Ephraim M. Hanks

Associate Professor of Statistics

Ephraim M. Hanks

Research Summary

Spatio-Temporal Statistics. Bayesian Hierarchical Modeling. Optimal Sampling. Animal Movement. Landscape Genetics.

Huck Affiliations

Publication Tags

Animals Prediction Telemetry Model Spatial Data Time Animal Sampling Movement Effect Statistical Models Autocorrelation Connectivity Ants Resource Selection Lakes Continuous Time Nutrients Single Nucleotide Polymorphism Bayesian Analysis Physiology Interaction Pathogens Individual Based Model Machine Learning

Most Recent Papers

A Sample Covariance-Based Approach For Spatial Binary Data

Sahar Zarmehri, Ephraim M. Hanks, Lin Lin, 2021, Journal of Agricultural, Biological, and Environmental Statistics

A novel quantitative framework for riverscape genetics

Shannon L. White, Ephraim M. Hanks, Tyler Wagner, 2020, Ecological Applications

Ecological prediction at macroscales using big data: Does sampling design matter?

Patricia A. Soranno, Kendra Spence Cheruvelil, Boyang Liu, Qi Wang, Pang Ning Tan, Jiayu Zhou, Katelyn B.S. King, Ian M. McCullough, Jemma Stachelek, Meridith Bartley, Christopher T. Filstrup, Ephraim M. Hanks, Jean François Lapierre, Noah R. Lottig, Erin M. Schliep, Tyler Wagner, Katherine E. Webster, 2020, Ecological Applications

A lattice and random intermediate point sampling design for animal movement

Elizabeth Eisenhauer, Ephraim Hanks, 2020, Environmetrics

Machine learning for modeling animal movement

Dhanushi A. Wijeyakulasuriya, Elizabeth W. Eisenhauer, Benjamin A. Shaby, Ephraim M. Hanks, 2020, PLoS One

Bayesian analysis of spatial generalized linear mixed models with Laplace moving average random fields

Adam Walder, Ephraim M. Hanks, 2020, Computational Statistics and Data Analysis

Effects of two centuries of global environmental variation on phenology and physiology of Arabidopsis thaliana

Victoria L. DeLeo, Duncan N.L. Menge, Ephraim M. Hanks, Thomas E. Juenger, Jesse R. Lasky, 2020, Global Change Biology on p. 523-538

Identifying and characterizing extrapolation in multivariate response data

Meridith L. Bartley, Ephraim M. Hanks, Erin M. Schliep, Patricia A. Soranno, Tyler Wagner, 2019, PLoS One

A Dynamic Individual-Based Model for High-Resolution Ant Interactions

Nathan B. Wikle, Ephraim M. Hanks, David P. Hughes, 2019, Journal of Agricultural, Biological, and Environmental Statistics on p. 589-609

Confronting models with data

Paul C. Cross, Diann J. Prosser, Andrew M. Ramey, Ephraim M. Hanks, Kim M. Pepin, 2019, Philosophical Transactions of the Royal Society B: Biological Sciences

Most-Cited Papers

Restricted spatial regression in practice

Ephraim M. Hanks, Erin M. Schliep, Mevin B. Hooten, Jennifer A. Hoeting, 2015, Environmetrics on p. 243-254

Spatial autoregressive models for statistical inference from ecological data

Jay M. Ver Hoef, Erin E. Peterson, Mevin B. Hooten, Ephraim M. Hanks, Marie Josèe Fortin, 2018, Ecological Monographs on p. 36-59

Continuous-time discrete-space models for animal movement

Ephraim M. Hanks, Mevin B. Hooten, Mat W. Alldredge, 2015, Annals of Applied Statistics on p. 145-165

Velocity-Based movement modeling for individual and population level inference

Ephraim M. Hanks, Mevin B. Hooten, Devin S. Johnson, Jeremy T. Sterling, 2011, PLoS One on p. e22795

Reconciling resource utilization and resource selection functions

Mevin B. Hooten, Ephraim M. Hanks, Devin S. Johnson, Mat W. Alldredge, 2013, Journal of Animal Ecology on p. 1146-1154

Circuit theory and model-based inference for landscape connectivity

Ephraim M. Hanks, Mevin B. Hooten, 2013, Journal of the American Statistical Association on p. 22-33

Hierarchical animal movement models for population-level inference

Mevin B. Hooten, Frances E. Buderman, Brian M. Brost, Ephraim M. Hanks, Jacob S. Ivan, 2016, Environmetrics on p. 322-333

Animal movement constraints improve resource selection inference in the presence of telemetry error

Brian M. Brost, Mevin B. Hooten, Ephraim M. Hanks, Robert J. Small, 2015, Ecology on p. 2590-2597

Social, spatial, and temporal organization in a complex insect society

Lauren E. Quevillon, Ephraim M. Hanks, Shweta Bansal, David P. Hughes, 2015, Scientific Reports

Reconciling multiple data sources to improve accuracy of large-scale prediction of forest disease incidence

Ephraim M. Hanks, Mevin B. Hooten, Fred A. Baker, 2011, Ecological Applications on p. 1173-1188