Mitchell Zimmerman: Manganese-Carbon Relationship Inconsistent Across Vegetation Types in Upper East River Watershed Soil

Authors:

Mitchell Zimmerman; Dana Chadwick; Kathleen Grant; Corey Lawrence

Abstract:

Soil organic carbon (SOC) holds a significant portion of terrestrial carbon stores, and studies have found that the stability of this carbon stock could be dependent on soil manganese (Mn) limiting microbial decomposition processes. The purpose of this study is to determine if the positive relationship between Mn availability in soil and the rate of decomposition of organic matter identified in past studies has any discernible influence on the SOC concentration in soil samples collected across sites with various vegetation and geology types in the Upper East River Watershed in Colorado. The samples were collected from 437 sites containing multiple conifer species, quaking aspen stands, riparian shrubs, and diverse meadows. In total, 487 samples were collected and processed to determine the SOC and Mn content of the fresh leaves, litter, soil, and roots. When comparing Mn and SOC levels across different vegetation types, we found conifer systems to have a significantly higher litter, foliar, and root Mn than the meadow, broadleaf, and shrub systems, and significantly higher SOC than every other vegetation type except wet meadow. Linear regression of SOC vs Mn found a slight positive relationship between the two, but had an insignificant p-value at the p = 0.05 level. In conifer systems, a statistically significant positive relationship was found, and in wet and mesic meadow systems we saw convincing negative relationships, both with significant p-values. In other words, the results were inconsistent across vegetation systems. Our findings imply, despite evidence that Mn is a convincing predictor of decomposition rates and long-term carbon sequestration in the soil, that it is difficult to use such knowledge to predict soil and topographic features in practice and that findings may not be transferable from boreal to temperate montane systems.

Presentation:

Video:

7 Comments on “Mitchell Zimmerman: Manganese-Carbon Relationship Inconsistent Across Vegetation Types in Upper East River Watershed Soil

  1. Hi Mitchell, great work! I really liked how clean and clear your slides were.

    It was interesting to hear that you got such different results than what was expected! I know you mentioned different ecosystems/vegetation types may have played a role in that, are there any other hypothesis behind what could have been driving this difference?

    I’d also love to hear a bit more about the motivation between this work. Given that SOC is a big terrestrial carbon store, is one the goals to understand it’s process/impacts in relation to climate change?

    • Hi Bianca,

      Yes, exactly! The primary motivation behind this project was the fact that the decomposition of SOC and other organic matter creates a significant annual flux of CO2 into the atmosphere– a flux nearly 6 times greater in magnitude than anthropogenic emissions. Therefore, understanding what limiting factors might play a role in regulating that massive carbon flux will be key to understanding climate change dynamics in the years to come. (I was going to mention this in the video, but it didn’t make the final cut when trying to whittle it down under 5 minutes.)

      To your question about other things that might be driving the difference we found, many of the past studies we looked at made their findings in experimental conditions meant to replicate or simulate natural ecosystems. Those that did take place out in natural ecosystems were still much more controlled and took place over a long period of time, with multiple measurements being taken over that period. In contrast, our analyses were done on a dataset comprised of one-time samples from non-experimental settings, meaning we were working with a snapshot of a large area rather than a timelapse of a small one. This difference in structure may well be the cause of the differences in our results. Another interesting factor that didn’t make the cut that we found might play a role are the chemical make-up of the parent rock of the soil (intrusive vs shale, etc). Did you have a hypothesis of your own, or were you just curious?

      Thanks!

      • Hi Mitchell,

        Great! Thanks for explaining – makes lots of sense.

        I was mostly just curious in regards to other hypotheses that might be driving this difference, sounds like you have a couple of good ideas!

        Great job, again!

  2. Mitchell,
    Excellent work wrangling such a large data set! Your presentation was a really nice summary as well. Do you think that looking at soil organic carbon inventories, as opposed to “mass loss” or carbon turnover times, obscures the correlation? Did you try looking at other correlations that might provide a proxy for carbon turnover – for example difference from the mean SOC vs Mn content?
    Kate

    • Hi Kate,
      Thank you! I think that is definitely a possibility, and I hadn’t considered the difference from the mean approach. That could be a very good follow-up analysis! I was planning to do something similar at one point and standardize the SOC data and put it against Mn, but it slipped through the cracks. If this kind of analysis did reveal significant correlations, I suspect that they, too, would vary by vegetation type.
      Mitchell

  3. Hi Mitchell-

    I enjoyed your video, you have an engaging presentation style and did a wonderful job clearly explaining your questions and findings. I love when there are inconsistent or unexpected findings because it makes research more exciting!

    In your response to Bianca’s question, you do a good job explaining how your investigation connects to climate change and in your response to Kate you bring up possible follow-up analyses. I’m curious as to what you think other next steps might be?

  4. Hi Mitchell,

    Thank you for your very clear explanation of your research! In your response to Bianca, you mentioned you were working with a one-time sample rather than one that was time-lapsed. How would your results be different if the samples were taken over a longer period of time, such as over one year?

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