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Dr. Mevin Hooten

Research Publications

  • 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 dependent ecological data. Ecology, 98: 632-646.
  • Small, R.J., B.M. Brost, M.B. Hooten, M. Castellote, and J. Mondragon. (2017). Anthropogenic noise in Cook Inlet beluga critical habitat: Potential for spatial displacement. Endangered Species Research, 32: 43-57.
  • Hefley, T.J., M.B. Hooten, E.M. Hanks, R.E. Russell, and D.P. Walsh. (2017). The Bayesian spatial group lasso. Journal of Agricultural, Biological, and Environmental Statistics, 22: 42-59.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Williams, P.J. and M.B. Hooten. (2016). Combining statistical inference and decisions in ecology. Ecological Applications, 26: 1930-1942.
  • Broms, K.M., M.B. Hooten, and R.M. Fitzpatrick. (2016). Model selection and assessment for multi­species occupancy models. Ecology, 97: 1759-1770.
  • Hefley, T.J. and M.B. Hooten. (2016). Hierarchical species distribution models. Current Landscape
    Ecology Reports, 1: 87-97.
  • Wikle, C.K., W.B. Leeds, and M.B. Hooten. (2016). Models for ecological models: Ocean primary productivity. Chance, 29 (2): 23.
  • 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.
  • 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.
  • Meredith, C.S., P. Budy, and M. Hooten, and M. O. Prates. 2016. Assessing abiotic conditions influencing the longitudinal distribution of exotic brown trout in a mountain stream: a spatially-explicit modeling approach. Biological Invasions. DOI 10.1007/s10530-016-1322-z. USGS IP-069503 Abstract | 
  • 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.
  • 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.
  • 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(3): 264–273.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Hobbs, N.T. and M.B. Hooten. (2015). Bayesian Models: A Statistical Primer for Ecologists. Princeton University Press.
  • Hobbs, N.T., C. Geremia, J. Treanor, 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.
  • 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.
  • 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.
  • 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, DOI: 10.1111/1365-2656.12370
  • 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.
  • Hooten, M.B. and N.T. Hobbs. (2015). A guide to Bayesian model selection for ecologists. Ecological Monographs. 85: 3-28.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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: 1456-1463. Ecology, In Press.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Hanks, E.M., M.B. Hooten, D.S. Johnson, and J. Sterling. (2011). A velocity-based approach for linking animal telemetry data to environmental drivers of movement. PLoS One, 6(8), e22795.
  • 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.
  • 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.
  • Schmelter, M.L., M.B. Hooten, and D.K. Stevens. (2011). Bayesian sediment transport model for uni-size bedload. Water Resources Research, 47: W11514.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Wikle, C.K. and M.B. Hooten (2010). A general science-based framework for nonlinear spatio-temporal dynamical models. TEST, 19: 417-451.
  • 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.
  • 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.
  • 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.
  • Cangelosi, A.R. and M.B. Hooten (2009). Models for Bounded Systems with Continuous Dynamics. Biometrics, 65: 850-856.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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(1): 59-70.
  • 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.
  • Arab, A., M.B. Hooten, and C.K. Wikle (2007). Hierarchical Spatial Models. In: Encyclopedia of Geographical Information Science. Springer.
  • 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.
  • 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.
  • 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.
  • 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.
  • Hooten, M.B. and C.K. Wikle (2007). Shifts in the spatio-temporal growth dynamics of shortleaf pine. Environmental and Ecological Statistics, 14(3): 207-227.
  • 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.
  • 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.
  • 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.
  • 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.
 

Current Staff

Federal Staff: 3

Masters Students: 4

Phd Students: 3

Post Docs: 1

University Staff: 2

5 Year Summary

Students graduated: 22

Scientific Publications: 82

Presentations: 87

 

Contact Us

Colorado Cooperative Fish and Wildlife Research Unit Fort Collins, CO 80523-1484 Phone: (970) 491 - 5396 Fax: (970) 491 - 1413 Our University Web Site

Unit Leader

Dana Winkelman
Dana Winkelman-Unit Leader

I received my BS degree in biology in 1984 and my MS degree in Biology in 1987 at the University of Nevada, Reno. I received my PhD in zoology from the University of Georgia in 1994. I joined the Cooperative Research Unit program in 1998 as Assistant Unit Leader at the Oklahoma Cooperative Fish and Wildlife Research Unit. In 2003 I became the Unit Leader at the Colorado Cooperative Fish and Wildlife Research Unit.

Colorado Cooperative Fish and Wildlife Research Unit Cooperators

  1. Colorado Parks and Wildlife
  2. Colorado State University
  3. U.S. Fish and Wildlife Service
  4. U.S. Geological Survey
  5. Wildlife Management Institute