DOI: 10.5061/dryad.9cnp5hqn5
title: Data from: Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data

description: A key challenge in the management of populations is to quantify the impact
 of interven-tions in the face of environmental and phenotypic variability.
 However, accurate estima-tion of the effects of management and
 environment, in large-scale ecological research is often limited by the
 expense of data collection, the inherent trade-off between quality and
 quantity, and missing data. In this paper we develop a novel modelling
 framework, and demographically informed imputation scheme, to
 comprehensively account for the uncertainty generated by miss-ing
 population, management, and herbicide resistance data. Using this
 framework and a large dataset (178 sites over 3 years) on the densities of
 a destructive arable weed (Alo-pecurus myosuroides) we investigate the
 effects of environment, management, and evolved herbicide resistance, on
 weed population dynamics. In this study we quantify the marginal effects
 of a suite of common management prac-tices, including cropping,
 cultivation, and herbicide pressure, and evolved herbicide re-sistance, on
 weed population dynamics. Using this framework, we provide the first
 empirically backed demonstration that herbi-cide resistance is a key
 driver of population dynamics in arable weeds at regional scales. Whilst
 cultivation type had minimal impact on weed density, crop rotation, and
 earlier cultivation and drill dates consistently reduced infestation
 severity. Synthesis and applications: As we demonstrate that high
 herbicide resistance levels can produce extremely severe weed
 infestations, monitoring of herbicide resistance is a pri-ority for famers
 across western Europe. Furthermore, developing non chemical control
 methods is essential to control current weed populations, and prevent
 further resistance evolution. We recommend that planning interventions
 that center on crop rotation and incorporate spring sewing and cultivation
 to provide the best reductions in weed densi-ties. More generally, by
 directly accounting for missing data our framework permits the analysis of
 management practices with data that would otherwise be severely
 compro-mised.
description: Data were collected from a network of UK farms using a density
 structured survey method outlined in Queensborough 2011. 
description: # Quantifying the impacts of management and herbicide resistance on
 regional plant population dynamics in the face of missing data
 Contained are the datasets
 and code required to replicate the analyses in Goodsell et al (2023),
 *Quantifying the impacts of management and herbicide resistance on
 regional plant population dynamics in the face of missing data.* ##
 Description of the data and file structure **Data**: Contains data
 required to run all stages in the analysis. Many files contain the same
 variable names, important variables have been described in the first
 object they appear in. **all_imputation_data.rds** - The data required to
 run the imputation scheme, this is an R list containing the following:
 **$Management** - data frame containing missing and observed values for
 management imputation FF & FFY: the specific field, and field year.
 year: the year. crop: crop cult_cat : cultivation category a_gly: number
 of autumn (post September 1st) glyphosate applications spray_days_total:
 total number of herbicide applications of all modes of action spray
 days_gw: number of grass weed specific herbicide applications d_date:
 drill date (in year-days) d_year: drill year (in year-days) c_date:
 cultivation date (in year-days)\ \ h_date: harvest date (in year-days)
 soil_group: soil category\ \ X&Y: spatial locations of each field.
 drop_years: years to drop from the analysis. \ **$Resistance** data frame
 containing missing and observed values for resistance imputation: FF:
 field ID\ mean_mort: average mortality **$raw_ds**: data frame with raw
 density state data containing only observed values QID: quadrat ID x &
 y: quadrat location with field DensityState: density state **$unobs_ds**:
 data frame with missing and observed density state data is_obs: index of
 whether density state was observed or not. **$init_ds** : data frame with
 density state data with missing values filled with random draws. Used in
 the initialisation of the imputation. --- **all_imptation_index.rds:** an
 index of missing fields required for imputation of missing density state
 data.\ **complete_cases.rds**: a data frame containing only the complete
 cases of management variables. **rotation_freqs.rds**: an object
 containing frequencies of different rotations used to produce
 supplementary figures. --- Within **imputation results** are the extracted
 results from the cross-validation procedure, as well as the
 multiply-imputed data and models fit to multiply-imputed data (suffixed
 \_DATA and \_MODS respectively). **_DATA** objects are R lists,
 containing:\ $conv - an index of imputation run number and mean density
 state by field to check for convergence. $management_data - imputed
 management data by imputation run. $cv - cross validation scores for each
 imputation run. $ ds_data - imputed density state data for each run. $
 all_data - imputed management and density state data. This is the most
 important data required for the subsequent analysis and contains many of
 the variables contained in **all_imputation_data**. This contains
 variables suffixed by the year of transition (_t1 / \_t2). Variables not
 indexed. New and or derived variables include: * d_diff - difference
 between drill dates between seasonal medians (spring & summer - given
 in julian days) * transition_year - factor variable for the years in which
 density state measurements were taken (e.g. 2015-2016) * rotation -
 sequence of crops between years in which measurements were taken.
 **cv_results.rds**: a list containing log-loss scores by hold-out fold and
 imputation for each model. --- Within **simulation results** are the
 results required to reproduce the sensitivity analysis (of weed responses
 to management) included in the manuscript. \ \
 **raw_coeffs_all_management.rds** - contained are the raw (i.e. mean)
 coefficients for the model including all management variables. \ \
 **simulated_coeffs_all_management.rds** the re-simulated (using MAP
 posterior simulation implemented in 'mgcv') coefficients to
 account for sampling uncertainty. **simRes_all_management.rds** - the
 simulation results for plotting figures.\ \
 **coef_summary_all_management.rds** - a summary of coefficients used for
 plotting. --- **Validation results** contains the results (imputed models
 and data) of the validation exercise for each management / resistance
 variable, where 20% of each variable was amputed then imputed to assess
 imputation performance. --- **Figures:** Output folder for main and
 supplementary figures. --- ## Sharing/Access information Management,
 resistance and weed density data were all collected from a network of UK
 farms. The Blackgrass network studied was established with funding from
 the Biotechnology and Biological Sciences Research Council (BBSRC;
 BB/L001489/1) and the Agriculture and Horticulture Development Board
 (AHDB), and is currently supported through the BBSRC “Growing Health”
 (BB/X010953/1) Institute Strategic Programme. ## Code/Software **R**:
 contains all R scripts required to replicate the analysis and is divided
 into two folders, **imputation** and **simulation**. **Imputation**
 contains scripts to check the cross validation results (check_cv.R), an
 example of how to run the imputation (example_imputation.R) as the full
 exercise took several hundred compute hours on a HPC cluster we did not
 include the full workflow. Functions to run the imputation are contained
 in the script imputation_helpers.R. validate_imputation.R and
 validation_helpers.R contain the code required to replicate the validation
 exercise. The scripts variable_relationships.R and missingness_patterns
 plot figures in the main text and supplementary. **Simulation** contains
 the scripts to replicate the sensitivity analysis. simulate_coefficients.R
 re-simulates coefficients to account for sampling uncertainty,
 simulate_steps_rs.R then takes these simulated coefficients and runs the
 population dynamic model (two-step projection) to assess weed sensitivity
 to management. plot_step_sim.R plots the results and
 simulation_functions.R contains the functions required to run the
 simulation.