DOI: 10.5061/dryad.wstqjq2gw
title: Data from: Nutrient availability controls the impact of mammalian herbivores on soil carbon and nitrogen pools in grasslands

description: Grasslands have been subject to considerable alteration due to human
 activities globally, including widespread changes in populations and
 composition of large mammalian herbivores and elevated supply of
 nutrients. Grassland soils remain important reservoirs of carbon (C) and
 nitrogen (N). Herbivores may affect both C and N pools and these changes
 likely interact with increases in soil nutrient availability. Given the
 scale of grassland soil fluxes, such changes can have striking
 consequences for atmospheric C concentrations and the
 climate. Here, we use the Nutrient Network experiment to examine
 the responses of soil C and N pools to mammalian herbivore exclusion
 across 22 grasslands, under ambient and elevated nutrient availabilities
 (fertilized with NPK + micronutrients). We show that the impact of
 herbivore exclusion on soil C and N pools depends on
 fertilization. Under ambient nutrient conditions, we observed no
 effect of herbivore exclusion, but under elevated nutrient supply, pools
 are smaller upon herbivore exclusion. The highest mean soil C and N pools
 were found in grazed and fertilized plots. The decrease in soil C and N
 upon herbivore exclusion in combination with fertilization correlated with
 a decrease in aboveground plant biomass and microbial activity, indicating
 a reduced storage of organic matter and microbial residues as soil C and
 N. The response of soil C and N pools to herbivore exclusion was
 contingent on temperature – herbivores likely cause losses of C and N in
 colder sites and increases in warmer sites. Additionally, grasslands that
 contain mammalian herbivores have the potential to sequester more N under
 increased temperature variability and nutrient enrichment than ungrazed
 grasslands. Our study highlights the importance of conserving
 mammalian herbivore populations in grasslands worldwide. We need to
 incorporate local-scale herbivory, and its interaction with
 nutrient enrichment and climate, within global-scale models to better
 predict land-atmosphere interactions under future climate change.
description: <b>Sitters.et.al.GCB.CNpool.RR.script</b> R script required to: (1) quantify the impact of herbivore exclusion and fertilization on soil C and N pools by linear mixed models with block nested within site as random effect. And (2) examine the impact of the exclusion of herbivores on soil C and N pools and C:N ratio under unfertilized and fertilized conditions by performing one sample t-tests on the log response ratios (RR = ln(fenced/unfenced)). If the 95% confidence interval values of the RRs did not overlap with zero, there was a significant decrease or increase with herbivore exclusion. <b>Sitters.et.al.GCB.MuMIn.script</b> R script required to examine: (1) which local controls over soil C and N were responsible for changes in soil C and N pools due to herbivore exclusion, and (2) which across-site environmental drivers affected the impact of herbivore exclusion on soil C and N pools. Multi-model inference was used by modelling the effects of the predictor variables (either the local controls or the environmental variables) on the C and N response ratios with a full LMM with site ID as a random effect. The models also included fertilization as a fixed factor to observe any significant interactions between fertilization and local controls/environmental drivers. Multi-model inference uses model averaging based on Akaike’s information criterion (AIC) to arrive at consistent parameter estimates of the most important explanatory variables in the full LMM, by averaging a set of top models which share similarly high levels of parsimony. Top models were defined as those that fell within 4 AIC units of the model with the lowest AIC value.  <b>soil.CNpool_4trt</b> Data file required to run the LMMs on soil C and N pools (analysis 1 in Sitters.et.al.GCB.CNpool.RR.script). Data included in the file are the site ID (site_code), the blocks at each site, the treatment (Control à in Fig. 2: +H-F, NPK à +H+F, Fence à -H-F, NPK+Fence à -H+F), the year soil samples were taken, the number of years treatments were in place before the soil samples were taken (after_n_treatment_yrs), soil C and N concentrations in % (pct_C and pct_N), soil bulk density in g/cm<sup>3</sup> (bulk_density) and soil C and N pools in kg m<sup>-2</sup> (kgm2_C and kgm2_N). <b>soil.RR.CNpool_local_env</b> Data file required to: (1) run the one sample t-tests on the RRs of the exclusion of herbivores on soil C and N pools (RR = ln(fenced/unfenced); analysis 2 in Sitters.et.al.GCB.CNpool.RR.script). And (2) use multi-model inference to examine which local controls and environmental drivers affect the impact of herbivore exclusion on soil C and N pools (analyses in Sitters.et.al.GCB.MuMIn.script). Data included in the file are the site ID (site_code), the blocks at each site, the fertilization treatment (Unfertilized or Fertilized) for which the RRs were calculated, the year soil samples were taken (year), the number of years treatments were in place before the soil samples were taken (after_n_treatment_yrs), log response ratios of soil C and N concentrations, pools and C:N ratio (RR_Ccon, RR_Ncon, RR_Cpool, RR_Npool, RR_CN), log response ratio of soil bulk density (RR_bulk_density). Then follow the candidate local controls used in the multi-model inference analysis: log response ratios of aboveground live plant biomass (RR_live.biom), aboveground dead biomass (RR_dead.biom), belowground plant biomass (RR_root.biom), microbial activity (RR_mic.act) and microbial biomass (RR_mic.biom). After that the candidate environmental controls used in the multi-model inference analysis: mean annual temperature (°C; MAT), temperature seasonality (SD of temperature among months; TEMP_VAR), mean temperature of wettest quarter (°C; TEMP_WET_Q), mean annual precipitation (mm; MAP), precipitation seasonality (coefficient of variation in precipitation among months; MAP_VAR), annual atmospheric N deposition (kg N ha<sup>-1</sup> year<sup>-1</sup>; N_Dep), pre-treatment aboveground plant biomass (plant.biom_yr0), and pre-treatment soil N concentrations (pct_N_y0).