scholarly journals Quantifying acoustic survey uncertainty using Bayesian hierarchical modeling with an application to assessing Mysis relicta population densities in Lake Ontario

2016 ◽  
Vol 73 (8) ◽  
pp. 2104-2111 ◽  
Author(s):  
Patrick J. Sullivan ◽  
Lars G. Rudstam
Keyword(s):  
Hierarchical Modeling ◽  
Survey Design ◽  
Lake Ontario ◽  
Sensitivity Analyses ◽  
Target Strength ◽  
Parameter Estimates ◽  
Data Set ◽  
Density Estimates ◽  
Mysis Relicta ◽  
Bayesian Hierarchical

Abstract A Bayesian hierarchical model was applied to acoustic backscattering data collected on Mysis relicta (opossum shrimp) populations in Lake Ontario in 2005 to estimate the combined uncertainty in mean density estimates as well as the individual contributions to that uncertainty from the various information sources involved in the calculation including calibration, target strength determination, threshold specification and survey sampling design. Traditional estimation approaches often only take into account the variability associated with the survey design, while assuming that all other intermediate parameter estimates used in the calculations are fixed and known. Unfortunately, unaccounted for variation in the steps leading up to the global density estimate may make significant contributions to the uncertainty of density estimates. While other studies have used sensitivity analyses to demonstrate the degree to which uncertainty in the various input parameters can influence estimates, including the uncertainty directly as demonstrated here using a Bayesian hierarchical approach allows for a more transparent representation of the true uncertainty and the mechanisms needed for its reduction. A Bayesian analysis of the mysid data examined here indicates that increasing the sample size of biological collections used in the target strength regression prove to be a more direct and practical way of reducing the overall variation in mean density estimates than similar steps employed to increase the number of transects surveyed. A doubling of target strength net tow samples resulted in a 23% reduction in variance relative to an 11% reduction that resulted from doubling the number of survey transects. This is an important difference as doubling the number of survey transects would add 5 days to the survey whereas doubling the number of net tows would add only one day. Although these results are specific to this particular data set, the method described is general.

scholarly journals Bayesian Inference for Correlations in the Presence of Measurement Error and Estimation Uncertainty

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Dora Matzke ◽  
Alexander Ly ◽  
Ravi Selker ◽  
Wouter D. Weeda ◽  
Benjamin Scheibehenne ◽  
...  
Keyword(s):  
Measurement Error ◽  
Bayesian Modeling ◽  
Pearson Correlation ◽  
Psychological Research ◽  
Uncertain Parameter ◽  
Parameter Estimates ◽  
Data Sets ◽  
Data Set ◽  
Bayesian Hierarchical ◽  
Estimation Uncertainty

Whenever parameter estimates are uncertain or observations are contaminated by measurement error, the Pearson correlation coefficient can severely underestimate the true strength of an association. Various approaches exist for inferring the correlation in the presence of estimation uncertainty and measurement error, but none are routinely applied in psychological research. Here we focus on a Bayesian hierarchical model proposed by Behseta, Berdyyeva, Olson, and Kass (2009) that allows researchers to infer the underlying correlation between error-contaminated observations. We show that this approach may be also applied to obtain the underlying correlation between uncertain parameter estimates as well as the correlation between uncertain parameter estimates and noisy observations. We illustrate the Bayesian modeling of correlations with two empirical data sets; in each data set, we first infer the posterior distribution of the underlying correlation and then compute Bayes factors to quantify the evidence that the data provide for the presence of an association.

scholarly journals Coho salmon escapement and trends in migration timing to a data-poor river: estimates from a Bayesian hierarchical model

2015 ◽  
Vol 72 (12) ◽  
pp. 1807-1816 ◽  
Author(s):  
Timothy E. Walsworth ◽  
Daniel E. Schindler
Keyword(s):  
Coho Salmon ◽  
Hierarchical Modeling ◽  
Economic Value ◽  
Population Characteristics ◽  
Parameter Estimates ◽  
Bayesian Hierarchical ◽  
Migration Timing ◽  
Timing Model ◽  
The Pacific ◽  
Spatial Locations

Data to inform fisheries management are often limited in remote or low economic value fisheries. Here, we use a Bayesian hierarchical modeling structure and two alternative migration timing models to estimate the annual escapement of coho salmon (Oncorhynchus kisutch) to the Chignik River (Alaska, USA) in years with little data, borrowing information from data-rich years to inform parameter estimates. Additionally, we examined trends in peak migration timing between 1922 and 2013 and relative to environmental conditions. Our analyses show that annual escapement estimates are prone to substantial errors unless daily escapement is enumerated for at least 7 days after peak migration date. Finally, peak migration date was negatively correlated with the strength of the Pacific Decadal Oscillation in May–August, and increased over time, though the significance of these associations was dependent on the specific form of the migration timing model used. The modeling approach we present here is easily adaptable to similar situations where data from alternative periods of time or spatial locations can be used to objectively inform local parameter estimates of population characteristics.

scholarly journals Evaluating Alternative Flight Plans in Thermal Drone Wildlife Surveys—Simulation Study

2021 ◽  
Vol 13 (6) ◽  
pp. 1102
Author(s):  
Julia Witczuk ◽  
Stanisław Pagacz
Keyword(s):  
Computer Simulations ◽  
Survey Design ◽  
Distance Sampling ◽  
Line Of Sight ◽  
Density Estimates ◽  
Accuracy And Precision ◽  
Survey Costs ◽  
Random Survey ◽  
Density Population

The rapidly developing technology of unmanned aerial vehicles (drones) extends to the availability of aerial surveys for wildlife research and management. However, regulations limiting drone operations to visual line of sight (VLOS) seriously affect the design of surveys, as flight paths must be concentrated within small sampling blocks. Such a design is inferior to spatially unrestricted randomized designs available if operations beyond visual line of sight (BVLOS) are allowed. We used computer simulations to assess whether the VLOS rule affects the accuracy and precision of wildlife density estimates derived from drone collected data. We tested two alternative flight plans (VLOS vs. BVLOS) in simulated surveys of low-, medium- and high-density populations of a hypothetical ungulate species with three levels of effort (one to three repetitions). The population density was estimated using the ratio estimate and distance sampling method. The observed differences in the accuracy and precision of estimates from the VLOS and BVLOS surveys were relatively small and negligible. Only in the case of the low-density population (2 ind./100 ha) surveyed once was the VLOS design inferior to BVLOS, delivering biased and less precise estimates. These results show that while the VLOS regulations complicate survey logistics and interfere with random survey design, the quality of derived estimates does not have to be compromised. We advise testing alternative survey variants with the aid of computer simulations to achieve reliable estimates while minimizing survey costs.

scholarly journals Variance prior specification for a basket trial design using Bayesian hierarchical modeling

2018 ◽  
Vol 16 (2) ◽  
pp. 142-153 ◽  
Author(s):  
Kristen M Cunanan ◽  
Alexia Iasonos ◽  
Ronglai Shen ◽  
Mithat Gönen
Keyword(s):  
Standard Deviation ◽  
Prior Distribution ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Bayesian Hierarchical ◽  
Inverse Gamma ◽  
Wide Range ◽  
Basket Trial ◽  
Basket Trials ◽  
Variance Parameter

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. Methods: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. Results: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero [Formula: see text], this can lead to unacceptably high false-positive rates [Formula: see text] in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. Conclusion: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

scholarly journals Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

2010 ◽  
Vol 14 (3) ◽  
pp. 545-556 ◽  
Author(s):  
J. Rings ◽  
J. A. Huisman ◽  
H. Vereecken
Keyword(s):  
Water Content ◽  
Particle Filtering ◽  
Field Data ◽  
Synthetic Data ◽  
Time Domain Reflectometry ◽  
Second Step ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Data Set ◽  
Resistivity Tomography

Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

Applicability of an extant batch respirometric assay in describing dynamics of ammonia and nitrite oxidation in a nitrifying bioreactor

2005 ◽  
Vol 52 (10-11) ◽  
pp. 503-508 ◽  
Author(s):  
K. Chandran ◽  
Z. Hu ◽  
B.F. Smets
Keyword(s):  
Kinetic Parameter ◽  
Sensitivity Analyses ◽  
Continuous Reactor ◽  
Parameter Estimates ◽  
Additional Parameter ◽  
Reactor Performance ◽  
Estimation Techniques ◽  
The Difference ◽  
The Impact ◽  
Best Fit

Several techniques have been proposed for biokinetic estimation of nitrification. Recently, an extant respirometric assay has been presented that yields kinetic parameters for both nitrification steps with minimal physiological change to the microorganisms during the assay. Herein, the ability of biokinetic parameter estimates from the extant respirometric assay to adequately describe concurrently obtained NH4+-N and NO2−-N substrate depletion profiles is evaluated. Based on our results, in general, the substrate depletion profiles resulted in a higher estimate of the maximum specific growth rate coefficient, μmax for both NH4+-N to NO2−-N oxidation and NO2−-N to NO3−-N oxidation compared to estimates from the extant respirograms. The trends in the kinetic parameter estimates from the different biokinetic estimation techniques are paralleled in the nature of substrate depletion profiles obtained from best-fit parameters. Based on a visual inspection, in general, best-fit parameters from optimally designed complete respirograms provided a better description of the substrate depletion profiles than estimates from isolated respirograms. Nevertheless, the sum of the squared errors for the best-fit respirometry based parameters was outside the 95% joint confidence interval computed for the best-fit substrate depletion based parameters. Notwithstanding the difference in kinetic parameter estimates determined in this study, the different biokinetic estimation techniques still are close to estimates reported in literature. Additional parameter identifiability and sensitivity analysis of parameters from substrate depletion assays revealed high precision of parameters and high parameter correlation. Although biokinetic estimation via automated extant respirometry is far more facile than via manual substrate depletion measurements, additional sensitivity analyses are needed to test the impact of differences in the resulting parameter values on continuous reactor performance.

scholarly journals Employee engagement and Emotional Exhaustion of Fly-In-Fly-Out Workers: A Diary Study

2018 ◽  
Author(s):  
Simon Albrecht ◽  
Jeromy Anglim
Keyword(s):  
Employee Engagement ◽  
Emotional Exhaustion ◽  
Hierarchical Modeling ◽  
Job Demands ◽  
Job Resources ◽  
Future Research ◽  
Bayesian Hierarchical ◽  
Emotional Demand ◽  
Work Cycle ◽  
Emotional Demands

Objective: Although Fly-in-Fly-Out (FIFO) work practices are widely used, little is known about their impact on the motivation and wellbeing of FIFO workers across the course of their work cycles. Drawing from the Job Demands-Resources model, we aimed to test for the within-person effects of time of work cycle, job demands, and job resources on emotional exhaustion and employee engagement at three day-intervals. Method: Fifty-two FIFO workers filled out three or more on-line diary surveys after every three days of their on-site work roster. The survey consisted of items drawn from previously validated scales. Bayesian hierarchical modeling of the day-level data was conducted. Results: Workers, on average, showed a decline in engagement and supervisor support, and an increase in emotional demand over the course of the work cycle. The results of the hierarchical modeling showed that day-level autonomy predicted day-level engagement and that day-level workload and emotional demands predicted emotional exhaustion. Conclusions: The findings highlight the importance of managing FIFO employees' day-to-day experiences of job demands and job resources because of their influence on employee engagement and emotional exhaustion. To best protect FIFO worker day-level wellbeing, employing organisations should ensure optimal levels of job autonomy, workload, and emotional demands. Practical implications, study limitations and areas for future research are outlined.

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