The challenges of predicting pesticide exposure of honey bees at landscape level (Simon Delso et al. 2017)

In this directory you will find all datasets, scripts and analyses outputs from the following paper

Simon-Delso, N., San
Martin, G., Bruneau, E., Delcourt, C., Hautier, L., 2017. The
challenges of predicting pesticide exposure of honey bees at
landscape level. Scientific Reports 7, 3801.
doi:10.1038/s41598-017-03467-5

`Bee_landscape.R` provides the code and `report_Bee_landscape.pdf` provides the ouputs of these analyses.

The `scripts` folder contains two scripts with functions that are used (sourced) in `Bee_landscape.R`.

The `data` folder provides the raw data with a `README.md` file providing detailed description of these data.
We were not allowed to make public the raw geodata (shapefile) used to compute the crop surfaces
around the apiaries. The apiaries coordinates have also been rounded to the second decimal
to protect the beekeepers privacy.

These raw data might look quite complex. So we have grouped most of the data in a more understandable structure in the `data_out` folder. These summary dataset are generated by the `Bee_landscape.R` script. This script is working directly with the raw data for more flexibility.

Summary of the paper :

To evaluate the
risks of pesticides for pollinators, we must not only evaluate their
toxicity but also understand how pollinators are exposed to these
xenobiotics in the field. We focused on this last point and modeled
honey bee exposure to pesticides at the landscape level. Pollen
pellet samples (n = 60) from 40 Belgian apiaries were collected
from late July to October 2011 and underwent palynological and
pesticide residue analyses. Areas of various crops around each apiary
were measured at 4 spatial scales. The most frequently detected
pesticides were the fungicides boscalid (n = 19, 31.7%) and
pyrimethanil (n = 10, 16.7%) and the insecticide dimethoate
(n = 10, 16.7%). We were able to predict exposure probability for
boscalid and dimethoate by using broad indicators of cropping
intensity, but it remained difficult to identify the precise source
of contamination (e.g. specific crops in which the use of the
pesticide is authorized). For pyrimethanil, we were not able to build
any convincing landscape model that could explain the contamination.
Our results, combined with the late sampling period, strongly suggest
that pesticides applied to crops unattractive to pollinators, and
therefore considered of no risk for them, may be sources of exposure
through weeds, drift to neighboring plants, or succeeding crops.