Soil property, microbial abundance, and plant and invertebrate biomass data across a natural soil temperature gradient in Iceland from August 2018
Data files
Mar 29, 2022 version files 26.08 KB
Abstract
This is a dataset of soil physiochemical properties, bacterial and fungal abundance, and above and belowground plant and invertebrate biomass, sampled at 40 plots in the Hengill geothermal valley, Iceland, from 15th to 22nd August 2018. The plots span a temperature gradient of 10-35 °C over the sampling period, and this temperature gradient is consistent over time. The dataset also includes data on the decomposition rate of soil organic matter, which was sampled at 60 plots in the Hengill valley from May to July 2015.
Methods
Study site
This is a dataset of soil physiochemical properties, bacterial and fungal abundance, and above and belowground plant and invertebrate biomass, sampled at 40 soil plots in the Hengill geothermal valley, Iceland, from 15th to 22nd August 2018. The plots, measuring approximately 1 m2, evenly span a temperature gradient of 10-35°C. The dataset also includes data on the decomposition rate of soil organic matter, which was sampled at 60 plots in the Hengill valley from May to July 2015 (see Robinson et al. 2021 for plot details and sampling regime).
Soil properties
Soil temperature was measured at 5 cm depth at each plot on 15th, 18th, and 22nd August, and a mean plot temperature calculated. Soil physiochemical properties were analysed from 3 soil cores of 3 cm in diameter, taken from the upper 10 cm soil stratum at each plot; one quarter of each subsample was pooled to obtain an estimate per plot. Aboveground plant matter, excluding roots, were removed from each core. Percentage soil moisture was calculated by measuring the weight of one pooled soil sample before and after drying for 24 h in a 70°C drying oven. Soil pH was obtained from 20 g of the dry soil by adding 100 ml distilled water, shaking for 5 min on 150 rpm, letting the sample stand for 2 h, and measuring soil pH from the water layer using an InoLab pH 720 (WTW) probe. Soil PO4, NH4, and NO3 concentrations were analysed from a second pooled soil; 60 g of fresh soil was extracted in 100 ml distilled water, filtered through a GF/C (1.2μm) glass microfiber filter (Whatman, GE Healthcare Europe GmbH), and analysed using a Lachat QuikChem 8000 analyser (Zallweger Analytics, Inc., Lachat Instruments Division, USA). Total mineral N was calculated as the sum of NH4 and NO3. Soil organic matter content (excluding dry root biomass) was calculated as the weight lost from an oven dried (105°C for 24 hours) soil sample after heating at 550 °C for 5 h.
Decomposition rate of soil organic matter was measured using the Cotton-strip Assay method (Tiegs et al. 2013) by placing a 2.5 cm x 8 cm strip of Fredrix-brand unprimed 12-oz. heavyweight cotton fabric (Style #548) 5 cm belowground at 60 plots, concurrently with a Maxim Integrated DS1921G Thermocron iButton temperature logger, on 13th May 2015. The strips were collected on 3rd July, rinsed with stream water to remove residual soil, soaked in 96% ethanol for 30 seconds to kill bacteria and halt decomposition, and dried at 60 °C for 12 h. Using a universal testing machine (Instron 5866 with 500 kN tensile holding clamps), maximum tensile strench of each cotton strip was measured. % tensile loss (proxy for decomposition) was calculated as (C-T) / C x 100, where T is the maximum tensile strength for each strip collected from the field, and C is the mean tensile strength of seven control strips, which had not been placed in the ground. See Robinson et al. 2021 for detailed description of plots sampled in 2015.
Microbial abundance
Bacterial and fungal abundance was estimated from additional soil cores of 3 cm in diameter taken from the upper 4 cm soil stratum (including the litter layer) at each plot. DNA was extracted using the PowerSoil DNA Isolation Kit (Qiagen, Germany). DNA was quantified using the high-sensitivity Qubit assay (Thermo Fisher Scientific, Switzerland). Relative abundances of bacterial and fungal communities were determined by quantitative PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). PCR amplification of partial bacterial small-subunit ribosomal RNA genes (region V1–V3 of 16S; primers 27F and 512R) and fungal ribosomal internal transcribed spacers (region ITS2; primers IT3 and ITS4) was performed as described previously (Frey et al. 2020, Frey et al. 2021). For qPCR analyses, 2.5 ng DNA in a total volume of 6.6 µL and 8.4 µL GoTaq qPCRMaster Mix (Promega, Switzerland), containing 1.8 mM of each primer and 0.2 mg mL-1 of BSA, were used. The PCR conditions consisted of an initial denaturation at 95 ºC for 10 min, 40 cycles of denaturation at 95 ºC for 40 s, annealing at 58 ºC for 40 s and elongation at 72 ºC for 60 s followed by the final data acquisition step at 80 ºC for 60 s. The specificity of the amplification products was confirmed by melting-curve analysis. Three standard curves per target region (correlations ≥0.997) were obtained using tenfold serial dilutions (10-1 to 10-9 copies) of plasmids generated from cloned targets (Frey et al. 2020). Data were converted to represent the average copy number of targets per μg DNA and per g soil.
Vegetation properties
Vascular plant biomass was measured from a randomly placed 30 x 30 cm quadrat at each plot. To measure aboveground biomass (AGB) of plants, the aboveground layer of vegetation was cut and removed, dried at 70 °C for 24 h and weighed to obtain biomass per unit area. AGB was estimated as the biomass of graminoids plus forbs; total biomass of mosses was also estimated. Graminoid leaf N concentration was analysed from dried and ground leaf material using a LECO CNS-2000 analyser (LECO Corporation, Saint Joseph, MI, USA). Belowground biomass (BGB) of vascular plants was estimated from a soil core of 3 cm in diameter taken from the 10 cm upper soil stratum (excluding aboveground plant material) at each quadrat. Roots were extracted from the soil cores by rinsing in water using a 250-μm sieve, dried at 70 °C for 24 hours and weighed to obtain biomass per unit area. Root to shoot ratio was calculated as dry weight of BGB per cm2 divided by dry weight of AGB per cm2, and the total vascular plant biomass as the sum of AGB and BGB.
Invertebrate community
Enchytraied and nematode biomass was estimated from 3 soil cores of 3 cm in diameter taken from the upper 4 cm soil stratum (including litter layer) at each plot. Enchytraieds were extracted using wet funnels (O'Connor 1962) from a pooled sample of one half of each of the three soil cores, counted live, and classified into size classes (length 0-2, 2.1-4, 4.1-6, 6.1-8, 8.1-10, 10.1-12 or >12 mm) and their biomass was calculated according to Abrahamsen (1973). Nematodes were also extracted using wet funnels (Sohlenius 1979) from a pooled sample of a quarter of each of the three soil cores, counted live and preserved in 70% ethanol. Fifty individuals from each sample were identified and classified by trophic group (bacterivore, fungivoe, herbivore, omnivore, predator; Yeates et al. 1993).
Soil micro-arthropods were extracted using a modified high-gradient-extractor (MacFayden 1961) from soil cores of 5.4 cm in diameter, taken from the upper 4 cm soil straum (including litter layer) at each plot. Total micro-arthropod biomass was calculated as the sum of all individual species' biomasses, obtained using length-weight regressions (see Robinson et al. 2021), and abundance of individual trophic groups (microbivore/detritivore, herbivore, omnivore, predator) calculated.
Epigeal invertebrates were sampled by deploying five pitfall traps in each plot. White plastic cups of 7 cm in diameter and 8.5 cm in depth were filled with 10 ml of ethylene glycol and 30 ml of stream water, and left for 48 h before collection. Samples from the five traps at each plot were combined into a 250-μm sieve and stored in 70% ethanol. Invertebrate activity density (abundance) was estimate as the total number of individuals in the five traps, and total biomass as the sum of all individual species' biomasses. Invertebrates were identified to species level where possible and split into trophic groups, exluding adult Diptera, Hymenoptera, and Lepidoptera. Further details of sampling and collection of epigeal invertebrates are detailed in Robinson et al. (2018).
References:
Abrahamsen G. (1973) Studies on body-volume, body-surface area, density, and live weight of enchytraeidae (Oligochaeta). Pedobiologia 13: 6–15.
Frey B, Carnol M, Dharmarajah A, Brunner I, Schleppi P. (2020) Only minor changes in the soil microbiome of a sub-alpine forest after 20 years of moderately increased nitrogen loads. Frontiers in Forests and Global Change 3: 77.
Frey B, Walthert L, Perez-Mon C, Stierli B, Köchli R, Dharmarajah A, Brunner I (2021) Deep soil layers of drough-exposed forests harbor poorly known bacterial and fungal communities. Frontiers in Microbiology 12: 1061.
MacFayden A. (1961) Improved funnel-type extractors for soil arthropods. Journal of Animal Ecology 30: 171–184.
O’Connor FB. (1962) The extraction of Enchytraeidae from soil. In: P. W. Murphy (Ed.) Progress in soil zoology. Butterworth, London, UK; 279–285.
Robinson SI, McLaughlin ÓB, Marteinsdóttir B, O'Gorman EJ. (2018) Soil temperature effects on the structure and diversity of plant and invertebrate communities in a natural warming experiment. Journal of Animal Ecology 87: 634–46.
Robinson SI, Mikola J, Ovaskainen O, O’Gorman EJ. (2021) Temperature effects on the temporal dynamics of a subarctic invertebrate community. Journal of Animal Ecology 90: 1217-1227.
Sohlenius B. (1979) A carbon budget for nematodes, rotifers and tardigrades in a Swedish coniferous forest soil. Holarctic Ecology 2: 30–40.
Tiegs SD, Clapcott JE, Griffiths NA, Boulton AJ. (2013) A standardized cotton-strip assay for measuring organic-matter decomposition in streams. Ecological Indicators 32: 131–139.
Yeates GW, Bongers T, De Goede RGM, Freckman DW, Georgieva SS. (1993) Feeding habits in soil nematode families and genera—an outline for soil ecologists. Journal of Nematology 25: 315–331.
Usage notes
See README_Robinson_Hengill2018.txt