Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data

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 herbici..., Data were collected from a network of UK farms using a density structured survey method outlined in Queensborough 2011. , , # 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 applicatio...

种群管理中的一项核心挑战，是在环境与表型变异存在的情况下，量化干预措施的影响。然而在大规模生态学研究中，准确估计管理措施与环境因子的效应，常受限于数据采集成本高昂、质量与数量间固有的权衡矛盾，以及数据缺失问题。

本文提出了一种全新的建模框架与基于种群统计学的插补方案，以全面处理由种群、管理措施及除草剂抗性（herbicide resistance）数据缺失所引发的不确定性。依托该框架，结合一份覆盖3年周期、178个样点的大型数据集（针对破坏性农田杂草看麦娘（Alopecurus myosuroides）的种群密度），我们探究了环境因子、管理措施以及进化产生的除草剂抗性对杂草种群动态的影响。

本研究旨在量化一系列常见耕作管理措施的边际效应，包括种植制度、耕作方式及除草剂[原文截断]。本研究数据源自英国农场网络，采用Queensborough（2011）中详述的密度结构化调查方法采集。

# 面对数据缺失时量化管理措施与除草剂抗性对区域植物种群动态的影响

本数据集包含复现Goodsell等人（2023）发表论文《面对数据缺失时量化管理措施与除草剂抗性对区域植物种群动态的影响》所需的全部数据与代码。

## 数据与文件结构说明

**Data**：包含执行分析全流程所需的全部数据。

多个文件共享相同的变量名，重要变量将在其首次出现的对象中完成说明。

**all_imputation_data.rds**：用于执行插补方案的数据集，为R语言列表格式，包含以下内容：

**$Management**：用于管理措施插补的、包含缺失值与观测值的数据框

FF与FFY：具体田块与田块年度

year：调查年份

crop：种植作物

cult_cat：耕作类别

a_gly：秋季（9月1日之后）草甘膦施药次数[原文截断]