Field site
We cultured fungi from soils of three CCASE treatments. CCASE is a
field-based climate change experiment established in 2013 at the
Hubbard Brook Experimental Forest in New Hampshire, USA. The
experiment consists of three climate treatments applied across six
plots – full experiment description can be found in Templer et al.
(2017).
Study organisms
We cultured fungi from soil collected from all six plots at CCASE in
July 2017, 3.5 years after soil temperature treatments began. We
collected a single soil sample to 10 cm depth from each quadrant
within each plot. Soils were sampled using a knife and a 10 × 10-cm
frame that was ethanol-sterilized between samples, following Garcia et
al. (Garcia et al., 2020). Soils were stored on ice, transported back
to the laboratory, and sieved through a 2 mm mesh sieved within 24
hours of sampling. Soil for fungal isolation was kept at 4oC. To
isolate fungi from soil samples, 5 g of soil was diluted in 50 mL of a
0.9% saline solution, serially diluted to 10-3, and plated on solid
Modified Melan-Norkins (MMN) media, a medium commonly used to isolate
soil fungi (Burke et al., 2014; Talbot et al., 2015). 100 ul of
solution was plated on each 9 cm plate. Three replicate aliquots of
diluted soil suspension were plated per quadrant (3 CCASE treatments x
2 plots x 4 quadrants x 3 dilution replicates = 72 total plates).
After 1 week of incubation, we then picked isolates from plates based
on morphology. We cultured and identified approximately 120 isolates
across the six plots. We identified fungal isolates taxonomically via
Sanger sequencing of the intergenic transcribed spacer region (ITS) of
rRNA of each isolate. We extracted DNA from culture tissue using the
Extract-N-Amp Tissue PCR kit (Sigma-Aldrich) and PCR amplified the ITS
region of DNA using ITS1F and ITS4 primers (Manter and Vivanco, 2007).
PCR products were sequenced using single pass Sanger sequencing (MGH
CCIB DNA Core, Boston, MA). Unidentified bases at the ends of
sequences were trimmed using Geneious software (Biomatters Ltd., New
Zealand). Sequences were assigned taxonomy by searching against
GenBank using BLASTn. Based on this approach, we collapsed isolates
into 74 unique strains (i.e. isolates that were found in different
plots or different species within a plot) across 43 unique fungal
species.
We used strains in our laboratory experiment only if they had a close
match (> 97% similarity) to an existing field OTU from Garcia et
al. (Garcia et al., 2020). Of the 74 unique strains we identified, 46
strains had an unequivocal match to a field OTU. These strains
represented 29 species, which we used in our laboratory experiment.
Species were categorized as “common” or “unique” depending on if they
had strains across treatments (“common” species) or within a single
treatment (“unique” species). Of the fungal species we identified, 20
were represented by a single strain that originated from only one
CCASE treatment. Nine species had two strains that each originated
from different CCASE treatments: reference and warmed (n = 4),
reference and warmed + FTC (n = 3), and warmed and warmed + FTC (n =
2). No species were found in all three CCASE treatments.
Laboratory experiment
To test the responses of fungi to different climate conditions, we
conducted a common garden experiment in which we incubated all fungal
strains under laboratory conditions that simulated CCASE temperature
treatments (Table S2). We use the term isolate “origin” to indicate
the CCASE field treatment (warmed, warmed + FTC, or reference) from
which we cultured each fungal isolate and “laboratory environment” to
indicate the climate treatment applied in our common garden experiment
(van Diepen et al., 2017). Laboratory environment conditions
replicated the average soil temperature of each season (fall, winter,
spring, and summer) at CCASE, with each field season represented by a
one-week incubation in the lab (4 weeks total incubation time). We
calculated the average soil temperature of each season as the soil
temperature observed in each CCASE field plot over a 3-month period,
averaged across the two plots per treatment (Sorensen et al., 2018).
We simulated the warmed CCASE treatment in the laboratory environment
by increasing reference soil temperatures by +5°C in the spring,
summer, and fall months (Table S2). We induced winter soil freeze/thaw
cycles in the laboratory environment by freezing the winter plates to
-9°C for approximately 6 hours and defrosting for 18 hours at 4°C. We
repeated this process four times throughout our simulated “winter”
week to simulate the warmed + FTC CCASE treatment.
The common garden laboratory experiment lasted a total of 28 days (we
simulated each season for one week). We grew isolates on a soil
extract media prepared with a composite soil created from soil
collected across CCASE plots as the sole carbon and nutrient source.
We soaked 400 grams of soil in 1 liter of water for 2 days, then
strained the solution through paper towels twice to ensure no large
particulate debris or roots remained in the solution. We combined the
solution with 15g of agar, autoclaved it, and plated 10 ml onto 50 mm
Sterilin petri dishes. We used five replicate plates for each of the
three simulated CCASE treatments, resulting in 15 plates per isolate
(3 climate treatments 46 isolates 5 replicates = 690 total samples).
After 28 days of incubation, we harvested four agar plugs from each
replicate experimental petri dish, flash-froze them in liquid N, and
stored them frozen at -80°C until analysis.
Enzyme and growth rate measurements
To measure the response of individual fungal isolates to changes in
climate conditions, we measured growth rate and extracellular enzyme
activity of each isolate in the laboratory experiment. We recorded
growth as biomass area on each petri dish at four time points prior to
harvesting; we scanned each replicate plate of each isolate at the end
of each week during the growth period (28 days) on an Epson Perfection
V600 photo scanner. We quantified total growth of each sample as area
in mm2 by highlighting all pixels containing fungal biomass in each
scanned image using Adobe Photoshop and dividing this number by the
number of pixels in a 100mm2 area. We also calculated lag time and
maximum growth rate for each sample using the fit_easylinear function
in the growthrates package in R (Hall et al., 2013). None of the
isolates reached the plate edge by the end of the incubation
experiment.
We measured extracellular enzyme activities on each replicate of each
isolate using a standard fluorometric/colorimetric protocol adapted
from German et al. (2011a). We chose to measure five hydrolytic
enzymes; cellobiohydrolase (CBH), β-Glucosidase (BG), and
α-Glucosidase (AG)—each involved in the breakdown of more labile plant
carbohydrates in soil—as well as N-acetyl-D-glucosaminidase (NAG),
which is involved in the breakdown of chitin found in fungal biomass,
and acid phosphatase (AP), an organic phosphorus-acquiring enzyme
(Sinsabaugh et al., 2002). We also measured polyphenol oxidase (PPO)
and peroxidase (PER) activities as an estimate of more recalcitrant C
(e.g. lignin, aromatic soil organic matter) breakdown (Talbot et al.,
2015). To measure enzyme activity, we blended frozen plugs in 15 ml
sodium acetate buffer and mixed an aliquot with either fluorometric
enzyme substrates (for measurement of hydrolase activities), L-DOPA
reagent (for measurement of oxidative enzyme activity), or
methylumbelliferone standard (to create a standard curve). We
calculated enzyme activity on a sample mass basis (nmol mg agar-1
hr-1).
References
Blomberg, S.P., Garland, T., and Ives, A.R. (2003). Testing for
phylogenetic signal in comparative data: behavioral traits are more
labile. Evolution 57(4), 717-745.
Burke, D.J., Smemo, K.A., and Hewins, C.R. (2014). Ectomycorrhizal
fungi isolated from old-growth northern hardwood forest display
variability in extracellular enzyme activity in the presence of plant
litter. Soil Biology and Biochemistry 68(0), 219-222. doi:
http://dx.doi.org/10.1016/j.soilbio.2013.10.013.
Freckleton, R.P., and Rees, M. (2019). Comparative analysis of
experimental data. Methods in Ecology and Evolution 10(8), 1308-1321.
doi: https://doi.org/10.1111/2041-210X.13164.
Garamszegi, L.Z. (2014). Modern phylogenetic comparative methods and
their application in evolutionary biology: Concepts and Practice.
London, UK: Springer-Verlag Berlin Heidelberg.
Garcia, M.O., Templer, P.H., Sorensen, P.O., Sanders-DeMott, R.,
Groffman, P.M., and Bhatnagar, J.M. (2020). Soil Microbes Trade-Off
Biogeochemical Cycling for Stress Tolerance Traits in Response to
Year-Round Climate Change. Frontiers in Microbiology 11(616). doi:
10.3389/fmicb.2020.00616.
German, D.P., Weintraub, M.N., Grandy, A.S., Lauber, C.L., Rinkes,
Z.L., and Allison, S.D. (2011a). Optimization of hydrolytic and
oxidative enzyme methods for ecosystem studies. Soil Biology &
Biochemistry 43(7), 1387-1397. doi: 10.1016/j.soilbio.2011.03.017.
German, D.P., Weintraub, M.N., Grandy, A.S., Lauber, C.L., Rinkes,
Z.L., and Allison, S.D. (2011b). Optimization of hydrolytic and
oxidative enzyme methods for ecosystem studies. Soil Biology and
Biochemistry 43(7), 1387-1397. doi:
http://dx.doi.org/10.1016/j.soilbio.2011.03.017.
Hall, B.G., Acar, H., Nandipati, A., and Barlow, M. (2013). Growth
Rates Made Easy. Molecular Biology and Evolution 31(1), 232-238. doi:
10.1093/molbev/mst187.
Harmon, L.J., Weir, J.T., Brock, C.D., Glor, R.E., and Challenger, W.
(2007). GEIGER: investigating evolutionary radiations. Bioinformatics
24(1), 129-131.
Hill, B.H., McCORMICK, F.H., Harvey, B.C., Johnson, S.L., Warren,
M.L., and Elonen, C.M. (2010). Microbial enzyme activity, nutrient
uptake and nutrient limitation in forested streams. Freshwater Biology
55(5), 1005-1019.
Kembel, S.W., Cowan, P.D., Helmus, M.R., Cornwell, W.K., Morlon, H.,
Ackerly, D.D., et al. (2010). Picante: R tools for integrating
phylogenies and ecology. Bioinformatics 26(11), 1463-1464.
Manter, D.K., and Vivanco, J.M. (2007). Use of the ITS primers, ITS1F
and ITS4, to characterize fungal abundance and diversity in
mixed-template samples by qPCR and length heterogeneity analysis.
Journal of Microbiological Methods 71(1), 7-14. doi:
10.1016/j.mimet.2007.06.016.
Sinsabaugh, R.L., Carreiro, M.M., and Alvarez, S. (2002). "Enzyme
and microbial dynamics of litter decomposition," in Enzymes in
the Environment, Activity, Ecology, and Applications, ed. R.G.B.a.R.P.
Dick. (New York, Basel: Marcel Dekker), 249-265.
Sorensen, P.O., Finzi, A.C., Giasson, M.-A., Reinmann, A.B.,
Sanders-DeMott, R., and Templer, P.H. (2018). Winter soil freeze-thaw
cycles lead to reductions in soil microbial biomass and activity not
compensated for by soil warming. Soil Biology and Biochemistry 116,
39-47. doi: https://doi.org/10.1016/j.soilbio.2017.09.026.
Talbot, J.M., Martin, F., Kohler, A., Henrissat, B., and Peay, K.G.
(2015). Functional guild classification predicts the enzymatic role of
fungi in litter and soil biogeochemistry. Soil Biology and
Biochemistry 88, 441-456. doi:
http://dx.doi.org/10.1016/j.soilbio.2015.05.006.
Templer, P.H., Reinmann, A.B., Sanders-DeMott, R., Sorensen, P.O.,
Juice, S.M., Bowles, F., et al. (2017). Climate Change Across Seasons
Experiment (CCASE): A new method for simulating future climate in
seasonally snow-covered ecosystems. PLOS ONE 12(2), e0171928. doi:
10.1371/journal.pone.0171928.
van Diepen, L.T.A., Frey, S.D., Landis, E.A., Morrison, E.W., and
Pringle, A. (2017). Fungi exposed to chronic nitrogen enrichment are
less able to decay leaf litter. Ecology 98(1), 5-11. doi:
10.1002/ecy.1635.
