Mevin Hooten

Mevin Hooten

Professor, Department of Statistics and Data Sciences, The University of Texas at Austin; ASA Fellow

Publications

Books

Papers

2024

  1. Koslovsky, M.D., A. Kaplan, V.A. Terranova, and M.B. Hooten. (In Press). A unified Bayesian framework for modeling measurement error in multinomial data. Bayesian Analysis.

  2. Leach, C.B., B. Weitzman, J. Bodkin, D. Esler, G.G. Esslinger, K.A. Kloecker, D. Monson, J.N. Womble, and M.B. Hooten. (In Press). The dynamics of sea otter prey selection under population growth and expansion. Ecosphere.

  3. McDevitt-Gales, T., A.T. Degaetano, S. Elmendorf, J.R. Foster, H.S Ginsberg, M.B. Hooten, S. LaDeau, K.M. McClure, S. Paull, E. Posthumus, I. Rochlin, and D. Grear. (In Press). Partly Cloudy with a Chance of Mosquitoes: Developing a macroecological approach to forecasting mosquito populations and phenology under changing climates. Ecosphere.

  4. Schwob, M.R., M.B. Hooten, and V. Narasimhan. (In Press). Composite dyadic models for spatio-temporal data. Biometrics.

  5. Wikle, C.K., M.B. Hooten, W. Kleiber, and D.W. Nychka. (In Press). Spatial statistics: Climate and the environment. Spatial Statistics.

  6. Valentine, G.P., X. Lu, C.A. Dolloff, C.N. Roghair, J.M. Rash, M.B. Hooten, and Y. Kanno. (In Press). Landscape influences on thermal sensitivity and predicted spatial variability among brook trout streams in the Southeastern USA. River Research and Applications.

  7. Van Ee, J.J., C.A. Hagen, D.C. Pavlacky Jr., D.A. Haukos, A.J. Lawrence, A.M. Tanner, B.A. Grisham, K.A. Fricke, L.G. Rossi, G.G. Beauprez, K.E. Kuklinski, R. Martin, M.D. Koslovsky, T.B. Rintz, and M.B. Hooten. (In Press). Melded integrated population models. Journal of Agricultural, Biological, and Environmental Statistics.

  8. Lu, X., Y. Kanno, G. Valentine, M. Kulp, and M.B. Hooten. (In Press). Regularized latent trajectory models for spatio-temporal population dynamics. Journal of Agricultural, Biological and Environmental Statistics.

  9. Valle, D., N. Attias, J.A. Cullen, M.B. Hooten, A. Giroux, L. Gustavo, R. Oliveira-Santos, A.L.J. Desbiez, and R.J. Fletcher. (In Press). Bridging the gap between movement data and connectivity analysis using the Time-Explicit Habitat Selection (TEHS) model. Movement Ecology.

  10. Hui, F., Q. Vu, and M.B. Hooten. (2024). Spatial confounding in joint species distribution models. Methods in Ecology and Evolution, 15: 1906-1921. (pdf)

  11. Lu, X., Y. Kanno, G. Valentine, G. Rash, and M.B. Hooten. (2024). Using multi-scale spatial models of dendritic ecosystems to infer abundance of a stream salmonid. Journal of Applied Ecology, 61: 1703-1715. (pdf)

  12. Neitlich, P.N., W. Wright, E. Di Meglio, A.E. Shiel, C.J. Hampton-Miller, and M.B. Hooten. (2024). Mixed trends in heavy metal-enriched fugitive dust on National Park Service lands along the Red Dog Mine haul road, Alaska, 2006-2017. PLoS One, 19: e0297777.

  13. Hooten, M.B., M.R. Schwob, and D.S. Johnson. (2024). Geostatistical capture-recapture models. Spatial Statistics, 59: 100817. (pdf) (video)

  14. Eisaguirre, J.M., P.J. Williams, and M.B. Hooten. (2024). Rayleigh step-selection functions and connections to continuous-time mechanistic movement models. Movement Ecology, 12: 14.

  15. Valentine, G.P., X. Lu, E. Childress, C.A. Dolloff, N.P. Hitt, M.A. Kulp, B.H. Letcher, K.C. Pregler, J.M. Rash, M.B. Hooten, and Y. Kanno. (2024). Spatial asynchrony and cross-scale climate interactions in populations of a coldwater stream fish. Global Change Biology, 30: e17029. (pdf)

2023

  1. Lu, X., M.B. Hooten, A.M. Raiho, D.K. Swanson, C.A. Roland, and S.E. Stehn. (2023). Latent trajectory models for spatio-temporal dynamics in Alaskan ecosystems. Biometrics, 79: 3664-3675.

  2. Van Ee, J., C. Hagen, D. Pavlacky, K. Fricke, M. Koslovsky, and M.B. Hooten. (2023). Melding wildlife surveys to improve conservation inference. Biometrics, 79: 3941-3953.

  3. Schwob, M.R., M.B. Hooten, T. McDevitt-Gales. (2023). Dynamic population models with temporal preferential sampling to infer phenology. Journal of Agricultural, Biological, and Environmental Statistics, 28: 774-791.

  4. Lepak, J.M., B.M. Johnson, M.B. Hooten, B.A. Wolff, and A.G. Hansen. (2023). Predicting sport fish mercury contamination in heavily managed reservoirs: Implications for human and ecological health. PLoS One, 18: e0285890. (pdf)

  5. Williams, P.J., X. Lu, H.R. Scharf, and M.B. Hooten. (2023). Embracing asymmetry in nature: How to account for skewness in ecological data. Ecological Informatics, 75: 102085. (pdf)

  6. Hooten, M.B., M.R. Schwob, D.S. Johnson, and J.S. Ivan. (2023). Multistage hierarchical capture-recapture models. Environmetrics, 34: e2799. (code)

  7. Eisaguirre, J.M., P.J. Williams, X. Lu, M.L. Kissling, P.A. Schuette, B.P. Weitzman, W.S. Beatty, G.G. Esslinger, J.N. Womble, and M.B. Hooten. (2023). Informing management of recovering predators and their prey with ecological diffusion models. Frontiers in Ecology and the Environment, 21: 479-488. (cover article)

  8. Leach, C.B., B.P. Weitzman, J. Bodkin, D. Esler, G.G. Esslinger, K.A. Kloecker, D. Monson, J.N. Womble, and M.B. Hooten. (2023). Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models. Journal of Animal Ecology, 92: 1230-1243. (pdf)

2022

  1. Scharf, H.R., A. Raiho, S. Pugh, C.A. Roland, D.K. Swanson, S.E. Stehn, and M.B. Hooten. (2022). Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity. Biometrics, 78: 1427-1440.

  2. Wright, W.J., P.N. Neitlich, A.E. Shiel, and M.B. Hooten. (2022). Mechanistic spatial models for heavy metal pollution. Environmetrics, 33: e2760.

  3. Wenger, S.J., E. Stowe, K. Gido, M. Freeman, Y. Kanno, N. Franssen, J.D. Olden, L. Poff, A. Walters, P. Bumpers, M. Mims, M.B. Hooten, and X. Lu. (2022). Simple statistical models can be sufficient for testing hypotheses with population time series data. Ecology and Evolution, 12: e9339.

  4. Okasaki, C., M.B. Hooten, and A.M. Berdahl. (2022). Source reconstruction for spatio-temporal physical statistical models. Spatial Statistics, 52: 100707. (pdf)

  5. Lu, X., M.B. Hooten, A. Kaplan, J.N. Womble, and M.R. Bower. (2022). Improving wildlife population inference from aerial imagery data through entity resolution. Journal of Agricultural, Biological, and Environmental Statistics, 27: 364-381. (pdf)

  6. Raiho, A., H.R. Scharf, C.A. Roland, D.K. Swanson, S.E. Stehn, and M.B. Hooten. (2022). Searching for refuge: A framework for identifying site factors conferring resistance to climate-driven vegetation change. Diversity and Distributions, 28: 793-809. (pdf)

  7. Van Ee, J.J., J.S. Ivan, and M.B. Hooten. (2022). Community confounding in joint species distribution models. Scientific Reports, 12: 12235. (pdf)

  8. Scharf, H.R., X. Lu, P.J. Williams, and M.B. Hooten. (2022). Constructing flexible, identifiable, and interpretable statistical models for binary data. International Statistical Review, 90: 328-345. (pdf)

  9. Zimmerman, S., C. Aldridge, S. Oyler-McCance, and M.B. Hooten. (2022). Scale-dependent influence of the sagebrush community on genetic connectivity of the sagebrush obligate Gunnison sage-grouse. Molecular Ecology, 31: 3267-3285. (pdf)

  10. Schafer, T.L.J., C.K. Wikle, and M.B. Hooten. (2022). Bayesian inverse reinforcement learning for collective animal movement. Annals of Applied Statistics, 16: 999-1013. (pdf)

  11. Johnson, D.S., B.M. Brost, and M.B. Hooten. (2022). Greater than the sum of its parts: Computationally flexible Bayesian hierarchical modeling. Journal of Agricultural, Biological, and Environmental Statistics, 27: 382-400. (pdf)

  12. Kim, S., M.B. Hooten, T.L. Darden, and Y. Kanno. (2022). Linking male reproductive success to effort within and among nests in a co-breeding stream fish. Ethology, 128: 489-498. (pdf)

  13. Leach, C.B., P.J. Williams, J.M. Eisaguirre, J.N. Womble, M.R. Bower, and M.B. Hooten. (2022). Recursive Bayesian computation facilitates adaptive optimal design in ecological studies. Ecology, 103: e03573. (pdf)

  14. Feuka, A.B., M.G. Nafus, A.A. Yackel Adams, L.L. Bailey, and M.B. Hooten. (2022). Endogenous and exogenous mechanisms affecting invasive reptile movement at multiple scales. Movement Ecology, 10: 2. (pdf)

2021

  1. Raiho, A., E.F. Nicklen, A. Foster, C.A. Roland, and M.B. Hooten. (2021). Bridging implementation gaps to connect large ecological datasets to complex models. Ecology and Evolution, 11: 18271-18287. (pdf)

  2. Lepak, J.M., A.G. Hansen, M.B. Hooten, D. Brauch, and E.M. Vigil. (2021). Rapid proliferation of the parasitic copepod Salmincola californiensis on kokanee salmon in a large Colorado reservoir. Journal of Fish Diseases, 45: 89-98. (pdf)

  3. Banks, D.L. and M.B. Hooten. (2021). Statistical challenges in agent-based modeling. The American Statistician, 75: 235-242.

  4. Eisaguirre, J.M., P.J. Williams, X. Lu, M.L. Kissling, W.W. Beatty, G.G. Esslinger, J.N. Womble, and M.B. Hooten. (2021). Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator. Movement Ecology, 9: 34. (pdf)

  5. Hooten, M.B., D.S. Johnson, and B.M. Brost. (2021). Making recursive Bayesian inference accessible. The American Statistician, 75: 185-194. (pdf) (video)

  6. McCaslin, H.M., A.B. Feuka, and M.B. Hooten. (2021). Hierarchical computing for hierarchical models in ecology. Methods in Ecology and Evolution, 12: 245-254. (pdf) (video)

  7. Williamson, M.A., B.G. Dickson, M.B. Hooten, R.A. Graves, M.N. Lubell, and M.W. Schwartz. (2021). Accounting for incomplete reporting improves inference about private land conservation. Conservation Biology, 35: 1174-1185. (pdf)

2020

  1. Lasky, J.R., M.B. Hooten, and P.B. Adler. (2020). What processes must we understand to forecast regional scale population dynamics? Proceedings of the Royal Society B, 287: 20202219. (pdf)

  2. Leach, C., J.A. Hoeting, K. Pepin, A. Eiras, M.B. Hooten, and C. Webb. (2020). Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling. PLoS Neglected Tropical Diseases, 14: e0008868. (pdf)

  3. Hooten, M.B., C.K. Wikle, and M.R. Schwob. (2020). Statistical implementations of agent-based demographic models. International Statistical Review, 88: 441-461. (pdf) (video)

  4. Brost, B.M., M.B. Hooten, R.J. Small. (2020). Model-based clustering reveals patterns in central place use of a marine top predator. Ecosphere, 11: e03123. (pdf)

  5. Hooten, M.B., X. Lu, M.J. Garlick, and J.A. Powell. (2020). Animal movement models with mechanistic selection functions. Spatial Statistics, 37: 100406. (pdf) (video)

  6. Lu, X., P.J. Williams, M.B. Hooten, J.A. Powell, J.N. Womble, and M.R. Bower. (2020). Nonlinear reaction-diffusion process models improve inference for population dynamics. Environmetrics, 31: e2604. (pdf)

  7. Hooten, M.B., S. Pugh, and C.A. Roland. (2020). Geary’s contiguity ratio (Geary’s c). Wiley StatsRef: Statistics Reference Online.

  8. Christianson, K.R., B.M. Johnson, and M.B. Hooten. (2020). Compound effects of water clarity, inflow, wind, and climate warming on mountain lake thermal regimes. Aquatic Sciences, 82: 6. (pdf)

2019

  1. Tipton, J.R., M.B. Hooten, C. Nolan, R.K. Booth, and J. McLachlan. (2019). Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction. Annals of Applied Statistics, 13: 2363-2388. (pdf)

  2. Gerber, B.D., M.B. Hooten, C.P. Peck, M.B. Rice, J.H. Gammonley, A.D. Apa, and A.J. Davis. (2019). Extreme site fidelity as an optimal strategy in an unpredictable and homogeneous environment. Functional Ecology, 33: 1695-1707. (pdf)

  3. Williams, P.J., W.L. Kendall, and M.B. Hooten. (2019). Selecting ecological models using multi-objective optimization. Ecological Modelling, 404: 21-26. (pdf)

  4. Hooten, M.B., E.M. Hanks, and J.M. Ver Hoef. (2019). Simultaneous autoregressive (SAR) model. Wiley StatsRef: Statistics Reference Online. (pdf)

  5. Hooten, M.B. and E.C. Cooch. (2019). Comparing Ecological Models. In: Quantitative Analyses in Wildlife Science, eds. Brennan, L.A., A.N. Tri, and B.G. Marcot (pgs. 63-76). Johns Hopkins University Press. (pdf)

  6. Scharf, H.R., M.B. Hooten, R.R. Wilson, G.M. Durner, and T.C. Atwood. (2019). Accounting for phenology in the analysis of animal movement. Biometrics, 75: 810-820. (pdf)

  7. Christianson, K.R., B.M. Johnson, M.B. Hooten, and J.J. Roberts. (2019). Estimating lake-climate responses from sparse data: An application to high elevation lakes. Limnology and Oceanography, 64: 1371-1385. (pdf)

  8. Williams, P.J., M.B. Hooten, G.G. Esslinger, J.N. Womble, J. Bodkin, and M.R. Bower. (2019). The rise of an apex predator following deglaciation. Diversity and Distributions, 25: 895-908. (pdf)

  9. Nolan, C., J. Tipton, R.K. Booth, M.B. Hooten, and S.T. Jackson. (2019). Comparing and improving methods for reconstructing peatland water table depth from testate amoebae. The Holocene, 29: 1350-1361. (pdf)

  10. Peterson, E.E., E.M. Hanks, M.B. Hooten, J.M. Ver Hoef, and M.-J. Fortin. (2019). Spatially structured statistical network models for landscape genetics. Ecological Monographs, 89: e01355. (pdf)

  11. Ketz, A.C., T.L. Johnson, M.B. Hooten, and N.T. Hobbs. (2019). A hierarchical Bayesian approach for handling missing classification data. Ecology and Evolution, 9: 3130-3140. (pdf)

  12. Hooten, M.B. and D.S. Johnson. (2019). Modeling Animal Movement. Gelfand, A.E., M. Fuentes, and J.A. Hoeting (eds). In Handbook of Environmental and Ecological Statistics. Chapman & Hall/CRC.

  13. Hooten, M.B., H.R. Scharf, and J.M. Morales. (2019). Running on empty: Recharge dynamics from animal movement data. Ecology Letters, 22: 377-389. (pdf) (video)

2018

  1. Dietze, M., A. Fox, L. Beck-Johnson, J.L. Betancourt, M.B. Hooten, C. Jarnevitch, T. Kiett, M. Kenney, C. Laney, L. Larsen, H. Loescher, C. Lunch, B. Pijanowski, J. Randerson, E. Reid, A. Tredennick, R. Vargas, K. Weathers, and E. White. (2018). Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences, 115: 1424-1432. (pdf)

  2. Scharf, H.R., M.B. Hooten, D.S. Johnson, and J. Durban. (2018). Process convolution approaches for modeling interacting trajectories. Environmetrics, 29: e2487. (pdf)

  3. Buderman, F.E., M.B. Hooten, M.W. Alldredge, E.M. Hanks, and J.S. Ivan. (2018). Time-varying predatory behavior is primary predictor of fine-scale movement of wildland-urban cougars. Movement Ecology, 6: 22. (pdf)

  4. Conn, P.B., D.S. Johnson, P.J. Williams, S. Melin, and M.B. Hooten. (2018). A guide to Bayesian model checking for ecologists. Ecological Monographs, 88: 526-542. (pdf)

  5. Gerber, B.D., M.B. Hooten, C.P. Peck, M.B. Rice, J.H. Gammonley, A.D. Apa, and A.J. Davis. (2018). Accounting for location uncertainty in azimuthal telemetry data improves ecological inference. Movement Ecology, 6: 14. (pdf)

  6. Hooten, M.B., H.R. Scharf, T.J. Hefley, A. Pearse, and M. Weegman. (2018). Animal movement models for migratory individuals and groups. Methods in Ecology and Evolution, 9: 1692-1705. (pdf)

  7. Pejchar, L., T. Gallo, M.B. Hooten, and G. Daily. (2018). Predicting effects of large-scale reforestation on native and exotic birds. Diversity and Distributions, 24: 811-819. (pdf)

  8. Ver Hoef, J.M., E.M. Hanks, and M.B. Hooten. (2018). On the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models. Spatial Statistics, 25: 68-85. (pdf)

  9. Ketz, A.C., T.L. Johnson, R.J. Monello, J. Mack, J.L. George, B.R. Kraft, M.A. Wild, M.B. Hooten, and N.T. Hobbs. (2018). Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements. Ecological Applications, 28: 816-825. (pdf)

  10. Williams, P.J., M.B. Hooten, J.N. Womble, G.G. Esslinger, and M.R. Bower. (2018). Monitoring dynamic spatio-temporal ecological processes optimally. Ecology, 99: 524-535. (pdf)

  11. Ver Hoef, J.M., E.E. Peterson, M.B. Hooten, E.M. Hanks, and M-J. Fortin. (2018). Spatial autoregressive models for statistical inference from ecological data. Ecological Monographs, 88: 36-59. (pdf)

  12. Itter, M.S., A.O. Finley, M.B. Hooten, P.E. Higuera, J.R. Marlon, R. Kelly, and J.S. McLachlan. (2018). A model-based approach to wildland fire reconstruction using sediment charcoal records. Environmetrics, 22: e2450. (pdf)

  13. Buderman, F.E., M.B. Hooten, J. Ivan, and T. Shenk. (2018). Large-scale movement behavior in a reintroduced predator population. Ecography, 41: 126-139. (pdf)

2017

  1. Williams, P.J., M.B. Hooten, J.N. Womble, G.G. Esslinger, M.R. Bower, and T.J. Hefley. (2017). Estimating occupancy and abundance using aerial images with imperfect detection. Methods in Ecology and Evolution, 8: 1679-1689. (pdf)

  2. Hefley, T.J., B.M. Brost, and M.B. Hooten. (2017). Bias correction of bounded location errors in presence-only data. Methods in Ecology and Evolution, 8: 1566-1573. (pdf)

  3. Steger, C., B. Butt, and M.B. Hooten. (2017). Safari Science: Assessing the reliability of citizen science data for wildlife surveys. Journal of Applied Ecology, 54: 2053-2062. (pdf)

  4. Hooten, M.B., R. King, and R. Langrock. (2017). Guest editor’s introduction to the special issue on “Animal Movement Modeling.” Journal of Agricultural, Biological, and Environmental Statistics, 22: 224-231. (pdf)

  5. Scharf, H.R., M.B. Hooten, and D.S. Johnson. (2017). Imputation approaches for animal movement modeling. Journal of Agricultural, Biological, and Environmental Statistics, 22: 335-352. (pdf)

  6. Hanks, E.M., D.S. Johnson, and M.B. Hooten. (2017). Reflected stochastic differential equation models for constrained animal movement. Journal of Agricultural, Biological, and Environmental Statistics, 22: 353-372. (pdf)

  7. Hooten, M.B. and D.S. Johnson. (2017). Basis function models for animal movement. Journal of the American Statistical Association, 112: 578-589. (pdf)

  8. Hefley, T.J., M.B. Hooten, R.E. Russell, D.P. Walsh, and J. Powell. (2017). When mechanism matters: Forecasting the spread of disease using ecological diffusion. Ecology Letters, 20: 640-650. (pdf)

  9. Pepin, K.M., S.L. Kay, B. Golas, S.S. Shriner, A.T. Gilbert, R.S. Miller, A.L. Graham, S. Riley, P.C. Cross, M.D. Samuel, M.B. Hooten, J.A. Hoeting, J.O. Lloyd-Smith, C.T. Webb, and M.B. Buhnerkempe. (2017). Inferring infection hazard in wildlife populations by linking data across individual and population scales. Ecology Letters, 20: 275-292. (pdf)

  10. Meredith, C.S., P. Budy, M.B. Hooten, and M.O. Prates. (2017). Assessing abiotic conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach. Biological Invasions, 19: 503-519. (pdf)

  11. Tredennick, A.T., M.B. Hooten, and P.B. Adler. (2017). Do we need demographic data to forecast the state of plant populations? Methods in Ecology and Evolution, 8: 541-551. (pdf)

  12. Hefley, T.J., M.B. Hooten, E.M. Hanks, R.E. Russell, and D.P. Walsh. (2017). Dynamic spatio-temporal models for spatial data. Spatial Statistics, 20: 206-220. (pdf)

  13. Hefley, T.J., K.M. Broms, B.M. Brost, F.E. Buderman, S.L. Kay, H.R. Scharf, J.R. Tipton, P.J. Williams, and M.B. Hooten. (2017). The basis function approach to modeling autocorrelation in ecological data. Ecology, 98: 632-646. (pdf)

  14. Roberts, J.J., K.D. Fausch, M.B. Hooten, and D.P. Peterson. (2017). Nonnative trout invasions combined with climate change threaten persistence of isolated cutthroat trout populations in the southern Rocky Mountains. North American Journal of Fisheries Management, 37: 314-325. (pdf)

  15. Williams, P.J., M.B. Hooten, J.N. Womble, G.G. Esslinger, M.R. Bower, and T.J. Hefley. (2017). An integrated data model to estimate spatio-temporal occupancy, abundance, and colonization dynamics. Ecology, 98: 328-336. (pdf)

  16. Small, R.J., B.M. Brost, M.B. Hooten, M. Castellote, and J. Mondragon. (2017). Potential for spatial displacement of Cook Inlet beluga whales by anthropogenic noise in critical habitat. Endangered Species Research, 32: 43-57. (pdf)

  17. Hefley, T.J., M.B. Hooten, E.M. Hanks, R.E. Russell, and D.P. Walsh. (2017). The Bayesian group lasso for confounded spatial data. Journal of Agricultural, Biological, and Environmental Statistics, 22: 42-59. (pdf)

  18. Tipton, J., M.B. Hooten, and S. Goring. (2017). Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression. Advances in Statistical Climatology, Meteorology and Oceanography, 3: 1-16. (pdf)

  19. Brost, B.M., M.B. Hooten, and R.J. Small. (2017). Leveraging constraints and biotelemetry data to pinpoint repetitively used spatial features. Ecology, 98: 12-20. (pdf)

  20. Arab, A., M.B. Hooten, and C.K. Wikle. (2017). Hierarchical Spatial Models. In: Encyclopedia of Geographical Information Science, Second Edition. Springer.

2016

  1. Davis, A.J., M.B. Hooten, R.S. Miller, M. Farnsworth, J. Lewis, M. Moxcey, and K.M. Pepin. (2016) Inferring invasive species abundance using removal data from management actions. Ecological Applications, 26: 2339-2346. (pdf)

  2. Northrup, J.M., C.R. Anderson, M.B. Hooten, and G. Wittemyer. (2016). Movement reveals scale-dependence in habitat selection of a large ungulate. Ecological Applications, 26: 2746-2757. (pdf)

  3. Lepak, J.M., M.B. Hooten, C.A. Eagles-Smith, M.A. Lutz, M.T. Tate, J.T. Ackerman, J.J. Willacker Jr., D.C. Evers, J. Davis, C.F. Pritz, J.G. Wiener. (2016). Assessing mercury concentrations in fish across western Canada and the United States: potential health risks to fish and humans. Science of the Total Environment, 571: 342-354. (pdf)

  4. Scharf, H.R., M.B. Hooten, B.K. Fosdick, D.S. Johnson, J.M. London, and J.W. Durban. (2016). Dynamic social networks based on movement. Annals of Applied Statistics, 10: 2182-2202. (pdf)

  5. Hefley, T.J., M.B. Hooten, J.M. Drake, R.E. Russell, and D.P. Walsh. (2016). When can the cause of a population decline be determined? Ecology Letters, 19: 1353-1362. (pdf)

  6. Tredennick, A.T., M.B. Hooten, C.L. Aldridge, C.G. Homer, A. Kleinhesselink, and P.B. Adler. (2016). Forecasting climate change impacts on plant populations over large spatial extents. Ecosphere, 7: e01525. (pdf)

  7. Williams, P.J. and M.B. Hooten. (2016). Combining statistical inference and decisions in ecology. Ecological Applications, 26: 1930-1942. (pdf)

  8. Ruiz-Gutierrez, V., M.B. Hooten, and E.H. Campbell Grant. (2016). Uncertainty in biological monitoring: a framework for data collection and analysis to account for multiple sources of sampling bias. Methods in Ecology and Evolution, 7: 900-909. (pdf)

  9. Broms, K.M., M.B. Hooten, and R.M. Fitzpatrick. (2016). Model selection and assessment for multi-species occupancy models. Ecology, 97: 1759-1770. (pdf)

  10. Hooten, M.B., F.E. Buderman, B.M. Brost, E.M. Hanks, and J.S. Ivan. (2016). Hierarchical animal movement models for population-level inference. Environmetrics, 27: 322-333. (pdf) (video)

  11. Hanks, E.M., M.B. Hooten, S.A. Knick, S.J. Oyler-McCance, J.A. Ficke, T.B. Cross, and M.K. Schwartz. (2016). Latent spatial models and sampling design for landscape genetics. Annals of Applied Statistics, 10: 1041-1062. (pdf)

  12. Hefley, T.J. and M.B. Hooten. (2016). Hierarchical species distribution models. Current Landscape Ecology Reports: 1-16. (pdf)

  13. Wikle, C.K., W.M. Leeds, and M.B. Hooten. (2016) Models for ecological models: Ocean primary productivity. Chance, 29 (2): 23.

  14. Tipton, J., M.B. Hooten, N. Pederson, M. Tingley, and D. Bishop. (2016). Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models. Environmetrics, 27: 42-54. (pdf) (ASA ENVR Student Paper Award, 2015)

  15. Buderman, F.E., M.B. Hooten, J.S. Ivan, and T.M. Shenk. (2016). A functional model for characterizing long distance movement behavior. Methods in Ecology and Evolution, 7: 264-273. (pdf)

  16. Broms, K.M., M.B. Hooten, D.S. Johnson, L.L. Conquest, and R. Altwegg. (2016). Dynamic occupancy models for explicit colonization processes. Ecology, 97: 194-204. (pdf)

2015

  1. Raiho, A., M.B. Hooten, S. Bates, and N.T. Hobbs. (2015). Forecasting the effects of fertility control on overabundant ungulates: White-tailed deer in the National Capital region. PLoS One, 10: e0143122. (pdf)

  2. Brost, B.M., M.B. Hooten, E.M. Hanks, and R.J. Small. (2015). Animal movement constraints improve resource selection inference in the presence of telemetry error. Ecology, 96: 2590-2597. (pdf)

  3. Hefley, T.J. and M.B. Hooten. (2015). On the existence of maximum likelihood estimates for presence-only data. Methods in Ecology and Evolution, 6: 648-655. (pdf)

  4. Hobbs, N.T., C. Geremia, J. Trainor, R. Wallen, P.J. White, M.B. Hooten, and J.C. Rhyan. (2015). State-space modeling to support adaptive management of brucellosis in the Yellowstone bison population. Ecological Monographs, 85: 525-556. (pdf)

  5. Schmelter, M.L., P. Wilcock, M.B. Hooten, and D.K. Stevens. (2015). Multi-fraction Bayesian sediment transport model. Journal of Marine Science and Engineering, 3: 1066-1092. (pdf)

  6. Ross, B.E., M.B. Hooten, J-M DeVink, and D.N. Koons. (2015). Combined effects of climate, predation, and density dependence on Greater and Lesser Scaup population dynamics. Ecological Applications, 25: 1606-1617. (pdf)

  7. Gerber, B.D., W.L. Kendall, M.B. Hooten, J.A. Dubovsky, and R.C. Drewien. (2015). Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations. Journal of Animal Ecology, 84: 1299-1310. (pdf)

  8. Hanks, E.M., M.B. Hooten, and M. Alldredge. (2015). Continuous-time discrete-space models for animal movement. Annals of Applied Statistics, 9: 145-165. (pdf)

  9. Conn, P.B., Johnson, D.S., J.M. Ver Hoef, M.B. Hooten, J.M. London, and P.L. Boveng. (2015). Using spatio-temporal models to estimate animal abundance and infer ecological dynamics from survey counts. Ecological Monographs, 85: 235-252. (pdf)

  10. Hanks, E.M., E. Schliep, M.B. Hooten, and J.A. Hoeting. (2015). Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification. Environmetrics, 26: 243-254. (pdf)

  11. Hooten, M.B. and N.T. Hobbs. (2015). A guide to Bayesian model selection for ecologists. Ecological Monographs, 85: 3-28. (pdf) (updated code)

  12. Broms, K.M., M.B. Hooten, and R. Fitzpatrick. (2015). Accounting for imperfect detection in Hill numbers for biodiversity studies. Methods in Ecology and Evolution, 6: 99-108. (pdf)

  13. Wikle, C.K. and M.B. Hooten. (2015). Hierarchical agent-based spatio-temporal dynamic models for discrete valued data. Davis, R., S. Holan, R. Lund, and N. Ravishanker (eds.), Handbook of Discrete-Valued Time Series.

2014

  1. Davis, A.J., M.B. Hooten, M.L. Phillips, and P.F. Doherty. (2014). An integrated modeling approach to estimating Gunnison sage-grouse population dynamics: Combining index and demographic data. Ecology and Evolution, 4: 4247-4257. (pdf)

  2. McClintock, B.T., D.S. Johnson, M.B. Hooten, J.M. Ver Hoef, and J.M. Morales. (2014). When to be discrete: the importance of time formulation in understanding animal movement. Movement Ecology, 2: 21. (pdf)

  3. Odei, J.B., J. Symanzik, and M.B. Hooten. (2014). A Bayesian hierarchical model for forecasting intermountain snow dynamics. Environmetrics, 25: 324-340. (pdf)

  4. Garlick, M.J., J.A. Powell, M.B. Hooten, and L. McFarlane. (2014). Homogenization, sex, and differential motility predict spread of chronic wasting disease in mule deer in Southern Utah. Journal of Mathematical Biology, 69: 369-399. (pdf)

  5. Hooten, M.B., E.M. Hanks, D.S. Johnson, and M.W. Alldredge. (2014). Temporal variation and scale in movement-based resource selection functions. Statistical Methodology, 17: 82-98. (pdf)

2013

  1. Milliff, R.F., J. Fiechter, W.B. Leeds, R. Herbei, C.K. Wikle, M.B. Hooten, A.M. Moore, T.M. Powell, and J.L. Br own. (2013). Uncertainty management in coupled physical-biological lower-trophic level ocean ecosystem models. Oceanography, 24: 98-115.

  2. Green, A.W., M.B. Hooten, E.H.C. Grant, and L.L. Bailey. (2013). Evaluating breeding and metamorph occupancy and vernal pool management effects for wood frogs using a hierarchical model. Journal of Applied Ecology, 50: 1116-1123. (pdf)

  3. Johnson, D.S., M.B. Hooten, and C.E. Kuhn. (2013). Estimating animal resource selection from telemetry data using point process models. Journal of Animal Ecology, 82: 1155-1164. (pdf)

  4. Hooten, M.B., E.M. Hanks, D.S. Johnson, and M.W. Alldredge. (2013). Reconciling resource utilization and resource selection functions. Journal of Animal Ecology, 82: 1146-1154. (pdf)

  5. Hooten, M.B., M.J. Garlick, and J.A. Powell. (2013). Computationally efficient statistical differential equation modeling using homogenization. Journal of Agricultural, Biological and Environmental Statistics, 18: 405-428. (pdf)

  6. Northrup, J.M., M.B. Hooten, C.R. Anderson, and G. Wittemyer. (2013). Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology, 94: 1456-1464. (pdf)

  7. Johnson, D.S., P.B. Conn, M.B. Hooten, J. Ray, and B. Pond. (2013). Spatial occupancy models for large data sets. Ecology, 94: 801-808. (pdf)

  8. Roberts, J.J., K.D. Fausch, D.P. Peterson, and M.B. Hooten. (2013). Fragmentation and thermal risks from climate change interact to affect persistence of native trout in the Colorado River basin. Global Change Biology, 19: 1383-1398. (pdf)

  9. Hanks, E.M. and M.B. Hooten. (2013). Circuit theory and model-based inference for landscape connectivity. Journal of the American Statistical Association, 108: 22-33. (pdf)

  10. Cruz, S.M., M.B. Hooten, K.P. Huyvaert, C. Proano, D.J. Anderson, J. Fox, and M. Wikelski. (2013). At-sea behavior varies with lunar phase in a nocturnal pelagic seabird, the swallow-tailed gull. PLoS One, 8: e56889. (pdf)

2012

  1. Ross, B.E., M.B. Hooten, and D.N. Koons. (2012). An accessible method for implementing hierarchical models with spatio-temporal abundance data. PLoS One, 7: e49395. (pdf)

  2. Lepak, J.M., C.N. Cathcart, and M.B. Hooten. (2012). Otolith weight as a predictor of age in kokanee salmon (Oncorhynchus nerka) from four Colorado reservoirs. Canadian Journal of Fisheries and Aquatic Sciences, 69: 1569-1575. (pdf)

  3. Lepak, J.M., M.B. Hooten, and B.M. Johnson. (2012). The influence of marine subsidies on diet, growth, and Hg concentrations of freshwater sport fish: tertiary impacts on fisheries and human health. Ecotoxicology, 21: 1878-1888. (pdf)

  4. Hooten, M.B., B.E. Ross, and C.K. Wikle. (2012). Optimal spatio-temporal monitoring designs for characterizing population trends. Gitzen, R.A., J.J. Millspaugh, A.B. Cooper, and D.S. Licht (eds). In: Design and Analysis of Long-Term Ecological Monitoring Studies. Cambridge University Press. (pdf)

2011

  1. Haas, S.E., M.B. Hooten, D. Rizzo, and R.K. Meentemeyer. (2011). Forest species diversity reduces disease risk in a generalist plant pathogen invasion. Ecology Letters, 14: 1108-1116. (pdf)

  2. Hooten, M.B., W.B. Leeds, J. Fiechter, and C.K. Wikle. (2011). Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models. Journal of Agricultural, Biological and Environmental Statistics, 16: 475-494. (pdf)

  3. Schmelter, M.L., M.B. Hooten and D.K. Stevens. (2011). Bayesian sediment transport model for uni-size bedload. Water Resources Research, 47, W11514. (pdf)

  4. Garlick, M.J., J.A. Powell, M.B. Hooten, and L. McFarlane. (2011). Homogenization of large-scale movement models in ecology. Bulletin of Mathematical Biology, 73: 2088-2108. (pdf)

  5. Xiao, X., E.P. White, M.B. Hooten, and S.L. Durham. (2011). On the use of log-transformation vs. nonlinear regression for analyzing biological power-laws. Ecology, 92: 1887-1894. (pdf)

  6. Hanks, E.M., M.B. Hooten, D.S. Johnson, and J. Sterling. (2011). Velocity based movement modeling for individual and population level inference. PLoS One, 6(8), e22795. (pdf)

  7. Hanks, E.M., M.B. Hooten, and F.A. Baker. (2011). Reconciling multiple data sources to improve accuracy of large-scale prediction of forest disease incidence. Ecological Applications, 24: 1173-1188. (pdf)

  8. Dalgleish, H.J., D.N. Koons, M.B. Hooten, C.A. Moffet, and P.B. Adler. (2011). The influence of climate on the demography of three dominant sagebrush steppe plants. Ecology, 92: 75-85. (pdf)

  9. Hooten, M.B. (2011). The State of Spatial and Spatio-Temporal Statistical Modeling. In: Predictive Modeling in Landscape Ecology. Drew A., F. Huettman, and Y. Wiersma (eds). In: Predictive Modeling in Landscape Ecology. (pdf)

2010

  1. Hanks, E.M., M.B. Hooten, L. McFarlane and K.E. Mock. (2010). Model based approaches for characterizing environmental effects on spatial gene flow. In JSM Proceedings, Section on Statistics and the Environment. Alexandria, VA: American Statistical Association. 4113-4126. (pdf)

  2. Hooten, M.B., Johnson, D.S., Hanks, E.M., and J.H. Lowry. (2010). Agent-based inference for animal movement and selection. Journal of Agricultural, Biological and Environmental Statistics, 15: 523-538. (pdf)

  3. Wikle, C.K. and M.B. Hooten (2010). A general science-based framework for nonlinear spatio-temporal dynamical models. Test, 19: 417-451. (pdf)

  4. Wilson, R.R., T.L. Blankenship, M.B. Hooten, and J.A. Shivik. (2010). Prey-mediated avoidance of an intraguild predator by its intraguild prey. Oecologia, 164: 921-929. (pdf)

  5. Wilson, R.R., M.B. Hooten, B.N. Strobel, and J.A. Shivik (2010). Accounting for individuals, uncertainty, and multi-scale clustering in core area estimation. Journal of Wildlife Management, 74: 1343-1352. (pdf)

  6. Hooten, M.B., Anderson, J., and L.A. Waller. (2010). Assessing North American influenza dynamics with a statistical SIRS model. Spatial and Spatio-Temporal Epidemiology, 1: 177-185. (pdf)

  7. Nippert, J.B., M.B. Hooten, D.R. Sandquist, and J.K. Ward (2010). A model for predicting El Nino events using tree-ring cellulose del18O. Journal of Geophysical Research, 115: 1-9. (pdf)

  8. Hooten, M.B. and C.K. Wikle (2010). Statistical agent-based models for discrete spatio-temporal systems. Journal of the American Statistical Association, 105: 236-248. (pdf)

  9. Wilson, T.L., J.B. Odei, M.B. Hooten, and T.C. Edwards (2010). Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance. Journal of Applied Ecology, 47: 401-409. (BBC News Feature) (pdf)

  10. Larsen, R.T., J.A. Bissonette, J.T. Flinders, M.B. Hooten, and T.L. Wilson (2010). Summer spatial patterning of Chukars in relation to free water in Western Utah. Landscape Ecology, 25: 135-145. (pdf)

2009

  1. Odei, J.B., M.B. Hooten, and J. Jin. (2009). Hierarchical spatio-temporal models for intermountain snow water storage. In JSM Proceedings, Section on Statistics and the Environment. Alexandria, VA: American Statistical Association. 870-878. (pdf)

  2. Hooten, M.B., C.K. Wikle, L.D. Carlile, R. Warner, and D. Pitts (2009). Hierarchical population models for the red-cockaded woodpecker. Rich, T.D., M. C. Arizmendi, D. Demarest and C. Thompson (eds). Tundra to Tropics: Connecting Birds, Habitats and People. Proceedings of the 4th International Partners in Flight Conference, 13-16 February 2008. McAllen, TX. University of Texas-Pan American Press. Edinburg, TX. pgs. 354-364. (pdf)

  3. Cangelosi, A.R. and M.B. Hooten (2009). Models for Bounded Systems with Continuous Dynamics. Biometrics, 65: 850-856. (pdf)

  4. Hooten, M.B., M.J. Garlick, and J.A. Powell. (2009). Advantageous change of support in inverse implementations of statistical differential equation models. In JSM Proceedings, Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association. 1847-1857. (pdf)

  5. Hooten, M.B., C.K. Wikle, S. Sheriff, and J. Rushin (2009). Optimal spatio-temporal hybrid sampling designs for ecological monitoring. Journal of Vegetation Science, 20: 639-649. (pdf)

2008

  1. Hooten, M.B, R.R. Wilson, and J.A. Shivik (2008). Hard core or soft core: On the characterization of animal space use. 2008 Proceedings of the American Statistical Association [CD-ROM], Alexandria, VA: American Statistical Association: pp. 1301-1308. (pdf)

  2. Mock, K.E., C.A. Rowe, M.B. Hooten, J. DeWoody, and V.D. Hipkins (2008). Clonal dynamics in western North American aspen (Populus tremuloides). Molecular Ecology, 17: 4827-4844. (pdf)

  3. Hooten, M.B. and C.K. Wikle (2008). A Hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove. Environmental and Ecological Statistics, 15: 59-70. (pdf)

2007

  1. Hooten, M.B. and C.K. Wikle (2007). Invasions, Epidemics, and Binary Data in a Cellular World. 2007 Proceedings of the American Statistical Association [CD-ROM], Alexandria, VA: American Statistical Association: pp. 3999-4010. (pdf)

  2. Cangelosi, A.R. and M.B. Hooten (2007). Approximations to Continuous Dynamical Processes in Hierarchical Models. 2007 Proceedings of the American Statistical Association [CD-ROM], Alexandria, VA: American Statistical Association: pp. 1281-1287. (pdf)

  3. Hooten, M.B. (2007). Journal of the American Statistical Association. Book Review: Le, N.D. and J.V. Zidek. (2006) Statistical Analysis of Environmental Space-Time Processes. Springer-Verlag.

  4. Arab, A., M.B. Hooten, and C.K. Wikle (2007). Hierarchical Spatial Models. In: Encyclopedia of Geographical Information Science. Springer. (pdf)

  5. Hooten, M.B., C.K. Wikle, R.M. Dorazio, and J.A. Royle (2007). Hierarchical spatio-temporal matrix models for characterizing invasions. Biometrics, 63: 558-567. (pdf)

  6. Hooten, M.B. and C.K. Wikle (2007). Shifts in the spatio-temporal growth dynamics of shortleaf pine. Environmental and Ecological Statistics, 14: 207-227. (pdf)

  7. He, H.S., D.C. Dey, X. Fan, M.B. Hooten, J.M. Kabrick, C.K. Wikle, and Z. Fan (2007). Mapping pre-European settlement vegetation using a hierarchical Bayesian model and GIS. Plant Ecology, 191: 85-94. (pdf)

2006

  1. Hooten, M.B. (2006). Hierarchical spatio-temporal models for ecological processes. Ph.D. Dissertation, University of Missouri. Columbia, Missouri. (pdf)

  2. Hooten, M.B. and C.K. Wikle (2006). Spatio-temporal processes in ecology: A gentle introduction. Journal of Biogeography, 33: 1150-1152. Book Review: Reiners, W.A. and K.L. Driese. (2004) Transport processes in nature: propagation of ecological influences through environmental space. Cambridge University Press.

  3. Wikle, C.K. and M.B. Hooten (2006). Hierarchical Bayesian Spatio-Temporal Models for Population Spread. Clark, J.S. and A. Gelfand (eds). In: Applications of Computational Statistics in the Environmental Sciences: Hierarchical Bayes and MCMC Methods. Oxford University Press. (pdf)

<2006

  1. Hooten, M.B., Larsen, D.R., and C.K. Wikle, (2003). Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model. Landscape Ecology , 18: 487-502. (pdf)

  2. Hooten, M.B. (2001). Modeling the distribution of ground flora on large spatial domains in the Missouri Ozarks. M.S. Thesis, University of Missouri. Columbia, Missouri. (pdf)