Microbial Community Analyses Inform Geochemical Reaction Network Models for Predicting Pathways of Greenhouse Gas Production

Abstract

Microbial Analyses Inform Geochemical Modeling the terminal electron acceptors are depleted and the system becomes increasingly methanogenic. This suggests that as permafrost regions thaw and dry palsas transition into wet fens, CH 4 emissions will rise, increasing the warming potential of these systems and accelerating climate warming feedbacks.

Functional capacities of microbial communities to carry out large scale geochemical processes are maintained during ex situ anaerobic incubation

“…The field CO 2 :CH 4 ratio in the deep collapsed palsa was highly variable and did not differ significantly from the incubation ratios, although over time, the average CO 2 :CH 4 declined somewhat. While high CO 2 :CH 4 ratios may call into question the existence of methanogens at this site, previous work from our group has measured methanogens in peat collected from the collapsed palsa feature confirming that there are methanogens at this site [63]. The decline in CO 2 :CH 4 ratios is consistent with increasing decomposition of the peat over time-expected in the incubations-leading to a more methanogenic system.…”

The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research

“…1B). By integrating these myriad types of datasets, the project has characterized how thaw-induced changes in hydrology and vegetation (Malmer et al, 2005;Johansson et al, 2006;Bäckstrand et al, 2010;Palace et al, 2018) drive changes in organic matter (Hodgkins et al, 2014(Hodgkins et al, , 2016Wilson et al, 2017;Wilson & Tfaily, 2018) and microbial and viral communities (Mondav et al, 2014;Trubl et al, 2016Trubl et al, , 2018Trubl et al, , 2019Singleton et al, 2018;Emerson et al, 2018;Woodcroft et al, 2018;Martinez et al, 2019;Wilson et al, 2019;Roux et al, 2019), giving rise to changes in carbon gas emissions (Wik et al, 2013(Wik et al, , 2018Hodgkins et al, 2014Hodgkins et al, , 2015McCalley et al, 2014;Burke et al, 2019;Perryman et al, 2020), and collectively these insights are allowing improvements in predictive models (Deng et al, 2014(Deng et al, , 2017Chang et al, 2019aChang et al, , 2019bWilson et al, 2019).…”

“…Understanding and predicting the behavior of complex natural systems requires collecting and integrating data across multiple disciplines (Michener & Jones, 2012). While data from a single discipline offer a limited view into the system as a whole, interdisciplinary data integration can provide independent and complementary views of the same phenomenon, as well as illuminate emergent non-additive behaviors (McCalley et al, 2014;Deng et al, 2017;Woodcroft et al, 2018;Wilson et al, 2019). Interdisciplinary, systems-scale integration of data-from isotopic and other geochemical measurements, to measures of microbial ecology and biochemistry, to climate data, to vegetation surveys and remote sensing-is essential to modeling and predicting biogeochemical cycling (Heffernan et al, 2014;Fei, Guo & Potter, 2016;Rose et al, 2017).…”

Peer Review #1 of "The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research (v0.2)"

“…Data from the IsoGenie and related projects (Table 1; Figure 1A) span multiple levels of study, with temporal scales ranging from minute-to decadal-resolution, and spatial scales ranging from nanoscale (e.g., elemental composition of soil and pore water), to microscale (e.g., microbial composition and metabolic processes), to macroscale (e.g., vegetation surveys and drone and satellite imagery) ( Figure 1B). By integrating these myriad types of datasets, the project has characterized how thawinduced changes in hydrology and vegetation (Malmer et al, 2005;Johansson et al, 2006;Bäckstrand et al, 2010;Palace et al, 2018) drive changes in organic matter (Hodgkins et al, 2014(Hodgkins et al, , 2016Wilson et al, 2017;Wilson & Tfaily, 2018) and microbial and viral communities (Mondav & Woodcroft et al, 2014;Trubl et al, 2016Trubl et al, , 2018Trubl et al, , 2019Singleton et al, 2018;Emerson et al, 2018;Woodcroft & Singleton et al, 2018;Martinez et al, 2019;Wilson et al, 2019;Roux et al, 2019), giving rise to changes in carbon gas emissions (Wik et al, 2013(Wik et al, , 2018Hodgkins et al, 2014Hodgkins et al, , 2015McCalley et al, 2014;Burke et al, 2019;Perryman et al, 2020), and collectively these insights are allowing improvements in predictive models (Deng et al, 2014(Deng et al, , 2017Chang et al, 2019b,a;Wilson et al, 2019).…”

“…Understanding and predicting the behavior of complex natural systems requires collecting and integrating data across multiple disciplines (Michener & Jones, 2012). While data from a single discipline offer a limited view into the system as a whole, interdisciplinary data integration can provide independent and complementary views of the same phenomenon, as well as illuminate emergent non-additive behaviors (e.g., McCalley et al, 2014;Deng et al, 2017;Woodcroft & Singleton et al, 2018;Wilson et al, 2019). Interdisciplinary, systems-scale integration of data-from isotopic and other geochemical measurements, to measures of microbial ecology and biochemistry, to climate data, to vegetation surveys and remote sensing-is essential to modeling and predicting biogeochemical cycling (Heffernan et al, 2014;Fei, Guo & Potter, 2016;Rose et al, 2017).…”