Pollimetry: Robust estimates of body size and co-varying traits in pollinating insects

Tools to estimate pollinator body size and co-varying ecological traits.

To install

if (!requireNamespace("devtools")) {
  install.packages("devtools")
}
devtools::install_github("liamkendall/pollimetry")
library(pollimetry)

We also recommend downloading the data package so the bodysize function runs more efficiently.

#Loading is slow (~ up to 26 Mb per model file)
if (!requireNamespace("devtools")) {
  install.packages("devtools")
}
devtools::install_github("liamkendall/pollimetrydata")
library(pollimetrydata)

Estimating body size

The bodysize function uses Bayesian generalised linear mixed models (BGLMMs) to provide posterior estimates (along with S.E. and 95% credible intervals) of pollinator body size (i.e. dry body weight (mg)) using the intertegular distance (ITD), species taxonomy or phylogeny (bees only type="phylo"), sex and biogeography (at present only Australia, Europe, North America and South America). Estimates (and variance components) are returned as four additional columns bound to the original dataframe. These models will be periodically updated using novel data as and when it becomes available. See bodysize details for more information.

Pre-existing equations for Diptera, Hymenoptera and Lepidopteran taxa using body length (lengthsize), body length * width (lengthwidthsize) and head width (headwidthsize) are also provided.

Allometric traits

Users can predict, i) bee tongue length (tonguelength) from Cariveau et al. (2015), ii) bee foraging distances (foragedist) from equations described in van Nieuwstadt and Iraheta (1996) for Meliponini, Greenleaf et al. (2007) and Guedot et al. (2009) for Osmia spp, iii) wing loading (wingloading) from Bullock et al. (1999) and, iv) field nectar loads (nectarload) from Henry and Rodet (2018).

Future traits of interest

Many other ecological traits crucial to pollination are likely to be allometric. Therefore, we hope to examine these unexplored body size - trait relationships with the aim of including new predictive models in the future.

Example

Install and call the pollimetry library as follows:

devtools::install_github("liamkendall/pollimetry")
library(pollimetry)

This will install pollimetry and it's dependency brms. You can check the raw data used in the paper as follows:

load("data/pollimetry_dataset.rdata")
head(pollimetry_dataset)
?pollimetry_dataset #for metadata.

Let's predict some body sizes from a dataframe using taxonomy:

(example <- cbind.data.frame(IT = c(1.2, 2.3), 
                            Sex = c("Female","Male"), 
                            Family = c("Apidae","Andrenidae"),
                            Region = c("NorthAmerica","Europe"),
                            Species = c("Ceratina_dupla","Andrena_flavipes")))

bodysize(x = example, taxa = "bee", type = "taxo")
?bodysize

Now let's calculate some foraging distances based only in ITDs:

foragedist(c(10,5,2), type = "GreenleafAll")

Manuscript R code

Figshare

liamkendall / pollimetry