Species Distribution and Habitat Modeling WILD 6900, 3 credits, CRN 57734 (Logan), Fall Semester 2020

Course Description This course provides participants with skills for building species distribution and habitat models (SDHM) for use in management and conservation planning, and ecological study. Topics covered include: (i) formulation of intended SDHM use statements; (ii) data acquisition, organization, and vetting; (iii) model construction and prediction; (iv) assessment and evaluation; and (v) decision-risk associated with SDHM implementation. Participants will learn to apply these skills in R through hands-on exercises, with an intent to transfer the skills learned to a SDHM related to their work or research environment.

Course Delivery This is considered an online course. As such, it includes pre- recorded lectures and a weekly live discussion, as well as student-required exercises to be accomplished individually, or in work groups using a variety of remote- based platforms such as Zoom, MS Teams, Moodle, or Canvas. Live discussion is every TU 1330-1430 hr MDT. Attendance is strongly encouraged but cannot be required.

Course Structure Formal course instruction runs from TU 8 Sep thru FR 20 Nov. The week of 31 Aug is for personal CPU and data preparation. Post-Thanksgiving (30 Nov -11 Dec) is devoted to individual data analyses and presentations. You must have a personal CPU (4MB RAM is OK; 8MB RAM preferred). Required freeware is R-3.6.3 (versions 4.0.X are not yet stable), RStudio, and few assorted R toolkits. Syllabus (as of 29 Jun 2020) is attached.

Requirements You must conversant in R, or have taken a course similar to baseR WILD6580. All course exercises will be completed within the RMarkdown environment.

For further info contact: Thomas Edwards, [email protected] Species Distribution and Habitat Modelling (Version 2020.2)

Thomas C. Edwards Research Ecologist and Professor U.S. Geological Survey, Utah Cooperative Fish and Wildlife Research Unit, and Department of Wildland Resources Utah State University, Logan, UT 84322-5230 [email protected] (EMAIL)

The Application of R for Species Distribution and Habitat Modelling

Hosted in collaboration with FWS Ecological Services, FWS National Conservation Training Center, the USGS Cooperative Research Units Program, and Utah State University

Species Distribution and Habitat Modelling - T. Edwards Course Outline - 1 Species Distribution and Habitat Modelling Using R Course Outline (Version 2020.2)

M1:Introduction to Species Distribution & Habitat Modelling 1.1.1 Some background on distribution and habitat models (SDHM) 1.1.2 Developing an intended use of a SDHM 1.1.3 Why must we be careful building SDHMs? 1.1.4 Important modelling caveats you should know about 1.1.5 Decision-risk and SDHMs M1: Some Statistical Underpinnings 1.2.1 Some background on regression and classification 1.2.2 An overview of statistical classification tools 1.2.3 Which classifier(s) to select? Intermezzio 1.3 Course conventions

M2: Data Acquisition, Vetting, and Organization 2.1.1 Overview 2.1.2 Setting a data acquisition goal 2.1.3 Data acquisition processes 2.1.4 Basic data quality assessment processes M2: Data Resolutions and SDHMs 2.2.1 Some terminology 2.2.2 Examples of varying resolutions of commonly used raster predictors 2.2.3 Pros and cons of standardized (common) variable resolutions in SDHMs 2.2.4 Basic analytical tools for changing resolutions

Species Distribution and Habitat Modelling - T. Edwards Course Outline - 2 Species Distribution and Habitat Modelling Using R Course Outline (Version 2020.2)

M2: Spatial Data, Sample, and Modelling Frames 2.3.1 Some terminology for spatial frames 2.3.2 Different origins of spatial frames 2.3.3 Importance of selecting defensible spatial frames 2.3.4 Why construction of a background FISHNET can help in SDHM modelling M2: Issues of Presence-Only Data for SDHMs 2.4.1 What constitutes a presence-only data structure 2.4.2 How presence-only data affect ability to build a SDHM for classification 2.4.3 Concept of pseudo- ("false") absences as means of generating absence data 2.4.4 The importance of defensible modelling frame when generating pseudo-absences M2: Building the SDHM Training Dataset 2.5.1 What defines a training dataset for SDHM construction 2.5.2 Building a SDHM training dataset M2: Exploring Training Datasets Prior to SDHM Construction 2.6.1 Some background on the importance of data exploration before SDHM construction 2.6.2 Processes for exploring the training dataset

Species Distribution and Habitat Modelling - T. Edwards Course Outline - 3 Species Distribution and Habitat Modelling Using R Course Outline (Version 2020.2)

M3: Model Assessment and Uncertainty 3.1 The Validation Theaters of SDHMs 3.2 A Validation Framework for Application to SDHMs 3.3 The Validation Process – Statistical Approaches 3.4 The Validation Process – Less-Than-Statistical Approaches M4: Building Prediction Map Products from SDHMs 4.1 Some historical (just ‘cause it’s fun) and current-day ecological uses of maps 4.2 Converting R statistical model objects to map products M5: Logistic Regression GLM 5.1.1 When to Use the Logistic GLM Model 5.1.2 The Statistical Model for the Logistic GLM 5.1.3 Basic Assumptions of the Logistic GLM 5.1.4 Related Techniques 5.1.5 Example Analysis Using R: Predicting the probability of bird nest occurrence as a function of habitat variables M5: Additive Logistic Regression GAM 5.2.1 When to Use the Additive Logistic GAM Model 5.2.2 The Statistical Model for the Additive Logistic GAM 5.2.3 Basic Assumptions of the Additive Logistic GAM 5.2.4 Related Techniques 5.2.5 Example Analysis Using R: Discriminating lodgepole pine from a background of coniferous forest: the interplay of remote-sensing and environmental predictors

Species Distribution and Habitat Modelling - T. Edwards Course Outline - 4 Species Distribution and Habitat Modelling Using R Course Outline (Version 2020.2)

M5: Maximum Entropy MAXENT 5.3.1 Characteristics of MAXENT analysis 5.3.2 The Statistical Model for Maximum Entropy MAXENT 5.3.3 Basic Assumptions of Maximum Entropy MAXENT 5.3.4 Related Techniques 5.3.5 Example Analysis Using R: Modelling critical habitat for a plant species proposed for listing under the U.S. Endangered Species Act: Graham’s Beardtongue Penstemon grahamii M5: Random Forests RF 5.4.1 Characteristics Random Forests RF (and Classification Trees) 5.4.2 The Statistical Model for Random Forests RF 5.4.3 Basic assumptions of the random forest RF 5.4.4 Related Techniques 5.4.5 Example Analysis Using R: Predicting epiphytic macrolichen presence across broad geographic ranges: how good are extrapolative models? M5: Boosted Regression Trees BRT 5.5.1 Characteristics of Boosted Regression Trees BRT 5.5.2 The Statistical Model for Boosted Regression Trees BRT 5.5.3 Basic assumptions of the boosted regression trees BRT 5.5.4 Related techniques 5.5.5 Example Analysis Using R: Setting the stage for climate-change projections: using SDHMs to model the current distribution of the Utah juniper Juniperus osteosperma

Species Distribution and Habitat Modelling - T. Edwards Course Outline - 5 Species Distribution and Habitat Modelling Using R Course Outline (Version 2020.2)

M6: Ensemble SDHMs 6.1 What Constitutes an Ensemble Model 6.2 The Two Basic Classes of Ensemble Models 6.3 The Ensemble Process 6.4 Example Analysis Using R: An ensemble model predicting the spatial distribution of critical habitat for Graham’s Beardtongue Penstemon grahamii M7: Developing Field Designs to Test SDHMs 7.1 What constitutes a field assessment? 7.2 The two basic classes of field assessments 7.3 Example Analysis Using R: Developing a post-delisting monitoring program using a SDHM: Graham’s Beardtongue Penstemon grahamii M8: The Dilemma(s) of SDHM Comparisons 8.1 A comparison of results from the class exercises 8.2 Some (random and unstructured) thoughts on SDHMs as forecast models

Appendices Appendix 1 Data Sources Appendix 2 Required R Packages for Workshop Appendix 3 Basic R Functions for Workshop

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