting (e.g., severe hail and wind) and could be used to improve climate studies and tornado and other hazard models for forecasters, planners, and insurance/reinsurance companies, as well as for the development and verification of storm-scale prediction systems.

Framework for Identification and Collection of Data Useful for Risk Assessments of Microbial Foodborne or Waterborne Hazards: A Report from the International Life Sciences Institute Research Foundation Advisory Committee on Data Collection for Microbial Risk Assessment

2007 ◽  
Vol 70 (7) ◽  
pp. 1744-1751 ◽  
Author(s):  
ISABEL WALLS
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Risk Management ◽  
Data Collection ◽  
Dose Response ◽  
Management Decision ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Response Data ◽  
Management Decision Making

A microbial risk assessment (MRA) can provide the scientific basis for risk management decision making. Much data are needed to complete an MRA, including quantitative data for pathogens in foods. The purpose of this document was to provide information on data needs and data collection approaches for MRAs that will be useful for national governments, particularly in developing countries. A framework was developed, which included the following activities: (i) identify the purpose of data collection—this should include stating the specific question(s) to be addressed; (ii) identify and gather existing data—this should include a determination of whether the data are sufficient to answer questions to be addressed; (iii) develop and implement a data collection strategy; (iv) analyze data and draw conclusions; and (v) use data to answer questions identified at the start of the process. The key data needs identified for an MRA were as follows: (i) burden of foodborne or waterborne disease; (ii) microbial contamination of foods; and (iii) consumption patterns. In addition, dose-response data may be necessary, if existing dose-response data cannot be used to estimate dose response for the population of interest. Data should be collected with a view to its use in risk management decision making. Standard sampling and analysis methods should be used to ensure representative samples are tested, and care should be taken to avoid bias when selecting data sets. A number of barriers to data collection were identified, including a lack of clear understanding of the type of data needed to undertake an MRA, which is addressed in this document.

scholarly journals Combining growth curves when a longitudinal study switches measurement tools

2016 ◽  
Vol 25 (6) ◽  
pp. 2925-2938 ◽  
Author(s):  
Jacob J Oleson ◽  
Joseph E Cavanaugh ◽  
J Bruce Tomblin ◽  
Elizabeth Walker ◽  
Camille Dunn
Keyword(s):  
Longitudinal Study ◽  
Cochlear Implants ◽  
Hierarchical Modeling ◽  
Growth Curves ◽  
Measurement Tool ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Borrowing Strength ◽  
Measurement Tools ◽  
The Impact

When longitudinal studies are performed to investigate the growth of traits in children, the measurement tool being used to quantify the trait may need to change as the subjects’ age throughout the study. Changing the measurement tool at some point in the longitudinal study makes the analysis of that growth challenging which, in turn, makes it difficult to determine what other factors influence the growth rate. We developed a Bayesian hierarchical modeling framework that relates the growth curves per individual for each of the different measurement tools and allows for covariates to influence the shapes of the curves by borrowing strength across curves. The method is motivated by and demonstrated by speech perception outcome measurements of children who were implanted with cochlear implants. Researchers are interested in assessing the impact of age at implantation and comparing the growth rates of children who are implanted under the age of two versus those implanted between the ages of two and four.

scholarly journals A Framework for Estimating Human-Wildlife Conflict Probabilities Conditional on Species Occupancy

2021 ◽  
Vol 2 ◽  
Author(s):  
Rekha Warrier ◽  
Barry R. Noon ◽  
Larissa L. Bailey
Keyword(s):  
Species Distribution ◽  
False Positive ◽  
Hierarchical Modeling ◽  
Management Strategies ◽  
False Negative ◽  
Distribution Data ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Tiger Conservation ◽  
A Site

Managing human-wildlife conflicts (HWCs) is an important conservation objective for the many terrestrial landscapes dominated by humans. Forecasting where future conflicts are likely to occur and assessing risks to lives and livelihoods posed by wildlife are central to informing HWC management strategies. Existing assessments of the spatial occurrence patterns of HWC are based on either understanding spatial patterns of past conflicts or patterns of species distribution. In the former case, the absence of conflicts at a site cannot be attributed to the absence of the species. In the latter case, the presence of a species may not be an accurate measure of the probability of conflict occurrence. We present a Bayesian hierarchical modeling framework that integrates conflict reporting data and species distribution data, thus allowing the estimation of the probability that conflicts with a species are reported from a site, conditional on the species being present. In doing so, our model corrects for both false-positive and false-negative conflict reporting errors. We provide study design recommendations using simulations that explore the performance of the model under a range of conflict reporting probabilities. We applied the model to data on wild boar (Sus scrofa) space use and conflicts collected from the Central Terai Landscape (CTL), an important tiger conservation landscape in India. We found that tolerance for wildlife was a predictor of the probability with which farmers report conflict with wild boars from sites not used by the species. We also discuss useful extensions of the model when conflict data are verified for potential false-positive errors and when landscapes are monitored over multiple seasons.

scholarly journals OCRbayes: A Bayesian hierarchical modeling framework for Seahorse extracellular flux oxygen consumption rate data analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253926
Author(s):  
Xiang Zhang ◽  
Taolin Yuan ◽  
Jaap Keijer ◽  
Vincent C. J. de Boer
Keyword(s):  
Oxygen Consumption ◽  
Data Structure ◽  
Consumption Rate ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Complex Data ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Extracellular Flux ◽  
Complex Data Structure

Background Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. Results To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical modeling framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. Conclusions We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.

Provocation in the Workplace: A Qualitative Analysis of Published Arbitration Decisions

2018 ◽  
Vol 44 (3) ◽  
pp. 262-280
Author(s):  
Margaret A. Lucero ◽  
Robert E. Allen
Keyword(s):  
Qualitative Analysis ◽  
Workplace Violence ◽  
Behavioral Responses ◽  
Management Decision ◽  
Future Research ◽  
Theory Approach ◽  
Key Factors ◽  
Management Decision Making ◽  
Grounded Theory Approach ◽  
Source Of Information

Workplace violence has become a serious concern for managers and unions. This study examined provocation, a precursor to aggression and retaliation, using a grounded theory approach to evaluate a set of published arbitration decisions. The qualitative analysis identified different forms of behavior that provoked targets, as well as the targets’ emotional and behavioral responses to the provocation. In addition, analysis of the arbitrators’ decision provides a rich source of information on the key factors that can improve management decision making and union responses to these difficult disciplinary issues. Implications of the findings for future research are also discussed.

scholarly journals OCRbayes: A Bayesian hierarchical modeling framework for Seahorse extracellular flux oxygen consumption rate data analysis

2021 ◽  
Author(s):  
Xiang Zhang ◽  
Taolin Yuan ◽  
Jaap Keijer ◽  
Vincent C. J. de Boer
Keyword(s):  
Oxygen Consumption ◽  
Data Structure ◽  
Consumption Rate ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Complex Data ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Extracellular Flux ◽  
Complex Data Structure

Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical modeling framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.

scholarly journals A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

2022 ◽  
Vol 26 (1) ◽  
pp. 149-166
Author(s):  
Álvaro Ossandón ◽  
Manuela I. Brunner ◽  
Balaji Rajagopalan ◽  
William Kleiber
Keyword(s):  
Lead Time ◽  
Hierarchical Modeling ◽  
Space Time ◽  
Bayesian Hierarchical Modeling ◽  
Modeling Framework ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Streamflow Extremes ◽  
Seasonal Streamflow ◽  
Space Time Model

Abstract. Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging, particularly under nonstationary conditions and if extremes are correlated in space. The goal of this study is to implement a space–time model for the projection of seasonal streamflow extremes that considers the nonstationarity (interannual variability) and spatiotemporal dependence of high flows. We develop a space–time model to project seasonal streamflow extremes for several lead times up to 2 months, using a Bayesian hierarchical modeling (BHM) framework. This model is based on the assumption that streamflow extremes (3 d maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates describing the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatiotemporal variability and uncertainty. We apply this modeling framework to predict 3 d maximum streamflow in spring (May–June) at seven gauges in the Upper Colorado River basin (UCRB) with 0- to 2-month lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Pacific Decadal Oscillation – as potential covariates for 3 d spring maximum streamflow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space–time variability in extreme streamflow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations, thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of extreme spring streamflows become available 2 months before actual flood occurrence.

Centralization and Effectiveness of Reward Management in Multinational Enterprises

2018 ◽  
Vol 17 (2) ◽  
pp. 55-65 ◽  
Author(s):  
Michael Tekieli ◽  
Marion Festing ◽  
Xavier Baeten
Keyword(s):  
Decision Making ◽  
Social Identity Theory ◽  
Multinational Enterprises ◽  
Role Identity ◽  
Management Decision ◽  
Role Identities ◽  
Management Decision Making ◽  
New Perspective ◽  
Subsidiary Managers ◽  
The Relationship

Abstract. Based on responses from 158 reward managers located at the headquarters or subsidiaries of multinational enterprises, the present study examines the relationship between the centralization of reward management decision making and its perceived effectiveness in multinational enterprises. Our results show that headquarters managers perceive a centralized approach as being more effective, while for subsidiary managers this relationship is moderated by the manager’s role identity. Referring to social identity theory, the present study enriches the standardization versus localization debate through a new perspective focusing on psychological processes, thereby indicating the importance of in-group favoritism in headquarters and the influence of subsidiary managers’ role identities on reward management decision making.

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