forests

Riparian Evapotranspiration (ET) Study (SEON) from the Middle Rio Grande River Bosque, New Mexico (1999-2011): Micrometeorological 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 are the meteorological data collected as part of this study.

Core Areas: 

Data set ID: 

311

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.

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: 

sev311_bosqueETmet_20160713.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)

Ecosystem-Scale Rainfall Manipulation in a Piñon-Juniper Forest at the Sevilleta National Wildlife Refuge, New Mexico: Sap Flow Data (2006-2013)

Abstract: 

Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability.  Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees.  Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide-spread mortality in both these species was observed. 

The focus of this study was to determine the effects of rainfall manipulation on our two target tree species.  Therefore, the analysis of the water relations of these trees was an essential component of the project.  Sap-flow within each individual target tree was monitored through the use of Granier probes.  These monitoring efforts provided a window on processes such as transpiration and the night-time re-filling of the xylem tissue.  Drought tolerance and adaptation strategies were also explored by comparing differences in sap-flow rates across treatment types and between species.

Core Areas: 

Data set ID: 

277

Additional Project roles: 

361
362
363
364
365
366

Keywords: 

Methods: 

Site Description

In total, our study site consisted of 12 experimental plots located in three replicate blocks that varied in slope % and aspect. Slope varied from 0-2% in experimental plots situated in level portions of the site, with steeper grades ranging from 6-18% for plots established on hill-slopes. Soil depth across the site ranged from 20 to ≥ 100 cm, with shallower soil depths occurring on hill-slopes where depth to caliche and/or bed-rock was only 20-30 cm in some instances. 

The study utilized four different experimental treatments applied in three replicate blocks. The four experimental treatments included 1) un-manipulated, ambient control plots, 2) drought plots, 3) supplemental irrigation plots, and 4) cover-control plots that have a similar infrastructure to the drought plots, but remove no precipitation.  The three replicated blocks differed in their slope and aspect. One block of four plots was located on south facing slopes, one on north facing slopes, and one in a flat area of the landscape.  

Experimental Treatment Design 

To effectively reduce water availability to trees, we installed treatments of sufficient size to minimize tree water uptake from outside of the plot. Thus, we constructed three replicated drought structures that were 40 m × 40 m (1600 m2). We targeted a 50% reduction in ambient precipitation through water removal troughs that covered ~50% of the land surface area. Drought plot infrastructure was positioned to insure that targeted Piñon pine and juniper were centrally located within each drought plot to provide the maximum distance between tree stems and the nearest plot boundary. Each drought and cover-control plot consists of 27 parallel troughs running across the 40 m plot. Each trough was constructed with overlapping 3ft ×10 ft (0.91 m × 3.05 m) pieces of thermoplastic polymer sheets (Makloron SL Polycarbonate Sheet, Sheffield Plastics Inc, Sheffield, MA) fixed with self-tapping metal screws to horizontal rails that are approximately waist height and are supported by vertical posts every 2.5-3.5 m. The plastic sheets were bent into a concave shape to collect and divert the precipitation off of the plot. The bending and spacing of the plastic resulted in 0.81 m (32 in) troughs separated by 0.56 m (22 in) walkways. 

Individual troughs often intersected the canopy of trees because of their height. The troughs were installed as close to the bole of the tree as possible without damaging branches in order to maximize the area covered by the plastic across the entire plot. An end-cap was attached to the downstream edge of the trough to prevent water from falling onto the base of the tree. The end-caps were 81 cm × 30 cm and made with the same plastic as the troughs. Each end-cap was fixed to the trough with a 75 cm piece of 20 gauge angle iron cut to match the curve of the bottom of the trough and held in place with self-tapping screws. The plastic junctures were then sealed with acrylic cement (Weld-On #3 epoxy, IPS Corp., Compton, CA). The middle of the end-cap was fitted with a 3 in (7.62 cm) PVC collar to allow water to flow through. A piece of 3 in (7.62 cm) PVC pipe or suction hose (used when the bole of a tree was directly below trough) was then attached to the downstream side of the end-cap, enabling water to flow into the trough on the other side of a tree. End-caps were also placed at the downhill end of the troughs on the edge of the plot and fitted with 90o fittings to divert water down into a 30 cm2 gutter (open on top) that ran perpendicular to the plot. Collected water was then channeled from the gutter into adjacent arroyos for drainage away from the study area. 

We built cover-control infrastructures to investigate the impact of the plastic drought structures independent of changes in precipitation. This was necessary because of the high radiation environment in central New Mexico, in which the clear plastic troughs can effectively act as a greenhouse structure. The cover-control treatment had the same dimensions as the drought plots with one key difference. The plastic was attached to the rails in a convex orientation so precipitation would fall on top of the plastic and then drain directly down onto the plot. The cover-control plots were designed to receive the same amount of precipitation as un-manipulated ambient plots, with the precipitation falling and draining into the walkways between the rows of troughs. Cover-control plots were constructed between June-21-07 and July-24-07; drought plots were constructed between August-09-07 and August-27-07.  The total plastic coverage in each plot is 45% ± 1% of the 1600 m2 plot area due to the variable terrain and canopy cover. A direct test of the amount of precipitation excluded via the plastic troughs was performed over a 2-week period during the summer monsoon season of 2008. Two rainfall collection gutters (7.6 cm width, 6.1 m length) were installed in a perpendicular arrangement across four plastic drought structures and four intervening open walkways. One gutter was located below the troughs (~0.6 m above ground), and the other was located just above (~1.35 m) and offset, to determine the interception of rainfall by the troughs. Rainfall totals collected via the perpendicular gutters were measured using Series 525 tipping bucket rain gauges (Texas Electronics, Dallas, TX). 

Our irrigation system consisted of above-canopy sprinkler nozzles configured to deliver supplemental rainstorm event(s) at a rate of 19 mm hr-1. Our irrigation system is a modified design of the above-canopy irrigation system outlined by Munster et al. (2006). Each of the three irrigation plots has three 2750 gal (10.41 m3) water storage tanks connected in parallel.  These tanks were filled with filtered reverse osmosis (RO) water brought to the site with multiple tractor-trailer trucks. During irrigation events, water is pumped from the tanks through a series of hoses that decrease from 7.62 cm (3 in) main lines out of the tank to 2.54 cm (1 in) hoses attached to 16 equally-spaced sprinklers within the plot. Each sprinkler is 6.1 m (20 ft) tall (2-3 m higher than mean tree height), and fitted with a sprinkler nozzle that creates an even circular distribution of water with a radius of 5 m on the ground. Due to the varying topography, sprinklers located downslope (if unregulated) would receive more pressure than those at the top of a hill and thus spray more water. To mitigate this problem, each sprinkler line was fitted with a pressure gauge and variable globe valve (inline water spigot with precise regulation) equidistant from the top of the sprinkler. Each sprinkler line was then set so that the pressure gauges were equal, thus ensuring equal distribution of water throughout the plot, regardless of elevational differences.  The irrigation systems were tested in October 2007 (2 mm supplemental), and full applications (19 mm) were applied in 2008 on 24-June, 15-July, and 26-August. During the 24-June event, we deployed six ~1 m2 circular trays across one of the irrigation plots to test the spatial variation of the wetting. Data from this test indicated that on average, collection trays received 19.5 (± 2.5) mm of water. 

Site Abiotic Monitoring

We utilized Campbell Scientific dataloggers to continuously monitor and record abiotic conditions and physiological measurements across the site.  All systems were connected to a solar-powered wireless network with NL100 relays (Campbell Scientific, Logan, UT).  Plots were instrumented with CR-1000 dataloggers (Campbell Scientific, Logan, UT).  Each CR-1000 datalogger was accompanied by AM25T and AM 16/32 multiplexers to expand sensor measurement capacity (Campbell Scientific, Logan, UT). The south facing block of experimental plots (the intensive physiology block) was extensively instrumented with sensors to measure both abiotic and plant physiological parameters. The non-intensive experimental plots in the north facing and flat blocks were initially only instrumented to monitor abiotic conditions.  

Plant Physiological Response

Multiple physiological characteristics of ten target trees (five piñon and five juniper) within each of the intensive measurement plots were continually monitored by automated sensors.  Stem sap-flow (JS) was measured using Granier heat dissipation sap flow sensors installed in 2007 in each intensive physiology plot within the south aspect block.  Trees in north facing (plots 5-8) and flat blocks (plots 1-4) were not instrumented with sap-flow sensors during the 2007 or 2008 seasons.  All target trees had two 10mm Granier sap-flow sensors installed in the outermost sapwood (Granier 1987).  Each sensor used the traditional two probe heated and unheated reference design (Granier 1987), with two additional probes located 5 cm to the right side of the primary probes to correct for axial temperature gradients in the stem (Goulden and Field 1994). We found that this compensation for axial temperature gradients is critical to reduce measurement noise resulting from the open-canopy and high radiation environment of this ecosystem.  In addition, stems were wrapped with reflective insulation (Reflectix Inc., Markleville, IN) in an effort to shield sap-flow probes from short term ambient temperature fluctuations and direct solar irradiance.  Sapflow (JS) was calculated according to the methods outlined in Granier (1987) and Goulden and Field (1994). Sapwood depth was generally greater than 10 mm on the majority of instrumented trees, thus only a small % of measurements required a correction due to sensor installation in non-functional stem heartwood (see Clearwater and others 1999).  All data from sap-flow sensors was recorded using Campbell Scientific AM16/32 multiplexers and CR1000 dataloggers (Campbell Scientific, Logan, UT).

Statistics 

Statistical tests for treatment differences in stem sap-flow (JS) were analyzed using a linear-effects mixed model with repeated measures.  For the repeated measures analysis, an autoregressive first order [AR(1)] covariance structure was utilized.  Differences between groups and/or treatment means were deemed significant at a threshold α-value of p = 0.05.   All mean values are reported with ± 1 S.E.  

Data Processing and Quality Assurance & Control (QA-QC)

Data processing and QA-QC were performed using either Matlab (The Mathworks, Inc.) or Microsoft Office 2010 Excel (Microsoft Corporation) software.  All raw and/or processed data traces were visually plotted and inspected for noisy, erroneous, or out of range data points or sensors traces.  All removed data points had a “NaN” value assigned.   Despite this QA-QC review and data cleaning, all data sets should still be evaluated for outliers, etc., as standard outlier statistical tests were not performed.

Data sources: 

sev277_pjsapflow06_20150608.txt
sev277_pjsapflow07_20150608.txt
sev277_pjsapflow08_20150608.txt
sev277_pjsapflow09_20150608.txt
sev277_pjsapflow10_20150608.txt
sev277_pjsapflow11_20150608.txt
sev277_pjsapflow12_20150608.txt
sev277_pjsapflow13_20150608.txt

Quality Assurance: 

Data processing and QA-QC were performed using either Matlab (The Mathworks, Inc.) or Microsoft Office 2010 Excel (Microsoft Corporation) software.  All raw and/or processed data traces were visually plotted and inspected for noisy, erroneous, or out of range data points or sensors traces.  All removed data points had a “NaN” value assigned.   Despite this QA-QC review and data cleaning, all data sets should still be evaluated for outliers, etc., as standard outlier statistical tests were not performed.

Additional information: 

Additional notes: The Sapflow Js data-set contains 15 minute interval data from 2006 thru 2012.   Data Qa/Qc has been performed on these files.   PJ day refers to days since start of project (i.e., 1/1/2006).   PJ Timestamp denotes/records each 15 minute interval entry from 1/1/2006.

The treatment classes provided in the file are as follows; ambient (1), drought (2), cover control (3), and irrigation (4).  The experiment used plot aspect as the blocking factor.   There are 3 different replicate blocks and block classifications designated in the files; flat aspect (1), north aspect (2), and south aspect (3).  This will be obvious when viewing the files.

The remaining cols in the data frame are the 15 minute sap-flow data for each tree in a particular plot.   This type of variable is commonly referred to as sapflow density, and it is represented by the symbol/abbreviation JS, in units of (g/m2 s).  

Tree numbers are always grouped by species as follows (regardless of plot); Trees 1-5 are original Pinus edulis, Trees 6-10 are original Juniper monosperma.   When one of these original trees died, an additional tree in the plot was added to retain an adequate sample size over time (i.e., multiple years+).   These additional trees are grouped as follows; Trees 11-15 are “replacement” Pinus edulis, Trees 16-20 are “replacement” Juniper monosperma.  “Replacement” is used here in a more restricted sense, as these additional trees have their separate and unique tree designation number.

So, in differing plots you will have differing numbers of trees depending on; 1) the number of trees for which data was collected, and 2) how many additional “replacement trees” had to be designated due to mortality (or partial mortality) of original trees.  Many plots have n=10 trees, based on the original T1-T5 & T6-10 designation, as these particular plots did not experience mortality.   However, a plot like P10 has a total of n=16 trees.  In P10, the original T1-5 & T6-T10 trees are listed, a replacement Pinon (T11) is listed, and five additional/replacement junipers (T16-T20).   In some cases you will see data present at the same time for both original and replacement junipers (plots 6 & 10).  This is fine, as juniper experiences a slow/partial canopy dieback, so we monitored the original and replacement trees at the same time in these two plots.     Finally, we only provide data on trees for which data was collected (so for example, in some instances you may only have n=4 cols of data for a particular species in a particular plot).  

Ecosystem-Scale Rainfall Manipulation in a Piñon-Juniper Forest at the Sevilleta National Wildlife Refuge, New Mexico: Volumetric Water Content (VWC) at 5 cm Depth Data (2006-2013)

Abstract: 

Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability.  Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees.  Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide-spread mortality in both these species was observed. 

Obviously, one of the important areas of interest in this experiment was the effects of elevated (greater-than-average) and decreased (less-than-average) precipitation levels on soil moisture.  The volumetric water content of the soil was monitored across all twelve plots, all four treatment types, and all three cover types.  The record created through these monitoring activities not only noted the initial “wetting-up” of the soil after a precipitation event but also tracked the “drying-down” of the soil after the event.  The water content of the soil and its associated storage capacity could then provide a frame of reference in which changes in the physiological properties of our two target tree species, such as water potential and sapflow rate, could be interpreted. 

Core Areas: 

Data set ID: 

276

Additional Project roles: 

206
207
208
209
210
211
212
213

Keywords: 

Methods: 

Site Description

The study utilized four different experimental treatments applied in three replicate blocks. The four experimental treatments included 1) un-manipulated, ambient control plots, 2) drought plots, 3) supplemental irrigation plots, and 4) cover-control plots that have a similar infrastructure to the drought plots, but remove no precipitation.  The three replicated blocks differed in their slope and aspect. One block of four plots was located on south facing slopes, one on north facing slopes, and one in a flat area of the landscape.  

Experimental Treatment Design (see Pangle et al. 2012 for detailed methodology)
To effectively reduce water availability to trees, we installed treatments of sufficient size to minimize tree water uptake from outside of the plot.  Thus, we constructed three replicated drought structures that were 40 m × 40 m (1600 m2). We targeted a 50% reduction in ambient precipitation through water removal troughs that covered ~50% of the land surface area. Drought plot infrastructure was positioned to insure that targeted Piñon pine and juniper were centrally located within each drought plot to provide the maximum distance between tree stems and the nearest plot boundary.  Each drought and cover-control plot consists of 27 parallel troughs running across the 40 m plot. Each trough was constructed with overlapping 3ft ×10 ft (0.91 m × 3.05 m) pieces of thermoplastic polymer sheets (Makloron SL Polycarbonate Sheet, Sheffield Plastics Inc, Sheffield, MA) fixed with self-tapping metal screws to horizontal rails that are approximately waist height and are supported by vertical posts every 2.5-3.5 m. The plastic sheets were bent into a concave shape to collect and divert the precipitation off of the plot. The bending and spacing of the plastic resulted in 0.81 m (32 in) troughs separated by 0.56 m (22 in) walkways.  

Individual troughs often intersected the canopy of trees because of their height. The troughs were installed as close to the bole of the tree as possible without damaging branches in order to maximize the area covered by the plastic across the entire plot. An end-cap was attached to the downstream edge of the trough to prevent water from falling onto the base of the tree.  A piece of 3 in (7.62 cm) PVC pipe or suction hose (used when the bole of a tree was directly below trough) was then attached to the downstream side of the end-cap, enabling water to flow into the trough on the other side of a tree. End-caps were also placed at the downhill end of the troughs on the edge of the plot and fitted with 90 degree fittings to divert water down into a 30 cm2 gutter (open on top) that ran perpendicular to the plot. Collected water was then channeled from the gutter into adjacent arroyos for drainage away from the study area.

We built cover-control infrastructures to investigate the impact of the plastic drought structures independent of changes in precipitation. This was necessary because of the high radiation environment in central New Mexico, in which the clear plastic troughs can effectively act as a greenhouse structure. The cover-control treatment had the same dimensions as the drought plots with one key difference. The plastic was attached to the rails in a convex orientation so precipitation would fall on top of the plastic and then drain directly down onto the plot. The cover-control plots were designed to receive the same amount of precipitation as un-manipulated ambient plots, with the precipitation falling and draining into the walkways between the rows of troughs. Cover-control plots were constructed between June-21-07 and July-24-07; drought plots were constructed between August-09-07 and August-27-07.  The total plastic coverage in each plot is 45% ± 1% of the 1600 m2 plot area due to the variable terrain and canopy cover.

Our irrigation system consisted of above-canopy sprinkler nozzles configured to deliver supplemental rainstorm event(s) at a rate of 19 mm hr-1. Our irrigation system is a modified design of the above-canopy irrigation system outlined by Munster et al. (2006). Each of the three irrigation plots has three 2750 gal (10.41 m3) water storage tanks connected in parallel.  These tanks were filled with filtered reverse osmosis (RO) water brought to the site with multiple tractor-trailer trucks. During irrigation events, water is pumped from the tanks through a series of hoses attached to 16 equally-spaced sprinklers within the plot. Each sprinkler is 6.1 m (20 ft) tall (2-3 m higher than mean tree height), and fitted with a sprinkler nozzle that creates an even circular distribution of water with a radius of 5 m on the ground.  The irrigation systems were tested in October 2007 (2 mm supplemental), and full applications (19 mm) were applied in 2008 on 24-June, 15-July, and 26-August. During subsequent years (2009-2012), a total of four to six irrigation events (19mm each) were applied (please contact Will Pockman and/or Robert Pangle for specific application dates and rates).   

Site Abiotic Monitoring

Site Abiotic Monitoring (please see Pangle et al. 2012 for more detailed methodology) We used Campbell Scientific dataloggers to continuously monitor and record abiotic conditions and physiological measurements across the site. All systems were connected to a solar-powered wireless network with NL100 relays (Campbell Scientific, Logan, UT). Plots were instrumented with CR-1000, CR-7, and CR-10X dataloggers (Campbell Scientific, Logan, UT). Each CR-1000 datalogger was accompanied by AM25T and AM 16/32 multiplexers to expand sensor measurement capacity (Campbell Scientific, Logan, UT). Abiotic conditions were measured under each cover type (n=3-5 locations per cover type): under piñon, juniper, and intercanopy areas between trees. These measurements included; a) soil temperature (TS) at –5 cm depth and shielded air temperature (TA) at 10 cm (above soil surface), both measured with 24 gauge Type–T thermocouples (Omega, Stamford, CT), b) shallow soil volumetric water content (VWC) at –5 cm measured using EC-20 ECH2O probes (Decagon, Pullman, WA), and c) soil VWC at depth using EC-5 soil moisture probes (Decagon, Pullman, WA). Soil VWC profiles had sensors installed at –15 cm, –20 cm, and as deep as possible (down to –100 cm, depending on soil conditions).

Data sources: 

sev276_pjvwc5cm06_20150707.csv
sev276_pjvwc5cm07_20150709.csv
sev276_pjvwc5cm08_20150708.csv
sev276_pjvwc5cm09_20150709.csv
sev276_pjvwc5cm10_20150709.csv
sev276_pjvwc5cm11_20150709.csv
sev276_pjvwc5cm12_20150709.csv
sev276_pjvwc5cm13_20150709.csv

Quality Assurance: 

Data processing and QA-QC were performed using either Matlab (The Mathworks, Inc.) or Microsoft Office 2010 Excel (Microsoft Corporation) software.  All raw and/or processed data traces were visually plotted and inspected for noisy, erroneous, or out of range data points or sensors traces.  All removed data points had a “NaN” value assigned.   Despite this QA-QC review and data cleaning, all data sets should still be evaluated for outliers, etc., as standard outlier statistical tests were not performed.

Additional information: 

The VWC_5cm depth data-set contains 15 minute interval data from 2006 thru 2012.   Data Qa/Qc has been performed on these files.   PJ day refers to days since start of project (i.e., 1/1/2006).   PJ Timestamp denotes/records each 15 minute interval entry from 1/1/2006.


The treatment classes provided in the file are as follows; ambient control (1), drought (2), cover control (3), and irrigation (4).  The experiment used plot aspect as the blocking factor.   There are 3 different replicate blocks and block classifications designated in the files; flat aspect (1), north aspect (2), and south aspect (3).  This will be obvious when viewing the files.


Values are reported in decimal % (in other words, a 0.25 data entry = 25%).  There are three cover types within each plot; 1) VWC (5cm) data under Piñon canopy cover, 2) VWC (5cm) under juniper canopy cover, and 3) VWC (5cm) at inter-canopy locations (i.e., bare, no canopy cover).  The VWC (5cm) data was collected from probes installed/buried at 5cm soil depth.


Detailed information on VWC-5cm header columns for the Tree_Number, SensorID, Species, and Sensor_Location variables.  Tree_Number refers to the label given to each sensor probe (i.e., it is installed beneath a specific target tree or a bare inter-canopy location).  The SensorID is an identifier that provides both the Tree_Number information and the soil depth of the probe.  Species indicates the cover type where the measurement was made; PIED, JUMO, or bare ground/intercanopy (INCA).   And the Sensor_Location simply indicates the depth where the soil moisture (VWC) probe is installed.   


Tree numbers are always grouped by species as follows (regardless of plot); Trees 1-5 are original Pinus edulis, Trees 6-10 are original Juniper monosperma.  B1 through B5 always designate an inter-canopy (i.e., bare) location.  Note, for the VWC_5cm data – there are no or very few “replacement” trees.  All (or most all) VWC_5cm measurements were made original target trees, i,e., the sensor installation positions/locations remained in their original locations regardless of any later tree death or mortality.


Similar to the Sapflow-JS data, there may be differing tree labels (and sample sizes, i.e., n=3, n=4, or n=5) for each cover type in differing plots depending on; 1) the specific target trees under which measurements were made, and 2) the total number of target trees in a given plot under which soil moisture probes were installed (this varies from n=3 to n=5 per cover type for differing plots).    This will be obvious when you view the files for different plots.

Ecosystem-Scale Rainfall Manipulation in a Piñon-Juniper Forest at the Sevilleta National Wildlife Refuge, New Mexico: Water Potential Data (2006-2013)

Abstract: 

Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability.  Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees.  Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide-spread mortality in both these species was observed. 

Water potential measurements were used to monitor the water stress of the two target species across the four treatment regimes.  Sampling for water potentials occurred twice daily.  One set of samples was collected hours before dawn and another set was collected at mid-day.  The predawn readings provided the “least-stressed” tree water content values as they were collected after the trees had returned to equilibrium over the evening and had yet to start transpiring.  The mid-day values, collected after tree-level respiration had been occurring for hours and when the daily temperatures were highest, represented the opposite “most-stressed” scenario. To gauge the effect of the irrigation treatment on the water content of the trees, we sampled water potentials just before and just after irrigation events.   

Core Areas: 

Data set ID: 

275

Additional Project roles: 

376
377
378
379
380
381
382
383

Keywords: 

Methods: 

Site Description

In total, our study site consisted of 12 experimental plots located in three replicate blocks that varied in slope % and aspect. Slope varied from 0-2% in experimental plots situated in level portions of the site, with steeper grades ranging from 6-18% for plots established on hill-slopes. Soil depth across the site ranged from 20 to ≥ 100 cm, with shallower soil depths occurring on hill-slopes where depth to caliche and/or bed-rock was only 20-30 cm in some instances. 

The study utilized four different experimental treatments applied in three replicate blocks. The four experimental treatments included 1) un-manipulated, ambient control plots, 2) drought plots, 3) supplemental irrigation plots, and 4) cover-control plots that have a similar infrastructure to the drought plots, but remove no precipitation.  The three replicated blocks differed in their slope and aspect. One block of four plots was located on south facing slopes, one on north facing slopes, and one in a flat area of the landscape.  

Experimental Treatment Design 

To effectively reduce water availability to trees, we installed treatments of sufficient size to minimize tree water uptake from outside of the plot. Thus, we constructed three replicated drought structures that were 40 m × 40 m (1600 m2). We targeted a 50% reduction in ambient precipitation through water removal troughs that covered ~50% of the land surface area. Drought plot infrastructure was positioned to insure that targeted Piñon pine and juniper were centrally located within each drought plot to provide the maximum distance between tree stems and the nearest plot boundary.  Each drought and cover-control plot consists of 27 parallel troughs running across the 40 m plot. Each trough was constructed with overlapping 3ft ×10 ft (0.91 m × 3.05 m) pieces of thermoplastic polymer sheets (Makloron SL Polycarbonate Sheet, Sheffield Plastics Inc, Sheffield, MA) fixed with self-tapping metal screws to horizontal rails that are approximately waist height and are supported by vertical posts every 2.5-3.5 m. The plastic sheets were bent into a concave shape to collect and divert the precipitation off of the plot. The bending and spacing of the plastic resulted in 0.81 m (32 in) troughs separated by 0.56 m (22 in) walkways. 

Individual troughs often intersected the canopy of trees because of their height. The troughs were installed as close to the bole of the tree as possible without damaging branches in order to maximize the area covered by the plastic across the entire plot. An end-cap was attached to the downstream edge of the trough to prevent water from falling onto the base of the tree. The end-caps were 81 cm × 30 cm and made with the same plastic as the troughs. Each end-cap was fixed to the trough with a 75 cm piece of 20 gauge angle iron cut to match the curve of the bottom of the trough and held in place with self-tapping screws. The plastic junctures were then sealed with acrylic cement (Weld-On #3 epoxy, IPS Corp., Compton, CA). The middle of the end-cap was fitted with a 3 in (7.62 cm) PVC collar to allow water to flow through. A piece of 3 in (7.62 cm) PVC pipe or suction hose (used when the bole of a tree was directly below trough) was then attached to the downstream side of the end-cap, enabling water to flow into the trough on the other side of a tree. End-caps were also placed at the downhill end of the troughs on the edge of the plot and fitted with 90o fittings to divert water down into a 30 cm2 gutter (open on top) that ran perpendicular to the plot. Collected water was then channeled from the gutter into adjacent arroyos for drainage away from the study area. 

We built cover-control infrastructures to investigate the impact of the plastic drought structures independent of changes in precipitation. This was necessary because of the high radiation environment in central New Mexico, in which the clear plastic troughs can effectively act as a greenhouse structure. The cover-control treatment had the same dimensions as the drought plots with one key difference. The plastic was attached to the rails in a convex orientation so precipitation would fall on top of the plastic and then drain directly down onto the plot. The cover-control plots were designed to receive the same amount of precipitation as un-manipulated ambient plots, with the precipitation falling and draining into the walkways between the rows of troughs. Cover-control plots were constructed between June-21-07 and July-24-07; drought plots were constructed between August-09-07 and August-27-07.  The total plastic coverage in each plot is 45% ± 1% of the 1600 m2 plot area due to the variable terrain and canopy cover. A direct test of the amount of precipitation excluded via the plastic troughs was performed over a 2-week period during the summer monsoon season of 2008. Two rainfall collection gutters (7.6 cm width, 6.1 m length) were installed in a perpendicular arrangement across four plastic drought structures and four intervening open walkways. One gutter was located below the troughs (~0.6 m above ground), and the other was located just above (~1.35 m) and offset, to determine the interception of rainfall by the troughs. Rainfall totals collected via the perpendicular gutters were measured using Series 525 tipping bucket rain gauges (Texas Electronics, Dallas, TX). 

Our irrigation system consisted of above-canopy sprinkler nozzles configured to deliver supplemental rainstorm event(s) at a rate of 19 mm hr-1. Our irrigation system is a modified design of the above-canopy irrigation system outlined by Munster et al. (2006). Each of the three irrigation plots has three 2750 gal (10.41 m3) water storage tanks connected in parallel.  These tanks were filled with filtered reverse osmosis (RO) water brought to the site with multiple tractor-trailer trucks. During irrigation events, water is pumped from the tanks through a series of hoses that decrease from 7.62 cm (3 in) main lines out of the tank to 2.54 cm (1 in) hoses attached to 16 equally-spaced sprinklers within the plot. Each sprinkler is 6.1 m (20 ft) tall (2-3 m higher than mean tree height), and fitted with a sprinkler nozzle that creates an even circular distribution of water with a radius of 5 m on the ground. Due to the varying topography, sprinklers located downslope (if unregulated) would receive more pressure than those at the top of a hill and thus spray more water. To mitigate this problem, each sprinkler line was fitted with a pressure gauge and variable globe valve (inline water spigot with precise regulation) equidistant from the top of the sprinkler. Each sprinkler line was then set so that the pressure gauges were equal, thus ensuring equal distribution of water throughout the plot, regardless of elevational differences.  The irrigation systems were tested in October 2007 (2 mm supplemental), and full applications (19 mm) were applied in 2008 on 24-June, 15-July, and 26-August. During the 24-June event, we deployed six ~1 m2 circular trays across one of the irrigation plots to test the spatial variation of the wetting. Data from this test indicated that on average, collection trays received 19.5 (± 2.5) mm of water. 

Plant Physiological Response 

Multiple physiological characteristics of ten target trees (five piñon and five juniper) within each of the intensive measurement plots were continually monitored by automated sensors or periodic manual measurements. Predawn (PD) and mid-day (MD) plant water potentials were measured with multiple Scholander-type pressure chambers (PMS Instrument Co, Albany, OR) on all target trees. When possible, PD was measured both before and after supplemental irrigation events.  

Data sources: 

sev275_pjwaterpot_20160328.txt

Quality Assurance: 

Data processing and QA-QC were performed using either Matlab (The Mathworks, Inc.) or Microsoft Office 2010 Excel (Microsoft Corporation) software.  All raw and/or processed data traces were visually plotted and inspected for noisy, erroneous, or out of range data points or sensors traces.  All removed data points had a “NaN” value assigned.   Despite this QA-QC review and data cleaning, all data sets should still be evaluated for outliers, etc., as standard outlier statistical tests were not performed.

Additional information: 

Additional notes: The water potential data-set contains periodic tree level water potential data from 2006 thru 2012.  Measurements were made at either predawn or midday.  Data Qa/Qc has been performed on these files.   PJ day refers to days since start of project (i.e., 1/1/2006).
 
The treatment classes provided in the file are as follows; ambient (1), drought (2), cover-control (3), and irrigation (4).  The experiment used plot aspect as the blocking factor.   There are 3 different replicate blocks and block classifications designated in the files; flat aspect (1), north aspect (2), and south aspect (3).  This will be obvious when viewing the files.

Tree numbers are always grouped by species as follows (regardless of plot); Trees 1-5 are original Pinus edulis, Trees 6-10 are original Juniper monosperma.   When one of these original trees died, an additional tree in the plot was added to retain an adequate sample size over time (i.e., multiple years+).   These additional trees are grouped as follows; Trees 11-15 are “replacement” Pinus edulis, Trees 16-20 are “replacement” Juniper monosperma.  “Replacement” is used here in a more restricted sense, as these additional trees have their separate and unique tree designation number.

So, in differing plots you will have differing numbers of trees depending on; 1) the number of trees for which data was collected, and 2) how many additional “replacement trees” had to be designated due to mortality (or partial mortality) of original trees.  Many plots have n=10 trees, based on the original T1-T5 & T6-10 designation, as these particular plots did not experience mortality.   However, a plot like P10 has a total of n=16 trees.  In P10, the original T1-5 & T6-T10 trees are listed, a replacement Pinon (T11) is listed, and five additional/replacement junipers (T16-T20).   In some cases you will see data present at the same time for both original and replacement junipers (plots 6 & 10).  This is fine, as juniper experiences a slow/partial canopy dieback, so we monitored the original and replacement trees at the same time in these two plots.  Finally, we only provide data on trees for which data was collected (so for example, in some instances you may only have n=4 cols of data for a particular species in a particular plot).  

Ecosystem-Scale Rainfall Manipulation in a Piñon-Juniper Forest at the Sevilleta National Wildlife Refuge, New Mexico: Soil Temperature Data (2006-2013)

Abstract: 

Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability.  Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees. Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide-spread mortality in both these species was observed. 

Soil temperature impacts both the abiotic and biotic processes at our site. The rate of evaporation, soil water content, VPD, and many other environmental factors are directly or indirectly affected by the temperature of the system. By monitoring the soil temperature at our site, we were able to determine its influence on the target trees and their associated physiological functions. Differences in soil temperature between plots can be impacted by the drought and cover-control structures used in our rainfall-manipulation treatments. Therefore, measuring soil temperatures in all three cover types and all four treatment regimes also allowed us to tease-out the temperature differences that were an artifact of the treatment structures as opposed to the actual treatments. 

Core Areas: 

Data set ID: 

274

Additional Project roles: 

248
249
250
251
252
253
254
255

Keywords: 

Methods: 

Site Description

In total, our study site consisted of 12 experimental plots located in three replicate blocks that varied in slope % and aspect. Slope varied from 0-2% in experimental plots situated in level portions of the site, with steeper grades ranging from 6-18% for plots established on hill-slopes. Soil depth across the site ranged from 20 to ≥ 100 cm, with shallower soil depths occurring on hill-slopes where depth to caliche and/or bed-rock was only 20-30 cm in some instances. 

The study utilized four different experimental treatments applied in three replicate blocks. The four experimental treatments included 1) un-manipulated, ambient control plots, 2) drought plots, 3) supplemental irrigation plots, and 4) cover-control plots that have a similar infrastructure to the drought plots, but remove no precipitation.  The three replicated blocks differed in their slope and aspect. One block of four plots was located on south facing slopes, one on north facing slopes, and one in a flat area of the landscape.  

Experimental Treatment Design 

To effectively reduce water availability to trees, we installed treatments of sufficient size to minimize tree water uptake from outside of the plot. Thus, we constructed three replicated drought structures that were 40 m × 40 m (1600 m2). We targeted a 50% reduction in ambient precipitation through water removal troughs that covered ~50% of the land surface area. Drought plot infrastructure was positioned to insure that targeted Piñon pine and juniper were centrally located within each drought plot to provide the maximum distance between tree stems and the nearest plot boundary.  Each drought and cover-control plot consists of 27 parallel troughs running across the 40 m plot. Each trough was constructed with overlapping 3ft ×10 ft (0.91 m × 3.05 m) pieces of thermoplastic polymer sheets (Makloron SL Polycarbonate Sheet, Sheffield Plastics Inc, Sheffield, MA) fixed with self-tapping metal screws to horizontal rails that are approximately waist height and are supported by vertical posts every 2.5-3.5 m. The plastic sheets were bent into a concave shape to collect and divert the precipitation off of the plot. The bending and spacing of the plastic resulted in 0.81 m (32 in) troughs separated by 0.56 m (22 in) walkways. 

Individual troughs often intersected the canopy of trees because of their height. The troughs were installed as close to the bole of the tree as possible without damaging branches in order to maximize the area covered by the plastic across the entire plot. An end-cap was attached to the downstream edge of the trough to prevent water from falling onto the base of the tree. The end-caps were 81 cm × 30 cm and made with the same plastic as the troughs. Each end-cap was fixed to the trough with a 75 cm piece of 20 gauge angle iron cut to match the curve of the bottom of the trough and held in place with self-tapping screws. The plastic junctures were then sealed with acrylic cement (Weld-On #3 epoxy, IPS Corp., Compton, CA). The middle of the end-cap was fitted with a 3 in (7.62 cm) PVC collar to allow water to flow through. A piece of 3 in (7.62 cm) PVC pipe or suction hose (used when the bole of a tree was directly below trough) was then attached to the downstream side of the end-cap, enabling water to flow into the trough on the other side of a tree. End-caps were also placed at the downhill end of the troughs on the edge of the plot and fitted with 90o fittings to divert water down into a 30 cm2 gutter (open on top) that ran perpendicular to the plot. Collected water was then channeled from the gutter into adjacent arroyos for drainage away from the study area. 

We built cover-control infrastructures to investigate the impact of the plastic drought structures independent of changes in precipitation. This was necessary because of the high radiation environment in central New Mexico, in which the clear plastic troughs can effectively act as a greenhouse structure. The cover-control treatment had the same dimensions as the drought plots with one key difference. The plastic was attached to the rails in a convex orientation so precipitation would fall on top of the plastic and then drain directly down onto the plot. The cover-control plots were designed to receive the same amount of precipitation as un-manipulated ambient plots, with the precipitation falling and draining into the walkways between the rows of troughs. Cover-control plots were constructed between June-21-07 and July-24-07; drought plots were constructed between August-09-07 and August-27-07.  The total plastic coverage in each plot is 45% ± 1% of the 1600 m2 plot area due to the variable terrain and canopy cover. A direct test of the amount of precipitation excluded via the plastic troughs was performed over a 2-week period during the summer monsoon season of 2008. Two rainfall collection gutters (7.6 cm width, 6.1 m length) were installed in a perpendicular arrangement across four plastic drought structures and four intervening open walkways. One gutter was located below the troughs (~0.6 m above ground), and the other was located just above (~1.35 m) and offset, to determine the interception of rainfall by the troughs. Rainfall totals collected via the perpendicular gutters were measured using Series 525 tipping bucket rain gauges (Texas Electronics, Dallas, TX). 

Our irrigation system consisted of above-canopy sprinkler nozzles configured to deliver supplemental rainstorm event(s) at a rate of 19 mm hr-1. Our irrigation system is a modified design of the above-canopy irrigation system outlined by Munster et al. (2006). Each of the three irrigation plots has three 2750 gal (10.41 m3) water storage tanks connected in parallel.  These tanks were filled with filtered reverse osmosis (RO) water brought to the site with multiple tractor-trailer trucks. During irrigation events, water is pumped from the tanks through a series of hoses that decrease from 7.62 cm (3 in) main lines out of the tank to 2.54 cm (1 in) hoses attached to 16 equally-spaced sprinklers within the plot. Each sprinkler is 6.1 m (20 ft) tall (2-3 m higher than mean tree height), and fitted with a sprinkler nozzle that creates an even circular distribution of water with a radius of 5 m on the ground. Due to the varying topography, sprinklers located downslope (if unregulated) would receive more pressure than those at the top of a hill and thus spray more water. To mitigate this problem, each sprinkler line was fitted with a pressure gauge and variable globe valve (inline water spigot with precise regulation) equidistant from the top of the sprinkler. Each sprinkler line was then set so that the pressure gauges were equal, thus ensuring equal distribution of water throughout the plot, regardless of elevational differences.  The irrigation systems were tested in October 2007 (2 mm supplemental), and full applications (19 mm) were applied in 2008 on 24-June, 15-July, and 26-August. During the 24-June event, we deployed six ~1 m2 circular trays across one of the irrigation plots to test the spatial variation of the wetting. Data from this test indicated that on average, collection trays received 19.5 (± 2.5) mm of water.  During subsequent years (2009-2012), a total of four to six irrigation events (19mm each) were applied (please contact Will Pockman and/or Robert Pangle for specific application dates and rates).

Site Abiotic Monitoring

We utilized Campbell Scientific dataloggers to continuously monitor and record abiotic conditions and physiological measurements across the site.  All systems were connected to a solar-powered wireless network with NL100 relays (Campbell Scientific, Logan, UT).  Plots were instrumented with CR-1000, CR-7, and CR-10X dataloggers (Campbell Scientific, Logan, UT).  Each CR-1000 datalogger was accompanied by AM25T and AM 16/32 multiplexers to expand sensor measurement capacity (Campbell Scientific, Logan, UT). Abiotic conditions were measured under each cover type (n=3-5 locations per cover type): under piñon, juniper, and intercanopy areas between trees.  These measurements included; a) soil temperature (TS) at –5 cm depth and shielded air temperature (TA) at 10 cm (above soil surface), both measured with 24 gauge Type–T thermocouples (Omega, Stamford, CT), b) shallow soil volumetric water content (VWC) at –5 cm measured using EC-20 ECH2O probes (Decagon, Pullman, WA), and c) soil VWC at depth using EC-5 soil moisture probes (Decagon, Pullman, WA).  Soil VWC profiles had sensors installed at –15 cm, –20 cm, and as deep as possible (down to –100 cm, depending on soil conditions).

Data sources: 

sev274_pjsoiltemp06_20140122.txt
sev274_pjsoiltemp07_20140124.txt
sev274_pjsoiltemp08_20140124.txt
sev274_pjsoiltemp09_20140127.txt
sev274_pjsoiltemp10_20140127.txt
sev274_pjsoiltemp11_20140127.txt
sev274_pjsoiltemp12_20150702.txt
sev274_pjsoiltemp13_20150702.txt

Quality Assurance: 

Data processing and QA-QC were performed using either Matlab (The Mathworks, Inc.) or Microsoft Office 2010 Excel (Microsoft Corporation) software.  All raw and/or processed data traces were visually plotted and inspected for noisy, erroneous, or out of range data points or sensors traces.  All removed data points had a “NaN” value assigned.   Despite this QA-QC review and data cleaning, all data sets should still be evaluated for outliers, etc., as standard outlier statistical tests were not performed.

Additional information: 

The Plot Temperature data-set contains 15 minute interval data from 2006 thru 2012.   Data Qa/Qc has been performed on these files.   PJ day refers to days since start of project (i.e., 1/1/2006).   PJ Timestamp denotes/records each 15 minute interval entry from 1/1/2006.

The treatment classes provided in the file are as follows; ambient control (1), drought (2), cover control (3), and irrigation (4).  The experiment used plot aspect as the blocking factor.   There are 3 different replicate blocks and block classifications designated in the files; flat aspect (1), north aspect (2), and south aspect (3).  This will be obvious when viewing the files.

Detailed information on header columns for the SensorID, Tree_Name, Species, and Sensor_Location variables.   SensorID refers to the label given to each thermocouple probe (it is installed beneath a target tree or a bare inter-canopy location).   The tree name is an identifier that provides both the SensorID information and the location of probe as either a soil temperature or air temperature measurement.  Species indicates the cover type where the measurement was made; PIED, JUMO, or bare ground/intercanopy (INCA).   And the Sensor_Location simply indicates weather the reported value is a soil or air temperature value (in Celsius degrees).  

Tree numbers are always grouped by species as follows (regardless of plot); Trees 1-5 are original Pinus edulis, Trees 6-10 are original Juniper monosperma.  B1 through B5 always designate an inter-canopy (i.e., bare) location.  Note, For the Tsoil and Tair data – there are no “replacement” trees.  All temperature measurements were made original target trees, i,e., the temperature probe installation positions/locations remained in their original locations regardless of any later tree death or mortality.

Similar to the Sapflow-JS data, there may be differing tree labels (and sample sizes, i.e., n=3, n=4, or n=5) for each cover type in differing plots depending on; 1) the specific target trees under which measurements were made, and 2) the total number of target trees in a given plot under which thermocouples were installed (this varies from n=3 to n=5 per cover type for differing plots).    This will be obvious when you view the files for different plots.

Contributions of Soil Communities to Ecosystem Respiration and Greenhouse Gas Emmisions in a Piñon-Juniper Woodland at the Sevilleta National Widlife Refuge, New Mexico (2011)

Abstract: 

Global climate change processes, especially prolonged droughts and increasingly high temperatures, are significantly affecting numerous arid ecosystems across the state of New Mexico.  One of the more adversely affected ecosystems in New Mexico is piñon-juniper woodland (PJ), which includes areas near Mountainair, New Mexico, USA.  Because changes in ambient temperature and decreases in water availability show pervasive effects on the above-ground status of existing PJ woodlands in New Mexico, it seems likely that the effects of changes in these two master variables will manifest themselves within soil processes such as soil organic matter (SOM) decomposition rates and soil respiration rates, as well as nutrient cycling rates and availabilities to both plants and soil microbial communities. 

We conducted analyses of soil physicochemical properties and soil fungal biomass via soil ergosterol content, as well as evaluating the activity rates of multiple hydrolytic exoenzymes, which are indicative of fungal activity in soils.  Samples were collected from multiple tree-to-tree competition gradients that were identified in May/June of 2011.  These gradients were established based on the type of mycorrhizal fungus types expected to occupy the soil community established beneath the canopy of a focal tree, with there being two focal trees in each gradient.  Gradients were established between two live piñon trees (Pinus edulis), two juniper trees (Juniperus monosperma), a live piñon and live juniper, and a dead piñon and live juniper.  We only sampled from under live trees at the control site.

In order to obtain these samples, we collected soil samples from two different sites in a PJ woodland located within the boundaries of the Deer Canyon ranch. Changes in soil conditions were captured by sampling from the two sites at multiple times throughout the summer of 2011.  We collected samples from Dr. Marcy Litvak’s girdled PJ woodland eddy-flux tower site in June, July, August and finally in late September.  We also collected samples from Dr. Litvak’s control PJ woodland tower site in June and September of 2011.  Significant differences in the activity rates of the hydrolytic exoenzymes alanine aminopeptidase, alkaline phosphatase, β-d-glucosidase, and β-N-acetyl glucosaminidase were observed within soils collected at multiple times from June through September when comparing the observed rates of activities under the trees in the live piñon to live piñon gradients vs. the juniper to juniper gradients.  These differences were observed in samples from multiple dates at the girdled site without there being significant differences in soil fungal biomass across seasons or study sites.  Continued work with the established sites on a year-to-year basis could provide an insight into how the fungal communities within New Mexican PJ woodlands will respond to future changes in soil conditions as global climate change processes advance in New Mexico.

Data set ID: 

250

Core Areas: 

Additional Project roles: 

266
267

Keywords: 

Methods: 

Experimental design: Randomized complete block design was established at 2 different study sites, girdled piñon-juniper (PJ) woodland and non-girdled (control) PJ woodland.  In late May, 2011, we set-up each study site to contain six complete blocks (plots), each with multiple tree-to-tree gradients.  At the girdled PJ site, each plot included five different tree-to-tree gradients: Live pine to live pine, live pine to dead pine, live pine to live juniper, dead pine to live juniper, and live juniper to live juniper.  At the control PJ site we also established 6 blocks (plots); however, at this site there were only three gradients: Live pine to live juniper, dead pine to live juniper, and live juniper to live juniper.

Setting up plots:  Plots and gradients were established by marking sampling locations with orange flagging tape and pin-flags by Daniel Warnock and Kimberly Elsenbroek on May 19 and 23, 2011. 

Sample collection, allocation and storage: Soil samples were collected monthly from the girdled PJ woodland to establish two pre-monsoonal (dry) season time points, with samples collected on June 6, 2011 and June 15, 2011 considered as being from single time point.  Soil samples collected on July 20, 2011 represented our second dry season time point.  Soil samples for our two post-monsoon moisture time points were collected on August 15, 2011 and September 28, 2011.  As with the girdled site, soils sample from the control PJ woodland site  were collected both before and after the onset of the monsoon season.  However, unlike the girdled PJ woodland site, we only have one pre-monsoon time point June 29, 2011 and one post monsoon time point, September 15, 2011. 

All soil samples were collected by combining three 0-10cm sub-samples into the same zipper-locking plastic storage bag.  Samples were collected from three different locations within each tree-to tree gradient.  Two of the three samples were collected from locations within 30cm of the trunk of each of the two focal trees within a gradient.  The other sample for each gradient was collected from a point at the center of a zone formed by the edges of the canopies from the two competing focal trees.  All samples were then transported to the lab for refrigeration.  

Within 24-72 hours of sample collection, 5mL sub-samples were taken from each bulked soil sample and placed into individual Corning 15mL screw-cap centrifuge tubes.  Each tube was then filled to the 10mL mark with an 0.8% KOH in Methanol solution.  These tubes were placed in the fridge for storage until analyzed for ergosterol content. After preparation of the samples for ergosterol analyses, 1g samples were placed into 125mL round Nalgene bottles for analyses of fungal exoenzyme actitity (EEA) rates from each sample.  All enzyme activity assays were performed within 1 to 5 days after collection. Further, for all but the final post-monsoonal time points, assays were performed within 2 to 3 days of sample collection. 

After all of the fresh, refrigerated samples were alloquated for ergosterol and EEA analyses we placed the remaining quantities of soil for each sample into labeled paper bags for air-drying on a lab bench.  After 1-2 weeks, 30g of each sample was placed into a labeled plastic bag for shipping to Ithaca, New York, USA for analyses of soil-physicochemical properties.  While taking the 30g sub-samples, a separate 5g sub-sample from the air-dried sample was placed into a labeled, no. 1 coin-envelope for storage until analysis of soil hyphal abundance.   After all sub-sampling was completed any remaining soil was kept in its sample bag and stored in the lab.

Hydrolytic exoenzyme activity (EEA) assays: All hydrolytic EEA assays were performed as follows: Each 125mL sample bottle was partially filled with 50mM sodium bicarbonate buffer solution and homogenized using a Kinematica Polytron CH 6010 (Lucerne, Switzerland).  Upon homogenization, sample bottles were filled to 125mL with buffer solution.  Sample bottles were then set aside until placement in black, 96-well, micro-plates.  At the time of placement, each sample suspension was poured into a glass crystalizing dish where it was stirred at high speed into the appropriate columns within each micro-plate.  These columns included a quench control (200 uL sample suspension + 50uL MUB or methylcoumrin substrate control), a sample control (200uL sample suspension + 50uL 50mM bicarbonate buffer) and an assay column (200uL suspension + 50ul 200mM substrate).  Samples were pipetted into four sets of plates with each set analyzing the activity rates of a single hydrolytic enzyme.  These enzymes included alanine amino peptidase, alkaline phosphatase, β-d-glucosidase, and N-acetyl-β-d-glucosiminidase.  Further, all three samples from a single gradient within a single plot were added to the same plate (e.g., all samples from the live-pine-to-live-pine gradient from plot one were pipetted into a single plate for analyzing the activity of the enzyme alkaline-phospotase.

Ultimately our plate layout was completed as follows usingt two other columns for substrate controls:  In column one, we added 200uL buffer and 50uL of a substrate standard, which accounts for the fluorescence emitted by either the MUB, or the methylcoumarin group that is a component of the substrate solution added to the assay wells.  In column six of each plate was a substrate control, which is a solution of 200uL buffer and 50uL of one the four different substrates used in our hydrolytic EEA assays.   Columns 3-5 were our quench controls, which accounts for the quantity of fluorescence emitted by the MUB or methylcoumarin molecule absorbed by the particles in the soil suspension itself.  Columns 7-9 were the sample controls and  account for the amount of fluorescence emitted by the soil suspension + buffer solution added to each well.  Finally, columns 10-12 were our assay wells.  From these wells we could determine enzyme activity by measuring the fluorescence emitted by the MUB or methylcoumarin molecules cleaved off of the substrates initially added to each well.  The substrates included in these assays included: 7-amino-4-methylcoumarin (Sigma-Aldrich), 4-MUB-phosphate (Sigma-Aldrich), 4-MUB-β-d-glucoside (Sigma-Aldrich), and 4-MUB-N-acetyl-β-d-glucosiminide (Sigma-Aldrich). 

Because the intrinsic EEA rates varied across our targeted exoenzymes, assay plates were scanned for flourscence in sets of two.  Alanine aminopeptidase plates and alkaline phosphatase plates were scanned twice, first at 30-40 minutes after substrate addion and again at 50-80 minutes after substrate addition.  β-d-glucosidase, and N-acetyl-β-d-glucosiminidase plates were all scanned at 3-4 hours after substrate addition.  The timing of the second enzyme activity time point depended on expected soil moisture conditions.  Here, the post monsoon soils were allowed to incubate for a total of 5-6 hours prior to the second scan and the pre-monsoon plates were incubated for a total of 7-9 hours. 

Fungal biomass measurements: Fungal biomass was quantified by measuring the concentration of ergosterol in a sub-sample taken from each soil sample collected from June to September.   Within 24-72 hours of sample collection, 5mL sub-samples were taken from each bulked soil sample and placed into individual Corning 15mL screw-cap centrifuge tubes.  Each tube was filled to the 10mL mark with an 0.8% KOH in methanol solution.  Tubes were refrigerated for storage until analyzed for fungal biomass by measuring the ergosterol content within each sample.  Ergosterol concentration for each sample was determined using HPLC with 100% methanol as the solvent at a flow rate of 1.5mL/ minute and a c-18 column.  Ergosterol was quantified by measuring the peak height that passed through a detector set to measure absorbance at 282nm, at 3.7min after the sample was injected into the column.  The height of each peak was then converted into μg ergosterol/g soil and finally converted to mg fungal biomass/ g soil by applying a conversion factor.  

 

Instrumentation: 

I

* Instrument Name: Polytron

* Manufacturer: Kinematica

* Model Number: CH 6010

* Instrument Name: GeoXT  

* Manufacturer: Trimble

* Model Number: GeoExplorer 3000 series

* Instrument Name: fmax         

* Manufacturer: Molecular devices

* Model Number: type 374


* Instrument Name: versamax tunable micro-plate reader

* Manufacturer: molecular devices

* Model Number: ?


* Instrument Name: SSI 222D isocratic HPLC pump          

* Manufacturer: SSI  

* Model Number: 222D


* Instrument Name: Thermo Seperation Products AS 1000 autosampler     

* Manufacturer: Thermo Seperation Products           

* Model Number: AS 1000


* Instrument Name: Acutect 500 UV/Vis Wavelength detector      

* Manufacturer: Acutect        

* Model Number: 500


* Instrument Name: HP 3396 series iii integrator                              

* Manufacturer: Hewlitt Packard

* Model Number:  3396

Additional information: 

Girdled and control PJ woodland: 34.36N, 106.27W.

Girdled PJ woodland sampled: 6/June/2011, 15/June/2011, 20/July/2011, 15/Aug/2011, 28/Sept/2011.

Control PJ woodland sampled: 29/June/2011, 15/Sept/2011.

Hobo Datalogger-Derived Precipitation Data from the Sevilleta National Wildlife Refuge, New Mexico (2008-present)

Abstract: 

Precipitation is recognized as the most spatially variable abiotic variable in arid ecosystems such as the Sevilleta National Wildlife Refuge (NWR). Water is also usually the limiting factor in such environments so the accurate measurement of precipitation in both space and time is vital to understanding ecosystem dynamics. In 2008, the acquisition of a number of tipping-bucket rain gauges with Hobo dataloggers permitted the deployment of gauges into an increased number of locations on the Sevilleta NWR. Most dataloggers were installed in the greater Five Points area and primarily placed around the site of the 2003 burn study. A few additional dataloggers were installed throughout the entire Sevilleta NWR to expand overall coverage.

Core Areas: 

Data set ID: 

234

Additional Project roles: 

277

Keywords: 

Data sources: 

sev234_hobo_20140220.txt

Methods: 

Datalogger specifications - Onset Tipping Bucket Rain gauge (8" opening); each tip records 0.01" (0.254 mm).

Data downloading - Data is collected from Hobo dataloggers using a Hobo shuttle. The data is then downloaded onto a PC computer using Boxcar Software.

Maintenance: 

01/19/11-Data and metadata compiled and updated through 2010. (JMM)03/19/10-Data and metadata compiled and updated through 2010. SEV project number assigned (SEV234) in Navicat and all data and metadata uploaded for public access. (JMM)

Pinon Branch Demography Study at the Sevilleta National Wildlife Refuge, New Mexico (1989-1993)

Abstract: 

This project was designed to investigate the response of plant growth and reproduction to short- and long-term variation in biotic and abiotic environmental variables. Several perennial taxa, including tree (Juniperus monsperma and Pinus edulis), shrub (Larrea tridentata) and bunch grasses (Oryzopsis hymenoides (now Achnaterum hymenoides) and Sporobolus contractus) species, were monitored at 1-3 sites differing in elevation and topography as well as edaphic variables and annual precipitation. The sites represented optimal or marginal/transitional zones for particular species. Demographic measurements were made biannually, after the 'wet' (fall) and 'dry' (spring) seasons. For tree and shrub species, estimates of growth and reproduction were based on branch demography, with ten branch tips from 10-20 individuals per species per site repeatedly measured from 1989-1993.  For J. monsperma, P. edulis and L. tridentata, vegetative growth (i.e., branch growth) as well as reproduction were monitored. Additional measurements included needle length for P. edulis and leaf production, leaf size and branchlet production for L. tridentata. For grasses, basal diameter, leaf length and reproduction were monitored for 100 individuals per species per site.

This project, SEV006, contains only data on pinon branch demography.  Data on other variables and species is contained in SEV024, SEV025, SEV026, SEV027, and SEV028.

Core Areas: 

Data set ID: 

6

Additional Project roles: 

224

Keywords: 

Data sources: 

sev6_pinyondemography_20160303.txt

Methods: 

Tree Selection - Ten numbered pinon trees were randomly selected at each study site; five on the north slope of a canyon and five on the south slope.  Numbers were assigned to trees which were then sampled by random selection.

Branch Selection - Ten branches approximately 1.5 meters above the ground were chosen and assigned numbers (1-10).

Tag Placement - Tags were placed about six centimeters from the tip of a selected branch.

Blength - The length of the branch from the tag wire (or paint mark) to the branch apex.

Fascicles - The length of the most distal cluster of leaves on the branch, the present year's cohort of leaves.

Needle1 - The length, from tip to attachment point, of one needle in a cluster included in a length-with-fascicles measurement.

Needle2 - A measurement of second needle in a cluster.

Male - A measure of the length of a branch containing male cones. If the tagged branch diverged before reaching the apex, producing two or more male cones, the individual lengths were added together as one male cone branch length.

Female - The number of female cones on a measured branch.

Codes - The codes used for species in this study follow Kartesz abbreviation standards (Kartesz, J.T. 1994. A Synonymized Checklist of the Vascular Flora of the United States, Canada, and Greenland. Timber Press, Oregon). Codes consist of four-to-six characters and are alphanumeric .  A comprehensive list of all plant species found on the Sevilleta National Wildlife Refuge and their associated codes can be found on the Sevilleta Information Management System (SIMS) at:

/export/db/local/plant/lib/species_kartesz_codes_.lst.

Maintenance: 

I. Condensed log of activity of plant demography data:
  a. Individual documentations iniatiated 1989; Troy Maddux
  b. File (combined documentations) 31 August 1990; Troy Maddux
  c. Abstract (Written by Diane Marshall and Charles Wisdom) put in documentation 31 August 1990; Troy Maddux
  d. Documentation expanded 2 September 1990; Troy Maddux
  e. Concatenation of individual documentations iniatiated September 1990, completed 17 December 1990; Michelle Murillo
  f. File expanded into rdb file 17 December 1990; Michelle Murillo
  g. Rdb file checked, and errors eliminated 18 December 1990; Michelle Murillo
  h. KEYWORDS added 19 December 1990; James Brunt
  i. Random error checking completed 20 December 1990; Michelle Murillo
  j. File complete and archived 21 December 1990; Michelle Murillo & Greg Shore
 
     Documentation changed to reflect changes in datafile structure; 1 March 91 T. Maddux  

II. Detailed log of alterations/modifications of plant demography data:

This portion of the log contains details of all alterations and modifications applied to this file by Michelle Murillo. The file demography.dat was initiated in September 1990 by concatenating individual files into the demography.dat file. A generic header was devised to apply to all individual files. The header reads as such: date season site species station plant# branch# #1 #2 #3 #4 #5 #6 #7 #8, and the detailed description of the numbers 1-8 are listed in the documentation section of this file. The individual files were then modified to follow this header, which entailed rearranging of columns (generally the season, site, species, station, plant#, and branch#) and the addition of the date column. As the columns numbered 1-8 did not pertain to all individual files, non-applicables (na) were inserted where necessary. Other alterations included:

    1. Juniper 1989: Sepultura Canyon; addition of na's to presence/absence of male or female cones, depending on sex of tree, i.e. if tree was female then na's were inserted in the male column. Goat Draw; same as Sepultura Canyon
    2. juniper 1990: (Sepultura Canyon site has been discontinued) Goat Draw (season 1); orginal file contained x,y and --'s; which were converted to 0 (absence) and 1 (presence) and appropriate na's were inserted, depending on sex of tree Goat Draw (season 2); orginal file contained +'s for presence of cones; which were converted to 1 (presence) and appropriate na's were inserted depending on sex of tree.

        NOTE: In 1989 width of branch was measured, and in 1990 this measurement was no longer taken. (The orginal data sheets for 1989-2 are unavailable at the time of archiving, assumption is that this measurement applies to season 2 also ).  In 1990 the length of branchlet was measured which was not measured in 1989. (See documentation).
        NOTE: In 1989 (both seasons) the variable, number of branchlets, was not included. In 1990 the variable was added, but measurements of this variable was sporadic, occuring only in a few plants.

    3. grasses 1989/1990: All sites: addition of na's to branch#

Error checking was done as follows:

Two files, grasses 1990-season 1, and creosote 1990-season 1, had not been entered at the time of concatenation of individual files, and were therefore entered by Michelle Murillo. These two files were error checked by Michelle Murillo by visual checking of original data sheets with the files on 18 December 1990. On 17 December 1990, the expand program was applied to the demography.dat file and placed into the demography.rdb file. The rdb file was then checked for various errors, and the elimination of these errors were completed on 18 December 1990. On 19-20 December 1990, random error checking was conducted by using 'Tables for Statistical Data-Analysis'. One hundred and fifty entries were checked and approximiately 45 percent of the numbers were from 1989-season 2, and because the data sheets were unavailable, the entries were not error checked. On 23 December 1990, visual checking with the original data sheets was conducted for an overall check.

File initiated June 1991: MLM
season 1 entered: MLM
season 2 entered: KPM
Black Butte, season 2 entered and error checked, 7 Jan 1992: MLM
data entry complete 25 October 1991:  KPM
data error checking completed 8 November 1991:  KPM

Documentation updated (New people and times added) on 29 Jan 1992 and inserted into data file by Troy Maddux.

  1992 log

    * Put the 1992 data in the data base on 22 Oct 1992 Troy Maddux.


THE FOLLOWING IS THE LOG FOR THE SPRING 1992 DEMOGRAPHY DATA


    * File initiated by Troy Maddux 24 Aug 1992
    * Goat Draw PIED information entered by Michael Bradley
    * and sent to Troy Maddux Wed Aug 19 15:31:03 1992 and these
    * data added to this file by T. Maddux on 15 Oct 1992
    * Black butte data added to this file 15 Oct 1992
    * Many SPCO4 plants in plots 2 and 3 of Five Points had no
    * data for the inflorescence # so this was added (it was 0)
    * on 15 Oct 1992 by Troy Maddux
    * Removed extra '0' from rs SPCO4 #531-534 - 16 Oct 1992, T.M.
    * Pulled \doc from 1991 demography data to change for 1992 data 16 Oct 1992; T.M.
    * Who and When data were collected was added 20 Oct 1992; T.M.  

THE FOLLOWING IS THE LOG FOR THE FALL 1992 DEMOGRAPHY DATA
    *File initiated 16 Sep 1992 by Tiffany Cotlar
    *Data file from Tiffany Cotlar and a data file from Robin Abell combined; also blank lines removed from both files; this was done by Troy Maddux on 20 Oct 1992.
    *File initiated 16 Sep 1992 by Robin Abell
    *PIED #'s 9,13,23,15,17,36,41,42 by Robin Abell 16 Sep 1992
    *JUMO #'s 26,23,24,28,49,5,8,17,13,22,36,41,47,4,15,27,32,46,42
    * by Robin Abell 16 Sep 1992
    *LATR2 #'s (Five Points) 2,3,5,26 by Robin Abell 23 Sep 1992
    *SPCO4 #'s (Station B) 61,92,93,94,95,99,101,103,104,105,108,109
    * 109.1,110,110.1,111,112,112.1,122,125,126,127,130,148,149,402
    * by Robin Abell 23 Sep 1992
    *LATR2 #'s (Five Points) 90,92,96 by Robin Abell 24 Sep 1992
    *LATR2#'s (Rio Salado) 1,14,17 by Robin Abell 1 Oct 1992
    *changed typo: juno to JUMO; 20 Oct 1992, T.M.
    *added additional field to grass data to make the field # correct 20 Oct 1992, TM.

File submitted for archival 22 OCT 1992 - Troy Maddux

File initiated by Troy Maddux 22 Apr 1993 combining data entered by Ursula Bonhage, Roger Stupf, Christian Heierli, Marilyn Altenbach, and
Eric Scherff.
----------------------------------------------------------------------
*Data for:    
*        PIED 28, 32, 36
*    entered by Ursula Bonhage 14 Apr 1993.
----------------------------------------------------------------------
*Data for:
*        PIED 4, 5, 8, 9, 13, 15
* entered by M. Altenbach & E. Scherff 14 April 93.
----------------------------------------------------------------------
*Data for:
*        pinyon 17,22,23,24 and
* entered by  M. Altenbach & 16 April 93.
----------------------------------------------------------------------
* Data entered for:
*        PIED 26,27,41,42,46,47,49
* by eric and marilyn on 13 and 14 April 1993
----------------------------------------------------------------------
* Documentation taken from 91-92 archive data set and modified for this data set.  18 Oct 1993, by Troy Maddux.
* Filled in "Who" and "When" sections from the data sheets - Troy Maddux - 16 Nov 1993.
----------------------------------------------------------------------
****************************************
    1993 FALL DATA
****************************************
File for Black Butte orhy data created by Eric Scherff and Cynthia Gregoire on September 21, 1993. Data collected by Marilyn Altenbach and Eric Scherff on September 14, 1993
****************************************
File for Five Points Sporobolus information created by Cynthia Gregoire & Eric Scherff on 21 September 1993. Data collected by Marilyn Altenbach & Eric Scherff on 14 September 1993 and 15 September 1993. File appended by Eric Scherff and Cynthia Gregoire on 22 September 1993. Changed "nd" representing "no data" to "na" to be consistent with other data bases. - Troy Maddux - 8 Nov 1993.
****************************************
File for pinyons created by Eric Scherff on 10 September 1993 File appended by Eric Scherff on 15 September 1993 Data collected by M. Altenbach & E. Scherff on 9 September, 10 September, and 13 September.
****************************************
Different fall plant data put together into single archive file by Troy Maddux on 8 Nov 1993.
****************************************
Spring and Fall data put together in Archive File format 17 Nov 1993. by Troy Maddux - also changed nd's and dashes to na's.            
****************************************
9 Dec 1993 - Demography file contained all species - all but the pinyon information was taken from this file by Troy Maddux.
****************************************
10 Dec 1993 - Documentation section changed to reflect only pinyon and not other measured species (e.g. jumo, latr, orhy, and spco). Troy Maddux
****************************************
10 Feb 1994 - Documentation section compared to Doc. Manual and necessary changes made by Rosemary Vigil; Troy Maddux replaced four occurances of "na" with 0.
****************************************

14 March 1994. Separated the big demography-89-90 file into four parts; grass, pinyon, creosote, and juniper.  Rupal Shah went through and separated the file and edited the documentation.

* 3 Jan 1995 - replaced a couple of enigmatic X's with "na"'s - Troy Maddux.

3/19/98 - Changed species codes to Kartesz. K. Taugher
    - Added a final line to dataset of: "{END OF DATA}". K. Taugher
    - Updated metadata to reflect species code changes. K. Taugher

9/23/09 - Added Kartesz reference. K. Taugher

Additional information: 

1989: First Census - May 8,9,11 of 1989; Second Census - September 5 of 1989.

1990: First Census - This equals season  1 in the header for the data.  The data were collected from 27 Apr 1990 to 17 May 1990. Second Census - This equals season  2 in the header for the data. The data were collected from 5 Sep 1990 - 20 Sep 1990.

1991: First Census - 16 Apr 1991 - 15 May 1991. Second Census - 4 Aug 1991 - 13 Sep 1991.

1992: First Census - The first census was performed on the following dates: 15 Apr, 16 Apr, 22 Apr, 23 Apr, 27 Apr, 28 Apr, 29 Apr, 30 Apr, 1 May, 19 May, 21 May, 27 May, 28 May, 1 Jun, 2 Jun, 12 Jun 1992. Second Census - The second census was performed on 14-17 Sep, 22-24 Sep, 29 Sep, 5-7 Oct 1992.

1993: First Census - Goat Draw pinyon and juniper were measured on: 14 Apr; and 15 Apr 1993. Second Census - Goat Draw pinyon and juniper were measured on: 9 Sep; 10 Sep; and 13 Sep 1993.

The data for the first census (May) were collected by Ann Evans (Asst. Professor/UNM), Troy Maddux (Head Plant Tech/LTER), Sam Loftin (Graduate Student/UNM), Marikay Ramsey (Head Animal Tech/LTER), Joran Viers (Plant Tech/LTER), Michelle Murillo (Plant Tech/LTER), Jennifer Franklin (Plant Tech/LTER), Amy Shortess (Plant Tech/LTER).  The data for the second census (September) were collected by Troy Maddux (Head Plant Tech/LTER), Amy Shortess (Plant Tech/LTER), David Keller (Plant Tech/LTER).

1991: The data for the first census  (May) were collected by Roger Mongold (Plant Technician), Brad Swanson (Plant Technician), Joran Viers (Plant Technician), Kathleen McGee (Plant Technician), Sam Loftin (Graduate Student Technician), and Troy Maddux (Head Plant Technician).  The data for the second census (May) were collected by Jim Stanton (Plant Technician), Susan Prichard (Plant Technician), and Troy Maddux (Head Plant Technician).
 
1992: The data for the first census (Apr-Jun) were collected by Troy Maddux (Head Plant Technician) and the following plant technicians: Marilyn Altenbach, Michael Bradley, Melissa Chavez, Anthony Collier, Julie Knight, Ivan Ortiz, Amanda Persaud, Monica Valdez.  The data for the second census (Aug-Oct) were collected by Troy Maddux (Head Plant Technician), Robin Abell (Plant Technician), and Tiffany Cotlar (Plant Technician).

1993: SPRING CENSUS - Roger Stupf (Volunteer from Switzerland), Ursula Bonhage (Volunteer from Switzerland), Christian Heierli (Volunteer from Switzerland), Marilyn Altenbach (Field Crew Chief), Eric Scherff (Field Tech), Troy Maddux (Vegetation Studies Program Manager). FALL CENSUS - Cynthia Gregoire (Volunteer from Vermont), Marilyn Altenbach (Field Crew Chief), Eric Scherff (Field Tech).

Core Research Site Web Quadrat Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico (1999-present)

Abstract: 

This dataset 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. The data from these plots is used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV157, "Net Primary Productivity (NPP) Weight Data." This biomass data is included in SEV182, "Seasonal Biomass and Seasonal and Annual NPP for Core Research Sites."

This dataset is designated as NA-US-011 in the Global Index of Vegetation-Plot Databases (GIVD). To aid tracking of the use of databases in this index, please also reference this number when citing this data. The GIVD report for SEV129 can be found in: Biodiversity and Ecology 4 - Vegetation Databases for the 21st Century (2012) by J. Dengler et al.

Core Areas: 

Data set ID: 

129

Additional Project roles: 

430
431
432

Keywords: 

Data sources: 

sev129_nppcorequadrat_20170621

Methods: 

Locating the Sampling Quadrats:

Three core sites (B, G, and C) contain five rodent trapping webs. Each web consists of twelve 100m transects radiating out from a central rebar stake marked #145. There are four permanently marked ANPP plots on each of the trapping webs. These plots are located 10 meters from the end of the transects extending in the four cardinal directions. Each plot consists of four quadrats oriented around a tall center stake. Each quadrat is marked by two short, orange fiberglass stakes. Quad 1 is northwest of the center stake followed in a clockwise direction by quads 2, 3, and 4. As of 2004, only quads 1 (northwest of center stake) and 3 (southeast of center stake) are read at each plot.

Note: Winter measurements of all sites except Creosote (C) ceased after 2006.

Note: On August 4, 2009, some of the webs and quadrats within the unburned Black Grama (G) core site were impacted a lightning-initiated fire.  Thus, webs 2 and 3 were abandoned and extra plots added to areas within webs 1, 4, and 5 that were not burned.  Changes were as follows:

Webs 1, 4, and 5: A plot was added to the northeast to compensate for the loss of all plots at webs 2 and 3.

Web 4: A plot was added to the northwest to compensate for the northern plot, which was burned.

Note: At the blue grama/grassland study site, webs four and five are oblong rather than round. Therefore, the west and east plots are only 100 m apart.

Collecting the Data:

Net primary production data is collected twice each year, spring and fall, for all sites. The Five Points Creosote Core Site is also sampled in winter. 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 phaecantha, Opuntia 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 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 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. 

Creosote Measurements till 2013:

To measure creosote (i.e., Larrea tridenta) break the observations into two categories:

1.) Small, individual clusters of foliage on a branch (i.e., branch systems): Measure the horizontal cover of each live (i.e., green) foliage cluster, ignoring small open spaces (keeping in mind the 15% guideline stated above). Then measure the vertical "height" of each cluster from the top of the foliage to a plane created by extending a line horizontally from the bottom of the foliage. Each individual foliage cluster within a bush is considered a separate observation.

2.) Stems: Measure the length of each stem from the base to the beginning of live (i.e., green) foliage. Calculate the cumulative total of all stem measurements. This value is entered under "height" with the species as "stem" for each quadrat containing creosote. All other variable receive a default entry of "1" for creosote stem measurements.

Do not measure dead stems or areas of dead foliage. If in doubt about whether a stem is alive, scrape the stem with your fingernail and check for the presence of green cambium.

Creosote Measurements 2013 and after:

Each creosote is only measured as one total cover. Each quad that contains creosote will have one cover observation for each creosote canopy in quad.

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 for sites B, G, and C should be as follows: npp_core.mm.dd.yy.abc.xls. For site P, the file format should be npp_pinj.mm.dd.yy.abc.xls. File names should be in lowercase.

Maintenance: 

01/12/2010 - Data was QA/QC'd and put in Navicat. Metadata was updated and compiled for 1999-2010. (JMM) 11/29/2009 - Data was QA/QC'd and put in Navicat. Metadata was updated and compiled for 1999-2009. Note: In fall of 2009, data from site G, webs 2, 3, and& 4 (plot N) was not collected due to unexpected fire at Sevilleta LTER sites. (YX) 01/05/2009 - Metadata was updated and compiled for 1999 - 2008. (YX) 01/06/2009 - As of 2007, winter season was no longer measured except at site C (creosotebush only). (YX) 12/05/2009 - NPP data from 1999-2008 was QA/QC'd in MySQL. 2006 (krw). In 2003, site B was added. In 2004, the number of quads was reduced to 40 per site (quads 2 and 4 at each plot are no longer read). I checked for duplicates and missing quads. These most often happened when a recorder mislabeled a particular quad. I also checked every plant code against the USDA Plants database online at http://plants.usda.gov/. All plant codes that have had nomenclature changes were updated. All previously unknown plants that have since been identified were also updated. All unknown plants that will never be identified were left in the database. All types were corrected. A list of codes not in the USDA list that are still in the data are as follows NONE = no plants in quad, OPUN = opuntia seedlings, SPOR = lumped Sporobolus spp (SPAI, SPCO4, SPCR, SPFL2), STEM = bare stem measurements for LATR2, U2 and UKFO18 and UKFO57 = unknowns that will never be identified, UKFO80 = unknown that has not yet been identified.A list of updates and the reason for the changes are below along with comments where identification is uncertain:OLD CODE,NEW CODE,NUMBER_ROWS_AFFECTED,REASON_FOR_CHANGEPOOL,POOL,2,TYPO-999,BOER4,3,ERROR_IN_DATA_MANAGEMENT ALLI1,ALMA4,2,IDENTIFIED_UKFO AMAR2,AMPA,8,IDENTIFIED_UKFO AMAR3,ACNE,9,IDENTIFIED_UKFO AMAR4,AMPA,4,IDENTIFIED_UKFO APIA1,CYMO,2,IDENTIFIED_UKFO ARDR4,ARLUL2,45,BELIEVED_MISIDENTIFICATION ARLUA,ARLUL2,3,BELIEVED_MISIDENTIFICATION ASTE13,SCMU6,31,IDENTIFIED_UKFO ASTE5,UKSH5,4,STILL_UNKNOWN ASTE7,TOAN,1,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION ASTRA,ASMIM,1,BEST_GUESS_FROM_DESCRIPTION BRAS1,LEDED,1,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION BRAS2,DRGL5,54,IDENTIFIED_UKFOBR BR2,BRCA3,4,ONLY_CHANGE_TO_BRCA3_AFTER_NEW_PJ_PLOT_DESIGN BREU,BREUC2,1,TYPO BRIC1,BRBR2,1,IDENTIFIED_UKFO BRIC3,BREUC2,5,IDENTIFIED_UKFO BRIC4,BREUC2,1,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION BRIC5,UKSH5,1,"KNOWN_FROM_LOCATION,_STILL_UNKNOWN" CACT,OPUN,4,"IN_ROW_ID_13908,_13919,_AND_184 47_CHANGE_COVER_TO_1,_THESE_ARE_LENGTH_BY_WIDTH_MEASUREMENTS,_OPUTIA_SPP_SEEDLINGS" CACT1,CACT1,0,NEVER_TO_BE_IDENTIFIED CADR6,HODR,630,NAME_CHANGE CAJA6,POJA5,8,NAME_CHANGE CHAL2,CHAL11,2,CODE_REDUNDANCY CHAM,CHMI7,1,IDENTIFIED_UKFO CHCO2,CHCO,5,ONLY_AT_SITE_MS CHEN1,TECO,58,IDENTIFIED_UKFO CHGO2,CHCO2,1,TYPO CHLA2,CHLA10,127,CODE_REDUNDANCY COAR4,VINE,10,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" COAU,COAU2,1,TYPO COEQ,VINE,3,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" CONV1,VINE,3,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" CONV3,VINE,7,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" CONV4,VINE,6,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" CRAB,PAOB,2,IDENTIFIED_UKFO CYMO,HYFIC,4,ONLY_AT_SITE_B DAJA,DABR,7,ONLY_AT_SITE_P DEOBO,DEPI,675,BELIEVED_MISIDENTIFICATION DEWO?,DEWO,1,CONFIRMED_ID ECFEF,ECCOC,2,BELIEVED_MISIDENTIFICATION ECFEF2,OPUN,1,ONLY_AT_PIS4 ECFEF2,ECCOC,3,ONLY_AT_SITE_P ECFEF2,ECFEF3,2,NAME_CHANGE ERCI,ERCI6,11,ONLY_ON_5/26/04_SITE_P ERCI6,ERCI,21,ALL_SITE_B ERDI2,ERFL,17,BELIEVED_MISIDENTIFICATION ERDI4,ERFL,32,BELIEVED_MISIDENTIFICATION ERRO2,ERPO4,1,ONLY_AT_SITE_P ERPU8,DAPU7,2006,NAME_CHANGE SCIND,ESVIV,3,ONLY_AT_MG EUGL3,CHGL13,1,NAME_CHANGE FABA1,LUBR2,1,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION FABA3,LOPL2,4,IDENTIFIED_UKFOF ORB1,FORB1,0,STILL_UNKNOWN FORB3,DIWI2,3,IDENTIFIED_UKFO GARR1,GACO5,2,IDENTIFIED_UKFO HEOB,HENA,9,BELIEVED_MISIDENTIFICATION HIJA,PLJA,1350,NAME_CHANGE HOGL2,HODR,985,BELIEVED_MISIDENTIFICATION HYVE,MILI3,1,ONLY_AT_SITE_P IPCO2,VINE,116,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" IPCO3,VINE,5,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" IPLE,VINE,1,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" IPLO,IPLO2,3,TYPO JF1,GACO5,2,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION JF3,PLPA2,13,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION JF5,POOL,1,MOST_COMMON_PORTULACA JG1,ARPUP6,2,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION JG2,BOGR2,4,"GRASS_SEEDLINGS,_LIKELY_BOGR2" JUM0,JUMO,1,SPELLED_WITH_A_ZERO KRLA,KRLA2,5,TYPO_NO_KRLA_AT_SITE_MS LARER,LAOCO,204,BELIEVED_MISIDENTIFICATION LITH1,LIIN2,3,IDENTIFIED_UKFO MAGR10,MAPIP,24,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" MIMU,MIOX,5,ONLY_FOR_P2R6 MUMI,MUTO2,1,QUESTIONABLE MUSQ,MOSQ,97,CODE_REDUNDANCY NEIN,ECIN2,12,NAME_CHANGE NYCT1,BOSP,1,IDENTIFIED_UKFO NYCT2,MILI3,14,IDENTIFIED_UKFO OEAL,OECAC2,17,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" OECEC2,OECAC2,806,CODE_REDUNDANCY ONAG1,GASUN,16,IDENTIFIED_UKFO OPEN,OPEN3,31,TYPO OPMAC,OPMA8,10,TYPO OPUN,OPUN,3,"IN_ROW_ID_19570,_20831,_AND_21055_CHANGE_COVER_TO_1,_THESE_ARE_LENGTH_BY_WIDTH_MEASUREMENTS,_OPUTIA_SPP_SEEDLINGS" OPUN1,OPUN,1,"IN_ROW_ID_38109,_THIS_IS_A_SEEDLING,_CHANGE_COVER_TO_1" PEPA20,SCPA10,1,NAME_CHANGE PF1,DRCUC,24,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION PF2,ARLUL2,15,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION PF3,PF3,0,NEVER_TO_BE_IDENTIFIED PF4,PF4,0,NEVER_TO_BE_IDENTIFIED PG1,HENE5,8,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION PHHEF,SOJA,3,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" POAC1,POAC1,0,NEVER_TO_BE_IDENTIFIED POAC11,POFE,25,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION POAC12,PAOB,3,IDENTIFIED_UKFO POAC14,POAC14,0,NEVER_TO_BE_IDENTIFIED POAC7,LYPH,3,IDENTIFIED_UKFO POLY1,CHGR2,98,IDENTIFIED_UKFO PORT1,POOL,1,MOST_COMMON_PORTULACA QUGR3,QUTU2,1244,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" SAKA,SATR12,588,BELIEVED_MISIDENTIFICATION SC?,SCPA10,1,GRAMA_CACTUS SCIND,ECIN2,19,NAME_CHANGE SCINI,ECIN2,46,BELIEVED_MISIDENTIFICATION SCSCN2,BOCU,8,BELIEVED_MISIDENTIFICATION SEED2,BELY,6,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION SOLA6,SOJA,14,IDENTIFIED_UKFO SOLA7,SOJA,2,IDENTIFIED_UKFO_PHHEF_LUMPED_WITH_SOJA SPAI,SPOR,39,ONLY_IN_SITE_P SPCO4,SPOR,2288,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" SPCR,SPOR,3603,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" SPFL2,SPOR,2485,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" SPHAE,SPWR,5,MOST_LIKELY_SPHAERALCEA SPORO,SPOR,2,"BAD_IDENTIFICATIONS,_I_RECOMMEND_LUMPING" STNE,HENE5,6,NAME_CHANGE STNE2,HENE5,72,NAME_CHANGE U1,MAFE,3,IDENTIFIED_UKFO U2,U2,0,NEVER_TO_BE_IDENTIFIED U3,U3,0,NEVER_TO_BE_IDENTIFIED U4,U4,0,NEVER_TO_BE_IDENTIFIED U5,VUOC,26,IDENTIFIED_UKFO U7,SCLA6,3,IDENTIFIED_UKFO UKAS2,ERFL,7,BEST_GUESS_FROM_DESCRIPTION UKCA,CHFE3,2,MOST_COMMON_CHAEMACYSE_IN_AREA UKCA1,MAHEH2,1,LOOKED_IN_FUTURE__DATA UKFO,SEDI3,1,BEST_GUESS_FROM_DESCRIPTION UKFO10,UKFO10,0,NEVER_TO_BE_IDENTIFIED UKFO13,UKFO13,0,NEVER_TO_BE_IDENTIFIED UKFO15,ARLUL2,2,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION UKFO16,BRBR2,3,IDENTIFIED_UKFO UKFO17,UKFO17,0,NEVER_TO_BE_IDENTIFIED UKFO18,UKFO18,0,NEVER_TO_BE_IDENTIFIED UKFO19,GUSA2,3,IDENTIFIED_UKFO UKFO20,SPLE,2,BEST_GUESS_FROM_DESCRIPTION UKFO21,DAJA,1,IDENTIFIED_UKFO UKFO22,SEDI3,1,IDENTIFIED_UKFO UKFO23,MESCS,1,IDENTIFIED_UKFO UKFO31,UKFO31,0,"COULD_BE_BADI,_GLWR_OR_NECA3" UKFO32,SACYH2,1,IDENTIFIED_UKFO UKFO51,UKFO51,0,NEVER_TO_BE_IDENTIFIED UKFO57,UKFO57,0,NEVER_TO_BE_IDENTIFIED UKFO61,THWR,186,IDENTIFIED_UKFO UKFO62,THWR,34,IDENTIFIED_UKFO UKFO7,ZIGR,2,IDENTIFIED_UKFO UKFO72,MILI3,27,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION UKFO72?,MILI3,1,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION UKFO73,HYVE,4,PROBABLE_IDENTIFICATION_FROM_UKFO_DESCRIPTION UKFO75,UKFO75,0,NEVER_TO_BE_IDENTIFIED UKFO76,UKFO75,3,NEVER_TO_BE_IDENTIFIED UKFO80,UKFO80,0,NOT_YET_IDENTIFIED UKGR2,LYPH,1,BEST_GUESS_FROM_DESCRIPTION UKSH1,SPLE,2,BEST_GUESS_FROM_DESCRIPTION UKSH4,BREUC2,8,IDENTIFIED_UKFO UKSH5,UKSH5,0,NOT_YET_IDENTIFIED

Additional information: 

Other researchers involved with collecting samples/data: Chandra Tucker (CAT; 04/2014-present), Megan McClung (MAM; 04/2013-present), Stephanie Baker (SRB; 09/2010-present), John Mulhouse (JMM; 08/2009-06/2013), Amaris Swann (ALS; 08/2008-01/2013), 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), Mike Friggens (MTF; 1999 - 09/2001), Tessa Edelen (MTE, 08/2004 - 08/2005).

Data updated 08/18/15: MOSQ changed to MUSQ3; ARPUP6 changed to ARPU9; SPWR changed to SPPO6; a single entry BOER changed to BOER4.

Pinon Juniper Net Primary Production Quadrat Data from the Sevilleta National Wildlife Refuge, New Mexico: 1999-2001

Abstract: 

This three-year study at the Sevilleta LTER was designed to monitor net primary production (NPP) across two distinct ecosystems: pinon/juniper woodland (P) and juniper savannah woodland (J). Net primary production (NPP) is a fundamental ecological variable that measures 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 of the community to a wide range of ecological processes. While measures of both below- and above-ground biomass are important in estimating NPP, this study focused on estimating above-ground biomass production (ANPP).

To measure ANPP (i.e., the change in plant biomass, represented by stems, flowers, fruit and foliage, over time), the vegetation variables in this dataset, including species composition and the cover and height of individuals, were sampled twice yearly (spring and fall) at permanent 1m x 1m plots. The data from these plots was used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV157, "Net Primary Productivity (NPP) Weight Data." In addition, volumetric measurements were obtained from permanent plots to build regressions correlating biomass and volume.

Spring measurements were taken in April or May when shrubs and spring annuals reached peak biomass. Fall measurements were taken in either September or October when summer annuals reached peak biomass but prior to killing frosts. Winter measurements were taken in February before the onset of spring growth.

Core Areas: 

Data set ID: 

187

Additional Project roles: 

36

Keywords: 

Data sources: 

sev187_pjnppquadrat_04122010

Methods: 

Collecting the Data:

Vegetation data is collected on a palm top computer. 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.

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-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 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 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.

Foliage canopy cover:

Cover and height are recorded for all separate vegetative units that fall within an infinite vertical column that is defined by the inside edge of the PVC-frame. A vegetative unit consists of an individual species with a unique cover and height. This includes vegetation that is rooted outside of the frame but has foliage that extends into the vertical column defined by the PVC-frame.

As mentioned above, cover is quantified by counting the number or fraction of 10 cm x 10 cm squares intercepted by each vegetative unit. It is possible to obtain a total percent cover greater than 100 for a quadrat because vegetative units often overlap (especially in shrubs and succulents). For perennial plants, cover is based only on the vegetative portion of the plant (stem and leaf). For annual plants, cover is based on both vegetative and reproductive (inflorescence) portions of the plant.

If the cover of a vegetative unit is less than 1, the increments used are as follows: 0.01, 0.05, 0.1, 0.25, 0.5, and 0.75. If cover is between 1 and 5, increments of 0.5 are used and, if greater than 5, increments of 1 are used.  Finally, if the cover is greater than 15, the total canopy cover is divided into smaller units and the cover and heights of each observation measured separately. This reduces the size of harvest samples.

Maintenance: 

January 7, 2008 KRW Data from the P and J sites from 1999 to 2002 were extracted from the ongoing npp database and put in its own table in navicat. Palmtop/pj_npp. NPP data from 1999-2001 was QAQC'd in MySQL. I checked for duplicates and missing quads. These most often happened when a recorder mislabeled a particular quad. I also checked every plant code against the USDA Plants database online at http://plants.usda.gov/. All plant codes that have had nomenclature changes were updated. All previously unknown plants that have since been identified were also updated. All unknown plants that will never be identified were left in the database. All types were corrected. A list of codes not in the USDA list are that are still in the data are as follows NONE = no plants in quad, and UKFO57 = unknowns that will never be identified, UKFO80 = unknown that has not yet been identified. A list of the updates and the reason for the change are in the table below along with comments where identifications were questionable.

Additional information: 

Employee History:Mike Friggens: 1999 to September 2001, Karen Wetherill: February 7, 2000 to August 2009, Terri Koontz: February 2000 to August 2003 and August 2006 to August 2010, Shana Penington: February 2000 to August 2000, Heather Simpson: August 2000 to August 2002, Chris Roberts: September 2001 to August 2002.

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