Biogeosciences Research Spotlight

Mushrooms Could Provide a Record of Grassland History

Scientists measured carbon isotopes in certain types of fungi to assess whether the organisms can track how climate change is affecting grasses.

Source: Journal of Geophysical Research: Biogeosciences


Since the Industrial Revolution, concentrations of carbon dioxide in the atmosphere have rapidly increased. How does this influx of atmospheric carbon affect ecosystems, such as forests, croplands, and the 40 million acres of American lawns? Clues to answer this may lie in an unexpected source: mushrooms.

Trees and grasses pull carbon out of the atmosphere during photosynthesis and thus play a key role in the global carbon cycle. Theoretically, researchers can study how vegetation changes over time to assess the effects of increasing concentrations of carbon dioxide. Unfortunately, studying historical changes in grass communities is difficult. Unlike trees, which build tree rings from year to year, grasses leave little behind when they die and decompose, so scientists must use creative methods to look at grassland ecosystems from years past.

One method involves using the two stable isotopes of carbon, 13C and 12C, as natural tracers. But where can record of these isotopes be found?

Perhaps in mushrooms, Hobbie et al. hypothesized. The authors tracked the 13C to 12C ratios in mushrooms from lawns in America’s Midwest to study the historical shift in grass varieties in the region. The fungi feed on dead plant matter, so changes in carbon isotopes within mushrooms from samples collected over time can allow researchers to look at what kinds of grasses the fungi had consumed from season to season and year to year.

To see how changes in temperature, precipitation, and carbon dioxide in the atmosphere can affect vegetation, the researchers looked at competition between two kinds of plants: C3 and C4 grasses, which use different metabolic pathways for photosynthesis. These different pathways produce different 13C:12C ratios in plant tissues.

C3 grasses—such as wheat, oats, and ryegrass—are called cool-season plants and thrive in a temperature range of 65°F–75°F. These grasses are highly productive in the spring and fall, but high summer temperatures reduce growth. C4 plants, on the other hand, flourish in warmer and drier environments. These warm-season plants include corn, crabgrass, and bluestem grasses and are more efficient than C3 plants at photosynthesis under low concentrations of carbon dioxide.

Fairy Ring
A fairy ring of Amanita thiersii mushrooms in a lawn in Charleston, Ill. Credit: Michael Kuo

The researchers used isotopic data from samples of the fungus Amanita thiersii collected between 1982 and 2009 from 26 locations in the southeastern and south central United States. The scientists combined these data with information on temperature, precipitation, and carbon dioxide concentrations over the same period to study changes in the balance between C3 and C4 plants.

They found that high temperatures were good predictors of a higher percentage of C4 grasses, whereas higher precipitation favored C3 productivity. Over the 1982–2009 period, C3 grass productivity increased 18.5%, which the scientists attributed to a 13% increase in atmospheric carbon dioxide during that time.

The researchers point out that shifting lawn management also could have played a role in the changing grass landscape. Despite this, the novel method of using mushrooms to study the vegetation landscape and plant competition over time could be used in the future to assess how grasslands are adapting to climate change and to increasing carbon dioxide concentrations. (Journal of Geophysical Research: Biogeosciences, 2017)

—Alexandra Branscombe, Freelance Writer

Citation: Branscombe, A. (2017), Mushrooms could provide a record of grassland history, Eos, 98, Published on 11 April 2017.
© 2017. The authors. CC BY-NC-ND 3.0
  • Okay, here’s the abstract:

    How climate and rising carbon dioxide concentrations (pCO2) have influenced competition between C3 and C4 plants over the last 50 years is a critical uncertainty in climate change research. Here we used carbon isotope (δ13C) values of the saprotrophic lawn fungus Amanita thiersii to integrate the signal of C3 and C4 carbon in samples collected between 1982 and 2009 from the Midwestern USA. We then calculated 13C fractionation (Δ) to assess the balance between C3 and C4 photosynthesis as influenced by mean annual temperature (MAT), mean annual precipitation over a 30 year period (MAP-30), and pCO2. Sporocarp Δ correlated negatively with MAT (−1.74‰ °C−1, 79% of variance) and positively with MAP (9.52‰ m−1, 15% of variance), reflecting the relative productivity of C3 and C4 grasses in lawns. In addition, Δ values correlated positively with pCO2 (0.072‰ ppm−1, 5% of variance). Reduced photorespiration with rising pCO2 accounted for 20% of this increased Δ, but the remaining 80% is consistent with increased assimilation of C3-derived carbon by Amanita thiersii resulting from increased productivity of C3 grasses with rising pCO2. Between 1982 and 2009, pCO2 rose by 46 ppm and the relative contribution of C3 photosynthesis to Amanita thiersii carbon increased 18.5%. The δ13C value of Amanita thiersii may integrate both lawn maintenance practices and the physiological responses of turf grasses to rising CO2 concentrations.

    That raises a few questions in my mind:

    1. So I guess C4 plants tend to absorb a higher ratio of 13C to 12C than do C3 plants.

    That’s interesting. I didn’t know that. Why is that true, and is there really enough of a difference to reliably detect what the ratio of C3 to C4 plants was?

    2. Around here, what grows in people’s lawns mostly reflects what they planted in those lawns. If these mushrooms were really from “Midwestern lawns” it is hard to imagine that any other factor could overwhelm that one.

    3. What are these “% of variance” numbers? Is that some sort of alternative to confidence intervals? What does it tell us?

    It doesn’t even sound like the right units (it sounds like the square of the right units)!

    4. I found a preprint of the full paper. It says:

    “2 Materials and Methods. In Wolfe et al. [2012], gill tissue was subsampled from 48 herbarium specimens of Amanita thiersii collected at 26 different locations between 1982 and 2009 in southeastern and south-central USA. Locations were between 29 °N and 40 °N and 86 °W and 100 °W. Samples were analyzed for %C, %N, δ13C, and δ15N as detailed in Wolfe et al. [2012]. We analyzed the underlying data set from Wolfe et al. [2012] (as provided by B. Wolfe) using multiple regressions…”

    Say WHAT? If I understand that correctly, they had a grand total of only 48 dried mushroom samples, collected from 26 different locations, over a 27 year period.

    That sounds like a joke, or maybe a caricature or expose of shoddy science! That can’t be right, can it? How could anything of significance possibly be determined from that?

    For one thing, if they had 48 samples from 26 locations, i.e., less than twice as many samples as sample locations, it means that some locations must have had only one sample. Only in “climate science” could someone claim to have calculated a trend at a location from one sample.

    5. The preprint also says, “Within the central United States the choice of lawn grasses depends on the climatic zone, with more C3 grasses selected in cooler regions and more C4 grasses selected in warmer regions.”

    Well, maybe. But here in North Carolina it’s Fescue country — and that’s a C3 grass. (However, some people also plant Centipede or Zoysia, and those are C4.)

    6. Am I wrong in thinking that Mean Annual Temperature is a very crude indicator of the influence of temperature on plants?

    7. Am I wrong in thinking that “mean annual precipitation over a 30 year period” is an even cruder indicator of the influence of precipitation on plants, especially short-lived plants like grasses, and especially in a study which spanned less than 30 years?