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Sustainable aviation fuel (SAF) production from lipids is a technologically mature approach for replacing conventional fossil fuel use in the aviation sector, and there is increasing demand for such feedstocks. The oilseed Brassica carinata (known as Ethiopian mustard or simply carinata) is a promising SAF feedstock that can be grown as a supplemental cash crop over the winter fallow season of various annual crop rotations in the Southeast US, avoiding land use changes and potentially achieving some of the soil carbon sequestration and ecosystem service benefits of winter cover crops. However, carinata may require more intensive management than traditional cover crops, potentially leading to additional soil greenhouse gas (GHG) emissions through increased carbon losses from soil tillage and nitrous oxide (N2O) emissions from nitrogen fertilizer application. In this work, the 2017 version of the process-based DayCent ecosystem model was used to establish initial expectations for the total regional SAF production potential and associated soil GHG emissions when carinata is integrated as a winter crop into the existing crop rotations across its current suitability range in southern Alabama, southern Georgia, and northern Florida. Using data from academic and industry carinata field trials in the region, DayCent was calibrated to reproduce carinata yield, nitrogen response, harvest index, and biomass carbon-to-nitrogen ratio. The resulting model was then used to simulate the integration of carinata every third winter across all 2.1 Mha of actively cultivated cropland in the study area. The model predicted regional average yields of 2.9–3.0 Mg carinata seed per hectare depending on crop management assumptions. That results in the production of more than two million Mg of carinata seed annually across the study area, enough to supply approximately one billion liters of SAF. Conventional management of carinata led to only modest increases in soil carbon storage that were largely offset by additional N2O emissions. Climate-smart management via adopting no-till carinata establishment or using poultry litter as a nitrogen source resulted in a substantial net soil GHG sink (0.23–0.31 Mg CO2e ha−1 y−1, or 0.24–0.32 Mg CO2e per Mg of seed produced) at the farms where carinata is cultivated.

Permafrost dynamics can drastically affect vegetation and soil carbon dynamics in northern high latitudes. Vegetation has significant influences on the energy balance of soil surface by impacting the short-wave radiation, long-wave radiation and surface sensible heat flux, affecting soil thermal dynamics, in turn, inducing vegetation shift, affecting carbon cycling. During winter, snow can also significantly impact soil temperature due to its insulative effect. However, these processes have not been fully modeled to date. To quantify the interactions between vegetation, snow, and soil thermal dynamics and their impacts on carbon dynamics over the circumpolar region (45–90° N), we revise a sophisticated ecosystem model to improve simulations of soil temperature profile and their influences on vegetation, ecosystem carbon pools and fluxes. We find that, with warmer soil temperature in winter and cooler soil temperature in summer simulated with the revised model considering vegetation shift and snow effects, the region will release 1.54 Pg C/year to the atmosphere for present-day and 66.77–87.95 Pg C in 2022–2100. The canopy effects due to vegetation shift, however, will get more carbon sequestered into the ecosystem at 1.00 Pg C/year for present day and 36.09–44.32 Pg C/year in 2022–2100. This study highlights the importance to consider the interactions between snow, vegetation shift and soil thermal dynamics in simulating carbon dynamics in the region.

We developed a new demographic vegetation model, BiomeE, to improve the representation of vegetation demographic dynamics and ecosystem biogeochemical cycles in the NASA Goddard Institute of Space Studies' ModelE Earth system model. This model includes the processes of plant growth, mortality, reproduction, vegetation structural dynamics, and soil carbon and nitrogen storage and transformations. The model combines the plant physiological processes of ModelE's original vegetation model, Ent, with minor adaptations to fit the new allometry and vegetation structure with the plant demographic and ecosystem nitrogen processes represented from Geophysical Fluid Dynamics Laboratory (GFDL)'s LM3-PPA. For global applications, we added a new set of plant functional types to represent global vegetation functional diversity, including trees, shrubs, and grasses, and a new phenology model to deal with seasonal changes in temperature and soil water availability. Competition for light and soil resources is individual based, which makes the modeling of transient compositional changes and vegetation succession possible. BiomeE will allow ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems. BiomeE simulates, with fidelity comparable to other models, the dynamics of vegetation and soil biogeochemistry, including leaf area index, vegetation structure (e.g., height, tree density, size distribution, crown organization), and ecosystem carbon and nitrogen storage and fluxes. Further, BiomeE will also allow for the simulations of transient vegetation dynamics and eco evolutionary optimal community assemblage in response to past and future climate changes by incorporating core ecological processes, including demography, competition, and community assembly. [ABSTRACT FROM AUTHOR]

The 2014–2016 Northeast Pacific marine heatwave (MHW) induced the warmest 3‐year period on record in the California Current Ecosystem. We tested whether larval fish assemblage structure, phenology, and diversity dynamics were comparable to past warming events from 1951 to 2013. First, we hypothesized, based on past observations of biological effect of warming, that mesopelagic species with southern distributions relative to southern California and Pacific sardine Sardinops sagax (a coastal pelagic species) would increase during the MHW while northern mesopelagics and northern anchovy Engraulis mordax (coastal pelagic) abundances would decline. Similar to past warming, southern mesopelagics increased and northern mesopelagics decreased. Unexpectedly, however, a common southern mesopelagic, Mexican lampfish Triphoturus mexicanus, was approximately three times more abundant than the previous annual high. Furthermore, whereas sardine abundance did not increase, larval anchovy abundance rose to near‐record highs in summer 2016. Second, we hypothesized that fishes would spawn earlier during the MHW. Fishes did not spawn in an earlier season within a year, but five of six southern mesopelagic taxa spawned earlier than typical within winter and spring. Third, we predicted that species richness would increase moderately due to an influx of southern and exodus of northern species. Richness, however, was very high in all seasons and the highest ever during the summer as multiple species with primarily southern distributions were recorded spawning for the first time in southern California. The richness of northern species was also unexpectedly high during the MHW. Northern species likely persisted in the study area because in addition to the warm water, pockets of cold water were consistently present. If, as predicted, conditions similar to the MHW become more common as oceans warm, this unique and largely unexpected combination of fishes may reflect future biological conditions. [ABSTRACT FROM AUTHOR]

Although quantifying trophic interactions is a critical path to understanding and forecasting ecosystem functioning, fitting trophic models to field data remains challenging. It requires flexible statistical tools to combine different sources of information from the literature and fieldwork samples. We present EcoDiet, a hierarchical Bayesian modeling framework to simultaneously estimate food‐web topology and diet composition of all consumers in the food web, by combining (1) a priori knowledge from the literature on both food‐web topology and diet proportions; (2) stomach content analyses, with frequencies of prey occurrence used as the primary source of data to update the prior knowledge on the topological food‐web structure; (3) and biotracers data through a mixing model (MM). Inferences are derived in a Bayesian probabilistic rationale that provides a formal way to incorporate prior information and quantifies uncertainty around both the topological structure of the food web and the dietary proportions. EcoDiet was implemented as an open‐source R package, providing a user‐friendly interface to execute the model, as well as examples and guidelines to familiarize with its use. We used simulated data to demonstrate the benefits of EcoDiet and how the framework can improve inferences on diet matrix by comparison with classical network MM. We applied EcoDiet to the Celtic Sea ecosystem, and showed how combining multiple data types within an integrated approach provides a more robust and holistic picture of the food‐web topology and diet matrices than the literature or classical MM approach alone. EcoDiet has the potential to become a reference method for building diet matrices as a preliminary step of ecosystem modeling and to improve our understanding of prey–predator interactions. [ABSTRACT FROM AUTHOR]

Small cetaceans (dolphins, porpoises, and small toothed whales) occur from the poles to the tropics, and from freshwater habitats to the open ocean. Most ecological research has focused on the influence of abiotic factors on the abundance, distribution, and behavior of these species. The ecological impacts of small cetaceans on communities and ecosystems remain poorly quantified. Their movement patterns, often high local and regional abundances across a range of ecosystems, and high metabolic rates suggest that small cetaceans could have large effects on ecosystem structure, dynamics, and function through a variety of mechanisms. These include top-down (e.g., direct predation and risk effects) and bottom-up effects (e.g., translocation of nutrients within and across ecosystems), but also behavior-mediated processes where these predators can facilitate access to resources to other predators or modify the physical properties of habitat (e.g., bioturbation). Most small cetaceans can be consumed by other marine predators, particularly killer whales (Orcinus orca) and large sharks. Although consumption rates of small cetaceans can be high, there is a paucity of information on their effects on population sizes or behaviors of their prey. Mass-balance ecosystem models suggest that small cetaceans may impact the populations of short-lived prey species (particularly fish and cephalopods), but other factors (e.g., eutrophication and fisheries) also affect ecosystem functioning and population trends. Delphinids can also mediate the translocation and recycling of limiting nutrients between spatially distinct ecosystems on a diel basis. Despite intriguing possibilities, large gaps remain in our understanding of the roles and importance of small cetaceans in aquatic ecosystems, both marine and freshwater.

Calcifying marine phytoplankton-coccolithophores- are some of the most successful yet enigmatic organisms in the ocean and are at risk from global change. To better understand how they will be affected, we need to know "why" coccolithophores calcify. We review coccolithophorid evolutionary history and cell biology as well as insights from recent experiments to provide a critical assessment of the costs and benefits of calcification. We conclude that calcification has high energy demands and that coccolithophores might have calcified initially to reduce grazing pressure but that additional benefits such as protection from photodamage and viral/bacterial attack further explain their high diversity and broad spectrum ecology. The cost-benefit aspect of these traits is illustrated by novel ecosystem modeling, although conclusive observations remain limited. In the future ocean, the trade-off between changing ecological and physiological costs of calcification and their benefits will ultimately decide how this important group is affected by ocean acidification and global warming.

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 (R2=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 R2=0.8) with MODIS GPP. GPP modeled in eastern temperate forests and most areas with lower mean annual air temperature (Ta), 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 Ta 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±0.01PgCyr−1 and a range of −1.16 to +0.87PgCyr−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.

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 R2 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.

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 R2 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.

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.

Ecosystem process models can be used to predict forest response to disturbances at a range of scales. Selection of the spatial class of model should depend on the scale of the research or management question, and model type should depend on the ecosystem attributes of interest. In some cases, multiple classes of models could be used to address a single research question, with evaluations at each scale having potential benefits and drawbacks. This study examines two classes of models relative to how fire return intervals impact carbon and species dynamics in a southeastern U.S. pineland. A model that can be run as a global class model (ED) and a landscape class model (LANDIS‐II) were parameterized with species inventory data from an experimental Pinus Palustris (longleaf pine) forest in southwest Georgia, and simulations were calibrated with literature values, then validated with eddy‐covariance data from the study site. A variety of fire scenarios that included prescribed fire with a 2‐yr return interval, fire exclusion, and three wildfire scenarios (20‐, 50‐, and 100‐yr return intervals) were used for model runs. Results were compared and evaluated with regard to ecosystem carbon and species dynamics. Both models illustrated that prescribed fire provided the greatest carbon sequestration potential and most stable aboveground biomass through time when compared to the wildfire scenarios. The fire exclusion scenario for LANDIS‐II was the only scenario where prescribed fire did not provide the greatest carbon sequestration potential. However, fire exclusion on the order of centuries was a condition of this outcome and the occurrence of such long fire‐free periods is considered unrealistic in this fire‐prone landscape. Differences between models were primarily the result of the underlying characteristics of each model class, namely the spatial resolution and number of species included. In the end, two vastly different scale models supported the conclusion that high frequency prescribed fire in southeastern U.S. pinelands stabilizes carbon and maintains species composition in an ecosystem that is a known ecological hotspot. [ABSTRACT FROM AUTHOR]

Invasive species exert a strong impact on the trophic structure and functioning of the ecosystem they occupy. A modeling software globally used to assess these impacts is Ecopath with Ecosim. This study measures the effects of two invasive species, African catfish Clarias gariepinus, in the food web of the Karapuzha reservoir ecosystem in India. The findings show that the two invasive species directly and indirectly influence several ecosystem elements. The high level of niche overlap (92%) and strong competition for similar resources was exhibited by C. gariepinus. The adverse effects of African catfish on all fish and crustaceans in the reservoir were revealed by mixed trophic impact. Oreochromis mossambicus impacts were both positive and negative. It had a beneficial impact on its predator aquatic birds and was negative towards minnows. This study will help fishery managers to make informed decisions for ecosystem-based fishery management of fish resources. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract Increasing vapor pressure deficit (VPD) increases atmospheric demand for water. While increased evapotranspiration (ET) in response to increased atmospheric demand seems intuitive, plants are capable of reducing ET in response to increased VPD by closing their stomata. We examine which effect dominates the response to increasing VPD: atmospheric demand and increases in ET or plant response (stomata closure) and decreases in ET. We use Penman‐Monteith, combined with semiempirical optimal stomatal regulation theory and underlying water use efficiency, to develop a theoretical framework for assessing ET response to VPD. The theory suggests that depending on the environment and plant characteristics, ET response to increasing VPD can vary from strongly decreasing to increasing, highlighting the diversity of plant water regulation strategies. The ET response varies due to (1) climate, with tropical and temperate climates more likely to exhibit a positive ET response to increasing VPD than boreal and arctic climates; (2) photosynthesis strategy, with C3 plants more likely to exhibit a positive ET response than C4 plants; and (3) plant type, with crops more likely to exhibit a positive ET response, and shrubs and gymniosperm trees more likely to exhibit a negative ET response. These results, derived from previous literature connecting plant parameters to plant and climate characteristics, highlight the utility of our simplified framework for understanding complex land‐atmosphere systems in terms of idealized scenarios in which ET responds to VPD only. This response is otherwise challenging to assess in an environment where many processes coevolve together.

Abstract Ecosystem process models can be used to predict forest response to disturbances at a range of scales. Selection of the spatial class of model should depend on the scale of the research or management question, and model type should depend on the ecosystem attributes of interest. In some cases, multiple classes of models could be used to address a single research question, with evaluations at each scale having potential benefits and drawbacks. This study examines two classes of models relative to how fire return intervals impact carbon and species dynamics in a southeastern U.S. pineland. A model that can be run as a global class model (ED) and a landscape class model (LANDIS‐II) were parameterized with species inventory data from an experimental Pinus Palustris (longleaf pine) forest in southwest Georgia, and simulations were calibrated with literature values, then validated with eddy‐covariance data from the study site. A variety of fire scenarios that included prescribed fire with a 2‐yr return interval, fire exclusion, and three wildfire scenarios (20‐, 50‐, and 100‐yr return intervals) were used for model runs. Results were compared and evaluated with regard to ecosystem carbon and species dynamics. Both models illustrated that prescribed fire provided the greatest carbon sequestration potential and most stable aboveground biomass through time when compared to the wildfire scenarios. The fire exclusion scenario for LANDIS‐II was the only scenario where prescribed fire did not provide the greatest carbon sequestration potential. However, fire exclusion on the order of centuries was a condition of this outcome and the occurrence of such long fire‐free periods is considered unrealistic in this fire‐prone landscape. Differences between models were primarily the result of the underlying characteristics of each model class, namely the spatial resolution and number of species included. In the end, two vastly different scale models supported the conclusion that high frequency prescribed fire in southeastern U.S. pinelands stabilizes carbon and maintains species composition in an ecosystem that is a known ecological hotspot.

In 2020, Brazil was the seventh largest emitter of GHG (greenhouse gases), releasing ~2.16 GtCO2e (gigatons of carbon dioxide equivalent) into the atmosphere. Activities related to land use contributed approximately 73% of national emissions in that year. Considering that pastures represent the primary land use in the country, occupying approximately 20% of the territory, the mapping and monitoring of C stocks in these areas is essential to determine their contribution to national emissions. In this study, based on the integrated use of the CENTURY model, georeferenced databases, and the R environment, we mapped and analyzed, for the first time, the C stocks dynamics associated with the pasture areas of the Cerrado biome between 2000 and 2019. The average C stocks in the soil (0–20 cm) and in the aboveground biomass estimated by modeling were ~31 MgC·ha−1 and ~4 MgC·ha−1, respectively, values close to those observed in the literature for the region. Furthermore, the model results corresponded to the edaphic patterns of the region, with the highest average estimated C stocks in Cambisols (~34 MgC·ha−1) and the lowest in Neosols (~29 MgC·ha−1). The temporal dynamics of soil C stocks in these areas are directly related to the age of the pastures. In fact, stocks tend to be reduced in recently converted areas and stabilized in areas that have been under this land use for a longer time (≥30 years). As a result, a loss of ~103 MtC (millions of tons of carbon) was estimated in the Cerrado pasture soils in twenty years. The mapping and monitoring of C stocks in this land use type through approaches such as the one presented in this study is essential to support the Brazilian government’s efforts to mitigate C emissions.

Premise: The point‐intercept method is one of the most commonly used approaches to measure species cover in ecosystems worldwide. In this approach, multiple points are sampled for presence/absence of a species, and the number of present points divided by the total number of sampled points provides an estimate of percent cover. Our purpose is to mathematically analyze the accuracy of the point‐intercept approach and establish guidelines for its use. Methods: We developed formulas that analyze the point‐intercept method and confirmed their effectiveness using simulations. Results: We find that a point‐intercept spacing of at least 80% of the largest plant diameter provides the most reliable results. We present a user‐friendly spreadsheet that calculates the number of intercepts needed for fieldwork, as well as the standard deviation, expected deviation, and confidence interval of the collected data. Discussion: We provide a variety of guidelines for establishing field protocols based on our results, including dealing with rare species and combining results for multiple species. Quadrat characteristics (intercept spacing, number of point intercepts) can now be easily calculated to guide research design prior to fieldwork; after fieldwork is complete, the accuracy of this technique can (and should) be reported in all future ecological studies in which it is used. [ABSTRACT FROM AUTHOR]

Abstract Premise The point‐intercept method is one of the most commonly used approaches to measure species cover in ecosystems worldwide. In this approach, multiple points are sampled for presence/absence of a species, and the number of present points divided by the total number of sampled points provides an estimate of percent cover. Our purpose is to mathematically analyze the accuracy of the point‐intercept approach and establish guidelines for its use. Methods We developed formulas that analyze the point‐intercept method and confirmed their effectiveness using simulations. Results We find that a point‐intercept spacing of at least 80% of the largest plant diameter provides the most reliable results. We present a user‐friendly spreadsheet that calculates the number of intercepts needed for fieldwork, as well as the standard deviation, expected deviation, and confidence interval of the collected data. Discussion We provide a variety of guidelines for establishing field protocols based on our results, including dealing with rare species and combining results for multiple species. Quadrat characteristics (intercept spacing, number of point intercepts) can now be easily calculated to guide research design prior to fieldwork; after fieldwork is complete, the accuracy of this technique can (and should) be reported in all future ecological studies in which it is used.

Management strategy evaluation (MSE) is a simulation approach that serves as a “light on the hill” (Smith, 1994) to test options for marine management, monitoring, and assessment against simulated ecosystem and fishery dynamics, including uncertainty in ecological and fishery processes and observations. MSE has become a key method to evaluate trade-offs between management objectives and to communicate with decision makers. Here we describe how and why MSE is continuing to grow from a single species approach to one relevant to multi-species and ecosystem-based management. In particular, different ecosystem modeling approaches can fit within the MSE process to meet particular natural resource management needs. We present four case studies that illustrate how MSE is expanding to include ecosystem considerations and ecosystem models as ‘operating models’ (i.e., virtual test worlds), to simulate monitoring, assessment, and harvest control rules, and to evaluate tradeoffs via performance metrics. We highlight United States case studies related to fisheries regulations and climate, which support NOAA’s policy goals related to the Ecosystem Based Fishery Roadmap and Climate Science Strategy but vary in the complexity of population, ecosystem, and assessment representation. We emphasize methods, tool development, and lessons learned that are relevant beyond the United States, and the additional benefits relative to single-species MSE approaches.

One of the significant challenges to using information and ideas generated through ecosystem models and analyses for ecosystem-based fisheries management is the disconnect between modeling and management needs. Here we present a case study from the U.S. West Coast, the stakeholder review of NOAA’s annual ecosystem status report for the California Current Ecosystem established by the Pacific Fisheries Management Council’s Fisheries Ecosystem Plan, showcasing a process to identify management priorities that require information from ecosystem models and analyses. We then assess potential ecosystem models and analyses that could help address the identified policy concerns. We screened stakeholder comments and found 17 comments highlighting the need for ecosystem-level synthesis. Policy needs for ecosystem science included: (1) assessment of how the environment affects productivity of target species to improve forecasts of biomass and reference points required for setting harvest limits, (2) assessment of shifts in the spatial distribution of target stocks and protected species to anticipate changes in availability and the potential for interactions between target and protected species, (3) identification of trophic interactions to better assess tradeoffs in the management of forage species between the diet needs of dependent predators, the resilience of fishing communities, and maintenance of the forage species themselves, and (4) synthesis of how the environment affects efficiency and profitability in fishing communities, either directly via extreme events (e.g., storms) or indirectly via climate-driven changes in target species availability. We conclude by exemplifying an existing management process established on the U.S. West Coast that could be used to enable the structured, iterative, and interactive communication between managers, stakeholders, and modelers that is key to refining existing ecosystem models and analyses for management use.

Antarctic krill (krill) are important within Southern Ocean ecosystems and support an expanding fishery. Toward understanding krill's response to environmental change, it is necessary to identify regions that support high krill larval production (spawning habitat). We develop a mechanistic model combining thermal and food requirements for krill egg production, with predation pressure post‐spawning, to predict regions of high‐quality spawning habitat. We optimize our model regionally and generate circumpolar predictions of spawning habitat quality. Our results indicate the southwest Atlantic accounts for almost half of all predicted high‐quality spawning habitat. Small‐scale management units (SSMUs) around the Antarctic Peninsula had high coverage of high‐quality spawning habitat. In contrast, the remaining SSMUs (except around South Georgia) were poorly covered, suggestive of population sinks reliant on input from external sources upstream. This implies strong potential for downstream impacts of fishing in key spawning areas, with implications for management of SSMUs and the krill fishery. Plain Language Summary: Antarctic krill (krill) are a key prey item for many Southern Ocean marine predators and also support an expanding commercial fishery. However, there is a need to identify areas of the Southern Ocean that are best for both the production of eggs, and subsequent survival until free‐swimming larvae. We create a model for spawning habitat which considers the temperature and food conditions that adult female krill need to successfully produce eggs, as well as the density of predators feeding on spawned eggs. We optimize our model using existing regional‐scale data of krill eggs and larvae and use this to predict where good spawning habitat may exist around the whole Southern Ocean. Our model found that nearly half of the best spawning habitat occurs in the southwest Atlantic. Within this area, small‐scale krill fishing management areas around the Antarctic Peninsula contain good‐quality spawning habitat. Krill fished here are likely to be locally produced. But, other small‐scale management areas contain little good‐quality spawning habitat (except around South Georgia) and probably rely on krill being imported by ocean currents. This is important for understanding how fishing may impact both local ecosystems and those that are downstream. Key Points: Mechanistic model for Antarctic krill spawning habitat uses thermal and food constraints on egg production, and predation on egg survivalOptimizing from regional data we generate circumpolar predictions to show half of quality spawning habitat is in the southwest AtlanticSpatial management units for the krill fishery do not all represent optimal spawning habitat, suggesting some areas rely on recruit import [ABSTRACT FROM AUTHOR]

Widespread tree mortality following droughts has emerged as an environmentally and economically devastating 'ecological surprise'. It is well established that tree physiology is important in understanding drought-driven mortality; however, the accuracy of predictions based on physiology alone has been limited. We propose that complicating factors at two levels stymie predictions of drought-driven mortality: (i) organismal-level physiological and site factors that obscure understanding of drought exposure and vulnerability and (ii) community-level ecological interactions, particularly with biotic agents whose effects on tree mortality may reverse expectations based on stress physiology. We conclude with a path forward that emphasizes the need for an integrative approach to stress physiology and biotic agent dynamics when assessing forest risk to drought-driven morality in a changing climate. Drought mortality has wide-ranging ramifications from environmental conservation to climate change mitigation efforts. Thus far, mortality prediction efforts using physiology alone have found limited success. Physiological interactions, such as within-species trait variation, trait covariation, and trait–environment covariation, can reverse or confound mortality predictions. Ecological complexity, particularly the degree to which biotic mortality agents are linked to stress physiology, is highly variable. Thus, the presence of biotic agents has strong potential to reverse or confound mortality predictions. We present a framework to integrate our understanding of complex drought physiology and biotic mortality agents. Future work is needed to understand where and when biotic mortality agents might amplify patterns of physiological stress and where and when the effects of biotic agents might be largely decoupled from physiological stress. [ABSTRACT FROM AUTHOR]

Climate change impacts on marine life in the world ocean are expected to accelerate over the 21st century, affecting the structure and functioning of food webs. We analyzed a key aspect of this issue, focusing on the impact of changes in biomass flow within marine food webs and the resulting effects on ecosystem biomass and production. We used a modeling framework based on a parsimonious quasi‐physical representation of biomass flow through the food web, to explore the future of marine consumer biomass and production at the global scale over the 21st century. Biomass flow is determined by three climate‐related factors: primary production entering the food web, trophic transfer efficiency describing losses in biomass transfers from one trophic level (TL) to the next, and flow kinetic measuring the speed of biomass transfers within the food web. Using climate projections of three earth system models, we calculated biomass and production at each TL on a 1° latitude ×1° longitude grid of the global ocean under two greenhouse gas emission scenarios. We show that the alterations of the trophic functioning of marine ecosystems, mainly driven by faster and less efficient biomass transfers and decreasing primary production, would lead to a projected decline in total consumer biomass by 18.5% by 2090–2099 relative to 1986–2005 under the "no mitigation policy" scenario. The projected decrease in transfer efficiency is expected to amplify impacts at higher TLs, leading to a 21.3% decrease in abundance of predators and thus to a change in the overall trophic structure of marine ecosystems. Marine animal production is also projected to decline but to a lesser extent than biomass. Our study highlights that the temporal and spatial projected changes in biomass and production would imply direct repercussions on the future of world fisheries and beyond all services provided by Ocean. [ABSTRACT FROM AUTHOR]

For decades crop models have been proven to help agronomists simulate plant growth interactions in the environment, for instance with soil, water, and nutrients. Now scientists are turning their attention to agronomic interactions with ecosystems, specifically wetlands. Wetlands are an integral part of the landscape both as a habitat, and as a buffer between agricultural areas and large watersheds. Process-based simulation models such as APEX, and ALMANAC are used for crops, but have now been applied to wetlands. These models simulate vegetation growth, plant competition, nutrient cycling, erosion, and hydrology. Recent research has allowed wetland plant growth to be simulated, and more complex modeling of the landscape has begun. Here we summarize advances in wetland plant simulation using crop modeling and application of these process-based crop models to wetland plants and ecosystems. [ABSTRACT FROM AUTHOR]

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]

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.

The 5G technology allows telecommunication operators to provide services beyond data broadband for consumers, employing tailored use-cases for enterprise and vertical markets. As a result, there is an increasing need for a collaborative service development through a de-centralized value-chain, along with new business models and shorter time-to-market. In this paper, a novel, use-case driven framework for a modular 5G service design and delivery is proposed. The framework implements agile and as-a-service principles to provide 5G use-case customers access not only to service usage, but also to design and operation. The framework integrates with the 3rd Generation Partnership Project (3GPP) specifications by mapping network-slicing procedures to the designed ecosystem lifecycle operations. The framework allows for the any-technology deployment, utilizing the available foundations for domain-specific automation. A structured collaboration between a 5G operator and a use-case user allows for the customization and improvement of the targeted use-case, where 5G network assets are updated with customer's vertical components through programmable user-stories. Laboratory testing of a 5G network-slicing for a private-telephony and merchandise-tracking service is performed to validate the effectiveness of the proposed approach for real-world applications.

Coastal erosion and wetland loss are affecting Louisiana to such an extent that the loss of land between 1932 and 2016 was close to 5,000 km2. To mitigate this decline, coastal protection and restoration projects are being planned and implemented by the State of Louisiana, United States. The Louisiana Coastal Master Plan (CMP) is an adaptive management approach that provides a suite of projects that are predicted to build or maintain land and protect coastal communities. Restoring the coast with this 50-year large-scale restoration and risk reduction plan has the potential to change the biomass and distribution of economically and ecologically important fisheries species in this region. However, not restoring the coast may have negative impacts on these species due to the loss of habitat. This research uses an ecosystem model to evaluate the effects of plan implementation versus a future without action (FWOA) on the biomass and distribution of fisheries species in the estuaries over 50 years of model simulations. By simulating effects using a spatially-explicit ecosystem model, not only can the changes in biomass in response to plan implementation be evaluated, but also the distribution of species in response to the planned restoration and risk reduction projects. Simulations are performed under two relative sea level rise (SLR) scenarios to understand the effects of climate change on project performance and subsequent fisheries species biomass and distribution. Simulation output of eight economically important fisheries species shows that the plan mostly results in increases in species biomass, but that the outcomes are species-specific and basin-specific. The SLR scenario highly affects the amount of wetland habitat maintained after 50 years (with higher levels of wetland loss under increased SLR) and, subsequently, the biomass of species depending on that habitat. Species distribution results can be used to identify expected changes for specific species on a regional basis. By making this type of information available to resource managers, precautionary measures of ecosystem management and adaptation can be implemented.

Although many countries have formally committed to Ecosystem-Based Fisheries Management (EBFM), actual progress toward these goals has been slow. This paper presents two independent case studies that have combined strategic advice from ecosystem modeling with the tactical advice of single-species assessment models to provide practical ecosystem-based management advice. With this approach, stock status, reference points, and initial target F are computed from a single-species model, then an ecosystem model rescales the target F according to ecosystem indicators without crossing pre-calculated single-species precautionary limits. Finally, the single-species model computes the quota advice from the rescaled target F, termed here Feco. Such a methodology incorporates both the detailed population reconstructions of the single-species model and the broader ecosystem perspective from ecosystem-based modeling, and fits into existing management schemes. The advocated method has arisen from independent work on EBFM in two international fisheries management systems: (1) Atlantic menhaden in the United States and (2) the multi species fisheries of the Irish Sea, in the Celtic Seas ecoregion. In the Atlantic menhaden example, the objective was to develop ecological reference points (ERPs) that account for the effect of menhaden harvest on predator populations and the tradeoffs associated with forage fish management. In the Irish Sea, the objective was to account for ecosystem variability when setting quotas for the individual target species. These two exercises were aimed at different management needs, but both arrived at a process of adjusting the target F used within the current single-species management. Although the approach has limitations, it represents a practical step toward EBFM, which can be adapted to a range of ecosystem objectives and applied within current management systems.

Both trophic structure and biomass flow within marine food webs are influenced by the abiotic environment and anthropogenic stressors such as fishing. The abiotic environment has a large effect on species spatial distribution patterns and productivity and, consequently, spatial co-occurrence between predators and prey, while fishing alters species abundances and food-web structure. In order to disentangle the impacts of the abiotic environment and fishing in the Celtic Sea ecosystem, we developed a spatio-temporal trophic model, specifically an Ecopath with Ecosim with Ecospace model, for the period 1985–2016. In this model, particular attention was paid to the parameterization of the responses of all trophic levels to abiotic environmental changes. Satellite remote sensing data were employed to determine the spatial distribution and annual fluctuations of primary production (PP). Spatial and temporal changes in the habitat favorable for zooplankton were predicted with a novel ecological-niche approach using daily detection of productivity fronts from satellite ocean color. Finally, functional responses characterizing the effect of several abiotic environmental variables (including, among others, temperature, salinity and dissolved oxygen concentration, both at the surface and at the bottom) on fish species groups’ habitat suitability were produced from the predictions of statistical habitat models fitted to presence-absence data collected by multiple fisheries-independent surveys. The dynamic component of our model (Ecosim) was driven by time-series of fishing effort, PP, zooplankton habitat suitability and abiotic environmental variables, and was fitted to abundance and fisheries catch data. The spatial component of our model (Ecospace) was constructed, for specific years of the period 1985–2016 with contrasted abiotic environmental conditions, to predict the variable distribution of the biomass of all functional groups. We found that fishing was the main driver of observed ecosystem changes in the Celtic Sea over the period 1985–2016. However, the integration of the environmental variability into the model and the subsequent improvement of the fit of the dynamic Ecosim component highlighted (i) the control of the overall pelagic production by PP and (ii) the influence of temperature on the productivity of several trophic levels in the Celtic Sea, especially on trophic groups with warm and cold water affinities. In addition, Ecospace predictions indicated that the spatial distributions of commercial fish species may have substantially changed over the studied period. These spatial changes mainly appeared to be driven by temperature and may, therefore, largely impact future fisheries given the continuity of climatic changes.

Abstract 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.

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]

Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

The marine ecosystem off British Columbia (BC), Canada, has experienced various changes in the last two decades, including reduced lipid-rich zooplankton biomass, increased marine mammals, and deteriorated commercial fisheries, particularly those targeting pelagic species such as Pacific Herring (Clupea pallasii). Understanding how stressors interactively and cumulatively affect commercially important fish species is key to moving toward ecosystem-based fisheries management. Because it is challenging to assess the cumulative effects of multiple stressors by using empirical data alone, a dynamic, individual-based spatially explicit ecosystem modeling platform such as Object-oriented Simulator of Marine Ecosystems (OSMOSE) represents a valuable tool to simulate ecological processes and comprehensively evaluate how stressors cumulatively impact modeled species. In this study, we employed OSMOSE to investigate the cumulative effects of fishing, plankton biomass change, and marine mammal consumption on the dynamics of some fish species and the BC marine ecosystem as a whole. We specifically simulated ecosystem dynamics during the last 20 years under two sets of scenarios: (1) unfavorable conditions from the perspective of commercial fish species (i.e., doubling fishing mortality rates, halving plankton biomass, and doubling marine mammal biomass, acting individually or collectively); and (2) favorable conditions with the three factors having opposite changes (i.e., halving fishing mortality rates, doubling plankton biomass, and halving marine mammal biomass, acting individually or collectively). Our results indicate that, under unfavorable conditions, the degree to which species biomass was reduced varied among species, and that negative synergistic and negative dampened effects were dominant under historical and doubled fishing mortality rates, respectively. Under favorable conditions, species biomasses did not increase as much as expected due to the existence of complex predator-prey interactions among fish species, and positive synergistic and positive dampened effects were prevailing under historical and halved fishing mortality rates, respectively. The ecosystem total biomass and the biomass to fisheries yield ratio were found to be good ecological indicators to represent ecosystem changes and track the impacts from the multiple drivers of change. Our research provides insights on how fisheries management should adapt to prepare for potential future impacts of climate change.

The Gulf of Nicoya is a large tropical estuary located on the Pacific coast of Costa Rica and accounts for the country’s main fish production. It is increasingly impacted not only by its fisheries, but also by urbanization, tourism developments, the input of pollutants from urban centers and agriculture activities in the catchment areas of large rivers entering the gulf. Similar to other coastal ecosystems, the gulf is particularly sensitive to short- and long-term changes in the climate, such as the precipitation rate, Sea Surface Temperature, wind speed and current regimes. While the gulf has been studied for decades, until now no attempt has been made to combine the analysis of the impacts of fishing and environmental changes on the ecosystem. By following a holistic approach that uses fisheries and environmental time series data from collaborative research and simulation models, this study aims at identifying the main drivers of the observed changes in the Gulf of Nicoya ecosystem. While the model simulations indicate that variations in the catch of some target species (e.g., cephalopods and shrimps) are mainly driven by the fisheries’ exploitation rates, several other species (e.g., corvina, snook, small pelagics, and crabs) are also substantially affected by climate variations, particularly during El Niño periods of high Sea Surface Temperature extremes and increased precipitation. During these periods, phytoplankton productivity and zooplankton biomass deceases with bottom–up effects on the entire food chain. Indicators of the environmental state of the system thus have to be considered along with regulations of the fishing pressure to form the basis for the development of appropriate management strategies.

Since 2010, the northern Gulf of Mexico (NGoM) has experienced two unique environmental stressors. First, the 2010 Deepwater Horizon oil spill (DWH) impacted a broad range of taxa and habitats and resulted in declines of small demersal reef fish over the study area (88.5–85.5°W and 29–30.5°N). Then, from 2011 to 2014 the invasive Indo-Pacific lionfish (Pterois volitans) underwent exponential population growth, leading to some of the highest densities in their invaded range. The primary objective of this study was to evaluate the effect of these stressors on reef ecosystems, and specifically how invasive lionfish and fishing may have impacted recovery following DWH. Site-specific datasets on fish density and diet composition were synthesized into an Ecopath with Ecosim food web model of a NGoM reef ecosystem. The model consisted of 63 biomass groups and was calibrated to time series of abundance from 2009 to 2016. The model accounted for mortality from the DWH using forcing functions derived from logistic dose-response curves and oil concentrations. Eight stressor scenarios were simulated, representing all combinations of DWH, lionfish, and fishing. Simulated biomass differed across model groups due to singular and cumulative impacts of stressors and direct and indirect effects arising through food web interactions. Species with high exploitation rates were influenced by fishing more than lionfish following DWH. Several small demersal fish groups were predicted to be strongly influenced by either the cumulative effects of lionfish and DWH or by lionfish alone. A second group of small demersal fish benefited in the stressor scenarios due to reduced top-down predation and competition in the combined stressor scenarios. We conclude that lionfish had a major impact on this ecosystem, based on both empirical data and simulation results. This caused slower recoveries following DWH and lower fish biomass and diversity. Additionally, the lack of recovery for some groups in the absence of lionfish suggests system reorganization may be preventing return to a pre-DWH state. We intended for this work to improve our understanding of how temperate reef ecosystems, like those in the NGoM, respond to broad scale stressors and advance the state of applied ecosystem modeling for resource damage assessment and restoration planning.