scholarly journals Consequences of ignoring variable and spatially-autocorrelated detection probability in spatial capture-recapture

2020 ◽  
Author(s):  
Ehsan M. Moqanaki ◽  
Cyril Milleret ◽  
Mahdieh Tourani ◽  
Pierre Dupont ◽  
Richard Bischof
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Detection Probability ◽  
Model Misspecification ◽  
Simulated Data ◽  
Parameter Estimates ◽  
Abundance Estimates ◽  
Credible Intervals ◽  
Capture Recapture ◽  
The Impact

AbstractContextSpatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, intrinsic and extrinsic to the study system.ObjectivesWe identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates.MethodsWe simulated realistic SCR data with spatially variable and autocorrelated detection probability. We then fitted a single-session SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences.ResultsHighly autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85 - 0.96), modulated by the magnitude of that variation, can lead to pronounced negative bias (up to 75%), reduction in precision (249%), and decreasing coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates.ConclusionsUnknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured unknown or partially unknown spatial variability in detection probability into account.

scholarly journals Consequences of ignoring variable and spatially autocorrelated detection probability in spatial capture-recapture

2021 ◽  
Author(s):  
Ehsan M. Moqanaki ◽  
Cyril Milleret ◽  
Mahdieh Tourani ◽  
Pierre Dupont ◽  
Richard Bischof
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Detection Probability ◽  
Model Misspecification ◽  
Simulated Data ◽  
Parameter Estimates ◽  
Reference Scenario ◽  
Abundance Estimates ◽  
Capture Recapture ◽  
The Impact

Abstract Context Spatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, both intrinsic and extrinsic to the study system. This is the case, for example, when covariates coding for variable effort and detection probability in general are incomplete or entirely lacking. Objectives We identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates. Methods We simulated SCR data with spatially variable and autocorrelated detection probability. We then fitted an SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences. Results Highly-autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85–0.96), modulated by the magnitude of the unmodeled heterogeneity, can lead to pronounced negative bias (up to 65%, or about 44-fold decrease compared to the reference scenario), reduction in precision (249% or 2.5-fold) and coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates. Conclusions Unknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured, unknown or partially unknown spatial variability in detection probability into account.

scholarly journals On the Estimation of Dispersal and Movement of Birds

The Condor ◽  
2004 ◽  
Vol 106 (4) ◽  
pp. 720-731 ◽  
Author(s):  
William L. Kendall ◽  
James D. Nichols
Keyword(s):  
Robust Design ◽  
Nonlinear Dynamic ◽  
Detection Probability ◽  
Cross Correlation ◽  
Statistical Methodology ◽  
Open Population ◽  
Abundance Estimates ◽  
Capture Recapture ◽  
Over Time

Abstract The estimation of dispersal and movement is important to evolutionary and population ecologists, as well as to wildlife managers. We review statistical methodology available to estimate movement probabilities. We begin with cases where individual birds can be marked and their movements estimated with the use of multisite capture-recapture methods. Movements can be monitored either directly, using telemetry, or by accounting for detection probability when conventional marks are used. When one or more sites are unobservable, telemetry, band recoveries, incidental observations, a closed- or open-population robust design, or partial determinism in movements can be used to estimate movement. When individuals cannot be marked, presence-absence data can be used to model changes in occupancy over time, providing indirect inferences about movement. Where abundance estimates over time are available for multiple sites, potential coupling of their dynamics can be investigated using linear cross-correlation or nonlinear dynamic tools. Sobre la Estimación de la Dispersión y el Movimiento de las Aves Resumen. La estimación de la dispersión y el movimiento es importante para los ecó logos evolutivos y de poblaciones, así como también para los encargados del manejo de vida silvestre. Revisamos la metodología estadística disponible para estimar probabilidades de movimiento. Empezamos con casos donde aves individuales pueden ser marcadas y sus movimientos estimados con el uso de métodos de captura-repactura para múltiples sitios. Los movimientos pueden ser monitoreados ya sea directamente, usando telemetría o teniendo en cuenta las probabilidades de detección cuando se usan marcas convencionales. Cuando uno o más sitios no pueden ser observados, se puede estimar el movimiento usando telemetría, recuperación de anillos, observaciones circunstanciales, un diseño poblacional robusto cerrado o abierto, o determinismo parcial de los movimientos. Cuando los individuos no pueden ser marcados, se pueden usar datos de presencia-ausencia para modelar los cambios en el tiempo de la ocupación, brindando inferencias indirectas sobre los movimientos. Cuando las estimaciones de abundancia a lo largo del tiempo están disponibles para varios sitios, se puede investigar la interrelación potencial de sus dinámicas usando correlaciones cruzadas lineales o herramientas para dinámica no lineal.

scholarly journals Improved Models for Plasma Radiometabolite Correction and their Impact on Kinetic Quantification in PET Studies

2015 ◽  
Vol 35 (9) ◽  
pp. 1462-1469 ◽  
Author(s):  
Matteo Tonietto ◽  
Mattia Veronese ◽  
Gaia Rizzo ◽  
Paolo Zanotti-Fregonara ◽  
Talakad G Lohith ◽  
...  
Keyword(s):  
Model Misspecification ◽  
Simulated Data ◽  
Input Function ◽  
Volume Of Distribution ◽  
Binding Potential ◽  
Weighted Residuals ◽  
Injection Duration ◽  
Positron Emission ◽  
Reliable Quantification ◽  
The Impact

The quantification of dynamic positron emission tomography studies performed with arterial sampling usually requires correcting the input function for the presence of radiometabolites by using a model of the plasma parent fraction (PPf). Here, we show how to include the duration of radioligand injection in the PPf model formulations to achieve a more physiologic description of the plasma measurements. This formulation (here called convoluted model) was tested on simulated data and on three datasets with different parent kinetics: [11C]NOP-1A, [11C]MePPEP, and [11C](R)-rolipram. Results showed that convoluted PPf models better described the fraction of unchanged parent in the plasma compared with standard models for all three datasets (weighted residuals sum of squares up to 25% lower). When considering the effect on tissue quantification, the overall impact on the total volume of distribution (VT) was low. However, the impact was significant and radioligand-dependent on the binding potential (BP) and the microparameters ( K1, k2, k3, and k4). Simulated data confirmed that quantification is sensitive to different degrees to PPf model misspecification. Including the injection duration allows obtaining a more accurate correction of the input function for the presence of radiometabolites and this yields a more reliable quantification of the tissue parameters.

scholarly journals Abundance estimation for line transect sampling: A comparison of distance sampling and spatial capture-recapture models

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252231
Author(s):  
Nathan J. Crum ◽  
Lisa C. Neyman ◽  
Timothy A. Gowan
Keyword(s):  
North Atlantic ◽  
Distance Sampling ◽  
Model Specification ◽  
Abundance Estimation ◽  
Line Transect ◽  
Abundance Estimates ◽  
Credible Intervals ◽  
Capture Recapture ◽  
Line Transects ◽  
Nominal Coverage

Accurate and precise abundance estimation is vital for informed wildlife conservation and management decision-making. Line transect surveys are a common sampling approach for abundance estimation. Distance sampling is often used to estimate abundance from line transect survey data; however, search encounter spatial capture-recapture can also be used when individuals in the population of interest are identifiable. The search encounter spatial capture-recapture model has rarely been applied, and its performance has not been compared to that of distance sampling. We analyzed simulated datasets to compare the performance of distance sampling and spatial capture-recapture abundance estimators. Additionally, we estimated the abundance of North Atlantic right whales in the southeastern United States with two formulations of each model and compared the estimates. Spatial capture-recapture abundance estimates had lower root mean squared error than distance sampling estimates. Spatial capture-recapture 95% credible intervals for abundance had nominal coverage, i.e., contained the simulating value for abundance in 95% of simulations, whereas distance sampling credible intervals had below nominal coverage. Moreover, North Atlantic right whale abundance estimates from distance sampling models were more sensitive to model specification compared to spatial capture-recapture estimates. When estimating abundance from line transect data, researchers should consider using search encounter spatial capture-recapture when individuals in the population of interest are identifiable, when line transects are surveyed over multiple occasions, when there is imperfect detection of individuals located on the line transect, and when it is safe to assume the population of interest is closed demographically. When line transects are surveyed over multiple occasions, researchers should be aware that individual space use may induce spatial autocorrelation in counts across transects. This is not accounted for in common distance sampling estimators and leads to overly precise abundance estimates.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

Analyzing the Impact of Healthy Behavior on Weight Change with a Provable Mathematical Model (Preprint)

2020 ◽  
Author(s):  
Ayan Chatterjee ◽  
Ram Bajpai ◽  
Pankaj Khatiwada
Keyword(s):  
Physical Activity ◽  
Mathematical Model ◽  
Health Behavior ◽  
Weight Change ◽  
Simulated Data ◽  
Negative Behavior ◽  
Healthy Behavior ◽  
Healthy Habits ◽  
Lifestyle Diseases ◽  
The Impact

BACKGROUND Lifestyle diseases are the primary cause of death worldwide. The gradual growth of negative behavior in humans due to physical inactivity, unhealthy habit, and improper nutrition expedites lifestyle diseases. In this study, we develop a mathematical model to analyze the impact of regular physical activity, healthy habits, and a proper diet on weight change, targeting obesity as a case study. Followed by, we design an algorithm for the verification of the proposed mathematical model with simulated data of artificial participants. OBJECTIVE This study intends to analyze the effect of healthy behavior (physical activity, healthy habits, and proper dietary pattern) on weight change with a proposed mathematical model and its verification with an algorithm where personalized habits are designed to change dynamically based on the rule. METHODS We developed a weight-change mathematical model as a function of activity, habit, and nutrition with the first law of thermodynamics, basal metabolic rate (BMR), total daily energy expenditure (TDEE), and body-mass-index (BMI) to establish a relationship between health behavior and weight change. Followed by, we verified the model with simulated data. RESULTS The proposed provable mathematical model showed a strong relationship between health behavior and weight change. We verified the mathematical model with the proposed algorithm using simulated data following the necessary constraints. The adoption of BMR and TDEE calculation following Harris-Benedict’s equation has increased the model's accuracy under defined settings. CONCLUSIONS This study helped us understand the impact of healthy behavior on obesity and overweight with numeric implications and the importance of adopting a healthy lifestyle abstaining from negative behavior change.

Applying the risk principle to optimize accuracy and equity in correctional risk assessment: Results From a Simulation

2021 ◽  
pp. 0306624X2098652
Author(s):  
Grant Duwe
Keyword(s):  
Risk Assessment ◽  
Predictive Accuracy ◽  
Ethnic Disparities ◽  
Simulated Data ◽  
Risk Assessments ◽  
Risk Level ◽  
Risk Levels ◽  
Risk Principle ◽  
Changes In Risk ◽  
The Impact

As the use of risk assessments for correctional populations has grown, so has concern that these instruments exacerbate existing racial and ethnic disparities. While much of the attention arising from this concern has focused on how algorithms are designed, relatively little consideration has been given to how risk assessments are used. To this end, the present study tests whether application of the risk principle would help preserve predictive accuracy while, at the same time, mitigate disparities. Using a sample of 9,529 inmates released from Minnesota prisons who had been assessed multiple times during their confinement on a fully-automated risk assessment, this study relies on both actual and simulated data to examine the impact of program assignment decisions on changes in risk level from intake to release. The findings showed that while the risk principle was used in practice to some extent, the simulated results showed that greater adherence to the risk principle would increase reductions in risk levels and minimize the disparities observed at intake. The simulated data further revealed the most favorable outcomes would be achieved by not only applying the risk principle, but also by expanding program capacity for the higher-risk inmates in order to adequately reduce their risk.

Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data

2021 ◽  
Vol 45 (3) ◽  
pp. 159-177
Author(s):  
Chen-Wei Liu
Keyword(s):  
Missing Data ◽  
Latent Variable ◽  
Bivariate Normal Distribution ◽  
Parameter Estimates ◽  
Missing Responses ◽  
Variable Distribution ◽  
Item Responses ◽  
Nonnormal Distribution ◽  
The Impact ◽  
Biased Estimates

Missing not at random (MNAR) modeling for non-ignorable missing responses usually assumes that the latent variable distribution is a bivariate normal distribution. Such an assumption is rarely verified and often employed as a standard in practice. Recent studies for “complete” item responses (i.e., no missing data) have shown that ignoring the nonnormal distribution of a unidimensional latent variable, especially skewed or bimodal, can yield biased estimates and misleading conclusion. However, dealing with the bivariate nonnormal latent variable distribution with present MNAR data has not been looked into. This article proposes to extend unidimensional empirical histogram and Davidian curve methods to simultaneously deal with nonnormal latent variable distribution and MNAR data. A simulation study is carried out to demonstrate the consequence of ignoring bivariate nonnormal distribution on parameter estimates, followed by an empirical analysis of “don’t know” item responses. The results presented in this article show that examining the assumption of bivariate nonnormal latent variable distribution should be considered as a routine for MNAR data to minimize the impact of nonnormality on parameter estimates.

scholarly journals Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín
Keyword(s):  
Statistical Theory ◽  
Learning Algorithm ◽  
Real Life ◽  
Simulated Data ◽  
Simultaneous Equation ◽  
Variational Bayes ◽  
Parameter Estimates ◽  
Step Method ◽  
The One ◽  
Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

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