maxent_modeling_code.R

# Your sloth species name
# Maxent modeling code

# Your name + your partner's name
# Date

# FIRST STEP: Rename this file by adding your species name and an underscore to the beginning of the file
# (for example, "tridactylus_maxent.R")


# Required packages -------------------------------------------------------

# Load all the packages you need for the analyses
# You need 1) the package for spatial thinning, 2) the package for mapping,
# 3) the packages from background_region_tutorial.Rmd,
# 4) the package used in partitioning occurrences


# Occurrence data ---------------------------------------------------------

# Import the dataset for your species from data/occurrence_data



# Visualize occurrence data -----------------------------------------------

# Use the ggmap package to plot the occurrence points for your species on a map
# Share this map in Slack



# Spatial thinning --------------------------------------------------------

# Refer to your code from intern_code/occ_data_cleaning.R
# (and also lesson_plans/s2_process_occ_data/sloth_cleaning_pt3.Rmd)

# Thin your occurrence data to a distance of 40 km


# Check how many rows were removed by spatial thinning
# Share this number in Slack


# Visualize which points were removed using ggmap


# Create background region ------------------------------------------------

# Refer to lesson_plans/s4_process_env_data/background_region_tutorial.Rmd

# Create a background region for your species (based on the thinned occurrence data!):
## B. variegatus: points buffered by 2 degrees
## B. tridactylus: MCP buffered by 1 degree
## B. torquatus: points buffered by 1 degree


# Make a map of your background region
# Share that map in Slack


# Remember to sample background points from your background region

# Partition occurrence data -----------------------------------------------

# Refer to lesson_plans/s5_partition_occ_data.Rmd

# Partition your thinned occurrence data:
## if your species has 25 or fewer thinned occurrences, use a jackknife partition
## if your species has >25 thinned occurrences, use a block partition


# Visualize the partitioned occurrence data on a map
# Share this map in Slack


# Build Maxent models -----------------------------------------------------

# Refer to lesson_plans/s6_build_eval_niche_model/ENMeval_tutorial.Rmd

# Use regularization multiples from 1 to 5 with a step value of 1


# Use feature classes "L", "LQ", "H", and "LQH"


# Run ENMevaluate()
# and unpack results data frame, list of models, and RasterStack of raw predictions



# Select Maxent model -----------------------------------------------------

# Refer to lesson_plans/s6_build_eval_niche_model/model_selection_tutorial.Rmd

# Sort the results data frame using AUC, OR, and/or AIC
# Select the "best" model according to your criteria


# Slack the name of the best model and the criteria you used to select it


# Visualize model ---------------------------------------------------------

# Generate the model prediction and plot it
# Share this map in Slack



# Project in space --------------------------------------------------------

# Project the model to the background region you selected and plot the projection
# Share this map in Slack


# Project the model to a bounding box for your species and plot the projection
# Share this map in Slack


# Project forward in time --------------------------------------------------------

# Find the resolution of your masked environmental data


# Set two different GCMs: HadGEM2-ES and CCSM4 (we are going to project to 2 different GCMs to compare the results)


# Set three different RCPs: 2.6, 6, and 8.5


# Set the year to be 2070


# Download the data for the 6 different combinations of GCM and RCP (all at the year 2070)

# Set the names of your environmental data

# Crop and mask the environmental data to the bounding box for your species


# Project the model into the future -- you will end up with 6 different projected models
# Plot the projected models


# Response curves --------------------------------------------------------

# Check which variables in the model have non-zero coefficients

# Plot response curves