README

The envi package is a suite of R functions to estimate the ecological niche of a species and predict the spatial distribution of the ecological niche – a version of environmental interpolation – with spatial kernel density estimation techniques. A two-group comparison (e.g., presence and absence locations of a single species) is conducted using the spatial relative risk function that is estimated using the sparr package. Internal cross-validation and basic visualization are also supported.

Installation

Available functions

Function	Description
`lrren`	Main function. Estimate an ecological niche using the spatial relative risk function and predict its location in geographic space.
`perlrren`	Sensitivity analysis for `lrren` whereby observation locations are spatially perturbed (‘jittered’) with specified radii, iteratively.
`plot_obs`	Display multiple plots of the estimated ecological niche from `lrren` output.
`plot_predict`	Display multiple plots of the predicted spatial distribution from `lrren` output.
`plot_cv`	Display multiple plots of internal k-fold cross-validation diagnostics from `lrren` output.
`plot_perturb`	Display multiple plots of output from `perlrren` including predicted spatial distribution of the summary statistics.
`div_plot`	Called within `plot_obs`, `plot_predict`, and `plot_perturb`, provides functionality for basic visualization of surfaces with diverging color palettes.
`seq_plot`	Called within `plot_perturb`, provides functionality for basic visualization of surfaces with sequential color palettes.
`pval_correct`	Called within `lrren` and `perlrren`, calculates various multiple testing corrections for the alpha level.

Authors

Usage

For the lrren() function

set.seed(1234) # for reproducibility

# ------------------ #
# Necessary packages #
# ------------------ #

library(envi)
library(spatstat.data)
library(spatstat.random)

# -------------- #
# Prepare inputs #
# -------------- #

# Using the 'bei' and 'bei.extra' data within {spatstat.data}

# Environmental Covariates
elev <- bei.extra[[1]]
grad <- bei.extra[[2]]
elev$v <- scale(elev)
grad$v <- scale(grad)
elev_raster <- rast(elev)
grad_raster <- rast(grad)

# Presence data
presence <- bei
marks(presence) <- data.frame(
  'presence' = rep(1, presence$n),
  'lon' = presence$x,
  'lat' = presence$y
)
marks(presence)$elev <- elev[presence]
marks(presence)$grad <- grad[presence]

# (Pseudo-)Absence data
absence <- rpoispp(0.008, win = elev)
marks(absence) <- data.frame(
  'presence' = rep(0, absence$n),
  'lon' = absence$x,
  'lat' = absence$y
)
marks(absence)$elev <- elev[absence]
marks(absence)$grad <- grad[absence]

# Combine
obs_locs <- superimpose(presence, absence, check = FALSE)
obs_locs <- marks(obs_locs)
obs_locs$id <- seq(1, nrow(obs_locs), 1)
obs_locs <- obs_locs[ , c(6, 2, 3, 1, 4, 5)]

# Prediction Data
predict_xy <- crds(elev_raster)
predict_locs <- as.data.frame(predict_xy)
predict_locs$elev <- extract(elev_raster, predict_xy)[ , 1]
predict_locs$grad <- extract(grad_raster, predict_xy)[ , 1]

# ----------- #
# Run lrren() #
# ----------- #

test1 <- lrren(
  obs_locs = obs_locs,
  predict_locs = predict_locs,
  predict = TRUE,
  verbose = TRUE,
  cv = TRUE
)
              
# -------------- #
# Run plot_obs() #
# -------------- #

plot_obs(test1)

# ------------------ #
# Run plot_predict() #
# ------------------ #

plot_predict(
  test1,
  cref0 = 'EPSG:5472',
  cref1 = 'EPSG:4326'
)

# ------------- #
# Run plot_cv() #
# ------------- #

plot_cv(test1)

# -------------------------------------- #
# Run lrren() with Bonferroni correction #
# -------------------------------------- #

test2 <- lrren(
  obs_locs = obs_locs,
  predict_locs = predict_locs,
  predict = TRUE,
  p_correct = 'Bonferroni'
)

# Note: Only showing third plot
plot_obs(test2)

# Note: Only showing second plot
plot_predict(
  test2,
  cref0 = 'EPSG:5472',
  cref1 = 'EPSG:4326'
)

# Note: plot_cv() will display the same results because cross-validation only performed for the log relative risk estimate

For the perlrren() function

set.seed(1234) # for reproducibility

# ------------------ #
# Necessary packages #
# ------------------ #

library(envi)
library(spatstat.data)
library(spatstat.random)

# -------------- #
# Prepare inputs #
# -------------- #

# Using the 'bei' and 'bei.extra' data within {spatstat.data}

# Scale environmental covariates
ims <- bei.extra
ims[[1]]$v <- scale(ims[[1]]$v)
ims[[2]]$v <- scale(ims[[2]]$v)

# Presence data
presence <- bei
marks(presence) <- data.frame(
  'presence' = rep(1, presence$n),
  'lon' = presence$x,
  'lat' = presence$y
)

# (Pseudo-)Absence data
absence <- rpoispp(0.008, win = ims[[1]])
marks(absence) <- data.frame(
  'presence' = rep(0, absence$n),
  'lon' = absence$x,
  'lat' = absence$y
)

# Combine and create 'id' and 'levels' features
obs_locs <- superimpose(presence, absence, check = FALSE)
marks(obs_locs)$id <- seq(1, obs_locs$n, 1)
marks(obs_locs)$levels <- as.factor(rpois(obs_locs$n, lambda = 0.05))
marks(obs_locs) <- marks(obs_locs)[ , c(4, 2, 3, 1, 5)]

# -------------- #
# Run perlrren() #
# -------------- #

# Uncertainty in observation locations
## Most observations within 10 meters
## Some observations within 100 meters
## Few observations within 500 meters

test3 <- perlrren(
  obs_ppp = obs_locs,
  covariates = ims,
  radii = c(10, 100, 500),
  verbose = FALSE, # may not be availabe if parallel = TRUE
  parallel = TRUE,
  n_sim = 100
)
                 
# ------------------ #
# Run plot_perturb() #
# ------------------ #

plot_perturb(
  test3,
  cref0 = 'EPSG:5472',
  cref1 = 'EPSG:4326',
  cov_labs = c('elev', 'grad')
)

envi: Environmental Interpolation using Spatial Kernel Density Estimation

Overview

Installation

Available functions

Authors

Usage

For the lrren() function

For the perlrren() function

Funding

Acknowledgments

Questions? Feedback?