Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model

Abstract

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change.Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two Accepted ArticleThis article is protected by copyright. All rights reserved remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data-model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change and broadening the impact of ecological research.

Clarifying the evidence for microbial‐ and plant‐derived soil organic matter, and the path toward a more quantitative understanding

“…In the context of plant and microbial contributions to SOM, numerical modeling studies have emphasized the vast potential for microbes to contribute to SOM formation (47%–80%; Fan & Liang, 2015; Liang et al, 2011). Numerical models integrate theoretical understanding and empirical measurements to make predictions about pool sizes and process rates, and to simulate the effects of perturbations on these pools and processes (Kyker‐Snowman et al, 2021). Modeling studies can therefore provide valuable insights to guide future theoretical and empirical work (Blankinship et al, 2018).…”

Global net biome CO2exchange predicted comparably well using parameter–environment relationships and plant functional types

“…These include whether and to what degree trait covariations (e.g., Peaucelle et al, 2019) should be explicitly preserved; whether different traits should be predicted based on fully independent filters; how complex or parsimonious EF regression models themselves should be; which environmental covariates are most relevant for predicting which traits; whether EF relationships should be included even if they contain little theoretical support; whether all traits benefit from EF-based assumptions or if a hybrid, super-predictive EF-and PFT-based approach can improve simulations; and so on. An additional consideration involves the mathematical interpretability and/or generality of EF relationships (Kyker-Snowman et al, 2022), which depends on the specific predictive framework selected for analysis (i.e., a machine learning-based approach is less interpretable than a simple linear regression). It is also not clear whether EF relationships developed offline can be used directly in differe nt TBMs with unique structures and dependencies, or whether the parameters of the EF relationships themselves would need local tuning for each specific TBM to avoid compensating errors (Koster et al, 2009;J-F Exbrayat et al, 2013).…”

Reimagine fire science for the anthropocene

“…We need to build upon the adaptive, integrated knowledge, and "use-inspired" approaches, such as those put forth by Kyker-Snowman et al (56) and Wall et al (57), by including empiricists, modelers, practitioners, and domain experts from broad disciplines where they are involved at every stage of data collection, idea development, and model integration. In this approach, the two-way exchange of ideas is emphasized in order to effectively incorporate domain expertise and knowledge into models of systems that can not only improve understanding, but eventually move toward forecasting capability (see Challenge 5).…”