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Quantifying ecosystem services in urban areas is complex. However, existing ecosystem service typologies and ecosystem modeling can provide a means towards understanding some key biophysical links between urban forests and ecosystem services. This project addresses broader concepts of sustainability by assessing the urban park system in Phoenix, Arizona’s hot urban climate. This project aims to quantify and demonstrate the multiple ecosystem services provided by Phoenix’s green infrastructure (i.e., urban park system), including its air pollution removal values, carbon sequestration and storage, avoided runoff, structural value, and the energy savings it provides for city residents. Modeling of ecosystem services of the urban park system revealed around 517,000 trees within the system, representing a 7.20% tree cover. These trees remove about 3630 tons (t) of carbon (at an associated value of $285,000) and about 272 t of air pollutants (at an associated value of $1.16 million) every year. Trees within Phoenix’s urban park system are estimated to reduce annual residential energy costs by $106,000 and their structural value is estimated at $692 million. The findings of this research will increase our knowledge of the value of green infrastructure services provided by different types of urban vegetation and assist in the future design, planning and management of green infrastructure in cities. Thus, this study has implications for both policy and practice, contributing to a better understanding of the multiple benefits of green infrastructure and improving the design of green spaces in hot arid urban climates around the globe.

Grape, olive, and wheat are traditional Mediterranean Basin crops that have immense cultural, economic, and ecological importance, and are the basis for producing wine, olive oil, and pasta and bread products. Of fruit crops, grape has the largest area and the highest economic importance globally. These traditional Mediterranean crop systems and related food products have global relevance, and yet globally, all regions with Mediterranean climate are especially vulnerable to climate change that threatens this Mediterranean bio-cultural heritage. However, how to analyze the complex tripartite ecological, economic, and social effects of climate change on these systems has been vexing and largely unexplored. Here we review how a bioeconomic approach using physiologically-based demographic models in the context of geographic information systems may be an important step in examining the complexity of these factors on grape. We show that with relatively modest data and funding, regional bioeconomic analysis of grape production under present weather and climate change is possible, and that management-relevant complexity can be included in a mechanistic way.

This viewpoint article examines Environmental Impact Assessment (EIA) practices in developed and transitioning nations, identifies weaknesses, and proposes a new quantitative approach. The literature indicates that there exists little to no standardization in EIA practice, transitioning nations rely on weak scientific impact analyses, and the establishment of baseline conditions is generally missing. The more fundamental issue is that the “receptor”-based approach leads to a qualitative and subjective EIA, as it does not adequately integrate the full measure of the complexity of ecosystems, ongoing project risks, and cumulative impacts. We propose the application of a new framework that aims to ensure full life cycle assessment of impacts applicable to any EIA process, within any jurisdictional context. System-Based EIA (SBEIA) is based on modeling to predict changes and rests on data analysis with a statistically rigorous approach to assess impacts. This global approach uses technologies and methodologies that are typically applied to characterize ecosystem structure and functioning, including remote sensing, modeling, and in situ monitoring. The aim of this approach is to provide a method that can produce quantifiable reproducible values of impact and risk and move EIA towards its substantive goal of sustainable development. The adoption of this approach would provide a better evaluation of economic costs and benefits for all stakeholders.

Soil carbon feedbacks to global change are uncertain, and the biological processes that govern soil organic matter decomposition are not resolved in current ecosystem models. Though it is recognized that microbial biodiversity influences decomposition rates, incorporating this relationship into ecosystem models is challenging because microbial communities are prohibitively diverse. It is likely necessary to distill microbial biodiversity by focusing on functional groups or ecological strategies. The ecological strategies that currently dominate the microbial ecology literature derive from macroecological theory, have clear weaknesses, and have had limited success when applied to predict soil carbon dynamics. Here, we present a new framework for soil microorganisms: Carbon Acquisition Ecological Strategies (CAES), and we outline a path toward incorporating microbial biodiversity into ecosystem models using this framework to enhance predictions of soil carbon feedbacks to global change. Because a microorganism's diet is central to its ecological niche and likely to covary with other ecologically significant traits, we posit that carbon acquisition may serve as a tractable foundation for developing ecological strategies. We describe four candidate ecological strategies for soil microorganisms: 1° decomposers that assimilate complex plant polymers, 2° decomposers that assimilate microbial necromass, passive consumers that assimilate dissolved organic carbon, and predatory microbes that assimilate live microbial biomass. These strategies are directly linked to soil carbon pools currently represented in ecosystem models and may provide a foundation for greater integration of microbial community dynamics into ecosystem models. [Display omitted] • Microbes have specialized traits to use distinct pools of soil organic carbon. • Carbon acquisition traits may serve as a strong foundation for ecological strategies. • Ecological strategies can help condense microbial diversity for inclusion in models. [ABSTRACT FROM AUTHOR]

End-to-end ecosystem modeling platforms, including OSMOSE, are key tools for informing ecosystem-based fisheries management (EBFM). End-to-end models ideally implement two-way interactions between model components, yet two-way interactions between high trophic level (HTL) functional groups and humans (fisheries managers and fishers) are currently missing in OSMOSE. We developed a management strategy evaluation (MSE) framework for OSMOSE, which allows for feedback between HTL functional groups and fisheries managers. This framework couples OSMOSE to a management procedure integrating decision rules and accounting for scientific uncertainty and the acceptable risk of overfishing. We applied the MSE framework to the OSMOSE model of the West Florida Shelf, so as to conduct an evaluation of total allowable catch (TAC) strategies for red grouper ( Epinephelus morio ) in a context of episodic events of natural mortality. Our simulations indicate that TAC strategies that assume higher scientific uncertainty and/or lower acceptable risk of overfishing result in higher biomass-related metrics for red grouper. However, the levels of scientific uncertainty and acceptable risk of overfishing impose a trade-off between biomass-related and catch-related metrics for red grouper. Our simulations also indicate that updating red grouper TAC more frequently in a context of episodic events of natural mortality does not have a large impact on biomass-related and catch-related metrics for red grouper and other functional groups. The MSE we conducted for red grouper is strategic, and its outcomes, which were obtained under a specific set of assumptions, must be considered preliminary. We discuss how future research could help enhance understanding of the possible impacts of TAC strategies for red grouper. The MSE framework designed for OSMOSE links the dynamics of HTL functional groups to that of fisheries managers, thereby allowing OSMOSE to be better suited for informing EBFM. This framework is an invaluable asset in assessing the performance of fisheries management strategies, but could also be used for other purposes, such as the evaluation of research monitoring programs. [ABSTRACT FROM AUTHOR]

Predicted increases in the frequency and intensity of droughts across the temperate biome have highlighted the need to examine the extent to which forests may differ in their sensitivity to water stress. At present, a rich body of literature exists on how leaf- and stem-level physiology influence tree drought responses; however, less is known regarding the dynamic interactions that occur belowground between roots and soil physical and biological factors. Hence, there is a need to better understand how and why processes occurring belowground influence forest sensitivity to drought. Here, we review what is known about tree species’ belowground strategies for dealing with drought, and how physical and biological characteristics of soils interact with rooting strategies to influence forest sensitivity to drought. Then, we highlight how a belowground perspective of drought can be used in models to reduce uncertainty in predicting the ecosystem consequences of droughts in forests. Finally, we describe the challenges and opportunities associated with managing forests under conditions of increasing drought frequency and intensity, and explain how a belowground perspective on drought may facilitate improved forest management. [ABSTRACT FROM AUTHOR]

In natural capital accounting, ecosystems are assets that provide ecosystem services to people. Assets can be measured using both physical and monetary units. In the international System of Environmental-Economic Accounting, ecosystem assets are generally valued on the basis of the net present value of the expected flow of ecosystem services. In this paper we argue that several additional conceptualisations of ecosystem assets are needed to understand ecosystems as assets, in support of ecosystem assessments, ecosystem accounting and ecosystem management. In particular, we define ecosystems’ capacity and capability to supply ecosystem services, as well as the potential supply of ecosystem services. Capacity relates to sustainable use levels of multiple ecosystem services, capability involves prioritising the use of one ecosystem service over a basket of services, and potential supply considers the ability of ecosystems to generate services regardless of demand for these services. We ground our definitions in the ecosystem services and accounting literature, and illustrate and compare the concepts of flow, capacity, capability, and potential supply with a range of conceptual and real-world examples drawn from case studies in Europe and North America. Our paper contributes to the development of measurement frameworks for natural capital to support environmental accounting and other assessment frameworks. [ABSTRACT FROM AUTHOR]

Renewable fuel standards in the US and elsewhere mandate the production of large quantities of cellulosic biofuels with low greenhouse gas ( GHG) footprints, a requirement which will likely entail extensive cultivation of dedicated bioenergy feedstock crops on marginal agricultural lands. Performance data for such systems is sparse, and non-linear interactions between the feedstock species, agronomic management intensity, and underlying soil and land characteristics complicate the development of sustainable landscape design strategies for low-impact commercial-scale feedstock production. Process-based ecosystem models are valuable for extrapolating field trial results and making predictions of productivity and associated environmental impacts that integrate the effects of spatially variable environmental factors across diverse production landscapes. However, there are few examples of ecosystem model parameterization against field trials on both prime and marginal lands or of conducting landscape-scale analyses at sufficient resolution to capture interactions between soil type, land use, and management intensity. In this work we used a data-diverse, multi-criteria approach to parameterize and validate the DayCent biogeochemistry model for upland and lowland switchgrass using data on yields, soil carbon changes, and soil nitrous oxide emissions from US field trials spanning a range of climates, soil types, and management conditions. We then conducted a high-resolution case study analysis of a real-world cellulosic biofuel landscape in Kansas in order to estimate feedstock production potential and associated direct biogenic GHG emissions footprint. Our results suggest that switchgrass yields and emissions balance can vary greatly across a landscape large enough to supply a biorefinery in response to variations in soil type and land-use history, but that within a given land base both of these performance factors can be widely modulated by changing management intensity. This in turn implies a large sustainable cellulosic biofuel landscape design space within which a system can be optimized to meet economic or environmental objectives. [ABSTRACT FROM AUTHOR]

Ocean acidification (OA) has the potential to restructure ecosystems due to variation in species sensitivity to the projected changes in ocean carbon chemistry. Ecological models can be forced with scenarios of OA to help scientists, managers, and other stakeholders understand how ecosystems might change. We present a novel methodology for developing estimates of species sensitivity to OA that are regionally specific, and applied the method to the California Current ecosystem. To do so, we built a database of all published literature on the sensitivity of temperate species to decreased pH. This database contains 393 papers on 285 species and 89 multi-species groups from temperate waters around the world. Research on urchins and oysters and on adult life stages dominates the literature. Almost a third of the temperate species studied to date occur in the California Current. However, most laboratory experiments use control pH conditions that are too high to represent average current chemistry conditions in the portion of the California Current water column where the majority of the species live. We developed estimates of sensitivity to OA for functional groups in the ecosystem, which can represent single species or taxonomically diverse groups of hundreds of species. We based these estimates on the amount of available evidence derived from published studies on species sensitivity, how well this evidence could inform species sensitivity in the California Current ecosystem, and the agreement of the available evidence for a species/species group. This approach is similar to that taken by the Intergovernmental Panel on Climate Change to characterize certainty when summarizing scientific findings. Most functional groups (26 of 34) responded negatively to OA conditions, but when uncertainty in sensitivity was considered, only 11 groups had relationships that were consistently negative. Thus, incorporating certainty about the sensitivity of species and functional groups to OA is an important part of developing robust scenarios for ecosystem projections. [ABSTRACT FROM AUTHOR]

Environmental policies employ metaphoric objectives such as ecosystem health, resilience and sustainable provision of ecosystem services, which influence corresponding sustainability assessments by means of normative settings such as assumptions on system description, indicator selection, aggregation of information and target setting. A heuristic approach is developed for sustainability assessments to avoid ambiguity and applications to the EU Marine Strategy Framework Directive (MSFD) and OSPAR assessments are presented. For MSFD, nineteen different assessment procedures have been proposed, but at present no agreed assessment procedure is available. The heuristic assessment framework is a functional-holistic approach comprising an ex-ante/ex-post assessment framework with specifically defined normative and systemic dimensions (EAEPNS). The outer normative dimension defines the ex-ante/ex-post framework, of which the latter branch delivers one measure of ecosystem health based on indicators and the former allows to account for the multi-dimensional nature of sustainability (social, economic, ecological) in terms of modeling approaches. For MSFD, the ex-ante/ex-post framework replaces the current distinction between assessments based on pressure and state descriptors. The ex-ante and the ex-post branch each comprise an inner normative and a systemic dimension. The inner normative dimension in the ex-post branch considers additive utility models and likelihood functions to standardize variables normalized with Bayesian modeling. Likelihood functions allow precautionary target setting. The ex-post systemic dimension considers a posteriori indicator selection by means of analysis of indicator space to avoid redundant indicator information as opposed to a priori indicator selection in deconstructive-structural approaches. Indicator information is expressed in terms of ecosystem variability by means of multivariate analysis procedures. The application to the OSPAR assessment for the southern North Sea showed, that with the selected 36 indicators 48% of ecosystem variability could be explained. Tools for the ex-ante branch are risk and ecosystem models with the capability to analyze trade-offs, generating model output for each of the pressure chains to allow for a phasing-out of human pressures. The Bayesian measure of ecosystem health is sensitive to trends in environmental features, but robust to ecosystem variability in line with state space models. The combination of the ex-ante and ex-post branch is essential to evaluate ecosystem resilience and to adopt adaptive management. Based on requirements of the heuristic approach, three possible developments of this concept can be envisioned, i.e. a governance driven approach built upon participatory processes, a science driven functional-holistic approach requiring extensive monitoring to analyze complete ecosystem variability, and an approach with emphasis on ex-ante modeling and ex-post assessment of well-studied subsystems. [ABSTRACT FROM AUTHOR]

Factors affecting population recovery from depletion are at the focus of wildlife management. Particularly, it has been debated how life-history characteristics might affect population recovery ability and productivity. Many exploited fish stocks have shown temporal changes towards earlier maturation and reduced adult body size, potentially owing to evolutionary responses to fishing. Whereas such life-history changes have been widely documented, their potential role on stock's ability to recover from exploitation often remains ignored by traditional fisheries management. We used a marine ecosystem model parameterized for Southeastern Australian ecosystem to explore how changes towards 'faster' life histories might affect population per capita growth rate r. We show that for most species changes towards earlier maturation during fishing have a negative effect (3-40% decrease) on r during the recovery phase. Faster juvenile growth and earlier maturation were beneficial early in life, but smaller adult body sizes reduced the lifetime reproductive output and increased adult natural mortality. However, both at intra- and inter-specific level natural mortality and trophic position of the species were as important in determining r as species longevity and age of maturation, suggesting that r cannot be predicted from life-history traits alone. Our study highlights that factors affecting population recovery ability and productivity should be explored in a multi-species context, where both age-specific fecundity and survival schedules are addressed simultaneously. It also suggests that contemporary life-history changes in harvested species are unlikely to increase their resilience and recovery ability. [ABSTRACT FROM AUTHOR]

Purpose: Improving land use assessment in life cycle assessment (LCA) is a priority. Recently, soil organic carbon (SOC) depletion has been proposed as a transformation and occupation midpoint indicator to estimate impacts on biotic production potential (BPP). SOC depletion is recommended by the European Union in the International Reference Life Cycle Data System (ILCD) Handbook as a land use indicator. There is a consensus method to calculate SOC depletion in LCA, and ILCD proposes a set of characterization factors (CFs), but these lack geographical discrimination. Methods: Our method of calculation for midpoint CFs follows Brandão and Milà i Canals (Int J Life Cycle Assess 18:1243-1252, ). We operationalize the method using SOC stocks from the LUCASOIL database of field measurements in Europe. We use potential natural vegetation (PNV) as the reference situation. CFs were calculated on a cell basis for 23 countries in Europe and grouped in three spatial scales (an administrative classification, NUTS II, and two biophysical classifications, ecoregion and climate region) according to soil type and land cover following a consensus map of cover classes. To evaluate the method's results, CFs were applied in a case study. Results and discussion: SOC stocks of European soils were obtained according to land use and soil type classes (excluding non-European Union countries) for the three spatial scales. A database of European transformation and occupation CFs is also presented and analyzed. The aggregation of CFs at biophysical scales (ecoregion and climate region) is similar, but NUTS II aggregation of CFs is problematic. The application of the CFs in the case study revealed significant differences compared to the outcome of using CFs collected from other land use models. Conclusions: This paper is the first operationalization using field measurements of an updated version of the ILCD-recommended model for land use impacts in LCA. We obtained CFs for SOC depletion in Europe that can be nested within CFs suggested by ILCD since our results possess better spatial resolution but are only for European Union countries. The case study application highlighted the need for inventories to improve the spatial resolution of the life cycle processes to match the detail of LCIA models. [ABSTRACT FROM AUTHOR]

Background: Ecosystem modeling applied to fisheries remains hampered by a lack of local information. Fishers’ knowledge could fill this gap, improving participation in and the management of fisheries. Methodology: The same fishing area was modeled using two approaches: based on fishers’ knowledge and based on scientific information. For the former, the data was collected by interviews through the Delphi methodology, and for the latter, the data was gathered from the literature. Agreement between the attributes generated by the fishers’ knowledge model and scientific model is discussed and explored, aiming to improve data availability, the ecosystem model, and fisheries management. Principal Findings: The ecosystem attributes produced from the fishers’ knowledge model were consistent with the ecosystem attributes produced by the scientific model, and elaborated using only the scientific data from literature. Conclusions/Significance: This study provides evidence that fishers’ knowledge may suitably complement scientific data, and may improve the modeling tools for the research and management of fisheries. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, grassland degradation has become a major environmental and economic problem, but little information is available on the response of grassland productivity to both climate change and grazing intensity. By developing a grazing module in a process-based ecosystem model, the dynamic land ecosystem model ( DLEM), we explore the roles of climate change, elevated CO2, and varying grazing intensities in affecting aboveground net primary productivity ( ANPP) across different grassland sites in Mongolia. Our results show that both growing season precipitation totals and average temperature exert important controls on annual ANPP across six sites over a precipitation gradient, explaining 65% and 45% of the interannual variations, respectively. Interannual variation in ANPP, measured as the ratio of standard deviation among years to long-term mean, increased from 9.5 to 18.9% to 23.9-32.5% along a gradient of high to low precipitation. Historical grazing resulted in a net reduction in ANPP across all sites ranging from 2% to 15.4%. Our results further show that grassland ANPP can be maintained at a grazing intensity of 1.0 and 0.5 sheep/ha at wet and dry sites, respectively, indicating that dry sites are more vulnerable to grazing compared to wet sites. In addition, precipitation use efficiency ( PUE) decreased while nitrogen use efficiency ( NUE) increased across a gradient of low to high precipitation. However, grazing resulted in a net reduction in both PUE and NUE by 47% and 67% across all sites. Our results indicate that seasonal precipitation totals, average temperatures and grazing are important regulators of grassland ANPP in Mongolia. These results have important implications for grassland productivity in semi-arid regions in Central Asia and beyond. [ABSTRACT FROM AUTHOR]

Predictions from forest ecosystem models are limited in part by large uncertainties in the current state of the land surface, as previous disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect. Likewise, future disturbances also present a challenge to prediction as their dynamics are episodic and complex and occur across a range of spatial and temporal scales. While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important. This study focuses on the impacts of these smaller disturbance events on the predictability of vegetation dynamics and carbon flux. Using data on vegetation structure collected for the same domain at two different times, i.e. “repeat lidar data”, we test high-resolution model predictions of vegetation dynamics and carbon flux across a range of spatial scales at an important tropical forest site at La Selva Biological Station, Costa Rica. We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha. We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances. The results of this study illustrate the strong impact fine-scale forest disturbances have on forest dynamics, ultimately limiting the spatial resolution of accurate model predictions. [ABSTRACT FROM AUTHOR]

Ecosystem responses to the increasing warming in recent decades across North America (NA) are spatially heterogeneous and partly uncertain. Here we examined the spatial and temporal variability of warming across different eco-regions of NA using long-term (1979–2010) climate data (North America Regional Reanalysis (NARR)) with 3-hourly time-step and 0.25° × 0.25° spatial resolution and run a comprehensive mathematical process model, ecosys to study the impacts of this variability in warming on gross primary productivity (GPP). In a site scale test of model results, annual GPP modeled for pixels which corresponded to the locations of 20 eddy covariance flux towers correlated well ( R 2 = 0.76) with annual GPP derived from the towers in 2005. At continental scale, long-term annual average modeled GPP correlated well (geographically weighed regression R 2 = 0.8) with MODIS GPP. GPP modeled in eastern temperate forests and most areas with lower mean annual air temperature ( T a ), such as those in northern forests and Taiga, increased due to early spring and late autumn warming observed in NARR and these eco-regions contributed 92% of the increases in NA GPP over the last three decades. However, modeled GPP declined in most southwestern regions of NA (accounting >50% of the ecosystems with declining GPP), due to water stress from rising T a and declining precipitation, implying that further warming and projected dryness in this region could further reduce NA carbon uptake. Overall, NA modeled GPP increased by 5.8% in the last 30 years, with a positive trend of +0.012 ± 0.01 Pg C yr −1 and a range of −1.16 to +0.87 Pg C yr −1 caused by interannual variability of GPP from the long-term (1980–2010) mean. This variability was the greatest in southwest of US and part of the Great Plains, which could be as a result of frequent El Niño–Southern Oscillation’ events that led to major droughts. [ABSTRACT FROM AUTHOR]

Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review, we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a 'theory of everything' for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy. [ABSTRACT FROM AUTHOR]

The quality of gridded weather and soil datasets is one of the main sources of uncertainty in modeling regional scale land-atmosphere carbon exchange. This uncertainty may be assessed by comparing net ecosystem productivity (NEP) modeled for selected grid cells with these datasets against NEP modeled at representative sites within that grid cell for which detailed site scale measurements of weather and soil are available. In this study, a comprehensive mathematical ecosystem model, ecosys , was used to simulate differences in NEP at six eddy covariance (EC) flux tower sites across North America caused by using inputs for weather and soil extracted from 0.25° × 0.25° gridded datasets for the grid cells within which the towers were located vs. those measured at each tower site during years with contrasting weather (cool vs. warm and wet vs. dry). NEP differences attributed to gridded vs. measured model inputs varied among sites when tested against EC-derived values. At some sites (e.g. coastal coniferous site in British Columbia), adverse impacts of summer warming on NEP under contrasting weather in 2001 (cooler) vs. 2004 (warmer) were accurately modeled with both inputs, giving R 2 with EC NEP values >0.80. At other sites, reduced accuracy in NEP modeled with gridded vs. measured inputs was attributed to shallower soil depth in the gridded soil database. This shallower depth caused site NEP in a boreal deciduous stand in Saskatchewan not to be well simulated in 2001 and 2003, the first and third years of a major drought in central North America, due to early soil drying. At yet other sites, reduced accuracy was attributed to overestimates of soil organic nitrogen (SON) calculated from general soil organic carbon (SOC):SON relationships used in the absence of SON values in the gridded soil database. These overestimates caused excessive productivity to be modeled with gridded inputs at a boreal black spruce site in Quebec with unusually large SOC:SON. Gridded weather and soil inputs that caused such differences in NEP would certainly affect regional and global carbon budget model estimates, and so need to be improved in future large scale model input datasets. [ABSTRACT FROM AUTHOR]

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation. [ABSTRACT FROM AUTHOR]

Context: Prediction of climate impacts on terrestrial ecosystems is limited by the complexity of the couplings between biosphere and atmosphere-what we define here as eco-climate. Critical transitions in ecosystem function and structure must be conceptualized, modeled, and ultimately predicted. Eco-climate system macrostate is a pattern of physical couplings between subsystems; each macrostate must be modeled differently because different physical processes are important. Critical transitions are less likely where the elasticity of macrostate is weak or absent. This motivates a fundamentally new complex systems approach. Objective: To model eco-climate macrostate, and its elasticity to seasonal climate forcing (air temperature and precipitation) and ecosystem biophysical state (phenophase). Methods: This Dynamical Process Network approach uses information flow to model an eco-climate system structure using timeseries observations from seven eddy-covariance tower sites in the United States. An aggregate power-law model estimates the elasticity of each location's macrostate to seasonal climate and phenophase. Results: Macrostate varies by both season and ecosystem type. Evergreen forests are highly elastic to air temperature and are more likely than agricultural or deciduous systems to experience state changes as the climate warms. Precipitation and phenophase elasticity is stronger in some agricultural, grassland, and deciduous forest systems. Conclusions: Different empirical model structures are needed based on season and location, to simulate ecosystem carbon dynamics and critical state transitions. Phenophase directly controls macrostate in some ecosystems. Flux data co-located with in situ ecological monitoring are essential for eco-climate model development and prediction using complex systems approaches. [ABSTRACT FROM AUTHOR]

A sequence of one-year combined state–parameter estimation experiments has been conducted in a North Atlantic and Arctic Ocean configuration of the coupled physical–biogeochemical model HYCOM-NORWECOM over the period 2007–2010. The aim is to evaluate the ability of an ensemble-based data assimilation method to calibrate ecosystem model parameters in a pre-operational setting, namely the production of the MyOcean pilot reanalysis of the Arctic biology. For that purpose, four biological parameters (two phyto- and two zooplankton mortality rates) are estimated by assimilating weekly data such as, satellite-derived Sea Surface Temperature, along-track Sea Level Anomalies, ice concentrations and chlorophyll-a concentrations with an Ensemble Kalman Filter. The set of optimized parameters locally exhibits seasonal variations suggesting that time-dependent parameters should be used in ocean ecosystem models. A clustering analysis of the optimized parameters is performed in order to identify consistent ecosystem regions. In the north part of the domain, where the ecosystem model is the most reliable, most of them can be associated with Longhurst provinces and new provinces emerge in the Arctic Ocean. However, the clusters do not coincide anymore with the Longhurst provinces in the Tropics due to large model errors. Regarding the ecosystem state variables, the assimilation of satellite-derived chlorophyll concentration leads to significant reduction of the RMS errors in the observed variables during the first year, i.e. 2008, compared to a free run simulation. However, local filter divergences of the parameter component occur in 2009 and result in an increase in the RMS error at the time of the spring bloom. [ABSTRACT FROM AUTHOR]

The food web of Lake Kinneret contains intraguild predation (IGP). Predatory invertebrates and planktivorous fish both feed on herbivorous zooplankton, while the planktivorous fish also feed on the predatory invertebrates. In this study, a complex mechanistic hydrodynamic-ecological model, coupled to a bioenergetics-based fish population model (DYCD-FISH), was employed with the aim of revealing IGP dynamics. The results indicate that the predation pressure of predatory zooplankton on herbivorous zooplankton varies widely, depending on the season. At the time of its annual peak, it is 10–20 times higher than the fish predation pressure. When the number of fish was significantly higher, as occurs in the lake after atypical meteorological years, the effect was a shift from a bottom-up controlled ecosystem, to the top-down control of planktivorous fish and a significant reduction of predatory and herbivorous zooplankton biomass. Yet, seasonally, the decrease in predatory-zooplankton biomass was followed by a decrease in their predation pressure on herbivorous zooplankton, leading to an increase of herbivorous zooplankton biomass to an extent similar to the base level. The analysis demonstrates the emergence of non-equilibrium IGP dynamics due to intra-annual and inter-annual changes in the physico-chemical characteristics of the lake, and suggests that IGP dynamics should be considered in food web models in order to more accurately capture mass transfer and trophic interactions.

• We developed a simplified mechanistic model of Pseudo-Nitzschia to disentangle the environmental factors leading to the production of the neurotoxin domoic acid. • Silicic acid limitation shows the strongest impact on domoic acid production. • Our simplified model can be embedded in complex ecosystem models to expand Pseudo-Nitzschia and domoic acid predictability across the U.S. Western Coast. Pseudo-nitzschia species are one of the leading causes of harmful algal blooms (HABs) along the western coast of the United States. Approximately half of known Pseudo-nitzschia strains can produce domoic acid (DA), a neurotoxin that can negatively impact wildlife and fisheries and put human life at risk through amnesic shellfish poisoning. Production and accumulation of DA, a secondary metabolite synthesized during periods of low primary metabolism, is triggered by environmental stressors such as nutrient limitation. To quantify and estimate the feedbacks between DA production and environmental conditions, we designed a simple mechanistic model of Pseudo-nitzschia and domoic acid dynamics, which we validate against batch and chemostat experiments. Our results suggest that, as nutrients other than nitrogen (i.e., silicon, phosphorus, and potentially iron) become limiting, DA production increases. Under Si limitation, we found an approximate doubling in DA production relative to N limitation. Additionally, our model indicates a positive relationship between light and DA production. These results support the idea that the relationship with nutrient limitation and light is based on direct impacts on Pseudo-nitzschia biosynthesis and biomass accumulation. Because it can easily be embedded within existing coupled physical-ecosystem models, our model represents a step forward toward modeling the occurrence of Pseudo-nitzschia HABs and DA across the U.S. West Coast. [ABSTRACT FROM AUTHOR]

Ecosystem modeling is a popular technique to incorporate the direct and indirect interactions of all ecosystem components within a simulation. However, these models are data-intensive. Relatively data-poor, deep-sea ecosystems (with depths greater than 200 m) have not received the same modeling effort as neritic zones. Nevertheless, deep-sea ecosystems are exposed to many of the same anthropogenic stressors as nearshore systems, suggesting that the development of ecosystem models is pertinent. A literature review was conducted to explore the global deep-sea modeling effort. More than 300 modeling publications were discovered that included the deep-sea as a component, either had a functional group or explicitly mentioned depths greater than 200 m. The annual rate of new publications increased after 2000, most likely because of the advances in modeling capabilities, the growth of ecosystem modeling as a practice, and the availability of new data. Only 39% of the publications mentioned a deep-sea component as a focal point in the model, indicating that although the deep-sea is incorporated in many models, its ecosystem services are not explicitly included. Models developed with the Ecopath with Ecosim framework accounted for 59% of the publications, suggesting that multi-model "ensemble" approaches are lacking, although different frameworks have been applied. Disparity exists among the locations of models where some regions had more than 40 publications (subarctic Atlantic Ocean), while some others had 0 publications. The initial modeling effort in a region generally characterized the trophic structure, while advanced efforts conducted scenarios of ecosystem responses to stressors (e.g., fisheries, climate change, and oil spills). In general, current modeling efforts are hindered by a paucity of life history information for several deep-sea species/functional groups, unavailability of time series, and frameworks that may not properly estimate the energetics and spatial utilization of deep-sea organisms. Due to the high degree of trophic connectivity in oceanic ecosystems, the refinement of deep-sea ecosystem models is necessary to understand and combat the synergistic effects of stressors and enact ecosystem-based resource management efforts. • Ecosystem models are useful in simulating complex ecosystems with synergistic stressors. • A systematic literature review was conducted for all ecosystem models inclusive of the deep sea. • 304 publications inclusive of the deep sea were found and the publication rate has increased since 2000. • The modeling effort is not uniformly distributed around the globe. • Factors limiting model development, efficacy, and implementation in management are discussed. [ABSTRACT FROM AUTHOR]

Fire is a dominant disturbance in temperate and boreal biomes, and increasing burned area with climate change may fundamentally alter forests. Improved information about how fire-induced changes to forests may feedback to affect subsequent burning at regional scales could inform forest management and climate-mitigation strategies. However, fire is simplistically represented in Earth System Models, and regional statistical fire models often assume sufficient fuels, contributing to uncertainty in future projections. To address this challenge, we developed the Dynamic Temperate and Boreal Fire and Forest-Ecosystem Simulator (DYNAFFOREST). DYNAFFOREST represents the hierarchical structuring of forests, from individual cohorts to continental extents, making it possible to simulate feedbacks between fire and forests at broad scales over decades to centuries. We parameterized DYNAFFOREST for the western United States of America and benchmarked simulations with observations. DYNAFFOREST recreated patterns of forest cover, structure, and downed fuels, and was capable of capturing average 20th-century fire activity. • Feedbacks between fires and forest are not well captured in broad-scale models. • DYNAFFOREST simulates temperate and boreal fire and forests as a coupled system. • The model captures forest and fire characteristics in the western United States of America. • DYNAFFOREST is a powerful framework for diagnosing the causes and consequences of increasing fire. [ABSTRACT FROM AUTHOR]

There is substantial interest in stopping and reversing the effects of subsidence in the Sacramento–San Joaquin Delta (Delta) where organic soils predominate. Also, the passage of California Assembly Bill 32 in 2006 created the potential to trade credits for carbon sequestered in wetlands on subsided Delta islands. The primary purpose of the work described here was to estimate future vertical accretion and understand processes that affect vertical accretion and carbon sequestration in impounded marshes on subsided Delta islands. Using a cohort-accounting model, we simulated vertical accretion from 4,700 calibrated years before present (BP) at a wetland area located within Franks Tract State Recreation Area (lat 38.059, long −121.611, hereafter, “Franks Wetland”)—a small, relatively undisturbed marsh island—and at the Twitchell Island subsidence-reversal demonstration project since 1997. We used physical and chemical data collected during the study as well as literature values for model inputs. Model results compared favorably with measured rates of vertical accretion, mass of carbon sequestered, bulk density and organic matter content. From 4,700 to model-estimated 350 years BP, the simulated rate of vertical accretion at Franks Wetland averaged about 0.12 cm yr-1, which is within the range of rates in tidal wetlands worldwide. Our model results indicate that large sediment inputs during the last 150 to 200 years resulted in a higher accretion rate of 0.3 cm yr -1. On Twitchell Island, greater organic inputs resulted in average vertical accretion rates as high as 9.2 cm yr -1. Future simulations indicate that the managed impounded marsh will accrete highly organic material at rates of about 3 cm yr -1. Model results coupled with GIS analysis indicate that large areas of the periphery of the Delta, if impounded and converted to freshwater marsh, could be restored to tidal elevations within 50 to 100 years. Most of the central Delta would require 50 to 250 years to be restored to projected mean sea level. A large portion of the western Delta could be restored to mean sea level within 50 to 150 years (large areas on Sherman, Jersey, and Bethel islands, and small areas on Bradford, Twitchell, and Brannan islands, and Webb Tract). We estimated that long-term carbon sequestration rates for impounded marshes such as the Twitchell Island demonstration ponds will range from 12 to 15 metric tons carbon ha-1 yr-1. Creation of impounded marshes on Delta islands can substantially benefit levee stability as demonstrated by cumulative force and hydraulic gradient calculations.

Evapotranspiration (ET) is a key component of the hydrologic cycle in terrestrial ecosystems and accurate description of ET processes is essential for developing reliable ecohydrological models. This study investigated the accuracy of ET prediction by the DRAINMOD-FOREST after its calibration/validation for predicting commonly measured hydrological variables. The model was tested by conducting an eight year simulation of drainage and shallow groundwater dynamics in a managed mid-rotation loblolly pine ( Pinus taeda L.) plantation located in the coastal plain of North Carolina, USA. Modeled daily ET rates were compared to those measured in the field using the eddy covariance technique. In addition, the wavelet transform and coherence analysis were used to compare ET predictions and measurements on the time–frequency domain. Results showed that DRAINMOD-FOREST accurately predicted annual and monthly ET after a successful calibration and validation using measured drainage rates and water table depth. The model under predicted ET on an annual basis by 2%, while the Nash–Sutcliffe coefficient of model predictions on a monthly basis was 0.78. Results from wavelet transform and coherence analysis demonstrated that the model reasonably captured the high power spectra of ET at an annual scale with significantly high model-data coherency. These results suggested that the calibrated DRAINMOD-FOREST collectively captured key factors and mechanisms controlling ET dynamics in the drained pine plantation. However, the global power spectrum revealed that the model over predicted the power spectrum of ET at an annual scale, suggesting the model may have under predicted canopy conductance during non-growing seasons. In addition, this study also suggested that DRAINMOD-FOREST did not properly capture the seasonal dynamics of ET under extreme drought conditions with deeper water table depths. These results suggested further refinement to parameters, particularly vegetation related, and structures of DRAINMOD-FOREST to achieve better agreement between ET predictions and measurements in the time–frequency domain. [ABSTRACT FROM AUTHOR]

The EcoTroph modeling approach was applied to five Mediterranean marine ecosystems to characterize their food webs and investigate their responses to several simulated fishing scenarios. First, EcoTroph was used to synthesize the outputs of five pre-existing heterogeneous Ecopath models in a common framework, and thus to compare different ecosystems through their trophic spectra of biomass, catch, and fishing mortalities. This approach contributes to our understanding of ecosystem functioning, from both ecological and fisheries perspectives. Then, we assessed the sensitivity of each ecosystem to fishery, using EcoTroph simulations. For the five ecosystems considered, we simulated the effects of increasing or decreasing fishing mortalities on both the biomass and the catch per trophic class. Our results emphasize that the Mediterranean Sea is strongly affected by the depletion of high trophic level organisms. Results also show that fisheries impacts, at the trophic level scale, differ between ecosystems according to their trophic structure and exploitation patterns. A top-down compensation effect is observed in some simulations where a fishing-induced decrease in the biomass of predators impacts their prey, leading to an increase in the biomass at lower trophic levels. The results of this comparative analysis highlight that ecosystems where top-down controls are observed are less sensitive to variations in fishing mortality in terms of total ecosystem biomass. This suggests that the magnitude of top-down control present in a system can affect its stability. [ABSTRACT FROM AUTHOR]

With a focus on resources of the Colorado River ecosystem below Glen Canyon Dam, the Glen Canyon Dam Adaptive Management Program has included a variety of experimental policy tests, ranging from manipulation of water releases from the dam to removal of non-native fish within Grand Canyon National Park. None of these field-scale experiments has yet produced unambiguous results in terms of management prescriptions. But there has been adaptive learning, mostly from unanticipated or surprising resource responses relative to predictions from ecosystem modeling. Surprise learning opportunities may often be viewed with dismay by some stakeholders who might not be clear about the purpose of science and modeling in adaptive management. However, the experimental results from the Glen Canyon Dam program actually represent scientific successes in terms of revealing new opportunities for developing better river management policies. A new long-term experimental management planning process for Glen Canyon Dam operations, started in 2011 by the U.S. Department of the Interior, provides an opportunity to refocus management objectives, identify and evaluate key uncertainties about the influence of dam releases, and refine monitoring for learning over the next several decades. Adaptive learning since 1995 is critical input to this long-term planning effort. Embracing uncertainty and surprise outcomes revealed by monitoring and ecosystem modeling will likely continue the advancement of resource objectives below the dam, and may also promote efficient learning in other complex programs. [ABSTRACT FROM AUTHOR]

505I.506II.506III.508IV.512V.513VI.514515References515 Summary: Fine roots acquire essential soil resources and mediate biogeochemical cycling in terrestrial ecosystems. Estimates of carbon and nutrient allocation to build and maintain these structures remain uncertain because of the challenges of consistently measuring and interpreting fine‐root systems. Traditionally, fine roots have been defined as all roots ≤ 2 mm in diameter, yet it is now recognized that this approach fails to capture the diversity of form and function observed among fine‐root orders. Here, we demonstrate how order‐based and functional classification frameworks improve our understanding of dynamic root processes in ecosystems dominated by perennial plants. In these frameworks, fine roots are either separated into individual root orders or functionally defined into a shorter‐lived absorptive pool and a longer‐lived transport fine‐root pool. Using these frameworks, we estimate that fine‐root production and turnover represent 22% of terrestrial net primary production globally – a c. 30% reduction from previous estimates assuming a single fine‐root pool. Future work developing tools to rapidly differentiate functional fine‐root classes, explicit incorporation of mycorrhizal fungi into fine‐root studies, and wider adoption of a two‐pool approach to model fine roots provide opportunities to better understand below‐ground processes in the terrestrial biosphere. [ABSTRACT FROM AUTHOR]