LTER network scientists work together to reveal key trends in organic matter processing, storage and transport across ecosystems.
LTER scientists meet at the 2018 LTER Science Council Meeting, where the Organic Matter Synthesis working group was born. Credit: LTER Network Office, CC BY_SA 4.0.
by Mareli Sanchez-Julia, PhD candidate at Tulane University
Organic matter (OM), or the carbon remaining after organisms die, fuels the engines of nearly all ecosystems on Earth. Organic matter affects how much carbon is stored in ecosystems, and as a result influences Earth’s biomes and climate patterns. Most research focuses on OM dynamics at local scales, making it hard to capture the full scope of OM’s temporal and spatial dynamics. A new, LTER network-wide synthesis published in Climate Change Ecology aligns local experiments with a global framework, and identifies processes and long-term trends in OM dynamics that are shared or unique to terrestrial, aquatic, and marine ecosystems.
Organic matter research is core to the LTER
The LTER network supports OM research through the lens of diverse scientific perspectives, facilitating synthesis of a complex topic at a global scale. “Organic matter is a term for a really complex pool with many components that can vary across ecosystems” said Dr. Tamara Harms, lead author of the study. The seed for the paper was planted at a 2018 LTER Science Council Meeting, and each topic grew collaboratively from 2018 through 2020, shifting to email correspondence during the global COVID-19 pandemic. Scientists from different LTER sites brought their unique perspectives and expertise to the sessions, breaking down the complexity of OM dynamics into workable units. The resulting paper addresses the complex nature of OM dynamics across ecosystems, and identifies key knowledge gaps in our understanding.
A conceptual model, and a survey
Two qualitative approaches form the backbone of the study. First, a conceptual model describes the major OM pools that are shared across terrestrial, aquatic and marine ecosystems. These include pools driven by producers or consumers, but also general categories, such as accessible or inaccessible organic matter. These pools are connected through the flux of OM, or the movement and transport of OM from one pool to another. The model identifies the processes that mediate those fluxes, such as animal migration, senescence, freezing, oxidation-reduction conditions, and sorption, and highlights their vulnerability to press and pulse disturbances. The conceptual model allowed researchers to visualize the processes that are either shared or unique to ecosystems. For example, export and burial processes affect OM flux in both terrestrial and aquatic ecosystems, yet these are infrequently studied in terrestrial ecosystems or included in conceptual models of terrestrial OM storage. The model also allowed scientists to see that multiple vectors may influence OM storage at the same time, but that the compound effect of different vectors is not well-represented in quantitative models.
Second, a survey sent to LTER scientists asked about factors influencing OM pools and transport. The survey also requested associated publications and invited sites to submit a vignette about OM dynamics at their site. Twenty-four out of the 28 LTER sites responded to the survey. “There are a lot of aspects of [organic matter] that we have not pinned down that we can’t write an equation for and put it in a model. But when you ask people to reflect on their interpretation of that complexity, then that’s another way of figuring out where we are on the topic” said Dr. Harms. “It really helps shake out where the thinking is”.
Diverse perspectives lead to better science
By merging the perspectives of ecologists, geochemists, agronomists and oceanographers, the authors can uncover the unifying principles that govern OM transport, processing, and storage across the network’s sites. “Even though there are these diverse LTER sites including open ocean, arid land, urban, hot and cold places, the central themes came out really easily,” says Dr. Harms. She attributes this ease as a strength of the LTER network, where scientists are charged with the long-term monitoring of things like nutrient cycling, disturbance and drought. “We are learning about similar processes across very disparate ecosystems.”
The authors highlight five key insights derived from synthesis of short- and long-term experiments across sites. They also identify areas where better data integration is needed to improve quantitative models.
1) Climate strongly affects OM dynamics. Survey results identified climate as the most important factor driving OM vulnerability to transformation and loss, and the projected effect of climate change was cited as the primary long-term catalyst of OM loss. Experiments that simulate climate change, such as increased drought, warming and saltwater intrusion, have shown different short- vs. long-term trends in OM processing and storage, emphasizing that climate change affects OM in non-linear ways. For example, soils at the Harvard Forest (HFR) site, a mid-latitude hardwood forest, showed high carbon loss right after warming but had periods of little to no carbon loss during the 26 years of experimentation. The clearest long-term trends of OM loss come from high-latitude and high elevation ecosystems. Warmer biomes show greater variation in OM dynamics.
2) There is strong coupling of nutrient cycles and OM dynamics, revealed by long-term fertilization experiments and natural fertility gradients. Changes to nitrogen and/or phosphorus tend to shift species composition and dominance, which re-organizes how OM is stored in ecosystems. OM storage is also contingent upon the relative strength of nutrient limitation for producers and consumers, which offset OM gains and losses, respectively, across many ecosystems. In marine ecosystems, nutrients are tightly coupled to primary production, which impacts how OM is transported vertically through the water column and how likely it is to be sequestered.
3) Land use changes and disturbances have complex legacies. Responses of organic matter to short term vs. long term disturbances differ along a trajectory of recovery. For example, “the change that you see immediately after taking agriculture out of rotation can be very different from what you see in the long term, and various facets of organic matter recover from those changes at different rates”, said Dr. Harms. The timescales of these recovery trajectories depend on the size of the OM pools within each ecosystem, the rate of primary production before and after the disturbance, and the impact of the disturbance on processes controlling OM inputs and losses.
4) The role of OM transport is often overlooked in terrestrial ecosystems. While multiple vectors of OM transport influence OM dynamics within or between terrestrial ecosystems, these are rarely included in predictive models or OM budgets. However, OM transport is well-studied in aquatic systems. Applying donor-recipient frameworks used in aquatic systems and emphasizing research on connectivity between land and water may help strengthen terrestrial models of OM fluxes between pools.
5) Predictive models need to know the chemical composition of OM. Long-term experiments have consistently revealed that there are “slow” and “fast” phases of decomposition across ecosystems, meaning that the chemical composition of organic matter influences the rate at which it is broken down. Quantifying the chemical composition of OM pools while tracking their pool size over time may improve our understanding of the rates at which OM is processed and stored. Further, studies that integrate microbial community information and OM chemical composition with long-term data on OM dynamics may also help to clarify projections of OM within ecosystems.
Snapshot studies help us know how OM behaves at a point in time, while long-term data can reveal patterns of OM flux in response to land use, climate and policy changes. This synthesis of LTER network sites integrated the perspectives of many disciplines to stimulate the discovery of patterns in OM dynamics across ecosystems. By combining short-term observation with long-term monitoring of OM dynamics, we can more accurately describe C dynamics and OM budgets under scenarios of continued or accelerating change.