DOI: 10.5061/dryad.9zw3r22t0
title: Dominant species drive biomass and diversity responses to nutrient inputs

description: Global change is enriching terrestrial ecosystems with multiple nutrients
 and amplifying interannual variation in precipitation. Grassland
 productivity may be co-limited by combinations of nitrogen (N), phosphorus
 (P), and potassium (K). How these nutrients may interact with each other
 or with varying precipitation to influence the contributions of dominant
 species and functional groups to aboveground net primary productivity
 (ANPP) and species diversity is rarely considered. We fertilized a mesic
 grassland for five years with all combinations of N, P, and K +
 micronutrients in the first year (Kµ) to test which nutrients 1) limited
 ANPP and functional group biomass, 2) reordered dominant species and
 impacted plant species diversity, and 3) interacted with annual
 precipitation to influence these responses. Adding N and P together
 disproportionately increased ANPP, but adding both N and P or N and Kµ
 disproportionately increased forb biomass to account for nearly all (90%)
 of ANPP. Grass biomass was correlated with light availability, not
 nutrients, and legume biomass decreased with added N, with or without
 other nutrients. Nutrient combinations (mainly NP and NPKµ) causing the
 greatest increases in forb biomass and ANPP also resulted in replacement
 of dominant species by an annual forb and decreased species diversity
 (Shannon index), evenness, and species richness. Nutrient combinations (P,
 Kµ, PKµ) not increasing biomass favored dominance by C4 grasses, and
 increased species richness. N effects on ANPP, species diversity, and
 richness were greater in years with higher annual precipitation. Annual
 precipitation interacted with all three nutrients to exert sometimes
 positive and sometimes negative feedback on the abundance of the most
 dominant species. Dominant species drive nutrient effects on community
 productivity and species diversity. An expanded definition of nutrient
 limitation incorporating constituent responses will improve understanding
 of anthropogenic nutrient inputs on ecosystem productivity and related
 ecosystem services.
description: # Dominant species drive biomass and diversity responses to nutrient
 inputs Dataset DOI:
 [10.5061/dryad.9zw3r22t0](https://doi.org/10.5061/dryad.9zw3r22t0) ##
 Description of the data and file structure Manuscript Title: Limiting and
 non-limiting nutrients impact species richness and dominant species
 abundance in a nitrogen-phosphorus co-limited grassland. PI: Philip Fay,
 USDA-ARS Grassland, Soil, and Water Research Laboratory, Temple, TX 76502
 USA. Data collected: Data are from a grassland site near Temple, Texas
 which conducted a multiple nutrient fertilization experiment for a minimum
 from 2008 to 2016. The experiment followed the Nutrient Network
 standardized experimental protocol. 5 m x 5 m experimental plots were
 fertilized with nitrogen, phosphorus, or potassium in factorial
 combinations with three replicates per nutrient combination. Current year
 live aboveground biomass was sampled by harvesting, and plant community
 composition was measured by visual estimates of the percent cover of each
 plant species. Protocol details are available at
 [www.nutnet.org](http://www.nutnet.org) and in (1) . Location of data
 collection: A never-plowed remnant tallgrass prairie at Temple, TX, USA
 (31°05’ N, 97°20’ W), in the Blackland Prairie region of the U.S. Central
 Plains. Missing value designation: empty cells. File: datasets.xlsx * Tab
 2008_2011: Data collected during years of the experiment when no
 management was being performed on the study site, as described in the
 manuscript Materials and Methods. - Tab 2012_2016: Data collected in years
 when senesced standing vegetation and litter were removed each spring
 prior to the start of the growing season as described in the manuscript
 Materials and Methods. * Variable names for both tabs are defined in the
 accompanying file metadata.xlsx. File: DCI_curves.xlsx * Contains a tab
 with species rank abundance data for each nutrient treatment, eight in
 total. File: metadata.xlsx * Tab metadata_2008_2011: the metadata for the
 tab 2008_2011 in the dataset file. - Tab metadata_2012_2016: the metadata
 for the tab 2012_2016 in the dataset file. * Tab rank_curves: the metadata
 tabs in DCI_curves.xlsx dataset. These three tabs contain the following
 columns: * Column A:  the Variable VARNUM, containing integer values used
 to number each variable. - Column B: the variable NAME, containing
 character values naming each variable. * Column C: the variable
 Description, containing the variable definitions. This dataset contains
 file ‘datasets.xlsx’, the metadata file ‘metadata.xlsx’, and files
 containing SAS code used to perform the analysis and make the graphics
 used in this paper.    Files containing SAS code have the .sas extension,
 and the filename begins with a number indicating the order in which the
 code files should be run. **Notes on the code files**
 **1_libnames_options_varlists.sas:** assigns library locations, creates
 variable lists, and sets certain SAS options. Beware that editing the
 variable lists will alter output from nearly every following program.
 **2_weather dataset build sds.sas:** Builds a dataset containing annual
 precipitation variables using local weather sources. Some variables coded
 here are not used in the final dataset. **3_AGB_core_sds.sas:** Reads
 aboveground biomass (AGB) data for the Temple.us site from a Nutrient
 Network dataset. Performs housekeeping and computes values for ANPP
 (agb_gm2) and forb, grass and legume biomass. Some response metrics are
 computed with this code but not used in the final dataset. **4_Read
 PAR.sas:** Reads canopy light variables from the same source dataset used
 in 3_AGB_core_sds.sas. Some responses metrics computed by this code are
 not used in the final dataset. **5_cover_core_sds.sas:** Reads plant
 species cover data for the Temple.us site from a Nutrient Network dataset.
 Performs housekeeping. **6_diversity dominance.sas:** Uses the dataset
 built in program 5 to compute compute the Dominance Candidate Index (DCI),
 identify dominant species, sum DCI by functional group and by
 dominant/subdominant status, produce data for rank abundance curves,
 compute species richness, Shannon diversity, and evenness variables. Some
 variables produced in this code are not used in the final dataset.
 **7_Build and stdize.sas: J**oins the biomass, cover, diversity, and
 precipitation datasets, standardizes the variables, and writes the dataset
 used in subsequent analyses. Also contains code writing the datasets
 uploaded to DRYAD. **8_means classes all vars.sas:** Generates means and
 standard errors organized for graphing in OriginPro. **9_AGB_wx.sas:**
 Fits linear mixed models to the response variables. Also computes slopes
 of precip-DCI for each dominant species and for the dominant species in
 aggregate.  These slopes were used to generate Figure 6 in the manuscript.
 **10_no_hay_AGB_wx.sas:** Fits linear mixed models to the 2008_2011
 dataset. **Code files numbered 11 through 19:** Write datasets used to
 make graphs in Originpro. Some write treatment means across years, others
 write treatment x year combinations. References: 1.            E. T. Borer
 *et al.*, Finding generality in ecology: a model for globally distributed
 experiments. *Methods Ecol. Evol.* **5**, 65-73 (2014).