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Animals occupying higher trophic levels can have disproportionately large influence on ecosystem structure and functioning, owning to intricate behavioral responses to their environment, but the effects of behavioral adaptations on aquatic ecosystem dynamics are underrepresented, especially in model studies. Here, we explore how adaptive behavior of fish can affect the dynamics of aquatics ecosystems. We frame fish behavior in the context of the central trade‐off between feeding and predation, calculating the optimal level of feeding determined by ambient food availability and predation risk. To explore whole‐ecosystem consequences of fish behavior, we embed our behavioral model within the Water Ecosystems Tool (WET), a contemporary end‐to‐end aquatic ecosystem model. The principle of optimality provides a robust and mechanistic framework for representing animal behavior that is relevant for complex models, and can provide a stabilizing effect on model dynamics. The model predicts an emergent functional response similar to Holling type III, but with richer dynamics and a more rigorous theoretical foundation. We show how adaptive fish behavior works to stabilize food web dynamics compared to a control model with no optimal behavior, and how changing the strength of the underlying trade‐off has profound effects on trophic control and food web structure. Furthermore, we demonstrate how including fish behavior allows for an overall more realistic response of the model system to environmental perturbation in the form of nutrient enhancement. We discuss the structuring effects of behavioral adaptations in real ecosystems, and how approaches like this one may benefit aquatic ecological modeling. Our study further highlights how a mechanistic approach based on concepts from theoretical ecology can be successfully implemented in complex operational models resulting in improved dynamics and descriptive power. [ABSTRACT FROM AUTHOR]

Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single‐species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost‐benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single‐species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost‐benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits. [ABSTRACT FROM AUTHOR]

The two phases of El‐Niño‐Southern Oscillation (ENSO) influence both regional and global terrestrial vegetation productivity on inter‐annual scales. However, the major drivers for the regional vegetation productivity and their controlling strengths during different phases of ENSO remain unclear. We herein disentangled the impacts of two phases of ENSO on regional carbon cycle using multiple data sets. We found that soil moisture predominantly accounts for ∼40% of the variability in regional vegetation productivity during ENSO events. Our results showed that the satellite‐derived vegetation productivity proxies, gross primary productivity from data‐driven models (FLUXCOM) and observation‐constrained ecosystem model (Carbon Cycle Data Assimilation System) generally agree in depicting the contribution of soil moisture and air temperature in modulating regional vegetation productivity. However, the ensemble of weakly constrained ecosystem models exhibits non‐negligible discrepancies in the roles of vapor pressure deficit and radiation over extra‐tropics. This study highlights the significance of water in regulating regional vegetation productivity during ENSO. Plain Language Summary: ENSO, a significant climate phenomenon, profoundly influences ecosystem productivity from regional to global scales. Yet, accurately pinpointing the attributions of different climate factors that control Gross Primary Productivity (GPP) during ENSO events remains challenging, partly due to large uncertainties in existing GPP data. Through extensive analysis using various data sets, we found that soil moisture plays a dominant role in controlling GPP across most continents. This implies that current terrestrial biosphere models might underestimate the importance of soil moisture in driving productivity during ENSO events. Addressing this gap in understanding is crucial for refining and properly constraining the related processes in terrestrial biosphere models. Key Points: Soil moisture predominantly governs vegetation productivity changes during El‐Niño‐Southern Oscillation (ENSO) eventsSatellite‐derived vegetation indices consistently support the predominant influence of water on vegetation productivityENSO triggers varying effects on vegetation productivity across different regions, with distinct impacts during El Niño and La Niña [ABSTRACT FROM AUTHOR]

Abstract Animals occupying higher trophic levels can have disproportionately large influence on ecosystem structure and functioning, owning to intricate behavioral responses to their environment, but the effects of behavioral adaptations on aquatic ecosystem dynamics are underrepresented, especially in model studies. Here, we explore how adaptive behavior of fish can affect the dynamics of aquatics ecosystems. We frame fish behavior in the context of the central trade‐off between feeding and predation, calculating the optimal level of feeding determined by ambient food availability and predation risk. To explore whole‐ecosystem consequences of fish behavior, we embed our behavioral model within the Water Ecosystems Tool (WET), a contemporary end‐to‐end aquatic ecosystem model. The principle of optimality provides a robust and mechanistic framework for representing animal behavior that is relevant for complex models, and can provide a stabilizing effect on model dynamics. The model predicts an emergent functional response similar to Holling type III, but with richer dynamics and a more rigorous theoretical foundation. We show how adaptive fish behavior works to stabilize food web dynamics compared to a control model with no optimal behavior, and how changing the strength of the underlying trade‐off has profound effects on trophic control and food web structure. Furthermore, we demonstrate how including fish behavior allows for an overall more realistic response of the model system to environmental perturbation in the form of nutrient enhancement. We discuss the structuring effects of behavioral adaptations in real ecosystems, and how approaches like this one may benefit aquatic ecological modeling. Our study further highlights how a mechanistic approach based on concepts from theoretical ecology can be successfully implemented in complex operational models resulting in improved dynamics and descriptive power.

Pseudo-nitzschia species are one of the leading causes of harmful algal blooms (HABs) along the western coast of the United States. Approximately half of known Pseudo-nitzschia strains can produce domoic acid (DA), a neurotoxin that can negatively impact wildlife and fisheries and put human life at risk through amnesic shellfish poisoning. Production and accumulation of DA, a secondary metabolite synthesized during periods of low primary metabolism, is triggered by environmental stressors such as nutrient limitation. To quantify and estimate the feedbacks between DA production and environmental conditions, we designed a simple mechanistic model of Pseudo-nitzschia and domoic acid dynamics, which we validate against batch and chemostat experiments. Our results suggest that, as nutrients other than nitrogen (i.e., silicon, phosphorus, and potentially iron) become limiting, DA production increases. Under Si limitation, we found an approximate doubling in DA production relative to N limitation. Additionally, our model indicates a positive relationship between light and DA production. These results support the idea that the relationship with nutrient limitation and light is based on direct impacts on Pseudo-nitzschia biosynthesis and biomass accumulation. Because it can easily be embedded within existing coupled physical-ecosystem models, our model represents a step forward toward modeling the occurrence of Pseudo-nitzschia HABs and DA across the U.S. West Coast.

Ecosystem models, such as Ecopath with Ecosim (EwE), provide a platform to simulate intricate policy scenarios where multiple species, pressures, and ecosystem services interact. Complex questions often return complex answers, necessitating evidence and advice to be communicated in terms of trade-offs, risks, and uncertainty. Calibration procedures for EwE, which can act as a source of uncertainty and bias in model results, have yet to be explored in a comprehensive way that communicates how sensitive model outputs are to different calibration approaches. As the EwE community has grown, multiple divergent approaches have been applied to calibrate models through the estimation of vulnerability multipliers: parameters that augment the consumption rate limits of predators. Here we explore the underlying principles of vulnerability multipliers as well as existing calibration approaches and their justification. Two case studies are presented: the first explores how vulnerability multipliers emerge based on the chosen calibration approach using simulated data, while the second takes two operational EwE models (Irish Sea and Northwest Atlantic Continental Shelf) and compares their outputs when calibrated following alternate calibration approaches. We show how calibration approaches can impact model-derived advice and provide a list of best practice recommendations for EwE calibration. [ABSTRACT FROM AUTHOR]

Accurately modeling gross primary productivity (GPP) is of great importance in diagnosing terrestrial carbon-climate feedbacks. Process-based terrestrial ecosystem models are often subject to substantial uncertainties, primarily attributed to inadequately calibrated parameters. Recent attention has identified carbonyl sulfide (COS) as a promising proxy of GPP, due to the close linkage between leaf exchange of COS and carbon dioxide (CO2) through their shared pathway of stomatal diffusion. However, most of the current modeling approaches for COS and CO2 did not explicitly consider the vegetation structural impacts, i.e. the differences between the sun-shade and sunlit leaves in COS uptake. This study used ecosystem COS fluxes data from 7 sites to optimize GPP estimation across various ecosystems with the Boreal Ecosystem Productivity Simulator (BEPS), which was further developed for simulating the leaf COS uptake under its state-of-the-art 'two-leaf' framework. Our results demonstrated the substantial improvement in GPP simulation across various ecosystems through the fusion of COS data into the 'two-leaf' model, with the ensemble mean of root mean square error (RMSE) for simulated GPP reduced by 18.99 % to 66.64 %. Notably, we also shed light on the remarkable identifiability of key parameters within the BEPS model, including the maximum carboxylation rate of Rubisco at 25 °C (Vcmax25), minimum stomatal conductance (...), and leaf nitrogen content (Nleaf), despite intricate interactions among COS-related parameters. Furthermore, our global sensitivity analysis delineated both shared and disparate sensitivities of COS and GPP to model parameters and suggested the unique treatment of parameters for each site in COS and GPP modeling. In summary, our study deepened insights into the sensitivity, identifiability, and interactions of parameters related to COS, and showcased the efficacy of COS in reducing uncertainty in GPP simulations. [ABSTRACT FROM AUTHOR]

Variation in the recruitment of salmon is often found to be correlated with marine climate indices, but mechanisms behind environment–recruitment relationships remain unclear and correlations often break down over time. We used an ecosystem modeling approach to explore bottom‐up and top‐down mechanisms linking a variable environment to salmon recruitment variations. Our ecosystem model incorporates a regional ocean circulation submodel for hydrodynamics, a nutrient‐phytoplankton‐zooplankton submodel for producing planktonic prey fields, and an individual‐based model (IBM) representing juvenile Chinook salmon (Oncorhynchus tshawytscha), combined with observations of foraging distributions and diet of a seabird predator. The salmon IBM consists of modules, including a juvenile salmon growth module based on temperature and salmon–prey availability, a behavior‐based movement module, and a juvenile salmon predation mortality module based on juvenile salmon size distribution and predator–prey interaction probability. Seabird–salmon interactions depend on spatial overlap and juvenile salmon size, whereby salmon that grow past the size range of the prey distribution of the predator will escape predation. We used a 21‐year historical simulation to explore interannual variability in juvenile Chinook salmon growth and predation‐mediated survival under a range of ocean conditions for sized‐based mortality scenarios. We based a series of increasingly complex predation scenarios on seabird observational data to explore variability in predation mortality on juvenile Chinook salmon. We initially included information about the predator spatial distribution, then added population size, and finally the predator's diet percentage made up of juvenile salmon. Model agreement improves with added predator complexity, especially during periods when predator abundance is high. Overall, our model found that when the fraction of juvenile salmon in seabird diet increased relative to alternate prey (e.g., Northern anchovy Engraulis mordax, and juvenile rockfish Sebastes spp.), there was a concomitant decrease in salmon cohort survival during their first year at sea. [ABSTRACT FROM AUTHOR]

Abstract The two phases of El‐Niño‐Southern Oscillation (ENSO) influence both regional and global terrestrial vegetation productivity on inter‐annual scales. However, the major drivers for the regional vegetation productivity and their controlling strengths during different phases of ENSO remain unclear. We herein disentangled the impacts of two phases of ENSO on regional carbon cycle using multiple data sets. We found that soil moisture predominantly accounts for ∼40% of the variability in regional vegetation productivity during ENSO events. Our results showed that the satellite‐derived vegetation productivity proxies, gross primary productivity from data‐driven models (FLUXCOM) and observation‐constrained ecosystem model (Carbon Cycle Data Assimilation System) generally agree in depicting the contribution of soil moisture and air temperature in modulating regional vegetation productivity. However, the ensemble of weakly constrained ecosystem models exhibits non‐negligible discrepancies in the roles of vapor pressure deficit and radiation over extra‐tropics. This study highlights the significance of water in regulating regional vegetation productivity during ENSO.

Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R2  = 0.62 across all sites), LWs accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

Downscaling physical forcing from global climate models is both time consuming and labor demanding and can delay or limit the physical forcing available for regional marine ecosystem modelers. Earlier studies have shown that downscaled physics is necessary for capturing the dynamics of primary production and lower trophic levels; however, it is not clear how higher trophic levels respond to the coarse resolution physics of global models. Here, we apply the Nordic and Barents Seas Atlantis ecosystem model (NoBa) to study the consequences of using physical forcing from global climate models versus using that from regional models. The study is therefore (i) a comparison between a regional model and its driving global model to investigate the extent to which a global climate model can be used for regional ecosystem predictions and (ii) a study of the impact of future climate change in the Nordic and Barents Seas. We found that few higher trophic level species were affected by using forcing from a global versus a regional model, and there was a general agreement in future biomass trends and distribution patterns. However, the slight difference in temperature between the models dramatically impacted Northeast Arctic cod (Gadus morhua), which highlights how species projection uncertainty could arise from poor physical representation of the physical forcing, in addition to uncertainty in the ecosystem model parameterization. [ABSTRACT FROM AUTHOR]

Intensification of human activities is pushing our use of ecosystems beyond thresholds of resiliency. Given the accelerating global crisis of ecological sustainability, there has been enormous growth in research related to ecological security. However, differences in opinions on ecological security have hindered understanding and effective applications of this concept. To understand the development of research on ecological security, we reviewed its achievements and limitations over the past 30 years from three dimensions: definition, evaluation method, and approach to identify measures to improve the ecological security level. We used the Web of Science search engine to retrieve peer-reviewed journal articles published from 1990 to 2021 containing the keywords "ecological security" or "ecological safety". There are three main ethical perspectives among the definitions of ecological security: nature-centric, human-centric, and eclectic; the human-centric view, which focuses on human well-being, is predominant in the field. Most studies employed the following three evaluation methods: quantitative comparison, composite indicators, and spatial analysis. However, the results of ecological security analyses were difficult to compare. Three main approaches (causality, correlation, and landscape) were used to identify the drivers of ecological security and propose measures for ensuring or improving ecological security. Owing to the complexity and heterogeneity of ecosystems, universally effective measures to ensure ecological security rarely exist. For the definition and evaluation of ecological security, a broader, non-anthropocentric perspective that incorporates the intrinsic value of non-humans in the context of cost–benefit, security–efficiency evaluations is essential. When proposing evaluation methods, the comparability of evaluation results should be given priority. To improve ecological security level, identifying the key drivers and/or potential optimal patterns of ecological security may be a promising solution. [ABSTRACT FROM AUTHOR]

One of the significant challenges to using information and ideas generated through ecosystem models and analyses for ecosystem-based fisheries management is the disconnect between modeling and management needs. Here we present a case study from the U.S. West Coast, the stakeholder review of NOAA’s annual ecosystem status report for the California Current Ecosystem established by the Pacific Fisheries Management Council’s Fisheries Ecosystem Plan, showcasing a process to identify management priorities that require information from ecosystem models and analyses. We then assess potential ecosystem models and analyses that could help address the identified policy concerns. We screened stakeholder comments and found 17 comments highlighting the need for ecosystem-level synthesis. Policy needs for ecosystem science included: (1) assessment of how the environment affects productivity of target species to improve forecasts of biomass and reference points required for setting harvest limits, (2) assessment of shifts in the spatial distribution of target stocks and protected species to anticipate changes in availability and the potential for interactions between target and protected species, (3) identification of trophic interactions to better assess tradeoffs in the management of forage species between the diet needs of dependent predators, the resilience of fishing communities, and maintenance of the forage species themselves, and (4) synthesis of how the environment affects efficiency and profitability in fishing communities, either directly via extreme events (e.g., storms) or indirectly via climate-driven changes in target species availability. We conclude by exemplifying an existing management process established on the U.S. West Coast that could be used to enable the structured, iterative, and interactive communication between managers, stakeholders, and modelers that is key to refining existing ecosystem models and analyses for management use.

Modeling Arctic-Boreal vegetation is a challenging but important task, since this highly dynamic ecosystem is undergoing rapid and substantial environmental change. In this work, we synthesized information on 18 dynamic vegetation models (DVMs) that can be used to project vegetation structure, composition, and function in North American Arctic-Boreal ecosystems. We reviewed the ecosystem properties and scaling assumptions these models make, reviewed their applications from the scholarly literature, and conducted a survey of expert opinion to determine which processes are important but lacking in DVMs. We then grouped the models into four categories (specific intention models, forest species models, cohort models, and carbon tracking models) using cluster analysis to highlight similarities among the models. Our application review identified 48 papers that addressed vegetation dynamics either directly (22) or indirectly (26). The expert survey results indicated a large desire for increased representation of active layer depth and permafrost in future model development. Ultimately, this paper serves as a summary of DVM development and application in Arctic-Boreal environments and can be used as a guide for potential model users, thereby prioritizing options for model development.

Wildfires significantly change boreal forest ecosystem carbon balance through both direct combustion and post-fire carbon dynamics. Affected vegetation influences soil thermal regime and carbon cycling by impacting the surface energy balance of boreal forests. This study uses a process-based biogeochemistry model to quantify carbon budget of North American boreal forests during 1986–2020 based on satellite-derived burn severity data. During the study period, burn severity generally increases. Fires remove ecosystem carbon of 2.4 Pg C and reduce net ecosystem production (NEP) from 32.6 to 0.8 Tg C yr ^−1 , making the forest ecosystems lose 3.5 Pg C, shifting a carbon sink to a source. The canopy’s cooling effect leads to lower soil temperature and lower net primary production due to lower nitrogen mineralization and uptake. Post-fire NEP decreases from 1.6 to 0.8 Tg C yr ^−1 . This reduction accounts for 50% of the simulated NEP when the effects of fire-affected canopy are not considered. Our study highlights the importance of wildfires and their induced-canopy changes in soil thermal and ecosystem carbon dynamics of boreal forests.

Bioregenerative life support systems (BLSS) are vital for long-duration and remote space missions to increase mission sustainability. These systems break down human waste materials into nutrients and CO2 for plants and other edible organisms, which in turn provide food, fresh water, and oxygen for astronauts. The central idea is to create a materially closed loop, which can significantly reduce mission mass and volume by cutting down or even eliminating disposable waste. In most BLSS studies only a fraction of the resources, such as food, are provided by the system itself, with the rest taken on board at departure or provided through resupply missions. However, for autonomous long-duration space missions without any possibility of resupply, a BLSS that generates all resources with minimal or no material loss, is essential. The goal of this study is to develop a stoichiometric model of a conceptually fully closed BLSS that provides all the metabolic needs of the crew and organisms. The MELiSSA concept of the European Space Agency is used as reference system, consisting of five interconnected compartments, each inhabited by different types of organisms. A detailed review of publicly available MELiSSA literature from 1989 to 2022 revealed that no existing stoichiometric model met the study’s requirements. Therefore, a new stoichiometric model was developed to describe the cycling of the elements C, H, O, and N through all five MELiSSA compartments and one auxiliary compartment. A compact set of chemical equations with fixed coefficients was established for this purpose. A spreadsheet model simulates the flow of all relevant compounds for a crew of six. By balancing the dimensions of the different compartments, a high degree of closure is attained at steady state, with 12 out of 14 compounds exhibiting zero loss, and oxygen and CO2 displaying only minor losses between iterations. This is the first stoichiometric model of a MELiSSA-inspired BLSS that describes a continuous provision of 100% of the food and oxygen needs of the crew. The stoichiometry serves as the foundation of an agent-based model of the MELiSSA loop, as part of the Evolving Asteroid Starships (E|A|S) research project.

Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics. [ABSTRACT FROM AUTHOR]

Terrestrial plants have transformed Earth's surface environments by altering water, energy, and biogeochemical cycles. Studying vegetation-climate interaction in deep time has necessarily relied on modern-plant analogs to represent paleo-ecosystems—as methods for reconstructing paleo- and, in particular, extinct-plant function were lacking. This approach is potentially compromised given that plant physiology has evolved through time, and some paleo-plants have no clear modern analog. Advancements in the quantitative reconstruction of whole-plant function provide new opportunities to replace modern-plant analogs and capture age-specific vegetation-climate interactions. Here, we review recent investigations of paleo-plant performance through the integration of fossil and geologic data with process-based ecosystem- to Earth system–scale models to explore how early vascular plants responded to and influenced climate. First, we present an argument for characterizing extinct plants in terms of ecological and evolutionary theory to provide a framework for advancing reconstructed vegetation-climate interactions in deep time. We discuss the novel mechanistic understanding provided by applying these approaches to plants of the late Paleozoic ever-wet tropics and at higher latitudes. Finally, we discuss preliminary applications to paleo-plants in a state-of-the-art Earth system model to highlight the potential implications of different plant functional strategies on our understanding of vegetation-climate interactions in deep time. For hundreds of millions of years, plants have been a keystone in maintaining the status of Earth's atmosphere, oceans, and climate. Extinct plants have functioned differently across time, limiting our understanding of how processes on Earth interact to produce climate. New methods, reviewed here, allow quantitative reconstruction of extinct-plant function based on the fossil record. Integrating extinct plants into ecosystem and climate models will expand our understanding of vegetation's role in past environmental change. [ABSTRACT FROM AUTHOR]

Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high-resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED-2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the eastern Amazon as initial conditions to ED-2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥66%) experienced water stress with declines in ET (up to 34%) and GPP (up to 35%) and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multiyear droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are driven by not only climate and deforestation but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.

In 2020, Brazil was the seventh largest emitter of GHG (greenhouse gases), releasing ~2.16 GtCO2e (gigatons of carbon dioxide equivalent) into the atmosphere. Activities related to land use contributed approximately 73% of national emissions in that year. Considering that pastures represent the primary land use in the country, occupying approximately 20% of the territory, the mapping and monitoring of C stocks in these areas is essential to determine their contribution to national emissions. In this study, based on the integrated use of the CENTURY model, georeferenced databases, and the R environment, we mapped and analyzed, for the first time, the C stocks dynamics associated with the pasture areas of the Cerrado biome between 2000 and 2019. The average C stocks in the soil (0–20 cm) and in the aboveground biomass estimated by modeling were ~31 MgC·ha−1 and ~4 MgC·ha−1, respectively, values close to those observed in the literature for the region. Furthermore, the model results corresponded to the edaphic patterns of the region, with the highest average estimated C stocks in Cambisols (~34 MgC·ha−1) and the lowest in Neosols (~29 MgC·ha−1). The temporal dynamics of soil C stocks in these areas are directly related to the age of the pastures. In fact, stocks tend to be reduced in recently converted areas and stabilized in areas that have been under this land use for a longer time (≥30 years). As a result, a loss of ~103 MtC (millions of tons of carbon) was estimated in the Cerrado pasture soils in twenty years. The mapping and monitoring of C stocks in this land use type through approaches such as the one presented in this study is essential to support the Brazilian government's efforts to mitigate C emissions. [ABSTRACT FROM AUTHOR]

Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.

Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.

Ecosystems worldwide receive large amounts of nutrients from both natural processes and human activities. While direct subsidy effects on primary production are relatively well-known (the green food web), the indirect effects of subsidies on producers as mediated by the brown food web and predators are poorly considered. With a dynamical green-brown food web model, parameterized using empirical estimates from the literature, we illustrate the effect of organic and inorganic nutrient subsidies on net primary production (NPP) (i.e., after removing loss to herbivory) in two idealized ecosystems—one terrestrial and one aquatic. We find that nutrient subsidies increase net primary production, an effect that saturates with increasing subsidies. Changing the quality of subsidies from inorganic to organic tends to increase net primary production in terrestrial ecosystems, but less often so in aquatic ecosystems. This occurs when organic nutrient inputs promote detritivores in the brown food web, and hence predators that in turn regulate herbivores, thereby promoting primary production. This previously largely overlooked effect is further enhanced by ecosystem properties such as fast decomposition and low rates of nutrient additions and demonstrates the importance of nutrient subsidy quality on ecosystem functioning.

Satellite-observed leaf area index (LAI) is often used to depict vegetation canopy structure and photosynthesis processes in terrestrial biogeochemical models. However, it remains unclear how the uncertainty of LAI among different satellite products propagates to the modeling of carbon (C), nitrogen (N), and phosphorus (P) cycles. Here, we separately drive a global biogeochemical model by three satellite-derived LAI products (i.e., GIMMS LAI3g, GLASS, and GLOBMAP) from 1982 to 2011. Using a traceability analysis, we explored the propagation of LAI-driven uncertainty to modeled C, N, and P storage among different biomes. The results showed that the data uncertainty of LAI was more considerable in the tropics than in non-tropical regions, whereas the modeling uncertainty of C, N, and P stocks showed a contrasting biogeographic pattern. The spread of simulated C, N, and P storage derived by different LAI datasets resulted from assimilation rates of elements in shrubland and C3 grassland but from the element residence time (τ) in deciduous needle leaf forest and tundra regions. Moreover, the assimilation rates of elements are the main contributing factor, with 67.6, 93.2, and 93% of vegetated grids for the modeled uncertainty of C, N, and P storage among the three simulations. We further traced the variations in τ to baseline residence times of different elements and the environmental scalars. These findings indicate that the data uncertainty of plant leaf traits can propagate to ecosystem processes in global biogeochemical models, especially in non-tropical forests.

The Visayan Sea is one of the major traditional fishing grounds in the Philippines and is likewise considered as a top contributor to the country’s overall fisheries production. However, fisheries surveys conducted over the past couple of decades have documented changes in the abundance and composition of catches and stocks toward an increasing dominance of invertebrates and low trophic levels fish families. Assessments of the commercially-important pelagic and demersal stocks were used to estimate population parameters that already exceed the sustainable reference limits. These traditional assessments point exclusively to fishing as the likely cause of the observed status and trends, but they fail to incorporate the potential influence of trophic interactions and variable environmental conditions despite a nationally-legislated mandate to apply an ecosystem approach to assessing and managing the country’s fisheries. Thus, an ecosystem model (Ecopath with Ecosim) of the Visayan Sea was constructed to investigate the role and extent of the fishery, trophic interactions, and changing environmental conditions as drivers of stock abundance and ecosystem dynamics. The results suggest that fishing and its corresponding trophic effects are the major drivers of invertebrate and fish biomass and catches in the Visayan Sea over the past two decades, while the environmental trends provide explanations for patterns that cannot be explained by fishing alone. Incorporating sea surface temperature variations and primary productivity anomalies produced the best-fitting models for most of the observed biomass and catch trends. The catches of the small pelagics (i.e., sardines and mackerels) were likewise found to be particularly affected by rainfall, while the biomass and catch of the small demersal fishes also show a sensitivity to chlorophyll-a concentrations. The primary productivity anomalies further showed a significant correlation with the Pacific Decadal Oscillation (PDO), which suggests that bottom-up trophic controls in the system may be affected by long-term regional climatic changes. These findings validate the applicability and necessity of the ecosystem modeling approach in assessing exploited marine ecosystems to complement the analyses from typically single-species population assessments, especially in the face of increasing environmental variability and projected climate change scenarios.

Alpine lakes on the Tibetan Plateau are highly sensitive to global change and have been recognized as the sentinel of climate warming. However, anthropogenic impacts in populated area are migrating to these remote areas via transporting particulate nutrients by atmospheric deposition. Whether warming and nutrient deposition would impose additive or synergistic effects on the lake ecosystem remains largely unknown. Here, we present multi-proxy (sediment pigment and geochemistry) records during the past two centuries at the Cuoqia Lake in the southeast Tibetan Plateau. We found that the lake exhibited rapid ecological changes since 1980 AD characterized by an increase in primary productivity due to algal proliferation, with more rapid growth of green algae and diatoms. These findings are in concert with many other lakes (e.g., Moon Lake and Shade Co) in the same area, suggesting a consistent pattern of ecosystem evolution at the region scale. Statistical analyses suggested that nutrient deposition and climate warming were strongly associated with the variations in primary productivity and algae composition, exerting both individual and interactive effects. In addition, scenario analyses with a well-established process-based ecosystem model further revealed that the two factors not only individually, but also synergistically promoted the algal proliferation and community succession. Such synergy is evident in that the effect of lake warming would be more pronounced under higher nutrient deposition scenario, which is potentially due to higher temperature-driven mineralization in warmer conditions, and higher efficiency of nutrient utilization under enhanced light availability attributing to declining ice thickness and duration in cold seasons. Overall, our study proposes the existence and quantifies the synergistic impacts of climate warming and anthropogenic activities in driving the ecological changes in remote alpine lakes on the Tibetan Plateau. The lake ecological consequences driven by individual factor would be worsen by such synergy, so that we cannot predict the lake ecosystem trajectory in the future based on each factor separately, and more efforts than previously expected would be needed for the lake restoration and management.

Nitrification regulates the potential for nitrogen (N) loss from ecosystems because it converts ammonium to nitrate, which is susceptible to leaching and gaseous emissions. Crops can suppress the microbes that perform nitrification by exuding nitrification‐inhibiting compounds from their roots and taking up available ammonium. However, the effect of nitrification suppression on agroecosystem N losses remains poorly characterized, and a lack of temporal synchrony between nitrification, N losses, and nitrification suppression by plants could limit the effect of nitrification suppression. We used the DayCent‐CABBI model to evaluate the effectiveness of the suppression of nitrification by sorghum to reduce N2O emissions and nitrate leaching in an energy sorghum/soybean rotation at the Energy Farm in Urbana‐Champaign, IL. We simulated nitrification suppression at the measured levels (MeasNS) and at the maximum measured level applied to the entire growing season (MaxNS), and we also explored ways to better utilize nitrification suppression by altering the timing of urea–ammonium–nitrate fertilizer applications. Model experiments showed that most nitrification occurred immediately after fertilizer was applied, whereas nitrification suppression began to ramp up more than a month after planting. On an annual basis, MeasNS experiments showed a 1%–2% reduction in annual N2O emissions relative to no nitrification suppression (NoNS), and MaxNS experiments showed a 4%–9% reduction in annual N2O emissions relative to NoNS. Both nitrification suppression levels showed <1% reduction in nitrate leaching. Altering the timing of fertilizer applications to better synchronize nitrification suppression with high soil ammonium levels had mixed effects on annual N2O emissions and nitrate leaching and sometimes resulted in increased N losses. The timing of simulated N2O emissions shifted with the timing of fertilization, and N2O emissions from denitrification increased when N2O emissions from nitrification decreased. Increasing N retention during the non‐growing season may be more effective than growing‐season nitrification suppression for reducing annual N losses in the rainfed Midwest. [ABSTRACT FROM AUTHOR]

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi‐empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi‐arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big‐leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations. Plain Language Summary: Earth system models are an essential tool for understanding the consequences of changing climate conditions on forest ecosystems. Models are rapidly incorporating more realistic representations of how drought impacts ecosystem carbon and water cycling. These advancements need to be thoroughly evaluated to ensure that the models adequately capture the plant functional response to drought stress. Here we merge underutilized measurements with new analytical tools to evaluate several model representations of plant response to drought. These tools allow us to both better understand relationships among drought stress and ecosystem response, as well as quantify model accuracy. We find that models generally perform similarly under unstressed conditions, but performance diverges under drought. Key Points: We evaluate several model formulations for coupling plant hydraulic and stomatal response using functional performance metricsInformation flows from soil water potential and vapor pressure deficit to transpiration reflect structural differences among modelsConsiderable biases in modeled canopy temperature propagate to a 5% offset in cumulative growing season transpiration [ABSTRACT FROM AUTHOR]

The microfossil record demonstrates the presence of eukaryotic organisms in the marine ecosystem by about 1,700 million years ago (Ma). Despite this, steranes, a biomarker indicator of eukaryotic organisms, do not appear in the rock record until about 780 Ma in what is known as the “rise of algae.” Before this, it is argued that eukaryotes were minor ecosystem members, with prokaryotes dominating both primary production and ecosystem dynamics. In this view, the rise of algae was possibly sparked by increased nutrient availability supplying the higher nutrient requirements of eukaryotic algae. Here, we challenge this view. We use a size-based ecosystem model to show that the size distribution of preserved eukaryotic microfossils from 1,700 Ma and onward required an active eukaryote ecosystem complete with phototrophy, osmotrophy, phagotrophy, and mixotrophy. Model results suggest that eukaryotes accounted for one-half or more of the living biomass, with eukaryotic algae contributing to about one-half of total marine primary production. These ecosystems lived with deep-water phosphate levels of at least 10% of modern levels. The general lack of steranes in the pre–780-Ma rock record could be a result of poor preservation. [ABSTRACT FROM AUTHOR]

Cat removal is already underway, but there is concern that a decrease in the cat population could release predation pressure on rats, potentially leading to increased rat predation on red-tailed tropicbirds ( I Phaethon rubricauda i ) (Baker et al., 2020; Han et al., 2020). The metabolic demands of rats and cats (which included the mass of rats and cats) and the mass and energy of adult birds influenced the estimates of cat equivalence most strongly. For example, if reducing cat populations by 50% costs twice as much as reducing rats by 50%, but each cat is worth 77 rats, then our cat equivalence metric suggests that removing cats would be the most cost-effective strategy, but if cat equivalence is 30 then removing rats would be more effective. [Extracted from the article]

Abstract Nitrification regulates the potential for nitrogen (N) loss from ecosystems because it converts ammonium to nitrate, which is susceptible to leaching and gaseous emissions. Crops can suppress the microbes that perform nitrification by exuding nitrification‐inhibiting compounds from their roots and taking up available ammonium. However, the effect of nitrification suppression on agroecosystem N losses remains poorly characterized, and a lack of temporal synchrony between nitrification, N losses, and nitrification suppression by plants could limit the effect of nitrification suppression. We used the DayCent‐CABBI model to evaluate the effectiveness of the suppression of nitrification by sorghum to reduce N2O emissions and nitrate leaching in an energy sorghum/soybean rotation at the Energy Farm in Urbana‐Champaign, IL. We simulated nitrification suppression at the measured levels (MeasNS) and at the maximum measured level applied to the entire growing season (MaxNS), and we also explored ways to better utilize nitrification suppression by altering the timing of urea–ammonium–nitrate fertilizer applications. Model experiments showed that most nitrification occurred immediately after fertilizer was applied, whereas nitrification suppression began to ramp up more than a month after planting. On an annual basis, MeasNS experiments showed a 1%–2% reduction in annual N2O emissions relative to no nitrification suppression (NoNS), and MaxNS experiments showed a 4%–9% reduction in annual N2O emissions relative to NoNS. Both nitrification suppression levels showed

Abstract Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi‐empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi‐arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big‐leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.

Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, "Introduction to Ecological Forecasting," at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students' ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students' abilities to engage and understand complex ecological concepts. [ABSTRACT FROM AUTHOR]

Oxygen availability is decreasing in many lakes and reservoirs worldwide, raising the urgency for understanding how anoxia (low oxygen) affects coupled biogeochemical cycling, which has major implications for water quality, food webs, and ecosystem functioning. Although the increasing magnitude and prevalence of anoxia has been documented in freshwaters globally, the challenges of disentangling oxygen and temperature responses have hindered assessment of the effects of anoxia on carbon, nitrogen, and phosphorus concentrations, stoichiometry (chemical ratios), and retention in freshwaters. The consequences of anoxia are likely severe and may be irreversible, necessitating ecosystem‐scale experimental investigation of decreasing freshwater oxygen availability. To address this gap, we devised and conducted REDOX (the Reservoir Ecosystem Dynamic Oxygenation eXperiment), an unprecedented, 7‐year experiment in which we manipulated and modeled bottom‐water (hypolimnetic) oxygen availability at the whole‐ecosystem scale in a eutrophic reservoir. Seven years of data reveal that anoxia significantly increased hypolimnetic carbon, nitrogen, and phosphorus concentrations and altered elemental stoichiometry by factors of 2–5× relative to oxic periods. Importantly, prolonged summer anoxia increased nitrogen export from the reservoir by six‐fold and changed the reservoir from a net sink to a net source of phosphorus and organic carbon downstream. While low oxygen in freshwaters is thought of as a response to land use and climate change, results from REDOX demonstrate that low oxygen can also be a driver of major changes to freshwater biogeochemical cycling, which may serve as an intensifying feedback that increases anoxia in downstream waterbodies. Consequently, as climate and land use change continue to increase the prevalence of anoxia in lakes and reservoirs globally, it is likely that anoxia will have major effects on freshwater carbon, nitrogen, and phosphorus budgets as well as water quality and ecosystem functioning. [ABSTRACT FROM AUTHOR]

The North American (NA) terrestrial biosphere has been a long-term carbon sink but impacts of climate extremes such as drought on ecosystem carbon exchange remain largely uncertain. Here, changes in biospheric carbon fluxes with recent climate change and impacts of the major droughts of the past 30 years on continental carbon cycle across NA were studied using a comprehensive mathematical process model, ecosys. In test of these model responses at continental scale, the spatial anomalies in modeled leaf area indices, fully prognostic in the model, from long-term (1980–2010) means during major drought events in 1988 and 2002 agreed well with those in AVHRR NDVI (R2=0.84 in 1988, 0.71 in 2002). Net ecosystem productivity (NEP) modeled across NA declined by 92% (0.50PgC yr−1) and 90% (0.49PgC yr−1) from the long-term mean (+0.54PgC yr−1), in 1988 and 2002 respectively. These significant drops in NEP offset 28% of the carbon gains modeled over the last three decades. Although the long-term average modeled terrestrial carbon sink was estimated to offset ∼30% of the fossil fuel emissions of NA, only 0.03% and 3.2% were offset in 1988 and 2002 leaving almost all fossil fuel emissions to the atmosphere. These major drought events controlled much of the continental-scale interannual variability and mainly occurred in parts of the Great Plains, southwest US and northern Mexico. Although warming in northern ecosystems caused increasing carbon sinks to be modeled as a result of greater gross primary productivity with longer growing seasons, elsewhere in the continent frequent drought events of the past 30 years reduced carbon uptake and hence net carbon sinks of the NA.

Hurricane Maria, a Category 4 storm, snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne Lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100–800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥ 1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13.5% of the study area had canopy heights ≤ 2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss > 1 m (2017–2018) followed by rapid height gain > 1 m (2018–2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no height changes larger than ± 1 m in either period. Canopy layers < 10 m tall accounted for most increases in canopy height and fractional cover between 2018 and 2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage and highlight the diversity of ecosystem impacts from heterogeneous spatial patterns and vertical stratification of forest regrowth following a major disturbance event. [ABSTRACT FROM AUTHOR]

Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environmental changes do not accurately represent the size, distribution and flux of C from the soil. Here, we modified the daily Century (DAYCENT) biogeochemical model by tuning decomposition rates of conceptual SOC pools to match measurable C fraction data, followed by historical and future simulations of SOC dynamics. Results showed that simulations using fraction‐constrained DAYCENT (DCfrac) led to better initialization of SOC stocks and distribution compared to default/SOC‐only‐constrained DAYCENT (DCdef) at long‐term research sites. Regional simulation using DCfrac demonstrated higher SOC stocks for both croplands (34.86 vs. 26.17 MgC ha−1) and grasslands (54.05 vs. 40.82 MgC ha−1) compared to DCdef for the contemporary period (2001–2005 average), which better matched observationally constrained data‐driven maps of current SOC distributions. Projection of SOC dynamics in response to land cover change under a high warming climate showed average absolute SOC loss of 8.44 and 10.43 MgC ha−1 for grasslands and croplands, respectively, using DCfrac whereas, SOC losses were 6.55 and 7.85 MgC ha−1 for grasslands and croplands, respectively, using DCdef. The projected SOC loss using DCfrac was 33% and 29% higher for croplands and grasslands compared to DCdef. Our modeling study demonstrates that initializing SOC pools with measurable C fraction data led to more accurate representation of SOC stocks and distribution of SOC into individual carbon pools resulting in the prediction of greater sensitivity to agricultural intensification and warming. Plain Language Summary: We aim to improve the representation of soil organic carbon (SOC) dynamics in the earth system model by matching the conceptual soil pools with carbon fraction data. We found large divergence in SOC stocks with higher absolute and relative losses under historical and projected climate and land use using the fraction‐constrained compared to the default/SOC‐only‐constrained model. This implies that the conceptual soil pools parameterized to match with carbon fraction data can better simulate SOC dynamics now and into the future. Key Points: The fraction‐constrained (optimized) model led to better initialization and distribution of soil organic carbon (SOC) stocks compared to the default modelThe fraction‐constrained (optimized) model led to larger absolute and relative losses of SOC compared to the default model during 1895–2005Under high warming, projected SOC losses were 33% for croplands and 29% for grasslands using the optimized compared to the default model [ABSTRACT FROM AUTHOR]

The terrestrial net ecosystem productivity (NEP) has increased during the past three decades, but the mechanisms responsible are still unclear. We analyzed 17 years (2001–2017) of eddy‐covariance measurements of NEP, evapotranspiration (ET) and light and water use efficiency from a boreal coniferous forest in Southern Finland for trends and inter‐annual variability (IAV). The forest was a mean annual carbon sink (252 [±42] gC m‐2a‐1), and NEP increased at rate +6.4–7.0 gC m‐2a‐1 (or ca. +2.5% a‐1) during the period. This was attributed to the increasing gross‐primary productivity GPP and occurred without detectable change in ET. The start of annual carbon uptake period was advanced by 0.7 d a‐1, and increase in GPP and NEP outside the main growing season contributed ca. one‐third and one‐fourth of the annual trend, respectively. Meteorological factors were responsible for the IAV of fluxes but did not explain the long‐term trends. The growing season GPP trend was strongest in ample light during the peak growing season. Using a multi‐layer ecosystem model, we showed that direct CO2 fertilization effect diminishes when moving from leaf to ecosystem, and only 30–40% of the observed ecosystem GPP increase could be attributed to CO2. The increasing trend in leaf‐area index (LAI), stimulated by forest thinning in 2002, was the main driver of the enhanced GPP and NEP of the mid‐rotation managed forest. It also compensated for the decrease of mean leaf stomatal conductance with increasing CO2 and LAI, explaining the apparent proportionality between observed GPP and CO2 trends. The results emphasize that attributing trends to their physical and physiological drivers is challenged by strong IAV, and uncertainty of LAI and species composition changes due to the dynamic flux footprint. The results enlighten the underlying mechanisms responsible for the increasing terrestrial carbon uptake in the boreal zone. [ABSTRACT FROM AUTHOR]

Abstract Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environmental changes do not accurately represent the size, distribution and flux of C from the soil. Here, we modified the daily Century (DAYCENT) biogeochemical model by tuning decomposition rates of conceptual SOC pools to match measurable C fraction data, followed by historical and future simulations of SOC dynamics. Results showed that simulations using fraction‐constrained DAYCENT (DCfrac) led to better initialization of SOC stocks and distribution compared to default/SOC‐only‐constrained DAYCENT (DCdef) at long‐term research sites. Regional simulation using DCfrac demonstrated higher SOC stocks for both croplands (34.86 vs. 26.17 MgC ha−1) and grasslands (54.05 vs. 40.82 MgC ha−1) compared to DCdef for the contemporary period (2001–2005 average), which better matched observationally constrained data‐driven maps of current SOC distributions. Projection of SOC dynamics in response to land cover change under a high warming climate showed average absolute SOC loss of 8.44 and 10.43 MgC ha−1 for grasslands and croplands, respectively, using DCfrac whereas, SOC losses were 6.55 and 7.85 MgC ha−1 for grasslands and croplands, respectively, using DCdef. The projected SOC loss using DCfrac was 33% and 29% higher for croplands and grasslands compared to DCdef. Our modeling study demonstrates that initializing SOC pools with measurable C fraction data led to more accurate representation of SOC stocks and distribution of SOC into individual carbon pools resulting in the prediction of greater sensitivity to agricultural intensification and warming.