---
title: "Mapping species richness in attribute space"
author: "Bruno Vilela"
date: "2025-07-07"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Mapping-species-richness-in-attribute-space}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  fig.height = 5,
  fig.width  = 8,
  fig.align  = "center"
)
```

## Overview

Species richness and community structure can also be represented in attribute space, where axes correspond to any quantitative information that can be attributed to a species. The `letsR` package provides tools to construct and analyze presence–absence matrices (PAMs) in attribute space, allowing researchers to examine biodiversity patterns beyond geography and environment.

This vignette demonstrates how to:

1. Build a PAM in attribute space using `lets.attrpam()`;
2. Visualize species richness with `lets.plot.attrpam()`;
3. Compute descriptors per attribute cell using `lets.attrcells()`;
4. Aggregate descriptors to the species level with `lets.summarizer.cells()`; and
5. Cross-map attribute metrics to geographic space for integrative analysis.


```{r}
# Load the package
library(letsR)
```

## Simulating trait data and building the attribute space PAM

We begin by generating a dataset of 2,000 species with two correlated traits:

```{r}
set.seed(123)
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 the attribute-space PAM
attr_obj <- lets.attrpam(df, n_bins = 30)
```

## Visualizing richness in attribute space

The `lets.plot.attrpam()` function plots the richness surface across the bivariate trait space.

```{r}
lets.plot.attrpam(attr_obj)
```

Each cell represents a unique combination of traits (binned values of `trait_a` and `trait_b`), and the color intensity indicates the number of species falling within that bin.


## Computing attribute-space descriptors

The function `lets.attrcells()` quantifies structural properties of each cell in the trait space, including measures of centrality, isolation, and border proximity.

```{r}
attr_desc <- lets.attrcells(attr_obj, perc = 0.2)
head(attr_desc)
```

We can visualize these metrics using `lets.plot.attrcells()`:

```{r}
lets.plot.attrcells(attr_obj, attr_desc)
```

Each panel represents a different descriptor (e.g., distance to midpoint, distance to border, weighted isolation) mapped across the trait space.


## Summarizing descriptors by species

To derive species-level summaries, we can aggregate descriptor values across all cells occupied by each species using the `lets.summarizer.cells()` function.

```{r}
attr_desc_by_sp <- lets.summaryze.cells(attr_obj, attr_desc, func = mean)
head(attr_desc_by_sp)
```

This produces a data frame in which each row corresponds to a species, and each column corresponds to the mean descriptor value across the cells where that species occurs.
`


## Linking attribute space to geographic space

When a geographic PAM generated by `lets.presab()` is supplied through the y argument, `lets.attrcells()` links the species occurring in each attribute cell to the geographic cells occupied by those species. This enables the computation of additional descriptors in geographic space.

```{r}
data("PAM")

n <- length(PAM$Species_name)
Species <- PAM$Species_name
trait_a <- rnorm(n)
trait_b <- trait_a * 0.2 + rnorm(n)
df <- data.frame(Species, trait_a, trait_b)

x <- lets.attrpam(df, n_bins = 4)

cell_desc_geo <- lets.attrcells(x, y = PAM)
head(cell_desc_geo)
```


When `y` is provided, the output includes additional variables:

- `Frequency`: number of geographic cells associated with each attribute cell;
- `Area`: summed area of those geographic cells; and
- geographic isolation summaries:
  - `Isolation (Min.)`
  - `Isolation (1st Qu.)`
  - `Isolation (Median)`
  - `Isolation (Mean)`
  - `Isolation (3rd Qu.)`
  - `Isolation (Max.)`

These variables allow direct integration of position in attribute space with occupancy and isolation patterns in geographic space.

The resulting descriptors can also be plotted:

```{r}
lets.plot.attrcells(x, cell_desc_geo)
```

## References

Vilela, B. & Villalobos, F. (2015). letsR: a new R package for data handling and analysis in macroecology. Methods in Ecology and Evolution, 6(10), 1229–1234.
