README

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envi: Environmental Interpolation using Spatial Kernel Density Estimation

Overview

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')
)

Funding

Acknowledgments

Questions? Feedback?

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.