Explore the vast range of titles available.

Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth’s climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface. Coastal systems are hotspots of ecological, geochemical and economic activity, yet their dynamics are not accurately represented in global models. In this Review, Ward and colleagues assess the current state of coastal science and recommend approaches for including the coastal interface in predictive models.

Despite the growing use of Aquatic Ecosystem Models for lake modeling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. Calibration is generally based on direct data comparison of observed versus modeled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model's ability to capture system‐scale behavior that is not easily perceivable in observations, but which may be important for resource management. The aim of this study is to compare the performance of “naïve” calibration and a “system‐inspired” calibration, an approach that augments the standard state‐based calibration with a range of system‐inspired metrics (e.g., thermocline depth, metalimnetic oxygen minima), to increase the coherence between the simulated and natural ecosystems. A coupled physical‐biogeochemical model was applied to a focal site to simulate two key state‐variables: water temperature and dissolved oxygen. The model was calibrated according to the new system‐inspired modeling convention, using formal calibration techniques. There was an improvement in the simulation using parameters optimized on the additional metrics, which helped to reduce uncertainty predicting aspects of the system relevant to reservoir management, such as the occurrence of the metalimnetic oxygen minima. Extending the use of system‐inspired metrics when calibrating models has the potential to improve model fidelity for capturing more complex ecosystem dynamics. Key Points We assessed the use of system‐inspired metrics in a novel approach to calibrating Aquatic Ecosystem Models (AEMs) The use of system‐inspired metrics in calibration improved model performance relative to traditional calibration methods Implementation of system‐inspired metrics has the potential to greatly improve model prediction of complex ecosystem dynamics

Ecological models help us understand how ecosystems function, predict responses to global change, and identify future research needs. However, widespread use of models is limited by the technical challenges of model-data synthesis and information management. To address these challenges, we present an ecoinformatic workflow, the Predictive Ecosystem Analyzer (PEcAn), which facilitates model analysis. Herein we describe the PEcAn modules that synthesize plant trait data to estimate model parameters, propagate parameter uncertainties through to model output, and evaluate the contribution of each parameter to model uncertainty. We illustrate a comprehensive approach to the estimation of parameter values, starting with a statement of prior knowledge that is refined by species-level data using Bayesian meta-analysis; this is the first use of a rigorous meta-analysis to inform the parameters of a mechanistic ecosystem model. Parameter uncertainty is propagated using ensemble methods to estimate model uncertainty. Variance decomposition allows us to quantify the contribution of each parameter to model uncertainty; this information can be used to prioritize subsequent data collection. By streamlining the use of models and focusing efforts to identify and constrain the dominant sources of uncertainty in model output, the approach used by PEcAn can speed scientific progress. We demonstrate PEcAn's ability to incorporate data to reduce uncertainty in productivity of a perennial grass monoculture ( Panicum virgatum L.) modeled by the Ecosystem Demography model. Prior estimates were specified for 15 model parameters, and species-level data were available for seven of these. Meta-analysis of species-level data substantially reduced the contribution of three parameters (specific leaf area, maximum carboxylation rate, and stomatal slope) to overall model uncertainty. By contrast, root turnover rate, root respiration rate, and leaf width had little effect on model output; therefore trait data had little impact on model uncertainty. For fine-root allocation, the decrease in parameter uncertainty was offset by an increase in model sensitivity. Remaining model uncertainty is driven by growth respiration, fine-root allocation, leaf turnover rater, and specific leaf area. By establishing robust channels of feedback between data collection and ecosystem modeling, PEcAn provides a framework for more efficient and integrative science.

The problem of scale has been a critical impediment to incorporating important fine-scale processes into global ecosystem models. Our knowledge of fine-scale physiological and ecological processes comes from a variety of measurements, ranging from forest plot inventories to remote sensing, made at spatial resolutions considerably smaller than the large scale at which global ecosystem models are defined. In this paper, we describe a new individual-based, terrestrial biosphere model, which we label the ecosystem demography model (ED). We then introduce a general method for scaling stochastic individual-based models of ecosystem dynamics (gap models) such as ED to large scales. The method accounts for the fine-scale spatial heterogeneity within an ecosystem caused by stochastic disturbance events, operating at scales down to individual canopy-tree-sized gaps. By conditioning appropriately on the occurrence of these events, we derive a size- and age-structured (SAS) approximation for the first moment of the stochastic ecosystem model. With this approximation, it is possible to make predictions about the large scales of interest from a description of the fine-scale physiological and population-dynamic processes without simulating the fate of every plant individually. We use the SAS approximation to implement our individual-based biosphere model over South America from 15° N to 15° S, showing that the SAS equations are accurate across a range of environmental conditions and resulting ecosystem types. We then compare the predictions of the biosphere model to regional data and to intensive data at specific sites. Analysis of the model at these sites illustrates the importance of fine-scale heterogeneity in governing large-scale ecosystem function, showing how population and community-level processes influence ecosystem composition and structure, patterns of aboveground carbon accumulation, and net ecosystem production.

Our limited ability to accurately simulate leaf phenology is a leading source of uncertainty in models of ecosystem carbon cycling. We evaluate if continuously updating canopy state variables with observations is beneficial for predicting phenological events. We employed ensemble adjustment Kalman filter (EAKF) to update predictions of leaf area index (LAI) and leaf extension using tower-based photosynthetically active radiation (PAR) and moderate resolution imaging spectrometer (MODIS) data for 2002-2005 at Willow Creek, Wisconsin, USA, a mature, even-aged, northern hardwood, deciduous forest. The ecosystem demography model version 2 (ED2) was used as the prediction model, forced by offline climate data. EAKF successfully incorporated information from both the observations and model predictions weighted by their respective uncertainties. The resulting estimate reproduced the observed leaf phenological cycle in the spring and the fall better than a parametric model prediction. These results indicate that during spring the observations contribute most in determining the correct bud-burst date, after which the model performs well, but accurately modeling fall leaf senesce requires continuous model updating from observations. While the predicted net ecosystem exchange (NEE) of CO 2 precedes tower observations and unassimilated model predictions in the spring, overall the prediction follows observed NEE better than the model alone. Our results show state data assimilation successfully simulates the evolution of plant leaf phenology and improves model predictions of forest NEE.

Managing ecosystems in the face of complex species interactions, and the associated uncertainty, presents a considerable ecological challenge. Altering those interactions via actions such as invasive species management or conservation translocations can result in unintended consequences, supporting the need to be able to make more informed decisions in the face of this uncertainty. We demonstrate the utility of ecosystem models to reduce uncertainty and inform future ecosystem management. We use Phillip Island, Australia, as a case study to investigate the impacts of two invasive species management options and consider whether a critically endangered mammal is likely to establish a population in the presence of invasive species. Qualitative models are used to determine the effects of apex predator removal (feral cats) and invasive prey removal (rabbits, rats, and mice). We extend this approach using Ensemble Ecosystem Models to consider how suppression, rather than eradication influences the species community; and consider whether an introduction of the critically endangered eastern barred bandicoot is likely to be successful in the presence of invasive species. Our analysis revealed the potential for unintended outcomes associated with feral cat control operations, with rats and rabbits expected to increase in abundance. A strategy based on managing prey species appeared to have the most ecosystem-wide benefits, with rodent control showing more favorable responses than a rabbit control strategy. Eastern barred bandicoots were predicted to persist under all feral cat control levels (including no control). Managing ecosystems is a complex and imprecise process. However, qualitative modeling and ensemble ecosystem modeling address uncertainty and are capable of improving and optimizing management practices. Our analysis shows that the best conservation outcomes may not always be associated with the topdown control of apex predators, and land managers should think more broadly in relation to managing bottom-up processes as well. Challenges faced in continuing to conserve biodiversity mean new, bolder, conservation actions are needed. We suggest that endangered species are capable of surviving in the presence of feral cats, potentially opening the door for more conservation translocations.

Afforestation projects for mitigating CO2 emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome‐BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11‐year‐old Eucommia ulmoides plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance (gsmax) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11‐year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30‐year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14–0.35 kg‐C m−2 y−1 in simulations using parameters of phases (i)–(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change. Afforestation projects for mitigating CO2 emissions require reliable monitoring of carbon storage and plant growth as key indicators of carbon fixation by afforestation. We developed the monitoring method for predicting carbon fixation in plantations, combining a process‐based ecosystem model and field data. We demonstrated how to estimate plant carbon pools and understand the parameter changes with plant growth stages and the uncertainty of predicted carbon fixation in a plantation by an optimization scheme using field survey data.

AimTrophic rewilding is proposed as an approach to tackle biodiversity loss by restoring ecosystem dynamics through the reintroduction of keystone species. Currently, evidence on the ecological consequences of reintroduction programmes is sparse and difficult to generalize. To better understand the ecological consequences of trophic rewilding, we simulated the extinction and reintroduction of large-bodied mammals under different environmental conditions.LocationEurope.MethodsWe selected four locations varying in productivity and seasonality in Europe and used a general ecosystem model called Madingley to run simulations. We initialized the model using body mass limits of a European Holocene baseline; we then removed large mammals and let the model converge to a new equilibrium. Next, we reintroduced the previously removed groups to assess whether the equilibrium would shift back to the initial condition. We tested three different reintroduction scenarios, in order to disentangle the importance of the different large mammal groups.ResultsThe removal of large-bodied mammals led to cascading effects, mainly resulting in increases in smaller-bodied herbivores and the release of mesopredators. Post-reintroduction, the system's new equilibrium state was closer to the initial equilibrium for stable and productive locations compared to highly seasonal and low-productive locations. The maximum trait space volume of the initial state and the post-reintroduction state varied by 9.1% on average over all locations, with an average decrease in trait combinations of 6.6%. The body mass distribution differed by 28%, comparing the initial state to the post-reintroduction state.Main ConclusionsOur simulation results suggest that reintroducing locally extinct large-bodied mammals can broadly restore shifts in ecosystem structure, roughly resembling the baseline ecosystem conditions. However, the extent to which the ecosystem's state resembles the original ecosystem is largely dependent on the reintroduction strategy (only herbivores and omnivores vs. also carnivores) and timing, as well as local environmental conditions.

Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO2. To address this challenge, we have updated and improved ECCO‐Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint‐based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data‐constrained ECCO physics, a Green's function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO‐Darwin exhibits broad‐scale consistency with observed surface ocean pCO2 and air‐sea CO2 flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO2 uptake occur in subpolar seasonally stratified biomes, where ECCO‐Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO‐Darwin has a time‐mean global ocean CO2 sink (2.47 ± 0.50 Pg C year−1) and interannual variability that are more consistent with interpolation‐based products. Compared to interpolation‐based methods, ECCO‐Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO‐Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate‐related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property‐conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies. Plain Language Summary Data‐driven estimates of how much carbon dioxide the ocean is absorbing (the so‐called “ocean carbon sink”) have improved substantially in recent years. However, computational ocean models that include biogeochemistry continue to play a critical role as they allow us to isolate and understand the individual processes that control ocean carbon sequestration. The ideal scenario is a combination of the above two methods, where data are ingested and then used to improve a model's fit to the observed ocean, also known as, data assimilation. While the physical oceanographic community has made great progress in developing data assimilation systems, for example, the Estimating the Circulation and Climate of the Ocean (ECCO) consortium, the biogeochemical community has generally lagged behind. The ECCO‐Darwin model presented in this paper represents an important technological step forward as it is the first global ocean biogeochemistry model that (1) ingests both physical and biogeochemical observations into the model in a realistic manner and (2) considers how the nature of the ocean carbon sink has changed over multiple decades. As the ECCO ocean circulation estimates become more accurate and lengthen in time, ECCO‐Darwin will become an ever more accurate and useful tool for climate‐related ocean carbon cycle and mitigation studies. Key Points ECCO‐Darwin is a global ocean biogeochemistry model that assimilates physical and biogeochemical observations in a conserving manner Air‐sea CO2 fluxes over seasonal to multidecadal timescales (1995–2017) are largely consistent with interpolation‐based products Contrary to interpolation‐based products, ECCO‐Darwin is impervious to sparse and irregularly sampled observations

During the implementation of the Belt and Road Initiative (BRI), simulating the change trends of terrestrial ecosystems in Eurasia under different climate scenarios is a key ecological issue. The HLZ ecosystem model was improved to simulate the changes in the spatial distribution and types of terrestrial ecosystems in Eurasia based on the climate data from Eurasian meteorological stations from 1981 to 2010 and the data from the RCP26, RCP45 and RCP85 scenarios released by CMIP5 from 2010 to 2100. Ecological diversity and patch connectivity index models were used to quantitatively calculate the future changes in ecological diversity and patch connectivity of terrestrial ecosystems in Eurasia. The results show that (1) cold temperate wet forest, cool temperate moist forest and desert are the major terrestrial ecosystem types and cover 36.71% of the total area of Eurasia. (2) Under all three scenarios, the polar/nival area would shrink more than other terrestrial ecosystem types and would decrease by 26.75 million km 2 per decade on average, and the subpolar/alpine moist tundra would have the fastest decreasing rate of 10.49% per decade on average from 2010 to 2100. (3) Under the RCP85 scenario, the rate of terrestrial ecosystem changes will be greater than that under the other two scenarios, and the subpolar/alpine moist tundra would exhibit the fastest decreasing rate of 10.88% per decade from 2010 to 2100. (4) The ecological diversity would generally show decreasing trends and decrease by 0.09%, 0.13% and 0.16% per decade on average under the RCP26, RCP45 and RCP85 scenarios, respectively. (5) The patch connectivity would first increase and then decrease under all three scenarios. In general, the trends of the changes in terrestrial ecosystems would show an obvious difference in the different regions throughout the BRI area.