Abstract. Earth System Models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this manuscript, we present the Ecosystem Demography Model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological cycles account for the horizontal and vertical heterogeneity of the ecosystem: the energy, water, and carbon cycles are solved separately for each group of individual trees of similar size and functional group (cohorts) living in a micro-environment with similar disturbance history (patches). We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the ordinary differential equations and guarantees excellent conservation of these quantities in long-term simulation ( < 0.1 % error over 50 years). We also show examples of ED-2.2 simulation results at single sites and across tropical South America. These results demonstrate the model's ability to characterize the variability of ecosystem structure, composition and functioning both at stand- and continental-scales. In addition, a detailed model evaluation was carried out and presented in a companion paper. Finally, we highlight some of the ongoing developments in ED-2.2 that aim at reducing the uncertainties identified in this study and the inclusion of processes hitherto not represented in the model.

Organic matter constitutes a key reservoir in global elemental cycles and the biological sequestration of carbon. However, our understanding of the dynamics of organic matter and its accumulation remains incomplete. Seemingly disparate hypotheses have been proposed to explain organic matter accumulation: the slow degradation of intrinsically recalcitrant substrates, the depletion to concentrations that inhibit microbial consumption, and a dependency on the consumption capabilities of nearby microbial populations. Here, using a mechanistic model, we develop a theoretical framework that explains how organic matter predictably accumulates due to biochemical, ecological, and environmental factors. The new framework subsumes the previous hypotheses. Changes in the microbial community or the environment can move a class of organic matter from a state of functional recalcitrance to a state of depletion by microbial consumers. The model explains the vertical profile of dissolved organic carbon in the ocean, and it connects microbial activity at subannual timescales to organic matter turnover at millenial timescales. The threshold behavior of the model implies that organic matter accumulation may respond nonlinearly to changes in temperature and other factors, providing hypotheses for the observed correlations between organic carbon reservoirs and temperature in past earth climates.

Microbes form the base of food webs and drive both aquatic and terrestrial biogeochemical cycling, thereby significantly influencing the global climate. Predicting how microbes will adapt to global change and the implications for global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here we present an approach for modeling multivariate trait evolution using orthogonal axes to define a trait-scape. We use empirical evolution data to first parameterize our framework followed by modeling thousands of possible adaptive walks. We find that only a limited number of phenotypes emerge, with some being more probable than others. Populations with historical bias in the direction of selection exhibited accelerated adaptation while highly convergent phenotypes emerged irrespective of the type of bias. Reproducible phenotypes further converged into several high-fitness regions in the collapsed trait-scape, thereby elucidating low-fitness regions. The emergence of nonrandom phenotypic solutions and high-fitness areas in an empirical algal trait-scape demonstrates that a limited set of evolutionary trajectories underlie the vast amount of possible trait correlation scenarios. Investigating microbial evolution through a reduced set of biogeochemically-important trait relationships lays the groundwork for incorporation into global change-driven ecosystem models where microbial trait dispersal can occur through different inheritance mechanisms. Identifying the probabilities of high-fitness outcomes based on trait correlations will be critical to directly connect microbial evolutionary responses to biogeochemical cycling under dynamic global change scenarios.Author SummaryMicroorganisms drive the base of global food webs and biogeochemical cycling, which significantly regulates Earth’s climate. Thus, it is critical to understand how their evolutionary responses to global change will affect global environmental processes. Microbial populations are highly diverse and both shape and are shaped by numerous environmental gradients happening simultaneously on different timescales. The sheer number of combinations to experimentally test exceeds our ability to do so, and so theoretical approaches that integrate biological and environmental variability onto a reduced set of representative axes can aid predictions of evolutionary outcomes. Here, we show that only a limited number of evolutionary solutions underlie thousands of possible biological trait correlation combinations, and that depending on historical bias, some phenotypes are more probable than others. These phenotypes further converge into high-fitness regions in a collapsed trait landscape derived from these representative axes. The emergence of only a handful of solutions from thousands of possible scenarios is a powerful tool to help predict probable microbial evolutionary trajectories given a vast array of combinations. This approach lays the groundwork to embed this framework into larger ecosystem models to examine the effects of these responses on biogeochemical cycling and global climate.

Abstract. The Ecosystem Demography Model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical and biogeochemical cycles of dynamic ecosystems while considering the role of vertical structure of plant communities and the heterogeneity of such structures across the landscape. In a companion paper, we described in detail how the model solves the energy, water, and carbon cycles, and verified the excellent conservation of such properties in long-term simulation. Here, we present a thorough assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles, with focus on the Amazon forest. We used multiple measurements from eddy covariance towers, forest inventory plots and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple time scales ranging from sub-daily to century-long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes and the storage of water, energy and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 also simulates emerging properties of the ecosystem found in observations, such as the relationship between biomass and mortality rates and wood density, although the relationships predicted by the model were biased. We also identified some of the model limitations, such as the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration, and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in ESMs, to realistically represent the role of forest structure and composition on energy, water, and carbon cycles, as well as the priority areas for further model development.

The organic sulfur compounds dimethylsulfoniopropionate (DMSP) and dimethyl sulfoxide (DMSO) play major roles in the marine microbial food web and have substantial climatic importance as sources and sinks of dimethyl sulfide (DMS). Seasonal shifts in the abundance and diversity of the phytoplankton and bacteria that cycle DMSP are likely to impact marine DMS (O) (P) concentrations, but the dynamic nature of these microbial interactions is still poorly resolved. Here, we examined the relationships between microbial community dynamics with DMS (O) (P) concentrations during a 2-year oceanographic time series conducted on the east Australian coast. Heterogenous temporal patterns were apparent in chlorophyll a (chl a) and DMSP concentrations, but the relationship between these parameters varied over time, suggesting the phytoplankton and bacterial community composition were affecting the net DMSP concentrations through differential DMSP production and degradation. Significant increases in DMSP were regularly measured in spring blooms dominated by predicted high DMSP-producing lineages of phytoplankton (Heterocapsa, Prorocentrum, Alexandrium, and Micromonas), while spring blooms that were dominated by predicted low DMSP-producing phytoplankton (Thalassiosira) demonstrated negligible increases in DMSP concentrations. During elevated DMSP concentrations, a significant increase in the relative abundance of the key copiotrophic bacterial lineage Rhodobacterales was accompanied by a three-fold increase in the gene, encoding the first step of DMSP demethylation (dmdA). Significant temporal shifts in DMS concentrations were measured and were significantly correlated with both fractions (0.2–2 μm and &gt;2 μm) of microbial DMSP lyase activity. Seasonal increases of the bacterial DMSP biosynthesis gene (dsyB) and the bacterial DMS oxidation gene (tmm) occurred during the spring-summer and coincided with peaks in DMSP and DMSO concentration, respectively. These findings, along with significant positive relationships between dsyB gene abundance and DMSP, and tmm gene abundance with DMSO, reinforce the significant role planktonic bacteria play in producing DMSP and DMSO in ocean surface waters. Our results highlight the highly dynamic nature and myriad of microbial interactions that govern sulfur cycling in coastal shelf waters and further underpin the importance of microbial ecology in mediating important marine biogeochemical processes.

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Abstract. Nitrification controls the oxidation state of bioavailable nitrogen. Distinct clades of chemoautotrophic microorganisms – predominantly ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB) – regulate the two steps of nitrification in the ocean, but explanations for their observed relative abundances and nitrification rates remain incomplete and their contributions to the global marine carbon cycle via carbon fixation remain unresolved. Using a mechanistic microbial ecosystem model with nitrifying functional types, we derive simple expressions for the controls on AOA and NOB in the deep, oxygenated open ocean. The relative biomass yields, loss rates, and cell quotas of AOA and NOB control their relative abundances, though we do not need to invoke a difference in loss rates to explain the observed relative abundances. The supply of ammonium, not the traits of AOA or NOB, controls the relatively equal ammonia and nitrite oxidation rates at steady state. The relative yields of AOA and NOB alone set their relative bulk carbon fixation rates in the water column. The quantitative relationships are consistent with multiple in situ datasets. In a complex global ecosystem model, nitrification emerges dynamically across diverse ocean environments, and ammonia and nitrite oxidation and their associated carbon fixation rates are decoupled due to physical transport and complex ecological interactions in some environments. Nevertheless, the simple expressions capture global patterns to first order. The model provides a mechanistic upper estimate on global chemoautotrophic carbon fixation of 0.2–0.5 Pg C yr−1, which is on the low end of the wide range of previous estimates. Modeled carbon fixation by AOA (0.2–0.3 Pg C yr−1) exceeds that of NOB (about 0.1 Pg C yr−1) because of the higher biomass yield of AOA. The simple expressions derived here can be used to quantify the biogeochemical impacts of additional metabolic pathways (i.e., mixotrophy) of nitrifying clades and to identify alternative metabolisms fueling carbon fixation in the deep ocean.

Oceanic time-series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use highfrequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in 20 the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset was dominated by four water column profile types: active upwelling, offshore influence, subsurface chlorophyll maximum, and surface bloom. On average, waters across the SPC were most similar to offshore profiles. On weekly timescales, the SPOT station was on average representative of 64% of profiles taken within the SPC, and SPOT was least similar to SPC locations that were closest to the Palos Verdes Peninsula. Subsurface chlorophyll maxima with co-located chlorophyll and particle 25 maxima were common in 2013 and 2014 suggesting that these subsurface chlorophyll maxima might contribute significantly to the local primary production. These results indicate that high-resolution in situ glider deployments can be used to determine the spatial domain of time-series data, allowing for broader application of these datasets and greater integration into modeling efforts.