Introduction
In many food webs, a consumer’s ability to eat a resource is limited
by their relative body sizes. The infer_edgelist() function
allows you to layer a numerical rule (like a predator-prey mass ratio)
on top of your categorical trait matching.
Prepare Numerical Data
First, ensure your taxon data includes a numeric column for size (e.g., body mass in kg or length in cm). Here we will modify our traits dataframe
```{r-modify_traits}
Adding body mass (kg) to our traits
traits_numeric <- traits %>% mutate(body_mass = case_when( species == “polar_bear” ~ 450, species == “seal” ~ 100, species == “orca” ~ 3000, species == “cod” ~ 5, species == “plankton” ~ 0.0001, TRUE ~ 10 # Default for others ))
head(traits_numeric)
# Define the Size Rule Function
The num_size_rule argument requires a function that takes two inputs (res_size, con_size) and returns 1 (feasible) or 0 (not feasible).
``` {r-size_rule}
my_size_rule <- function(res_size, con_size) {
ratio <- con_size / res_size
ifelse(ratio >= 2 & ratio <= 100, 1, 0)
}
Run Inference with Size Constraints
When col_num_size is provided, the function treats it as
an additional trait type. If certainty_req = "all", the
interaction must satisfy all categorical matches AND the numerical size
rule.
```{r-build_list}
edgelist_size <- infer_edgelist( data = traits_numeric, cat_combo_list = feeding_rules, col_taxon = “species”, col_num_size = “body_mass”, # Point to the numeric column num_size_rule = my_size_rule, # Apply our custom logic certainty_req = “all”, hide_printout = TRUE )
head(edgelist_size) ```