Exploring Mammal Diversity in Peru with perumammals

In perumammals: Taxonomic Backbone and Name Validation Tools for Mammals of Peru

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(perumammals)
library(dplyr)
library(ggplot2)
library(tidyr)

Introduction

Peru is widely recognized as one of the world's megadiverse countries, ranking second in the Neotropics and the Americas for mammalian diversity, with 573 species across 13 orders (Pacheco et al., 2021). The perumammals package provides direct access to this updated taxonomic dataset, enabling researchers, students, and conservation practitioners to explore, validate, and analyze mammals occurring in Peru.

Dataset Background

The updated mammalian checklist by Pacheco et al. (2021) compiles:

• 573 species, 223 genera, 51 families, 13 orders
• 87 endemic species (15.2% of total)
• Presence across 9 ecoregions, from coastal deserts to Amazonian rainforests
• Continuous updates reflecting recent systematic revisions

The package adopts this taxonomic framework and includes tools for validating species names and handling synonyms or misspelled entries via fuzzy matching.

Loading the Dataset

data("peru_mammals")

glimpse(peru_mammals)

Taxonomic Diversity

Orders and Species Richness

order_summary <- pm_list_orders() |> 
  dplyr::arrange(dplyr::desc(n_species)) |> 
  dplyr::mutate(percentage = round(n_species / sum(n_species) * 100, 1))

order_summary

ggplot(order_summary, 
       aes(x = reorder(order, n_species),
           y = n_species)) +
  geom_col(fill = "steelblue") +
  geom_text(aes(label = n_species),
            hjust = -0.2, size = 3) +
  coord_flip() +
  labs(
    title = "Mammalian Diversity in Peru by Order",
    subtitle = "Based on Pacheco et al. (2021)",
    x = "Order",
    y = "Number of Species"
  ) +
  theme_minimal()

Rodentia (194 species) and Chiroptera (189 species) together account for nearly 67% of Peru’s mammalian diversity.

Endemic Species

pm_list_endemic(include_rate = TRUE)

Rodentia contains the largest number of endemic species (56), representing 64.4% of all mammalian endemics in Peru.

Geographic Distribution

Ecoregional Diversity

# Obtener datos de ecoregiones
ecoregion_diversity <- pm_list_ecoregions(include_endemic = TRUE)

# Preparar datos para el gráfico tipo waffle
waffle_data <- ecoregion_diversity |> 
  select(ecoregion_label, pct_endemic) |> 
  mutate(
    total_pct = paste0(round(pct_endemic, 1), "% endemic"),
    ecoregion_full = paste0(ecoregion_label, "\n", total_pct)
  ) |> 
  rowwise() |> 
  mutate(
    grid_data = list(
      expand.grid(x = 1:10, y = 1:10) |> 
        mutate(
          id = row_number(),
          type = ifelse(id <= round(pct_endemic), "Endemic", "Common")
        )
    )
  ) |> 
  unnest(grid_data)

# Crear el gráfico
ggplot(waffle_data, aes(x = x, y = y, fill = type)) +
  geom_tile(color = "white", linewidth = 0.4) +
  facet_wrap(~ecoregion_full, ncol = 2) +
  scale_fill_manual(
    name = NULL,
    values = c("Common" = "#CBD5E1", "Endemic" = "#E07A5F"),
    labels = c("Common species", "Endemic species")
  ) +
  coord_equal() +
  labs(
    title = "Mammal Endemism by Ecoregion in Peru",
    subtitle = "Each square represents 1% of species. The percentage indicates the proportion of endemic species"
  ) +
  theme_void() +
  theme(
    plot.title = element_text(
      face = "bold", 
      size = 16, 
      hjust = 0.5, 
      margin = margin(b = 5)
    ),
    plot.subtitle = element_text(
      hjust = 0.5, 
      size = 10, 
      color = "gray30", 
      margin = margin(b = 20)
    ),
    legend.position = "bottom",
    legend.text = element_text(size = 10),
    legend.key.size = unit(0.7, "cm"),
    legend.margin = margin(t = 15),
    strip.text = element_text(
      size = 9,
      face = "bold", 
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)  # Aumentar márgenes
    ),
    strip.clip = "off",  # Permitir que el texto se extienda fuera del panel
    plot.background = element_rect(fill = "white", color = NA),
    panel.spacing.x = unit(2, "lines"),  # Aumentar espacio horizontal
    panel.spacing.y = unit(2.5, "lines"),  # Aumentar espacio vertical
    plot.margin = margin(15, 15, 15, 15)
  )

The Selva Baja contains the highest species richness, while the Yungas harbor the greatest number of endemic mammals.

Name Validation and Fuzzy Matching

species_list <- c(
  "Tremarctos ornatos",
  "Leopardus pardalis",
  "Odocoileus virginanus",
  "Lagothrix flavicauda",
  "Alouatta seniculus",
  "Puma concolor"
)

validated <- validate_peru_mammals(
  species_list,
  quiet = TRUE
)

validated %>%
  select(Orig.Name, Matched.Name, Match.Level, valid_rank)

The fuzzy matching tool corrects common misspellings and returns standardized scientific names while preserving the original input.

Practical Applications

Conservation Assessment

endemic_primates <- peru_mammals %>%
  filter(order == "Primates", endemic == TRUE) %>%
  select(genus, species, common_name)

endemic_primates

Research Data Cleaning

field_data <- data.frame(
  site = rep(c("Site_A", "Site_B", "Site_C"), each = 3),
  species = c(
    "Tremarctos ornatos", "Mazama rufina", "Odocoileus virginianus",
    "Leopardus pardalis", "Puma concolor", "Leopardus jacobita",
    "Lagothrix flavicauda", "Ateles belzebuth", "Alouatta seniculus"
  ),
  count = c(2, 1, 3, 1, 2, 1, 5, 3, 2)
)

cleaned_data <- field_data %>%
  left_join(
    validate_peru_mammals(field_data$species),
    by = c("species" = "Orig.Name")
  ) %>%
  select(site, original = species, validated = Matched.Name, count, Match.Level)

cleaned_data

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perumammals documentation built on Jan. 6, 2026, 5:06 p.m.