DOI: 10.5061/dryad.wpzgmsc28
title: A model for regional-scale oak savanna management: the roles of fire, canopy, and soils for understory plant diversity

description: We used these data to analyze direct effects of prescribed fire and
 mechanical thinning on oak savanna groundlayer plant diversity, indirect
 effects via changes to ecosystem structure (canopy openness and litter
 depth), and how these effects depended on soil productivity context and
 the spatial scale at with diversity was analyzed (mean 1-m2 plant species
 richness vs. total plot 1000-m2 plant species richness). The dataset is
 comprised of those two scales of richness, which are the primary response
 variables. We analyzed the effects of three primary management variables:
 fire frequency (number of fires since 2000), canopy thinning (the
 proportion of woody stems &gt; 5 cm DBH removed), and shrub/sapling
 thinning (the proportion of woody stems &lt; 5 cm DBH removed).
 Indirect effects via management effects on ecosystem structure (% canopy
 openness and mean litter depth) were modeled using structural equation
 modeling, and context-dependence was analyzed using interaction terms
 between soil productivity (in the form of a PCA axis) and each management
 variable in mixed models.
description: # A model for regional-scale oak savanna management: the roles of fire,
 canopy, and soils for understory plant diversity Dataset DOI:
 [10.5061/dryad.wpzgmsc28](10.5061/dryad.wpzgmsc28) ## Description of the
 data and file structure We assessed how variation in management history in
 oak savannas affected groundlayer plant communities by sampling 100 sites
 within the U.S. Great Lakes region of Illinois, Indiana, Ohio, Michigan,
 and Wisconsin. We sampled sites that historically supported oak savannas,
 are currently undergoing restoration for oak savanna using a variety of
 management approaches, and that represent a range of soil resource
 conditions. We identified geographically-proximal clusters of two to seven
 edaphically similar sites that represented a variety of management
 histories, differing in the frequency and intensity of canopy thinning,
 shrub and sapling thinning, and prescribed fire. Most clusters included at
 least one site with each of the following histories – one or more
 prescribed fires, mechanical canopy or shrub and sapling thinning, a
 combination of burning and thinning, and no management with fire or
 thinning in the 40 years prior to sampling. Our goal in identifying a
 range of management histories within clusters was to control for measured
 (e.g., soil characteristics) and unmeasured (e.g., land-use history)
 environmental gradients, while testing for differences among management
 approaches. ### Files and variables #### File: GLRI_ms1_data.csv
 **Description:** The dataset used in analyses. ##### Variables * cluster -
 Cluster name: name of site grouping containing 2-7 plots in similar
 landscape * site.name - Site name: Name for site containing one 0.1 ha
 plot * year - Year of sampling (If spring and fall sampling occurred in
 different year, this is year of fall sampling) * burns.since.2000 - Fire
 frequency: Number of prescribed fires conducted between 2000 and the year
 of sampling * pct.sand - Percent soil sand content: Mean of 40 soil cores
 (4 taken every 5 meters along transect) * pct.water - Percent soil water
 holding capacity: Proportion of oven dried weight to saturated wet weight,
 pooled site-level sample * soilPC1.inv - Larger values = higher organic
 matter, silt and clay content, and water holding capacity: Soils principle
 component axis (Includes soil organic matter, soil texture (percent sand,
 silt, and clay content), Mehlich-III phosphorus, Bray-II phosphorus, pH,
 total ion exchange capacity, and water holding capacity. The first PC axis
 described 60% of variation in soil variables and was positively related to
 percent silt, percent clay, soil organic matter, total exchange capacity,
 and water holding capacity and negatively related to percent sand.) *
 mean.dens.cor - Canopy openness: Mean of 40 densiometer readings (4 taken
 every 5 meters along transect)(Proportion of 96 cells not covered by
 canopy X 1.04) * mean.litdp - Leaf litter depth: Mean of 40 depth
 measurements (4 taken every 5 meters along transect) * thinning.canopy -
 Canopy thinning intensity: total DBH of cut stumps/(total DBH of cut
 stumps + total DBH of standing trees)(Trees = 5 + cm DBH) * thinning.shrub
 - Shrub thinning intesnity: # cut shrub and sapling stems/(# cut shrub and
 sapling stems + # live shrub and sapling stems) * mean.plotSR - Average
 quadrat groundlayer species richness: Mean of SR in 10, 1X1 m quadrats *
 bigplotSR - Total plot species richness: All species observed in 0.1 ha
 plot #### File: GLRI_ms1_data_with_metadata.xlsx **Description:** The
 dataset used in analyses, along with a metadata tab describing variables.
 ##### Variables: The same as in "GLRI_ms1_data.csv" 
 
 ##### File: GLRI_OSE_forDryad.R **Description:** R code for all analyses
 and figures included in manuscript, including Appendices. ## Code/software
 We conducted analyses in R v.4.0.3 (R Core Team 2020), using the following
 packages: lavaan, MuMIn, car, visreg, lme4. Code is organized by the
 questions addressed in the manuscript, and all variables are contained in
 the file "GLRI_ms1_data.csv". The file
 ""GLRI_ms1_data_with_metadata.xlsx" is provided for a quick
 reference of variable description. Question 1, "How do management and
 soil influence richness directly and via structure?", is analyzed
 with SEM (lavaan package); Question 2, "How do management effects
 vary across a soil productivity gradient?", is analyzed with
 generalized linear mixed models (lme4 package); Question 3, "Are
 effects on richness scale-dependent?", is analyzed by conducting
 analyses under Questions 1 and 2 on both small (1-m^2^) and large
 (1000-m^2^) richness. Code for visualizing data also included below
 primary analyses. Figures 3a-d (partial regression plots) represent
 individual SEM paths constructed from multiple regression models with
 structure similar to SEMs.