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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]

This study investigates enclosed water bodies to understand the intricate interactions among physical, chemical, and biological processes. A one-dimensional model, integrating sediment and ecosystem components, was developed to analyze oxygen depletion, nutrient dynamics, and the influence of sediment organic matter on oxygen concentrations due to stratification. Oxygen and nutrient concentrations were derived by combining results from a one-dimensional vertical diffusion model with the ecosystem model. The dissolution, adsorption, and desorption of inorganic substances in bottom mud react differently under anaerobic and aerobic conditions, necessitating separate formulations for reduction and oxidation reactions in the bottom mud model. In Lake Biwa, Japan's largest lake, dissolved oxygen levels have decreased despite regulatory efforts since the 1970s, prompting an investigation into the causes of oxygen-depleted water masses. The model was thus applied to examine oxygen consumption in Lake Biwa's deep sediments, shedding light on anoxia in enclosed water bodies. This study emphasizes the significance of bottom sediments in water quality models and their implications for ecosystems. Furthermore, the adaptive nature of this model allows for its application in various water bodies, including freshwater reservoirs, lakes, and saltwater bays. [ABSTRACT FROM AUTHOR]

This article, published in Frontiers in Marine Science, provides a comprehensive analysis of biogeochemical processes in Chilika Lagoon, India. The study examines factors such as primary production, dissolved oxygen concentrations, ammonia and nitrification processes, and phosphorus dynamics. The results indicate that water quality fluctuations in the lagoon are closely linked to algal growth and nutrient regeneration from sediment. The article also discusses the complex relationship between phytoplankton carbon biomass and chlorophyll concentration, the role of zooplankton in the ecosystem, and the importance of measuring dissolved oxygen concentration. The study concludes that the lagoonal ecosystem shows resilience and further investigations are needed. This article is a valuable resource for researchers studying similar ecosystems. [Extracted from the article]

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.

Continuous cover forestry (CCF) has gained interest as an alternative to even-aged management particularly on drained peatland forests. However, relatively little is known about the physiological response of suppressed trees when larger trees are removed as a part of CCF practices. Consequently, studies concentrating on process-level modeling of the response of trees to selection harvesting are also rare. Here, we compared, modeled and measured harvest response of previously suppressed Norway spruce (Picea abies) trees to a selection harvest. We quantified the harvest response by collecting Norway spruce tree-ring samples in a drained peatland forest site and measuring the change in stable carbon and oxygen isotopic ratios of wood formed during 2010–20, including five post-harvest years. The measured isotopic ratios were compared with ecosystem-level process model predictions for |${\kern0em }^{13}$| C discrimination and |${\kern0em }^{18}$| O leaf water enrichment. We found that the model predicted similar but lower harvest response than the measurements. Furthermore, accounting for mesophyll conductance was important for capturing the variation in |${\kern0em }^{13}$| C discrimination. In addition, we performed sensitivity analysis on the model, which suggests that the modeled |${\kern0em }^{13}$| C discrimination is sensitive to parameters related to CO2 transport through stomata to the mesophyll. [ABSTRACT FROM AUTHOR]

This study investigates enclosed water bodies to understand the intricate interactions among physical, chemical, and biological processes. A one-dimensional model, integrating sediment and ecosystem components, was developed to analyze oxygen depletion, nutrient dynamics, and the influence of sediment organic matter on oxygen concentrations due to stratification. Oxygen and nutrient concentrations were derived by combining results from a one-dimensional vertical diffusion model with the ecosystem model. The dissolution, adsorption, and desorption of inorganic substances in bottom mud react differently under anaerobic and aerobic conditions, necessitating separate formulations for reduction and oxidation reactions in the bottom mud model. In Lake Biwa, Japan’s largest lake, dissolved oxygen levels have decreased despite regulatory efforts since the 1970s, prompting an investigation into the causes of oxygen-depleted water masses. The model was thus applied to examine oxygen consumption in Lake Biwa’s deep sediments, shedding light on anoxia in enclosed water bodies. This study emphasizes the significance of bottom sediments in water quality models and their implications for ecosystems. Furthermore, the adaptive nature of this model allows for its application in various water bodies, including freshwater reservoirs, lakes, and saltwater bays.

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.

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.

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.

Summary: Evolutionary history plays a key role driving patterns of trait variation across plant species. For scaling and modeling purposes, grass species are typically organized into C3 vs C4 plant functional types (PFTs). Plant functional type groupings may obscure important functional differences among species. Rather, grouping grasses by evolutionary lineage may better represent grass functional diversity.We measured 11 structural and physiological traits in situ from 75 grass species within the North American tallgrass prairie. We tested whether traits differed significantly among photosynthetic pathways or lineages (tribe) in annual and perennial grass species.Critically, we found evidence that grass traits varied among lineages, including independent origins of C4 photosynthesis. Using a rigorous model selection approach, tribe was included in the top models for five of nine traits for perennial species. Tribes were separable in a multivariate and phylogenetically controlled analysis of traits, owing to coordination of important structural and ecophysiological characteristics.Our findings suggest grouping grass species by photosynthetic pathway overlooks variation in several functional traits, particularly for C4 species. These results indicate that further assessment of lineage‐based differences at other sites and across other grass species distributions may improve representation of C4 species in trait comparison analyses and modeling investigations. [ABSTRACT FROM AUTHOR]

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]

Two decades have passed since the initiation of the National Oceanic and Atmospheric Administration’s research program aimed at advancing the understanding of estuary and ocean ecology of United States West Coast Pacific salmon (Oncorhynchus spp.). In this review and prospectus, we summarize key findings from this program and describe a plan for transitioning it to better support Ecosystem-Based Management (EBM). While we focus on salmon research, our approach applies to research design generally. Our path forward involves increasing understanding of ecosystem processes to improve the dependability of scenario testing under novel conditions. Over the past two decades, we developed a conceptual model for how climate, predators, prey, fisheries, and human activities influence salmon. Knowledge gaps we identified from our conceptual model include limited understanding of salmon distributions, behavior, maturation dynamics, and population dynamics, and salmon interactions with predators, competitors, and prey during winter. We consider emerging risks and vulnerabilities facing salmon and propose analysis frameworks for evaluating them. Increased predator populations, coupled with climate change, pose increasing threats to West Coast salmon and will require new strategies and actions to mitigate their negative impacts. We propose research to support the development of decision-support tools to evaluate tradeoffs associated with alternative management strategies and to inform an adaptive ecosystem management system to improve the resilience of salmon populations and salmon-dependent fisheries.

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]

Amazon droughts, including the 2015-2016 El Niño, may reduce forest net primary productivity and increase canopy tree mortality, thereby altering both the short- and the long-term net forest carbon balance. Given the broad extent of drought impacts, inventory plots or eddy flux towers may not capture regional variability in forest response to drought. We used multi-temporal airborne Lidar data and field measurements of coarse woody debris to estimate patterns of canopy turnover and associated carbon losses in intact and fragmented forests in the central Brazilian Amazon between 2013-2014 and 2014-2016. Average annualized canopy turnover rates increased by 65% during the drought period in both intact and fragmented forests. The average size and height of turnover events was similar for both time intervals, in contrast to expectations that the 2015-2016 El Niño drought would disproportionally affect large trees. Lidar-biomass relationships between canopy turnover and field measurements of coarse woody debris were modest (R2  ≈ 0.3), given similar coarse woody debris production and Lidar-derived changes in canopy volume from single tree and multiple branch fall events. Our findings suggest that El Niño conditions accelerated canopy turnover in central Amazon forests, increasing coarse woody debris production by 62% to 1.22 Mg C ha-1  yr-1 in drought years .

The metabolic cost of foraging is the dark energy of ecological systems. It is much harder to observe and to measure than its beneficial counterpart, prey consumption, yet it is not inconsequential for the dynamics of prey and predator populations. Here I define the metabolic response as the change in energy expenditure of predators in response to changes in prey density. It is analogous and intrinsically linked to the functional response, which is the change in consumption rate with prey density, as they are both shaped by adjustments in foraging activity. These adjustments are adaptive, ubiquitous in nature, and are implicitly assumed by models of predator–prey dynamics that impose consumption saturation in functional responses. By ignoring the associated metabolic responses, these models violate the principle of energy conservation and likely underestimate the strength of predator–prey interactions. Using analytical and numerical approaches, I show that missing this component of interaction has broad consequences for dynamical stability and for the robustness of ecosystems to persistent environmental or anthropogenic stressors. Negative metabolic responses – those resulting from decreases in foraging activity when more prey is available, and arguably the most common – lead to lower local stability of food webs and a faster pace of change in population sizes, including higher excitability, higher frequency of oscillations, and quicker return times to equilibrium when stable. They can also buffer the effects of press perturbations, such as harvesting, on target populations and on their prey through top-down trophic cascades, but are expected to magnify bottom-up cascades, including the effects of nutrient enrichment or the effects of altering lower trophic levels that can be caused by environmental forcing and climate change. These results have implications for any resource management approach that relies on models of food web dynamics, which is the case of many applications of ecosystem-based fisheries management. Finally, besides having their own individual effects, metabolic responses have the potential to greatly alter, or even invert, functional response-stability relationships, and therefore can be critical to an integral understanding of predation and its influence on population dynamics and persistence.

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]

The maximum seasonal vegetation photosynthesis (Pho max ) is crucial to regulating the global carbon dynamics. Of particular importance are the seasonal increments in vegetation photosynthesis (ΔPho), which provide key insights into understanding Pho max . However, the interannual variability of ΔPho within the photosynthesis-up period (PUP) and its influencing factors remain unclear. To address this gap, we identified PUP and quantified the multi-year characteristics of ΔPho using satellite-derived solar-induced chlorophyll fluorescence. We further investigated the response of ΔPho in northern ecosystems to climate change and vegetation greening by integrating climate data and the normalized difference vegetation index. In the northern ecosystems, longer PUP often spatially correlated with a higher ΔPho. An increasing trend was evident regarding the multi-year variations in ΔPho, suggesting enhanced vegetation photosynthesis within the PUP. This phenomenon is primarily driven by increased solar radiation and intensified vegetation greening. Additionally, based on the results derived from satellite data, we found three pieces of evidence for the decoupling trend between vegetation photosynthesis and greening under the influence of climate change: first, the inconsistent trends between ΔPho and greening; second, the declining moving trend in the correlation coefficient between ΔPho and greening, approximately 9.17 × 10 -4 ; and third, the weakened dominant role of greening on ΔPho. These findings were further supported by results from ecosystem model simulations. In summary, this study provides insights into the interannual variability of ΔPho and its influencing factors and indicates that vegetation dynamics and terrestrial carbon cycle are likely to become more complex under future climate change scenarios., (© 2024 John Wiley & Sons Ltd.)

Underwater noise generated by human activities, such as shipping and seismic surveys, has emerged as a growing concern. Despite the mounting evidence that noise negatively impacts marine mammals, fish, and invertebrates, end-to-end ecosystem models often overlook noise as a stressor. This omission is due to the complexity of studying noise's influence on entire populations and ecosystems, making it difficult to gauge the potential total effects accurately. In this study, we implemented potential effects of underwater noise in the Atlantis ecosystem modeling framework. Noise effects on organisms were simulated through reduced growth and consumption rates, increased mortality and movement away from noisy areas. The noise module was tested by a Morris sensitivity analysis on most of the fish, mammal and invertebrate species in the model. The species were aggregated into 12 groups perturbed with six different noise levels, with the spatial dimension taken into account by repeating the analysis under varying spatial configurations. The results revealed substantial systemic effects from increased vulnerability to noise from zooplankton, while marine mammal noise vulnerability had relatively little impact, in line with earlier Atlantis studies. Additionally, the coastal area exhibited significantly higher biomass variability, indicating a need for more research in this region where noise is expected to increase the most. These results provide an initial estimation of the potential effects of noise at the ecosystem level in the Nordic and Barents Seas. However, for improved realism of the noise module in future studies, we emphasize the need to develop response functions for each species' sensitivity to noise. Understanding such species-specific sensitivities will be crucial in devising effective strategies to mitigate the detrimental consequences of underwater noise on marine ecosystems. • Underwater noise is implemented as a submodule in the Atlantis framework. • A Morris sensitivity analysis shows ecosystem effects from increased vulnerability to noise from zooplankton. • Spatial distribution of noise is important for the ecosystem response. [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]

Changes in rainfall associated with climate change are expected to affect the tightly coupled water‐carbon ecosystem dynamics. Here, we study the effects of altered rainfall at 33 sites in North America, as projected by the high‐resolution/high‐fidelity (∼4 km, 1 hr) continental‐wide Weather Research Forecasting (WRF) convection‐permitting model under a high‐emission scenario (RCP 8.5). We make use of a stochastic weather generator to extend WRF outputs, accounting for natural variability and simultaneously separate the changes in total rainfall, its seasonality, and its intraseasonal pattern. We used these rainfall scenarios to study ecosystem responses with the state‐of‐the‐art Tethys‐Chloris terrestrial biosphere model. Model simulations suggest that increases in mean annual rainfall dominate ecosystem responses at dry sites, while wet sites are less sensitive to rainfall changes. Sites of intermediate wetness face reductions in productivity, due to reduced growing season rainfall and increased water losses under altered seasonality, which outpace any possible benefits induced by increases in mean annual totals. Changes in the fine‐scale temporal structure of rainfall have an insignificant impact on ecosystem productivity and only alter hydrological dynamics, contradicting expectations based on some field experiments, which, however, are not tailored to directly quantify climate change impacts, but rather to understand the mechanisms leading to ecosystem responses. We further demonstrate how approaches following the "fewer but larger rainfall events" concept might exacerbate ecosystem responses. Plain Language Summary: Rainfall is expected to change with global warming and this can affect ecosystems. Here, we investigate the impacts of changes in rainfall over 33 North American ecosystems, based on high‐resolution projections of future end‐of‐century climate. We extend these projections with the use of statistical tools to study their effects on ecosystem response with a state‐of‐the‐art model. In dry ecosystems where naturally water demand far exceeds water supply, an increase in mean annual rainfall alleviates chronic water stress and stimulates vegetation productivity. In wet ecosystems rarely facing water stress, the increase in mean annual rainfall does not affect vegetation and water is mostly lost as runoff, or recharges the aquifers. In ecosystems where water supply roughly equals water demand, rainfall during the growing season—when water is much needed—is reduced, due to changes in the monthly distribution of rainfall and, hence, vegetation productivity is negatively affected. We also demonstrate that changes in storm characteristics and frequency do not significantly modify vegetation productivity, but are more important for runoff generation and aquifer recharge. Key Points: Increase in mean rainfall and its seasonality affects ecosystem productivityChanges in rainfall structure at fine temporal scales only affect water partitioning, not ecosystem productivityField experiments manipulate rainfall outside convection‐permitting model projections and can overestimate ecosystem responses [ABSTRACT FROM AUTHOR]

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]

Our results indicate that the abundance of forage fish rarely affects predator productivity, which suggests that the extra-precautionary management of forage fish would rarely achieve the intended benefits for marine predator populations. Keywords: ecosystem-based fisheries management; ecosystem models; forage fish; marine mammals; precautionary management; predator-prey dynamics; seabirds; small pelagics; aves marinas; dinámicas depredador-presa; mamíferos marinos; manejo de pesquerías basado en el ecosistema; manejo precautorio; modelos de ecosistemas; peces pelágicos pequeños EN ecosystem-based fisheries management ecosystem models forage fish marine mammals precautionary management predator-prey dynamics seabirds small pelagics aves marinas dinámicas depredador-presa mamíferos marinos manejo de pesquerías basado en el ecosistema manejo precautorio modelos de ecosistemas peces pelágicos pequeños 1540 1551 12 10/05/21 20211001 NES 211001 Introduction Forage fish are small pelagic fish (e.g., herrings, sardines, and anchovies) that provide benefits to both people and marine ecosystems. We quantified the frequency with which forage fish abundance influences predator productivity and used these findings to reflect on the extent to which extra-precautionary regulations on forage fish are likely to have the intended benefits for their predators. [Extracted from the article]

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]

Forest ecosystems play a significant role in climate change mitigation and uptake a larger amount of atmospheric CO2 than other terrestrial ecosystems via photosynthesis process in form of gross primary production (GPP). The photosynthesis or GPP is largely determined by the photosynthetic capacity of vegetation (i.e., maximum rate of carboxylation, Vcmax) in ecosystem models. However, considerable uncertainties of Vcmax estimates may limit our potential to address scientific issues of GPP related to the increasing emission of atmospheric CO2. Recently, solar-induced chlorophyll fluorescence (SIF) signals have been used as a proxy for resolving photosynthesis. In this study, the biochemical and structural parameters were retrieved from hyperspectral reflectance and fluorescence quantum efficiency (FQEs) was retrieved from ground-based SIF. Then, retrieved parameters were incorporated into the Soil Canopy Observation Photosynthesis and Energy (SCOPE) model to explore the potential of ground-based SIF to track Vcmax variability for a subtropical evergreen mixed forest. Then, SIF-derived Vcmax was used to parameterize Boreal Ecosystem Production Simulator (BEPS) model to simulate the GPP. With retrieved vegetative parameters and FQEs, the ground-based SIF was strongly correlated with the model-based SIF simulation at O2-B and O2-A bands, demonstrating that the coefficient of determination (R2) improved from 0.15 (constant values) to 0.60 (retrieved values) for SIFB and from 0.79 to 0.94 for SIFA simulation. Using SIF-derived Vcmax, the R2 value of simulated GPP against eddy covariance-based measurements substantially increased from 0.18 (constant Vcmax) to 0.38 (SIF-derived Vcmax) for dry season and from 0.56 to 0.67 for wet season respectively. The utilization of SIF-derived Vcmax with its corrected temperature response function reduced the relative error in annual GPP simulations by 24.9%. Our results support the significant references toward reducing unbiased SIF simulation and highlighting the potential of ground SIF in deriving Vcmax at the site scale for defining forest management options.

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.

Global climate change is a key driver of change in coastal waters with clear effects on biological communities and marine ecosystems. Human activities in combination with climate change exert a tremendous pressure on marine ecosystems and threaten their integrity, structure, and functioning. The protection of these ecosystems is a major target of the 14th United Nations sustainable development goal “Conserve and sustainably use the oceans, seas and marine resources for sustainable development.” However, due to the complexity of processes and interactions of stressors, the status assessment of ecosystems remains a challenge. Holistic food web models, including biological and environmental data, could provide a suitable basis to assess ecosystem health. Here, we review climate change impacts on different trophic levels of coastal ecosystems ranging from plankton to ecologically and economically important fish and shellfish species. Furthermore, we show different food web model approaches, their advantages and limitations. To effectively manage coastal ecosystems, we need both a detailed knowledge base of each trophic level and a holistic modeling approach for assessment and prediction of future scenarios on food web-scales. A new model approach with a seamless coupling of physical ocean models and food web models could provide a future tool for guiding ecosystem-based management.

Water availability is the dominant control of global terrestrial primary productivity with concurrent effects on evapotranspiration and ecosystem respiration, especially in water-limited ecosystems. Process-oriented ecosystem models are critical tools for understanding land-atmosphere exchanges and for up-scaling this information to regional and global scales. Thus, it is important to understand how ecosystem models simulate ecosystem fluxes under changing weather conditions. Here, we applied both time-series analysis and meta-analysis techniques to study how five ecosystem process-oriented models-simulated gross primary production (GPP), ecosystem respiration (Reco), and evapotranspiration (ET). Ecosystem fluxes were simulated for 3 years at a daily time step from four evergreen and three deciduous Mediterranean oak woodlands (21 site-year measurements; 105 site-year-simulations). Mediterranean ecosystems are important test-beds for studying the interannual dynamics of soil moisture on ecosystem mass and energy exchange as they experience cool, wet winters with hot, dry summers and are typically subject to drought. Results show data-model disagreements at multiple temporal scales for GPP, Reco, and ET at both plant functional types. Overall there was a systematic underestimation of the temporal variation of Reco at both plant functional types at temporal scales between weeks and months, and an overestimation at the yearly scale. Modeled Reco was systematically overestimated during drought for all sites, but daily GPP was systematically underestimated only for deciduous sites during drought. In contrast, daily estimates of ET showed good data-model agreement even during drought conditions. This meta-analysis brings attention to the importance of drought conditions for modeling purposes in representing forest dynamics in water-limited ecosystems. © 2013 Springer Science+Business Media New York.

Computationally complex systems models are needed to advance research and implement policy in theoretical and applied population biology. Difference and differential equations used to build lumped dynamic models (LDMs) may have the advantage of clarity, but are limited in their inability to include fine-scale spatial information and individual-specific physical, physiological, immunological, neural and behavioral states. Current formulations of agent-based models (ABMs) are too idiosyncratic and freewheeling to provide a general, coherent framework for dynamically linking the inner and outer worlds of organisms. Here I propose principles for a general, modular, hierarchically scalable, framework for building computational population models (CPMs) designed to treat the inner world of individual agents as complex dynamical systems that take information from their spatially detailed outer worlds to drive the dynamic inner worlds of these agents, simulate their ecology and the evolutionary pathways of their progeny. All the modeling elements are in place, although improvements in software technology will be helpful; but most of all we need a cultural shift in the way population biologists communicate and share model components and the models themselves, fit, test, refute, and refine models, to make the progress needed to meet the ecosystems management challenges posed by global change biology.

Two decades have passed since the initiation of the National Oceanic and Atmospheric Administration’s research program aimed at advancing the understanding of estuary and ocean ecology of United States West Coast Pacific salmon (Oncorhynchus spp.). In this review and prospectus, we summarize key findings from this program and describe a plan for transitioning it to better support Ecosystem-Based Management (EBM). While we focus on salmon research, our approach applies to research design generally. Our path forward involves increasing understanding of ecosystem processes to improve the dependability of scenario testing under novel conditions. Over the past two decades, we developed a conceptual model for how climate, predators, prey, fisheries, and human activities influence salmon. Knowledge gaps we identified from our conceptual model include limited understanding of salmon distributions, behavior, maturation dynamics, and population dynamics, and salmon interactions with predators, competitors, and prey during winter. We consider emerging risks and vulnerabilities facing salmon and propose analysis frameworks for evaluating them. Increased predator populations, coupled with climate change, pose increasing threats to West Coast salmon and will require new strategies and actions to mitigate their negative impacts. We propose research to support the development of decision-support tools to evaluate tradeoffs associated with alternative management strategies and to inform an adaptive ecosystem management system to improve the resilience of salmon populations and salmon-dependent fisheries.

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]

The representation of carbon allocation (CA) in ecosystem differs tremendously among models, resulting in diverse responses of carbon cycling and storage to global change. Several studies have highlighted discrepancies between empirical observations and model predictions, attributing these differences to problems of model structure. We analyzed the mathematical representation of CA in models using concepts from dynamical systems theory; we reviewed a representative sample of models of CA in vegetation and developed a model database within the Python package bgc-md. We asked whether these representations can be generalized as a linear system, or whether a more general framework is needed to accommodate nonlinearities. Some of the vegetation systems simulated with the reviewed models have a fixed partitioning of photosynthetic products, independent of environmental forcing. Vegetation is often represented as a linear system without storage compartments. Yet, other structures with nonlinearities have also been proposed, with important consequences on the temporal trajectories of ecosystem carbon compartments. The proposed mathematical framework unifies the representation of alternative CA schemes, facilitating their classification according to mathematical properties as well as their potential temporal behaviour. It can represent complex processes in a compact form, which can potentially facilitate dialog among empiricists, theoreticians, and modellers. [ABSTRACT FROM AUTHOR]

Aim: An emerging framework for tropical ecosystems states that fire activity is either "fuel build‐up limited" or "fuel moisture limited", that is, as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build‐up, or by dry season rainfall driving fuel moisture. Location: Pantropical savannas and grasslands. Time period: 2002–2016. Methods: We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how fuel moisture and fuel build‐up effect (accumulated rainfall during 6 and 24 months prior to the end of the burning season, respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships. Results: Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were fuel build‐up limited against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm/year, Africa at 800 mm/year and Australia at 1,000 mm/year of mean annual rainfall. Main conclusions: In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions—local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change. [ABSTRACT FROM AUTHOR]

• WUE increases with air temperature, peaking at ca. 10 ℃ and decreased thereafter. • Annual precipitation is positively correlated with WUE when < 1000 mm. • LAI and Ci/Ca are the two key factors dominating the spatial WUE patterns. • LAI positively affects WUE through biophysically constraining soil water evaporation. • LAI positively correlates with WUE within sites with low vapor pressure deficit. Ecosystem water use efficiency (WUE) is an important property for understanding the coupling between ecosystem carbon and water cycles and for constraining land models, whereas the mechanism of its spatial variations remains unclear. Here we proposed a framework to decompose ecosystem WUE into component processes, with which the physiological and non-physiological processes controlling ecosystem WUE were included. Further, with the data of measurements by eddy covariance at global Fluxnet sites, we investigated the spatial patterns and determinants of ecosystem WUE with the framework proposed. Our results showed that crop exhibited the highest value of WUE, followed by forest, grassland, wetland, and shrub. The cross-site difference in WUE was mainly determined by that of gross primary productivity (GPP) and the physiological component, i.e., the ratio of GPP to plant transpiration. Spatially, ecosystem WUE showed an overall pattern of increase with air temperature, peaking at ca. 10 ℃ and decreased thereafter. Mean annual precipitation was positively correlated with WUE when < 1000 mm. Moreover, leaf area index (LAI) and the ratio of ambient to intercellular CO 2 concentration at canopy level (C i / C a) were the two key factors dominating the spatial ecosystem WUE patterns. LAI affected ecosystem WUE mainly through biophysically controlling the fraction of soil water evaporation. Our findings highlight the importance of LAI and canopy level C i / C a on controlling the spatial variations in ecosystem WUE, which warrants more attentions and investigations in ecosystem models. [ABSTRACT FROM AUTHOR]

Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02–0.29 and root mean square errors ranging from 399–821 gC m−2yr−1. These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models.

The Southern California Coastal Ocean (SCCO), defined as the region inclusive of the Southern California Bight and the Santa Barbara Channel, from the shoreline to beyond the continental shelf, has significant anthropogenic injections of many materials through the air, rivers, runoff, outfalls, sediments, and marine spills. These inputs are superposed on a dynamic system of internal processes including water motions, biological production in the surface layers, particle sinking, dissolution, and scavenging. In spite of lengthy and continuing sequences of SCCO measurements at various scales, a synthetical understanding and an ability to make skillful predictions and comprehensive assessments of the fate and transport of these materials in the SCCO remains illusive. In many cases the interpretation of localized measurements lacks a regional context. Conversely, there is little information on the regional impacts of localized sources. It is clear that an integrated computational model of the most relevant processes is needed before these goals can be achieved. An integrated model will be of great value to environmental managers responsible for sewage discharges, storm water discharges, and coastal fisheries. The overall project objective is to develop, test, and use for practical purposes an integrated computational model of the physical, chemical, and biological processes relevant to the distributions of natural and anthropogenically injected materials in the Southern California.

Background: Studies that attempt to measure shifts in species distributions often consider a single species in isolation. However, understanding changes in spatial overlap between predators and their prey might provide deeper insight into how species redistribution affects food web dynamics. Predator–prey overlap metrics: Here, we review a suite of 10 metrics [range overlap, area overlap, the local index of collocation (Pianka's O), Hurlbert's index, biomass‐weighted overlap, asymmetrical alpha, Schoener's D, Bhattacharyya's coefficient, the global index of collocation and the AB ratio] that describe how two species overlap in space, using concepts such as binary co‐occurrence, encounter rates, spatial niche similarity, spatial independence, geographical similarity and trophic transfer. We describe the specific ecological insights that can be gained using each overlap metric, in order to determine which is most appropriate for describing spatial predator–prey interactions for different applications. Simulation and case study: We use simulated predator and prey distributions to demonstrate how the 10 metrics respond to variation in three types of predator–prey interactions: changing spatial overlap between predator and prey, changing predator population size and changing patterns of predator aggregation in response to prey density. We also apply these overlap metrics to a case study of a predatory fish (arrowtooth flounder, Atheresthes stomias) and its prey (juvenile walleye pollock, Gadus chalcogrammus) in the Eastern Bering Sea, AK, USA. We show how the metrics can be applied to understand spatial and temporal variation in the overlap of species distributions in this rapidly changing Arctic ecosystem. Conclusions: Using both simulated and empirical data, we provide a roadmap for ecologists and other practitioners to select overlap metrics to describe particular aspects of spatial predator–prey interactions. We outline a range of research and management applications for which each metric may be suited. [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]

Abstract Fisheries resources in the U.S. Gulf of Mexico (GoM) are under increasing pressure from both natural and anthropogenic stressors that have potentially broad effects on the ecosystem and introduce considerable uncertainty into management outcomes. To address these issues, more holistic, ecosystem-based tools are needed to inform decision-making. A Scoping Workshop with scientists, managers, and other stakeholders was held to identify and prioritize challenges in the GoM that could be addressed using ecosystem models, and how best to incorporate those models into the existing fisheries assessment and management framework. Challenges identified were associated with uncertainty in stock assessments, environmental stressors, multi-species reference points, invasive species, habitat effects, spatial management, and forage fisheries. Short-term priorities included those that address critical assumptions in stock assessments and inform imminent decision making, whereas long-term priorities were those associated with environmental stressors and novel management approaches. This information is intended to guide future ecosystem modeling efforts and help advance ecosystem based fisheries management in the region. Highlights • Fisheries in the GoM are affected by natural and anthropogenic stressors with potentially broad impacts on the ecosystem. • A workshop was held to identify key management challenges in the GoM that could be addressed using ecosystem models. • There is an urgent need to integrate ecosystem processes into stock assessments and inform single species catch limits. • Other challenges are associated with long-term environmental change and development of alternative management strategies. [ABSTRACT FROM AUTHOR]