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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, weighted = FALSE)

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.

weighted

If TRUE, argument func is ignored, and weighted mean is calculated. Weights are attributed to each species according to 1/N cells that the species occur.

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)
} # }