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Abstract Background Natural disturbance is a fundamental component of the functioning of tropical rainforests let to natural dynamics, with tree mortality the driving force of forest renewal. With ongoing global (i.e. land-use and climate) changes, tropical forests are currently facing deep and rapid modifications in disturbance regimes that may hamper their recovering capacity so that developing robust predictive model able to predict ecosystem resilience and recovery becomes of primary importance for decision-making: (i) Do regenerating forests recover faster than mature forests given the same level of disturbance? (ii) Is the local topography an important predictor of the post-disturbance forest trajectories? (iii) Is the community functional composition, assessed with community weighted-mean functional traits, a good predictor of carbon stock recovery? (iv) How important is the climate stress (seasonal drought and/or soil water saturation) in shaping the recovery trajectory? Methods Paracou is a large scale forest disturbance experiment set up in 1984 with nine 6.25 ha plots spanning on a large disturbance gradient where 15 to 60% of the initial forest ecosystem biomass were removed. More than 70,000 trees belonging to ca. 700 tree species have then been censused every 2 years up today. Using this unique dataset, we aim at deciphering the endogenous (forest structure and composition) and exogenous (local environment and climate stress) drivers of ecosystem recovery in time. To do so, we disentangle carbon recovery into demographic processes (recruitment, growth, mortality fluxes) and cohorts (recruited trees, survivors). Results Variations in the pre-disturbance forest structure or in local environment do not shape significantly the ecosystem recovery rates. Variations in the pre-disturbance forest composition and in the post-disturbance climate significantly change the forest recovery trajectory. Pioneer-rich forests have slower recovery rates than assemblages of late-successional species. Soil water saturation during the wet season strongly impedes ecosystem recovery but not seasonal drought. From a sensitivity analysis, we highlight the pre-disturbance forest composition and the post-disturbance climate conditions as the primary factors controlling the recovery trajectory. Conclusions Highly-disturbed forests and secondary forests because they are composed of a lot of pioneer species will be less able to cope with new disturbance. In the context of increasing tree mortality due to both (i) severe droughts imputable to climate change and (ii) human-induced perturbations, tropical forest management should focus on reducing disturbances by developing Reduced Impact Logging techniques.

Ecosystems continuously adapt to interacting environmental drivers that change over time. Consequently, the carbon balance of terrestrial ecosystem may presently still be affected by past anthropogenic disturbances (e.g., deforestation) and other environmental changes (e.g., climate change). However, even though such so‐called "legacy effects" are implicitly included in many carbon cycle modeling studies, they are typically not explicitly quantified and therefore scientists might not be aware of their long‐term importance. Here, we use the ecosystem model LPJ‐GUESS to quantify legacy effects for the 21st century and the respective contributions of the following environmental drivers: climate change, CO2 fertilization, land use change, wood harvest, nitrogen deposition, and nitrogen fertilization. According to our simulations, the combined legacy effects of historical (1850–2015) environmental changes result in a land carbon uptake of +126 Gt C over the future (2015–2099) period. This by far exceeds the impacts of future environmental changes (range −53 Gt C to +16 Gt C for three scenarios) and is comparable in magnitude to historical carbon losses (−154 Gt C). Legacy effects can mainly be attributed to ecosystems still adapting to historical increases in atmospheric CO2 (+65 Gt C) and nitrogen deposition (+33 Gt C), but long‐term vegetation regrowth following agricultural abandonment (+8 Gt C) and wood harvest (+19 Gt C) also play a role. The response of the biosphere to historical environmental changes dominates future terrestrial carbon cycling at least until midcentury. Legacy effects persist many decades after environmental changes occurred and need to be considered when interpreting changes and estimating terrestrial carbon uptake potentials. Key Points: Terrestrial ecosystems only slowly adapt to environmental changesMost of the modeled future (2015–2099) carbon uptake can be attributed to historical (1850–2015) rather than future environmental changesLegacy effects are mostly a result of CO2 fertilization and also due to historical nitrogen deposition, land use change, and wood harvest [ABSTRACT FROM AUTHOR]

Biofuel and bioenergy systems are integral to most climate stabilization scenarios for displacement of transport sector fossil fuel use and for producing negative emissions via carbon capture and storage (CCS). However, the net greenhouse gas mitigation benefit of such pathways is controversial due to concerns around ecosystem carbon losses from land use change and foregone sequestration benefits from alternative land uses. Here, we couple bottom-up ecosystem simulation with models of cellulosic biofuel production and CCS in order to track ecosystem and supply chain carbon flows for current and future biofuel systems, with comparison to competing land-based biological mitigation schemes. Analyzing three contrasting US case study sites, we show that on land transitioning out of crops or pasture, switchgrass cultivation for cellulosic ethanol production has per-hectare mitigation potential comparable to reforestation and severalfold greater than grassland restoration. In contrast, harvesting and converting existing secondary forest at those sites incurs large initial carbon debt requiring long payback periods. We also highlight how plausible future improvements in energy crop yields and biorefining technology together with CCS would achieve mitigation potential 4 and 15 times greater than forest and grassland restoration, respectively. Finally, we show that recent estimates of induced land use change are small relative to the opportunities for improving system performance that we quantify here. While climate and other ecosystem service benefits cannot be taken for granted from cellulosic biofuel deployment, our scenarios illustrate how conventional and carbon-negative biofuel systems could make a nearterm, robust, and distinctive contribution to the climate challenge. [ABSTRACT FROM AUTHOR]

San Francisco Bay (SFB) is a complex ecosystem that has been heavily impacted by human activities. It has experienced strong year-to-year variations in physical, chemical, and biological conditions, due to both natural climate variation and human activities. There is a need to investigate this long-term variation, with a focus on management practices and needs. The study aims to construct a coupled hydrodynamic and ecosystem model SCHISM/CoSiNE, to investigate interannual variability of chlorophyll and nutrient dynamics in SFB over the 10-year period of 2005–2014. The coupled SCHISM/CoSiNE model captures the long-term observations well. It shows that high chlorophyll concentrations in South Bay persist during warm months, while chlorophyll in North Bay is low and has strong interannual variation. Nutrient concentrations are higher in North Bay where large river outflow influences their distribution and interannual variation, and in lower South Bay. The model results show that phytoplankton blooms in Suisun Bay tend to occur when flow rates are between 100 and 250 m3/s and ammonium concentration is in the range of 1–2 mmol/m3. Model sensitivity experiments show that benthic grazing can potentially reduce phytoplankton biomass, and its effect on chlorophyll concentration is modulated by river outflow. We demonstrate the potential of this open-source model for exploring water quality conditions and options for river flow management of SFB. [ABSTRACT FROM AUTHOR]

The problems of transformation of Russia's land ecosystems amid climate change and anthropogenic impacts were discussed at a meeting of the Presidium of the Russian Academy of Sciences on December 10, 2019. Its participants recognized that the current condition of land ecosystems increases the environmental risks and economic consequences of such changes. At the same time, the discussion participants emphasized that the rational use of plant resources of the Russian Federation is the most important condition for the country's sustainable socioeconomic development; therefore, the academic community needs to develop effective measures to preserve them. [ABSTRACT FROM AUTHOR]

Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems change over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same. [ABSTRACT FROM AUTHOR]

Purpose: The present study assesses environmental performance of seafood production by demersal trawling in Tunisia (Gulf of Gabes) in order to analyze the contribution of each production stage to environmental impacts and to understand drivers of the impacts using life cycle assessment (LCA). Then a set of ecosystem quality indicators were determined using an ecosystem modeling tool, Ecopath with Ecosim (EwE), and were combined with LCA to increase the relevance of both tools' assessments when applied to fisheries. Methods: The approach consisted of conducting LCA and calculating ecosystem indicators to provide a complete assessment of trawling's environmental impacts and the ecosystem characteristics associated with seafood production. The functional unit for the LCA was set to 1 t of landed seafood, and system boundaries included several operational stages related to demersal trawling. Several ecosystem indicators from EwE were calculated. Demersal trawling in the exploited ecosystem of the Gulf of Gabes (southern Tunisia) was used as a case study to illustrate the applicability of the approach. Several management plans were simulated and their influence on environmental performance was assessed. Ecospace, the spatial module of EwE, was used to simulate management scenarios: establishment of marine protected areas, extension of the biological rest period, and decrease in the number of demersal trawlers. Results and discussion: LCA revealed that fuel consumption by fishing vessels, fuel production, and paint and antifouling production contributed most to environmental impacts. All management plans simulated decreased environmental impacts compared with the baseline scenario. The most effective management plan is extending the rest period, which increases demersal trawler yield and greatly decreases the PPR/catch of demersal trawlers. Conclusions: The method developed in this study is relevant for supplementing LCA of fisheries and potentially that of seafood production systems. It provides policy makers with practical information to help implement effective management plans in the context of an ecosystem approach to fisheries. [ABSTRACT FROM AUTHOR]

Leaf maintenance respiration (Rleaf,m) is a major but poorly understood component of the terrestrial carbon cycle (C). Earth systems models (ESMs) use simple sub‐models relating Rleaf,m to leaf traits, applied at canopy scale. Rleaf,m models vary depending on which leaf N traits they incorporate (e.g., mass or area based) and the form of relationship (linear or nonlinear). To simulate vegetation responses to global change, some ESMs include ecological optimization to identify canopy structures that maximize net C accumulation. However, the implications for optimization of using alternate leaf‐scale empirical Rleaf,m models are undetermined. Here we combine alternate well‐known empirical models of Rleaf,m with a process model of canopy photosynthesis. We quantify how net canopy exports of C vary with leaf area index (LAI) and total canopy N (TCN). Using data from tropical and arctic canopies, we show that estimates of canopy Rleaf,m vary widely among the three models. Using an optimization framework, we show that the LAI and TCN values maximizing C export depends strongly on the Rleaf,m model used. No single model could match observed arctic and tropical LAI‐TCN patterns with predictions of optimal LAI‐TCN. We recommend caution in using leaf‐scale empirical models for components of ESMs at canopy‐scale. Rleaf,m models may produce reasonable results for a specified LAI, but, due to their varied representations of Rleaf,mfoliar N sensitivity, are associated with different and potentially unrealistic optimization dynamics at canopy scale. We recommend ESMs to be evaluated using response surfaces of canopy C export in LAI‐TCN space to understand and mitigate these risks. Plain Language Summary: While we have good understanding of plant photosynthesis, its links to climate and leaf nitrogen and process models to estimate photosynthesis; the same is not true for respiration. Measurements of leaf respiration are used to calibrate simple respiration models, which are applied at canopy‐scale. Here we investigate the risks associated with using various alternate simple respiration models in the context of viewing plant canopies as economic structures that must produce more carbon through photosynthesis than they use in respiration and tissue construction. We model the carbon economy of canopies with measured properties (leaf coverage and nitrogen) in arctic and in the tropical ecosystems, comparing the results using three different respiration models. First, we note the respiration estimates vary greatly among the models, so the models are not consistent. Second, we show that the optimal canopy properties (those most economically successful) also depend strongly on the respiration model used. This means that the choice of respiration model will have significant effects on the predictions of canopy response, and therefore C cycling, under global change. Our research highlights the need for more robust, process modeling of respiration. Key Points: Estimates of annual canopy maintenance respiration, a globally important C flux, vary widely among models calibrated from leaf trait dataUsing an optimization framework, we show that the canopy properties maximizing C export depend strongly on the respiration model usedLeaf‐scale empirical models should be applied cautiously at the canopy‐scale, particularly in Earth system models with canopy optimization. [ABSTRACT FROM AUTHOR]

Soil temperature and moisture are important factors that control many ecosystem processes. However, interactions between soil thermal and hydrological processes are not adequately understood in cold regions, where the frozen soil, fire disturbance, and soil drainage play important roles in controlling interactions among these processes. These interactions were investigated with a new ecosystem model framework, the dynamic organic soil version of the Terrestrial Ecosystem Model, that incorporates an efficient and stable numerical scheme for simulating soil thermal and hydrological dynamics within soil profiles that contain a live moss horizon, fibrous and amorphous organic horizons, and mineral soil horizons. The performance of the model was evaluated for a tundra burn site that had both preburn and postburn measurements, two black spruce fire chronosequences (representing space-for-time substitutions in well and intermediately drained conditions), and a poorly drained black spruce site. Although space-for-time substitutions present challenges in model-data comparison, the model demonstrates substantial ability in simulating the dynamics of evapotranspiration, soil temperature, active layer depth, soil moisture, and water table depth in response to both climate variability and fire disturbance. Several differences between model simulations and field measurements identified key challenges for evaluating/improving model performance that include (1) proper representation of discrepancies between air temperature and ground surface temperature; (2) minimization of precipitation biases in the driving data sets; (3) improvement of the measurement accuracy of soil moisture in surface organic horizons; and (4) proper specification of organic horizon depth/properties, and soil thermal conductivity.

Vegetation dynamics play a crucial role in understanding the complex interactions between climate variability, ecosystem changes and human land use. In this study, we use the Hindcasting Ecosystems Model (HEMO) to reconstruct the paleo Normalized Difference Vegetation Index (paleo-NDVI), which provides valuable insights into the historical vegetation patterns. Our research focuses on the El Alto Ancasti mountain range, a region characterized by its rich environmental diversity, and archaeological evidence of pre-Hispanic agricultural village landscapes, showcasing the knowledge and management of diverse environments and their fluctuations. To reconstruct the paleo-NDVI, we employed a Neuronal Network trained with two datasets. First, we utilized NDVI data extracted from AVHRR/NOAA satellite images spanning from 1982 to 2015, representing 8 km pixels from the Global Inventory Modeling and Mapping Studies. Second, we incorporated growth ring chronologies from Northwest Argentina sourced from the International Tree Ring Data Bank, enabling the calibration of the NDVI-ring relationship. This methodology allowed us to reconstruct the paleo-NDVI back to 442 AD and to calculate anomalies. Our analysis revealed significant negative anomalies around 600 AD, coinciding with previous research conducted in the nearby Ambato valley. These anomalies may be attributed to the eruption of Tres Cruces volcano in Catamarca during that period. Additionally, extensive positive anomalies emerged from 1600 AD onwards, which align with anomalies detected in earlier studies. The eruption of Huaynaputina volcano in southern Peru in 1600 AD is a possible factor contributing to these positive anomalies. Ongoing investigations focusing on the paleoenvironment and human occupations in the El Alto-Ancasti area aim to provide further insights into how these detected changes could have influenced environmental management decisions. By integrating multidisciplinary approaches, we can better comprehend the complex relationship between vegetation dynamics, climatic events and human activities, ultimately enhancing our understanding of past environmental transformations and their implications for sustainable management strategies. • Use of tree rings and NDVI data for paleoenvironmental retrodictions by means of artificial neural networks (NN). • Retrodiction of paleo-NDVI up to 442DC in Alto Ancasti (Catamarca, Argentina) by means of artificial neural networks (NN). • Coherence between paleo-NDVI retrodiction of Sierra Alto Ancasti with that of the Ambato Valley. • Finding of relation between paleo-NDVI variations and climate forcings such as volcanic events and ENSO. • Evidence of interrelations between ecosystem dynamics and environmental management of past occupations. [ABSTRACT FROM AUTHOR]

Spatiotemporal simulation modeling is used in the context of ecosystem-based fisheries management to investigate different management options, including the size and allocation of marine protected and fisheries restricted areas. Here, we used ecospace to assess the effectiveness of existing and potential future spatiotemporal fishing restrictions in the heavily exploited Thermaikos Gulf, Greece for the years 2000–2025 (calibration period 2000–2016; projection period 2017–2025). ecospace combines temporal biomass and commercial catch data with spatial habitat and other environmental data, as well as species ecological preferences, feeding, and dispersal rates to depict changes in trophic interactions, biomasses, and commercial catches in time and space. ecospace simulations supported the empirical data demonstrating that fisheries restricted areas are effective tools for rebuilding the biomass of exploited stocks, with their size and location playing a significant role in the way that different organisms respond to protection. Nevertheless, our results suggested that in order to achieve the highest benefits of protection, fisheries restricted areas would need to be accompanied by a parallel reduction in total fishing effort, rather than a redistribution of fishing activities. Such redistribution would just move the pressure on the boundaries of protected areas, causing a local increase of commercial catches owing to the beneficial spillover effects of protection. One of the tested spatiotemporal restriction scenarios (MPA 5) suggests certain additional management measures on top of the existing restrictions for all four fishing fleets operating in the area. This scenario predicted a considerable increase in the biomass of key commercial and vulnerable species groups, including hake, flatfishes, anglerfish, sharks, and rays and skates, by the end of the simulation period in 2025. [ABSTRACT FROM AUTHOR]

• The systematic parameter sensitivity analysis and model calibration approach has been implemented into BEPS to calibrate parameters over 10 ecosystems of China. • The constraint of soil hydrological processes together with the physiological processes promotes better simulation of GPP over arid regions. • Higher productivity over the southern regions of China has been detected. Terrestrial ecosystems are the largest sink for carbon, and their ecosystem gross primary productivity (GPP) regulates variations in atmospheric carbon dioxide (CO 2) concentrations. Current process-based ecosystem models used for estimating GPP are subject to large uncertainties due to poorly constrained parameter values. In this study, we implemented a global sensitivity analysis (GSA) on parameters in the Boreal Ecosystem Productivity Simulator (BEPS) considering the parameters' second-order impacts. We also applied the generalized likelihood estimation (GLUE) method, which is flexible for a multi-parameter calibration, to optimize the GPP simulation by BEPS for 10 sites covering 7 plant functional types (PFT) over China. Our optimized results significantly reduced the uncertainty of the simulated GPP over all the sites by 17 % to 82 % and showed that the GPP is sensitive to not only the photosynthesis-related parameters but also the parameters related to the soil water uptake as well as to the energy balance. The optimized GPP across South China showed that the mix forest, shrub, and grass have a higher GPP and are more controlled by the soil water availability. This study showed that the GLUE method together with the GSA scheme could constrain the ecosystem model well when simulating GPP across multiple ecosystems and provide a reasonable estimate of the spatial and temporal distribution of the ecosystem GPP over China. We call for more observations from more sites, as well as data on plant traits, to be collected in China in order to better constrain ecosystem carbon cycle modeling and understand its response to climate change. [ABSTRACT FROM AUTHOR]

This study used the Biome Biogeochemical Cycles (Biome-BGC) process model to simulate boreal forest dynamics, compared the results with a variety of measured carbon content and flux data from two boreal chronosequences in northern Manitoba, Canada, and examined how model output was affected by water and nitrogen limitations on simulated plant production and decomposition. Vascular and nonvascular plant growth were modeled over 151 years in well-drained and poorly drained forests, using as many site-specific model parameters as possible. Measured data included (1) leaf area and carbon content from site-specific allometry data, (2) aboveground and belowground net primary production from allometry and root cores, and (3) flux data, including biometry-based net ecosystem production and tower-based net ecosystem exchange. The simulation used three vegetation types or functional groups (evergreen needleaf trees, deciduous broadleaf trees, and bryophytes). Model output matched some of the observed data well, with net primary production, biomass, and net ecosystem production (NEP) values usually (50–80% of data) within the errors of observed values. Leaf area was generally underpredicted. In the simulation, nitrogen limitation increased with stand age, while soil anoxia limited vascular plant growth in the poorly drained simulation. NEP was most sensitive to climate variability in the poorly drained stands. Simulation results are discussed with respect to conceptual issues in, and parameterization of, the Biome-BGC model.

Ecologists are increasingly using macrosystems approaches to understand population, community, and ecosystem dynamics across interconnected spatial and temporal scales. Consequently, integrating macrosystems skills, including simulation modeling and sensor data analysis, into undergraduate and graduate curricula is needed to train future environmental biologists. Through the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration) program, we developed four teaching modules to introduce macrosystems ecology to ecology and biology students. Modules combine high-frequency sensor data from GLEON (Global Lake Ecological Observatory Network) and NEON (National Ecological Observatory Network) sites with ecosystem simulation models. Pre- and post-module assessments of 319 students across 24 classrooms indicate that hands-on, inquiry-based modules increase students’ understanding of macrosystems ecology, including complex processes that occur across multiple spatial and temporal scales. Following module use, students were more likely to correctly define macrosystems concepts, interpret complex data visualizations and apply macrosystems approaches in new contexts. In addition, there was an increase in student’s self-perceived proficiency and confidence using both long-term and high-frequency data; key macrosystems ecology techniques. Our results suggest that integrating short (1–3 h) macrosystems activities into ecology courses can improve students’ ability to interpret complex and non-linear ecological processes. In addition, our study serves as one of the first documented instances for directly incorporating concepts in macrosystems ecology into undergraduate and graduate ecology and biology curricula.

This paper examines the structure and dynamics of the marine ecosystem surrounding Haida Gwaii (an archipelago in the southeastern Gulf of Alaska). Based on previous research, a set of improved mass-balanced models was constructed in Ecopath with Ecosim (EwE) to represent ecosystem states existing circa 1900, 1950, and currently. These models feature increased taxonomic and ecological resolution relative to their predecessors across trophic levels and size classes from zooplankton to whales. A more detailed representation of Pacific herring (Clupea pallasii), including its age structure, predators, and prey, was introduced to permit modeling of the ecosystem role of herring as a forage fish, as well as the ecological impacts of herring fisheries. Gross ecosystem structure and herring trophodynamics were compared across ecosystem states using size spectra and ecological indicators, including mixed trophic impacts (MTIs). The 1950 model was fitted to a comprehensive set of biomass and catch time series. Dynamic ecosystem simulations evaluated the influence of fishing, predation, other natural mortality, and primary productivity trends on ecosystem behavior since 1950, as well as the relative importance of top-down versus bottom-up forcing. Size spectra and ecosystem indicators suggest that the Haida Gwaii ecosystem has not undergone a radical structural shift since 1900, despite heavy exploitation of numerous marine mammals and fish. Moreover, MTIs show that herring constitutes an important mid-trophic level node in the food web, participating in complex interactions with many predators, prey, and competitors. Dynamic ecosystem simulations demonstrate that trends in fishing mortality, trophic interactions, and primary productivity (correlated with the Pacific Decadal Oscillation) are all necessary to explain historical Haida Gwaii ecosystem behavior. These interacting drivers yield a mosaic of top-down and bottom-up trophic control for trophic interactions involving herring and throughout the food web. Simulation results also suggest that production of several herring, salmon, and groundfish stocks may have recently become partially decoupled from primary productivity, perhaps due to changes in copepod guild composition. These results also indicate that a biodiversity decline and “fishing down the food web” occurred off Haida Gwaii since 1950. Finally, the fitted 1950 model provides a robust platform for dynamic ecosystem simulations.

Worldwide fisheries management has been undergoing a paradigm shift from a single-species approach to ecosystem approaches. In the United States, NOAA has adopted a policy statement and Road Map to guide the development and implementation of ecosystem-based fisheries management (EBFM). NOAA’s EBFM policy supports addressing the ecosystem interconnections to help maintain resilient and productive ecosystems, even as they respond to climate, habitat, ecological, and social and economic changes. Managing natural marine resources while taking into account their interactions with their environment and our human interactions with our resources and environment requires the support of ecosystem science, modeling, and analysis. Implementing EBFM will require using existing mandates and approaches that fit regional management structures and cultures. The primary mandate for managing marine fisheries in the United States is the Magnuson-Stevens Fishery Conservation and Management Act. Many tenets of the Act align well with the EBFM policy, however, incorporating ecosystem analysis and models into fisheries management processes has faced procedural challenges in many jurisdictions. In this paper, we review example cases where scientists have had success in using ecosystem analysis and modeling to inform management priorities, and identify practices that help bring new ecosystem science information into existing policy processes. A key to these successes is regular communication and collaborative discourse among modelers, stakeholders, and resource managers to tailor models and ensure they addressed the management needs as directly as possible.

Abstract Forests have a prominent role in carbon sequestration and storage. Climate change and anthropogenic forcing have altered the dominant characteristics of some forested ecosystems through changes to their disturbance regimes, particularly fire. Ecosystems that historically burned frequently, like pinelands in the southeastern United States, risk changes in their structure and function when the fire regime they require is altered. Although the carbon storage potential in an unburned southeastern U.S. forest would be larger, this scenario is unrealistic due to the likelihood of wildfire. Additionally, fire exclusion can have negative consequences on these forests health, biodiversity, and species endemism. There is a need, specifically for the southeast, to estimate carbon and species dynamics based on the differences between various fire regimes, and particularly the differences between prescribed fire and wildfire. These are important factors to consider given that prescribed fire is a common tool used in the southeast, and wildfires are ever more present. Field data from an experimental Pinus palustris (longleaf pine) forest of southwest Georgia were used to parametrize the forest landscape model LANDIS‐II. The model simulated how carbon and species dynamics differ under a fire exclusion, a prescribed fire, and multiple wildfire scenarios. All scenarios except fire exclusion resulted in net emissions to the atmosphere, but prescribed fire produced the least carbon emissions from fire and maintained the most stable aboveground biomass compared to wildfire scenarios. Removing fire for approximately a century was necessary to obtain an average stand‐level biomass greater than that of prescribed fire and net emissions less than that of prescribed fire. The prescribed fire scenario produced a longleaf pine‐dominated forest, the exclusion scenario converted to predominantly oak species Quercus virginiana (live oak), Q. stellata (post oak), and Q. margaretta (sand post oak), while scenarios with intermediate wildfire regimes supported a mix of other fire‐facilitator hardwoods and pine species, such as Q. incana (bluejack oak) and Pinus elliotti (slash pine). Overall, this study supports prescribed fire regimes in southeastern U.S. pinelands to both minimize carbon emissions and preserve native biodiversity.

Modeling tools that can demonstrate possible consequences of strategies designed to operationalize ecosystem-based fisheries management (EBFM) should be able to address tradeoffs over a wide suite of considerations representing the scope of marine management objectives. Coupled ecological-economic modeling, where models for ecological and economic subsystems are linked through their inputs and outputs, allows for quantification of such tradeoffs. Here, we link the harvest output from fishery management scenarios implemented in an end-to-end ecosystem model (Atlantis) to an input–output regional economic model for the Northeast United States to calculate changes in socio-economic indicators, including the consequences of management action for regional sales, wages, and employment. We implement three simple scenarios (maintain, decrease, or increase current fishing effort), and compare model-projected values for systematic and sector-specific indicators. Systematic indicators revealed different ecological and economic outcomes, with large ecological responses and clear tradeoffs among the catch and biomass of species groups. Economic indicators for the region responded similarly to fishery yield; however, changes in total sales did not match those in landed catch. Under increased fishing effort, a lower proportional increase in sales relative to total landed catch arose due to increased yield from lower value species groups. Average fisheries income changed little among scenarios, but was highest when effort was maintained at current levels, likely a reflection of fleet and catch stability. Our results serve to demonstrate that consequences of management may be felt disproportionately among species through the region and across different fisheries sectors. With our coupled modeling approach of passing Atlantis ecosystem model outputs to an input–output economic model, we were able to assess effects of fisheries management across a broader suite of indicators that have relevance for policymakers across multiple objectives.

Finding suitable places to establish a mussel farm is challenging, as many aspects like mussel growth, clearance effect and the risk of low oxygen conditions, have to be considered. We present a tailor-made approach, combining field experiments with a spatially explicit model tool, to support the planning process. A case study was set up in the German part of Szczecin (Oder) Lagoon (Baltic Sea), as it shows all typical eutrophication problems and has a strong need and high potential for nutrient retention measures. Farming zebra mussels (Dreissena polymorpha) is an innovative approach that utilizes a species which is often perceived as a pest. The practical applicability and water quality improvement potential was proven by a pilot farm. Combining the gained knowledge with the simulation model led to a cascade of mussel farm options that differ in purpose, location, and biomass. Placing a mussel farm in an enclosed bay resulted in a remarkable water quality improvement (Secchi Depth increased up to 2 m), but the effect stayed local, the growth was limited and the potential annual nutrient removal reached a threshold of ~30 t N and 2.8 t P. The same nutrient removal could be reached with much smaller farms in an open sea area, whereas the change of water transparency or bottom oxygen conditions were neglectable. A maximal nutrient removal potential of 1,750 t N and 160 t P per year was estimated, when nearly the entire German part of Szczecin Lagoon with mussel farms was used. This led to a strong reduction of phytoplankton and an increase of Secchi Depth, but also a rising risk of anoxia. Overall, all mussel farm options are only a supportive measure, but not sufficient to reach the Good Environmental Status demanded by the Water Framework Directive. At once, the nutrient export from Szczecin Lagoon to the open Baltic was reduced by up to 3,500 t N and 420 t P per year, making the large-scale mussel farm option also a potential measure within the Marine Strategy Framework Directive.

Technology evolution describes a change in a technology performance over time. The modeling of technology evolution is crucial for designers, entrepreneurs, and government officials to set reasonable R&D targets, invest in promising technology, and develop effective incentive policies. Scientists and engineers have developed several mathematical functions such as logistic function and exponential function (Moore’s Law) to model technology evolution. However, these models focus on how a technology evolves in isolation and do not consider how the technology interacts with other technologies. Here, we extend the Lotka-Volterra equations from community ecology to model a technology ecosystem with system, component, and fundamental layers. We model the technology ecosystem of passenger aircraft using the Lotka-Volterra equations. The results show limited trickle-down effect in the technology ecosystem, where we refer to the impact from an upper layer technology to a lower layer technology as a trickle-down effect. The limited trickle-down effect suggests that the advance of the system technology (passenger aircraft) is not able to automatically promote the performance of the component technology (turbofan aero-engine) and the fundamental technology (engine blade superalloy) that constitute the system. Our research warns that it may not be effective to maintain the prosperity of a technology ecosystem through government incentives on system technologies only. Decision makers should consider supporting the innovations of key component or fundamental technologies. [ABSTRACT FROM AUTHOR]

Highlights • Spatialized dynamic model linking the coupled model Eco3M-S/SYMPHONIE to OSMOSE. • The model was calibrated and evaluated with satellite, biomass, landings, and trophic data. • Outputs of the end-to-end OSMOSE-GoL model are similar to field and literature data. Abstract An end-to-end model named OSMOSE-GoL has been built for the Gulf of Lions, the main French Mediterranean fishing area. This spatialized dynamic model links the coupled hydrodynamic and biogeochemical model Eco3M-S/SYMPHONIE (LTL – low trophic level model) to OSMOSE (HTL – high trophic level model). It includes 15 compartments of living organisms, five from the LTL model (i.e. nanophytoplankton, microphytoplankton, nanozooplankton, microzooplankton and mesozooplankton) and ten from the HTL model (northern krill, southern shortfin squid, European pilchard, European anchovy, European sprat, Atlantic horse mackerel, Atlantic mackerel, blue whiting, European hake and Atlantic bluefin tuna). With the exception of northern krill and European sprat, all HTL species are commercially exploited and undergo fisheries mortality pressure. The modeled species represent more than 70% of annual catches in this area. This paper presents the parameterization, calibration and evaluation of this model with satellite data for phytoplankton and with biomass, landings, diet and trophic level data for HTL groups. For most species, the diets in output of OSMOSE-GoL are similar to field and literature data in terms of dominant prey groups and species. However, some differences were observed. Various reasons may explain the mismatch between the modeled diet and field data. Benthic prey sometimes observed in the stomach content of the HTL predators were not modeled in OSMOSE-GoL. Field studies were carried out at specific periods and locations, while our data concern the period 2001–2004 and the entire modeled domain. Inter- and intra-annual variations in spatial distribution and density of prey may also explain these differences. The model estimates trophic level values similar to those cited in the literature for all the HTL compartments. These values are also close to the trophic levels estimated by a previous Ecopath model for the same area and period. Even though some improvements are still possible, this model may already be of use to explore fishery or Marine Protected Areas scenarios for socio-ecosystem management issues. [ABSTRACT FROM AUTHOR]

Paleoclimate records identified a severe drought lasting approximately a decade on the Mongolian Plateau during the 2000s, the severity of which was only exceeded by a single drought during the last two millennia. Under high‐emission scenarios, arid and semiarid areas are projected to continue to experience a drying trend over the coming decades; therefore, understanding how ecosystems respond to long‐lasting drought has global implications. Here we used a process‐based ecosystem model to examine the interannual and intra‐annual variations in net ecosystem productivity in response to climate extremes across the Mongolian Plateau. We find that the recent‐decade drought caused Mongolian terrestrial ecosystems to shift from a carbon (C) sink to a C source, canceling 40% of climate‐induced C accumulation over the entire twentieth century. Our study details a shortened C sequestering season, increased summer C source, and accelerated C depletion during the 2000s drought. Plain Language Summary: Multiple lines of evidence have shown detrimental effects of drought events on ecosystem production and carbon dynamics, but it remains uncertain how arid and semiarid ecosystems respond to long‐lasting drought. Here we reveal that a severe drought during the first decade of the 2000s on the Mongolian Plateau considerably weakened C sequestration capacity and accelerated C depletion. This work has broad implications for understanding impacts of persistent droughts on terrestrial C dynamics as a drying trend, in many arid and semiarid areas, is projected to continue over the coming decades. Key Points: The recent decade (2000–2009) has been identified as one of the driest periods on the Mongolian Plateau over the most recent ~2,000 yearsThis long‐duration drought shortened the C sequestration season, increased the summer C source, and led to a net annual C releaseThe NEP amplitude in this dry decade increased due to reduced C uptake and increased C emissions, implying an accelerated C depletion [ABSTRACT FROM AUTHOR]

Understanding drivers of ecosystem structure and function is a pervasive goal in academic and applied research. We used 24 synthetic ecosystem-level indices derived from trophic models, and independently derived data for Net Primary Productivity, to investigate drivers of ecosystem structure and function for 43 marine ecosystems distributed in all oceans of the world and including coastal, estuaries, mid-ocean islands, open-ocean, coral reef, continental shelf, and upwelling ecosystems. Of these indices, ecosystem Biomass, Primary Production, Respiration, the ratio of Biomass to Total System Throughput (sum of total energy flow into and out of an ecosystem as well as between ecosystem components), the ratio of Production to Biomass, Residence Time (mean time that a unit of energy remains in the ecosystem), Average Trophic Level, and Relative Ascendency (index of organization and complexity of a food web) displayed relationships with measures of Net Primary Productivity (NPP). Across all ecosystems, relationships were stronger with seasonal and interannual variability of NPP as compared to mean NPP. Both measures of temporal variability were combined into multivariate predictive relationships for each ecosystem index, with r2 values ranging from 0.14 to 0.49 and Akaike's information criteria values from − 8.44 to 3.26. Our results indicate that despite large geographic and environmental differences, temporal variability of NPP is strongly linked to the structure and function of marine ecosystems. [ABSTRACT FROM AUTHOR]

Using end-to-end models for ecosystem-based management requires knowledge of the structure, uncertainty and sensitivity of the model. The Norwegian and Barents Seas (NoBa) Atlantis model was implemented for use in ‘what if’ scenarios, combining fisheries management strategies with the influences of climate change and climate variability. Before being used for this purpose, we wanted to evaluate and identify sensitive parameters and whether the species position in the foodweb influenced their sensitivity to parameter perturbation. Perturbing recruitment, mortality, prey consumption and growth by +/- 25% for nine biomass-dominating key species in the Barents Sea, while keeping the physical climate constant, proved the growth rate to be the most sensitive parameter in the model. Their trophic position in the ecosystem (lower trophic level, mid trophic level, top predators) influenced their responses to the perturbations. Top-predators, being generalists, responded mostly to perturbations on their individual life-history parameters. Mid-level species were the most vulnerable to perturbations, not only to their own individual life-history parameters, but also to perturbations on other trophic levels (higher or lower). Perturbations on the lower trophic levels had by far the strongest impact on the system, resulting in biomass changes for nearly all components in the system. Combined perturbations often resulted in non-additive model responses, including both dampened effects and increased impact of combined perturbations. Identifying sensitive parameters and species in end-to-end models will not only provide insights about the structure and functioning of the ecosystem in the model, but also highlight areas where more information and research would be useful—both for model parameterization, but also for constraining or quantifying model uncertainty. [ABSTRACT FROM AUTHOR]

We consider a discrete-time dynamical system generated by a nonlinear operator (with four real parameters a , b , c , d) of ocean ecosystem. We find conditions on the parameters under which the operator is reduced to a ℓ -Volterra quadratic stochastic operator mapping two-dimensional simplex to itself. We show that if c d (c + d) = 0 , then (under some conditions on a , b) this ℓ -Volterra operator may have up to three or a countable set of fixed points; if c d (c + d) ≠ 0 , then the operator has up to three fixed points. Depending on the parameters, the fixed points may be attracting, repelling or saddle points. The limit behaviors of trajectories of the dynamical system are studied. It is shown that independently on values of parameters and on initial (starting) point, all trajectories converge. Thus, the operator (dynamical system) is regular. We give some biological interpretations of our results. [ABSTRACT FROM AUTHOR]

Economic, environmental, and social advantages have been achieved over the years through byproducts and waste exchanges between industries. These Eco-Industrial Parks (EIPs) are touted to be ecologically similar, however when they are analyzed using Ecological Network Analysis (ENA) techniques it has been found that they do not successfully mimic analogous ecosystems. ENA coupled with average food webs characteristics are used here to create a bio-inspired design optimization for the water distribution network of the Kalundborg EIP in Denmark. The bio-inspired solution is compared to a cost-based solution to illustrate what the former can offer beyond a conventional approach. Both solutions similarly minimize freshwater consumption, however the bio-inspired solution has additional benefits that suggest a more sustainable and robust design, such as the ability to maintain network function in the event of a connection losses. The results suggest that consumption and cost reductions alone may not be the best optimization route. [ABSTRACT FROM AUTHOR]

The application of manure and mineral nitrogen (N) fertilizer, and livestock excreta deposition are the main drivers of nitrous oxide (N2O) emissions in agricultural systems. However, the magnitude and spatiotemporal variations of N2O emissions due to different management practices (excreta deposition and manure/fertilizer application) from grassland ecosystems remain unclear. In this study, we used the Dynamic Land Ecosystem Model to simulate the spatiotemporal variation in global N2O emissions and their attribution to different sources from both intensively managed (pasturelands) and extensively managed (rangelands) grasslands during 1961–2014. Over the study period, pasturelands and rangelands experienced a significant increase in N2O emissions from 1.74 Tg N2O‐N in 1961 to 3.11 Tg N2O‐N in 2014 (p < 0.05). Globally, pasturelands and rangelands were responsible for 54% (2.2 Tg N2O‐N) of the total agricultural N2O emissions (4.1 Tg N2O‐N) in 2006. Natural and anthropogenic sources contributed 26% (0.64 Tg N2O‐N/year) and 74% (1.78 Tg N2O‐N/year) of the net emissions, respectively. Across different biomes, pasturelands (i.e., C3 and C4) were the single largest contributor to N2O fluxes, accounting for 86% of the net global emissions from grasslands. Among different sources, livestock excreta deposition contributed 54% of the net emissions, followed by manure N (13%) and mineral N (7%) application. Regionally, southern Asia contributed 38% of the total emissions, followed by Europe (29%) and North America (16%). Our modeling study demonstrates that livestock excreta deposition and manure/fertilizer application have dramatically altered the N cycle in pasturelands, with a substantial impact on the climate system. Key Points: Natural and anthropogenic sources contributed 26% (0.64 Tg N2O‐N/year) and 74% (1.78 Tg N2O‐N/year) of the net N2O emissions, respectivelyPasturelands were the single largest contributor to N2O fluxes, accounting for 86% of the net N2O emissions (2.4 Tg N2O‐N/year)Among different sources, livestock excreta N deposition was the largest source of N2O contributing to 54% of the net N2O emissions [ABSTRACT FROM AUTHOR]

Different ecosystem models often provide contrasting predictions (model uncertainty), which is perceived to be a major challenge impeding their use to support ecosystem-based fisheries management (EBFM). The focus of this manuscript is to examine the extent of model disagreements which could impact management advice for EBFM in the central Baltic Sea. We compare how much three models (EwE, Gadget and a multispecies stock production model) differ in 1) their estimates of fishing mortality rates (Fs) satisfying alternative hypothetical management scenario objectives and 2) the outcomes of those scenarios in terms of performance indicators (spawning stock biomasses, catches, profits). Uncertainty in future environmental conditions affecting fish was taken into account by considering two seal population growth scenarios and two nutrient load scenarios. Differences in the development of the stocks, yields and profits exist among the models but the general patterns are also sufficiently similar to appear promising in the context of strategic fishery advice. Thus, we suggest that disagreements among the ecosystem models will not impede their use for providing strategic advice on how to reach management objectives that go beyond the traditional maximum yield targets and for informing on the potential consequences of pursuing such objectives. This is especially true for scenarios aiming at exploiting forage fish sprat and herring, for which the agreement was the largest among our models. However, the quantitative response to altering fishing pressure differed among models. This was due to the diverse environmental covariates and the different number of trophic relationships and their functional forms considered in the models. This suggests that ecosystem models can be used to provide quantitative advice only after more targeted research is conducted to gain a deeper understanding into the relationship between trophic links and fish population dynamics in the Baltic Sea. [ABSTRACT FROM AUTHOR]

ContentsSummary693I.Introduction693II.Comparison of various definitions and measurement techniques of minimum conductance694III.Cuticular conductance695IV.Contribution of stomata696V.Environmental and ecological variation in minimum conductance696VI.Use of minimum conductance in models698VII.Conclusions703Acknowledgements703References703 Summary: When the rate of photosynthesis is greatly diminished, such as during severe drought, extreme temperature or low light, it seems advantageous for plants to close stomata and completely halt water loss. However, water loss continues through the cuticle and incompletely closed stomata, together constituting the leaf minimum conductance (gmin). In this review, we critically evaluate the sources of variation in gmin, quantitatively compare various methods for its estimation, and illustrate the role of gmin in models of leaf gas exchange. A literature compilation of gmin as measured by the weight loss of detached leaves is presented, which shows much variation in this trait, which is not clearly related to species groups, climate of origin or leaf type. Much evidence points to the idea that gmin is highly responsive to the growing conditions of the plant, including soil water availability, temperature and air humidity – as we further demonstrate with two case studies. We pay special attention to the role of the minimum conductance in the Ball–Berry model of stomatal conductance, and caution against the usual regression‐based method for its estimation. The synthesis presented here provides guidelines for the use of gmin in ecosystem models, and points to clear research gaps for this drought tolerance trait. [ABSTRACT FROM AUTHOR]

Benthic—pelagic coupling plays a pivotal role in aquatic ecosystems but the effects of fishery driven interactions on its functioning has been largely overlooked. Disentangling the benthic—pelagic links including effects of mixed fisheries, however, needs sketching a whole description of ecosystem interactions using quantitative tools. A holistic food web model has been here developed in order to understand the interplay between the benthic-pelagic coupling and mixed fisheries in a Mediterranean system such as the Strait of Sicily. The reconstruction of the food web required review and integration of a vast set of local and regional biological information from bacteria to large pelagic species that were aggregated into 72 functional groups. Fisheries were described by 18 fleet segments resulting from combination of fishing gears and fishing vessel size. The input-output analysis on the food web of energy pathways allowed identifying effects of biological and fishery components. Results showed that the structure of the Strait of Sicily food web is complex. Similarly to other Mediterranean areas, the food web of the Strait of Sicily encompasses 4.5 trophic levels (TLs) with the highest TLs reached by bluefin tuna, swordfish and large hake and largely impacted by bottom trawling and large longline. Importantly, benthic-pelagic coupling is affected by direct and indirect impacts among groups of species, fleets and fleets-species through the whole trophic spectrum of the food web. Moreover, functional groups able to move on large spatial scales or life history of which is spent between shelf and slope domains play a key role in linking subsystems together and mediate interactions in the Mediterranean mixed fisheries. [ABSTRACT FROM AUTHOR]

We developed a Lagrangian larval dispersal model to estimate trajectories for eleven fish taxa inhabiting the Gulf of Mexico (GOM). Dispersal models are at family level resolution for Scaridae, Lutjanidae, Scombridae, Labridae, Ophichthidae, and Ophidiidae, at genus level resolution for Hemanthias , and at species level resolution for Trachurus lathami, Decapterus punctatus, Katsuwonus pelamis, and Euthynnus alleteratus. Hydrodynamics are provided by the West Florida Coastal Ocean Model (WFCOM). Larval samples are from the spring and fall SEAMAP ichthyoplankton surveys from 2007 to 2011. The Lagrangian model was run backwards/forwards in time from the sampling event to estimate spawning/settlement locations. Results were used to update larval dispersal dynamics in the GOM Atlantis 'end-to-end' ecosystem model for twelve functional groups. We compare dispersal and non-dispersal scenarios in the Gulf of Mexico Atlantis model and find differences in stock abundance and distribution of fish. This highlights that the abundance and distribution of fishery resources are sensitive to changing circulation patterns. This work takes an interdisciplinary approach to understanding larval dynamics and their impacts on ecosystems at the intersection of predictive statistical modeling, hydrodynamic modeling, and ecosystem modeling. • Synchronous data collection and modeling efforts are needed for place-based studies. • Both retention and export of larvae occur on the West Florida Shelf. • Spawning and settling sites reflect both ocean conditions and behavioral choices. • Including dispersal in ecosystem models can change the distribution of production. • Understanding larval movement can play a key role in adaptive management. [ABSTRACT FROM AUTHOR]

Changes of quantitative composition and trophic structure of the nekton community in the western Bering Sea are considered for the last decades and role of pacific salmons in dynamics of trophic flows is evaluated in dependence on their abundance using the ecosystem model Ecopath. Two models are developed that describe trophic structure of the community in two cases: 1) low biomass of salmons and high biomass of walleye pollock (in 1980s, the year 1986 is analyzed as an example) and 2) decreased pollock biomass and increasing salmon biomass (in 2000s, the year 2006 as an example). Besides, a hypothetical situation is modeled with the salmon biomass multiplied by 1.5 relative to its level in 2006. Significant decrease of pollock abundance between 1980s and 2000s caused twofold reduction of total food consumption by nekton species; the heightened consumption by salmons and squids in the 2000s compensated only a small part of this reduction. However, the tenfold increase of salmons biomass changed their main diets with lowering of the prey trophic level from amphipods and squids to euphausiids, copepods, and pteropods. Now the salmons are the only numerous predator group of the fourth trophic level in the upper pelagic layer of offshore waters in the western Bering Sea. Due to their high trophic plasticity, they can feed by wide range of prey belonged to 2-3rd trophic levels that supplies them by a large amount of food. Even in the modeled case of increasing of the salmons biomass in 1.5 times relative to the level of 2000s, the current level of forage resources is able to support their populations. There is concluded that carrying capacity of the western Bering Sea is excessively sufficient for pacific salmons in periods of their high abundance.

Across the Arctic, the net ecosystem carbon (C) balance of tundra ecosystems is highly uncertain due to substantial temporal variability of C fluxes and to landscape heterogeneity. We modeled both carbon dioxide (CO2) and methane (CH4) fluxes for the dominant land cover types in a ~100‐km2 sub‐Arctic tundra region in northeast European Russia for the period of 2006–2015 using process‐based biogeochemical models. Modeled net annual CO2 fluxes ranged from −300 g C m−2 year−1 [net uptake] in a willow fen to 3 g C m−2 year−1 [net source] in dry lichen tundra. Modeled annual CH4 emissions ranged from −0.2 to 22.3 g C m−2 year−1 at a peat plateau site and a willow fen site, respectively. Interannual variability over the decade was relatively small (20%–25%) in comparison with variability among the land cover types (150%). Using high‐resolution land cover classification, the region was a net sink of atmospheric CO2 across most land cover types but a net source of CH4 to the atmosphere due to high emissions from permafrost‐free fens. Using a lower resolution for land cover classification resulted in a 20%–65% underestimation of regional CH4 flux relative to high‐resolution classification and smaller (10%) overestimation of regional CO2 uptake due to the underestimation of wetland area by 60%. The relative fraction of uplands versus wetlands was key to determining the net regional C balance at this and other Arctic tundra sites because wetlands were hot spots for C cycling in Arctic tundra ecosystems. We modeled the regional carbon balance of sub‐Arctic tundra over a decade in a region with lakes, wetlands, and uplands using process‐based biogeochemical models. Interannual variability over the decade was relatively small in comparison with variability among the land cover types. Wetlands were hot spots for C cycling in this sub‐Arctic tundra ecosystem. Capturing the relative fraction of uplands versus wetlands was key to determining the net regional C balance at this and other Arctic tundra sites. [ABSTRACT FROM AUTHOR]

As demand for global food security increases and arable land decreases, utilization of marine ecosystems intensifies. In particular, coastal marine ecosystems which are easily accessible, highly productive, and often protected from large storm perturbations, make ideal areas for expansion in ocean food systems. Whether an ecosystem has been identified as a potential new site for food production or whether it has had long-established food production activities, proper management of resources is paramount for sustainability of both the ecosystem and food industry. One way to approach sustainable management is by taking a carrying capacity approach using food web modeling. Our study area focuses on a highly productive estuarine embayment that has had long-standing practices of both mussel aquaculture and artisanal fisheries since the 1960’s. Using ecological modeling with Ecopath software, we have calculated the current ecological carrying capacity and production carrying capacity of the system. Complementing this new analysis, we performed a review of ecosystem ecology indicators to compare relative carrying capacities. We found that current mussel aquaculture biomass (1718 t km −2 ) have exceeded ecological carrying capacity (773 t km −2 ) but it is still below production carrying capacity (2164 t km −2 ). Based on Ecopath maturity index and omnivory index output values, Ria de Arousa can be considered a mature ecosystem relative to other coastal and estuarine systems. This high level of maturity may be interpreted as the resilience of this system to cope with changes brought about over time by the intensification of food production and human activity. [ABSTRACT FROM AUTHOR]

Agricultural land management often involves trade-offs between ecosystem services (ES) and disservices (EDS). Balancing these trade-offs to achieve low-impact production of agricultural commodities requires rigorous approaches for quantifying and optimizing ES and EDS, reconciling biophysical constraints and different management objectives. In this study, we demonstrate a high-resolution spatially-explicit analysis of ES and EDS trade-offs for irrigated corn production systems in the South Platte River Basin, Colorado, USA, as a case study. The analysis integrated a biogeochemical model (DayCent) with optimization algorithms to assess trade-offs between multiple ES and EDS indicators, including net primary production, soil organic carbon, water use, nitrogen leaching, and greenhouse gas emissions. Our results show a large fraction of total potential system productivity (up to 21 Mg ha −1 year −1 ) can be realized at minimal ecosystem impacts through careful land management decisions. Our analysis also explores how different land management objectives imply different landscape configurations. [ABSTRACT FROM AUTHOR]

Abstract: Models representing exchange of carbon between the atmosphere and the terrestrial biosphere include a large variety of processes and mechanisms, and have increased in complexity in the last decades. These models are no exception of the simulation versus understanding conundrum previously articulated for models of the physical climate, which states that increasing detail in process representation in models, and the simulations they produce, hinders understanding of holistic system behavior. However, recent theoretical progress on the mathematical representation of the carbon cycle in ecosystems may help to provide a general framework for the qualitative understanding of models without compromising detail in process representation. Here we (1) briefly review recent ideas on the theory of transient dynamics of the terrestrial carbon cycle and its matrix representation, pointing out issues of interpretation, (2) show that these ideas can be further generalized in the mathematical concept of nonautonomous compartmental systems, and (3) provide thoughts on how this framework can be used to address a new set of questions in carbon cycle science. [ABSTRACT FROM AUTHOR]

Summary: This research investigates the impact of e‐commerce on energy consumption in all four sectors of the U.S. economy (commercial, industrial, residential, and transportation), using macroeconomic data from 1992 to 2015. These data capture all the development phases of e‐commerce, as well as direct and rebound effects in and across sectors. Empirical dynamic models (EDMs), a novel methodology in industrial ecology, are applied to the e‐commerce/energy relationship to accommodate for complex system behavior and state‐dependent effects. The results of these data‐driven methods suggest that e‐commerce increases energy consumption mainly through increases in the residential and commercial sectors. These findings contrast with extant research that focuses on transportation effects, which appear less prominent in this investigation. E‐commerce effects also demonstrate state dependence, varying over the magnitude of e‐commerce as a percentage of the total retail sector, particularly in commercial and transportation realms. Assuming these effects will continue in the future, the findings imply that policy makers should focus on mitigating the environmentally deteriorating effects of e‐commerce in the residential sector. However, this investigation cannot provide root causes for the uncovered e‐commerce effects. Robustness of the empirical findings, limitations of the novel EDM methodology, and respective avenues for future methodological and substantial research are discussed. [ABSTRACT FROM AUTHOR]

Photodegradation accelerates litter decay in arid grasslands where plant growth and litter decay are strongly controlled by precipitation and evapotranspiration. However, the effects of photodegradation on ecosystem C and N dynamics are not well understood. We examined the effects using an ecosystem biogeochemical model DayCent-UV with photodegradation explicitly represented and validated. The model was parameterized for a California grassland where photodegradation was documented to release CO2 from litter. The model was parameterized with an inverse modeling approach using an extensive data set of six years of daily observed carbon and water gas exchange (gross primary production, ecosystem respiration, and evapotranspiration), soil temperature, and soil moisture. DayCent-UV correctly simulated the seasonal patterns of the observed gas exchange and closely simulated the inter-annual variation in the gas exchange and biomass production rates. The simulations suggested that the inter-annual variation is driven more by actual evapotranspiration than by precipitation because a large portion of precipitation is lost as runoff during wet years. Photodegradation in DayCent-UV accelerated C and N cycling, decreasing system C and N by 9.2% and 9.5% and C and N residence times by 9.4% and 18.2%. Accelerated N cycling made a greater fraction of system N available for plants, increasing net N mineralization and plant production for a given amount of system N. Increased net N mineralization was due to decreased immobilization by microbes in the aboveground organic matter. Photodegradation did not alter the control on plant production by evapotranspiration. These results suggest that at the ecosystem level, the central effect of photodegradation is to suppress microbial activity. We conclude that photodegradation accelerates N cycling at the expense of microbes in this grassland, making it more efficient in supporting plant growth for a given amount of N in the system. [ABSTRACT FROM AUTHOR]