Cooperative Research Units
Education, Research And Technical Assistance For Managing Our Natural Resources
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Utah Education Activities

Training Offered

  • Species Distribution Modelling Using R
    The course covers major parametric, semi- and non-parametric models currently used in species distribution models (SDM), ranging from GLMs to GAMs to Random Forests / Boosted Regression Trees. Course also covers design issues, principally those related to (i) presence only vs. true presence-absence data, and (ii) sample frame issues leading to grossly unbalanced training data (i.e., prevalence) and their effects on model output. Exercise data ranges from plants to animals, rare to common species, and small to large extents.
    (Thomas Edwards July 2017)
  • graphR - Basic Graphing in R
    Basics of graphing in R, with an emphasis on the creation of publication-quality graphs for journals. Five Modules on (i) plot layouts, (ii) saving graphics output, (iii) plot size and resolution, (iv) plot axes, symbols and lines, and (v) six basic graphs in R. Nine exercises associated with the course.
    (Thomas Edwards February 2017)
  • gisR – Basic Geographic Information Systems Analysis
    Covers common GIS data manipulations and analyses using raster-based, and polygon, line and point shapefiles. Twelve exercises are associated with the course.
    (Thomas Edwards January 2017)
  • Species Distribution Modelling Using R
    The course covers major parametric, semi- and non-parametric models currently used in species distribution models (SDM), ranging from GLMs to GAMs to Random Forests / Boosted Regression Trees. Course also covers design issues, principally those related to (i) presence only vs. true presence-absence data, and (ii) sample frame issues leading to grossly unbalanced training data (i.e., prevalence) and their effects on model output. Exercise data ranges from plants to animals, rare to common species, and small to large extents.
    (Thomas Edwards September 2015)
  • gisR – Basic Geographic Information Systems Analysis
    Covers common GIS data manipulations and analyses using raster-based, and polygon, line and point shapefiles. Twelve exercises are associated with the course.
    (Thomas Edwards September 2015)
  • statR-I – Descriptive Statistics in R
    Common descriptive statistics in R. Five modules on (i) numerical descriptive statistics, (ii) graphical descriptive statistics, (iii) assessing distributions, (iv) measures of correlation, (v) basic graphs for portraying descriptive data. Seven exercises associated with the course.
    (Thomas Edwards August 2015)
  • baseR – Data Management and Manipulation in R
    An introduction to R, including Sections on: (i) The R Environment (8 Modules), (ii) Basics of Data Management (9 Modules), and (iii) Data Manipulation in R (9 Modules). Eighteen exercises associated with the course.
    (Thomas Edwards August 2015)
  • baseR – Data Management and Manipulation in R
    An introduction to R, including Sections on: (i) The R Environment (8 Modules), (ii) Basics of Data Management (9 Modules), and (iii) Data Manipulation in R (9 Modules). Eighteen exercises associated with the course.
    (Thomas Edwards July 2015)
  • gisR – Basic Geographic Information Systems Analysis
    Covers common GIS data manipulations and analyses using raster-based, and polygon, line and point shapefiles. Twelve exercises are associated with the course.
    (Thomas Edwards July 2015)
  • Species Distribution Modelling Using R
    The course covers major parametric, semi- and non-parametric models currently used in species distribution models (SDM), ranging from GLMs to GAMs to Random Forests / Boosted Regression Trees. Course also covers design issues, principally those related to (i) presence only vs. true presence-absence data, and (ii) sample frame issues leading to grossly unbalanced training data (i.e., prevalence) and their effects on model output. Exercise data ranges from plants to animals, rare to common species, and small to large extents.
    (Thomas Edwards July 2015)
  • gisR – Basic Geographic Information Systems Analysis
    Covers common GIS data manipulations and analyses using raster-based, and polygon, line and point shapefiles. Twelve exercises are associated with the course.
    (Thomas Edwards April 2015)
  • baseR – Data Management and Manipulation in R
    An introduction to R, including Sections on: (i) The R Environment (8 Modules), (ii) Basics of Data Management (9 Modules), and (iii) Data Manipulation in R (9 Modules). Eighteen exercises associated with the course.
    (Thomas Edwards January 2015)
  • Bayesain Statistics for Ecologists. Shortcourse objectives were to: (i) Gain an understanding of Bayesian Methods; (ii) Learn to specify Bayesian models (write them down in mathematical notation); (iii) Learn how to read and interpret applied Bayesian literature; (iv) Gain an understanding of Bayesian computation (largely through MCMC); (v) Learn how to display and interpret Bayesian model output; and (vi) Learn how to connect Bayesian models with scientific inquiry. Team-taught with M. Hooten, COCFWRU. Attended by 12 MS and PhD graduate students, Utah State University, 12-16 Mar 2012. (Thomas Edwards, Mevin Hooten March 2012)
  • baseR for DWR Biologists. Workshop on data organization and manipulations in R often needed to prepare collected field data for analysis. Attended by 34 DWR and FWS biologists, 3 Fridays 10, 17 & 24 February 2012. (Thomas Edwards February 2012)
  • baseR for Natural Resource Graduate students. Workshop on data organization and manipulations in R often needed to prepare collected field data for analysis. Attended by 27 MS and PhD graduate students, Utah State University, 5 sessions, 26 Sept, 3, 10, 17, & 24 Oct 2011. (Thomas Edwards October 2011)
  • ECOCHANGE #2: Predictive habitat distribution models: tools for building projections of global change impact on biodiversity. Swiss Federal Research Lab WSL, Birmensdorf, Switzerland, 19-24 September 2010.
    Course objectives are to train participants in both theoretical and practical aspects of the process of species distribution modelling, from conception to model fitting to model evaluation to spatial predictions. Course content includes lectures on underlying theories behind distribution modelling, and habitat suitability modelling exercises with R and the BIOMOD package, as well as poster presentations and general discussions. This year, a special focus is on application of habitat suitability models in conservation.
    (Thomas Edwards September 2010)
  • NCTC Data Analysis IV: Bioclimatic Forecast Modeling Using R. Panama City, FL 18-23 April 2010
    This course will entail use of statistical models first introduced in DAIIA to predict new plant and animal distributions as a result of global change, including both climate and land-use impacts. It begins with discussion of overviews of land-use and climate change, and the importance defining an appropriate forecast frame. Forecast frames for several different plant and animals will be illustrated using BADEN, a structured approach requiring development of Biotic, Abiotic, and Dispersal ENvelopes. Statistical models first discussed in DAIIIA will next be evaluated using an ensemble models structure, whereby no single model form portrays future landscape, but rather output from many different model forms is averaged. This will be accomplished using the BIOMOD analytical structure, and include several variants of this process. Procedures for assessing the uncertainty of forecast models will be discussed, and will involve methods for reporting and portraying this uncertainty. The course will use the same examples from DAIIIA to ensure course continuity, but include new data structures of projected climate and land use change.
    (Thomas Edwards April 2010)
  • Species Distribution Modelling Using R. Utah State University, Logan, Utah, 15-19 March 2010
    The class covers major parametric, semi- and non-parametric models currently used in species distribution models, ranging from the tried and true GLMs to GAMs to Random Forests. Workshop also covers design issues, principally those related to (i) presence only vs. true presence-absence data, and (ii) sample frame issues leading to grossly unbalanced training data (ie prevalence) and their effects on model output. All analyses would be taught using R.
    (Thomas Edwards March 2010)
  • Instructor, NCTC Course #CSP4220, Data Analysis IIIA: Species Distribution Modeling Using R. Panama City, FL, 4-8 January 2010. (15 FWS students) (Thomas Edwards January 2010)
  • NCTC Data Analysis III: Species Distribution Modelling Using R. Panama City, Florida, 6-11 December 2009.
    The course covers major parametric, semi- and non-parametric models currently used in species distribution models, ranging from the tried and true GLMs to GAMs to Random Forests. Workshop also covers design issues, principally those related to (i) presence only vs. true presence-absence data, and (ii) sample frame issues leading to grossly unbalanced training data (ie prevalence) and their effects on model output. All analyses would be taught using R.
    (Thomas Edwards December 2009)
  • ECOCHANGE #1: Predictive habitat distribution models: tools for building projections of global change impact on biodiversity. University of Lausanne, Lausanne, Switzerland, 7-10 September 2009.
    Course objectives are to train participants in both theoretical and practical aspects of the process of species distribution modelling, from conception to model fitting to model evaluation to spatial predictions. Course content includes lectures on underlying theories behind distribution modelling, and habitat suitability modelling exercises with R and the BIOMOD package, as well as poster presentations and general discussions.
    (Thomas Edwards September 2009)
  • Workshop on Random Forests, 2009 Meeting of the U.S. Chapter of the International Association for Landscape Ecology, Snowbird, Utah, 4/15/2009.
    Random Forests: applications to conservation issues (subtitled: Is the forest really better than the tree? And what if you’re in the grasslands?).
    (Thomas Edwards April 2009)
  • Principles of Stream Restoration, Part I, Utah State University, Department of Watershed Sciences. Co-taught with J. Schmidt, M. Kondolf, and P. Wilcox. 5-day short course. 2003, 2004, 2005, 2006, 2007, 2008. (Phaedra Budy July 2008)
  • Principles of Stream Restoration, Part I, Utah State University, Department of Watershed Sciences. Co-taught with J. Schmidt, M. Kondolf, and P. Wilcox. 5-day short course. 2007. (Phaedra Budy June 2007)
  • Bioenergetics in fisheries: concepts and applications. (Phaedra Budy March 2007)
  • Coordinator- Continuing Education Short Course, USU, Departments of AWER and FWRS. August 21-24, 2006. MARK Mark-Resight Workshop. Mary Conner- Instructor. (Phaedra Budy August 2006)
  • Stream Restoration, Utah State University, Department of Aquatic, Watershed, and Earth Resources. Co-taught with J. Schmidt, M. Kondolf, and P. Wilcox. 2006. (Phaedra Budy May 2006)
  • Coordinator and Co-Instructor- Continuing Education Short Course, USU, Dept. of AWER. May 9-13, 2005. The principles and practice of stream restoration: Part I. Jack Schmidt, Peter Wilcox, and Matt Kondolf- Co-Instructors. (Phaedra Budy May 2005)
  • Coordinator and Co-Instructor- Continuing Education Short Course, USU, Dept. of AWER. August 17-27, 2004. The principles and practice of stream restoration. Jack Schmidt- Co-Instructor. (Phaedra Budy August 2004)
  • June 4-8, 2001. Program MARK Workshop. Sponsored by the Wildlife Management Ins. & taught by Dave Anderson, Ken Burnham, Colorado Cooperative Fish and Wildlife Unit, and Gary White, Colorado State University. (Phaedra Budy June 2004)
  • Population Assessment Short Course. Continued Education, AFS. (Phaedra Budy March 2003)
  • Population Assessment Short Course. Continued Education, Bonneville AFS. (Phaedra Budy February 2001)
  • Integral Projection Models Workshop. IPMs represent the next generation of stage-classified demographic models by offering all of the advantages of discrete matrix models in a more general framework. In the simplest form of an IPM, individuals of a population are classified along a continuous state variable (e.g., volume, height, weight), and the vital rates involved in the life cycle of the species (e.g., survival, growth, reproduction) are modeled through a series of simple, biologically intuitive regressions. More complex IPMs can include age × size × habitat interactions, stochastic modeling, and coupling of genetic information on population dynamics. (Thomas Edwards August 2012)
 

Current Staff

Federal Staff: 2

Masters Students: 5

Phd Students: 2

Post Docs: 1

University Staff: 2

5 Year Summary

Students graduated: 12

Scientific Publications: 28

Presentations: 127

 

Utah Cooperative Fish and Wildlife Research Unit Cooperators

  1. U.S. Fish and Wildlife Service
  2. U.S. Geological Survey
  3. Utah Division of Wildlife Resources
  4. Utah State University/College of Natural Resources
  5. Wildlife Management Institute