Data for: Comparative biogeography of marine and terrestrial microbes and macrobiota using eDNA
Data files
Apr 29, 2026 version files 10.90 MB
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1_data_prepare_ed.R
3.63 KB
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2_run_gf_ed.R
2.43 KB
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2025cvrf_alpha_estimates_dunes_multi_uni.csv
2.41 KB
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2025cvrf_alpha_estimates_estuaries_multi_uni.csv
1.85 KB
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2025cvrf_alpha_estimates_marine_multi_uni.csv
2.52 KB
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3_permu_gf_ed.R
1.84 KB
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Coast_12S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
10.08 KB
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Coast_16S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
2.03 MB
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Coast_18S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced.csv
1.23 MB
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Coast_CO1_70_sum_Ruggiero_consolidated_renamed_caledna_taxonomy_balanced_no_na.csv
452.20 KB
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Coast_FITS_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
621.75 KB
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Coast_PITS_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
159.31 KB
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COI_cov_raref85.csv
411.36 KB
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Dunes_multicellular_glom2025_cvrf85.RData
13.33 KB
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dunes_multicellular_Species_list_2025_cvrf85.csv
7.47 KB
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Dunes_Unicellular_glom2025_cvrf85.RData
30.38 KB
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dunes_Unicellular_Species_list_2025_cvrf85.csv
57.10 KB
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estuaries_multicellular_Species_list_2025_cvrf85.csv
7.50 KB
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estuaries_Unicellular_Species_list_2025_cvrf85.csv
51.30 KB
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Estuary_Multicellular_glom2025_cvrf85.RData
11.80 KB
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Estuary_Unicellular_glom2025_cvrf85.RData
26.38 KB
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Final_metadata_Coast_balanced.csv
34.56 KB
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FITS_cov_raref85.csv
594.98 KB
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inext_cvrf.Rmd
7.88 KB
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macro_samples_raup_df_cr85.csv
21.67 KB
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Metadata_Multicellular_UnicellularDec2025.csv
45.28 KB
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micro_samples_raup_df_cr85.csv
62.62 KB
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Multicellular_cvrf85_glom2025.RData
34.78 KB
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phy_deco_all_2025_cvrf85flt.RData
730.36 KB
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phy_rare_covrf85_all_2025.RData
726.74 KB
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PITS_cov_raref85.csv
124.74 KB
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plotting_ed.R
4.25 KB
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README.md
30.44 KB
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S12S_cov_raref85.csv
5.15 KB
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S16S_cov_raref85.csv
1.92 MB
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S18S_cov_raref85.csv
1.19 MB
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Step1_coverage_raref.Rmd
4.30 KB
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Step2_data_objects_coverage_rarefaction2025.Rmd
8.26 KB
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Step3_CoastalTest2025_covrf_ed20251212.Rmd
18.06 KB
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Step4_RaupCrick_betas20251223.Rmd
12.92 KB
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Step5_RC_betadiversity_plots.Rmd
5.74 KB
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Step6_CAP_coastal_fig_viridis_raup2026cvrf_grey.Rmd
16.94 KB
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Swash_multicellular_glom2025_cvrf85.RData
14.95 KB
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Swash_multicellular_Species_list_2025_cvrf85.csv
3.17 KB
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Swash_Unicellular_glom2025_cvrf85.RData
29.22 KB
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Swash_Unicellular_Species_list_2025_cvrf85.csv
45.37 KB
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Unicellular_cvrf85_glom2025.RData
76.95 KB
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util_ed.R
6.07 KB
Abstract
Quantifying how patterns of biodiversity and their underlying processes vary across diverse environments and ecological communities is a long-standing challenge in biogeography. The logistics of sampling entire communities over different geographic scales are barriers to determining biogeographic theory and conceptual integration at large scales. Here, we use environmental DNA (eDNA) to overcome these challenges and examine biogeography hypotheses contrasting diversity in marine versus terrestrial realms and unicellular versus multicellular organisms. Within two days, we sampled three habitats (terrestrial, estuarine, marine) along a ~1,400 km coastal transect in California. We found that eDNA exhibited some novel and familiar alpha diversity patterns. Marine diversity was lower than terrestrial diversity in multicellular biota, though statistically indistinguishable in unicellular biota. Richness was modestly correlated within habitats and was generally higher in unicellular biota. Beta diversity was, unexpectedly, less similar within marine than terrestrial assemblages. Unicellular assemblages were consistently more similar than multicellular assemblages, and their beta diversity correlated. These patterns are consistent with predictions based on body size and nuanced effects of environmental heterogeneity. However, there was considerable heterogeneity in both datasets. Overall, eDNA provides a common currency for studying biodiversity patterns and underlying processes (e.g. drift, migration, selection) across diverse environments and complex communities and may be the foundation of a unifying macroecological framework.
Raw reads were deposited in NCBI Sequence Read Archive (Accession: PRJNA702201).
Photographs from the sampling sites and the habitat were taken and uploaded with the kobo tool app to the CALeDNA webpage under the Transect Research Project (https://data.ucedna.com/research_projects/transect/pages/overview)
DATA & CODE FILE OVERVIEW
This data repository consists of 35 data files, 12 code scripts, and this README document, with the following data and code filenames and variables
Data files and variables
1. Final_metadata_Coast_balanced.csv
Description: Metadata for sample collections, including coordinates, site description, environmental variables, and diversity indexes. Used with the taxonomy tables to create initial phyloseq objects.
Variables: MatchName: Sample site's name assigned by CALeDNA naming scheme reported by volunteers.
Longitude: Longitude of sample sites reported by volunteers. Units: decimal degrees
Latitude: Latitude of sample sites reported by volunteers. Units: decimal degrees
Date: Date of sampling reported by volunteers.
location: Name of places visited reported by volunteers.
location code: Two letter abbreviation for the sites name.
ecoregion: Habitat as described in Level III Ecoregions of the Continuous United States, United States Environmental Protection Agency
majorhab: Major habitat type classified according to California Wildlife Habitat Relationships System, California Department of Fish and Wildlife
minorhab: Habitat Minor habitat type classified according to California Wildlife Habitat Relationships System, California Department of Fish and Wildlife
microhab: Minor habitat type reported by volunteers.
habitat_coarse: General habitat type classification reported by volunteers.
bio1: BioClim Annual Mean Temperature. Units: C*100
bio2: BioClim Mean Diurnal Range (Mean of monthly (max temp - min temp)). Units: C*100
bio3: BioClim Isothermality (BIO2/BIO7) (* 100)
bio4: BioClim Temperature Seasonality (standard deviation *100). Unit: C
bio5: BioClim Max Temperature of Warmest Month. Units: C*100
bio6: BioClim Min Temperature of Coldest Month. Units: C*100
bio7: BioClim Temperature Annual Range (BIO5-BIO6). Units: C*100
bio8: BioClim Mean Temperature of Wettest Quarter. Units: C*100
bio9: BioClim Mean Temperature of Driest Quarter. Units: C*100
bio10: BioClim Mean Temperature of Warmest Quarter. Units: C*100
bio11: BioClim Mean Temperature of Coldest Quarter. Units: C*100
bio12: BioClim Annual Precipitation. Units: mm
bio13: BioClim Precipitation of Wettest Month. Units: mm
bio14: BioClim Precipitation of Driest Month. Units: mm
bio15: BioClim Precipitation Seasonality (Coefficient of Variation). Units: mm
bio16: BioClim Precipitation of Wettest Quarter. Units: mm
bio17: BioClim Precipitation of Driest Quarter. Units: mm
bio18: BioClim Precipitation of Warmest Quarter. Units: mm
bio19: BioClim Precipitation of Coldest Quarter. Units: mm
sndppt: Soil Properties Sand content (50 to 2000 micro meter) mass fraction in % at depth 0.00 m from SoilGrid
sltppt: Soil Properties weight percentage of the silt particles (0.0002–0.05 mm) from SoilGrid
clyppt: Soil Properties Clay content (0 ~ 2 micro meter) mass fraction in % at depth 0.00m from SoilGrid
bldfie: Soil Properties Bulk density (fine earth) in kg / cubic meter at depth 0.00 m from SoilGrid
SoS: Soil Properties Volunteers' classification of Sediment/ Soil/ Sand
Elev: Topology Elevation of sampling site Elevation Derivatives for National Applications (EDNA)
Slope: Topology The rate of change of elevation for each digital elevation model (DEM) cell Elevation Derivatives for National Applications (EDNA)
NDVIS2: Vegetation Normalized Difference Vegetation Index calculated from Sentinel bands B4, B8 Sentinel-2
NDVI32: Vegetation Normalized Difference Vegetation Index in 32 days period LANDSAT8 TIER 1
Greenness: Vegetation Annual Greenest Pixel in the year of 2017 LANDSAT8 TIER 1
2. Coast_CO1_70_sum_Ruggiero_consolidated_renamed_caledna_taxonomy_balanced_no_na.csv
Description: Decontaminated ASV table for CO1 marker, taxonomic identity at 70%. Samples were decontaminated by eliminating from all the samples the sequences that appeared in the extraction blanks. We keep the sequences that never appeared in the blank. Blank samples columns were then removed. Taxa with low read count were filtered out using the filter_taxa function in phyloseq. The minimum total for a taxa was set at 5 read total. filter_taxa(phyloseq_object, function(x) sum(x) > 4, T)
Variables:
sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline
K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
3. Coast_PITS_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
Description: Decontaminated ASV table for PITS marker, taxonomic identity at 60%. Samples were decontaminated by eliminating from all the samples the sequences that appeared in the extraction blanks. We keep the sequences that never appeared in the blank. Blank samples columns were then removed. Taxa with low read count were filtered out using the filter_taxa function in phyloseq. The minimum total for a taxa was set at 5 read total. filter_taxa(phyloseq_object, function(x) sum(x) > 4, T)
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline
K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
4. Coast_18S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced.csv
Description: Decontaminated ASV table for 18S marker, taxonomic identity at 70%. Samples were decontaminated by eliminating from all the samples the sequences that appeared in the extraction blanks. We keep the sequences that never appeared in the blank. Blank samples columns were then removed. Taxa with low read count were filtered out using the filter_taxa function in phyloseq. The minimum total for a taxa was set at 5 read total. filter_taxa(phyloseq_object, function(x) sum(x) > 4, T)
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline
K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
5. Coast_12S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
Description: Decontaminated ASV table for 12S marker, taxonomic identity at 70%. Samples were decontaminated by eliminating from all the samples the sequences that appeared in the extraction blanks. We keep the sequences that never appeared in the blank. Blank samples columns were then removed. Taxa with low read count were filtered out using the filter_taxa function in phyloseq. The minimum total for a taxa was set at 5 read total. filter_taxa(phyloseq_object, function(x) sum(x) > 4, T)
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline
K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
6. Coast_FITS_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
Description: Decontaminated ASV table for FITS marker, taxonomic identity at 70%. Samples were decontaminated by eliminating from all the samples the sequences that appeared in the extraction blanks. We keep the sequences that never appeared in the blank. Blank samples columns were then removed. Taxa with low read count were filtered out using the filter_taxa function in phyloseq. The minimum total for a taxa was set at 5 read total. filter_taxa(phyloseq_object, function(x) sum(x) > 4, T)
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline
K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
7. Coast_16S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
Description: Decontaminated ASV table for 16S marker, taxonomic identity at 60%. Samples were decontaminated by eliminating from all the samples the sequences that appeared in the extraction blanks. We keep the sequences that never appeared in the blank. Blank samples columns were then removed. Taxa with low read count were filtered out using the filter_taxa function in phyloseq. The minimum total for a taxa was set at 5 read total. filter_taxa(phyloseq_object, function(x) sum(x) > 4, T)
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline.
K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
8. S12S_cov_raref85.csv
Description: ASV table for marker S12S after coverage rarefaction. Samples were rarefied to a 0.85 coverage value with the function phyloseq_coverage_raref in R package metagMisc (Mikryukov 2021).
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline K0063B2 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
9. COI_cov_raref85.csv
Description: ASV table for marker CO1 after coverage rarefaction. Samples were rarefied to a 0.85 coverage value with the function phyloseq_coverage_raref in R package metagMisc (Mikryukov 2021).
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
10. PITS_cov_raref85.csv
Description: ASV table for marker PITS after coverage rarefaction. Samples were rarefied to a 0.85 coverage value with the function phyloseq_coverage_raref in R package metagMisc (Mikryukov 2021).
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
11. S18S_cov_raref85.csv
Description: ASV table for marker 18S after coverage rarefaction. Samples were rarefied to a 0.85 coverage value with the function phyloseq_coverage_raref in R package metagMisc (Mikryukov 2021).
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
12. FITS_cov_raref85.csv
Description: ASV table for marker FITS after coverage rarefaction. Samples were rarefied to a 0.85 coverage value with the function phyloseq_coverage_raref in R package metagMisc (Mikryukov 2021).
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline K0030A1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
13. S16S_cov_raref85.csv
Description: ASV table for marker 16S after coverage rarefaction. Samples were rarefied to a 0.85 coverage value with the function phyloseq_coverage_raref in R package metagMisc (Mikryukov 2021).
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline K0030B1 to K0219C1: Sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
14. Metadata_Multicellular_UnicellularDec2025.csv
Description: Metadata for sample collections, including coordinates, site description, environmental variables, and diversity indexes. Include samples retained after 85% coverage rarefaction. File used in tests and plots included in Step3_CoastalTest2025_covrf_ed20251212.Rmd; Step4_RaupCrick_betas20251223.Rmd; Step5_RC_betadiversity_plots.Rmd; Step6_CAP_coastal_fig_viridis_raup2026cvrf_grey.Rmd. Also used as metadata file in the Gradient Forest analyses.
Variables: MatchName: Sample site's name assigned by CALeDNA naming scheme reported by volunteers.
Longitude: Longitude of sample sites reported by volunteers. Units: decimal degrees
Latitude: Latitude of sample sites reported by volunteers. Units: decimal degrees
Date: Date of sampling reported by volunteers.
location: Name of places visited reported by volunteers.
location code: Two letter abbreviation for the sites name.
ecoregion: Habitat as described in Level III Ecoregions of the Continuous United States, United States Environmental Protection Agency
majorhab: Major habitat type classified according to California Wildlife Habitat Relationships System, California Department of Fish and Wildlife
minorhab: Habitat Minor habitat type classified according to California Wildlife Habitat Relationships System, California Department of Fish and Wildlife
microhab: Minor habitat type reported by volunteers.
habitat: General habitat type classification reported by volunteers.
Max day temp F: Maximum daily temperature in degree Fahrenheit for the day of the sample collection
Min day temp F: Minimum daily temperature in degree Fahrenheit for the day of the sample collection. Mean day temp F : Average daily temperature in degree Fahrenheit for the day of the sample collection
Mean day temp C: Average daily temperature in degree Celsius for the day of the sample collection.
NOAA_station: NOAA weather station from where data was obtained.
bio1: BioClim Annual Mean Temperature. Units: C*100
bio2: BioClim Mean Diurnal Range (Mean of monthly (max temp - min temp)). Units: C*100
bio3: BioClim Isothermality (BIO2/BIO7) (* 100)
bio4: BioClim Temperature Seasonality (standard deviation *100). Unit: C
bio5: BioClim Max Temperature of Warmest Month. Units: C*100
bio6: BioClim Min Temperature of Coldest Month. Units: C*100
bio7: BioClim Temperature Annual Range (BIO5-BIO6). Units: C*100
bio8: BioClim Mean Temperature of Wettest Quarter. Units: C*100
bio9: BioClim Mean Temperature of Driest Quarter. Units: C*100
bio10: BioClim Mean Temperature of Warmest Quarter. Units: C*100
bio11: BioClim Mean Temperature of Coldest Quarter. Units: C*100
bio12: BioClim Annual Precipitation. Units: mm
bio13: BioClim Precipitation of Wettest Month. Units: mm
bio14: BioClim Precipitation of Driest Month. Units: mm
bio15: BioClim Precipitation Seasonality (Coefficient of Variation). Units: mm
bio16: BioClim Precipitation of Wettest Quarter. Units: mm
bio17: BioClim Precipitation of Driest Quarter. Units: mm
bio18: BioClim Precipitation of Warmest Quarter. Units: mm
bio19: BioClim Precipitation of Coldest Quarter. Units: mm
sndppt: Soil Properties Sand content (50 to 2000 micro meter) mass fraction in % at depth 0.00 m from SoilGrid
sltppt: Soil Properties weight percentage of the silt particles (0.0002–0.05 mm) from SoilGrid
clyppt: Soil Properties Clay content (0 ~ 2 micro meter) mass fraction in % at depth 0.00m from SoilGrid
bldfie: Soil Properties Bulk density (fine earth) in kg / cubic meter at depth 0.00 m from SoilGrid
SoS: Soil Properties Volunteers' classification of Sediment/ Soil/ Sand
Elev: Topology Elevation of sampling site Elevation Derivatives for National Applications (EDNA)
Slope: Topology The rate of change of elevation for each digital elevation model (DEM) cell Elevation Derivatives for National Applications (EDNA)
NDVIS2: Vegetation Normalized Difference Vegetation Index calculated from Sentinel bands B4, B8 Sentinel-2
NDVI32: Vegetation Normalized Difference Vegetation Index in 32 days period LANDSAT8 TIER 1
Greenness: Vegetation Annual Greenest Pixel in the year of 2017 LANDSAT8 TIER 1
Observed.Uni.25cvrf: Unicellular Species richness (observed)
Chao1.Uni.25cvrf: Unicellular Chao1 index
se.chao1.Uni.25cvrf:standard error for unicellular Chao1 index
Shannon.Uni.25cvrf: Unicellular Shannon Index
InvSimpson.Uni.25cvrf: Unicellular Simpson Index
Observed.Multi.25cvrf: Multicellular Species richness (observed)
Chao1.Multi.25cvrf:Multicellular Chao1 index
se.chao1.Multi.25cvrf: standard error for multicellular Chao1 index
Shannon.Multi.25cvrf:Multicellular Shannon Index
InvSimpson.Multi.25cvrf:Multicellular Simpson Index
RC_mean.val.multi: Multicellular mean Raup-Crick values
RC_mean.val.uni: Unicellular mean Raup-Crick values
15. phy_rare_covrf85_all_2025.RData
Description: Intermediate file. Phyloseq data object of remaining samples after rarefaction to 85% coverage.
16. phy_deco_all_2025_cvrf85flt.RData
Description: intermediate file, same as output file phy_rare_covrf85_all_2025.RData but with taxonomy columns corrected, use this to create the Unicellular and multicellular datasets in Step2_habitat_data_objects_coverage_rarefaction2025.Rmd; and All samples in the Gradient Forest analyses.
17. Multicellular_cvrf85_glom2025.RData
Description: Intermediate file. Phyloseq data for the multicellular dataset
Variables: OTU table: sample table: tax table:
18. Unicellular_cvrf85_glom2025.RData
Description: Intermediate file. Phyloseq data object for the unicellular dataset
19. Swash_multicellular_glom2025_cvrf85.RData
Description: Intermediate file. Phyloseq data object for the multicellular organisms in the marine realm.
20. Estuary_Multicellular_glom2025_cvrf85.RData
Description: Intermediate file. Phyloseq data object for the multicellular organisms in the estuarine realm.
21. Dunes_multicellular_glom2025_cvrf85.RData
Description: Intermediate file. Phyloseq data object for the multicellular organisms in the terrestrial realm.
22. Swash_Unicellular_glom2025_cvrf85.RData
Description: Intermediate file. Phyloseq data object for the unicellular organisms in the marine realm.
23. Estuary_Unicellular_glom2025_cvrf85.RData
Description: Intermediate file. Phyloseq data object for the unicellular organisms in the estuarine realm.
24. Dunes_Unicellular_glom2025_cvrf85.RData
Description: Intermediate file. Phyloseq data object for the unicellular organisms in the terrestrial realm.
25. 2025cvrf_alpha_estimates_marine_multi_uni.csv
Description: Results of the alpha diversity estimates for multicellular and unicellular organisms in the marine realm
Variables: MatchName: Sample site's name assigned by CALeDNA naming scheme reported by volunteers. Observed.MM: species richness multicellular, Chao1.MM: Chao1 index multicellular, se.chao1.MM: standard error Chao1 index multicellular, Shannon.MM: Shannon diversity index multicellular, InvSimpson.MM: Simpson Index multicellular, Observed.MU: species richness unicellular, Chao1.MU: Chao1 index unicellular, se.chao1.MU: standard error Chao1 index unicellular, Shannon.MU: Shannon Index unicellular, InvSimpson.MU: Simpson Index multicellular
26. 2025cvrf_alpha_estimates_dunes_multi_uni.csv
Description: Results of the alpha diversity estimates for multicellular and unicellular organisms in the terrestrial realm
Variables: MatchName: Sample site's name assigned by CALeDNA naming scheme reported by volunteers. Observed.DM: species richness multicellular, Chao1.DM: Chao1 index multicellular, se.chao1.DM: standard error Chao1 index multicellular, Shannon.DM: Shannon diversity index multicellular, InvSimpson.DM: Simpson Index multicellular, Observed.DU: species richness unicellular, Chao1.DU: Chao1 index unicellular, se.chao1.DU: standard error Chao1 index unicellular, Shannon.DU: Shannon Index unicellular, InvSimpson.DU: Simpson Index multicellular
27. 2025cvrf_alpha_estimates_estuaries_multi_uni.csv
Description: Results of the alpha diversity estimates for multicellular and unicellular organisms in the estuarine realm
Variables: MatchName: Sample site's name assigned by CALeDNA naming scheme reported by volunteers. Observed.EM: species richness multicellular, Chao1.EM: Chao1 index multicellular, se.chao1.EM: standard error Chao1 index multicellular, Shannon.EM: Shannon diversity index multicellular, InvSimpson.EM: Simpson Index multicellular, Observed.EU: species richness unicellular, Chao1.EU: Chao1 index unicellular, se.chao1.EU: standard error Chao1 index unicellular, Shannon.EU: Shannon Index unicellular, InvSimpson.EU: Simpson Index multicellular
28. macro_samples_raup_df_cr85.csv
Description: Results of the beta diversity estimates for multicellular organisms
Variables: MatchName: Sample site's name assigned by CALeDNA naming scheme reported by volunteers. Each column is sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
29. micro_samples_raup_df_cr85.csv
Description: Results of the beta diversity estimates for unicellular organisms
Variables: MatchName: Sample site's name assigned by CALeDNA naming scheme reported by volunteers. Each column is sample site's name assigned by CALeDNA naming scheme. Sample kit number, 4-digit kit number, A/B/C are 3 sample sites, 1/2 are replicates within sites. See metadata for sites details.
30. Swash_Unicellular_Species_list_2025_cvrf85.csv
Description: Corrected taxonomy list of unicellular species found in the marine realm, after 85% coverage rarefaction.
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline. Other columns represent taxonomic classifications of Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species.
31. Swash_multicellular_Species_list_2025_cvrf85.csv
Description: Corrected taxonomy list of multicellular species found in the marine realm, after 85% coverage rarefaction.
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline. Other columns represent taxonomic classifications of Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species.
32. estuaries_Unicellular_Species_list_2025_cvrf85.csv
Description: Corrected taxonomy list of unicellular species found in the estuarine realm, after 85% coverage rarefaction.
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline. Other columns represent taxonomic classifications of Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species.
33. estuaries_multicellular_Species_list_2025_cvrf85.csv
Description: Corrected taxonomy list of multicellular species found in the estuarine realm, after 85% coverage rarefaction.
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline. Other columns represent taxonomic classifications of Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species.
34. dunes_Unicellular_Species_list_2025_cvrf85.csv
Description: Corrected taxonomy list of unicellular species found in the terrestrial realm, after 85% coverage rarefaction.
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline. Other columns represent taxonomic classifications of Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species.
35. dunes_multicellular_Species_list_2025_cvrf85.csv
Description: Corrected taxonomy list of multicellular species found in the terrestrial realm, after 85% coverage rarefaction.
Variables: sum.taxonomy: Summary of taxonomy assigned by the ANACAPA pipeline. Other columns represent taxonomic classifications of Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species.
Code scripts and workflow
1. Step1_coverage_raref.Rmd
Description: This script prepares first phyloseq objects and performs coverage rarefaction.
Input files: Coast_PITS_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
Coast_18S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced.csv
Coast_CO1_70_sum_Ruggiero_consolidated_renamed_caledna_taxonomy_balanced_no_na.csv
Coast_FITS_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
Coast_12S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
Coast_16S_ASV_sum_by_taxonomy_60_decontaminated_converted_caledna_taxonomy_balanced_no_na.csv
Final_metadata_Coast_balanced.csv
Output file:
phy_rare_covrf85_all_2025.RData
2. Step2_data_objects_coverage_rarefaction2025.Rmd
Description: This script creates the unicellular/multicellular data objects, run after Step 1.
Input files:
phy_rare_covrf85_all_2025.RData
Output files:
phy_deco_all_2025_cvrf85flt.RData (intermediate file with correct taxonomic order)
Multicellular_flt2025_cvrf85.RData
Multicellular_cvrf85_glom2025.RData
Unicellular_flt2025_cvrf85.RData
Unicellular_cvrf85_glom2025.RData
3. Step3_CoastalTest2025_covrf_ed20251212.Rmd
Description: Run after Step 1 and 2. This r notebook contains the scripts to create the habitat phyloseq objects. Plots from figure 3, and test for alpha diversity.
Input files:
Unicellular_cvrf85_glom2025.RData
Multicellular_cvrf85_glom2025.RData
Output files:
Swash_multicellular_glom2025_cvrf85.RData
Estuary_Multicellular_glom2025_cvrf85.RData
Dunes_multicellular_glom2025_cvrf85.RData
Swash_Unicellular_glom2025_cvrf85.RData
Estuary_Unicellular_glom2025_cvrf85.RData
Dunes_Unicellular_glom2025_cvrf85.RData
4. Step4_RaupCrick_betas20251223.Rmd
Description: Run after Step 1 and 2. This r notebook contains the scripts to calculate Raup-Crick dissimilarities. Input files:
Multicellular_cvrf85_glom2025.RData
Unicellular_cvrf85_glom2025.RData
Swash_multicellular_glom2025_cvrf85.RData
Estuary_Multicellular_glom2025_cvrf85.RData
Dunes_multicellular_glom2025_cvrf85.RData
Swash_Unicellular_glom2025_cvrf85.RData
Estuary_Unicellular_glom2025_cvrf85.RData
Dunes_Unicellular_glom2025_cvrf85.RData
Output files:
macro_samples_raup_df_cr85.csv
micro_samples_raup_df_cr85.csv
5. Step5_RC_betadiversity_plots.R
Description: Run after Step 4. This r notebook contains the scripts to plot Figure 4.
Input files: macro_samples_raup_df_cr85.csv
micro_samples_raup_df_cr85.csv
Metadata_Multicellular_UnicellularDec2025.csv
6. Step6_CAP_coastal_fig_viridis_raup2026cvrf_grey.Rmd
Description: Run after Step 4. This r notebook contains the scripts to plot Figure 5.
Input files:
Swash_multicellular_glom2025_cvrf85.RData
Estuary_Multicellular_glom2025_cvrf85.RData
Dunes_multicellular_glom2025_cvrf85.RData
Swash_Unicellular_glom2025_cvrf85.RData
Estuary_Unicellular_glom2025_cvrf85.RData
Dunes_Unicellular_glom2025_cvrf85.RData Metadata_Multicellular_UnicellularDec2025.csv
7. inext_cvrf.Rmd
Description: This r notebook contains the scripts to plot Figure S2.
Input files:
Multicellular_cvrf85_glom2025.RData
Unicellular_cvrf85_glom2025.RData
Swash_multicellular_glom2025_cvrf85.RData
Estuary_Multicellular_glom2025_cvrf85.RData
Dunes_multicellular_glom2025_cvrf85.RData
Swash_Unicellular_glom2025_cvrf85.RData
Estuary_Unicellular_glom2025_cvrf85.RData
Dunes_Unicellular_glom2025_cvrf85.RData
8. 1_data_prepare_ed.R
Description: Prepares the data for the gradient forest analyses
Input file: phy_deco_all_2025_cvrf85flt.RData
Multicellular_cvrf85_glom2025.RData
Unicellular_cvrf85_glom2025.RData
Metadata_Multicellular_UnicellularDec2025.csv
9. 2_run_gf_ed.R
Description: Run gradient forest in coastal transect
10. 3_permu_gf_ed.R
Description: Shuffle gradient forest
11. plotting_ed.R
Description: Plot figure S3.
12. util_ed.R
Description:Functions for preparing for gradient forest
SOFTWARE VERSIONS
R version 4.3.1
Packages used:
biomformat 1.30.0
cluster 2.1.8.1
cowplot 1.2.0
decontam 1.22.0
dplyr 1.1.4
ggforce 0.5.0
ggplot2 4.0.0
ggrepel 0.9.6
gradientForest 0.1-17 (R version 3.5.3)
gridBase 0.4-7
here 1.0.2
Hmisc 5.2-3
iNEXT 3.0.2
metagMisc 0.5.0
optparse 1.7.5
pairwiseAdonis 0.4.1
phyloseq 1.46.0
plotly 4.11.0
qiime2R 0.99.6
ranacapa 0.1.0
reshape2 1.4.4
rmarkdown 2.30
tibble 3.3.0
REFERENCES
Lin, M., Simons, A. L., Harrigan, R. J., Curd, E. E., Schneider, F. D., Ruiz-Ramos, D. V., Gold, Z., Osborne, M. G., Shirazi, S., Schweizer, T. M., Moore, T. N., Fox, E. A., Turba, R., Garcia-Vedrenne, A. E., Helman, S. K., Rutledge, K., Mejia, M. P., Marwayana, O., Munguia Ramos, M. N., Wetzer, R., Pentcheff, N.D., McTavish, E.J., Dawson, MN., Shapiro, B., Wayne, R.K., & Meyer, R. S. (2021). Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California. Ecological Applications, 31(6), e02379. https://doi.org/10.1002/eap.2379
Curd, E. E., Gold, Z., Kandlikar, G. S., Gomer, J., Ogden, M., O’Connell, T., Pipes, L., Schweizer, T. M., Rabichow, L., & Lin, M. (2019). Anacapa Toolkit: An environmental DNA toolkit for processing multilocus metabarcode datasets. Methods in Ecology and Evolution, 10(9), 1469–1475.
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A., & Callahan, B. J. (2018). Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome, 6, 1–14.
McMurdie, P. J., & Holmes, S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE, 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217
Mikryukov V. 2021 metagMisc: miscellaneous functions for metagenomic analysis. R package version 0.0.4. (doi:10.5281/zenodo.1172500)
R Development Core Team. (2008). R: A language and environment for statistical computing. [Computer software]. R Foundation for Statistical Computing.
Sample collection: California Environmental DNA (CALeDNA) kits (n = 23) were sent to co-authors and collaborators across the state to sample 22 locations (Figure 2; Table S1). We used one kit for each location, except for Morro Bay (n = 2). At each location we sampled 3 habitats: marine, terrestrial and estuarine. Marine habitat was sampled at the swash zone, defined as the area of the beach influenced by wave action; the swash was characterized by sandy beach or rocky intertidal. Terrestrial habitat included dunes and bluffs. Estuarine habitat included the river mouth, tidal flat or tidal marsh. Habitats were characterized in more detail using the updated California Wildlife Habitat Relationship system (CWHR) for coastal habitats (Shaffer 2002).
At each habitat we sampled 2 sites (Figure 2b), separated at least 33 m apart (Figure 2c, d), and collected 1.5 ml of sample (sand, sediment, or soil) in triplicate (each replicate being ~30 cm from another). In places where the swash habitat was rocky intertidal, samples were collected at the very lowest point of the tide cycle and on a day that had a very low tide. Sediment was taken near tidepools, from under rocks. Photographs from the sampling area and the habitat were taken and uploaded with the kobo tool app to the CALeDNA webpage http://www.ucedna.com. Samples were stored at -80˚C until extraction.
DNA extraction, amplification, and sequencing: Prior to DNA extraction, and in order to recover a representative community per sampling site, we pooled the triplicate samples collected at each site. For these samples, approximately 0.08 mg of sample was taken for each replicate, pooled and vortexed before extraction. The DNA from this pool was extracted using the Qiagen DNeasy PowerSoil kit (Qiagen Inc., Valencia, CA, USA) following the manufacturer's instructions. We included an extraction blank with every round of extractions (up to 17 pooled samples) to track extraction-level introduced contamination.
We reconstructed the community of organisms found at a site by PCR amplifying eDNA with five universal primers (Table S2, Curd et. al 2019). The ITS1 region was used to amplify fungal DNA (“FITS”; ITS5 and 5.8S; White et al. 1990; Epp et al. 2012), ITS2 region was used to amplify plant DNA (“PITS”; ITS-S2F and ITS-S3R; Gu et al. 2013); CO1 (mlCOIintF and Fol-degen-rev; Yu et al., 2012; Leray et al. 2013) and 18S (Euk_1391f and EukBr; Amaral-Zettler et al. 2009) were used to amplify DNA from eukaryotes, and 16S (515F and 806R; Caporaso et al. 2012) was used to identify microbes. Primers were modified to include a 5’ linker matching the region of the Nextera transposome that allows amplification with Nextera Indices (Illumina, San Diego, CA, USA). PCR amplification was performed in triplicate using 15 µl PCR reactions to amplify 2 µL of DNA template with 0.15 μL of each primer (10 μmol/L each), 7.5 µl 2x Qiagen Multiplex PCR Master Mix polymerase (Qiagen Inc., Valencia, CA, USA), and 5.2 μL of dH2O (see Table S2.B for PCR conditions). PCR products were visualized using a 2 % agarose gel.
PCR replicates were pooled by sample and cleaned with SPRI beads (Applied Biological Materials, Vancouver, British Columbia, Canada) at a ratio of 1.3:1 beads to DNA that was experimentally shown to capture products greater than 200 bp, thus eliminating unincorporated primer. The clean product was then quantified on the VICTOR3 Multilabel plate reader (Perkin Elmer, Waltham, Massachusetts, USA) using Quant-iT™ dsDNA Assay Kit high sensitivity (ThermoFisher Scientific, Waltham, MA, USA). For each sample, amplicons of the five markers were pooled equally by copy number (number of amplicon copies = (concentration of DNA (ng) * 6.022x1023 molecules/mol) / ((length of the amplicon * 650 g/mol)* 1x109 g/ng), and the final concentration was adjusted to 5 ng per uL. Amplicons were indexed with Illumina Nextera Indexing Primers (Nextera Index Kit; Illumina, San Diego, California, USA) using 0.625 μL of primer i7 (10µM), 0.625 μL of primer i5 (10µM), 12.5 µL of HiFi HotStart Ready Mix (Kapa Biosystems, Woburn, Massachusetts, USA) with the following PCR conditions: hold at 95˚C for 5 min, then 12 cycles of 20 sec at 98˚C, 30 sec at 56˚C, and 3 min at 72˚C, and a final hold at 72˚C for 5 min, before cooling to 8˚C to end the reaction. Libraries were run on 2% agarose gels to confirm amplification, again cleaned using SPRI beads and quantified as described above. All samples were pooled at equimolar concentrations (target read depth per metabarcoding locus per sample was 25,000 reads) for a final concentration of approximately 45 nM and sequenced with 30% PhiX on an Illumina MiSeq using kit v3 for 2x300 bp paired end reads at the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley.
Sequence processing: Demultiplexed raw reads from the MiSeq were run through the Anacapa QC and Dada2 module (Curd et al. 2019). Briefly, reads were sorted by marker, primers and low-quality bases were trimmed, and run through Dada2 for dereplication and chimera checking. The Anacapa Classifier (default setting) was used to assign taxonomy to the Dada2 amplicon sequence variants (ASV), using CRUX libraries made for each marker (Curd et al 2019). Taxonomic paths for ASVs with a bootstrap confidence of 60% or above were retained based on previous benchmarking (Curd et al. 2019, Gold et al. 2021). Taxonomic lineages were converted to higher classification levels used in Ruggiero et al. (2015) based on the Order name. The taxonomy table was decontaminated using R library decontam (Davis et al. 2018) which determined the probability (at alpha = 0.1) that a taxonomic call was contamination based on its presence in the field and lab blanks. Decontaminated taxonomy tables of each marker were normalized by rarefaction using the ranacapa and phyloseq R packages with 10 repetitions and random seed (Curd et al 2019, Kandalikar et al 2018). PITS,12S and CO1 were rarefied to 1000 reads depth, 16S to 3000 reads, and 18S and FITS to 4000 reads depth. Then all rarefied phyloseq objects were merged into a single phyloseq object.
Diversity analyses: We created three main datasets from the normalized, decontaminated taxonomy tables: (1) a dataset containing all taxa identified with all the markers, (2) a ‘multicellular’ dataset composed of multicellular plants and animals identified with all markers, and (3) a ‘unicellular' dataset including the bacteria, archaea, fungi, protista, chromista, amoeba, unicellular algae and unicellular fungi identified with all markers. Multicellularity was defined as organisms composed of more than one cell, with cells that show some level of specialization (Bell 1997), thus colonial bacteria, amoebas, ciliates and microalgae (like Volvox and diatoms) were considered Unicellular organisms (Grosberg and Strathmann 2007). Fungi were considered Unicellular, except for those within the Agaricomycotina (Basidiomycota) or the Pezizomycotina (Ascomycota), because most members of those groups have multicellular differentiated tissues (Stajich et al. 2009, Nagy et al. 2018). The Multicellular and Unicellular data were partitioned by kingdoms (i.e., Bacteria, Fungi, Animalia), as defined by Ruggiero et al. 2015, and by the habitat in which the sample was collected (marine, terrestrial, estuarine).
Taxa with less than 5 reads total were filtered out of each dataset (Flynn et al. 2015). Statistical analyses were run in R v. 3.5.2 (R Development Core Team, 2008). Alpha-diversity was calculated and plotted using the phyloseq package (McMurdie and Holmes 2013). Indices of beta-diversity (Jaccard index) were calculated with the betadiver function in vegan (Dixon, 2003; Oksanen et al. 2018) and plotted with phyloseq (McMurdie and Holmes 2013). Chao1 beta-diversity was calculated with SpadeR (Chao et al. 2016). To evaluate if there were variations in community organizing patterns in the multicellular and unicellular realms, we plotted the pairwise Jaccard dissimilarity measures within each realm for each site pair. Since beta-diversity only estimates species shared among site pairs, we used the R package ZETADIV (Latombe et al. 2018) to calculate species turnover (zeta decay) and retention (zeta ratio) among all sites.
