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species

Source: vignettes/species.Rmd
species.Rmd

The species_define function works mostly behind the scenes to set up the parameters needed to simulate individual body size measurements. Given any of: (1) the AOU as used in the North American Breeding Bird Survey (Pardieck et al. 2019), (2) the scientific name, (3) the species’ mean body size, or (4) the species’ mean and standard deviation body size, species_define returns the parameters used by pop_generate and community_generate to simulate individual size measurements for individuals of that species, or returns an error message asking for more or different information. In most instances, species_define is called under-the-hood by the generate functions, and users do not need to interact with it directly.

Species known to birdsize

birdsize includes species-level parameters for 443 species of birds common in the North American Breeding Bird Survey. To view the list of species included, examine the known_species data table, included:

head(known_species)
#> # A tibble: 6 × 3
#>     AOU genus      species    
#>   <int> <chr>      <chr>      
#> 1  2881 Perdix     perdix     
#> 2  2882 Alectoris  chukar     
#> 3  2890 Colinus    virginianus
#> 4  2920 Oreortyx   pictus     
#> 5  2930 Callipepla squamata   
#> 6  2940 Callipepla californica

Species included in known_species can be retrieved via either their AOU or scientific name. For example, the hummingbird Selasphorous calliope has an AOU of 4360.

AOU lookup 

hummingbird_AOU_parameters <- species_define(AOU = 4360)

hummingbird_AOU_parameters
#> $AOU
#> [1] 4360
#> 
#> $scientific_name
#> [1] "Selasphorus calliope"
#> 
#> $mean_size
#> [1] 2.65
#> 
#> $sd_size
#> [1] 0.1818394
#> 
#> $sd_method
#> [1] "AOU lookup"
#> 
#> $sim_species_id
#> [1] 4360

Scientific name lookup 

hummingbird_name_parameters <- species_define(scientific_name = "Selasphorus calliope")

hummingbird_name_parameters
#> $AOU
#> [1] 4360
#> 
#> $scientific_name
#> [1] "Selasphorus calliope"
#> 
#> $mean_size
#> [1] 2.65
#> 
#> $sd_size
#> [1] 0.1818394
#> 
#> $sd_method
#> [1] "Scientific name lookup"
#> 
#> $sim_species_id
#> [1] 4360

Note that the sd_method field tells us which method we used to look up the parameters. This field propagates throughout the pop_generate and community_generate functions to keep track of the underlying methodology.

Unknown species or AOUs

Attempting to use species_define with an AOU or species not known to birdsize will return an error:

try(species_define(AOU = 100))
#> Error in species_lookup(AOU = AOU) : `AOU` is invalid.
try(species_define(scientific_name = "Swiftus Taylor"))
#> Error in species_lookup(scientific_name = scientific_name) : 
#>   Scientific name is invalid.

Species not known to birdsize

Some users may want to use this methodology with species not included in known_species, or to use different species-level parameters than those built-in to birdsize (for example, to explore intraspecific variation in body size over time or space). To do this, supply species_define with mean, or mean and standard deviation, values for each species. To help keep track of species-parameter matches, use the sim_species_id field to assign a species identifier to each novel species.

Manually supplying species parameters

Suppose we want to work with a hypothetical species with a mean body size of 40g and a standard deviation of 2.5. Because this species doesn’t have a scientific name or AOU included in birdsize, we label it using the arbitrary sim_species_id of 1.

hypothetical_species_parameters <- species_define(mean_size = 40, sd_size = 2.5, sim_species_id = 1)

hypothetical_species_parameters
#> $AOU
#> [1] NA
#> 
#> $scientific_name
#> [1] NA
#> 
#> $mean_size
#> [1] 40
#> 
#> $sd_size
#> [1] 2.5
#> 
#> $sd_method
#> [1] "Mean and SD provided"
#> 
#> $sim_species_id
#> [1] 1

This can be particularly useful when working with multiple new species. For example, if we have 3 new species, we can store their information in a separate table and iterate over sim_species_id to generate parameters for each species. This happens under the hood in community_generate.

multiple_species_info <- data.frame(
  mean_size = c(10, 40, 50),
  sd_size = c(1, 2.5, 3),
  sim_species_id = 1:3
)


pmap_df(multiple_species_info, species_define)
#> # A tibble: 3 × 6
#>     AOU scientific_name mean_size sd_size sd_method            sim_species_id
#>   <int> <chr>               <dbl>   <dbl> <chr>                         <int>
#> 1    NA NA                     10     1   Mean and SD provided              1
#> 2    NA NA                     40     2.5 Mean and SD provided              2
#> 3    NA NA                     50     3   Mean and SD provided              3

If the standard deviation is not provided, species_define will estimate it (see the scaling vignette):

multiple_species_info_no_sd <- data.frame(
  mean_size = c(10, 40, 50),
  sim_species_id = 1:3
)


pmap_df(multiple_species_info_no_sd, species_define)
#> # A tibble: 3 × 6
#>     AOU scientific_name mean_size sd_size sd_method              sim_species_id
#>   <int> <chr>               <dbl>   <dbl> <chr>                           <int>
#> 1    NA NA                     10   0.693 SD estimated from mean              1
#> 2    NA NA                     40   2.80  SD estimated from mean              2
#> 3    NA NA                     50   3.51  SD estimated from mean              3

Order of operations

If multiple sets of information are provided (e.g. both AOU and scientific_name), species_define will use it in this order of preference:

  1. AOU
  2. Scientific name
  3. Manually provided mean and standard deviation
  4. Manually provided mean and estimated standard deviation

References

Pardieck, K.L., Ziolkowski Jr., D.J., Lutmerding, M., Aponte, V., and Hudson, M-A.R., 2019, North American Breeding Bird Survey Dataset 1966 - 2018 (ver. 2018.0): U.S. Geological Survey, Patuxent Wildlife Research Center, https://doi.org/10.5066/P9HE8XYJ.