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Relevant bibliographies by topics / Mixed linear models

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Academic literature on the topic 'Mixed linear models'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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Journal articles on the topic "Mixed linear models"

1

Varga, Štefan. "Quadratic estimations in mixed linear models." Applications of Mathematics 36, no. 2 (1991): 134–44. http://dx.doi.org/10.21136/am.1991.104450.

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Landwehr, Jan R., Andreas Herrmann, and Mark Heitmann. "Linear Mixed Models." Marketing ZFP 30, no. 3 (2008): 175–90. http://dx.doi.org/10.15358/0344-1369-2008-3-175.

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Volaufová, Júlia, and Viktor Witkovský. "Estimation of variance components in mixed linear models." Applications of Mathematics 37, no. 2 (1992): 139–48. http://dx.doi.org/10.21136/am.1992.104497.

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Varga, Štefan. "Estimations of covariance components in mixed linear models." Mathematica Bohemica 121, no. 1 (1996): 29–33. http://dx.doi.org/10.21136/mb.1996.125947.

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Verbeke, Geert, Bart Spiessens, and Emmanuel Lesaffre. "Conditional Linear Mixed Models." American Statistician 55, no. 1 (2001): 25–34. http://dx.doi.org/10.1198/000313001300339905.

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Geraci, Marco, and Matteo Bottai. "Linear quantile mixed models." Statistics and Computing 24, no. 3 (2013): 461–79. http://dx.doi.org/10.1007/s11222-013-9381-9.

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Morrell, Christopher H., Jay D. Pearson, and Larry J. Brant. "Linear Transformations of Linear Mixed-Effects Models." American Statistician 51, no. 4 (1997): 338. http://dx.doi.org/10.2307/2685902.

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Morrell, Christopher H., Jay D. Pearson, and Larry J. Brant. "Linear Transformations of Linear Mixed-Effects Models." American Statistician 51, no. 4 (1997): 338–43. http://dx.doi.org/10.1080/00031305.1997.10474409.

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Stępniak, Czesław. "Admissible linear estimators in mixed linear models." Journal of Multivariate Analysis 31, no. 1 (1989): 90–106. http://dx.doi.org/10.1016/0047-259x(89)90052-3.

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Arellano-Valle, R. B., H. Bolfarine, and V. H. Lachos. "Skew-normal Linear Mixed Models." Journal of Data Science 3, no. 4 (2021): 415–38. http://dx.doi.org/10.6339/jds.2005.03(4).238.

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Dissertations / Theses on the topic "Mixed linear models"

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Gory, Jeffrey J. "Marginally Interpretable Generalized Linear Mixed Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497966698387606.

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Möls, Märt. "Linear mixed models with equivalent predictors /." Online version, 2004. http://dspace.utlib.ee/dspace/bitstream/10062/1339/5/Mols.pdf.

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Randell, David. "Bayes linear variance learning for mixed linear temporal models." Thesis, Durham University, 2012. http://etheses.dur.ac.uk/3646/.

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Modelling of complex corroding industrial systems is ritical to effective inspection and maintenance for ssurance of system integrity. Wall thickness and corrosion rate are modelled for multiple dependent corroding omponents, given observations of minimum wall thickness per component. At each inspection, partial observations of the system are considered. A Bayes Linear approach is adopted simplifying parameter estimation and avoiding often unrealistic distributional assumptions. Key system variances are modelled, making exchangeability assumptions to facilitate analysis for sparse inspection time-series. A utility based criterion is used to assess quality of inspection design and aid decision making. The model is applied to inspection data from pipework networks on a full-scale offshore platform.

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Overstall, Antony Marshall. "Default Bayesian model determination for generalised linear mixed models." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170229/.

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In this thesis, an automatic, default, fully Bayesian model determination strategy for GLMMs is considered. This strategy must address the two key issues of default prior specification and computation. Default prior distributions for the model parameters, that are based on a unit information concept, are proposed. A two-phase computational strategy, that uses a reversible jump algorithm and implementation of bridge sampling, is also proposed. This strategy is applied to four examples throughout this thesis.

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Sima, Adam. "Accounting for Model Uncertainty in Linear Mixed-Effects Models." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2950.

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Standard statistical decision-making tools, such as inference, confidence intervals and forecasting, are contingent on the assumption that the statistical model used in the analysis is the true model. In linear mixed-effect models, ignoring model uncertainty results in an underestimation of the residual variance, contributing to hypothesis tests that demonstrate larger than nominal Type-I errors and confidence intervals with smaller than nominal coverage probabilities. A novel utilization of the generalized degrees of freedom developed by Zhang et al. (2012) is used to adjust the estimate of the residual variance for model uncertainty. Additionally, the general global linear approximation is extended to linear mixed-effect models to adjust the standard errors of the parameter estimates for model uncertainty. Both of these methods use a perturbation method for estimation, where random noise is added to the response variable and, conditional on the observed responses, the corresponding estimate is calculated. A simulation study demonstrates that when the proposed methodologies are utilized, both the variance and standard errors are inflated for model uncertainty. However, when a data-driven strategy is employed, the proposed methodologies show limited usefulness. These methods are evaluated with a trial assessing the performance of cervical traction in the treatment of cervical radiculopathy.

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Heinzl, Felix. "Clustering in linear and additive mixed models." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-157169.

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Casale, Francesco Paolo. "Multivariate linear mixed models for statistical genetics." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/267465.

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In the last decade, genome-wide association studies have helped to advance our understanding of the genetic architecture of many important traits, including diseases. However, the statistical analysis of genotype-phenotype associations remains challenging due to multiple factors. First, many traits have polygenic architectures, which means that they are controlled by a large number of variants with small individual effects. Second, as increasingly deep phenotype data are being generated there is a need for multivariate analysis approaches to leverage multiple related phenotypes while retaining computational efficiency. Additionally, genetic analyses are confronted by strong confounding factors that can create spurious associations when not properly accounted for in the statistical model. We here derive more flexible methods that allow integrating genetic effects across variants and multiple quantitative traits. To do so, we build on the classical linear mixed model (LMM), a widely adopted framework for genetic studies. The first contribution of this thesis is mtSet, an efficient mixed-model approach that enables genome-wide association testing between sets of genetic variants and multiple traits while accounting for confounding factors. In both simulations and real-data applications we demonstrate that mtSet effectively combines the advantages of variant-set and multi-trait analyses. Next, we present a new model for gene-context interactions that builds on mtSet. The proposed interaction set test (iSet) yields increased statistical power for detecting polygenic interactions. Additionally, iSet enables the identification of genetic loci that are associated with different configurations of causal variants across contexts. After benchmarking the proposed method using simulated data, we consider two applications to real datasets, where we investigate genetic effects on gene expression across different cellular contexts and sex-specific genetic effects on lipid levels. Finally, we describe LIMIX, a software framework for the flexible implementation of different LMMs. Most of the models considered in this thesis, including mtSet and iSet, are implemented and available in LIMIX. A unique aspect of the software is an inference framework that allows a large class of genetic models to be defined and, in many cases, to be efficiently fitted by exploiting specific algebraic properties. We demonstrate the utility of this software suite in two applied collaboration projects. Taken together, this thesis demonstrates the value of flexible and integrative modelling in genetics and contributes new statistical methods for genetic analysis. These approaches generalise previous models, yet retain the computational efficiency that is needed to tackle large genetic datasets.

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Min, Min. "Asymptotic normality in generalized linear mixed models." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7758.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Lee, Yi-Ching. "An Approach to Estimation and Selection in Linear Mixed Models with Missing Data." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1562754262770979.

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Greenaway, Mark Jonathan. "Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20233.

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Approximate Bayesian inference methods offer methodology for fitting Bayesian models as fast alternatives to Markov Chain Monte Carlo methods that sometimes have only a slight loss of accuracy. In this thesis, we consider variable selection for linear models, and zero inflated mixed models. Variable selection for linear regression models are ubiquitous in applied statistics. We use the popular g-prior (Zellner, 1986) for model selection of linear models with normal priors where g is a prior hyperparameter. We derive exact expressions for the model selection Bayes Factors in terms of special functions depending on the sample size, number of covariates and R-squared of the model. We show that these expressions are accurate, fast to evaluate, and numerically stable. An R package blma for doing Bayesian linear model averaging using these exact expressions has been released on GitHub. We extend the Particle EM method of (Rockova, 2017) using Particle Variational Approximation and the exact posterior marginal likelihood expressions to derive a computationally efficient algorithm for model selection on data sets with many covariates. Our algorithm performs well relative to existing algorithms, completing in 8 seconds on a model selection problem with a sample size of 600 and 7200 covariates. We consider zero-inflated models that have many applications in areas such as manufacturing and public health, but pose numerical issues when fitting them to data. We apply a variational approximation to zero-inflated Poisson mixed models with Gaussian distributed random effects using a combination of VB and the Gaussian Variational Approximation (GVA). We also incorporate a novel parameterisation of the covariance of the GVA using the Cholesky factor of the precision matrix, similar to Tan and Nott (2018) to resolve associated numerical difficulties.

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Books on the topic "Mixed linear models"

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McCulloch, Charles E. Generalized Linear Mixed Models. Institute of Mathematical Statistics and American Statistical Association, 2003. http://dx.doi.org/10.1214/cbms/1462106059.

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Conference Board of the Mathematical Sciences. and National Science Foundation (U.S.), eds. Generalized linear mixed models. Institute of Mathematical Statistics, 2003.

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McCulloch, Charles E. Generalized, linear, and mixed models. John Wiley & Sons, 2001.

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Verbeke, Geert, and Geert Molenberghs. Linear Mixed Models in Practice. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2294-1.

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McCulloch, Charles E. Generalized, Linear, and Mixed Models. John Wiley & Sons, Ltd., 2005.

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McCulloch, Charles E. Generalized, linear, and mixed models. 2nd ed. Wiley, 2008.

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Khuri, André I. Statistical tests in mixed linear models. Wiley, 1998.

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Geert, Molenberghs, ed. Linear mixed models for longitudinal data. Springer, 2009.

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Filipiak, Katarzyna, Augustyn Markiewicz, and Dietrich von Rosen, eds. Multivariate, Multilinear and Mixed Linear Models. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75494-5.

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Khuri, André I., Thomas Mathew, and Bimal K. Sinha. Statistical Texts for Mixed Linear Models. John Wiley & Sons, Inc., 1998. http://dx.doi.org/10.1002/9781118164860.

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Book chapters on the topic "Mixed linear models"

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Bapat, R. B. "Linear Mixed Models." In Linear Algebra and Linear Models. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2739-0_9.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Models for Repeated-Measures Data: The Rat Brain Example." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-5.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Two-Level Models for Clustered Data: The Rat Pup Example." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-3.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Random Coefficient Models for Longitudinal Data: The Autism Example." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-6.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Models for Data with Crossed Random Factors: The SAT Score Example." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-8.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Three-Level Models for Clustered Data: The Classroom Example." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-4.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Power Analysis and Sample Size Calculations for Linear Mixed Models." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-9.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Models for Clustered Longitudinal Data: The Dental Veneer Example." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-7.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Linear Mixed Models: An Overview." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-2.

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West, Brady T., Kathleen B. Welch, Andrzej T. Gałecki, and Brenda W. Gillespie. "Introduction." In Linear Mixed Models, 3rd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003181064-1.

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Conference papers on the topic "Mixed linear models"

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Liu, Xiang, Xueling Liu, Jing Diao, et al. "Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822861.

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Adane, Kofi Freeman, and Aaron Fuhr. "Corrosion in Dense Slurry and Models Performance." In CONFERENCE 2022. AMPP, 2022. https://doi.org/10.5006/c2022-17743.

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Abstract A linear polarization resistance probe was used to study corrosion rates in oil sands dense slurries. The study was conducted in a pilot-scale horizontal slurry flow loop with nine test conditions evaluated. Slurry consisted of a bimodal (sand and rocks) particle distribution mixed with municipal water. The sand had a median size, d50 of approximately 0.776 mm and spread (d90 / d10) ratios in the range of 2.5 to 3.5. The rocks ranged in size from 6 to 12 mm (0.25 to 0.5 inches). The study was done in a 193.7 mm (7.625 inch) carbon steel pipe flow loop with mixture velocity and solids concentration of 4.5-7.0 m/s and 19.3-29.1 v/v%, respectively. The oxygen was injected to achieve a dissolved oxygen concentration of 1.0 ppm to 11.7 ppm. The measured corrosion rates were used to validate five existing corrosion rate models. The interaction of slurry flow characteristics and corrosion is a complex phenomenon and poses a significant challenge for any modeling attempts. Nonetheless, several findings in this work can be leveraged for corrosion predictions. The slurry effect on corrosion was not linear but depended highly on slurry flow regime, fines content, dissolved oxygen concentration, and local solids concentration and velocity distributions. In general, most existing models either overestimated or underestimated corrosion rates, with a couple showing some promise for further evaluation.

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Ahmed, Abdulhakeem, and Ana I. Torres. "Design and Optimization of Circular Economy Networks: A Case Study of Polyethylene Terephthalate (PET)." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.154237.

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Circular systems design is an emerging approach for promoting sustainable development. Despite its perceived advantages, the characterization of circular systems remains loosely defined and ambiguous. This work proposes a network optimization framework that evaluates three objective functions related to economic and environmental domains and employs a Pareto analysis to illuminate the trade-offs between objectives. The US polyethylene terephthalate (PET) value chain is selected as a case study and represented via a superstructure containing various recycling pathways. The superstructure optimization problems are modeled as a mixed integer linear program (MILP) and linear programs (LPs), implemented in Pyomo, and solved with CPLEX for a one-year assessment horizon. Solutions to the circular economy models are then compared to the corresponding solutions of linear economy models. Preliminary results show that the optimal circular network is advantageous over the optimal linear network for all objectives subject to the current market supply of raw materials and the total cost of production. However, when considering the present chemical processing infrastructure of the US economy and unrestricted biomass feedstock availability, a linear economy is favorable as an outcome of low operating cost and carbon sequestration.

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Kere, Kiswendsida Jules, and Qindan Huang. "Development of Probabilistic Models of Defect Interaction Identification and Failure Pressure for Pipelines with Colony of Corrosion Defects." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-19404.

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Abstract The interaction of defects in a defect colony has a higher pipeline failure risk than the case when such interaction is not considered. The goal of this study is to develop probabilistic interaction rule and predictive failure pressure model for pipelines with colony of corrosion defects. The proposed interaction rule is developed based on the logistic regression algorithm by considering pipe properties and colony configurations as the independent variables. A performance comparison with the existing interaction rules shows that the proposed interaction rule has the most accurate predictions. The failure pressure prediction model is developed by adding a correction factor to the Mixed-Type Interaction (MTI) method; and the correction factor is modeled using a multivariate linear regression using the pipe properties and adjacent defects characteristics as the independent variables. It is found that the proposed model provides unbiased and more accurate predictions. The results of the reliability analysis of an example pipeline with colony defects show that the interaction effect on the failure prediction plays a critical role in the structural performance of pipelines.

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Hurtado, M., N. von Ellenreider, C. Muravchik, and A. Nehorai. "Sparse component analysis for linear mixed models." In 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2010. http://dx.doi.org/10.1109/sam.2010.5606719.

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Lin, Lina, Mathias Drton, and Ali Shojaie. "Statistical Significance in High-dimensional Linear Mixed Models." In FODS '20: ACM-IMS Foundations of Data Science Conference. ACM, 2020. http://dx.doi.org/10.1145/3412815.3416883.

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Coelho, Carlos A., Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Symposium: Inference and Estimation in Mixed Linear Models." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3497902.

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Lindsten, Fredrik, and Thomas B. Schon. "Identification of mixed linear/nonlinear state-space models." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717191.

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Savasci, Alper, Adedoyin Inaolaji, and Sumit Paudyal. "Performance Analysis of Mixed-Integer Conic and Mixed-Integer Linear Unit Commitment Models." In 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020. http://dx.doi.org/10.1109/pesgm41954.2020.9281466.

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Shiqing, Wang, Ma Ying, and Feng Zhijun. "Integral Expression Form of Admissible Linear Estimators of Effects in Linear Mixed Models." In 2010 International Conference on Computing, Control and Industrial Engineering. IEEE, 2010. http://dx.doi.org/10.1109/ccie.2010.133.

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Reports on the topic "Mixed linear models"

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Juricek, Ben C. Generalized Linear Mixed-Effects Models in R. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada413561.

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Rao, C. R. A Unified Approach to Estimation in Linear Models with Fixed and Mixed Effects. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada193060.

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Goulet Coulombe, Philippe, Massimiliano Marcellino, and Dalibor Stevanovic. Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables. CIRANO, 2025. https://doi.org/10.54932/qgja3449.

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We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models are better suited to handle cross-sectional nonlinearities. Results show that predictive improvements are broad-based and that even a few high frequency state indicators contribute substantially to forecast accuracy. Our findings highlight the complementarity between flexible modeling and cross-sectional pooling, making panel neural networks a powerful tool for timely and accurate fiscal monitoring in heterogeneous settings.

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ธรรมรักษ์, เผด็จ. ปัจจัยที่มีผลต่อการตายก่อนหย่านมของลูกสุกร ในฟาร์มสุกรเชิงพาณิชย์ในประเทศไทย : รายงานฉบับสมบูรณ์. จุฬาลงกรณ์มหาวิทยาลัย, 2015. https://doi.org/10.58837/chula.res.2015.77.

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ในอุตสาหกรรมการเลี้ยงสุกร จำนวนลูกสุกรแรกคลอดมีชีวิตต่อครอกเพิ่มขึ้นอย่างมากเนื่องจากการปรับปรุงพันธุกรรมของสุกรมีความเจริญก้าวหน้าอย่างรวดเร็ว อย่างไรก็ตามความรู้ความเข้าใจทางด้านการจัดการหลังคลอดเพื่อลดการตายของลูกสุกรก่อนหย่านมยังไม่เพียงพอ การวิจัยครั้งนี้ทำขึ้นเพื่อศึกษาการตายของลูกสุกรก่อนหย่านมของฟาร์มสุกรในประเทศไทยสัมพันธ์กับจำนวนลูกสุกรที่เลี้ยงต่อครอกและน้ำหนักแรกคลอดของลูกสุกร ข้อมูลที่ศึกษามาจาก 11,154 ครอกจากแม่สุกร 3,574 แม่ที่คลอด ระหว่างเดือน มกราคม ค.ศ. 2009 ถึง เดือนธันวาคม ค.ศ. 2012 จำนวนลูกแรกคลอดมีชีวิตต่อครอก (หลังการย้ายฝาก) แบ่งเป็น 4 กลุ่ม ได้แก่ 1 – 7 ตัว 8 – 10 ตัว 11 – 12 ตัว และ 13 – 15 ตัว/ครอก น้ำหนักแรกคลอดเฉลี่ยแบ่งเป็น ต่ำ (< 1.30 กิโลกรัม/ตัว) ปานกลาง (1.30 – 1.79 กิโลกรัม/ตัว) และสูง (≥ 1.80 กิโลกรัม/ตัว) เปอร์เซ็นต์การตายก่อนหย่านมของลูกสุกรถูกคำนวณและแปลงเป็นค่า log แล้วทำการวิเคราะห์ทางสถิติด้วยวิธี General Linear Mixed Models ผลการศึกษาพบว่าอัตราการตายก่อนหย่านมเฉลี่ยเท่ากับ 14.5% (median = 10.0%) การตายของลูกสุกรก่อนหย่านมในครอกที่มีลูกสุกร 13 – 15 ตัวต่อครอก (24.1%) สูงกว่ากับครอกที่มีลูกสุกร 1 – 7 ตัว (11.9% P < 0.001) 8 – 10 ตัว (11.8% P < 0.001) และ 11 – 12 ตัว (14.6% P < 0.001) ตัวต่อครอก อย่างมีนัยสำคัญ ครอกที่มีน้ำหนักแรกคลอดต่ำ (18.8%) มีอัตราการตายก่อนหย่านมสูงกว่าครอกที่มีน้ำหนักแรกคลอดปานกลาง (15.7% P < 0.001) และสูง (12.1% P < 0.001) อย่างมีนัยสำคัญ โดยสรุปการลดอัตราการตายก่อนหย่านมของลูกสุกรในฟาร์มสุกรควรเน้นการจัดการดูแลลูกสุกรในครอกที่มีจำนวนลูกสุกรมากกว่า 13 ตัวต่อครอก และลูกสุกรที่มีน้ำหนักแรกคลอดต่ำกว่า 1.30 กิโลกรัม มากเป็นพิเศษ

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5

Coscia, Jordan, Wendy Cass, and Leighton J. Evaluation of 20 years of vegetation change in the Big Meadows landscape of Shenandoah National Park in response to mowing and burning: Application of historic data to identify best practices for future meadow management. National Park Service, 2025. https://doi.org/10.36967/2312599.

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The Big Meadows landscape is an open habitat that supports unique biodiversity not found elsewhere in Shenandoah National Park. To support this biodiversity and to maintain the Meadow’s historic landscape characteristics, Park staff have managed the meadows using prescribed burns, mowing, and the targeted cutting and removal of woody plants since 1998. To assess the impacts of these management actions on the plant community in Big Meadows and to guide future land management recommendations, we visualized changes in the plant community composition between 1998 and 2022 using non-metric multidimensional scaling and used generalized linear mixed-effects regression to model the impacts of climate and land management actions on eight plant community composition variables. Ordination revealed that the plant community across Big Meadows has moved in the direction of undesired increases in Rubus and Celastrus presence between 2004 and 2022 (stress = 0.13, non-metric R2 = 0.98, linear R2 = 0.91), a direction correlated with longer intervals between prescribed burns (R2 = 0.36, P = 0.01). Our models suggest that changes in the plant community were correlated with both changes in climate and changes in management frequency, providing strong evidence that longer intervals between prescribed burns were associated with undesired increases in woody plant cover (estimate ± SE = 0.19 ± 0.06, P < 0.001) and decreases in desired forb cover (estimate ± SE = −0.20 ± 0.04, P < 0.001 and moderate evidence that later spring freezes were associated with increases in desired graminoid cover (estimate ± SE = 0.11 ± 0.04, P = 0.01) and decreases in undesired Celastrus cover (estimate ± SE = −1.02 ± 0.48 , P = 0.04). Overall, though targeted experimentation will be needed to identify a management interval that balances the costs and effects of changes in management frequencies, our results suggest that increases in the frequency of prescribed burns and other management actions may assist in the maintenance of desired landscape conditions in Big Meadows.

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Kalen, Nicholas. Remediated for accessibility per Section 508. National Park Service, 2023. http://dx.doi.org/10.36967/2298454.

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I conducted bat surveys at Richmond National Battlefield Park to assess the status of bat communities following potential impacts of the disease white-nose syndrome. This disease, caused by the fungus Pseudogymnoascus destructans, has severely reduced populations of several bat species in the eastern United States, threatening some with regional extirpation. Most affected species include the little brown bat (Myotis lucifugus), northern long-eared bat (Myotis septentrionalis), Indiana bat (Myotis sodalis), and tricolored bat (Perimyotis subflavus). During the summers of 2016-2020, I sampled sites with acoustic bat detectors and conducted capture surveys using mist nets to characterize bat communities with a focus on documenting WNS-imperiled species. I also conducted non-reproductive, or dormant, season acoustic and capture surveys from 2017-2021, to investigate potential local wintering by bats, especially northern long-eared bats, which have recently been discovered wintering in the Coastal Plain of North Carolina. Acoustic results identified the presence of ten bat species by echolocation calls: big brown bat (Eptesicus fuscus), eastern red bat (Lasiurus borealis), hoary bat (Lasiurus cinereus), silver-haired bat (Lasionycteris noctivagans), little brown bat, northern long-eared bat, Indiana bat, evening bat (Nycticeius humeralis), tricolored bat, and Mexican free-tailed bat (Tadarida brasiliensis). Capture surveys documented big brown bats, eastern red bats, silver-haired bats, and evening bats. To examine habitat associations of bat species, I used generalized linear mixed models of a selection of variable candidates: habitat type, distance to water, minimum nightly temperature, and nightly precipitation to predict summer activity. Activity of big brown, eastern red, hoary, and Mexican free-tailed bats was highest in open habitats. Myotis spp. were most associated with mixed forest habitats. Tricolored bat activity was highest and evening bat activity was lowest in riparian and wetland habitats. To examine seasonality in bat species occurrence, I modeled acoustic activity in passes/night by Julian date using generalized additive models. Activity of big brown, eastern red, little brown, and tricolored bats was highest during summer. Activity of northern long-eared and Indiana bats was very low overall but was also highest in the summer. Tree bat species hoary, silver-haired, and Mexican free-tailed bat activity was highest in the spring and fall. Dormant season results suggest some winter occurrences for most bat species. Visual validation of echolocation calls revealed misidentifications were problematic for Myotis spp. little brown bat, northern long-eared bat, and Indiana bat, but diagnostic calls of all three species were recorded. Acoustic passes identified as northern long-eared bats suggest some individuals may be wintering nearby, but winter activity was very low and not indicative of large overwintering populations.

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7

Kalen, Nicholas. Bats of Richmond National Battlefield Park following white-nose syndrome: Public version. National Park Service, 2023. http://dx.doi.org/10.36967/2299295.

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I conducted bat surveys at Richmond National Battlefield Park to assess the status of bat communities following potential impacts of the disease white-nose syndrome. This disease, caused by the fungus Pseudogymnoascus destructans, has severely reduced populations of several bat species in the eastern United States, threatening some with regional extirpation. Most affected species include the little brown bat (Myotis lucifugus), northern long-eared bat (Myotis septentrionalis), Indiana bat (Myotis sodalis), and tricolored bat (Perimyotis subflavus). During the summers of 2016–2020, I sampled sites with acoustic bat detectors and conducted capture surveys using mist nets to characterize bat communities with a focus on documenting WNS-imperiled species. I also conducted non-reproductive, or dormant, season acoustic and capture surveys from 2017–2021, to investigate potential local wintering by bats, especially northern long-eared bats, which have recently been discovered wintering in the Coastal Plain of North Carolina. Acoustic results identified the presence of ten bat species by echolocation calls: big brown bat (Eptesicus fuscus), eastern red bat (Lasiurus borealis), hoary bat (Lasiurus cinereus), silver-haired bat (Lasionycteris noctivagans), little brown bat, northern long-eared bat, Indiana bat, evening bat (Nycticeius humeralis), tricolored bat, and Mexican free-tailed bat (Tadarida brasiliensis). Capture surveys documented big brown bats, eastern red bats, silver-haired bats, and evening bats. To examine habitat associations of bat species, I used generalized linear mixed models of a selection of variable candidates: habitat type, distance to water, minimum nightly temperature, and nightly precipitation to predict summer activity. Activity of big brown, eastern red, hoary, and Mexican free-tailed bats was highest in open habitats. Myotis spp. were most associated with mixed forest habitats. Tricolored bat activity was highest and evening bat activity was lowest in riparian and wetland habitats. To examine seasonality in bat species occurrence, I modeled acoustic activity in passes/night by Julian date using generalized additive models. Activity of big brown, eastern red, little brown, and tricolored bats was highest during summer. Activity of northern long-eared and Indiana bats was very low overall but was also highest in the summer. Tree bat species hoary, silver-haired, and Mexican free-tailed bat activity was highest in the spring and fall. Dormant season results suggest some winter occurrences for most bat species. Visual validation of echolocation calls revealed misidentifications were problematic for Myotis spp. little brown bat, northern long-eared bat, and Indiana bat, but diagnostic calls of all three species were recorded. Acoustic passes identified as northern long-eared bats suggest some individuals may be wintering nearby, but winter activity was very low and not indicative of large overwintering populations. An internal NPS version of this document that includes an appendix with capture survey site coordinates is available and may be requested as needed.

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Kalen, Nicholas. Bats of Petersburg National Battlefield following white-nose syndrome. National Park Service, 2023. http://dx.doi.org/10.36967/2299217.

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I conducted bat surveys at Petersburg National Battlefield Park (PETE) to assess the status of bat communities following potential impacts of the disease white-nose syndrome. This disease, caused by the fungus Pseudogymnoascus destructans, has severely reduced populations of several bat species in the eastern United States, threatening some with regional extirpation. Most affected species include the little brown bat (Myotis lucifugus), the federally-endangered northern long-eared bat (Myotis septentrionalis) and Indiana bat (Myotis sodalis) (USFWS 2007, USFWS 2022a), as well as the tricolored bat (Perimyotis subflavus), which has been proposed for endangered status (USFWS 2022b). I sampled sites with acoustic bat detectors from the summer of 2016 through fall 2021 and conducted capture surveys using mist nets from 2016-2019 to characterize seasonal occurrence of bat species with a focus on documenting WNS-imperiled species. Surveys also sought to document potential over-wintering of bats at PETE, especially northern long-eared bats, which have recently been discovered wintering in the Coastal Plain of North Carolina. Acoustic results identified the presence of nine bat species by echolocation calls: big brown bat (Eptesicus fuscus), eastern red bat (Lasiurus borealis), hoary bat (Lasiurus cinereus), silver-haired bat (Lasionycteris noctivagans), little brown bat, Indiana bat, evening bat (Nycticeius humeralis), tricolored bat, and Mexican free-tailed bat (Tadarida brasiliensis). Northern long-eared bats were identified acoustically by software, but identifications were invalidated by visual review. Acoustic misidentifications also occurred for little brown and Indiana bats, but diagnostic calls were confirmed. Capture surveys documented big brown bats, eastern red bats, evening bats, and a single little brown bat. To examine habitat associations of bat species, I used generalized linear mixed models of a selection of variable candidates: habitat type, distance to water, minimum nightly temperature, and nightly precipitation to predict summer activity. Activity of big brown, hoary, silver-haired, and Mexican free-tailed bats was highest in open habitats. Little brown and eastern red bats were most associated with mixed forest habitats. Evening bat activity was highest in deciduous forests. Habitat type was not a significant predictor of activity for Indiana and tricolored bat activity. To examine seasonality in bat species occurrence, I modeled acoustic activity in passes/night by Julian date using generalized additive models. Activity of big brown, eastern red, little brown, tricolored, and evening bats was highest during summer. Activity identified as Indiana bats was very low overall but was also highest in the summer. Activity of tree bat species hoary, silver-haired, and Mexican free-tailed bat was highest during the dormant season and peak activity appeared associated with migration. Dormant season results suggest some winter activity for most bat species identified as present in the park.

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9

bell, Matthew, Marcel P. Huijser, and David Kack. Exploring Apex Predator Effects on Wildlife-Vehicle Collisions: A Case Study on Wolf Reintroductions in Yellowstone. Western Transportation Institute, 2024. http://dx.doi.org/10.15788/1727735675.

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This study investigates the impact of wolf reintroduction on wildlife-vehicle collisions (WVCs) along a segment of US-191 bordering Yellowstone National Park. Wolves were reintroduced in 1995–1996, and subsequent wolf pack establishment may have influenced the behavior and population dynamics of prey species, potentially altering WVC patterns. Using carcass data collected from 1989 to 2021, the analysis was divided into two primary phases: before wolves (1989–1996) and after wolves (1997–2021). A series of linear mixed-effects models were developed to assess changes in WVCs across these time periods. Predictor variables included average annual daily traffic (AADT), elk population estimates, and wolf counts. Results showed that WVCs significantly declined in the post-wolf period, suggesting that the presence of wolves may reduce WVCs directly by modifying prey behavior and movement patterns, or indirectly by reducing prey population densities. Further analysis revealed that while elk populations were a significant predictor of WVCs before wolves were reintroduced, this relationship weakened post-reintroduction. Traffic volume did not significantly influence WVC patterns in either period, nor did it interact significantly with wolf presence. The inclusion of wolf counts as a continuous variable showed a negative relationship with WVCs, indicating that higher wolf densities may contribute to a further reduction in collisions over time. These findings suggest that apex predators can play a role in mitigating human-wildlife conflicts, such as WVCs, by influencing prey species’ behavior and distribution. The study provides valuable insights for wildlife managers and transportation planners, highlighting the potential benefits of predator conservation for road safety and ecosystem health.

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Mohr, Michelle, Jennifer Cribbs, Joan Dudney, et al. Inventory of white pine blister rust, fire, and bark beetles in sugar pine at Yosemite National Park. National Park Service, 2025. https://doi.org/10.36967/2308879.

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Invasive white pine blister rust (Cronartium ribicola, WPBR) threatens white pine populations throughout North America. Sugar pine (Pinus lambertiana), for example, has been declining in Sequoia and King Canyon National Parks (SEKI) due to WPBR, as well as other mortality agents, including fire, drought, and mountain pine beetle (Dendroctonus ponderosae, MPB). Whether these declines reflect population trends farther north in Yosemite National Park, however, is largely unknown. To this end, we surveyed 56 plots between June and October, 2023 in Yosemite. To test whether fire impacted WPBR or sugar pine recruitment, 23 plots were established in the Rim Fire footprint. Average extent (% of plots with ? 1 infection) and infection rate (% of all live stems with ? 1 infection) was 48.2% and 3.7%, respectively, though the extent was much lower in the Rim Fire plots (26.1%) compared to the primary plots (63.6%). Our generalized linear mixed models of infection rate suggested that fire and the presence of alternate hosts were important correlates of WPBR. As fire severity increased, WPBR infections declined, suggesting that high severity fire may dampen infection rates. Additionally, MPB was typically found in larger diameter stems, and the extent was higher in primary plots (54.6%) compared to the Rim Fire plots (30.4%). Recent surveys from SEKI show that sugar pine and WPBR infection rates are declining, likely due to complex interactions with drought, MPB, and fire. Comparable infection rates in Yosemite suggest that both sugar pine and WPBR may be following a similar trajectory. Though long-term monitoring is needed to quantify trends, our results underscore that restoration will help ensure the persistence of this ecologically important species.

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