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GDRarity.Rmd

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

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

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")

Output Examples

Results from the first combination of methods

ANOVA Results for Every Method and Restriction

Best Models 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 GLFRPR, which explains up to 23.2% of the variation in flowering window and incorporates:

  • Local Geographic Rarity (GL)
  • Regional Functional Rarity (FR)
  • Regional Phylogenetic Rarity (PR)

Methods Used

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

Thresholds Applied

  • Geographic rarity: < 15%
  • Functional rarity: > 75%
  • Phylogenetic rarity: > 75%

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

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")))

Best Models 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 GLFRLPRH, which explains up to 78.6% of the variation in flowering window and incorporates:

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