DOI: 10.5061/dryad.2z34tmppq
title: Data for: Mechanisms of fire-maintained plant species diversity in species-rich wet pine savannas

description: Temperate savannas and grasslands maintained by frequent, low-intensity
 disturbances such as fire contain among the most species-rich plant
 communities in the world. Precisely how these disturbances maintain such
 high fine-scale diversity is poorly understood. This study examined the
 effects of the frequency of simulated fire (clipping combined with litter
 removal) and the relative importance of recruitment and survival on
 species diversity and trait and species composition at each of two pine
 savannas in southeastern Mississippi (USA) that had not been recently
 burned. Ten 2 × 2 m plots at each site were clipped/cleared annually from
 2014 to 2019 and again in spring 2021 (annual frequency). The other 10
 clipping plots were not clipped from 2018 to 2020 (reduced frequency).
 Vegetation in small subplots in annual frequency and reduced frequency
 plots was compared in August 2021 to test the effects of a short period
 without clipping on diversity and composition. To test the relative
 importance of recruitment and survival on diversity and composition, four
 0.25 × 0.25 m quarter plots were established within each of 10
 annual-frequency plots per site following a clipping treatment in fall
 2019 and assigned a 2 × 2 factorial arrangement of transplantation of sods
 from long-unburned areas and herbicide application. Reducing the frequency
 of clipping reduced plant diversity and altered composition at both sites.
 A comparison of diversity and trait composition responses to transplant
 and herbicide treatments revealed how recruitment and survival combined to
 affect species diversity. Partial or complete recovery of diversity
 following clipping and litter removal at both sites was driven by rapid
 increases in short-lived, resilient species that show fire-stimulated
 emergence from a seed bank and the persistence of long-lived species
 capable of surviving the prolonged period without fire or clipping.
 Species with reduced resilience and persistence were more likely to be
 lost in the reduced frequency treatment. Results are consistent with a
 model of short-term coexistence of maximum species diversity maintained by
 the most frequent fire regimes fuels will permit.
description: <strong><em>Experiment 1. </em>Effects of
 Clipping History and Herbicide-Generated Mortality on Fine-Scale Plant
 Diversity and Composition.</strong> To test the relative importance of recruitment and
 survival in explaining species diversity responses to frequent clipping, I
 examined the responses of diversity, composition, and functional traits of
 all identified species from 2020 to 2021 to transplantation of sods from
 long-unburned and unclipped areas into annual frequency plots and to
 herbicide application within these plots (Fig. 2). I established a 0.6
 × 0.6 m subplot within the center of each of the 10 2
 × 2 m Annual Frequency plots at each site in October 2019
 (Fig. 2). I then subdivided each subplot into 4 equal (0.25
 × 0.25 m) quarter plots and randomly assigned a 2
 × 2 factorial arrangement of the transplantation and
 herbicide treatments to the quarter plots in each subplot (Fig. 2). I then
 transplanted two 0.25 × 0.25 m vegetated sods excavated from stratified random
 locations of portions of the savanna that had not been recently burned or
 clipped to the quarter plots randomly chosen to receive the transplant
 treatment. I chose sods in a stratified random fashion so as to avoid
 splitting bunchgrass clumps in half. Because many clumps of <em>A.
 beyrichiana</em> at Grand Bay were large enough to encompass most of
 a sod, and to avoid damage to these plants, I took most sods from
 locations that either were directly adjacent to an <em>A.
 beyrichiana</em> clump or that included a small (&lt;~ 10 cm in
 diameter) clump of the species. This procedure resulted in an
 underestimate of <em>A. beyrichiana</em> within unclipped
 areas of the site. I therefore excluded <em>A.
 beyrichiana</em> from statistical analyses of the transplant
 treatment effect (see <em>Data Analysis of Experimental
 Results</em>). I established two transplant control quarter plots by
 trenching (once) around the periphery of the intact (non-transplanted)
 quarter plots (Fig. 2).  After transplanting the sods, I clipped all
 vegetation within the 2 ×
 2 m plot to the soil surface and removed
 all clippings and surface litter, as in previous years. One week later, as
 vegetation was beginning to resprout, I applied the herbicide treatment to
 a transplanted sod and a non-transplanted sod by carefully applying
 glyphosate herbicide (Roundup<sup>®</sup>) to all vegetation
 with live, aboveground green parts.
 <strong>Vegetation Sampling for Experiment
 1.</strong> I monitored
 the vegetation responses to the quarter-plot treatments by determining
 presence-absence of all identifiable vascular plant species, excluding
 woody plants taller than 1 m, within each of four (0.125
 × 0.125
 m) quadrants of each 0.25
 × 0.25 m quarter plot
 (Fig. 2). I censused vegetation in early April 2020, late May 2020,
 October 2020, early May 2021 (before implementation of the clipping
 treatment), and August 2021. I pooled the April and May 2020 censuses of
 spring-flowering species with the October 2020 census of summer and
 fall-flowering species to provide an estimate of composition in 2020. I
 pooled the May 2021 census of the spring-flowering species and August 2021
 census of late summer- and fall-flowering species to obtain an estimate of
 composition in 2021. Nomenclature followed Weakley (2020) and when
 necessary the USDA Plants Database.             To determine
 whether species richness was more negatively associated with the amount
 flammable fuels than with green vegetation, I quantified fine fuels, green
 vegetation, bare ground and species richness in May 2021 in the Annual
 Frequency plots. This census was conducted before the May 2021 clipping
 treatment and thus 19 months since the last clipping treatment.
 Quantification of fuels, green vegetation, and bare ground entailed taking
 an overhead digital photograph of each quarter plot, superimposing a 3 × 3
 (9-cell) grid on each photograph, and then counting the number of cells in
 which the majority of the area of each cell was occupied by either brown
 vegetation and/or surface litter (hereafter, flammable fuel), green
 vegetation, or bare ground. I then examined the relationship between the
 species richness and the relative amounts of flammable fuel, green
 vegetation, bare ground, and the transplant and herbicide treatments in
 May 2021, before the May 2021 clipping treatment was implemented.
   <em>Experiment 2.
 Effects of Reduced vs. Annual and Increased Frequency on Fine-Scale Plant
 Diversity and Composition. </em>I assessed the effect of reducing
 the frequency of simulated fire from once every year (annual frequency) to
 once at the beginning and end of a four-year period (reduced frequency) on
 plant species diversity and trait and species composition in 2021. I
 established a 0.25 × 0.25 m small subplot within each of the 10 reduced
 frequency plots at each site in May 2021 (Fig. 3). To determine the effect
 of reduced frequency on plant diversity and composition, I compared the
 August 2021 composition of the non-treated quarter plots in the annual
 frequency plots to August 2021 composition in the small subplots in the
 reduced frequency plots (Fig. 3). To investigate how diversity and
 composition differed between areas that experienced a recent reduction in
 clipping to those that experienced a recent increase in the frequency of
 clipping, I compared the composition of subplots in the reduced frequency
 plots to those quarter plots in the annual frequency plots that had not
 received herbicide but had been transplanted from areas that had not been
 clipped or burned since at least 2010 (Grand Bay) or since 2012 (Sandy
 Creek; Fig. 3). <em>Functional
 Traits and their Analysis.</em> To determine the treatment effects
 on functional traits, I extended the analysis of herbaceous species used
 in Brewer and Zee (2021) by including woody plants and species encountered
 in the current study that were not encountered in that study. I described
 all vascular plant species encountered in plots and that I could identify
 with respect to 25 traits (Appendix 2). Of particular interest were traits
 directly related to size and life history (e.g., height, root length,
 presence of belowground perennating organs, perenniality, presence of seed
 bank) and that were indicative of fire-mediated phenotypic plasticity
 (fire-stimulated flowering, fire-stimulated emergence, vegetative dormancy
 in years without fire). I described both categorical (e.g., photosynthetic
 pathway) and quantitative traits (e.g., leaf moisture content). I also
 measured traits commonly used in general functional trait analyses,
 including specific leaf mass (SLM), leaf moisture content, flowering
 season, photosynthetic pathway, and presence of nitrogen-fixing symbionts.
 I also measured traits that were common in wet and/or wet, nutrient poor
 habitats (root porosity, a proxy for aerenchyma tissue production, and
 carnivory; Brewer et al. 2011). I
 measured quantitative traits that were continuous (e.g., specific leaf
 mass, leaf moisture, lateness of flowering (spore-producing) season) as
 well as those that were classified into two binary categories: greater
 than the median value or less than or equal to the median value of the
 species pool (e.g., maximum root length, rhizome thickness, rhizome
 length, root porosity). For some binary variables, there were inadequate
 data to classify some species. For example, for the trait “fire-stimulated
 emergence”, evidence for or against fire-stimulated emergence was lacking
 for some species. I considered responses for those species as
 missing. I used
 the <em>functcomp</em> function in R (package FD) to calculate
 community-weighted trait means (CWM, Lavorel et al. 2008, hereafter CW
 trait means) using all identified species encountered in all plots for
 Experiments 1 and 2. For a continuous trait (e.g., lateness of flowering,
 specific leaf mass), the CW trait mean was simply the mean trait value of
 all species present in the sample (after excluding species with no
 measurement), weighted by their relative abundances. For binary variables,
 I specified <em>CWM.type</em> = “all”, which returned the
 summed relative abundances of all species in each class (excluding those
 that could not be scored) divided by the summed relative abundances of all
 species. This approach resulted in there being a mean relative abundance
 for each binary category (e.g., one for “carnivorous” and one for “not
 carnivorous”). In many cases, the CW means for the two categories had the
 same absolute value but differed in sign. In cases in which data were
 lacking for some species (e.g., fire-stimulated emergence), CW means could
 differ in both sign and absolute value. <em>Data Analysis
 of Experimental Results.</em> For Experiment 1, I analyzed the
 effects of <em>herbicide</em> (herbicide, no herbicide),
 <em>transplant</em> (transplanted, not transplanted),
 <em>year</em>, <em>site</em>, and
 <em>plot</em> on species richness and Shannon species
 diversity with linear mixed models (function <em>lmer</em> in
 the lmerTest package of R; version 3.2.4). For the Sandy Creek site, I
 treated <em>herbicide</em>, <em>transplant</em>,
 <em>year</em>,<em> </em>and their interactions as
 fixed effects and <em>plot</em> as a random effect. Because of
 the unavoidable underestimation of <em>A. beyrichiana</em>
 abundance in transplanted sods at Grand Bay, I ran separate analyses of
 the herbicide and transplant effects on species richness and Shannon
 diversity. I conducted analyses of the herbicide effect and its
 interaction with year on non-transplanted quarter plots and included
 <em>A. beyrichiana</em> in the analyses. In contrast, I
 conducted analyses of the transplant effect and its interaction with year
 on quarter plots that were not treated with herbicide and excluded
 <em>A. beyrichiana</em> from the analyses. <em>Aristida
 beyrichiana</em> was absent from all plots at Sandy Creek. To
 increase statistical power of the tests of the transplant effect on
 richness and Shannon diversity, I combined data from no-herbicide quarter
 plots from both sites (excluding <em>A. beyrichiana</em>) and
 tested the main between-subjects effect of transplant for both years and
 its interactions with site and year. I
 analyzed treatment effects on species and functional trait composition
 (based on CW trait means) for each site separately using two integrated
 approaches. One approach involved permutation multivariate analysis of
 variance (permanova) (Anderson 2001) using the
 <em>adonis2</em> function in the vegan package of R. I used
 the method “bray” to conduct the analysis on Bray-Curtis distance matrices
 derived from quadrant sums (hereafter, abundance). To examine which
 species were most important in distinguishing composition between
 treatments, the second approach involved running a distance-based
 redundancy analysis and interpreting species or community-weighted mean
 trait scores (dbRDA; function <em>capscale</em> in the vegan
 package of R; McArdle and Anderson 2001). I analyzed functional trait
 composition in much the same way. As with the analysis of species
 composition, I used scores associated with CW traits to determine which
 traits were most important in distinguishing the treatments and to which
 treatment group each trait was most strongly associated. For the
 Sandy Creek data, I examined changes in composition as a function of the
 transplant treatment by conducting a repeated measures permanova of the
 within-subjects effect of <em>year</em> and its interaction
 with <em>transplant</em>. This test was done without including
 the herbicide treatment in the model due to its overwhelming effect on
 mortality in both transplanted and non-transplanted quarter plots. In a
 separate analysis, I examined changes in composition as a function of the
 herbicide treatment and its interaction with <em>transplant
 </em>by conducting a repeated measures permanova of the
 within-subjects of <em>year</em> and its interactions with
 <em>herbicide</em>, <em>transplant</em>, and
 <em>herbicide</em> × <em>transplant</em>
 (disregarding the <em>year</em> ×
 <em>transplant</em> test). For both analyses, I also included
 a grouping variable, which accounted for all the between-subjects
 variation and removed its associated degrees of freedom from the residual
 error term (see Appendix 3 to see the code for the
 <em>adonis2</em> model statements). To isolate the effect of
 each treatment on compositional change and associated species or trait
 scores, I ran a separate partial dbRDA for each treatment × year
 interaction, wherein the test of each was conditioned on the
 <em>grouping variable</em> and <em>year</em>
 effects and the remaining treatment × year interactions in the model (see
 Appendix 3 to see the code for the <em>capscale</em> model
 statements). To generate the appropriate model statements, I converted
 each of the treatment interactions with year to a main effect (Appendix
 4). In addition to running a permanova to determine the effects of the
 treatments on changes in composition, I ran separate permanovas on the
 2020 abundance data (pooled April, May, October data) and the 2021 data
 (pooled May, August data). To examine which species and traits were most
 important in driving the treatment effects within each year, specifically,
 the <em>herbicide</em>, <em>transplant</em>, and
 <em>herbicide</em> × <em>transplant</em> effects,
 I ran a separate partial dbRDA for each of these effects and obtained
 species or CW trait scores. As with the partial dbRDAs of the treatment ×
 year interactions, I isolated the main effect of each treatment on
 composition in each year conditioned on <em>plot</em>, in
 which the treatment was nested, and the remaining treatment effect and the
 interaction. To contrast the effects of herbicide and the transplant
 treatments on composition, I also used permanova and dbRDA to examine
 differences between the herbicide-only treatment and the transplant-only
 treatments in each year and for the change in composition between
 years. For the
 Grand Bay data, I ran repeated measures and single-year permanovas and
 dbRDAs separately for the herbicide and transplant effects. As with the
 analyses of species richness and diversity, I analyzed the effects of
 herbicide on non-transplanted quarter plots and the effects of the
 transplant treatment on quarter plots that did not receive the herbicide
 treatment. To contrast the effects of the transplant and herbicide
 treatments on composition, I also used permanova and dbRDA to examine
 differences between the herbicide-only treatment and the transplant-only
 treatments in each year and for the change in composition between years.
 Because of the problem of not adequately sampling <em>A.
 beyrichiana</em> in transplanted sods at Grand Bay, I removed this
 species from all analyses involving transplanted sods. I ran
 between-subjects permanovas of treatment effects on composition averaged
 over the two years when the treatment × year interactions were not
 significant. Between-subjects tests were based on principal coordinates
 analysis axis scores rather than on abundances (Anderson et al. 2008),
 which precluded direct identification of which species or functional
 traits drove compositional differences associated with the treatments
 averaged over the two years (Appendix 5 for the R code used to conduct the
 between-subjects tests). I used
 linear mixed models to test the relationship between species richness and
 the fraction of flammable fuels, green vegetation, and bare ground and the
 transplant and herbicide treatments in May 2021. Before running the linear
 mixed models, I ran principal components analysis (using
 <em>princomp</em> in R) to quantify differences among the
 quarter plots with respect to flammable fuel, green vegetation, and bare
 ground. Two principal components were extracted. The first principal
 component, which explained 66% of the variation in cover among quarter
 plots was highly negatively correlated with flammable fuel
 (r<sub>brown vegetation </sub>= -0.989), strongly positively
 correlated with bare ground (r = 0.773), and modestly positively
 correlated with green vegetation (r = 0.426). The second principal
 component, which explained 34% of the variation in cover among quarter
 plots was highly negatively correlated with green vegetation
 (r<sub>green vegetation</sub> = -0.905), positively correlated
 with bare ground (r = 0.634) and uncorrelated with flammable fuel (r =
 0.142). Hence, the first principal component was considered a flammable
 fuel/bare ground axis and the second principal component was considered a
 green vegetation/bare ground axis. I then fit species richness to a linear
 mixed model that included transplant, herbicide, their interaction, and
 the two principal components as fixed effects, and site and plot as random
 effects, with plot nested within site. For Experiment 2, I analyzed the effect of the
 <em>frequency</em> treatment (annual, reduced) on species
 richness and Shannon species diversity for both sites together, treating
 site as either a random effect (using linear mixed models) or as a fixed
 effect. When I analyzed site as a fixed effect, I used linear models
 (function <em>lm</em> package of R) to determine if there was
 a significant main effect of site and a site × frequency interaction. I conducted two separate treatment comparisons for each
 site. For one comparison (Effect of a Recent Reduction in Frequency), I
 compared the control quarter plots in the annual frequency plots to the
 subplots in the reduced frequency plots. For the second analysis (Effect
 of a Recent Increase in Frequency), I compared the transplanted quarter
 plots that were not treated with herbicide in the annual frequency plots
 to the subplots in the reduced frequency plots. I analyzed the effect of
 the <em>frequency</em> treatment on composition both together
 and separately for each site using permanova and dbRDA. When I examined
 both sites together, I included site and the site × frequency interaction in the permanova model, and the partial dbRDA examined the
 effect of the frequency treatment conditioned on site and the
 site × frequency interaction.
description: All files can be opened using software capable of reading an
 MS-DOS comma separated values or plain text files.