nutrients

Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico (2008 - present)

Abstract: 

Two of the most pervasive human impacts on ecosystems are alteration of global nutrient budgets and changes in the abundance and identity of consumers. Fossil fuel combustion and agricultural fertilization have doubled and quintupled, respectively, global pools of nitrogen and phosphorus relative to pre-industrial levels. In spite of the global impacts of these human activities, there have been no globally coordinated experiments to quantify the general impacts on ecological systems. This experiment seeks to determine how nutrient availability controls plant biomass, diversity, and species composition in a desert grassland. This has important implications for understanding how future atmospheric deposition of nutrients (N, S, Ca, K) might affect community and ecosystem-level responses. This study is part of a larger coordinated research network that includes more than 40 grassland sites around the world. By using a standardized experimental setup that is consistent across all study sites, we are addressing the questions of whether diversity and productivity are co-limited by multiple nutrients and if so, whether these trends are predictable on a global scale.

Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and and foliage, over time and incoporates growth as well as loss to death and decomposition. To measure this change the vegetation variables, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots within each site. Volumetric measurements are made using vegetation data from permanent plots (SEV231, "Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet NPP Quadrat Sampling") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."

Data set ID: 

293

Core Areas: 

Keywords: 

Data sources: 

sev293_nutnetbiomass_20150325.txt

Methods: 

Data Processing Techniques to Derive Biomass and NPP:

Data from SEV231 and SEV157 are used used to calculate seasonal and annual production of each species in each quadrat for a given year. Allometric equations derived from harvested samples of each species for each season are applied to the measured cover, height, and count of each species in each quadrat. This provides seasonal biomass for winter, spring, and fall.

Seasonal NPP is derived by subtracting the previous season's biomass from the biomass for the current season. For example, spring NPP is calculated by subtracting the winter weight from the spring weight for each species in a given quadrat. Negative differences are considered to be 0. Likewise, fall production is computed by subtracting spring biomass from fall biomass. Annual biomass is taken as the sum of spring and fall NPP.

Additional information: 

Additional Information on the Data Collection Period

Species composition and net primary production was sampled semiannually (spring and fall) in 2007, 2008, and 2009. Soil was sampled and analyzed in the fall in 2007 and 2008. Plots were fertilized annually starting in 2008.

In August 2009, a wildfire burned all 40 of the NutNet plots causing no Fall 2009 vegetation measurements.

Special Codes for Vegetation Ids:

SPORSP- Unknown Sporobolus

SPSP- Unknown Sphaeralcea

UNKFO- Unknown Forb

Rio Grande Water Chemistry Data from Bernalillo County, New Mexico (2006-2007)

Abstract: 

Human populations in Colorado, New Mexico and Texas depend on the Rio Grande for municipal water, agricultural irrigation, and recreation. The Rio Grande and its riparian corridor also support thousands of species of plants, invertebrates and vertebrates, some of which include over 300 species of migratory birds and the endangered Rio Grande silvery minnow and southwestern willow flycatcher. Eutrophication and salinization are the two most important types of water quality degradation which negatively impact the human and nonhuman biological communities in this water poor region. In spite of their significance, few published studies have investigated anthropogenic and natural sources of nutrients and dissolved solids to the Rio Grande. This study investigated the patterns and trends of nutrients and dissolved solids in the Middle Rio Grande (MRG) on a monthly basis from September 2005 – January 2008. During all months, wastewater treatment plants were the major source of nutrients to the MRG. Under high flow conditions, nutrient levels remained elevated for 260 river kilometers below the wastewater inputs. During months when significant portions of the river flow were diverted for irrigation, nitrate and phosphate were removed from the MRG and concentrations at the downstream end of the reach were returned to levels comparable to the un-impacted northern reach of river. Dissolved solids were added to the river by both wastewater and saline tributary inputs. Both anthropogenic and natural inputs of dissolved solids were found to affect water quality in the MRG. Continuous real-time measurements of temperature, pH, turbidity, dissolved oxygen, and conductivity also were initiated at four sites above and through the urban reach of the City of Albuquerque. Preliminary results show increasing turbidity and dissolved oxygen depletions associated with storm runoff from urban areas. 

Data set ID: 

180

Additional Project roles: 

200
201

Core Areas: 

Keywords: 

Purpose: 

The objectives of this study were to: 1) conduct a detailed assessment of the temporal and spatial trends in water quality of the MRG, 2) determine sources of eutrophication and salinization along the MRG, 3) estimate instream nutrient processing and retention, 4) calculate the effects of urbanization on dissolved oxygen and stream metabolism values in the MRG, and 5) provide baseline data for future water-quality monitoring and assessment in the MRG. 

Methods: 

Experimental Design:  

Samples were collected along the lenth of the MRG over a two to three day period, approximately monthly. Single grab samples were collected at each site. During 'Monthly' collections samples were taken from just the mainstem of the MRG. During 'Synoptic' collections samples were taken from both the mainstem sites and all of the major tributaries to the MRG. Mainstem sites were located ~ 5 km downstream of each major tributary to  the MRG to allow complete mixing of the tributary and mainstem water bodies and tributaries were sampled just prior to their convergence with the mainstem of the Rio Grande. Samples were collected during periods of stable flow (samples were not collected during storm pulses). 

Field methods: 

Surface-water samples were collected for measurement of temperature, pH, and conductivity, and analysis of major dissolved inorganic nutrients (nitrate, phosphate, and ammonium), major cations (sodium, potassium, magnesium and calcium), major anions (sulfate, bromide and chloride), dissolved organic and inorganic carbon (DOC, DIC), specific ultraviolet absorbance (SUVA), and chlorophyll a at each site. Sampling began in September 2005 and continued through February 2008. All samples were collected as close to the stream thalweg as flows permitted. Water samples for analysis of nutrients, cations, and anions were collected as grab samples in 130 ml syringes and immediately filtered in the field through ashed 0.7 um pore size glass fiber filters. Unfiltered water samples for chlorophyll-a analysis were collected in acid washed or unused HDPE bottles. All samples were placed on ice and transported to the laboratory for analysis.

Laboratory Procedures:  

Ammonium samples were analyzed using the phenyl hypochlorite method and a 10 cm flow path modified from Hansen and Koroleff (Hansen and Koroleff 1983). Bromide, chloride, nitrate, phosphate and sulphate were analyzed by ion chromatography (Dionex, Standard Method EPA 300.1, 2).  Organic and inorganic carbon were analyzed using a Shimadzu TOC-5050A carbon analyzer using Standard Method 5310 B (Clesceri et al. 1998). Sodium, potassium, magnesium, and calcium were analyzed using a Perkin Elmer Optima 5300 DV ICP using Standard Method 3120 B (EPA 200.7) (Clesceri et al. 1998). Clesceri, L. S., A. E. Greenberg, and A. D. Eaton, editors. 1998. Standard Methods for the Examination of Water and Wastewater. 20 edition. American Public Health Association, American Water Works Association, Water Environment Federation, Baltimore. Hansen, H. P. and F. Koroleff. 1983. Determination of Nutrients. Pages 159-226 in K. Grasshoff, K. Kremling, and M. Ehrhardt, editors. Methods of Seawater Analysis. Weinheim: Verlag Chemie.

Data sources: 

sev180_waterchemistry_02282012.txt

Instrumentation: 

* Instrument Name: Carbon Analyzer

* Manufacturer: Shimadzu

* Model Number: TOC-5050A

* Instrument Name: Ion Chromatograph

* Manufacturer: Dionex

* Model Number: 

* Instrument Name: Inductively Coupled Plasma Optical Emission Spectrometer

* Manufacturer: Perkin Elmer

* Model Number: Optima 5300 DV ICP 

Rio Grande River Sonde Data from Bernalillo County, New Mexico (2006-2007)

Abstract: 

Human populations in Colorado, New Mexico and Texas depend on the Rio Grande for municipal water, agricultural irrigation, and recreation. The Rio Grande and its riparian corridor also support thousands of species of plants, invertebrates and vertebrates, some of which include over 300 species of migratory birds and the endangered Rio Grande silvery minnow and southwestern willow flycatcher. Eutrophication and salinization are the two most important types of water quality degradation which negatively impact the human and nonhuman biological communities in this water poor region. In spite of their significance, few published studies have investigated anthropogenic and natural sources of nutrients and dissolved solids to the Rio Grande. This study investigated the patterns and trends of nutrients and dissolved solids in the Middle Rio Grande (MRG) on a monthly basis from September 2005 – January 2008. During all months, wastewater treatment plants were the major source of nutrients to the MRG. Under high flow conditions, nutrient levels remained elevated for 260 river kilometers below the wastewater inputs. During months when significant portions of the river flow were diverted for irrigation, nitrate and phosphate were removed from the MRG and concentrations at the downstream end of the reach were returned to levels comparable to the un-impacted northern reach of river. Dissolved solids were added to the river by both wastewater and saline tributary inputs. Both anthropogenic and natural inputs of dissolved solids were found to affect water quality in the MRG. Continuous real-time measurements of temperature, pH, turbidity, dissolved oxygen, and conductivity also were initiated at four sites above and through the urban reach of the City of Albuquerque. Preliminary results show increasing turbidity and dissolved oxygen depletions associated with storm runoff from urban areas. 

Core Areas: 

Data set ID: 

190

Additional Project roles: 

268
269

Keywords: 

Purpose: 

The objectives of this study were to: 1) conduct a detailed assessment of the temporal and spatial trends in water quality of the MRG, 2) determine sources of eutrophication and salinization along the MRG, 3) estimate instream nutrient processing and retention, 4) calculate the effects of urbanization on dissolved oxygen and stream metabolism values in the MRG, and 5) provide baseline data for future water-quality monitoring and assessment in the MRG. 

Methods: 

Experimental Design:  

Four sites were chosen within the Albuquerque reach for continuous measurement of five water-quality field parameters; temperature, conductivity, pH, turbidity, and dissolved oxygen. Sites were chosen to provide instrument stability and a gradient of urban influence with the most northern site located above urban wastewater inputs and the southern site below the Bernalillo, Rio Rancho, and Albuquerque wastewater treatment plants. 

Sampling Design:

 Readings were collected every fifteen minutes. 

Field methods: 

Sondes were recalibrated in the field every two to four weeks following manufacturers specifications.

Data sources: 

sev190_rgsonde_20121022.txt

Instrumentation: 

* Instrument Name: Multi-Parameter Water Quality Sonde

* Manufacturer: Yellow Spring Instruments

* Model Number: YSI 6920 

Quality Assurance: 

This data has been visually inspected to: 1) identify outliers and periods when the units were buried by sediment or were not functioning properly, and 2) examine the data for consistency of individual observations with temporal and spatial trends seen at upstream and downstream units.

Additional information: 

Additional Site Information:

site_number,site_name,site,latitude,longitude,river_kilometer,rgtrib,IsSonde

48,Alameda_Drain,ALMDRAIN,35.20,-106.64,98.91,T,0

49,Albuqerque_Riverside_Drai,ABQRSDRN,34.95,-106.68,131.13,T,0

14,Albuqerque_WWTP,ABQCWWTP,35.02,-106.67,122.86,T,0

17,Atrisco_Drain,ATRSCDRN,34.95,-106.68,131.08,T,0

2,Bernalillo_WWTP,BERNWWTP,35.31,-106.56,81.90,T,0

12,Central_Bride_Drain,CENDRAIN,35.10,-106.69,112.51,T,0

45,Elephant_Butte,ELEBUTTE,33.15,-107.22,375.41,M,0

8,LCRDR,LCRDRAIN,35.16,-106.67,104.48,M,0

33,LFCC_at_NB_Gate_BD,LFCCNGBD,33.87,-106.85,267.50,T,0

32,LFCC_at_NCP,FCCLNCP,33.96,-106.85,257.65,T,0

35,LFCC_at_SB_Gate_BD,LFCCSGBD,33.72,-106.91,287.09,T,0

44,LFCC_at_ST_PK_Gate,LFCCSTPK,33.63,-107.00,304.64,T,0

19,LL_WWTP,LOSLWWTP,34.78,-106.73,152.10,T,0

23,LPDR1,LPDR1DRN,34.66,-106.74,166.92,T,0

25,LPDR2,LPDR2DRN,34.59,-106.75,175.02,T,0

28,L_&_J_Drain,LNJDRAIN,34.37,-106.84,203.48,T,0

10,Oxbow_Drain,OXBDRAIN,35.14,-106.69,107.70,T,0

21,Peralta,Wasteway,PERALTWW,34.69,-106.74,162.83,T,0

41,RG_Above_Isleta_(I_25),RGABVISL,34.95,-106.68,131.11,M,1

7,RG_Alameda,RGALAMED,35.16,-106.67,104.46,M,0

40,RG_Angostura,RGANGOST,35.38,-106.50,70.84,M,0

13,RG_at_Central_Br,RGCENTBR,35.09,-106.68,113.59,M,0

9,RG_at_LCRDR,RGLCRDR,35.13,-106.69,107.81,M,0

42,RG_at_NCP,RGATNCP,33.96,-106.85,257.87,M,0

11,RG_at_Oxbow,RGOXBOW,35.11,-106.69,111.10,M,0

46,RG_at_Rio_Bravo,RGRIOBRV,35.03,-106.67,121.82,M,1

43,RG_at_Shirk,RGATSHRK,0.00,0.00,128.00,M,0

37,RG_at_ST_PK_Gate,RGSTPKGT,33.58,-107.06,312.87,M,0

24,RG_Belen_Bridge,RGBLENBR,34.59,-106.75,175.02,M,0

26,RG_Belen_Tower_Site,RGBLENTW,34.55,-106.76,180.91,M,0

15,RG_Below_ABQ_WWT,RGABQWWT,0.00,0.00,125.00,M,0

3,RG_Below_Bernalillo_WWTP,RGBERLWW,35.28,-106.60,86.65,M,0

18,RG_Below_Isleta,RGBELISL,34.87,-106.72,141.95,M,0

20,RG_Below_LL_WWTP,RGLLWWTP,34.69,-106.74,162.70,M,0

22,RG_Below_Peralta,RGBLPERT,0.00,0.00,162.50,M,0

5,RG_Below_RR_WWTP,RGRRWWTP,35.20,-106.64,99.13,M,1

30,RG_Below_San_F,RGBLSANF,34.31,-106.85,211.14,M,0

1,RG_Bernalillo_550,RGBER550,35.32,-106.56,80.29,M,1

39,RG_Buckman,RGBUCKMN,35.84,-106.16,0.00,M,0

47,RG_LL_Bridge,RGLLBRDG,34.81,-106.72,148.97,M,0

34,RG_NB_GATE_BD,RGNBGTBD,33.87,-106.85,267.55,M,0

31,RG_San_Acacia,RGACACIA,34.26,-106.89,220.86,M,0

36,RG_SB_GATE_BD,RGSBGTBD,33.72,-106.91,287.23,M,0

29,Rio_Puerco,RIOPUERC,34.41,-106.85,201.73,T,0

4,Rio_Rancho_WWTP,RIORWWTP,35.26,-106.60,89.02,T,0

38,Rock_House,ROCKHOUS,33.38,-107.16,338.72,M,0

27,SanFrancisco_Drain,SFRANDRN,34.37,-106.84,203.33,T,0

50,Unit_7_Drain,UNITSVDR,34.26,-106.89,220.74,T,0

Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet NPP Quadrat Sampling at the Sevilleta National Wildlife Refuge, New Mexico (2007-present)

Abstract: 

Two of the most pervasive human impacts on ecosystems are alteration of global nutrient budgets and changes in the abundance and identity of consumers. Fossil fuel combustion and agricultural fertilization have doubled and quintupled, respectively, global pools of nitrogen and phosphorus relative to pre-industrial levels. In spite of the global impacts of these human activities, there have been no globally coordinated experiments to quantify the general impacts on ecological systems. This experiment seeks to determine how nutrient availability controls plant biomass, diversity, and species composition in a desert grassland. This has important implications for understanding how future atmospheric deposition of nutrients (N, S, Ca, K) might affect community and ecosystem-level responses. This study is part of a larger coordinated research network that includes more than 40 grassland sites around the world. By using a standardized experimental setup that is consistent across all study sites, we are addressing the questions of whether diversity and productivity are co-limited by multiple nutrients and if so, whether these trends are predictable on a global scale.

Core Areas: 

Data set ID: 

231

Additional Project roles: 

466
467
468

Keywords: 

Methods: 

Methods: 

Nutrient addition treatments and sampling sites are located in an area of desert grassland dominated by black grama, Bouteloua eriopoda. The experimental design is completely randomized with 8 treatments replicated 5 times each.  The nutrients added include N (nitrogen), P (phosphorus), and K (potassium plus other nutrients). Treatments are: +N+P+K, +N+P, +N+K, +N, +P+K, +P, +K, and control (no nutrients added).  Treatments were randomly assigned to 40-25 m2 plots with 1m separating each plot. Response variables measured include: plant community composition; percent ground cover of live perennial grasses, herbaceous dicots, shrubs, cactus, litter, and bare ground; aboveground net primary production; light availability, and several soil parameters (moisture, organic matter content, pH, P, field available nitrogen (NO3-N and NH4-N), potentially mineralizable N).

This experiment was initiated in May 2007 with one year of pre-treatment data and 3 years of post-treatment data collected thus far.  Nutrients are applied annually at the beginning of the growing season starting in 2008.  Plant community composition, percent cover of individual plant species, and aboveground net primary production will continue to be monitored semiannually (spring and fall) in a permanently marked 1m2 subplot in each plot. Soil will be collected each year and will be shipped to collaborators for analyses.

Net Primary Productivity (NPP) Measurements                               

Collecting the Data:

Net primary production data is collected twice each year, spring and fall. Spring measurements are taken in April or May when shrubs and spring annuals have reached peak biomass. Fall measurements are taken in either September or October when summer annuals have reached peak biomass but prior to killing frosts. Winter measurements are taken in February before the onset of spring growth.

Vegetation data is collected on a palm top computer. A 1-m2 PVC-frame is placed over the fiberglass stakes that mark the diagonal corners of each quadrat. When measuring cover it is important to stay centered over the vegetation in the quadrat to prevent errors caused by angle of view (parallax). Each PVC-frame is divided into 100 squares with nylon string. The dimensions of each square are 10cm x 10cm and represent 1 percent of the total area.

The cover (area) and height of each individual live (green) vegetative unit that falls within the one square meter quadrat is measured. A vegetative unit consists of an individual size class (as defined by a unique cover and height) of a particular species within a quadrat. Cover is quantified by counting the number of 10cm x 10cm squares filled by each vegetative unit.

Niners and plexidecs are additional tools that help accurately determine the cover a vegetative unit. A niner is a small, hand-held PVC frame that can be used to measure canopies. Like the larger PVC frame it is divided into 10cm x 10cm squares, each square representing 1% of the total cover. However, there are only nine squares within the frame, hence the name “niner.” A plexidec can help determine the cover of vegetative units with covers less than 1%. Plexidecs are clear plastic squares that are held above vegetation. Each plexidec represents a cover of 0.5% and has smaller dimensions etched onto the surface that correspond to 0.01%, 0.05%, 0.1%, and 0.25% cover.

It is extremely important that cover and height measurements remain consistent over time to ensure that regressions based on this data remain valid. Field crew members should calibrate with each other to ensure that observer bias does not influence data collection.

In 2013, percent cover of litter and bare soil were added for each quadrat.

Cover Measurements:

Grasses-To determine the cover of a grass clump, envision a perimeter around the central mass or densest portion of the plant, excluding individual long leaves, wispy ends, or more open upper regions of the plant. Live foliage is frequently mixed with dead foliage in grass clumps and this must be kept in mind during measurement as our goal is to measure only plant biomass for the current season. In general, recently dead foliage is yellow and dead foliage is gray. Within reason, try to include only yellow or green portions of the plant in cover measurement while excluding portions of the plant that are gray. This is particularly important for measurements made in the winter when there is little or no green foliage present. In winter, sometimes measurements will be based mainly on yellow foliage. Stoloniferous stems of grasses that are not rooted should be ignored. If a stem is rooted it should be recorded as a separate observation from the parent plant.

Forbs-The cover of forbs is measured as the perimeter of the densest portion of the plant. If the forb is an annual it is acceptable to include the inflorescence in this measurement. If the forb is a perennial, do not include the inflorescence as part of the cover measurement. Measure all foliage that was produced during the current season, including any recently dead (yellow) foliage. Avoid measuring gray foliage that died in a previous season.

Cacti-For cacti that consist of a series of pads or jointed stems (Opuntia phaecantha, Opuntia imbricata) measure the average length and width of each pad instead of cover and height. Cacti that occur as a dense ball/clump of stems (Opuntia leptocaulis) are measured using the same protocol as shrubs. Pincushion or hedgehog cacti (Escobaria vivipara, Schlerocactus intertextus, Echinocereus fendleri) that occur as single (or clustered) cylindrical stems are measured as a single cover.

Yuccas-Make separate observations for the leaves and caudex (thick basal stem). Break the observations into sections of leaves that are approximately the same height and record the cover as the perimeter around this group of leaf blades. The caudex is measured as a single cover. The thick leaves of yuccas make it difficult to make a cover measurement by centering yourself over the caudex of the plant. The cover of the caudex may be estimated by holding a niner next to it or using a tape measure to measure to approximate the area.

Height Measurements:

Height is recorded as a whole number in centimeters. All heights are vertical heights but they are not necessarily perpendicular to the ground if the ground is sloping.

Annual grasses and all forbs-Measure the height from the base of the plant to the top of the inflorescence (if present). Otherwise, measure to the top of the green foliage.

Perennial grasses-Measure the height from the base of the plant to the top of the live green foliage. Do not include the inflorescence in the height measurement. The presence of live green foliage may be difficult to see in the winter. Check carefully at the base of the plant for the presence of green foliage. If none is found it may be necessary to pull the leaf sheaths off of several plants outside the quadrat. From this you may be able to make some observations about where green foliage is likely to occur.

Perennial shrub and sub-shrubs-Measure the height from the base of the green foliage to the top of the green foliage, ignoring all bare stems. Do not measure to the ground unless the foliage reaches the ground.

Plants rooted outside but hanging into a quadrat-Do not measure the height from the ground. Measure only the height of the portion of the plant that is within the quadrat.

Recording the Data:

Excel spreadsheets are used for data entry and file names should begin with the overall study (npp), followed by the date (mm.dd.yy) and the initials of the recorder (.abc). Finally, the site abbreviation should be added (i.e., c, g, b, p). The final format should be as follows: npp.mm.dd.yy.abcg.xls. File names should be in lowercase.


Data sources: 

sev231_nppnutnetquadrat_20161215.csv

Quality Assurance: 

All data were QA/QC'd by use of filters in Excel and imported into MySQL.

Additional information: 

Additional Information on the Data Collection Period

Species composition and net primary production was sampled semiannually (spring and fall) in 2007, 2008, and 2009. Soil was sampled and analyzed in the fall in 2007 and 2008. Plots were fertilized annually starting in 2008.

In August 2009, a wildfire burned all 40 of the NutNet plots causing no Fall 2009 vegetation measurements.

Special Codes for Vegetation Ids:

SPORSP- Unknown Sporobolus

SPSP- Unknown Sphaeralcea

UNKFO- Unknown Forb

On 08/20/2015, the following taxonomic changes were made to the data: ARPUP6 was changed to ARPU9, OECAC2 was changed to OECA10, SPWR was changed to SPPO6

Soil Nutrient Distributions in Chihuahuan Desert Grasslands and Shrublands at the Sevilleta National Wildlife Refuge, New Mexico (1989)

Abstract: 

Vegetation throughout the southwestern United States has changed from perennial grassland to woody shrubland over the past century. Previous studies on the development of 'islands of fertility' focused primarily on only the most limiting, plant-essential element, soil nitrogen (N). The research presented here addressed the question of whether other plant-essential elements, namely phosphorus (P) and potassium (K), showed similar concentration gradients under the desert shrub Larrea tridentata (creosotebush). It also examined whether the spatial distribution of N, P, and K differed from that of essential, but non-limiting nutrients, namely calcium (Ca), magnesium (Mg), and sulfur (S), and non-essential elements, namely sodium (Na), chloride (Cl), and fluoride (F). Within adjacent grassland and shrubland plots, surface soils were collected under and between vegetation and analyzed for a suite of soil nutrients. Soil nutrient distribution followed a uniform pattern that mirrored the spatial homogeneity of bunchgrasses in the grassland, but followed a patchy distribution that mirrored the spatial heterogeneity of individual shrubs in the shrubland. The main differences were that in the grassland, all elements were uniformly distributed, but in the shrubland the plant-essential elements, nitrogen, phosphorus, and potassium, were concentrated under the shrub canopy, and the non-limiting and non-essential elements were either concentrated in the intershrub spaces or were equally concentrated under shrubs and in the interspaces. Our results show how vegetation shifts from grassland to shrubland contribute to long-term, widespread change in the structure and function of desert ecosystems.

Data set ID: 

152

Core Areas: 

Keywords: 

Purpose: 

The research presented here addressed the question of whether other plant-essential elements, namely phosphorus (P) and potassium (K), showed similar concentration gradients under the desert shrub Larrea tridentata (creosotebush). It also examined whether the spatial distribution of N, P, and K differed from that of essential, but non-limiting nutrients, namely calcium (Ca), magnesium (Mg), and sulfur (S), and non-essential elements, namely sodium (Na), chloride (Cl), and fluoride (F).

Data sources: 

sev152_crossions_20121024.txt

Methods: 

Plot establishment

Four 10 X 10-m plots were established in grassland and shrubland sites in 1989. In the grassland, two plots were located where B. eriopoda dominates and two plots were located where B. gracilis dominates. In the shrubland, paired plots were located in two shrubland areas dominated by L. tridentata.

Biomass estimation

Aboveground plant biomass was estimated for each vegetation type at the height of the summer growing season in July 1989. Estimates of grassland biomass were based on clippings of aboveground plant material from a composite of three separate 1 m2 quadrats adjacent to each grassland plot. Estimates of shrub biomass were calculated from shrub volume measures – one height and two canopy diameter measurements – taken on all L. tridentata shrubs in each shrubland plot following Ludwig et al. (1975). Volume measurements were not taken on the few desiccated or dead individuals of the sub-shrub G. sarothrae in the plots.

Ludwig, J. A., Reynolds, J. F. & Whitson, P. D. 1975. Size-biomass relationships of several Chihuahuan Desert shrubs. Am. Midl. Nat. 94:451-461.

Soil Sampling

To characterize overall soil nutrient composition and soil properties, we collected 25 soil samples from 0–10 cm in depth using a stratified-random sampling design in each of the eight 10 X 10-m plots, noting whether the sample was taken from beneath vegetation or in the bare space between plants. This resulted in a total of 100 soil samples from the grassland and 100 samples from the shrubland habitats. Soil samples were taken at the height of summer drought and although they appeared dry to the touch, all samples were air dried and sieved through a standard 2 mm mesh sieve prior to analysis.

Soil Nutrient Analysis

We analyzed all soil samples for NO3-N, total N, K, total organic C, Ca, Mg, SO4-S, F, Cl, Na, and P. Ground soil samples were analyzed for total organic carbon and total nitrogen using a Carlo-Erba CHN Analyzer. Anions, except for phosphorus, were extracted by shaking a 6-g sample in 30 ml of deionized H2O for 30 min. The extract was filtered through a 0.45 um millipore filter, and analyzed with a Dionex 2010i ion chromatograph. Cations were extracted by shaking a 10-g soil subsample with 50 ml of NH4C2H2O2 (ammonium acetate) at pH 7.0. The extract was filtered gravimetrically through a #40 Whatman filter and analyzer with a Perkin Elmer 3100 Atomic Absorption Spectrophotometer. Phosphorus was extracted using a modified sequential Hedley fractionation (Tiessen et al. 1984; Tiessen &Moir 1993). A 2-g soil sample was placed in a 50 ml plastic centrifuge tube with 30 ml of deionized water and a 2.5 cm2 anion exchange membrane (AR- 204UZR-412 Ionics, Watertown, MA). Samples were shaken end-over-end for 16 h at 25 degrees C. The anion-exchange membrane was removed and phosphorus retained on the membrane was eluted by shaking the strip with 30 ml of 1 M HCl for 4 h (resin-extractable P). Subsequently,the remaining soil sample was extracted with 30 ml of 0.5 M NaHCO3 (pH 8.5) in the 50-ml centrifuge tube (bicarbonate-extractable P). This process was repeated with increasingly stronger reagents that remove more tightly bound, less plant-available, fractions using NaOH, HCl, and H2SO4-H2O2. Each sample was also sonicated and resuspended in NaOH to remove P that is otherwise encapsulated in Al and Fe minerals. All extracts were analyzed for orthophosphate with the Total Phosphorus procedure for the TRAACS 800 Autoanalyzer. 

Tiessen, H., Stewart, J. W. B. & Cole, C. V. 1984. Pathways of phosphate transformations in soils of differing pedogenesis. Soil Sci. Soc. Am. J. 48: 853-858.

Tiessen, H. & Moir, J. O. 1993. Characterization of available P by sequential fractionation. Pp. 75-86. In: Carter, M. R. (ed.), Soil sampling and methods of analysis. Lewis Publishers, Boca Raton.

Additional information: 

Livestock Exclosure Nutrient Study from a Chihuahuan Desert Grassland at the Sevilleta National Wildlife Refuge, New Mexico (2003)

Abstract: 

Data on soil characteristics and dominant grass and soil chemical composition gathered on active rangeland, livestock exclosures on active rangeland, and the Sevilleta NWR.

Data set ID: 

159

Core Areas: 

Keywords: 

Data sources: 

sev159_exclosnutrient_09212005.txt

Methods: 

Experimental Design

This study compares grazed grassland (treatment G), grassland within livestock exclosures (treatment X) and undisturbed (by livestock) grassland (treatment S - for Sevilleta). During the winter of 1992/1993, three 300m x 300m plots were delineated for each of these three treatments. Plots are referred to as G1, G2, G3, X1, X2, X3, S1, S2, and S3 (number increasing from west to east). Each of these nine plots are separated by at least 300 m. Exclosure plots are surrounded by wire fencing, and the Sevilleta plots are located within the NWR boundary. A rodent trapping web is centered within each plot. Within the framework of this mammal trapping web, twelve 3x4 m quadrats were randomly placed. Corners of plots are marked by aluminum fence posts and locations were recorded with GPS. Centers and lines of mammal trapping webs are marked by 1m high (centers and end of lines) and .25m high (lines) rebar and aluminum numbered tags. Corners of quadrats are marked by short rebar. The location of mammal trapping webs and quadrats is the same within each plot.

For this particular study a subsample of four quadrats, chosen randomly for each plot, within each plot was sampled. Data collected include soil texture, field water content, 50% water holding capacity, organic matter content, percent C, percent N, percent P, and dominant grass (usually Bouteloua eriopoda) percent C, percent N, and percent P. Soil samples were collected as cores to 10cm depth, with 5-10 cores taken along a diagonal from one corner of the quadrat to the other corner, then pooled in a plastic bag as one sample from each quadrat. Grass samples of green above-ground tissue (growth from summer/fall 03) include tissue from 5-10 individual plants within the quadrat. All analyses were conducted for each quadrat excepting soil texture, for which equal weights of soil from each quadrat were pooled into one sample per plot. Soil samples were collected on 8/10/03 and plant samples were collected on 10/18/03, before and after the most significant rains of the season.

Soil texture was analyzed using the hydrometer method, and data are recorded as sand, silt, and clay. Soil field water content was measured as the difference in weight before and after the drying of a subsample of soil. Soil organic matter was measured as the difference in weight before and after combustion of a subsample of dry soil. Soil 50% water holding capacity was measured using standard Sevilleta techniques (after White). Soil and plant percent C and N were measured using a Carlo Erba AutoAnalyzer. Soil and plant percent P was measured as extractable P, converted to PO4-P, using a Technicon AutoAnalyzer.

Additional information: 

Additional field crew member: Ben Zimmerman

Additional Study Area Information

Study Area Name: Pino Gate

Study Area Location: The study site was located near the base of the Los Pinos mountains and directly adjacent to the nothern fencline of the SNWR at Pino Gate

Elevation: 1600 m

Vegetation: Burrograss (Scleropogon brevifolius), sand dropseed (Sporobolus ryptandrus), and black grama (Bouteloua eriopoda) were the dominant vegetation.

Soils: Deep clayey loam soils

Geology: On an upper bajada slope, in a broad swale

Climate: Long-term mean annual precipitation is 243 mm, about 60% of which occurs during the summer. Long-term mean monthly temperatures for January and July are 1.5°C and 25.1°C, respectively.

Site history: Historically, prairie dogs were common throughout the area, but were exterminated by the early 1970’s (John Ford, United States Department of Agriculture Wildlife Services, personal communication). Gunnison’s prairie dogs began to re-colonize the study site from adjacent private land in 1998. During our study, the colony occurred within a 5 ha area, near the base of the Los Piños Mountains in an area with deep clayey loam soils. The site has been long inhabited by kangaroo rats, and represents typical northern Chihuahuan Desert grassland.

North Coordinate:34.406954
South Coordinate:34.406954
East Coordinate:-106.606269
West Coordinate:-106.606269

Additional Metadata

Animal grazers affect grassland disturbance patterns via changes in plant community composition and structure, nutrient cycling, and soil structure. Human-managed livestock grazing is a significant grassland disturbance worldwide.

Research Questions/Hypotheses:

a. Is livestock grazing related to changes in arid grassland soil structure and chemical composition or plant nutrient composition? If so, how?

(hypothesis) Grazed rangeland will have more compact soil (lower water holding capacity), lower levels of nutrients (C, N, P) and lower organic matter content than grassland unaffected by livestock grazing. Grazed rangeland will produce grasses of lower nutritional quality (i.e. higher C:N and C:P ratios) than grassland unaffected by livestock grazing.

b. When livestock are exclosed from a rangeland, does soil structure and chemical composition or plant nutrient composition `recover', exhibit resilience, align more closely with values characteristic of grassland unaffected by livestock grazing?

(hypothesis) Within livestock exclosures (free of livestock for 9 years) soil water holding capacity will be higher, levels of nutrients (C, N, P) and organic matter content will be higher, and plant nutritional quality will be higher (C:N and C:P ratios will be lower) than on grazed rangeland. These structural and chemical characteristics will be intermediate between those of grazed rangeland and grassland unaffected by livestock grazing.

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