31  Analysis example: Using AusTraits for trait-climate analyses

31.1 Introduction

A more involved analysis is merging trait data with climatic parameters.

Please note the data summary and analysis presented here is similar to that in a manuscript currently in review, Towers et al, “Revisiting the role of mean annual precipitation in shaping functional trait distributions at a continental scale”, doi: 10.1101/2023.06.29.546983

The code presented here must be run offline on your machine, as the climate data files required are not hosted on the traits.build-book GitHub repository.

library(dplyr)
library(stringr)
library(terra)
library(purrr)
library(ggplot2)

This example uses the most recent AusTraits release, version 5.0.0

most_recent <- austraits::get_versions()[["doi"]][1]

most_recent

[1] "10.5281/zenodo.10156222"

austraits <- austraits::load_austraits(doi = most_recent)

Loading data from 'data/austraits/austraits-5.0.0.rds'

31.2 Extract data from AusTraits

For the analysis here, two subsets of AusTraits data are required.

First, extracting data on woodiness. There are three traits for which AusTraits has near-complete taxon coverage. Woodiness & plant growth form are in Wenk_2022 and life history is in Wenk_2023_1:

woody_taxa <-
  austraits$traits %>%
  dplyr::filter(trait_name == "woodiness_detailed") %>%
  dplyr::filter(dataset_id == "Wenk_2022") %>%
  dplyr::select(taxon_name, value) %>%
  dplyr::mutate(woodiness = ifelse(stringr::str_detect(value, "herb"), "herbaceous", "woody")) %>%
  dplyr::select(-value) %>%
  dplyr::distinct(taxon_name, .keep_all = TRUE)

Second, extract the numeric trait data you want to analyse. Only field-collected data on adult plants with reported coordinates is used:

trait_data <-
  (austraits %>% join_locations())$traits %>%
  rename(latitude = `latitude (deg)`, longitude = `longitude (deg)`) %>%
  dplyr::filter(!is.na(latitude)) %>%
  dplyr::filter(trait_name == "leaf_mass_per_area") %>%
  dplyr::filter(basis_of_record == "field") %>%
  dplyr::filter(life_stage == "adult") %>%
  dplyr::group_by(dataset_id, location_name, taxon_name) %>%
  dplyr::mutate(value = mean(as.numeric(value))) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(
    ID = row_number(),
    latitude = as.numeric(latitude),
    longitude = as.numeric(longitude)
  ) %>%
  distinct(value, dataset_id, location_name, taxon_name, .keep_all = TRUE) %>%
  dplyr::filter(!is.na(latitude)) %>%
  dplyr::filter(!is.na(longitude)) %>%
  dplyr::left_join(woody_taxa, by = "taxon_name")

31.3 Download and open spatial climatic data

Download the climatic layers of interest from WorldClim:

https://www.worldclim.org/data/worldclim21.html

Download a shapefile of Australia:

https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/digital-boundary-files

Load a target raster, to set the baseline Coordinate Reference System (CRS) and resolution:

target_raster <- terra::rast("export/wc2.1_2.5m_bio_12.tif")

Set the target CRS:

target_crs <- terra::crs(target_raster)

Load australia as a vector file to bound observations, using the target crs from above:

australia <- terra::vect("export/AUS_2021_AUST_SHP_GDA2020/", crs=target_crs)

Crop the extent of the target raster with the vector file above and confirm the data are as expected:

precip_data_cropped <- target_raster %>%
  terra::crop(australia)

plot(precip_data_cropped)

Project the AusTraits data into a spatial format and confirm the data plot as expected:

occurrence_data_species_family_vect <- terra::vect(trait_data,
                                                   geom = c("longitude", "latitude"),
                                                   crs = terra::crs(precip_data_cropped))

plot(occurrence_data_species_family_vect)

Use the function terra::extract to extract climate variables for each AusTraits data point:

extracted_tmp <-
  terra::extract(
    x = precip_data_cropped,                   # climatic data
    y = occurrence_data_species_family_vect,   # AusTraits trait data
    method = "simple",
    cells = TRUE, bind = TRUE, ID = FALSE, xy = TRUE)

Turn extracted values into a dataframe and separate data for herbaceous versus woody taxa:

extracted <-
  terra::values(extracted_tmp) %>%
    mutate(
      value_herbs = ifelse(woodiness == "herbaceous", value, NA),
      value_woody = ifelse(woodiness == "woody", value, NA)
    )

31.4 Plot your data

ggplot() +
  geom_point(
    data = extracted,
    aes(x = wc2.1_2.5m_bio_12,
        y = value_herbs),
        shape = 16,
        alpha = 0.4,
        color = "darkseagreen3"
    ) +
  geom_point(
    data = extracted,
    aes(x = wc2.1_2.5m_bio_12,
        y = value_woody),
    shape = 16,
    alpha = 0.4,
    color = "coral"
  ) +
  scale_x_continuous(
    trans = "log10"
  ) +
  scale_y_continuous(
    trans = "log10"
  ) +
  labs(
    x = "Annual precipitation (mm)",
    y = "Leaf mass per area (g/m2)")