primary production

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

Biophysicochemical properties and hyperspectral reflectance values of biocrust and soil samples, Fryxell Basin, McMurdo Dry Valleys, Antarctica (2022)


This data package includes biophysicochemical properties of biocrust and soil samples collected in-situ within the Lake Fryxell basin of the McMurdo Dry Valleys, Antarctica during December 2022 at 64 different terrestrial locations. These parameters include biological (ash-free dry mass, pigment concentration, soil invertebrate counts), physical (gravimetric water content, electrical conductivity, pH), and chemical (inorganic nitrogen, inorganic phosphorous, total nitrogen, soil organic carbon) properties of the surface soil, biocrust, and underlying soil. This package also contains reflectance measurements of individual grab samples of biocrust, soil, and rock collected from each of the field sites, acquired in a laboratory using a hyperspectral spectrometer. Additionally, this package contains parameters derived solely from geospatial data for each of the field sites, including aspect, slope, elevation, gravimetric water content, and snow frequency. These data were used to model habitat suitability of biocrusts in the Fryxell Basin using both in-situ field survey data and geospatial parameters. They aid in our understanding of the ecology of biocrust communities, where they are located throughout the Fryxell basin, and the structure and functioning of these microhabitats as well as improving understanding of the regional carbon budget. These data also highlight the importance of snow patch-associated biocrust communities, which are understudied but likely an important piece of the overall carbon budget in this region. 

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In-situ Environmental Sampling & Analyses

At 64 locations throughout the Fryxell basin, we documented whether biocrust was present. At each site, surface layer samples of soil or biocrust (if present) of known area were collected for pigment analysis, organic matter content, and hyperspectral reflectance analysis later in the laboratory. We also collected underlying soil down to 10 cm below the surface for gravimetric water content (GWC), electrical conductivity (EC), pH, inorganic nitrogen (N) concentration in the form of ammonium (NH4+) and nitrate (NO3-), inorganic phosphorus (P) concentration in the form of phosphate (PO4-3), soil organic carbon (SOC), total nitrogen (TN), and invertebrate abundance (nematodes, tardigrades, and rotifers).

Ash-free dry mass (AFDM). The surface layer soil and biocrust samples were measured for AFDM by weighing a known area of sample, oven drying, weighing, then combusting at 550 °C for 24 hr using a muffle furnace, gently stirring samples halfway through combustion, and reweighing after cooling in a desiccator. The rehydration of clays was assumed negligible, so we did not rewet samples.

Pigments. We estimated pigment concentration on the surface ~1 cm layer soil and biocrust samples using a trichromatic spectrophotometric method for chlorophyll-a, carotenoids, and scytonemin at 663, 490, and 384 nm, respectively (Garcia‐Pichel and Castenholz, 1991). Samples were not exposed to direct light throughout the entire process. The samples were dried at 105 °C for 24 hr, sieved through a 4 mm sieve, and extracted for 24 hr at ambient temperature in 90% unbuffered acetone using a 3.75:10 soil to solvent ratio for samples which contained none or minimal biocrust and a 0.5:10 biocrust to solvent ratio for samples containing dense biocrust, based on protocols from Couradeau et al. (2016) and the McMurdo Dry Valleys Long Term Ecological Research Program (MCM LTER) standard methods. After centrifugation, the extracts were analyzed on a spectrophotometer using 10 mL cuvettes. The absorbances contributed by each pigment were calculated using the trichromatic equations outlined in Garcia‐Pichel and Castenholz (1991), and the pigment concentrations were calculated using the Beer-Lambert Law with the extinction coefficients of 89.7 L g-1 cm-1 for chlorophyll-a (Couradeau et al., 2016), 112.6 L g-1 cm-1 for scytonemin (Brenowitz and Castenholz, 1997), and 262 L g-1 cm-1 for carotenoids (Thrane et al., 2015).

Invertebrate counts. The number of soil organisms (nematodes, rotifers and tardigrades), divided by species, sex and maturity was determined at each of the 64 sites. Invertebrate abundance was enumerated using inverted light microscopy on soil solutions using a modified sugar-centrifugation extraction procedure described by Freckman and Virginia (1993).

Gravimetric water content (GWC). Water content was determined as the mass of water per mass of dry soil. This was conducted by weighing field moist soil, drying in an oven at 105 °C for 24 hr, and reweighing the soil after.

Electrical conductivity (EC). We measured electrical conductivity using a 1:5 soil to DI H2O slurry using a benchtop conductivity probe.

pH. We measured pH using a 1:2 soil to DI H2O slurry using a benchtop pH probe.

Inorganic N and P. We extracted inorganic N (NH4+ and NO3-) in 2 M potassium chloride and inorganic P (PO4-3) in 0.5 M sodium bicarbonate. Inorganic N and P were measured on extracts using a Lachat flow injection analyzer.

Total N and Soil Organic C (SOC). We measured SOC and TN using an Elementar Vario MAX Cube analyzer after fumigating samples with concentrated hydrochloric acid to remove the influence of carbonates on SOC values.

Remote Sensing Analyses

Geospatial Parameters. Parameters based solely on geospatial data were extracted for each of the 64 sites as well. These include aspect, slope, elevation, GWC, and snow frequency. Aspect, slope, and elevation were extracted from a high-resolution DEM (Fountain et al. 2017), the rasterized GWC layer was developed for this region by Salvatore et al. (2023) using 21 WorldView-2 and -3 images, and the rasterized snow frequency layer was also developed for this region using albedo thresholds with multiple images over the course of the austral summer (see also, Thapa-Magar et al. in review). Snow frequency was calculated as the percentage of time (the proportion of the total images captured during the austral summer) that each pixel was identified as being snow covered (e.g., 0% representing no snow observed in any of the imagery and 100% representing snow cover in all summer images analyzed).

Hyperspectral reflectance of field samples. Spectra were acquired of samples from each of the 64 sites using an Analytical Spectral Devices (ASD) FieldSpec4 high-resolution hyperspectral reflectance spectrometer set up for use in a stable lab environment. Data were collected between 0.35 µm and 2.5 µm at a 0.001 µm sampling interval. A halogen lamp was used to illuminate the samples at 30° off-nadir, while reflectance was measured at nadir using the ASD’s bare fiber optic cable roughly 3 cm above the sample surface. Spectra were collected of the samples in moisture conditions ranging from dry, damp (field-moist: moisture conditions present at time of sample collection), to wet which were sprayed twice with an aerosolizing spray bottle with distilled water. The samples were placed under the halogen illuminating lamp and let sit for 60 seconds before a spectrum was acquired. This was performed identically for each sample to ensure consistency. To minimize instrument noise, particularly at the longest and shortest wavelengths where the output of the halogen bulb is lowest, we averaged 50 individual spectra for each sample.

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Funding for this work was provided by the National Science Foundation (NSF) grants #OPP-1637708 and #OPP-2224760 to the MCM LTER.


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