primary production

Plant growth in most ecosystems forms the base or “primary” component of the food web. The amount and type of plant growth in an ecosystem helps to determine the amount and kind of animals (or “secondary” productivity) that can survive there.

Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011): Energy Balance Data

Abstract: 

This study originated with the objective of parameterizing riparian evapotranspiration (ET) in the water budget of the Middle Rio Grande. We hypothesized that flooding and invasions of non-native species would strongly impact ecosystem water use. Our objectives were to measure and compare water use of native (Rio Grande cottonwood, Populus deltoides ssp. wizleni) and non-native (saltcedar, Tamarix chinensis & Russian olive, Eleagnus angustifolia) vegetation and to evaluate how water use is affected by climatic variability resulting in high river flows and flooding as well as drought conditions and deep water tables. Eddy covariance flux towers to measure ET and shallow wells to monitor water tables were instrumented in 1999. Active sites in their second decade of monitoring include a xeroriparian, non-flooding salt cedar woodland within Sevilleta National Wildlife Refuge (NWR) and a dense, monotypic salt cedar stand at Bosque del Apache NWR, which is subject to flood pulses associated with high river flows. These data are energy balance data collected as part of this study.

Core Areas: 

Data set ID: 

308

Keywords: 

Methods: 

Three-dimensional eddy covariance: Measures fluxes of latent heat, sensible heat, and momentum, integrated over an area such as a vegetation canopy. High frequency measurements are made of vertical wind speed and water vapor, and their covariance over thirty minutes is used to compute latent heat flux, the heat absorbed by evaporation, from the canopy surface. Latent heat flux (LE) is converted to a direct measurement of evapotranspiration (ET). Simultaneous, high frequency measurements of temperature are used with vertical wind speed to compute the sensible heat flux (H), the heat transfer due to vertical temperature gradients. Measuring net radiation (Rn) and ground heat flux (G), allows the energy balance to be calculated (Rn = LE + H + G), providing a self-check for accuracy and closure error.

Sites: Two Rio Grande riparian locations in P. deltoides forests, two in T. chinensis forests. In each forest type, one of the two sites is prone to flooding from elevated Rio Grande flows, and the other site does not flood. A fifth site was located in a mix of non-native Eleagnus angustifolia (Russian olive) and native Salix exigua (coyote willow) prone to flooding.

*Design: Eddy covariance systems were mounted on towers in the turbulent surface layer 2-2.5 m above the canopy. Measurement period was 10 Hz and the covariance period was 30 minutes. Additional energy fluxes were made at 1 Hz and averaged over 30 minutes.

Data sources: 

sev308_bosqueEB_20160727.csv

Instrumentation: 

*Instrument Name: 3-D Sonic Anemometer
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: CSAT3

*Instrument Name: CO2/H2O Analyzer
*Manufacturer: Li-Cor, Inc. (Lincoln, NE)
*Model Number: LI-7500

*Instrument Name: Net Radiometer
*Manufacturer: Kipp & Zonen (Delft, The Netherlands)
*Model Number: CNR1

*Instrument Name: Barometric Pressure Sensor
*Manufacturer: Vaisala (Helsinki, Finland)
*Model Number: CS105

*Instrument Name: Temperature and Relative Humidity Probe
*Manufacturer: Vaisala (Helsinki, Finland)
*Model Number: HMP45C

*Instrument Name: Wind Sentry (Anemometer and Vane)
*Manufacturer: R.M. Young (Traverse City, MI)
*Model Number: 03001

*Instrument Name: Tipping Bucket Rain Gage
*Manufacturer: Texas Electronics, Inc. (Dallas, TX)
*Model Number: TE525

*Instrument Name: Quantum Sensor (PAR)
*Manufacturer: Li-Cor, Inc. (Lincoln, NE)
*Model Number: LI-190

*Instrument Name: Water Content Reflectometer
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: CS616

*Instrument Name: Soil Heat Flux Plate
*Manufacturer: Radiation and Energy Balance Systems, Inc. (Bellevue, WA)
*Model Number: HFT3

*Instrument Name: Averaging Soil Thermocouple Probe
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: TCAV

*Instrument Name: Measurement and Control System (Datalogger)
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: CR5000

*Instrument Name: Levelogger and Barologger (Water Table)
*Manufacturer: Solinst Canada Ltd. (Georgetown, ON, Canada)
*Model Number: 3001 LT M10 and 3001 LT M1.5

*Instrument Name: Mini-Diver, Cera-Diver, and Baro-Diver (Water Table)
*Manufacturer: Van Essen Instruments ((Delft, The Netherlands)
*Model Number: DI501, DI701, and DI500

Discontinued Instruments:

*Instrument Name: Krypton Hygrometer
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: KH2O

*Instrument Name: Net Radiometer
*Manufacturer: Radiation and Energy Balance Systems, Inc. (Bellevue, WA)
*Model Number: Q-7.1

*Instrument Name: Pyranometer
*Manufacturer: Kipp & Zonen (Delft, The Netherlands)
*Model Number: CM3

*Instrument Name: Micrologger
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: CR23X

*Instrument Name: Submersible Sensor Pressure Transducer (Water Table)
*Manufacturer: Electronic Engineering Innovations (Las Cruces, NM)
*Model Number: 2.0 (2 m) and 5.0 (4 m)

Quality Assurance: 

a] Before ET is computed from LE, various standard corrections are applied. These include: coordinate rotation to align the wind vector with the sonic anemometer, corrections developed from frequency response relationships that incorporate sensor line averaging and separation (Massman corrections), and corrections to account for flux effects on vapor density as opposed to mixing ratio measurements. Corrections are made in a data analysis (Perl) program. See Cleverly, et al., Hydrological Processes 20: 3207-3225, 2006 for more detail and references.

b] On days in which 1-4 of the 30 min LE values are missing, a general linear regression model between LE and Rn is used to estimate missing data whenever the regression coefficient was significantly different from 0 (i.e. p > 0.5). ET is not calculated from LE on days that do not match the above criteria.

c] Other missing data required for derived data values, as well as out of range data are filtered out in data analysis (Perl) programs.

d] Closure of the energy balance is achieved by adding the measured Bowen Ration (H/LE) components to H and LE. Closure represents the error introduced when applying the energy balance method to estimate ET: closure = Rn - LE - H - G. The measured Bowen Ratio, H / LE, is used to parse the closure value into component H and LE values.

e] Soil water content data are calibrated with soil water content (% vol) values measured from field samples by linear regression in a data analysis (Perl) program.

Additional information: 

Note: the data are not continuous--all sites have numerous breaks in the data. Additionally, instruments were introduced and retired at various times. For example, measurements of latent heat flux began with krypton hygrometers (LE_kh_c) and were replaced by infrared gas analyzers (LE_irga_c), which also commenced CO2 data (Fc_c, co2_a). Soil water content (soil_water) and a backup barometric sensor (P_mb_2) were added in 2003. Solar radiation (Rg_AVG) and photosynthetically active radiation (PAR_AVG) were added in 2006.

Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011 ): CO2 Concentration and Flux data

Abstract: 

This study originated with the objective of parameterizing riparian evapotranspiration (ET) in the water budget of the Middle Rio Grande. We hypothesized that flooding and invasions of non-native species would strongly impact ecosystem water use. Our objectives were to measure and compare water use of native (Rio Grande cottonwood, Populus deltoides ssp. wizleni) and non-native (saltcedar, Tamarix chinensis, Russian olive, Eleagnus angustifolia) vegetation and to evaluate how water use is affected by climatic variability resulting in high river flows and flooding as well as drought conditions and deep water tables. Eddy covariance flux towers to measure ET and shallow wells to monitor water tables were instrumented in 1999. Active sites in their second decade of monitoring include a xeroriparian, non-flooding salt cedar woodland within Sevilleta National Wildlife Refuge and a dense, monotypic salt cedar stand at Bosque del Apache NWR, which is subject to flood pulses associated with high river flows. These data are CO2 concentration at canopy and CO2 flux from canopy collected as part of this study.

Core Areas: 

Data set ID: 

312

Keywords: 

Methods: 

Three-dimensional eddy covariance: Measures fluxes of latent heat, sensible heat, and momentum, integrated over an area such as a vegetation canopy. High frequency measurements are made of vertical wind speed and water vapor, and their covariance over thirty minutes is used to compute latent heat flux, the heat absorbed by evaporation, from the canopy surface. Latent heat flux (LE) is converted to a direct measurement of evapotranspiration (ET). Simultaneous, high frequency measurements of temperature are used with vertical wind speed to compute the sensible heat flux (H), the heat transfer due to vertical temperature gradients. Measuring net radiation (Rn) and ground heat flux (G), allows the energy balance to be calculated (Rn = LE + H + G), providing a self-check for accuracy and closure error.

Sites: Two Rio Grande riparian locations in P. deltoides forests, two in T. chinensis forests. In each forest type, one of the two sites is prone to flooding from elevated Rio Grande flows, and the other site does not flood. A fifth site was located in a mix of non-native Eleagnus angustifolia (Russian olive) and native Salix exigua (coyote willow) prone to flooding.

Design: Eddy covariance systems were mounted on towers in the turbulent surface layer 2-2.5 m above the canopy. Measurement period was 10 Hz and the covariance period was 30 minutes. Additional energy fluxes were made at 1 Hz and averaged over 30 minutes.

Data sources: 

sev312_bosqueETCO2_20160720.txt

Instrumentation: 

Current Instruments:

*Instrument Name: 3-D Sonic Anemometer
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: CSAT3

*Instrument Name: CO2/H2O Analyzer
*Manufacturer: Li-Cor, Inc. (Lincoln, NE)
*Model Number: LI-7500

*Instrument Name: Net Radiometer
*Manufacturer: Kipp & Zonen (Delft, The Netherlands)
*Model Number: CNR1

*Instrument Name: Barometric Pressure Sensor
*Manufacturer: Vaisala (Helsinki, Finland)
*Model Number: CS105

*Instrument Name: Temperature and Relative Humidity Probe
*Manufacturer: Vaisala (Helsinki, Finland)
*Model Number: HMP45C

*Instrument Name: Wind Sentry (Anemometer and Vane)
*Manufacturer: R.M. Young (Traverse City, MI)
*Model Number: 03001

*Instrument Name: Tipping Bucket Rain Gage
*Manufacturer: Texas Electronics, Inc. (Dallas, TX)
*Model Number: TE525

*Instrument Name: Quantum Sensor (PAR)
*Manufacturer: Li-Cor, Inc. (Lincoln, NE)
*Model Number: LI-190

*Instrument Name: Water Content Reflectometer
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: CS616

*Instrument Name: Soil Heat Flux Plate
*Manufacturer: Radiation and Energy Balance Systems, Inc. (Bellevue, WA)
*Model Number: HFT3

*Instrument Name: Averaging Soil Thermocouple Probe
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: TCAV

*Instrument Name: Measurement and Control System (Datalogger)
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: CR5000

*Instrument Name: Levelogger and Barologger (Water Table)
*Manufacturer: Solinst Canada Ltd. (Georgetown, ON, Canada)
*Model Number: 3001 LT M10 and 3001 LT M1.5

*Instrument Name: Mini-Diver, Cera-Diver, and Baro-Diver (Water Table)
*Manufacturer: Van Essen Instruments ((Delft, The Netherlands)
*Model Number: DI501, DI701, and DI500

Discontinued Instruments:

*Instrument Name: Krypton Hygrometer
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: KH2O

*Instrument Name: Net Radiometer
*Manufacturer: Radiation and Energy Balance Systems, Inc. (Bellevue, WA)
*Model Number: Q-7.1

*Instrument Name: Pyranometer
*Manufacturer: Kipp & Zonen (Delft, The Netherlands)
*Model Number: CM3

*Instrument Name: Micrologger
*Manufacturer: Campbell Scientific, Inc. (Logan, UT)
*Model Number: CR23X

*Instrument Name: Submersible Sensor Pressure Transducer (Water Table)
*Manufacturer: Electronic Engineering Innovations (Las Cruces, NM)
*Model Number: 2.0 (2 m) and 5.0 (4 m)

Quality Assurance: 

a] Before ET is computed from LE, various standard corrections are applied. These include: coordinate rotation to align the wind vector with the sonic anemometer, corrections developed from frequency response relationships that incorporate sensor line averaging and separation (Massman corrections), and corrections to account for flux effects on vapor density as opposed to mixing ratio measurements. Corrections are made in a data analysis (Perl) program. See Cleverly, et al., Hydrological Processes 20: 3207-3225, 2006 for more detail and references.

b] On days in which 1-4 of the 30 min LE values are missing, a general linear regression model between LE and Rn is used to estimate missing data whenever the regression coefficient was significantly different from 0 (i.e. p > 0.5). ET is not calculated from LE on days that do not match the above criteria.

c] Other missing data required for derived data values, as well as out of range data are filtered out in data analysis (Perl) programs.

d] Closure of the energy balance is achieved by adding the measured Bowen Ration (H/LE) components to H and LE. Closure represents the error introduced when applying the energy balance method to estimate ET: closure = Rn - LE - H - G. The measured Bowen Ratio, H / LE, is used to parse the closure value into component H and LE values.

e] Soil water content data are calibrated with soil water content (% vol) values measured from field samples by linear regression in a data analysis (Perl) program.

Additional information: 

SEV ET 3/18/2000--12/31/2011, water table 4/16/1999--12/31/2014
BDAS ET 3/16/2000--12/31/2011, water table 4/17/1999--6/30/2014
SHK ET 3/18/2000--12/31/2007, water table 3/16/2000--12/31/2013
BLN ET 3/22/2000--5/17/2004, water table 3/15/2000--11/18/2008
LARO ET 3/6/2003--12/17/2008, water table 4/16/2003--12/31/2009

Note: the data are not continuous--all sites have numerous breaks in the data. Additionally, instruments were introduced and retired at various times. For example, measurements of latent heat flux began with krypton hygrometers (LE_kh_c) and were replaced by infrared gas analyzers (LE_irga_c), which also commenced CO2 data (Fc_c, co2_a). Soil water content (soil_water) and a backup barometric sensor (P_mb_2) were added in 2003. Solar radiation (Rg_AVG) and photosynthetically active radiation (PAR_AVG) were added in 2006.

Monsoon Rainfall Manipulation Experiment (MRME): Soil Temperature Data from the Sevilleta Wildlife Refuge, NM (2010 - present)

Abstract: 

The Monsoon Rainfall Manipulation Experiment (MRME) is to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, which alters the pulses of soil moisture that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. These data are soil temperature data collected at two depths. They are used in combination with the CO2 concentration and VWC data to calculate carbon flux.

Core Areas: 

Data set ID: 

303

Keywords: 

Data sources: 

sev303_mrmeSoilTemp_20151214.txt

Monsoon Rainfall Manipulation Experiment (MRME): Soil Carbon Dioxide Concentration Data from the Sevilleta National Wildlife Refuge, NM (2010 - present)

Abstract: 

The Monsoon Rainfall Manipulation Experiment (MRME) is to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, which alters the pulses of soil moisture that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics.  These data are CO2 concentrations collected at three depths.  

Data set ID: 

302

Additional Project roles: 

515

Core Areas: 

Keywords: 

Methods: 

MRME contains three ambient precipitation plots and five replicates of the following treatments: 1) ambient plus a weekly addition of 5 mm rainfall, 2) ambient plus a monthly addition of 20 mm rainfall. Rainfall is added during the monsoon season (July-Sept) by an overhead (7 m) system fitted with sprinkler heads that deliver rainfall quality droplets. At the end of the summer, each treatment has received the same total amount of added precipitation, delivered in different sized events. 

Data sources: 

sev302_mrmeCO2_20160323.txt

Extreme Drought in Grassland Ecosystems (EDGE) Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico (2013- present)

Abstract: 

Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes.  While measures of both below- and above-ground biomass are important in estimating total NPP, this study focuses on above-ground net primary production (ANPP). Above-ground net primary production is the change in plant biomass, including loss to death and decomposition, over a given period of time. Volumetric measurements are made using vegetation data from permanent plots collected in SEV297, "Extreme Drought in Grassland Ecosystems (EDGE) Net Primary Production Quadrat Data" and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."

Data set ID: 

298

Core Areas: 

Additional Project roles: 

469

Keywords: 

Methods: 

Derivation of Biomass and Net primary Production:

Data from SEV297 and SEV157 are used to calculate the seasonal and annual production (i.e., biomass) 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 net primary production (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.

Data sources: 

sev298_edgebiomass_20150818

Additional information: 

The bounding box coordinates for the corners of the polygon which encompasses the full EDGE black site are:NW: -106.729227  34.337913 Decimal DegreesNE: -106.728434  34.337937 Decimal DegreesSW: -106.729144  34.337298 Decimal DegreesSE: -106.728392  34.337310 Decimal DegreesEDGE blue:NW: -106.622610  34.342141 Decimal DegreesNE: -106.621689  34.342079 Decimal DegreesSW: -106.623365  34.341518 Decimal DegreesSE: -106.622711  34.341015 Decimal Degrees

Extreme Drought in Grassland Ecosystems (EDGE) Net Primary Production Quadrat Data at the Sevilleta National Wildlife Refuge, New Mexico (2012-present)

Abstract: 

EDGE is located at six grassland sites that encompass a range of ecosystems in the Central US - from desert grasslands to short-, mixed-, and tallgrass prairie. We envision EDGE as a research platform that will not only advance our understanding of patterns and mechanisms of ecosystem sensitivity to climate change, but also will benefit the broader scientific community. Identical infrastructure for manipulating growing season precipitation will be deployed at all sites. Within the relatively large treatment plots (36 m2), we will measure with comparable methods, a broad spectrum of ecological responses particularly related to the interaction between carbon fluxes (NPP, soil respiration) and species response traits, as well as environmental parameters that are critical for the integrated experiment-modeling framework, as well as for site-based analyses. By designing EDGE as a research platform open to the broader scientific community, with subplots in all replicates (n = 180 plots) set-aside for additional studies, and by making data available to the broader ecological community EDGE will have value beyond what we envision here. 

Data set ID: 

297

Core Areas: 

Additional Project roles: 

503

Keywords: 

Methods: 

Study Sites

The six sites were selected to capture the key environmental and ecological gradients of Central US grasslands and represent the major grassland ecosystem types (desert, shortgrass, mixedgrass, and tallgrass) of the region. Site selection criteria included: site characteristics (mean annual precipitation and temperature, dominant vegetation), access and site security, permission to build experimental infrastructure, participation in an existing or future network (e.g., LTER, NEON), and available site support and supporting data (e.g., LTER, USFWS or ARS).

Experimental Treatments and Plots

Our approach will be to impose a significant reduction in growing season precipitation (-66 % of ambient) over a 4-yr period. This is the equivalent of a ca. 50% reduction in annual precipitation because at all sites about 60-75% of annual precipitation falls in the growing season. We will impose this long-term drought either by reducing the size of each rainfall event (event size reduction, E) or by reducing the number of events (delayed rainfall treatment, D).

The control (C) treatment is included for comparison. At each site, the ambient (C) rainfall pattern will be reduced in two ways to impose a severe drought over a 4-yr period.

For the event size reduction treatment (E), each rainfall event will be passively reduced by a fixed proportion. Note that rain event number and the average number of days between events does not differ from ambient treatment.

For the reduced event number (D) treatment, shelters roofs will be removable to permit periods of complete rain exclusion alternating with periods of ambient rainfall inputs. Here, a + 10 mm rule is used to determine when roofs are on or off. When the cumulative precipitation amount in this D treatment falls 10 mm below the E treatment, the roofs are removed until the cumulative precipitation total is 10 mm greater than the E treatment. In this way, total precipitation amounts will be similar at the end of the growing season, but event number will be reduced and the average number of days between events increased, with no change in event size compared to the C treatment.

Plot Setup

At each site, we will establish replicate 6 x 6 m experimental plots (n = 10 per treatment, including the control treatment) in a relatively homogeneous area (similar soils, vegetation, etc.) that is representative of the overall site. Plots will be arrayed such that each treatment will be co-located in a single block (n=10 blocks per site), with each block located at least 5 m apart. 

The blocking will help control for environmental gradients if present. For each site, all plots within a block (including the control) will be located at least 2 m apart and trenched to 1-1.5 m and surrounded by a 6 mil plastic barrier to hydrologically isolate them from the adjacent soil, and each plot will be covered by the rainfall manipulation infrastructure. The 6 x 6 m plot size includes a 0.5 m external buffer to allow access to the plots and minimize edge effects associated with the infrastructure. The resulting 5 x 5 m area will be divided into 4 2.5 x 2.5 m subplots. One subplot will be designated for plant species composition sampling, two will for destructive sampling (ANPP, belowground productivity, soil sampling, etc.), and the fourth set aside for opportunistic studies.

Rainfall Manipulation Infrastructure

We will passively alter rainfall reaching the plots by using a version of a rainfall reduction shelter (Fig. 6) designed by Yahdjian and Sala (2002). Versions of these shelters (ranging from ~2 to 100 m2 ) are being used by the co-PIs at the Sevilleta, Konza Prairie and Shortgrass Steppe LTERs, as well as by many other ecologists, and thus, they are proven technology. The most significant environmental artifacts of these shelters are a 5- 10% reduction in light due to the acrylic Vshaped shingles and a ~ 20 cm edge effect (Yahdjian and Sala 2002). Shelters will consist of a steel frame that supports a roof. To cover the 36 m2 plots, the shelters will be constructed as modular 3 x 3 m units, with four units per plot. The roof of each modular unit will be slanted at 15° toward the edge of the plot, creating a 6 m long peak along the mid-line of the plot, with two lower 6 m long edges with gutters to move rainwater away from the plots. The peaked roof will facilitate run-off of rainfall and access to the plot, and the lower edge will be oriented to the prevailing wind direction to minimize blow-in. Average leaf canopy height varies among the desert/short-, midand tallgrass prairie sites (~0.2 to 0.6 m), and to maintain a consistent roof-to-canopy distance, peak height of the shelters will be 1.3, 1.55 and 1.8 m, with lower edges of the shelters at 0.5, 0.75 and 1.0 m, respectively, for the four grassland types. Construction of the shelters will begin in Yr 1 (after pretreatment measurements are taken) and treatments will be operational by the early spring of YR 2. For the ESR treatment, the roof will consist of clear acrylic (high light transmission, low yellowness index, UV transparent) v-shaped shingles arrayed at a density to passively reducing each rainfall event by ~66% (Fig. 6). For the REN treatment, the roof will consist of clear, corrugated polycarbonate (high light transmission, low yellowness index, UV transparent) to completely exclude rainfall. For both treatments, the roofs will be constructed to facilitate easy removal via a clamping system. The REN treatment roofs will then be manually deployed and removed at intermittent intervals (see Fig. 6 for more detail). Ambient plots will have a deer netting roof to achieve an average reduction in light similar to the rainfall reduction roofs.

Plant species composition, species traits, stem density, and light availability

In the subplot designated for species composition, we will establish a permanent 2 x 2 m sampling plots, which will be divided into four 1 x 1m quadrats in which canopy cover of each species will be visually estimated to the nearest 1%. For each site, these measures will be repeated at least twice during the growing season of each year to sample early and late season species. Maximum cover values of each species will be used to determine richness, diversity and dominance and changes in composition, species turnover, and species associations over time. 

Collecting the Data:

Net primary production data is collected twice each year, spring and fall, for both sites. 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.

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, shrubs and sub-shrubs (non-creosote)-The cover of forbs, shrubs and sub-shrubs is measured as the horizontal area of the plant. If the species is an annual it is acceptable to include the inflorescence in this measurement if it increases cover. If the species 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 phaecanthaOpuntia imbricata) measure the length and width of each pad to the nearest cm 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 viviparaSchlerocactus intertextusEchinocereus 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 shrubs and sub-shrubs (non-creosote)-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. 

Data sources: 

sev297_edgequadrat_20160815

Additional information: 

Additional Information on the personnel associated with the Data Collection / Data Processing

Nathan Gehres 2014-present; Michell Thomey 2012-2014

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

Core Site Grid Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico (2013 - present)

Abstract: 

Begun in spring 2013, this project is part of a long-term study at the Sevilleta LTER measuring net primary production (NPP) across three distinct ecosystems: creosote-dominant shrubland (Site C), black grama-dominant grassland (Site G), and blue grama-dominant grassland (Site B). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes.

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 in this dataset, 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. A third sampling at Site C is performed in the winter. Volumetric measurements are made using vegetation data from permanent plots (SEV289, "Core Site Grid Quadrat Data for the Net Primary Production Study") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."

Data set ID: 

291

Core Areas: 

Additional Project roles: 

433
434
435
436

Keywords: 

Methods: 

Data Processing Techniques to Derive Biomass and NPP:

Data from SEV289 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.

Data sources: 

sev291_coregridbiomass_20150818

Additional information: 

Other researchers involved with collecting samples/data: Chandra Tucker (CAT; 04/2014-present), Megan McClung (MAM; 04/2013-present), Stephanie Baker (SRB; 2013-present), John Mulhouse (JMM; 2013).

Pinon-Juniper (Core Site) Seasonal Biomass and Seasonal and Annual NPP Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico (2003-present)

Abstract: 

This dataset contains pinon-juniper woodland biomass data and is part of a long-term study at the Sevilleta LTER measuring net primary production (NPP) across four distinct ecosystems: creosote-dominant shrubland (Site C, est. winter 1999), black grama-dominant grassland (Site G, est. winter 1999), blue grama-dominant grassland (Site B, est. winter 2002), and pinon-juniper woodland (Site P, est. winter 2003). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes.

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 in this dataset, 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. A third sampling at Site C is performed in the winter. Volumetric measurements are made using vegetation data from permanent plots (SEV278, "Pinon-Juniper (Core Site) Quadrat Data for the Net Primary Production Study") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."

Data set ID: 

290

Core Areas: 

Additional Project roles: 

482
483
484
485

Keywords: 

Methods: 

Data Processing Techniques to Derive Biomass and NPP:

Data from SEV278 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.

Data sources: 

289_npppinjbiomass_20150824

Additional information: 

Other researchers involved with collecting samples/data: Chandra Tucker (CAT; 04/2014-present),  Megan McClung (MAM; 04/2013-present), Stephanie Baker (SRB; 10/2010-present), John Mulhouse (JMM; 08/2009-06/2013), Amaris Swann (ALS; 08/2008-present), Maya Kapoor (MLK; 08/2003 - 01/2005, 05/2010 - 03/2011), Terri Koontz (TLK; 02/2000 - 08/2003, 08/2006 - 08/2010), Yang Xia (YX; 01/2005 - 03/2010), Karen Wetherill (KRW; 02/2000 - 08/2009);  Michell Thomey (MLT; 09/2005 - 08/2008), Heather Simpson (HLS; 08/2000 - 08/2002), Chris Roberts (CR; 09/2001- 08/2002), Shana Penington (SBP; 01/2000 - 08/2000), Seth Munson (SMM; 09/2002 - 06/2004), Jay McLeod (JRM; 01/2006 - 08/2006); Caleb Hickman (CRH; 09/2002 - 11/2004), Charity Hall (CLH; 01/2005 -  01/2006), Tessa Edelen (MTE, 08/2004 - 08/2005).

Biome Transition Along Elevational Gradients in New Mexico (SEON) Study: Flux Tower Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico (2011 to present)

Abstract: 

The varied topography and large elevation gradients that characterize the arid and semi-arid Southwest create a wide range of climatic conditions - and associated biomes - within relatively short distances. This creates an ideal experimental system in which to study the effects of climate on ecosystems. Such studies are critical givien that the Southwestern U.S. has already experienced changes in climate that have altered precipitation patterns (Mote et al. 2005), and stands to experience dramatic climate change in the coming decades (Seager et al. 2007; Ting et al. 2007). Climate models currently predict an imminent transition to a warmer, more arid climate in the Southwest (Seager et al. 2007; Ting et al. 2007). Thus, high elevation ecosystems, which currently experience relatively cool and mesic climates, will likely resemble their lower elevation counterparts, which experience a hotter and drier climate. In order to predict regional changes in carbon storage, hydrologic partitioning and water resources in response to these potential shifts, it is critical to understand how both temperature and soil moisture affect processes such as evaportranspiration (ET), total carbon uptake through gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange of carbon, water and energy across elevational gradients.

We are using a sequence of six widespread biomes along an elevational gradient in New Mexico -- ranging from hot, arid ecosystems at low elevations to cool, mesic ecosystems at high elevation to test specific hypotheses related to how climatic controls over ecosystem processes change across this gradient. We have an eddy covariance tower and associated meteorological instruments in each biome which we are using to directly measure the exchange of carbon, water and energy between the ecosystem and the atmosphere. This gradient offers us a unique opportunity to test the interactive effects of temperature and soil moisture on ecosystem processes, as temperature decreases and soil moisture increases markedly along the gradient and varies through time within sites.

This dataset examines how different stages of burn affects above-ground biomass production (ANPP) in a mixed desert-grassland. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes.  Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and foliage, over time and incorporates growth as well as loss to death and decomposition. To measure this change the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots. Volumetric measurements are made using vegetation data from permanent plots (SEV253, "Flux Tower Net Primary Productivity (NPP) Quadrat Study") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."

Data set ID: 

292

Core Areas: 

Additional Project roles: 

470
471
472
473

Keywords: 

Methods: 

Data Processing Techniques to Derive Biomass and NPP:

Data from SEV253 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.

Data sources: 

sev292_fluxbiomass_20150305.txt

Additional information: 

Other researchers involved with collecting samples/data: Chandra Tucker (CAT; 04/2014-present), Megan McClung (MAM; 04/2013-present), Stephanie Baker (SRB; 2011-present), John Mulhouse (JMM; 2011-05/2013), Amaris Swann (2011-01/2013)

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