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After thinning, the environmental niche is summarised by a
multivariate ellipsoid via fit_ellipsoid(). Two estimators
are available:
"covmat" — classical sample mean and covariance."mve" — robust Minimum Volume Ellipsoid (Rousseeuw,
1985).The ellipsoid boundary is defined by a chi-square cutoff on the
squared Mahalanobis distance, controlled by level (default
95 %).
library(bean)
data(origin_dat_prepared, package = "bean")
data(thinned_stochastic, package = "bean")
data(thinned_deterministic, package = "bean")
env_vars <- c("bio_1", "bio_4", "bio_12", "bio_15")We fit one ellipsoid to the raw prepared data and one to each of the thinned datasets, so we can compare what bias correction does to the inferred niche.
origin_ellipse <- fit_ellipsoid(
data = origin_dat_prepared,
env_vars = env_vars,
method = "covmat",
level = 0.95
)
stochastic_ellipse <- fit_ellipsoid(
data = thinned_stochastic$thinned_data,
env_vars = env_vars,
method = "covmat",
level = 0.95
)
deterministic_ellipse <- fit_ellipsoid(
data = thinned_deterministic$thinned_points,
env_vars = env_vars,
method = "covmat",
level = 0.95
)
origin_ellipse
#> -- Bean Environmental Niche Ellipsoid --
#> Method : covmat
#> Dimensions : 4 (bio_1, bio_4, bio_12, bio_15)
#> Level : 95.00%
#> Points used : 1024 (inside: 947, 92.5%)
#> Centroid:
#> bio_1 bio_4 bio_12 bio_15
#> -1.1433128 -0.1851743 -0.4804313 -0.4194209
stochastic_ellipse
#> -- Bean Environmental Niche Ellipsoid --
#> Method : covmat
#> Dimensions : 4 (bio_1, bio_4, bio_12, bio_15)
#> Level : 95.00%
#> Points used : 78 (inside: 71, 91.0%)
#> Centroid:
#> bio_1 bio_4 bio_12 bio_15
#> -0.3502912 -0.3690432 -0.1938518 -0.4200464
deterministic_ellipse
#> -- Bean Environmental Niche Ellipsoid --
#> Method : covmat
#> Dimensions : 4 (bio_1, bio_4, bio_12, bio_15)
#> Level : 95.00%
#> Points used : 56 (inside: 53, 94.6%)
#> Centroid:
#> bio_1 bio_4 bio_12 bio_15
#> -0.3839286 -0.4732143 -0.1607143 -0.4464286
For an interactive 3-D view, install the optional package
rgl and call
plot(origin_ellipse, dims = c(1, 2, 3)). If
rgl is not installed, the plot falls back to the 2-D view
of the first two requested variables.
predict() returns Mahalanobis distance and a Gaussian
suitability score \(s = \exp(-D^2/2)\).
It accepts a data.frame or, more usefully, a
terra::SpatRaster. Below we project each ellipsoid back
into geographic space and compare the resulting
suitability layers (the Mahalanobis layers behave
analogously, so we omit them here).
library(terra)
env <- terra::rast(system.file("extdata", "thai_env.tif", package = "bean"))
env_scaled <- terra::scale(env)
origin_pred <- predict(
object = origin_ellipse,
newdata = env_scaled,
include_suitability = TRUE,
suitability_truncated = FALSE,
include_mahalanobis = FALSE
)
stochastic_pred <- predict(
object = stochastic_ellipse,
newdata = env_scaled,
include_suitability = TRUE,
suitability_truncated = FALSE,
include_mahalanobis = FALSE
)
deterministic_pred <- predict(
object = deterministic_ellipse,
newdata = env_scaled,
include_suitability = TRUE,
suitability_truncated = FALSE,
include_mahalanobis = FALSE
)suit_stack <- c(origin_pred[["suitability"]],
stochastic_pred[["suitability"]],
deterministic_pred[["suitability"]])
names(suit_stack) <- c("Raw (unthinned)",
"Stochastic thinning",
"Deterministic thinning")
terra::plot(suit_stack, nc = 3, mar = c(2, 2, 2.5, 4))
Each panel uses the same colour scale, so darker / lighter shading across panels reflects a genuine shift in the modelled niche. Because the raw data is biased toward heavily sampled areas, the raw model typically over-predicts those conditions; the thinned models give a narrower, less inflated suitability surface.
If you prefer only the chi-square interior, set
suitability_truncated = TRUE:
origin_trunc <- predict(origin_ellipse,env_scaled,
include_suitability = FALSE,
suitability_truncated = TRUE,
include_mahalanobis = FALSE)
stoch_trunc <- predict(stochastic_ellipse, env_scaled,
include_suitability = FALSE,
suitability_truncated = TRUE,
include_mahalanobis = FALSE)
det_trunc <- predict(deterministic_ellipse, env_scaled,
include_suitability = FALSE,
suitability_truncated = TRUE,
include_mahalanobis = FALSE)
trunc_stack <- c(origin_trunc[["suitability_trunc"]],
stoch_trunc[["suitability_trunc"]],
det_trunc[["suitability_trunc"]])
names(trunc_stack) <- c("Raw (unthinned)",
"Stochastic thinning",
"Deterministic thinning")
terra::plot(trunc_stack, nc = 3, mar = c(2, 2, 2.5, 4))
Values outside the chi-square contour are NA, leaving
only the “core” niche.
summary_df <- data.frame(
model = c("Raw", "Stochastic", "Deterministic"),
mean_suit = sapply(list(origin_pred, stochastic_pred, deterministic_pred),
function(p) mean(terra::values(p[["suitability"]]), na.rm = TRUE)),
median_suit = sapply(list(origin_pred, stochastic_pred, deterministic_pred),
function(p) median(terra::values(p[["suitability"]]), na.rm = TRUE))
)
summary_df
#> model mean_suit median_suit
#> 1 Raw 0.01575929 8.268550e-06
#> 2 Stochastic 0.12812961 7.113131e-02
#> 3 Deterministic 0.15473723 9.700130e-02A drop in mean / median suitability after thinning is the expected effect: bias correction makes the model less willing to call under-sampled environments “highly suitable”.
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They may not be fully stable and should be used with caution. We make no claims about them.
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