hellometry: Wrapper function to estimate size (mm) and biomass (mg) from...

In hellometry: Biomass Estimation from Allometric Relationships

Wrapper function to estimate size (mm) and biomass (mg) from an input dataset

Description

Wrapper function, input your data and get size and measurement. Please name column with number of specimen "abundance", column with measurement "size_col", column with life stage (e.g. larva/adult) "stage", column with biomass type (dry/wet) "biomass_type", and the column with biomass "biomass_col". If you do not have a numerical measurement for a given specimen, the function can do size estimations if you input "small", "medium", "large" or "unknown". In this case, the algorithm will use existing size measurements for the species, and use the size distribution to estimate "small", "medium" and "large" inputs, or a weighted average of all measurements if the input is "unknown". For biomass measurements, just leave NA for those cells you want estimates in.

Usage

hellometry(
  dats,
  level_vec,
  biomass_type = "dry",
  model = "lm",
  r_square_cutoff_upper = 0.95,
  r_square_cutoff_lower = 0,
  p_val_cutoff = 0.05,
  measurements = NULL
)

Arguments

dats	The input data table, please include columns "abundance", "size_col", "biomass_col", "stage" (larva/pupa/adult).
level_vec	A character vector indicating the taxonomic levels present in your data, in increasing order of coarseness.
biomass_type	"dry"/"wet". Should data used in inference be "dry" for just dry biomass (default), or "wet" for just wet biomass. See dry_wet() for more information on how this works.
model	What kind of allometric model should be computed, "lm" (default) for a linear model on the log10-log10 scale, or "poisson" for a Poisson glm with a log link.
r_square_cutoff_upper	Upper cutoff for R2 in allometric models, models with values above it will not be used in estimation. Default is 0.95 to avoid overfit models
r_square_cutoff_lower	Lower cutoff for R2 in allometric models, models with values below it will not be used in estimation. Default is 0.
p_val_cutoff	Upper cutoff for p-value of allometric models, models with p_value above it will not be used in estimation. Default is 0.05.
measurements	Optional dataframe of reference measurements, only used to build the measurement table. Needs columns "size_col", "biomass_col", "biomass_type", "stage". Default is NULL, i.e. measurements are in dats.

Details

Please make sure that the level_vec argument has levels in increasing order of coarseness, e.g. from species to order.

Value

A list with three tibbles:

• data: the input data with added size and biomass estimates, and new columns with the taxonomic level and name of the taxon at which the estimate was made.

• size_estimates: tibble with size estimates that were used, with columns for taxonomic level, name and size category of the estimation.

• model_estimates: tibble with biomass models that were used, with columns for taxonomic level and name. See full_estimation_table() to get all possible size estimates and models for your data

Examples

# Reference measurements for one family, renamed to the columns the package expects
measurements <-
  bromeliad_inverts_measurements() %>%
  dplyr::filter(family == "Culicidae") %>%
  dplyr::rename(size_col = body_size_mm,
                biomass_col = body_mass_mg,
                biomass_type = mass_type) %>%
  dplyr::mutate(size_col = as.character(size_col),
                biomass_col = as.numeric(biomass_col))

# Taxa to estimate, with no measurement of their own
communities <-
  trini_communities() %>%
  dplyr::filter(family == "Culicidae") %>%
  dplyr::rename(abundance = n) %>%
  dplyr::mutate(size_col = "unknown",
                biomass_col = NA,
                biomass_type = "dry")

level_vec <-
  c("species", "genus", "family")

res <-
  hellometry(dats = communities, level_vec = level_vec,
             biomass_type = "dry", measurements = measurements)

# The estimates, joined back to the data
res$data[, c("genus", "size_col", "biomass_col", "size_level", "model_level")]

# The size estimates and the models they came from
res$size_estimates
res$model_estimates

hellometry documentation built on Nov. 11, 2026, 5:06 p.m.