DOI: 10.5061/dryad.crjdfn339
title: Large ecosystem-scale effects of restoration fail to mitigate impacts of land-use legacies

description: Ecological restoration is a global priority, with potential to reverse
 biodiversity declines and promote ecosystem functioning. Yet, successful
 restoration is challenged by lingering legacies of past land-use
 activities, which are pervasive on lands available for restoration.
 Although legacies can persist for centuries following cessation of human
 land uses such as agriculture, we currently lack understanding of how
 land-use legacies affect entire ecosystems, how they influence restoration
 outcomes, or whether restoration can mitigate legacy effects. Using a
 large-scale experiment, we evaluated how restoration by tree thinning and
 land-use legacies from prior cultivation and subsequent conversion to pine
 plantations affect fire-suppressed longleaf pine savannas. We evaluated 45
 ecological properties across four categories: 1) abiotic attributes, 2)
 organism abundances, 3) species diversity, and 4) species interactions.
 The effects of restoration and land-use legacies were pervasive, shaping
 all categories of properties, with restoration effects roughly twice the
 magnitude of legacy effects. Restoration effects were of comparable
 magnitude in savannas with and without a history of intensive human land
 use; however, restoration did not mitigate numerous legacy effects present
 prior to restoration. As a result, savannas with a history of intensive
 human land use supported altered properties, especially related to soils,
 even after restoration. The signature of past human land-use activities
 can be remarkably persistent in the face of intensive restoration,
 influencing the outcome of restoration across diverse ecological
 properties. Understanding and mitigating land-use legacies will maximize
 the potential to restore degraded ecosystems.
description: This research took place within a
 large-scale experiment at the Department of Energy Savannah River Site
 (SRS), a National Environmental Research Park in South Carolina, USA
 (33.20°N, 81.40°W) (Fig. 1). The sandy uplands at SRS historically
 supported open canopy longleaf pine savannas, which were largely converted
 to corn, cotton, and other crops between 1865 and 1950. All agriculture
 was abandoned in 1951 when the United States government acquired SRS and
 these fields were subsequently converted to plantations of longleaf
 (<i>Pinus palustris</i>), loblolly (<i>P.
 taeda</i>), and slash pine (<i>P.
 elliotii</i>).             The experiment included 126
 1-ha (100×100 m) plots grouped into 27 blocks. Each block was centered
 around the boundary between a longleaf pine savanna with no known history
 of agriculture and a former agricultural field supporting a mature pine
 plantation (<i>P. palustris</i> where possible). We determined
 land-use history using historical aerial photography and confirmed no
 differences in soil types on plots with and without agricultural history.
 Blocks included at least four and as many as 10 plots, depending on the
 sizes of areas with and without agricultural history, with half of the
 plots located within the savanna lacking agricultural history and half of
 the plots within the post-agricultural pine plantation. Due to a history
 of fire suppression, all plots supported closed canopy woodland at the
 onset of the experiment. To restore open canopy conditions, we randomly
 assigned a restoration thinning treatment in 2011 to half of the plots
 with and half of the plots without agricultural history, using logging
 equipment to remove trees from plots. The thinning treatment reduced tree
 density from an average of 650 trees/ha to 10 trees/ha . One or more
 prescribed surface fires were subsequently conducted within each block. In
 sum, this resulted in a 2 × 2 factorial manipulation of
 agricultural/plantation history and restoration thinning.             Between 2012 and 2017 we
 quantified abiotic conditions, the abundance of individuals, the diversity
 of species, and species interactions within the experimental plots (.
 Abiotic variables included temperature and light, the percentage ground
 cover by leaf litter, depth of the O horizon (leaf litter and duff),
 percent canopy closure (which influences understory light availability),
 soil water holding capacity, the percentage of ground area burned during
 prescribed fires, soil compaction, percent soil moisture, soil pH, soil
 organic matter, and soil phosphorus. Abundance variables included the
 summed captures of three rodent species using live traps, the number of
 observations of three individual rodent species using remote trail
 cameras, counts of grasshoppers, the total number of individuals
 established from seed addition of 12 herbaceous understory plants, numbers
 of fire ant (<i>Solenopsis invicta</i>) mounds, numbers of
 pyramid ant (<i>Dorymyrmex bureni</i>) mounds, counts of bees,
 and floral cover. Species diversity variables included the richness of
 grasshoppers, rodents, soil bacteria, soil fungi, bees, vascular plants
 naturally occurring in plots, and the richness of 12 herbaceous understory
 plants added through seed addition. Measures of species interactions
 included rates of herbivory on four understory herbs, rates of pollination
 to sentinel black mustard (<i>Brassica nigra</i>) plants, the
 effects of root competition on four understory herbs, and rates of seed
 removal, as a measure of granivory, on the seeds of six understory herbs
 and one tree species (<i>Quercus nigra</i>). We excluded any
 data collected within 10 m of the land-use boundary, to avoid potential
 influence of edge effects.              We used meta-analysis
 techniques to evaluate responses of the 45 response variables to land-use
 history and restoration. We first calculated standardized effect sizes,
 using absolute values of the difference between treatments because
 directionality does not have a consistent interpretation across the
 variables we measured. This allowed us to focus on the magnitude of
 responses across variables, though we do consider directionality during
 interpretation of individual variable responses. For each variable, we
 then averaged subsamples to get a single value for each of the four
 treatment combinations (plot types) within each block, providing 7-27
 replicates per variable. We did this because some variables had multiple
 measurements per plot and some blocks had multiple replicate plots of each
 treatment combination. We then calculated the mean and standard deviation
 across blocks for each variable in each of the four treatment
 combinations: unrestored post-agricultural/plantation, unrestored
 non-post-agricultural, restored post-agricultural/plantation, restored
 non-post-agricultural. We used these values to calculate Hedge's g, a
 measure of standardized effect size, for the differences between each pair
 of treatment combinations by using the escalc function in the metafor
 package in R. For example, to quantify how restoration affects the
 ecological properties within post-agricultural/plantation areas
 (“Restoration +Ag. history”) we calculated the effect size of the
 difference between unrestored post-agricultural/planation plots and
 restored post-agricultural/plantation plots. This was repeated for each
 treatment combination, resulting in four total effect sizes: the effect of
 agricultural and planation history in unrestored plots (Ag. history -
 Restoration), the effect of agricultural and planation history in restored
 plots (Ag. history + Restoration), the effect of restoration in
 post-agricultural/planation plots (Restoration + Ag. history), and the
 effect of restoration in plots without agricultural/planation history
 (Restoration - Ag. history) . We next fit meta-analysis models
 with the rma function in the metafor package to statistically test our
 four questions, using the effect sizes, variances, and sample size we
 obtained from the above Hedge’s g calculations. The models included
 ‘dataset’ as a random effect to account for potential non-independence of
 variables collected from the same project.  We then ran post-hoc tests
 using the glht function in the multcomp package to obtain p-values for the
 contrasts among each of the four effect sizes. 
description: 20200807 Remnant project meta analysis data.csv is the main
 dataset with raw data from the field experiment. The other files are
 outputs from the R code that are used for the analyses and making
 figures.