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Map species-level attributes over the attribute-space grid

Source: R/lets_maplizer_attr.R
lets.maplizer.attr.Rd

Summarizes species attributes (e.g., trait values, description year) within each **attribute-space** cell of a presence–absence matrix produced by lets.attrpam. A summary function (func) is applied across the attributes of species present in each cell, producing a per-cell attribute surface and an optional raster for visualization.

Usage

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

Arguments

x

An object returned by lets.attrpam, containing the attribute-space PAM (matrix) and its raster.

y

A numeric vector with species attributes to summarize (one value per species in z). If y is a factor, it is coerced to numeric by level codes.

z

A character vector of species names corresponding to y. These names must match the species columns in the attribute-space PAM within x.

func

A function to summarize attributes across species within each cell (e.g., mean, median, sum). Must return a single numeric value.

ras

Logical; if TRUE, include the attribute map as a SpatRaster in the output.

Value

A list with:

  • Matrix_attr: matrix/data.frame with summarized attribute values per attribute cell (first columns are the cell ID and the two attribute axes).

  • Attr_Raster: SpatRaster with the summarized attribute mapped in attribute space.

Details

Map species attributes in attribute space (based on AttrPAM)

Internally, the function multiplies each species presence column by its attribute value in y (matched by name via z), sets zeros to NA so that summary functions ignore absences, applies func across species for each cell, and rasterizes the resulting per-cell summaries onto the attribute raster template. Cells with no species remain as NA.

See also

lets.attrpam, lets.plot.attrpam, lets.attrcells

Author

Bruno Vilela

Examples

if (FALSE) { # \dontrun{
# Simulate a dataset of 2000 species with two traits
n <- 2000
Species <- paste0("sp", 1:n)
trait_a <- rnorm(n)
trait_b <- trait_a * 0.2 + rnorm(n)
df <- data.frame(Species, trait_a, trait_b)

# Build attribute-space PAM
x <- lets.attrpam(df, n_bins = 30)
lets.plot.attrpam(x)

# Map species-level attribute (here, trait_b) by cell using the mean
res <- lets.maplizer.attr(x, y = trait_b, z = Species, func = mean)

# Plot attribute raster
plot(res$Attr_Raster)
} # }