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Often there are several complex ecosystem models available to address a specific question. However, structural differences, systematic discrepancies and uncertainties mean that they typically produce different outputs. Rather than selecting a single 'best' model, it is desirable to combine them to give a coherent answer to the question at hand.Many methods of combining ecosystem models assume that one of the models is exactly correct, which is unlikely to be the case. Furthermore, models may not be fitted to the same data, have the same outputs, nor be run for the same time period, making many common methods difficult to implement. In this paper, we use a statistical model to describe the relationship between the ecosystem models, prior beliefs and observations to make coherent predictions of the true state of the ecosystem with robust quantification of uncertainty.We introduce EcoEnsemble, an R package that takes advantage of the statistical model's structure to efficiently fit the ensemble model, either sampling from the posterior distribution or maximising the posterior density.We demonstrate EcoEnsemble by investigating what would happen to four fish species in the North Sea under future management scenarios. Although developed for applications in ecology, EcoEnsemble can be used to combine any group of mechanistic models, for example in climate modelling, epidemiology or biology. [ABSTRACT FROM AUTHOR]

In the context of ecosystem-based fisheries management (EBFM), multi-species models offer a potential alternative to traditional single-species models for managing key species, particularly in mixed-fishery settings. These models account for interactions between different species, providing a more holistic approach to fisheries compared to traditional single-species management. There is currently no comprehensive list or recent analysis of the diverse methods used to account for species interactions in fisheries worldwide. We conducted a systematic review to objectively present the current multi-species models used in fisheries. The systematic search identified 86 multi-species models, which were then evaluated to assess their similarities. Employing a clustering analysis, three distinct groups were identified: extensions of single-species/dynamic multi-species models, aggregated ecosystem models, and end-to-end/coupled and hybrid models. The first group was among the most diverse, owing to their ability to integrate biological components, while maintaining an intermediate level of complexity. The second group, primarily defined by the EwE method, features an aggregated biomass pool structure incorporating biological components and environmental effects. The third cluster featured the most complex models, which included a comprehensive representation of size and age structure, the ability to incorporate biological components and environmental effects, as well as spatial representation. The application of these methods is primarily concentrated on small pelagic and demersal species from North America and Europe. This analysis provides a comprehensive guide for stakeholders on the development and use of multi-species models, considering data constraints and regional contexts. [ABSTRACT FROM AUTHOR]

Abstract: Information on the response of vegetation to different environmental drivers, including rainfall, forms a critical input to ecosystem models. Currently, such models are run based on parameters that, in some cases, are either assumed or lack supporting evidence (e.g., that vegetation growth across Africa is rainfall‐driven). A limited number of studies have reported that the onset of rain across Africa does not fully explain the onset of vegetation growth, for example, drawing on the observation of prerain flush effects in some parts of Africa. The spatial extent of this prerain green‐up effect, however, remains unknown, leaving a large gap in our understanding that may bias ecosystem modelling. This paper provides the most comprehensive spatial assessment to‐date of the magnitude and frequency of the different patterns of phenology response to rainfall across Africa and for different vegetation types. To define the relations between phenology and rainfall, we investigated the spatial variation in the difference, in number of days, between the start of rainy season (SRS) and start of vegetation growing season (SOS); and between the end of rainy season (ERS) and end of vegetation growing season (EOS). We reveal a much more extensive spread of prerain green‐up over Africa than previously reported, with prerain green‐up being the norm rather than the exception. We also show the relative sparsity of postrain green‐up, confined largely to the Sudano‐Sahel region. While the prerain green‐up phenomenon is well documented, its large spatial extent was not anticipated. Our results, thus, contrast with the widely held view that rainfall drives the onset and end of the vegetation growing season across Africa. Our findings point to a much more nuanced role of rainfall in Africa's vegetation growth cycle than previously thought, specifically as one of a set of several drivers, with important implications for ecosystem modelling. [ABSTRACT FROM AUTHOR]

The primary goal of the Western Arctic Linkage Experiment (WALE) was to better understand uncertainties of simulated hydrologic and ecosystem dynamics of the western Arctic in the context of 1) uncertainties in the data available to drive the models and 2) different approaches to simulating regional hydrology and ecosystem dynamics. Analyses of datasets on climate available for driving hydrologic and ecosystem models within the western Arctic during the late twentieth century indicate that there are substantial differences among the mean states of datasets for temperature, precipitation, vapor pressure, and radiation variables. Among the studies that examined temporal trends among the alternative climate datasets, there is not much consensus on trends among the datasets. In contrast, monthly and interannual variations of some variables showed some correlation across the datasets. The application of hydrology models driven by alternative climate drivers revealed that the simulation of regional hydrology was sensitive to precipitation and water vapor differences among the driving datasets and that accurate simulation of regional water balance is limited by biases in the forcing data. Satellite-based analyses for the region indicate that vegetation productivity of the region increased during the last two decades of the twentieth century because of earlier spring thaw, and the temporal variability of vegetation productivity simulated by different models from 1980 to 2000 was generally consistent with estimates based on the satellite record for applications driven with alternative climate datasets. However, the magnitude of the fluxes differed by as much as a factor of 2.5 among applications driven with different climate data, and spatial patterns of temporal trends in carbon dynamics were quite different among simulations. Finally, the study identified that the simulation of fire by ecosystem models is particularly sensitive to alternative climate datasets, with little or no fire simulated for some datasets. The results of WALE identify the importance of conducting retrospective analyses prior to coupling hydrology and ecosystem models with climate system models. For applications of hydrology and ecosystem models driven by projections of future climate, the authors recommend a coupling strategy in which future changes from climate model simulations are superimposed on the present mean climate of the most reliable datasets of historical climate. [ABSTRACT FROM AUTHOR]

Fisheries scientists habitually consider uncertainty in parameter values, but often neglect uncertainty about model structure, an issue of increasing importance as ecosystem models are devised to support the move to an ecosystem approach to fisheries (EAF). This paper sets out pragmatic approaches with which to account for uncertainties in model structure and we review current ways of dealing with this issue in fisheries and other disciplines. All involve considering a set of alternative models representing different structural assumptions, but differ in how those models are used. The models can be asked to identify bounds on possible outcomes, find management actions that will perform adequately irrespective of the true model, find management actions that best achieve one or more objectives given weights assigned to each model, or formalize hypotheses for evaluation through experimentation. Data availability is likely to limit the use of approaches that involve weighting alternative models in an ecosystem setting, and the cost of experimentation is likely to limit its use. Practical implementation of an EAF should therefore be based on management approaches that acknowledge the uncertainty inherent in model predictions and are robust to it. Model results must be presented in ways that represent the risks and trade-offs associated with alternative actions and the degree of uncertainty in predictions. This presentation should not disguise the fact that, in many cases, estimates of model uncertainty may be based on subjective criteria. The problem of model uncertainty is far from unique to fisheries, and a dialogue among fisheries modellers and modellers from other scientific communities will therefore be helpful. [ABSTRACT FROM AUTHOR]

This paper presents key challenges in modeling water quality processes of riparian ecosystems: How can the spatial and temporal extent of water and solute mixing in the riparian zone be modeled? What level of model complexity is justified? How can processes at the riparian scale be quantified? How can the impact of riparian ecosystems be determined at the watershed scale? Flexible models need to be introduced that can simulate varying levels of hillslope-riparian mixing dictated by topography, upland and riparian depths, and moisture conditions. Model simulations need to account for storm event peak flow conditions when upland solute loadings may either bypass or overwhelm the riparian zone. Model complexity should be dictated by the level of detail in measured data. Model algorithms need to be developed using new macro-scale and meso-scale experiments that capture process dynamics at the hillslope or landscape scales. Monte Carlo simulations should be an integral part of model simulations and rigorous tests that go beyond simple time series, and point-output comparisons need to be introduced. The impact of riparian zones on watershed-scale water quality can be assessed by performing simulations for representative hillslope-riparian scenarios. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystem models are built, among several reasons, to explore how the Earth’s biosphere responds to climate change and to the projected continual increase of atmospheric CO2 concentration. Many of these models adopt the Farquhar et al. approach, in which leaf carbon assimilation of C3 plants is regulated by two limitations depending on the rate of Rubisco activity and ribulose-1, 5-bisphosphate regeneration (RuBP). This approach was expanded upon by others to include a third limitation that expresses the occurrence, in some plant species, of a photosynthetic downregulation under high concentrations of ambient CO2. Several ecosystem models, however, constrain leaf photosynthesis using only two limitations according to the original formulation of Farquhar et al. and thus neglect the limitation that represents the downregulation of photosynthesis under elevated atmospheric CO2. In this study, the authors first reviewed the effect of elevated CO2 on photosynthesis of C3 plants, which illustrated that short-term observations are likely to considerably underestimate the number of plant species that exhibit a photosynthetic downregulation. Several recent long-term field observations have shown that such downregulation starts to be effective only after several seasons/years of plant exposure to elevated CO2. Second, an ecosystem model was used to illustrate that neglecting the photosynthetic downregulation may significantly bias predictions of net primary production of the middle and high latitudes under high atmospheric CO2 concentrations. Based on both review of field observations and results of simulations, the authors conclude that a more appropriate representation of plant physiology and choice of plant functional types may be required in ecosystem models in order to accurately simulate plant responses to changing environmental conditions. [ABSTRACT FROM AUTHOR]

Abstract Often there are several complex ecosystem models available to address a specific question. However, structural differences, systematic discrepancies and uncertainties mean that they typically produce different outputs. Rather than selecting a single ‘best’ model, it is desirable to combine them to give a coherent answer to the question at hand. Many methods of combining ecosystem models assume that one of the models is exactly correct, which is unlikely to be the case. Furthermore, models may not be fitted to the same data, have the same outputs, nor be run for the same time period, making many common methods difficult to implement. In this paper, we use a statistical model to describe the relationship between the ecosystem models, prior beliefs and observations to make coherent predictions of the true state of the ecosystem with robust quantification of uncertainty. We introduce EcoEnsemble, an R package that takes advantage of the statistical model's structure to efficiently fit the ensemble model, either sampling from the posterior distribution or maximising the posterior density. We demonstrate EcoEnsemble by investigating what would happen to four fish species in the North Sea under future management scenarios. Although developed for applications in ecology, EcoEnsemble can be used to combine any group of mechanistic models, for example in climate modelling, epidemiology or biology.

The need to implement an ecosystem-based fisheries management (EBFM) is enshrined in numerous regulations and strategies, at both global and European level. In practice, it is challenging to implement EBFM because it requires a complex evaluation of interlinked management effects and environmental and climate forcing on multi-species interactions, habitat status and human activities. Ecosystem models are one of the most critical research tools to inform EBFM, because they can integrate a wide variety of data, examine multiple and complex ecosystem interactions, and can make forecasts based on specific management scenarios. However, despite clear progress in marine ecosystem modelling, many models do not address policy goals and targets, which hinders uptake in policy. In this paper, we review the global and European policies and implementing bodies which directly or indirectly have a repercussion on the implementation of EBFM. Moreover, we highlight specific stakeholder needs related to the implementation of EBFM in European waters, which ecosystem models could help address. We review the policy commitments that drive these needs and the concerns raised by stakeholders during a survey and dedicated workshop. Key topics of concern were effects of climate change; bycatch; protected areas/fisheries restricted areas; and reducing the impacts of trawling. Stakeholders also provided specific questions related to these topics which ecosystem models could help address. Scenario and data results visualizations, as well as specific barriers in using the results of ecosystem models for decision-making are also discussed. A close involvement of stakeholders in scenario development and in designing graphical outputs is important, and can help overcome some of the main barriers that can hinder uptake of models and scenarios, including a lack of understanding of the benefits and limits of ecosystem models; insufficient involvement and interaction with stakeholders; and inadequate characterization of uncertainties.

Ecosystem-based fisheries management (EBFM) aims to go beyond single-species management by incorporating ecosystem considerations to guarantee the sustainable use of marine resources. Although many countries have formally committed to the implementation of EBFM, at a practical level progress has been very slow. At the analytical level, many advances have been made, developing sophisticated ecosystem models and, on the other side, rigorous stock assessment models. Yet it is still unclear how these two parallel approaches can be brought together to provide an efficient EBFM. Here we first present the two groups of models, we then discuss ways to integrate both approaches and their resulting possible implications for fisheries advice towards sustainable management, with a special focus on spatially explicit information. [ABSTRACT FROM AUTHOR]

The management of natural resources is currently more challenging than ever before. Climate change and human population growth pose a threat to marine ecosystems as we know them. In order to preserve ecosystems, biodiversity and ecosystem services, management of biological resources must adopt a holistic strategy. Ecosystem‐Based Fisheries Management (EBFM) enables this by managing natural resources at the ecosystem level. However, EBFM objectives and implementation can be unclear at times, particularly when framed in the context of shifting conditions. In this research, the strategies available for managing marine biological resources within the EBFM framework and in a changing environment are reviewed. The purpose of this publication is to guide the decision on whether and how to change current management strategies in order to achieve policy goals. The manuscript starts with a revision of ecosystem indicators and ecosystem models used to detect and describe changes in ecosystem dynamics and stocks productivities under present and future conditions. Then, the different frameworks and methods available for integrating this information into the decision‐making process are summarised. Currently, some of the options available to include ecosystem realism into the fisheries advice include using ecosystem models in the Management Strategy Evaluation (MSE) process, adjusting single species reference points with ecosystem information and implementing risk‐equivalent empirical approaches. However, barriers that are impeding the adoption of these techniques exist. I concluded the study by identifying them and providing literature‐based solutions to overcome them from an interdisciplinary perspective. [ABSTRACT FROM AUTHOR]

It is commonly accepted that no ecosystem model is the 'best', but rather that ecosystem models should be used in ensembles. This is also the case for the Barents Sea ecosystem, where we have used two different ecosystem models to explore the role of the top‐predator Northeast Arctic (NEA) stock of Atlantic cod (Gadus morhua, Gadidae) in the food web. The two models differ in complexity; Gompertz being less complex in terms of food web (7 components) and processes compared to the complex Nordic and Barents Seas Atlantis model (53 components). On the other hand, Gompertz provides thousands of stochastic realizations for each scenario, whereas Atlantis provides only one deterministic simulation. To compare the response to changes in NEA cod on two key prey species, capelin (Mallotus villosus, Osmeridae) and polar cod (Boreogadus saida, Gadidae), we perturbed the historical fishing pressure by ±50% and used the same NEA cod biomass in both models. Even though the links between NEA cod and the prey species are similar in the two models, the results from the study reveal that indirect effects through other food‐web components might be as important as direct predator–prey interactions. Differences in spatial structure and overlap between species also influence the species response to the perturbations. In this study, we focus on the mechanisms that drives the changes in the models, and advise on potential consequences for fisheries management. The two models can complement each other, and the differences between them point to areas where more knowledge is needed. [ABSTRACT FROM AUTHOR]

Climate changes may have immediate implications for forest productivity and may produce dramatic shifts in tree species distributions in the future. Quantifying these implications is significant for both scientists and managers. Cunninghamia lanceolata is an important coniferous timber species due to its fast growth and wide distribution in China. This paper proposes a methodology aiming at enhancing the distribution and productivity of C. lanceolata against a background of climate change. First, we simulated the potential distributions and establishment probabilities of C. lanceolata based on a species distribution model. Second, a process-based model, the PnET-II model, was calibrated and its parameterization of water balance improved. Finally, the improved PnET-II model was used to simulate the net primary productivity (NPP) of C. lanceolata. The simulated NPP and potential distribution were combined to produce an integrated indicator, the estimated total NPP, which serves to comprehensively characterize the productivity of the forest under climate change. The results of the analysis showed that (1) the distribution of C. lanceolata will increase in central China, but the mean probability of establishment will decrease in the 2050s; (2) the PnET-II model was improved, calibrated, and successfully validated for the simulation of the NPP of C. lanceolata in China; and (3) all scenarios predicted a reduction in total NPP in the 2050s, with a markedly lower reduction under the a2 scenario than under the b2 scenario. The changes in NPP suggested that forest productivity will show a large decrease in southern China and a mild increase in central China. All of these findings could improve our understanding of the impact of climate change on forest ecosystem structure and function and could provide a basis for policy-makers to apply adaptive measures and overcome the unfavorable influences of climate change. [ABSTRACT FROM AUTHOR]

Context: Understanding the variability and dynamics of ecosystems, as well as their responses to climate or land use change, is challenging for policy makers and natural resource managers. Virtual reality (VR) can be used to render virtual landscapes as immersive, visceral experiences and communicate ecosystem dynamics to users in an effective and engaging way. Objectives: To illustrate the potential and believability of VR, a team of landscape ecologists and immersive visualisation researchers modelled a reference Australian Box Gum Grassy Woodland landscape, an endangered eucalypt woodland ecosystem that is difficult to observe in its pre-European colonisation form. Methods: We document considerations for designing the immersive virtual landscape, including the creation of animated three-dimensional (3D) plants that alternate between the seasons, and soundscapes that change through the course of a simulated day. We used a heuristic evaluation with experts to assess the potential of immersive VR landscape modeling. Results: This cross disciplinary collaboration resulted in a VR experience that was evaluated in a series of meetings by 27 ecologists and managers in biodiversity conservation, many of whom were familiar with Box Gum Grassy Woodlands. 88% of participants stated that the simulation was believable and participants thought that virtual landscapes held great potential for education, public engagement and land management. Conclusions: Possible future directions include open-source libraries of ecological 3D models, and the visual simulation of historic landscapes and future climate change scenarios. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a personal perspective on the future of ecosystem based fisheries management (EBFM). I begin with the question, “if we did single species management well, would EBFM be necessary.” The answer to this is yes, because pure single-species management does not consider impacts on non-target species, trophic interactions among species, and habitat-destroying fishing practices. Pure single-species management conflicts with a range of legislation designed to protect non-target species and habitats within the U.S. and a number of other countries. The most important elements of EBFM are keeping fishing mortality rates low enough to prevent ecosystem-wide overfishing, reducing or eliminating by-catch and avoiding habitat-destroying fishing methods. There is a second phase of EBFM I call “extended EBFM” that consists of considering trophic interactions and area-based management. While there are now models of the trophic interactions for most highly managed ecosystems, and there are area-based management efforts underway in many places, I am not convinced that we are really ready, scientifically and administratively, to apply these forms of EBFM, because they are expensive and require complex trade-offs that are often ill-defined. [Copyright &y& Elsevier]

Ecosystems are often modeled as stocks of matter or energy connected by flows. Network environ analysis (NEA) is a set of mathematical methods for using powers of matrices to trace energy and material flows through such models. NEA has revealed several interesting properties of flow–storage networks, including dominance of indirect effects and the tendency for networks to create mutually positive interactions between species. However, the applicability of NEA is greatly limited by the fact that it can only be applied to models at constant steady states. In this paper, we present a new, computationally oriented approach to environ analysis called dynamic environ approximation (DEA). As a test of DEA, we use it to compute compartment throughflow in two implementations of a model of energy flow through an oyster reef ecosystem. We use a newly derived equation to compute model throughflow and compare its output to that of DEA. We find that DEA approximates the exact results given by this equation quite closely – in this particular case, with a mean Euclidean error ranging between 0.0008 and 0.21 – which gives a sense of how closely it reproduces other NEA-related quantities that cannot be exactly computed and discuss how to reduce this error. An application to calculating indirect flows in ecosystems is also discussed and dominance of indirect effects in a nonlinear model is demonstrated. [Copyright &y& Elsevier]

Abstract: Accurate information concerning regional to ecosystem-scale carbon dynamics within tropical rainforests is important because of the increasing certainty that the global climate will change significantly within the next century. Tropical forests of north Queensland, Australia, are highly sensitive to climate change and substantial shifts in the distribution of these forests are likely to occur with minor variations in climate. The focus of this research was the development of a model-based system for assessing forest growth and biomass accumulation dynamics within Australia''s tropical rainforest bioregion and predicting the impacts of climate change on these dynamics. This paper presents the parameterisation and calibration of (a) the 3-PG (Physiological Principles Predicting Growth) model to a selection of restored rainforest and commercial timber plantations and (b) a modified version 3-PGS which uses satellite data, enabling the spatial assessment of mature tropical rainforest growth and production throughout the wet tropics bioregion. Statistically significant relationships were observed between 3-PG and 3-PGS modelled and field measured estimates of stand structural attributes including, basal area (BA), diameter at breast height (DBH) and above-ground biomass (AGB) throughout the bioregion. 3-PG and 3-PGS modelled leaf area index (LAI) and net primary production (NPP) related well to published estimates at other similar rainforest sites. These results indicate that the simple, process-based models are effective at capturing the growth dynamics of structurally complex old-growth, restoration and plantation rainforests. [Copyright &y& Elsevier]

Coupled biogeochemical cycles drive ecosystem ecology by influencing individual-to-community scale behaviors; yet the development of process-based models that accurately capture these dynamics remains elusive. Soil organic matter (SOM) decomposition in particular is influenced by resource stoichiometry that dictates microbial nutrient acquisition ('ecological stoichiometry'). Despite its basis in biogeochemical modeling, ecological stoichiometry is only implicitly considered in high-resolution microbial investigations and the metabolic models they inform. State-of-science SOM decomposition models in both fields have advanced largely separately, but they agree on a need to move beyond seminal pool-based models. This presents an opportunity and a challenge to maximize the strengths of various models across different scales and environmental contexts. To address this challenge, we contend that ecological stoichiometry provides a framework for merging biogeochemical and microbiological models, as both explicitly consider substrate chemistries that are the basis of ecological stoichiometry as applied to SOM decomposition. We highlight two gaps that limit our understanding of SOM decomposition: (1) understanding how individual microorganisms alter metabolic strategies in response to substrate stoichiometry and (2) translating this knowledge to the scale of biogeochemical models. We suggest iterative information exchange to refine the objectives of high-resolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models. Assimilating theoretical and modelling frameworks from different scientific domains is the next frontier in SOM decomposition modelling; advancing technologies in the context of stoichiometric theory provides a consistent framework for interpreting molecular data, and further distilling this information into tractable SOM decomposition models. [ABSTRACT FROM AUTHOR]

Non-spatial dynamics are core to landscape simulations. Unit models simulate system interactions aggregated within one space unit of resolution used within a spatial model. For unit models to be applicable to spatial simulations they have to be formulated in a general enough way to simulate all habitat elements within the landscape. Within the Patuxent River watershed, human dominated land uses, such as agriculture and urban land, are already 50% of the current land use, while urban land is replacing forests, agriculture and wetlands at a rapid rate. The Patuxent Landscape Model (PLM) with the Patuxent General Unit Model as core (Pat-GEM) was developed as a predictive policy tool to estimate environmental impacts of such land use changes. The Pat-GEM is based on the General Ecosystem Model (GEM) developed by [Ecol. Modelling 88 1996 263]. Previous calibrations of the Pat-GEM for anthropogenic land uses have not been satisfactory due to the scarcity of appropriate data. This paper shows Pat-GEM simulations of biomass growth and nutrient uptake for crops typical within the Patuxent watershed. The Pat-GEM was expanded to include processes and fluxes that characterize agricultural land use. The most important extension was to include crop rotation into the model. Additionally, we refined the processes for planting, harvesting and fertilization by introducing specific growth parameters. Our revised Pat-GEM was calibrated against the results from Erosion Productivity Impact Calculator (EPIC) a widely used and calibrated agricultural model. We achieved high correlation between results generated with Pat-GEM and EPIC. The correlation coefficients (r2) varied between 0.87 and 0.98, with the simulation results for winter wheat showing the lowest correlation coefficients. Intercalibration using EPIC is a powerful method for calibrating the Pat-GEM model for agricultural land use. EPIC was able (a) to provide about 30% of the input data required for running the Pat-GEM model; and (b) to provide time series output data (with a daily time step) to calibrate the output variables biomass production and nutrient uptake. [Copyright &y& Elsevier]

Management Strategy Evaluation (MSE) is a framework to explore the tradeoffs amongst fishing strategies and assess the consequences for achieving management goals provided sources of uncertainty by means of simulation models (referred to as operating models). Single-species stock assessment often implements simulations for MSE, but the operating models often omit the dynamics of key biological interactions. This could be a disadvantage for the evaluation of tradeoffs as species interactions could have an impact on the performance of harvesting strategies. Tools for conducting ecosystem-based fisheries management (EBFM), such as integrated ecosystem assessments, include executing MSEs with ecosystem models, many of which explicitly include biological interactions. Although the support for EBFM has grown over the years, the amount of information provided by MSEs based on ecosystem models appears to be limited. A clear summary of such efforts would provide beneficial information for future efforts for EBFM. Herein, an inventory of applications simulating MSEs with ecosystem models that explicitly include biological interactions was developed based on findings from a literature review. First, the methodologies and foci across all identified applications are analyzed. Next, summaries of each application are provided. Lastly, general observations are provided along with recommendations for future applications. [ABSTRACT FROM AUTHOR]

Globally important services are supported by Southern Ocean ecosystems, underpinned by the structure, function, and dynamics of complex interconnected and regionally distinctive food webs. These food webs vary in response to a combination of physical and chemical processes that alter productivity, species composition and the relative abundance and dynamics of organisms. Combined with regional and seasonal variability, climate-induced changes and human activities have and are expected to continue to drive important structural and functional changes to Southern Ocean food webs. However, our current understanding of food web structure, function, status, and trends is patchy in space and time, and methods for systematically assessing and comparing community-level responses to change within and across regional and temporal scales are not well developed. Insights gained from food web modelling studies—ranging from theoretical analyses of ecosystem resilience and adaptation, to qualitative and quantitative descriptions of the system—can assist in resolving patterns of energy flow and the ecological mechanisms that drive food web structure, function, and responses to drivers (such as fishing and climate change). This understanding is required to inform robust management strategies to conserve Southern Ocean food webs and the ecosystem services they underpin in the face of change. This paper synthesises the current state of knowledge regarding Southern Ocean pelagic food webs, highlighting the distinct regional food web characteristics, including key drivers of energy flow, dominant species, and network properties that may indicate system resilience. In particular, the insights, gaps, and potential integration of existing knowledge and Southern Ocean food web models are evaluated as a basis for developing integrated food web assessments that can be used to test the efficacy of alternative management and policy options. We discuss key limitations of existing models for assessing change resulting from various drivers, summarise priorities for model development and identify that significant progress could be made to support policy by advancing the development of food web models coupled to projected biogeochemical models, such as in Earth System models.

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century. [ABSTRACT FROM AUTHOR]

Ecosystem models are important tools for exploring the temporal and spatial patterns of ecosystem processes and their responses to climate change. However, the implications of uncertainty in model parameters are often overlooked, especially in regional ecosystem model simulations. Here, we use eddy‐covariance observations to estimate parameters in an ecosystem model, which was developed from Shuttleworth‐Wallace model, and examine the effect on estimates of evapotranspiration (ET). Using a simple ecosystem model as an example, we use Monte Carlo techniques to optimize key model parameters using eddy covariance (EC) data from 163 FLUXNET sites. The optimization process revealed a strong spatial correlation between key parameters and environmental variables, particularly leaf area index (LAI) and soil characteristics (e.g., clay fraction). The optimization of parameters related to canopy conductance and soil surface resistance greatly improved model performance, particularly when incorporating the identified spatial variation of parameters. The improved model agreed well with the measurements with an increase in the coefficient of determination (R2) from 73% to 80% in the 8‐day averaged ET estimation and a decrease in the root mean square error (RMSE) from 130.2 to 104.3 mm year−1 compared with the original model. The results suggest the potential of eddy‐covariance flux observations to identify predictable spatial variations of key parameters, which can be used to better constrain ecosystem models. And in this case, a universal and efficient method for reducing the uncertainties in key parameters across different PFTs and ecosystem applications is suggested. Key Points: Global flux towers provide an opportunity for improving ecosystem models via yielding information of spatial variations in parametersKey parameters of ecosystem model were optimized with Fluxnet towersObvious improvements were found when the knowledge of the spatial variations in parameters was incorporated in an ecosystem model [ABSTRACT FROM AUTHOR]

Ecosystem-based fisheries management (EBFM) is a globally mandated approach with the intention to jointly address ecological and human (social-cultural, economic and institutional) dimensions. Indicators to measure performance against objectives have been suggested, tested, and refined but with a strong bias towards ecological indicators. In this paper, current use and application of indicators related to the human dimension in EBFM research and ecosystem models are analysed. It is found that compared to ecological counterparts, few indicators related to the human dimension are commonly associated with EBFM, and they mainly report on economic objectives related to fisheries. Similarly, in the most common ecosystem models, economic indicators are the most frequently used related to the human dimension, both in terms of model outputs and inputs. The prospect is small that indicators mainly related to profitable fishing economy are able to report on meeting the broad range of EBFM objectives and to successfully evaluate progress in achieving EBFM goals. To fully conform with EBFM principles, it is necessary to recognise that ecological and human indicators are inter-dependent. Moreover, the end-to-end ecosystem models used in EBFM will need to be further developed to allow a fuller spectrum of social-cultural, institutional, and economic objectives to be reported against. • Ecosystem-based fisheries management (EBFM) includes ecological and human objectives. • Research focus has been on mainly ecological EBFM indicators. • Human indicators and to which EBFM objectives they relate were reviewed. • Only a few indicators were commonly used, mainly related to fishing economy. • It is important to appropriate address all dimensions in operationalizing EBFM. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) can be released from soils to the atmosphere through microbial decomposition of plant residues or soil organic carbon, root emission, evaporation of litter‐stored BVOCs, and other physical processes. Soils can also act as a sink of BVOCs through biotic and abiotic uptake. Currently, the source and sink capabilities of soils have not been explicitly accounted for in global BVOC estimates from the terrestrial biosphere. In this review, we summarize the current knowledge of soil BVOC processes and aim to propose a generic framework for modelling soil BVOCs based on current understanding and data availability. To achieve this target, we start by reviewing measured sources and sinks of soil BVOCs and summarize commonly reported compounds. Next, we strive to disentangle the drivers for the underlying biotic and abiotic processes. We have ranked the list of compounds, known to be emitted from soils, based on our current understanding of how each process controls emission and uptake. We then present a modelling framework to describe soil BVOC emissions. The proposed framework is an important step toward initializing modelling exercises related to soil BVOC fluxes. Finally, we also provide suggestions for measurements needed to separate individual processes, as well as explore long‐term and large‐scale patterns in soil BVOC fluxes. Plain Language Summary: Living plants emit biogenic volatile organic compounds (BVOCs), which have impacts on regional and global climate. However, BVOCs can also be released from fallen leaf litter, plant roots, and soil organic matter, and some compounds are also consumed by soil microbes. In this article, we begin by sorting out the processes that govern soil emissions and uptakes of BVOCs and summarize the current understanding and available data for each process. Furthermore, we propose a generic modelling framework to add soil BVOC‐related processes into the typical structure existing in many ecosystem models. We also provide suggestions for future measurements that would help with model‐data integration. Key Points: We present biotic and abiotic drivers for soil BVOC emissions and sinksWe summarize the often‐reported compounds and key processes regulating their fluxesWe propose a generic framework for including soil BVOCs in ecosystem models [ABSTRACT FROM AUTHOR]