Climate change could negate U.S. forest ecosystem service benefits gained through reductions in nitrogen and sulfur deposition.

Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) impact the health and productivity of forests. Here, we explored the potential impacts of these environmental stressors on ecosystem services provided by future forests in the contiguous U.S. We found that all stand-level services benefitted (+ 2.6 to 8.1%) from reductions in N+S deposition, largely attributable to positive responses to reduced S that offset the net negative effects of lower N levels. Sawtimber responded positively (+ 0.5 to 0.6%) to some climate change, but negatively (- 2.4 to - 3.8%) to the most extreme scenarios. Aboveground carbon (C) sequestration and forest diversity were negatively impacted by all modelled changes in climate. Notably, the most extreme climate scenario eliminated gains in all three services achieved through reduced deposition. As individual tree species responded differently to climate change and atmospheric deposition, associated services unique to each species increased or decreased under future scenarios. Our results suggest that climate change should be considered when evaluating the benefits of N and S air pollution policies on the services provided by U.S. forests.

Future climate change effects on US forest composition may offset benefits of reduced atmospheric deposition of N and S.

Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) are important drivers of forest demography. Here we apply previously derived growth and survival responses for 94 tree species, representing >90% of the contiguous US forest basal area, to project how changes in mean annual temperature, precipitation, and N and S deposition from 20 different future scenarios may affect forest composition to 2100. We find that under the low climate change scenario (RCP 4.5), reductions in aboveground tree biomass from higher temperatures are roughly offset by increases in aboveground tree biomass from reductions in N and S deposition. However, under the higher climate change scenario (RCP 8.5) the decreases from climate change overwhelm increases from reductions in N and S deposition. These broad trends underlie wide variation among species. We found averaged across temperature scenarios the relative abundance of 60 species were projected to decrease more than 5% and 20 species were projected to increase more than 5%; and reductions of N and S deposition led to a decrease for 13 species and an increase for 40 species. This suggests large shifts in the composition of US forests in the future. Negative climate effects were mostly from elevated temperature and were not offset by scenarios with wetter conditions. We found that by 2100 an estimated 1 billion trees under the RCP 4.5 scenario and 20 billion trees under the RCP 8.5 scenario may be pushed outside the temperature record upon which these relationships were derived. These results may not fully capture future changes in forest composition as several other factors were not included. Overall efforts to reduce atmospheric deposition of N and S will likely be insufficient to overcome climate change impacts on forest demography across much of the United States unless we adhere to the low climate change scenario.

Above-ground tree carbon storage in response to nitrogen deposition in the U.S. is heterogeneous and may have weakened.

Changes in nitrogen (N) availability affect the ability for forest ecosystems to store carbon (C). Here we extend an analysis of the growth and survival of 94 tree species and 1.2 million trees, to estimate the incremental effects of N deposition on changes in aboveground C (dC/dN) across the contiguous U.S. (CONUS). We find that although the average effect of N deposition on aboveground C is positive for the CONUS (dC/dN=+9 kg C per kg N), there is wide variation among species and regions. Furthermore, in the Northeastern U.S. where we may compare responses from 2000-2016 with those from the 1980s-90s, we find the recent estimate of dC/dN is weaker than from the 1980s-90s due to species-level changes in responses to N deposition. This suggests that the U.S. forest C-sink varies widely across forests and may be weakening overall, possibly necessitating more aggressive climate policies than originally thought.

Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models.

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.

Assessing opportunities and inequities in undergraduate ecological forecasting education.

Conducting ecological research in a way that addresses complex, real-world problems requires a diverse, interdisciplinary and quantitatively trained ecology and environmental science workforce. This begins with equitably training students in ecology, interdisciplinary science, and quantitative skills at the undergraduate level. Understanding the current undergraduate curriculum landscape in ecology and environmental sciences allows for targeted interventions to improve equitable educational opportunities. Ecological forecasting is a sub-discipline of ecology with roots in interdisciplinary and quantitative science. We use ecological forecasting to show how ecology and environmental science undergraduate curriculum could be evaluated and ultimately restructured to address the needs of the 21st century workforce. To characterize the current state of ecological forecasting education, we compiled existing resources for teaching and learning ecological forecasting at three curriculum levels: online resources; US university courses on ecological forecasting; and US university courses on topics related to ecological forecasting. We found persistent patterns (1) in what topics are taught to US undergraduate students at each of the curriculum levels; and (2) in the accessibility of resources, in terms of course availability at higher education institutions in the United States. We developed and implemented programs to increase the accessibility and comprehensiveness of ecological forecasting undergraduate education, including initiatives to engage specifically with Native American undergraduates and online resources for learning quantitative concepts at the undergraduate level. Such steps enhance the capacity of ecological forecasting to be more inclusive to undergraduate students from diverse backgrounds and expose more students to quantitative training.

Progress and opportunities in advancing near-term forecasting of freshwater quality.

Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

Anoxia decreases the magnitude of the carbon, nitrogen, and phosphorus sink in freshwaters.

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.

Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability.

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

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

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 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.

Increased adoption of best practices in ecological forecasting enables comparisons of forecastability.

Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1-7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.

The contribution of wildland fire emissions to deposition in the U S: implications for tree growth and survival in the Northwest.

Ecosystems require access to key nutrients like nitrogen (N) and sulfur (S) to sustain growth and healthy function. However, excessive deposition can also damage ecosystems through nutrient imbalances, leading to changes in productivity and shifts in ecosystem structure. While wildland fires are a known source of atmospheric N and S, little has been done to examine the implications of wildland fire deposition for vulnerable ecosystems. We combine wildland fire emission estimates, atmospheric chemistry modeling, and forest inventory data to (a) quantify the contribution of wildland fire emissions to N and S deposition across the U S, and (b) assess the subsequent impacts on tree growth and survival rates in areas where impacts are likely meaningful based on the relative contribution of fire to total deposition. We estimate that wildland fires contributed 0.2 kg N ha-1 yr-1 and 0.04 kg S ha-1 yr-1 on average across the U S during 2008-2012, with maxima up to 1.4 kg N ha-1 yr-1 and 0.6 kg S ha-1 yr-1 in the Northwest representing over ~30% of total deposition in some areas. Based on these fluxes, exceedances of S critical loads as a result of wildland fires are minimal, but exceedances for N may affect the survival and growth rates of 16 tree species across 4.2 million hectares, with the most concentrated impacts occurring in Oregon, northern California, and Idaho. Understanding the broader environmental impacts of wildland fires in the U S will inform future decision making related to both fire management and ecosystem services conservation.

Environmental factors affecting ecological niche of Coccidioides species and spatial dynamics of valley fever in the United States.

Coccidioidomycosis is an understudied infectious disease acquired by inhaling fungal spores of Coccidioides species. While historically connected to the southwestern United States, the endemic region for this disease is not well defined. This study's objective was to estimate the impact of climate, soil, elevation and land cover on the Coccidioides species' ecological niche. This research used maximum entropy ecological niche modeling based on disease case data from 2015 to 2016. Results found mean temperature of the driest quarter, and barren, shrub, and cultivated land covers influential in characterizing the niche. In addition to hotspots in central California and Arizona, the Columbia Plateau ecoregion of Washington and Oregon showed more favorable conditions for fungus presence than surrounding areas. The identification of influential spatial drivers will assist in future modeling efforts, and the potential distribution map generated may aid public health officials in watching for potential hotspots, assessing vulnerability, and refining endemicity.

Nitrogen deposition and climate change effects on tree species composition and ecosystem services for a forest cohort.

The composition of forests in the northeastern United States and the ecosystem services they provide to future generations will depend on several factors. In this paper, we isolate the effects of two environmental drivers, nitrogen (N) deposition and climate (temperature and precipitation) change, through an analysis of a single cohort of 24 dominant tree species. We assembled a tree database using data from U.S. Forest Service Forest Inventory and Analysis monitoring plots. Applying observed species-specific growth and survival responses, we simulated how forest stands in a 19-state study area would change from 2005 to 2100 under 12 different future N deposition-climate scenarios. We then estimated implications for three selected forest ecosystem services: merchantable timber, aboveground carbon sequestration, and tree diversity. Total tree biomass (for 24 species combined) was positively associated with both increased N deposition and temperatures; however, due to differences in the direction and magnitude of species-specific responses, forest composition varied across scenarios. For example, red maple (Acer rubrum) trees gained biomass under scenarios with more N deposition and more climate change, whereas biomass of yellow birch (Betula alleghaniensis) and red pine (Pinus resinosa) was negatively affected. Projections for ecosystem services also varied across scenarios. Carbon sequestration, which is positively associated with biomass accumulation, increased with N deposition and increasing climate change. Total timber values also increased with overall biomass; however, scenarios with increasing climate change tended to favor species with lower merchantable value, whereas more N deposition favored species with higher merchantable value. Tree species diversity was projected to decrease with greater changes in climate (warmer temperatures), especially in the northwestern, central, and southeastern portions of the study area. In contrast, the effects of N deposition on diversity varied greatly in magnitude and direction across the study area. This study highlights species-specific and regional effects of N deposition and climate change in northeastern U.S. forests, which can inform management decision for air quality and forests in the region, as well as climate policy. It also provides a foundation for future studies that may incorporate other important factors such as multiple cohorts, sulfur deposition, insects, and diseases.

Correction: Growth and survival relationships of 71 tree species with nitrogen and sulfur deposition across the conterminous U.S.

[This corrects the article DOI: 10.1371/journal.pone.0205296.].

Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions.

Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon-nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and-the newly developed-5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO2) enrichment with meta-analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET-MTE observations. Simulations with N and CO2 enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections.

Growth and survival relationships of 71 tree species with nitrogen and sulfur deposition across the conterminous U.S.

Atmospheric deposition of nitrogen (N) influences forest demographics and carbon (C) uptake through multiple mechanisms that vary among tree species. Prior studies have estimated the effects of atmospheric N deposition on temperate forests by leveraging forest inventory measurements across regional gradients in deposition. However, in the United States (U.S.), these previous studies were limited in the number of species and the spatial scale of analysis, and did not include sulfur (S) deposition as a potential covariate. Here, we present a comprehensive analysis of how tree growth and survival for 71 species vary with N and S deposition across the conterminous U.S. Our analysis of 1,423,455 trees from forest plots inventoried between 2000 and 2016 reveals that the growth and/or survival of the vast majority of species in the analysis (n = 66, or 93%) were significantly affected by atmospheric deposition. Species co-occurred across the conterminous U.S. that had decreasing and increasing relationships between growth (or survival) and N deposition, with just over half of species responding negatively in either growth or survival to increased N deposition somewhere in their range (42 out of 71). Averaged across species and conterminous U.S., however, we found that an increase in deposition above current rates of N deposition would coincide with a small net increase in tree growth (1.7% per Δ kg N ha-1 yr-1), and a small net decrease in tree survival (-0.22% per Δ kg N ha-1 yr-1), with substantial regional and among-species variation. Adding S as a predictor improved the overall model performance for 70% of the species in the analysis. Our findings have potential to help inform ecosystem management and air pollution policy across the conterminous U.S., and suggest that N and S deposition have likely altered forest demographics in the U.S.

A mid-century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity.

Ecological forecasting of forest productivity involves integrating observations into a process-based model and propagating the dominant components of uncertainty to generate probability distributions for future states and fluxes. Here, we develop a forecast for the biomass change in loblolly pine (Pinus taeda) forests of the southeastern United States and evaluate the relative contribution of different forms of uncertainty to the total forecast uncertainty. Specifically, we assimilated observations of carbon and flux stocks and fluxes from sites across the region, including global change experiments, into a forest ecosystem model to calibrate the parameter distributions and estimate the process uncertainty (i.e., model structure uncertainty revealed in the residuals of the calibration). Using this calibration, we forecasted the change in biomass within each 12-digit Hydrologic (H12) unit across the native range of loblolly pine between 2010 and 2055 under the Representative Concentration Pathway 8.5 scenario. Averaged across the region, productivity is predicted to increase by a mean of 31% between 2010 and 2055 with an average forecast 95% quantile interval of ±15 percentage units. The largest increases were predicted in cooler locations, corresponding to the largest projected changes in temperature. The forecasted mean change varied considerably among the H12 units (3-80% productivity increase), but only units in the warmest and driest extents of the loblolly pine range had forecast distributions with probabilities of a decline in productivity that exceeded 5%. By isolating the individual components of the forecast uncertainty, we found that ecosystem model process uncertainty made the largest individual contribution. Ecosystem model parameter and climate model uncertainty had similar contributions to the overall forecast uncertainty, but with differing spatial patterns across the study region. The probabilistic framework developed here could be modified to include additional sources of uncertainty, including changes due to fire, insects, and pests: processes that would result in lower productivity changes than forecasted here. Overall, this study presents an ecological forecast at the ecosystem management scale so that land managers can explicitly account for uncertainty in decision analysis. Furthermore, it highlights that future work should focus on quantifying, propagating, and reducing ecosystem model process uncertainty.

Constraining estimates of global soil respiration by quantifying sources of variability.

Quantifying global soil respiration (RSG ) and its response to temperature change are critical for predicting the turnover of terrestrial carbon stocks and their feedbacks to climate change. Currently, estimates of RSG range from 68 to 98 Pg C year-1 , causing considerable uncertainty in the global carbon budget. We argue the source of this variability lies in the upscaling assumptions regarding the model format, data timescales, and precipitation component. To quantify the variability and constrain RSG , we developed RSG models using Random Forest and exponential models, and used different timescales (daily, monthly, and annual) of soil respiration (RS ) and climate data to predict RSG . From the resulting RSG estimates (range = 66.62-100.72 Pg), we calculated variability associated with each assumption. Among model formats, using monthly RS data rather than annual data decreased RSG by 7.43-9.46 Pg; however, RSG calculated from daily RS data was only 1.83 Pg lower than the RSG from monthly data. Using mean annual precipitation and temperature data instead of monthly data caused +4.84 and -4.36 Pg C differences, respectively. If the timescale of RS data is constant, RSG estimated by the first-order exponential (93.2 Pg) was greater than the Random Forest (78.76 Pg) or second-order exponential (76.18 Pg) estimates. These results highlight the importance of variation at subannual timescales for upscaling to RSG. The results indicated RSG is lower than in recent papers and the current benchmark for land models (98 Pg C year-1 ), and thus may change the predicted rates of terrestrial carbon turnover and the carbon to climate feedback as global temperatures rise.

Global patterns and substrate-based mechanisms of the terrestrial nitrogen cycle.

Nitrogen (N) deposition is impacting the services that ecosystems provide to humanity. However, the mechanisms determining impacts on the N cycle are not fully understood. To explore the mechanistic underpinnings of N impacts on N cycle processes, we reviewed and synthesised recent progress in ecosystem N research through empirical studies, conceptual analysis and model simulations. Experimental and observational studies have revealed that the stimulation of plant N uptake and soil retention generally diminishes as N loading increases, while dissolved and gaseous losses of N occur at low N availability but increase exponentially and become the dominant fate of N at high loading rates. The original N saturation hypothesis emphasises sequential N saturation from plant uptake to soil retention before N losses occur. However, biogeochemical models that simulate simultaneous competition for soil N substrates by multiple processes match the observed patterns of N losses better than models based on sequential competition. To enable better prediction of terrestrial N cycle responses to N loading, we recommend that future research identifies the response functions of different N processes to substrate availability using manipulative experiments, and incorporates the measured N saturation response functions into conceptual, theoretical and quantitative analyses.

Terrestrial and marine perspectives on modeling organic matter degradation pathways.

Organic matter (OM) plays a major role in both terrestrial and oceanic biogeochemical cycles. The amount of carbon stored in these systems is far greater than that of carbon dioxide (CO2 ) in the atmosphere, and annual fluxes of CO2 from these pools to the atmosphere exceed those from fossil fuel combustion. Understanding the processes that determine the fate of detrital material is important for predicting the effects that climate change will have on feedbacks to the global carbon cycle. However, Earth System Models (ESMs) typically utilize very simple formulations of processes affecting the mineralization and storage of detrital OM. Recent changes in our view of the nature of this material and the factors controlling its transformation have yet to find their way into models. In this review, we highlight the current understanding of the role and cycling of detrital OM in terrestrial and marine systems and examine how this pool of material is represented in ESMs. We include a discussion of the different mineralization pathways available as organic matter moves from soils, through inland waters to coastal systems and ultimately into open ocean environments. We argue that there is strong commonality between aspects of OM transformation in both terrestrial and marine systems and that our respective scientific communities would benefit from closer collaboration.