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.

Dataset DOI: 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 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: Joins 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).

Dominant species drive biomass and diversity responses to nutrient inputs

Data files

Abstract

Description of the data and file structure

Dominant species drive biomass and diversity responses to nutrient inputs

Data files

Abstract

README: Dominant species drive biomass and diversity responses to nutrient inputs

Description of the data and file structure