DOI: 10.5061/dryad.rjdfn2zm0
title: Data from: Temporal invasion regime attributes influence community synchrony and stability in an arid land system

description: Invasive species have become a major threat to ecosystems across the
 globe, causing significant ecological and economic damage. To anticipate
 how communities may respond to future invasions, it is crucial to refine
 how invader impacts are evaluated, especially in historically uninvaded
 and highly variable systems such as arid lands. While invader abundance is
 typically used to predict invader impacts, it may not effectively capture
 the dynamics that occur over time for established invaders that experience
 cyclical dynamics (i.e., boom-bust patterns), making it more challenging
 to track invader impacts. To address this issue, we leveraged a long-term
 vegetation dataset to develop a novel invasion regime framework for a
 dominant annual invader in North American deserts, Brassica tournefortii.
 Using abundance data over time, we evaluated how attributes of this
 invader’s boom-bust dynamics (i.e., invasion level, boom frequency and
 magnitude) influence the long-term synchrony and stability of invaded
 Aeolian sand dunes communities. We found that attributes that captured the
 temporal effects of the invader were  strong indicators
 of the impacts of an invader on long-term attributes of communities.
 Specifically, the mean magnitude of invader booms led to a decrease in
 species asynchrony and community stability. Increasing boom frequency also
 decreased community stability but this was more muted. Mean magnitude of
 invader booms also mediated shifts in the relationship between synchrony
 and stability, with this relationship becoming more shallow with
 increasing boom magnitudes. Our research emphasizes the significance of
 using community metrics that capture temporal dynamics to document
 invasion impacts within dynamic arid land systems. The invasion regime
 framework can additionally offer insights into the mechanisms that may
 enable the persistence of the invader over time. Together this knowledge
 can be helpful in guiding decision-making and land management strategies
 aimed at effectively controlling and mitigating the impact of invasive
 species.
description: <em>Study Design & Sampling</em> – we
 leveraged long-term observational transects that were established in 2002
 as part of the Coachella Valley Multiple Species Habitat Conservation Plan
 Monitoring Program (Allen et al. 2005). Nineteen belt transects were
 arranged as 100 m x 10 m (0.1ha) rectangles (Figure 2A). Within each belt
 transect, four 1 m<sup>2</sup> quadrats plots are spatially
 grouped within a cluster, with clusters spaced 44 meters apart (3 clusters
 per transect: A,B,C; n=12 plots, Figure 2B). Plots were alternated along
 the center line of the transect for a total of 228 plots. Due to some
 plots missing vegetation data, we reduced our dataset to 226 plots.
 Vegetation within these plots was surveyed annually from 2003-2019 using
 visual estimates of cover for each species present within the aboveground
 vegetation during peak biomass in the spring, (March-April, dependent on
 phenology). To assess traditional metrics of
 community-level responses to invasion, we created linear-mixed effect
 models using the ‘lme4’ package in r (Bates et al. 2015). For the first
 model, we fit Shannon-Weiner index as the response variable, with invasion
 level (low, moderate, high) as a fixed factor, with cluster nested within
 transect as a random factor. We used the same model structure as above for
 the second model but replaced the Shannon-Weiner index with species
 richness as the response variable. We then compared individual treatment
 levels in our models using Tukey's HSD post-hoc tests.
 To assess how attributes of <em>Brassica
 tournefortii’s</em> invasion regime attributes influence community
 synchrony and stability across an invasion gradient, we created two
 separate mixed effect models, one with community synchrony as the response
 variable, and the second with community stability as the response
 variable. Within these models, the following were included as fixed
 factors: boom frequency, mean boom magnitude and their interactions, with
 cluster nested within transect as random factors. To make sure our models
 met the assumptions and to test for collinearity among our predictor
 variables, we used the performance package in r. VIF values were kept
 below 10. We included cluster nested within transect as random factors. We
 used the same model structure for our second model, replacing community
 stability as the new response variable. Lastly, to test if attributes of
 an invasion regime mediate the relationship between community synchrony
 and community stability, we created a third model where we used community
 stability as a response variable, with community synchrony, mean boom
 magnitude, and their interactions as fixed factors and cluster nested
 within transect as a random factor. We focus on the mean boom magnitude
 invader attribute, as this measure provides the greatest potential to
 capture carryover effects via large propagule pulse events.  Finally, we
 used the ‘sjPlot’ package in r to visualize our results and plot the
 marginal effects of our regression models that had significant
 interactions (Lüdecke, 2024).
description: # DATA FROM: Temporal Invasion Regime Attributes Influence Community
 Synchrony and Stability in an Arid Land System
 [https://doi.org/10.5061/dryad.rjdfn2zm0](https://doi.org/10.5061/dryad.rjdfn2zm0) ## Files in this repository *Files* contain data collected on plant cover from 2003-2019 as a part of the Coachella Valley Multiple Species Habitat Conservation Plan Monitoring Program. **DATA FILES:** **fulldatasetCCB.csv**: this file contains all plant cover data collected for each of the 228 plots from 2003-2019. First 7 columns define the plot information, and the remainder of the columns provide % cover data within the quadrat. Each column containing cover is titled in the order as follows Genus_species for plants, or Percent_soiltype for soil conditions (soil not used in this analysis) Yr_TRANSECT - year and transect identifier together YEAR - year sampling occurred plot_year - plot identifier with year sampled transect- identifier for belt transect (19 transects total). The letters 'CA, L, H' at the beginning of the transect name indicate transects that are grouped together geographically.  plot- identifier for plot cluster- an identifier for 4 plot clusters (A,B, or C) quadrat- an identifier for which quadrat within the larger cluster **ECY240411**_**Condensed.csv**: this file contains a condensed version of fulldataset_CCB. The data contains 228 rows, each corresponding to a plot. This dataset has summaries of boom magnitude and frequency, as well as diversity, synchrony, and stability measures for the study extent per plot. transect- identifier for belt transect (19 transects total). The letters 'CA, L, H' at the beginning of the transect name indicate transects that are grouped together geographically.  plot- identifier for plot cluster- an identifier for 4 plot clusters (A, B, or C) quadrat- an identifier for which quadrat within the larger cluster BOOM_MEAN- mean boom magnitude boom_frequency - total frequency of boom events throughout the study extent mean_return_interval- the mean return interval between each boom event (not used in analysis) non_zeroMustard - this is the average mustard cover of the plot for the study extent, when years with 0 cover are removed invasion_level - contains the categorical value of invasion level that was assigned based on non_zeroMustard column value stability- measure of community stability for each plot syncrhony_L - Loreau's measure of community synchrony for each plot avg_richness - mean species richness for each plot (meter squared) mean. div - mean Shannon diversity for each plot (meter squared) **prism_ccb**_**annual.csv:** This file contains annual climate data derived from PRISM used to create AS1A. values for precipitation (mm), mean, minimum and maximum temperature (c), minimum and maximum vapor pressure deficit (hPa, and average native and nonnative diversity at our study sites for the entirety of the study extent.  **R script** **ECY24_0411.R**: contains code to reproduce the analysis and figures from the manuscript using free r studio software. The code contains an annotated table of contents to separate each section. If the reader has any questions, please feel free to contact the corresponding author.