electivity: Algorithms for electivity indices and measures of resource use versus availability

Desi Quintans (2019). electivity: Algorithms for Electivity Indices. R package version 1.0.2. https://github.com/DesiQuintans/electivity

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This package is essentially Lechowicz (1982) turned into an R package. It includes all algorithms that were described therein plus the example data that was provided for gypsy moth resource utilisation.

Lechowicz, M.J., 1982. The sampling characteristics of electivity indices. Oecologia 52, 22–30. https://doi.org/10.1007/BF00349007

Users are encouraged to read the original paper before deciding which algorithm is most useful for them. Lechowicz recommended Vanderploeg and Scavia’s E* index (implemented in this package as vs_electivity()) as “the single best, but not perfect, electivity index” because “E* embodies a measure of the feeder’s perception of a food’s value as a function of both its abundance and the abundance of other food types present.” In practice, he found that all indices returned nearly identical rank orders of preferred hosts except for Strauss’ linear index (L).

Installing

# Installing from CRAN
install.packages(electivity)

# Installing from GitHub
install.packages(remotes)
remotes::install_github("DesiQuintans/electivity")

library(electivity)

Built-in datasets

data(moth_distrib)  # Table 2 from Lechowicz (1982), raw data
data(moth_elect)    # Table 3 from Lechowicz (1982), calculated indices

head(moth_distrib)

##              binomen n_indiv dbh_cm_sum larva_mean_sum        r        p
## 1 Acer_pensylvanicum       1        8.5            0.5 2.86e-05 0.000566
## 2        Acer_rubrum       3       67.7            5.0 2.86e-04 0.004510
## 3     Acer_saccharum     158     2344.0         1342.0 7.68e-02 0.156000
## 4      Acer_spicatum       4       39.2           23.5 1.34e-03 0.002610
## 5     Amelanchier_sp       3       27.4           25.5 1.46e-03 0.001820
## 6  Betula_papyrifera      47      696.3           69.0 3.95e-03 0.046400

head(moth_elect)

##              binomen    E_i E_prime_i    D_i log_Q_i    L_i   W_i E_star_i
## 1 Acer_pensylvanicum -0.904     0.050 -0.904  -1.297 -0.001 0.006   -0.787
## 2        Acer_rubrum -0.881     0.063 -0.881  -1.199 -0.004 0.008   -0.739
## 3     Acer_saccharum -0.341     0.492 -0.380  -0.347 -0.079 0.061    0.075
## 4      Acer_spicatum -0.320     0.515 -0.321  -0.289 -0.001 0.064    0.098
## 5     Amelanchier_sp -0.111     0.800 -0.112  -0.097  0.000 0.099    0.308
## 6  Betula_papyrifera -0.843     0.085 -0.849  -1.089 -0.042 0.011   -0.665

Examples

Calculating food preference for a single animal

gypsy_moth_prefs <- vs_electivity(moth_distrib$r, moth_distrib$p)

names(gypsy_moth_prefs) <- moth_distrib$binomen

sort(gypsy_moth_prefs, decreasing = TRUE)

## Populus_grandidentata         Quercus_rubra     Ostrya_virginiana 
##            0.58107791            0.57940916            0.36470127 
##        Amelanchier_sp         Acer_spicatum       Juglans_cinerea 
##            0.30891287            0.09590718            0.08485274 
##        Acer_saccharum     Fagus_grandifolia       Tilia_americana 
##            0.07507596           -0.11166165           -0.17621830 
##         Pinus_strobus     Carya_cordiformis           Ulmus_rubra 
##           -0.32062903           -0.37337994           -0.50262587 
##   Prunus_pensylvanica     Betula_papyrifera    Fraxinus_americana 
##           -0.58618841           -0.66529208           -0.73713206 
##          Betula_lutea           Acer_rubrum    Acer_pensylvanicum 
##           -0.73883938           -0.73955078           -0.78682859 
##       Prunus_serotina 
##           -0.93239165

Calculating food preference for many animals at many sites (Tidy approach)

library(tidyr)
library(dplyr)
library(purrr)

# Example data
df <- tibble::tribble(
           ~snail,     ~site,      ~food, ~pieces_eaten, ~pieces_present,
           "Bert", "outside",   "Carrot",             3,               7,
           "Bert", "outside", "Broccoli",             3,               8,
           "Bert", "outside",     "Kale",             1,               1,
           "Bert",  "inside",   "Carrot",             5,              11,
           "Bert",  "inside", "Broccoli",             7,               3,
           "Bert",  "inside",     "Kale",             2,               4,
          "Ernie", "outside",   "Carrot",             6,               7,
          "Ernie", "outside", "Broccoli",             4,               8,
          "Ernie", "outside",     "Kale",             1,               1,
          "Ernie",  "inside",   "Carrot",             3,              11,
          "Ernie",  "inside", "Broccoli",             1,               3,
          "Ernie",  "inside",     "Kale",             4,               4
          )


prefs <- 
    df %>% 
    # Nest the data for each snail x site pair
    nest(-snail, -site) %>% 
    # Apply vs_electivity() (or any other function) to each nested dataframe
    mutate(score = map(data, ~ vs_electivity(.$pieces_eaten, .$pieces_present))) %>% 
    # Expand the result (score) into new rows
    unnest(score, data) %>% 
    # Omit unwanted columns
    select(-pieces_eaten, -pieces_present) %>% 
    # Turn the sites into columns that show electivity for each snail x food pair.
    spread(key = site, value = score)

knitr::kable(prefs, format = "markdown")

snail	food	inside	outside
Bert	Broccoli	0.3608247	-0.2317073
Bert	Carrot	-0.4136808	-0.1676301
Bert	Kale	-0.3734177	0.2490706
Ernie	Broccoli	-0.2325581	-0.2222222
Ernie	Carrot	-0.3250000	0.0434783
Ernie	Kale	0.3026316	0.1200000

Contributors

• Desi Quintans https://github.com/DesiQuintans

Contributions are welcome!