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Walter Applied Spatial Ecology Laboratory
Manual of Applied Spatial Ecology
Chapter 10 - Miscellaneous Code
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Chapter10_2016.pdf
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The effects of targeted removal of deer groups on the epidemiology of chronic wasting disease in wild white-tailed deer in Pennsylvania
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Modeling potential habitat for pheasant population restoration
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Acknowledgments
Preface
Chapter 1 - Data Manipulation and Management
Chapter 1 - Data Manipulation and Management
Chapter 1 - Data Manipulation and Management Overview
1.1 Load R software and packages
1.2 Geographic coordinate systems
1.3 Projected coordinate systems
1.4 Transformations Between Coordinate Systems
1.5 Import and Format Datasets
1.6 Manipulate Polygon Layer
1.7 Manipulate Raster Data Layer
1.8 Creating a Hexagonal Polygon Grid Over a Study Area
1.9 Creating a Square Polygon Grid Over a Study Area
1.10 Creating buffers
Chapter 2 - Climate Data Interpolation
Chapter 2 - Climate Data Interpolation
Chapter 2 - Climate Data Interpolation Overview
Introduction
2.1 Incorporating Background Spatial Layers
2.2 Accessing Climate Data
2.3 Cleaning Raw Climate Data
2.4 Using data in R
2.5 Importing Dynamically Downscaled Global Climate Data
Chapter 3 - Movement Methods
Chapter 3 - Movement Methods
Chapter 3 - Movement Methods Overview
Introduction
3.1 Importing datasets from a web source
3.2 Movement trajectories
3.3 Distance between locations
3.4 First Passage Time (FPT)
3.5 Regular Trajectories
3.6 Net Squared Displacement
3.7 Movement Trajectory Animation
Chapter 4 - Home Range Estimation
Chapter 4 - Home Range Estimation
Chapter 4 - Home Range Estimation Overview
4.1 Kernel Density Estimation (KDE) with reference bandwidth selection (href)
4.2 KDE with least-squares cross validation bandwidth selection (hlscv)
4.3 KDE with plug-in bandwidth selection (hplug-in)
4.4 Brownian Bridge Movement Models (BBMM)
4.5 Movement-based Kernel Density Estimation (MKDE)
4.6 Dynamic Brownian Bridge Movement Model (dBBMM)
4.7 Characteristic Hull Polygons (CHP)
4.8 Local Convex Hull (LoCoH)
Chapter 5 - Overlap Indices
Chapter 5 - Overlap Indices
Chapter 5 - Overlap Indices Overview
Introduction
5.1 Percent overlap
5.2 Probability overlap
5.3 Bhattacharyya’s affinity
5.4 Utilization distribution overlap index
5.5 Hellinger’s distance
5.6 Volume of intersection index
Chapter 6 - Three-dimensional Analysis
Chapter 6 - Three-dimensional Analysis
Chapter 6 - Three-dimensional Analysis Overview
6.1 Three-dimensional home range
6.2 Three-dimensional exploration of digital elevation models (DEMs)
Chapter 7 - Landscape Metrics
Chapter 7 - Landscape Metrics
Chapter 7 - Landscape Metrics Overview
Introduction
7.1 Landscape metrics for a single area
7.2 Landscape metrics within polygons
7.3 Landscape Metrics within buffers
Chapter 8 - Resource Selection
Chapter 8 - Resource Selection
Chapter 8 - Resource Selection Overview
Introduction
8.1 Minimum Convex Polygon
8.2 Preparing linear measures
8.3 Preparing additional covariates
8.4 Selection ratios
8.5 Resource selection functions
Chapter 9 - Spatial Epidemiology in WinBUGS
Chapter 9 - Spatial Epidemiology in WinBUGS
Chapter 9 - Spatial Epidemiology in WinBUGS Overview
Introduction
9.1 Data preparation in R
9.2 Raster manipulation in R
9.3 Combine Data Formats in R
9.4 Data preparation of NDVI covariate
9.5 Data preparation for R2WinBUGS
9.6 Data preparation within ArcMap
9.7 Check model
9.8 Load data
9.9 Compiling chains
9.10 Load initial values
9.11 Sample monitor tool
9.12 Update tool
9.13 Further considerations
Chapter 10 - Miscellaneous Code
Chapter 10 - Miscellaneous Code
Chapter 10 - Miscellaneous Code Overview
10.1 Remove or search for duplicated GPS locations in a data frame
10.2 Need to convert back to a matrix to be able to export the data or manipulate the data
10.3 Remove quotations marks around values in results table or printout
10.4 Bin numeric variables into categories
10.5 Recode variables in Rcmdr
10.6 Jitter UTM coordinates before making SpatialPointsDataFrame
10.7 Remove extraneous locations or remove all data for a single animal by animal ID
10.8 Generate sequential numbers as ID’s for each location then add back to original dataset
10.9 Rename data by deleting a portion of the string
10.10 Grab text included in strings of varying lengths
10.11 Rename levels of factor
10.12 Recode numeric values as factors into categories
10.13 Add leading zeros to single digit numbers to match datasets
10.14 Force a DBRB class output to a data frame
10.15 Subset GPS locations by a date range
10.16 Drivers for rdgal input/ouput but run command for complete list if needed
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Chapter10_2016.pdf