http://lod.bco-dmo.org/id/dataset/725483
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2018-01-31
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Eelgrass (Zostera marina) physiological and morphological traits measured between March of 2013 and August of 2014 using eelgrass collected in Bodega Bay, CA in May of 2012
2018-06-28
publication
2018-06-28
revision
BCO-DMO Linked Data URI
2018-06-28
creation
http://lod.bco-dmo.org/id/dataset/725483
John J. Stachowicz
University of California-Davis
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Stachowicz, J. J. (2018) Eelgrass (Zostera marina) physiological and morphological traits measured between March of 2013 and August of 2014 using eelgrass collected in Bodega Bay, CA in May of 2012. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 2) Version Date 2018-06-28 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/725483 [access date]
eelgrass traits Dataset Description: <p>Site information for transects conducted in Bodega Bay in 2012 can be found in the&nbsp;dataset "Eelgrass transect information"&nbsp;https://www.bco-dmo.org/dataset/725435.</p> Methods and Sampling: Methods below are excerpts from Abbot et al., in press.
In May 2012 we collected 20 ramets harvested at 2 m intervals along a 40 m transect at each of three tidal heights (high intertidal, low intertidal, and subtidal) at five sites within Bodega Harbor, CA. The five eelgrass collection sites are distributed throughout the harbor, between 0.45 and 3.2 km apart. We transported the 260 eelgrass ramets collected from these sites to the Bodega Marine Laboratory (~ 2-4 km), where we trimmed the ramets to a single shoot with 3 cm of rhizome and 30 cm of leaf length and planted them in 11.4 cm diameter by 9.5 cm high plastic flowerpots. We placed all pots in a single common garden flow-through seawater tank; we randomly assigned the pots to an initial position and rotated the pot position weekly. We collected leaf clips from each ramet for genetic analysis. We delineated genotypes and estimated relatedness using 11 microsatellite loci selected from a pool of >30 loci developed specifically for Zostera marina. We identified a total of 219 unique genotypes from the 260 ramets we collected. From this pool we selected 40 genotypes to ensure that we included (1) a wide range of pairwise relatedness values and (2) genotypes from all tidal heights and sites. We transplanted these 40 genotypes into 3.79 L plastic flowerpots and grew them in an outdoor common garden flow-through seawater tank for the duration of trait measurements. We rotated pot position weekly to avoid position effects. The common garden tank was 4.5 m long and 1 m wide and held approximately 3800 L of seawater. Seawater flowed into the tank via 10 inflow valves that were distributed along the length of the tank with a combined seawater flow rate of approximately 16 L per min. We then measured a range of performance and resource-acquisition traits for these 40 genotypes. We measured these traits only on new shoots produced while the plant was in the common garden, and not until shoots had acclimated to the common garden conditions for at least six months and had produced a minimum of three new shoots.
Morphology and production
We harvested five shoots of each genotype from the primary common garden, standardized each to 3 cm of rhizome and 30 cm shoot length, and planted them individually in 11.4 cm diameter by 9.5 cm high plastic flowerpots. The pots were placed in a second identical outdoor common garden in May at the beginning of the growing season. We started a second common garden so that we could retain the original clones in the common garden, while also destructively harvesting shoots for growth and morphological measurements. After a growth period of 10 weeks, we harvested the plants and measured shoot width and length, number of leaves, total rhizome length, rhizome diameter, maximum root length, new shoots produced, and the biomasses of roots, rhizomes, new shoots, and the terminal shoots. One week prior to harvesting plants we punched holes in terminal shoots to measure leaf growth rate (Williams and Ruckelshaus 1993).
Nutrient uptake rate
We quantified biomass-specific leaf nitrate uptake and root/rhizome ammonium uptake rates using two-compartment chambers similar to the design in Terrados and Williams (1997). Eelgrass plants can absorb multiple forms of nitrogen in all their tissues; however, because nitrate is most available in the water column and ammonium in the sediments we measured nitrate uptake in leaf shoots and ammonium uptake in roots/rhizomes at ambient concentrations. Eelgrass shoots were collected from the primary common garden of 40 genotypes, cleaned of all sediment, epiphytes, and invertebrates, and their rhizomes were cut to 3 cm the day prior to allow wound healing. The roots and rhizomes of each shoot were compartmentalized from the leaf shoots by inserting the shoot through a slit in a watertight rubber stopper that was then inserted into a 40 mL opaque plastic chamber filled with 35 mL of nitrogen-free artificial seawater spiked with ammonium to 100 M using a 5M NH4-N stock solution prepared with ammonium sulfate. The root/rhizome chambers with shoots inserted were then placed into 2 L clear acrylic cylindrical chambers filled with 1 L of nitrogen-free artificial seawater spiked with nitrate to 40 M using a 2 M NO3-N stock solution prepared with sodium nitrate. Sixteen acrylic chambers were seated in a water bath with water circulating through a chiller to keep chamber seawater temperature between 10-12C (Bracken et al. 2011). To provide sufficient water flow to prevent mass transfer limitation of uptake, we attached submersible pumps to each chamber via inflow and outflow pipes. Full spectrum quartz halite lamps surrounding the water bath provided the chambers with photosynthesis-saturating light (~700 mol photons m-2s-1). We took water samples from the root/rhizome chambers and shoot chambers prior to the start of the experiment, then sampled the shoot chambers every hour for 4 hours, at which time we detached the root/rhizome chambers from the shoot chamber, removed the shoots and took a final sample. We analyzed the shoot chamber samples (nitrate) using a Lachat 8000 series flow injection auto-analyzer and root/rhizome chamber samples (ammonium) using a Beckman Coulter DU640 spectrophotometer (Koroleff 1976). After removing plants from the chambers, we divided them into shoots, roots, and rhizomes, and dried them at 60C for at least 48 hours and then weighed each for biomass-specific uptake rate. We ran uptake trials with between 10-14 genotypes per day and measured all genotypes each week for nine weeks. Within a week eelgrass genotypes were randomly assigned a day and position in the water bath. Some genotypes did not yield enough shoots in the common garden for the full set of nine replicates (one genotype had only enough shoots for three replicates, but most had seven to nine successful replicates).
Leaf phenolic content
We analyzed total phenolic content using approximately 4 mg of dried, ground leaf material from each genotype (pooled from 3 leaves) following a modified Folin-Ciocalteu method (see Bolser et al. 1998). We extracted phenolics with 2 mL of 80% methanol for 24 hours, and then quantified them with a spectrophotometer using caffeic acid as a standard. Ferulic and caffeic acids are two of the most abundant phenolics in Zostera marina (Quackenbush et al. 1986; Vergeer and Develi 1997), and previous work showed that caffeic, ferulic, or gallic acids standards for eelgrass phenolic content from shoots collected in Bodega Bay produced similar results (Tomas et al. 2011).
Photosynthetic rate
We evaluated the photosynthetic performance of each genotype using a Diving-PAM® (Pulse Amplitude Modulated) fluorometer (Walz, Germany) to measure maximum quantum yield (potential photosynthetic efficiency, FV/Fm) and rapid light curves (RLC), which determine the effective quantum yield as a function of irradiance and can be used to assess light adaptation (Ralph and Gademann 2005, Williams et al. 2009). First, we dark-acclimated the outer leaves of each shoot for 30 minutes by placing a Waltz 4 mm opaque leaf clip 20 cm from the sediment surface on a leaf cleaned of epiphytes, then we immediately took maximum quantum yield and rapid light curve (RLC) measurements. RLCs comprised eight incremental steps of actinic light irradiance from 30 to 1129 PAR (mol photons m-2s-1), and the resulting yield measurements were converted into a relative electron transport rate (rETR) using the following equation:
rETR = F/Fm *PAR*0.5*AF,
where F/Fm is the effective quantum yield, F is the difference between background fluorescence F and F at each PAR increment, Fm is the maximum fluorescence, 0.5 assumes that photons absorbed are equally distributed between photosystems I and II (Genty et al. 1989), and AF is the standard absorption factor (0.55) for seagrasses (Durako 2007). To compare RLCs among genotypes we used curve fitting methods outlined in Ralph and Gademann (2005) to estimate characteristic parameters for each curve including: , initial slope of the curve (rate of increase in photosynthesis with increasing light in light-limited region of the RLC or photosynthetic efficiency); , slope of the curve where yield declines (strength of photoinhibition); and Ps, the maximum potential rETR. We fit each curve to a double exponential decay function (Platt et al. 1980) using the “nls” function in the stats package in R 3.0.2.
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1234345 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1234345
completed
John J. Stachowicz
University of California-Davis
530-752-1113
Department of Evolution and Ecology 1 Shields Ave
Davis
CA
95616
USA
jjstachowicz@ucdavis.edu
pointOfContact
asNeeded
Dataset Version: 2
Unknown
Geno
Site
Height
Pam_a
Pam_b
Pam_ps
Pam_etrmax
ammon
nitrate
ammon_res
nitrate_res
phenolics
TERM
RHIZ
ROOT
NEW
GROWTH
Tot_below
Tot_term
Total
Tot_above
GrowthXwidth_dayean
Tot_Growth_dayean
shoots
width
length
term_rhiz
new_rhiz
rhiz_D
max_root
tot_rhiz
ratio
theme
None, User defined
sample description
No BCO-DMO term
tide
Ammonium
Nitrate
count
wet_weight_biomass
featureType
BCO-DMO Standard Parameters
Stachowicz_Eelgrass_Transects_2012
service
Deployment Activity
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Connecting genetic diversity to ecosystem functioning: links between genetic diversity, relatedness and trait variation in a seagrass community
https://osprey.bco-dmo.org/project/564447
Connecting genetic diversity to ecosystem functioning: links between genetic diversity, relatedness and trait variation in a seagrass community
<p>There is growing evidence that genetic variation within and among populations of key species plays an important role in marine ecosystem processes. Several experiments provide compelling evidence that the number of genotypes in an assemblage (genotypic richness) can influence critical ecosystem functions including productivity, resistance to disturbance and invasion or colonization success. However, these studies use only the number of genotypes as a measure of genetic diversity. Recent analyses of species diversity experiments show that phylogenetic diversity may be a more reliable predictor of ecosystem functioning than simply the number of species. However, such approaches have not yet been applied to understanding the effects of genetics on ecosystem functioning. While genetic relatedness within a species holds the potential to predict the outcome of intraspecific interactions, and the functioning of ecosystems that depend on those species, we currently have few data to assess the shape or strength of this relationship. The investigators will build on their own previous work, and that of others, in eelgrass (<em>Zostera marina</em>) ecosystems showing strong effects of genotypic richness on a spectrum of critical ecosystem processes. The investigators will ask whether genotypic richness, or - as in studies at the level of species diversity - genetic relatedness/distance better predicts ecosystem functioning? If genetic relatedness measures are better predictors, then what mechanisms underlie this relationship? Can genetic relatedness predict ecological relatedness?</p>
<p>Although the current focus is on eelgrass, the research should be applicable to many systems. The project will assess the relationship between genetic relatedness and phenotypic distinctiveness of a key marine foundation species and use manipulative experiments to test the relative importance of the number of genotypes in an assemblage vs. their genetic relatedness and trait diversity for ecosystem functioning. Specifically, experiments will:<br />
(1) characterize the relationship between genetic relatedness and trait similarity among individual genotypes of eelgrass, including responses to experimental warming;<br />
(2) compare the effects of genetic relatedness and trait similarity among genotypes on the outcome of intraspecific competitive interactions; and<br />
(3) test the relative effect of genetic relatedness vs. number of genotypes of eelgrass on the growth of eelgrass, its associated ecosystem functions it (e.g., primary production, nutrient dynamics, trophic transfer, habitat provision, and detrital production and decomposition).</p>
<p>Seagrass ecosystems provide important services to coastal regions including primary production, nutrient cycling, habitat for fisheries species, and erosion control. Previous studies have shown these services can be compromised by reduction in the numbers of species of grazers or genotypes, but this study will allow a more predictive approach to diversity loss by integrating the effects of multiple components of diversity and clarifying the extent to which diversity effects can be predicted by the genetic or ecological uniqueness of component genotypes.</p>
Genetic Div to Ecosys Functioning
largerWorkCitation
project
eng; USA
oceans
-122.9574
-122.9406
38.305
38.3337
2013-03-01
2014-08-31
0
BCO-DMO catalogue of parameters from Eelgrass (Zostera marina) physiological and morphological traits measured between March of 2013 and August of 2014 using eelgrass collected in Bodega Bay, CA in May of 2012
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/739488.rdf
Name: Geno
Units: unitless
Description: <p>eelgrass (Zostera marina) genotype identifier</p>
http://lod.bco-dmo.org/id/dataset-parameter/739489.rdf
Name: Site
Units: unitless
Description: <p>Site code (MM=mason's marina, WP = westside park, CC = Campbell Cove, DP = Doran Park, J = jetty</p>
http://lod.bco-dmo.org/id/dataset-parameter/739490.rdf
Name: Height
Units: unitless
Description: <p>Tidal height (HI = high intertidal, LI = low intertidal, S = subtidal)</p>
http://lod.bco-dmo.org/id/dataset-parameter/739491.rdf
Name: Pam_a
Units: unitless
Description: <p>alpha parameter (rise) for exponential decay function fit to each rapid light curve (RLC). It is the initial slope of the RLC. See Ralph and Gadelmann, 2005.</p>
http://lod.bco-dmo.org/id/dataset-parameter/739492.rdf
Name: Pam_b
Units: unitless
Description: <p>beta parameter (fall) for exponential decay function fit to each rapid light curve (RLC). It is the slope of the curve where yield declines (strength of photoinhibition). See Ralph and Gadelmann, 2005.</p>
http://lod.bco-dmo.org/id/dataset-parameter/739493.rdf
Name: Pam_ps
Units: unitless
Description: <p>saturation coefficient for exponential decay function fit to each rapid light curve (RLC). It is the scaling factor that determines the height of the RLC. See Ralph and Gadelmann, 2005.</p>
http://lod.bco-dmo.org/id/dataset-parameter/739494.rdf
Name: Pam_etrmax
Units: unitless
Description: <p>the maximum potential relative Electron Transport Rate (ETR)</p>
http://lod.bco-dmo.org/id/dataset-parameter/739495.rdf
Name: ammon
Units: micromoles per gram per hour (µmol/g/hr)
Description: <p>biomass-specific ammonium uptake rate in roots/rhizomes</p>
http://lod.bco-dmo.org/id/dataset-parameter/739496.rdf
Name: nitrate
Units: micromoles per gram per hour (µmol/g/hr)
Description: <p>biomass-specific nitrate uptake rate in leaves</p>
http://lod.bco-dmo.org/id/dataset-parameter/739497.rdf
Name: ammon_res
Units: micromoles per gram per hour (µmol/g/hr)
Description: <p>residuals of ammonium uptake rate by sampling week (significant week effect)</p>
http://lod.bco-dmo.org/id/dataset-parameter/739498.rdf
Name: nitrate_res
Units: micromoles per gram per hour (µmol/g/hr)
Description: <p>residuals of nitrate uptake rate by sampling week (significant week effect)</p>
http://lod.bco-dmo.org/id/dataset-parameter/739499.rdf
Name: phenolics
Units: percent (dry mass)
Description: <p>Phenolic content</p>
http://lod.bco-dmo.org/id/dataset-parameter/739500.rdf
Name: TERM
Units: grams (g)
Description: <p>biomass of terminal shoot</p>
http://lod.bco-dmo.org/id/dataset-parameter/739501.rdf
Name: RHIZ
Units: grams (g)
Description: <p>biomass of rhizome</p>
http://lod.bco-dmo.org/id/dataset-parameter/739502.rdf
Name: ROOT
Units: grams (g)
Description: <p>biomass of roots</p>
http://lod.bco-dmo.org/id/dataset-parameter/739503.rdf
Name: NEW
Units: grams (g)
Description: <p>biomass of newly produced shoots</p>
http://lod.bco-dmo.org/id/dataset-parameter/739504.rdf
Name: GROWTH
Units: grams (g)
Description: <p>biomass of growth (segment of leaf between punches)</p>
http://lod.bco-dmo.org/id/dataset-parameter/739505.rdf
Name: Tot_below
Units: grams (g)
Description: <p>total belowground biomass</p>
http://lod.bco-dmo.org/id/dataset-parameter/739506.rdf
Name: Tot_term
Units: grams (g)
Description: <p>total terminal shoot biomass (terminal shoot + growth)</p>
http://lod.bco-dmo.org/id/dataset-parameter/739507.rdf
Name: Total
Units: grams (g)
Description: <p>total biomass of plant</p>
http://lod.bco-dmo.org/id/dataset-parameter/739508.rdf
Name: Tot_above
Units: grams (g)
Description: <p>total aboveground biomas (terminal+growth+new shoots)</p>
http://lod.bco-dmo.org/id/dataset-parameter/739509.rdf
Name: GrowthXwidth_dayean
Units: centimeters per day (cm/day)
Description: <p>area in cm of growth per shoot per day</p>
http://lod.bco-dmo.org/id/dataset-parameter/739510.rdf
Name: Tot_Growth_dayean
Units: centimeters per day (cm/day)
Description: <p>length in cm of growth per shoot per daty</p>
http://lod.bco-dmo.org/id/dataset-parameter/739511.rdf
Name: shoots
Units: per individual
Description: <p>number of shoots</p>
http://lod.bco-dmo.org/id/dataset-parameter/739512.rdf
Name: width
Units: millimeters (mm)
Description: <p>width of terminal shoot</p>
http://lod.bco-dmo.org/id/dataset-parameter/739513.rdf
Name: length
Units: centimeters (cm)
Description: <p>length of terminal shoot</p>
http://lod.bco-dmo.org/id/dataset-parameter/739514.rdf
Name: term_rhiz
Units: centimeters (cm)
Description: <p>length of terminal rhizome</p>
http://lod.bco-dmo.org/id/dataset-parameter/739515.rdf
Name: new_rhiz
Units: centimeters (cm)
Description: <p>total length of rhiz of new shoots</p>
http://lod.bco-dmo.org/id/dataset-parameter/739516.rdf
Name: rhiz_D
Units: millimeters (mm)
Description: <p>rhizome diameter</p>
http://lod.bco-dmo.org/id/dataset-parameter/739517.rdf
Name: max_root
Units: centimeters (cm)
Description: <p>max root length</p>
http://lod.bco-dmo.org/id/dataset-parameter/739518.rdf
Name: tot_rhiz
Units: centimeters (cm)
Description: <p>total rhizome length (new+terminal)</p>
http://lod.bco-dmo.org/id/dataset-parameter/739519.rdf
Name: ratio
Units: dimensionless
Description: <p>ratio of above to belowground biomass</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
7346
https://datadocs.bco-dmo.org/file/0AAMN8BsklGlqK/EelgrassTraits.csv
EelgrassTraits.csv
Primary data file for dataset ID 725483
download
https://osprey.bco-dmo.org/dataset/725483/data/download
download
onLine
dataset
Methods below are excerpts from Abbot et al., in press.
In May 2012 we collected 20 ramets harvested at 2 m intervals along a 40 m transect at each of three tidal heights (high intertidal, low intertidal, and subtidal) at five sites within Bodega Harbor, CA. The five eelgrass collection sites are distributed throughout the harbor, between 0.45 and 3.2 km apart. We transported the 260 eelgrass ramets collected from these sites to the Bodega Marine Laboratory (~ 2-4 km), where we trimmed the ramets to a single shoot with 3 cm of rhizome and 30 cm of leaf length and planted them in 11.4 cm diameter by 9.5 cm high plastic flowerpots. We placed all pots in a single common garden flow-through seawater tank; we randomly assigned the pots to an initial position and rotated the pot position weekly. We collected leaf clips from each ramet for genetic analysis. We delineated genotypes and estimated relatedness using 11 microsatellite loci selected from a pool of >30 loci developed specifically for Zostera marina. We identified a total of 219 unique genotypes from the 260 ramets we collected. From this pool we selected 40 genotypes to ensure that we included (1) a wide range of pairwise relatedness values and (2) genotypes from all tidal heights and sites. We transplanted these 40 genotypes into 3.79 L plastic flowerpots and grew them in an outdoor common garden flow-through seawater tank for the duration of trait measurements. We rotated pot position weekly to avoid position effects. The common garden tank was 4.5 m long and 1 m wide and held approximately 3800 L of seawater. Seawater flowed into the tank via 10 inflow valves that were distributed along the length of the tank with a combined seawater flow rate of approximately 16 L per min. We then measured a range of performance and resource-acquisition traits for these 40 genotypes. We measured these traits only on new shoots produced while the plant was in the common garden, and not until shoots had acclimated to the common garden conditions for at least six months and had produced a minimum of three new shoots.
Morphology and production
We harvested five shoots of each genotype from the primary common garden, standardized each to 3 cm of rhizome and 30 cm shoot length, and planted them individually in 11.4 cm diameter by 9.5 cm high plastic flowerpots. The pots were placed in a second identical outdoor common garden in May at the beginning of the growing season. We started a second common garden so that we could retain the original clones in the common garden, while also destructively harvesting shoots for growth and morphological measurements. After a growth period of 10 weeks, we harvested the plants and measured shoot width and length, number of leaves, total rhizome length, rhizome diameter, maximum root length, new shoots produced, and the biomasses of roots, rhizomes, new shoots, and the terminal shoots. One week prior to harvesting plants we punched holes in terminal shoots to measure leaf growth rate (Williams and Ruckelshaus 1993).
Nutrient uptake rate
We quantified biomass-specific leaf nitrate uptake and root/rhizome ammonium uptake rates using two-compartment chambers similar to the design in Terrados and Williams (1997). Eelgrass plants can absorb multiple forms of nitrogen in all their tissues; however, because nitrate is most available in the water column and ammonium in the sediments we measured nitrate uptake in leaf shoots and ammonium uptake in roots/rhizomes at ambient concentrations. Eelgrass shoots were collected from the primary common garden of 40 genotypes, cleaned of all sediment, epiphytes, and invertebrates, and their rhizomes were cut to 3 cm the day prior to allow wound healing. The roots and rhizomes of each shoot were compartmentalized from the leaf shoots by inserting the shoot through a slit in a watertight rubber stopper that was then inserted into a 40 mL opaque plastic chamber filled with 35 mL of nitrogen-free artificial seawater spiked with ammonium to 100 M using a 5M NH4-N stock solution prepared with ammonium sulfate. The root/rhizome chambers with shoots inserted were then placed into 2 L clear acrylic cylindrical chambers filled with 1 L of nitrogen-free artificial seawater spiked with nitrate to 40 M using a 2 M NO3-N stock solution prepared with sodium nitrate. Sixteen acrylic chambers were seated in a water bath with water circulating through a chiller to keep chamber seawater temperature between 10-12C (Bracken et al. 2011). To provide sufficient water flow to prevent mass transfer limitation of uptake, we attached submersible pumps to each chamber via inflow and outflow pipes. Full spectrum quartz halite lamps surrounding the water bath provided the chambers with photosynthesis-saturating light (~700 mol photons m-2s-1). We took water samples from the root/rhizome chambers and shoot chambers prior to the start of the experiment, then sampled the shoot chambers every hour for 4 hours, at which time we detached the root/rhizome chambers from the shoot chamber, removed the shoots and took a final sample. We analyzed the shoot chamber samples (nitrate) using a Lachat 8000 series flow injection auto-analyzer and root/rhizome chamber samples (ammonium) using a Beckman Coulter DU640 spectrophotometer (Koroleff 1976). After removing plants from the chambers, we divided them into shoots, roots, and rhizomes, and dried them at 60C for at least 48 hours and then weighed each for biomass-specific uptake rate. We ran uptake trials with between 10-14 genotypes per day and measured all genotypes each week for nine weeks. Within a week eelgrass genotypes were randomly assigned a day and position in the water bath. Some genotypes did not yield enough shoots in the common garden for the full set of nine replicates (one genotype had only enough shoots for three replicates, but most had seven to nine successful replicates).
Leaf phenolic content
We analyzed total phenolic content using approximately 4 mg of dried, ground leaf material from each genotype (pooled from 3 leaves) following a modified Folin-Ciocalteu method (see Bolser et al. 1998). We extracted phenolics with 2 mL of 80% methanol for 24 hours, and then quantified them with a spectrophotometer using caffeic acid as a standard. Ferulic and caffeic acids are two of the most abundant phenolics in Zostera marina (Quackenbush et al. 1986; Vergeer and Develi 1997), and previous work showed that caffeic, ferulic, or gallic acids standards for eelgrass phenolic content from shoots collected in Bodega Bay produced similar results (Tomas et al. 2011).
Photosynthetic rate
We evaluated the photosynthetic performance of each genotype using a Diving-PAM® (Pulse Amplitude Modulated) fluorometer (Walz, Germany) to measure maximum quantum yield (potential photosynthetic efficiency, FV/Fm) and rapid light curves (RLC), which determine the effective quantum yield as a function of irradiance and can be used to assess light adaptation (Ralph and Gademann 2005, Williams et al. 2009). First, we dark-acclimated the outer leaves of each shoot for 30 minutes by placing a Waltz 4 mm opaque leaf clip 20 cm from the sediment surface on a leaf cleaned of epiphytes, then we immediately took maximum quantum yield and rapid light curve (RLC) measurements. RLCs comprised eight incremental steps of actinic light irradiance from 30 to 1129 PAR (mol photons m-2s-1), and the resulting yield measurements were converted into a relative electron transport rate (rETR) using the following equation:
rETR = F/Fm *PAR*0.5*AF,
where F/Fm is the effective quantum yield, F is the difference between background fluorescence F and F at each PAR increment, Fm is the maximum fluorescence, 0.5 assumes that photons absorbed are equally distributed between photosystems I and II (Genty et al. 1989), and AF is the standard absorption factor (0.55) for seagrasses (Durako 2007). To compare RLCs among genotypes we used curve fitting methods outlined in Ralph and Gademann (2005) to estimate characteristic parameters for each curve including: , initial slope of the curve (rate of increase in photosynthesis with increasing light in light-limited region of the RLC or photosynthetic efficiency); , slope of the curve where yield declines (strength of photoinhibition); and Ps, the maximum potential rETR. We fit each curve to a double exponential decay function (Platt et al. 1980) using the “nls” function in the stats package in R 3.0.2.
Specified by the Principal Investigator(s)
<p>BCO-DMO Data Manager Processing Notes:
* added a conventional header with dataset name, PI name, version date
* modified parameter names to conform with BCO-DMO naming conventions
* All numeric values with more than three decimal places to three decimal places
* data version 2 (2018-06-28) replaces data version 1 (2018-01-31). Data version 2 includes a new parameter Pam_b, and removes "Flag" which was for internal use and not meaningful for re-use of the data since it was replaced by genotype identifier. Previously documented processing steps took place for both data version 1 and data version 2.
* rounded decimal places based on feedback from the data contributor. All numeric values with more than three decimal places to three decimal places except the following:
* rounded to one decimal place: shoots, width, rhiz_D
* rounded to two decimals: length, term_rhiz, new_rhiz, max_root, tot_rhiz
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
Deployment: Stachowicz_Eelgrass_Transects_2012
Stachowicz_Eelgrass_Transects_2012
Bodega Harbor
shoreside
Stachowicz_Eelgrass_Transects_2012
John J. Stachowicz
University of California-Davis
Bodega Harbor
shoreside