DOI: 10.5061/dryad.fj6q5747x
title: Data from: Management inputs, site conditions, and fire history shape outcomes of invasive plant control and native recovery

description: Millions of dollars and countless hours are spent on invasive plant
 management, and the field of invasion ecology has gained increasing
 attention in recent decades. Yet, despite these efforts to control and
 understand plant invasions, successful management is often elusive.
 Budgetary constraints are a common factor limiting invasive plant
 management programs, and therefore, optimizing control strategies are
 essential. However, such optimization requires data on management inputs
 and outcomes, and these data are often missing, lacking, or underutilized.
 To address this knowledge gap and identify predictors of successful weed
 control in natural areas, we examined nearly 20 years of invasive plant
 treatment data in the world’s largest urban national park – Santa Monica
 Mountains National Recreation Area of southern California. We resurveyed
 279 sites that had undergone control in the past two decades, collecting
 data on the abundance of native and invasive plant species to evaluate
 long-term management outcomes. We used multiple statistical approaches to
 identify management inputs and site characteristics that are predictors of
 eradication, invasive plant cover, and native species recovery. We found
 that the greater the initial size or percent cover of an infestation, the
 lower the probability of eradication. We also found that weed infestations
 on steeper slopes and in areas that have burned more frequently are less
 likely to be eradicated. Promisingly, our results also showed that greater
 reductions in invasives generally benefited native plant communities,
 though not in all cases. These analyses also highlighted that persistence
 is key; more frequent treatments (both chemical and nonchemical) and
 greater investment of labor resulted in larger reductions in invasive
 plants. Our results highlight how site characteristics and limited
 resources can complicate invasive plant management, while demonstrating
 the value of analyzing treatment and monitoring data to identify effective
 control strategies and guide adaptive management decisions.
description: # Data from: Management inputs, site conditions, and fire history shape
 outcomes of invasive plant control and native recovery Dataset DOI:
 [10.5061/dryad.fj6q5747x](https://doi.org/10.5061/dryad.fj6q5747x) ##
 Description of the data and file structure This dataset contains
 site-level monitoring data for multiple invasive plant species managed
 within the Santa Monica Mountains National Recreation Area (SMMNRA). Each
 row represents a single site or infestation polygon with associated
 measurements of infestation size, percent cover, target species identity,
 slope, elevation, fire history, and treatment outcomes. The dataset
 supports analyses of: * invasive species abundance and change through time
 * site conditions associated with invasion severity * treatment
 effectiveness * environmental drivers (topography, fire regime, vegetation
 type) Data were compiled from field observations, GIS analyses, and
 land-management records. ### Files and variables #### File:
 EAP24-0881_data.csv **Description:** Primary dataset containing 44 columns
 and 279 rows (one per site). **Variables**: | 
 
 | 
 
 | | :------------------------- |
 :---------------------------------------------------------------------------------- | | **Variable** | **Description** | | **site\_ID** | Unique identifier for each site or infestation polygon. | | **peak\_initial\_cover** | Maximum percent cover of the target invasive species at the initial assessment. | | **site\_ha** | Total site area (hectares). | | **inf\_size\_ha** | Initial infestation area of the target invasive species (hectares). | | **target** | Four-letter invasive species code for the focal species being monitored or treated. | | **target\_cover\_23** | Percent cover of the target invasive species in 2023. | | **eradicated** | Whether the target species was eradicated from the site (Yes/No). | | **inf\_size\_23** | Infestation area of the target invasive species in 2023 (hectares). | | **cover\_change** | Change in percent cover from baseline to 2023 (percentage points). | | **change\_infst\_ha** | Change in infestation area from baseline to 2023 (hectares). | | **trt\_start** | Year initial treatment began. | | **trt\_end** | Year most recent treatment occurred. | | **yrs\_trtd** | Total number of years during which treatment occurred. | | **yrs\_since\_trtd** | Number of years since the last treatment. | | **treatment\_type** | Treatment category: chemical, nonchemical, or both. | | **foliar\_spot** | Number of times foliar spot-spray treatment was applied. | | **foliar\_broadcast** | Number of times foliar broadcast-spray treatment was applied. | | **mow** | Number of times mowing treatment was conducted. | | **brushcut** | Number of times brush-cutting or brush-removal treatment occurred. | | **manual** | Number of times manual hand-pulling or hand-removal treatments were conducted. | | **cut\_apply** | Number of times cut-stump herbicide treatment was applied. | | **basal\_bark** | Number of times basal-bark herbicide treatment was applied. | | **times\_treated** | Total number of treatment actions at the site (sum of all methods). | | **labor\_hours** | Total labor hours spent treating the site. | | **hours\_per\_ha** | Labor hours standardized by site area (hours per hectare). | | **survey\_date** | Date the site was surveyed in 2023. | | **native\_cover** | Percent cover of native plant species in 2023. | | **prop\_native** | Proportion of total vegetation cover composed of native species (0–1). | | **nonnative\_cover\_23** | Percent cover of nonnative species in 2023. | | **native\_rich** | Number of native plant species observed (species richness). | | **natives\_m2** | Native plant density standardized per square meter. | | **nonnative\_rich** | Number of nonnative plant species observed (species richness). | | **aspect\_azm** | Aspect in azimuth degrees (0–360°). | | **min\_slope** | Minimum slope at the site (degrees). | | **max\_slope** | Maximum slope at the site (degrees). | | **mean\_slope** | Mean slope across the site (degrees). | | **major\_veg** | Dominant vegetation type at the site (e.g., chaparral, grassland). | | **fire\_freq** | Number of recorded fires at the site. | | **fire\_return\_interval** | Average interval between fires (years). | | **last\_fire** | Year of the most recent fire. | | **yrs\_postfire** | Number of years since the most recent fire. | | **elev** | Elevation (meters above sea level). | | **restored** | Whether ecological restoration activities were conducted (Yes/No). | | **fuel\_mod** | Whether fuel-modification work occurred (Yes/No). | **Target species** (column 'target'): ## Code/software All statistical analyses and data processing were conducted using **R version 4.4.1** (R Core Team 2024), an open-source programming language and environment for statistical computing. Analyses relied on base R functions as well as several freely available R packages. No proprietary software was used. All workflows can be executed on Windows, macOS, or Linux systems capable of running R 4.4.1 or later. The following packages were used in the analyses: * **tidyverse** (v2.0.0) for data manipulation, cleaning, and visualization * **lme4** (v1.1–35.1) for fitting linear and generalized linear mixed-effect models * **car** (v3.1–2) for ANOVA and regression diagnostics * **MASS** (v7.3–60.0) for additional statistical tools * **randomForest** (v4.7–1.1) for random forest models * **rfPermute** (v2.5.2) for permutation-based importance testing in random forests * **ggplot2** (v3.4.4) for data visualization * **stats** (base R) for parametric and nonparametric hypothesis testing * **vegan** (v2.6–4) for diversity calculations (when evaluating richness metrics) All code used to run the analyses can be executed using these packages with no additional dependencies.