GDRarity

Vignette Info

This vignette provides an introduction to the usage of GDRarity.

We will:

  1. Find the best restriction and accompanying method to explain variation in flowering phenology for 70 British plant species.
  2. Analyse the rarity of species using this restriction.

Setup

library(GDRarity)

Import Data

species_70_path <- system.file("extdata", "species_70.csv", package = "GDRarity")
abundance_70_path <- system.file("extdata", "abundance_70.csv", package = "GDRarity")

species_70 <- read.csv(species_70_path)
abundance_70 <- read.csv(abundance_70_path)

Run All Methods on All 63 Restrictions of GDR using Phylogenetic Linear Models

Only running taxonomic frequency as a measure of regional geographic rarity due to computational requirements associated with calculating range sizes using GBIF.

all_combinations <- restriction_performance_pipeline(species_df = species_70, abundance_df = abundance_70, trait_cols = c("SLA", "seed_mass", "canopy_height"), geo_methods = "taxonomic", response_vars = "flowering_duration", use_phylolm = TRUE)

Output Examples

Results from the first combination of methods

Results of Coefficients using every Method and Restriction

Best Restrictions Explaining Flowering Phenology

Parameter Combinations

Threshold Sets

Best Restriction for Flowering Phenology

The best restriction to explain variation in flowering phenology in the 70 British species is GRFLPL, which explains up to 33.7% of the variation in flowering window and incorporates:

  • Regional Geographic Rarity (GR)
  • Local Functional Rarity (FL)
  • Local Phylogenetic Rarity (PL)

Methods Used

  • Regional Geographic Rarity (GR): Taxonomic frequency
  • Regional Functional Rarity (FR): Minimum Euclidean distance
  • Local Phylogenetic Rarity (PR): Abundance-weighted phylogenetic mean pair-wise distance

Thresholds Applied

  • Geographic rarity: < 15%
  • Functional rarity: > 90%
  • Phylogenetic rarity: > 90%

Incorporating Additional Dimensions

Additional dimensions of rarity can also be incorporated but must be specified by users

Example: Adding Random Habitat Specificity

set.seed(123) # for reproducibility
species_70_hs <- species_70
species_70_hs$habitat_specificity <- runif(nrow(species_70_hs), min = 0, max = 1)

Re-run Methods with Habitat Specificity using Linear Models

When using user-specified rarity dimensions, you must specify all thresholds and directions.

# Must specify all thresholds and directions if using user-specified rarity dimension(s)
hs_combinations <- restriction_performance_pipeline(species_df = species_70_hs, abundance_df = abundance_70, additional_dimensions = "habitat_specificity", trait_cols = c("SLA", "seed_mass", "canopy_height"), geo_methods = "taxonomic", response_vars = "flowering_duration", threshold_sets = list(list(GR = 0.15, GL = 0.15, FR = 0.75, FL = 0.75, PR = 0.75, PL = 0.75, H = 0.85)), direction_sets = list(list(GR = "low", GL = "low", FR = "high", FL = "high", PR = "high", PL = "high", H = "high")), use_phylolm = TRUE)

Best Restrictions with Habitat Specificity

Best Restriction for Flowering Phenology Incorporating Habitat Specificity

The best restriction to explain variation in flowering phenology in the 70 British species incorporating a user-specified axis of habitat specificity is GRLFRLPRLH, which explains 79.3% of the variation in flowering window and incorporates all tested dimensions of rarity:

  • Regional Geographic Rarity (GR)
  • Local Geographic Rarity (GL)
  • Regional Functional Rarity (FR)
  • Local Functional Rarity (FL)
  • Regional Phylogenetic Rarity (PR)
  • Local Phylogenetic Rarity (PL)
  • Habitat Specificity (H)