Data from: Environmental stress and resource availability affect the maintenance of genetic variation in a dominant marsh plant (Spartina alterniflora)
Data files
Nov 25, 2024 version files 266.45 KB
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flower_data.csv
7.60 KB
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Genotype_survival_S16_subsetnoreps.xls
51.20 KB
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Genotypes_Nexp_field_similarity.csv
6.35 KB
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Nexp_Bruvodistance_perplot.csv
18.80 KB
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Nexp_GD_genotypic_composition_overtime.csv
17.40 KB
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Nexp_GDyrtime_richness.csv
8.31 KB
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README.md
18.07 KB
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Seedmom_flowerdata.csv
1.29 KB
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Tidal_GE_final_subset.csv
4.12 KB
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Wrack_measurements_Nexp.csv
4.68 KB
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Y1-3_EnvironmentCombo_forGenoDive.xlsx
128.64 KB
Abstract
Changes in genetic variation, and particularly documented declines in genetic diversity, influence not only evolutionary potential but also current ecological function. Given this context, it is essential to understand what abiotic and biotic factors promote or disrupt the maintenance of genetic variation in natural populations. To address this knowledge gap in the context of salt marsh plants, we established a 3-year field experiment testing the independent and interactive effects of nutrient availability and physical stress on the maintenance of plant (Spartina alterniflora) genotypic diversity. We found that in environments with high physical stress (i.e., low marsh elevations), diversity declined over time. However, the addition of nutrients promoted the maintenance of Spartina genotypic diversity across the physical stress gradient. We also observed changes in genotypic composition and genetic divergence across environmental stress treatments, indicating variation among Spartina genotypes in their response to these factors. Our results suggest that tidal inundation acts a selective gradient within coastal marshes, altering genotypic diversity and composition across the landscape. Moreover, our work highlights that the effects of increasing inundation due to continued sea level rise on the maintenance of diversity may be modulated by concomitant changes in nutrient inputs, with cascading effects on marsh structure and function.
README: Environmental stress and resource availability affect the maintenance of genetic variation in a dominant marsh plant (Spartina alterniflora)
https://doi.org/10.5061/dryad.msbcc2g78
Description of the data and file structure
Title: Environmental stress and resource availability affect the maintenance of genetic variation in a dominant marsh plant (Spartina alterniflora)
Data Points of Contact: Robyn A. Zerebecki1,2* and A. Randall Hughes1
- Marine Science Center and Coastal Sustainability Institute, Northeastern University, Nahant, MA, USA
- Department of Biology, University of Louisiana – Lafayette, 410 E. St. Mary Blvd, Lafayette, LA 70503
Corresponding Email addresses: robyn.zerebecki@louisiana.edu
Date of Data Collection: 2013-2016
Geographic Location of Data Collection: Field experiment conducted in Florida, USA.
Methodological Information: Description of methods and data collection for field experiment and genetic data in Zerebecki & Hughes Molecular Ecology
Files and variables
File: Nexp_Bruvodistance_perplot.csv
Description: pairwise Bruvo distance between all genotypes within plots (calculated in GenoDive)
Variables
- Row: Tidal elevation as row (High, Mid or Low marsh)
- Plot: Unique plot number
- Polyculture: Polyculture combination identity (e.g., P8) of plots
- Nutrient: Elevated – nutrients added; none – ambient nutrient treatment
- Year: Year that data was collected in (2013-2016)
- Plot ID_Year: Combination of Plot # and Year
- Total genotypic richness: # of unique Spartina genotypes in plot (including both planted and recruited genotypes)
Next 6 columns with the identity of documented genotypes (up to 6 potential genotypes) within the plot at each individual time point (there is no particular order to genotypes); recruit genotypes are identified as DIFFERENT #; if there are less than 6 genotypes later columns are left blank
- G1: Identity of genotype 1 in plot
- G2: Identity of genotype 2 in plot
- G3: Identity of genotype 3 in plot
- G4: Identity of genotype 4 in plot
- G5: Identity of genotype 5 in plot
- G6: Identity of genotype 6 in plot
## When fewer than 6 genotypes existed in the plot, cells contain NA for any pairwise comparison that did not exist.
- B: G1-G2: Bruvo distance between G1 (genotype 1; identified in column G1) and G2 (genotype 2, identified in column G2)
- B: G1-G3: Bruvo distance between G1 (genotype 1; identified in column G1) and G3 (genotype 3, identified in column G3)
- B:G1-G4: Bruvo distance between G1 (genotype 1; identified in column G1) and G4 (genotype 4, identified in column G4)
- B:G1-G5: Bruvo distance between G1 (genotype 1; identified in column G1) and G5 (genotype 5, identified in column G5)
- B:G1-G6: Bruvo distance between G1 (genotype 1; identified in column G1) and G6 (genotype 6, identified in column G6)
- B: G2-G3: Bruvo distance between G2 (genotype 2; identified in column G2) and G3 (genotype 3, identified in column G3)
- B: G2-G4: Bruvo distance between G2 (genotype 2; identified in column G2) and G4 (genotype 4, identified in column G4)
- B:G2-G5: Bruvo distance between G2 (genotype 2; identified in column G2) and G5 (genotype 5, identified in column G5)
- B: G2-G6: Bruvo distance between G2 (genotype 2; identified in column G2) and G6 (genotype 6, identified in column G6)
- B:G3-G4: Bruvo distance between G3 (genotype 3; identified in column G3) and G4 (genotype 4, identified in column G4)
- B:G3-G5: Bruvo distance between G3 (genotype 3; identified in column G3) and G5 (genotype 5, identified in column G5)
- B:G3-G6: Bruvo distance between G3 (genotype 3; identified in column G3) and G6 (genotype 6, identified in column G6)
- B: G4-G5: Bruvo distance between G4 (genotype 4; identified in column G4) and G5 (genotype 5, identified in column G5)
- B: G4-G6: Bruvo distance between G4 (genotype 4; identified in column G4) and G6 (genotype 6, identified in column G6)
- B:G5-G6: Bruvo distance between G5 (genotype 5; identified in column G5) and G6 (genotype 6, identified in column G6)
- AVERAGE BRUVO DISTANCE: mean pairwise Bruvo distance across all pairwise comparisons in the plot
File: Nexp_GD_genotypic_composition_overtime.csv
Description: relative abundance of each Spartina genotype in plots over time
Variables
- Row: Tidal elevation as row (High, Mid or Low marsh)
- Plot #: Unique plot number
- Genotype: Polyculture combination identity (e.g., P8) of plots
- Nutrient: Elevated – nutrients added; none – ambient nutrient treatment
- Diversity: diversity treatment (all polycultures)
- Date: Year and month that data was collected in (2013-2016)
- Total Density: stem density measured in plot at individual time point
- A-I2: relative abundance of Spartina genotype AI-2
- D-I3: relative abundance of Spartina genotype DI-3
- F-S3.5: relative abundance of Spartina genotype F-S3.5
- I12-1A: relative abundance of Spartina genotype I12-1A
- I12-1: relative abundance of Spartina genotype I12-1
- I11-3: relative abundance of Spartina genotype I11-3
- I3-1: relative abundance of Spartina genotype I3-1
- I5-1: relative abundance of Spartina genotype I5-1
- I5-2: relative abundance of Spartina genotype I5-2
- I6-2: relative abundance of Spartina genotype I6-2
- S2-1: relative abundance of Spartina genotype S2-1
- S5-3: relative abundance of Spartina genotype S5-3
- S6-1: relative abundance of Spartina genotype S6-1
- S6-4: relative abundance of Spartina genotype S6-4
- S8-1: relative abundance of Spartina genotype S8-1
- I5-3: relative abundance of Spartina genotype I5-3
- New genotype: relative abundance of all Spartina recruit genotypes (combined all recruits present)
File: flower_data.csv
Description: reproductive metrics (flowers and seed production) in fall 2015
Variables
- Row #: Row #: Tidal elevation as row (1 = high elevation, 2 = mid, and 3 = low marsh)
- Pot Location: Plot number per row
- Plot #: Unique plot number
Gen 1: Genotype identity (e.g., I12-1) or polyculture combination identity (e.g., P8) of plots; empty if no initial planted Spartina
Nutrient treatment: Elevated – nutrients added; none – ambient nutrient treatment
Polyculture: Planted diversity treatment; No corresponds to monoculture (one genotype); None corresponds to no planted Spartina; Yes corresponds to polyculture (6 genotypes planted)
October Density: stem density measured in plot at reproductive sampling point
Flowered?: indicating whether there were flowering in the plot or not (yes = 1; no = 0)
Total #reproductive stems over time: total number of flowering stems counted throughout the growing season (Oct-Nov)
Ratio Flower: Veg: proportion of number of flowering stems to number of Spartina stems in the plot
Number of Seeds: Total number of seeds on all flowering stems collected from the plot in October 2015
# Reproductive stem at seed collection time: the number of flowering stems collected in October 2015
Seeds per stem: (Number of Seeds) divided by (# Reproductive stem at seed collection time); empty cells are NA as there was no flowering stems present
File: Seedmom_flowerdata.csv
Description: genotypic identity of flowering stems compared to # of genotypes in plot
Variables
- Row: Tidal elevation as row (High, Mid or Low marsh)
Plot #: Unique plot number
Nutrient treatment: Elevated – nutrients added; none – ambient nutrient treatment
Polyculture: Polyculture combination identity (e.g., P8) of plots
# different genotypes that flowered: number of unique *Spartina *genotypes that flowered
Total # flowers genotyped: total number of flowers that we collected and samples genotyped
Genotypic Richness: *Spartina *genotypic richness in the plot in October 2015 when flowers were collected
File: Wrack_measurements_Nexp.csv
Description: measurements of wrack within plots throughout the experiment
Variables
Row #: Tidal elevation as row (1 = high elevation, 2 = mid, and 3 = low marsh)
Pot Location: Plot number per row
Gen 1: Genotype identity (e.g., I12-1) or polyculture combination identity (e.g., P8) of plots; empty if no initial planted Spartina
Nutrient treatment: Elevated – nutrients added; none – ambient nutrient treatment
Wrack Sept 2014: depth of wrack (organic debris; typically seagrass leaves) in cm within plot during September 2014 field survey
WRACK April 2015: depth of wrack (organic debris; typically seagrass leaves) in cm within plot during April 2015 field survey
Wrack (depth May 2014) : depth of wrack (organic debris; typically seagrass leaves) in cm within plot during May 2014 field survey
Wrack (winter 2014): depth of wrack (organic debris; typically seagrass leaves) in cm within plot during Nov 2014 field survey
Avg Wrack depth: average depth of wrack across all 4 sampling period
File: Nexp_GDyrtime_richness.csv
Description: *Spartina *genotypic richness (total, planted and recruit) over time
Variables
Row: Tidal elevation as row (High, Mid or Low marsh)
Plot #: Unique plot number
Polyculture: Polyculture combination identity (e.g., P8) of plots
Nutrient: Elevated – nutrients added; none – ambient nutrient treatment
Year: Year that data was collected in (2013-2016)
Plot ID_Year: Combination of Plot # and Year
Total genotypic richness: # of unique Spartina genotypes in plot (including both planted and recruited genotypes)
# Original Genotypes Present: # of unique Spartina genotypes in plot that were originally planted
# of new genotypes: # of unique Spartina genotypes in plot that recruited to plot (i.e., were not planted)
Colonizer.Yes: Did the plot contain recruited genotypes (Yes = 1, No = 0)
File: Genotype_survival_S16_subsetnoreps.xls
Description: survival of individual Spartina genotypes across plots and environmental treatments at the end of year 3
Variables
Tidal Elevation: row plot planted in; high, mid or low marsh
Nutrient: Elevated – nutrients added; none – ambient nutrient treatment
Polyculture: Polyculture combination identity (e.g., P8) of plots
Plot: Plot number within row
Genotype: Focal Spartina genotype
Presence S16: survival of genotype within plot at the end of the experiment (1 = present/alive; 0 = absence/lost)
File: Genotypes_Nexp_field_similarity.csv
Description: a matrix of allele frequencies, with each row representing an individual and each column representing an allele to visualize variation in genetic similarity among all genotypes using PCA approach
Variables
- Genotype ID:Genotype: Spartina genotypic identity of either planted (e.g., A-I2, D-I3, etc.) or recruited (numbered)
## Other columns are all possible alleles for each locus (10); for each individual (row) a allele would either be absent (0), present as homozygote (1) or a heterozygote with that allele present (0.5).
- Spar9_271: Potential allele in our samples for Spar9 loci
- Spar9_273: Potential allele in our samples for Spar9 loci
- Spar9_275: Potential allele in our samples for Spar9 loci
- Spar9_277: Potential allele in our samples for Spar9 loci
- Spar9_279: Potential allele in our samples for Spar9 loci
- Spar9_282: Potential allele in our samples for Spar9 loci
- Spar9_284: Potential allele in our samples for Spar9 loci
- Spar9_285: Potential allele in our samples for Spar9 loci
- Spar9_286: Potential allele in our samples for Spar9 loci
- Spar5_192: Potential allele in our samples for Spar5 loci
- Spar5_194: Potential allele in our samples for Spar5 loci
- Spar5_200: Potential allele in our samples for Spar5 loci
- Spar5_202: Potential allele in our samples for Spar5 loci
- Spar5_204: Potential allele in our samples for Spar5 loci
- Spar5_205: Potential allele in our samples for Spar5 loci
- Spar5_206: Potential allele in our samples for Spar5 loci
- Spar5_208: Potential allele in our samples for Spar5 loci
- Spar5_212: Potential allele in our samples for Spar5 loci
- Spar5_214: Potential allele in our samples for Spar5 loci
- Spar5_220: Potential allele in our samples for Spar5 loci
- Spar10_336: Potential allele in our samples for Spar10 loci
- Spar10_338: Potential allele in our samples for Spar10 loci
- Spar10_340: Potential allele in our samples for Spar10 loci
- Spar10_342: Potential allele in our samples for Spar10 loci
- Spar10_344: Potential allele in our samples for Spar10 loci
- Spar10_346: Potential allele in our samples for Spar10 loci
- Spar10_348: Potential allele in our samples for Spar10 loci
- Spar2_190: Potential allele in our samples for Spar2 loci
- Spar2_192: Potential allele in our samples for Spar2 loci
- Spar2_193: Potential allele in our samples for Spar2 loci
- Spar2_197: Potential allele in our samples for Spar2 loci
- Spar2_207: Potential allele in our samples for Spar2 loci
- Spar2_209: Potential allele in our samples for Spar2 loci
- Spar3_315: Potential allele in our samples for Spar3 loci
- Spar3_318: Potential allele in our samples for Spar3 loci
- Spar3_321: Potential allele in our samples for Spar3 loci
- Spar34_367: Potential allele in our samples for Spar34 loci
- Spar34_368: Potential allele in our samples for Spar34 loci
- Spar34_369: Potential allele in our samples for Spar34 loci
- Spar34_370: Potential allele in our samples for Spar34 loci
- Spar34_372: Potential allele in our samples for Spar34 loci
- Spar34_373: Potential allele in our samples for Spar34 loci
- Spar34_374: Potential allele in our samples for Spar34 loci
- Spar14_228: Potential allele in our samples for Spar14 loci
- Spar14_230: Potential allele in our samples for Spar14 loci
- Spar14_231: Potential allele in our samples for Spar14 loci
- Spar16_366: Potential allele in our samples for Spar16 loci
- Spar16_372: Potential allele in our samples for Spar16 loci
- Spar16_375: Potential allele in our samples for Spar16 loci
- Spar16_376: Potential allele in our samples for Spar16 loci
- Spar16_378: Potential allele in our samples for Spar16 loci
- Spar16_380: Potential allele in our samples for Spar16 loci
- Spar7_274: Potential allele in our samples for Spar7 loci
- Spar7_279: Potential allele in our samples for Spar7 loci
- Spar7_282: Potential allele in our samples for Spar7 loci
- Spar7_284: Potential allele in our samples for Spar7 loci
- Spar7_288: Potential allele in our samples for Spar7 loci
- Spar7_291: Potential allele in our samples for Spar7 loci
- Spar7_294: Potential allele in our samples for Spar7 loci
- Spar7_296: Potential allele in our samples for Spar7 loci
- Spar7_298: Potential allele in our samples for Spar7 loci
- Spar7_299: Potential allele in our samples for Spar7 loci
- Spar7_300: Potential allele in our samples for Spar7 loci
- Spar7_301: Potential allele in our samples for Spar7 loci
- Spar8_182: Potential allele in our samples for Spar8 loci
- Spar8_184: Potential allele in our samples for Spar8 loci
- Spar8_190: Potential allele in our samples for Spar8 loci
- Spar8_192: Potential allele in our samples for Spar8 loci
- Spar8_194: Potential allele in our samples for Spar8 loci *
File: Tidal_GE_final_subset.csv
Description: data from companion field experiment (Appendix 3) that planted a subset of individual genotypes across tidal elevation
Variables
Tidal Height: Tidal Height: row as move from low (1) to higher marsh (4); rows spaced
Plot #: Plot number within row (tidal height)
Genotype: *Spartina *genotypic identity
Survival: survival of transplants at the end of the experiment (1 = alive; 0= dead)
Final Live Density: number of live stems in pot at end of experiment
Final Dead Density: number of dead stems in pot at end of experiment
Final average Height: average Spartina stem height (cm) at end of experiment; blank cells are NA as there was no live stems to measure height on
Aboveground Biomass: aboveground biomass (stems) harvested at the end of experiment, dried for 24 hours at 65C and weighted
Belowground Biomass: belowground biomass (roots and rhizomes) harvested at the end of experiment, dried for 24 hours at 65C and weighted
File: Y1-3_EnvironmentCombo_forGenoDive.xlsx
Description: Allele calls (3 digits per allele; ploidy level 2) for all 10 DNA microsatellite loci used to genotype Spartina samples
Variables
Population: Each number represents a environmental treatment combination (i.e., tidal elevation x nutrient addition treatment) by year. (Individual names denote how numbers corresponded)
Clone: Spartina genotypic identity
Individual: Recorded as Tidal Elevation_Nutrient Treatment_Plot#_Tissue Sample (GD1-18)_Year (F15 for 2015, S16 for 2016, or blank for 2014)
The following columns are for values from alleles score for each individual microsatellite locus. Every allele is coded with 3 digits, and 2 alleles (diploid) for each locus. Missing data is denoted by appropriate number of zeros (3 per allele)
- SPAR9_FAM: allele calls for locus Spar09
- SPAR05_VIC: allele calls for locus Spar05
- SPAR10_NED: allele calls for locus Spar10
- SPAR02_FAM: allele calls for locus Spar02
- SPAR03_VIC: allele calls for locus Spar03
- SPAR34_VIC: allele calls for locus Spar34
- SPAR14_NED: allele calls for locus Spar14
- SPAR16_FAM: allele calls for locus Spar16
- SPAR07_NED: allele calls for locus Spar07
- SPAR08_PET: allele calls for locus Spar08
Methods
Description of methods and data collection for field experiment and genetic data processing and analysis in Zerebecki & Hughes Molecular Ecology