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Map species attributes in both environmental and geographic spaces

Source: R/lets_maplizer_env.R
lets.maplizer.env.Rd

Summarize species-level attributes (e.g., traits or conservation data) within each cell of environmental and geographic presence–absence matrices, enabling trait-based mapping across environmental gradients and geographic space.

Usage

lets.maplizer.env(x, y, z, func = mean, ras = TRUE)

Arguments

x

An object produced by lets.envpam.

y

A numeric vector containing the species attributes to be summarized (e.g., description year, body size).

z

A character vector with species names corresponding to the values in y. These names must match the species columns in the presence-absence matrices in `x`.

func

A function used to summarize the attribute across species in each cell (e.g., mean, median, sum). Must return a single numeric value.

ras

Logical. If TRUE, the result includes the attribute maps as SpatRaster objects.

Value

A list with the following elements:

  • Matrix_env: A matrix with summarized attribute values in environmental space.

  • Matrix_geo: A matrix with summarized attribute values in geographic space.

  • Env_Raster: A SpatRaster with the attribute values mapped in environmental space.

  • Geo_Raster: A SpatRaster with the attribute values mapped in geographic space.

Details

This function is useful for trait-based macroecological analyses that aim to understand how species attributes vary across environments or space. It uses the output of lets.envpam, applies a summary function to the trait values of all species present in each cell, and returns raster layers for visualization.

See also

lets.envpam, lets.maplizer, lets.plot.envpam

Author

Bruno Vilela

Examples

if (FALSE) { # \dontrun{
# Load data
data("Phyllomedusa")
data("prec")
data("temp")
data("IUCN")

prec <- unwrap(prec)
temp <- unwrap(temp)
PAM <- lets.presab(Phyllomedusa, remove.cells = FALSE)
envs <- lets.addvar(PAM, c(temp, prec), onlyvar = TRUE)
colnames(envs) <- c("Temperature", "Preciptation")
wrld_simpl <- get(utils::data("wrld_simpl", package = "letsR"))
PAM <- lets.pamcrop(PAM, vect(wrld_simpl))

# Create environmental PAM
res <- lets.envpam(PAM, envs, remove.cells = FALSE)

# Map mean description year
res_map <- lets.maplizer.env(res, 
                             y = IUCN$Description_Year,
                             z = IUCN$Species)

# Plotting trait maps
lets.plot.envpam(res_map)
} # }