Search

Understanding complexities in biodiversity is one of the fundamental goals of ecology and its monitoring is significant for ecosystem sustainability, maintenance, and conservation. However, biodiversity monitoring needs improvement to handle complex datasets and their analyses. This study attempts to understand these ecological complexities quickly, efficiently, and easily. The aim is to provide an alternative to ecologists, researchers, instructors, and stakeholders for biodiversity monitoring with the flexibility to visualize and customize outputs without software knowledge. A novel web‐based technique is applied to monitor the biodiversity of a complex mountain ecosystem using a national database. The species–environment relationships of different vegetation types across a mountain ecosystem's elevation gradient are investigated using open‐source climatic, physiographic, and socioeconomic variables. The proposed interactive tool to monitor biodiversity and understand its complexities is designed to visualize the data structure, summary, correlations, and sampling effectiveness quickly and easily. Plant species richness patterns and life forms (herb, shrub, and tree) across elevational gradients are investigated. We highlight the preliminary investigation of the data structure and their spatial distribution and apply the multicollinearity test to select variables for modeling. The drop‐down menu helps users browse different datasets and select those datasets for instant visualization. Preliminary investigations on interactions between variables and species richness of vegetation types along elevation gradient interactively displayed with options to select variables, plant richness, and an elevational range. Species–environment relationships are investigated using multiple modeling protocols, and results are interactively displayed with options to download in different file formats and colors at the click of a button. This visualization tool helps to understand ecosystem structure, species richness patterns and species–environment relationships easily and efficiently. The R‐codes used in this tool are reproducible and can be implemented with multiple datasets to monitor ecosystems. [ABSTRACT FROM AUTHOR]

A single‐column coupled physical and biological model based on the North Pacific Ecosystem Model for Understanding Regional Oceanography (NEMURO) with nitrogen and silicon cycles is adapted for the Bay of Bengal (BoB) environment. The model simulated plankton biomass and nutrients along the track of Bio‐Argos over East and West BoB (from 2016 to 2017) are validated with the observations. The model reasonably simulates the perennial structure of subsurface chlorophyll maximum (SCM). Further, three experiments are carried out to know the limitations in primary and secondary production in terms of nitrogen (NO3 + NH4) and silicate (Si(OH)4) in the open ocean BoB. In a "no‐NO3"experiment, the nitrate limiting term [that is, NO3/(NO3 + KNO3)] is set to zero so that the difference from the control case gives the role of "regenerated production" in the total primary and secondary production. Similarly, a no‐NH4 experiment was conducted to infer the role of "new production." The new (regenerated) production fuels 85 ± 1% (28 ± 6%) of the living biomass in the East part of open ocean BoB (East BoB). The corresponding values for the west part (West BoB) are 86 ± 1% (42 ± 2%). Among the primary producers, the new (regenerated) production contributed 72 ± 1% (24 ± 6%) in the East BoB and 74 ± 1% (37 ± 2%) in the West BoB. The silicate limits the diatom production by 46% ± 22% (45% ± 27%) of the actual amount of diatom in the East BoB (West BoB) diatom. Surface to 60 m depth, we observed severe nitrate limitation over East BoB and equal limitation of nitrate and silicate over West BoB from June to September; the remaining months suffered from moderate nitrate limitation in both regions. This study shows that nitrate appears to be a limiting nutrient compared to silicate at the surface of both East and West BoB. Since silicicline is deeper than nitracline, silicate is the potential limiting nutrient in BoB from 65 to 105 m depth in all seasons. Plain Language Summary: We adopted a medium complex single‐column coupled ecosystem model called NEMURO to the Bay of Bengal (BoB) environment. The model simulated the timely progression of the 13 state variables; these variables represent two functional groups of phytoplankton (such as flagellates and diatoms), three groups of zooplankton (e.g., ciliates, copepods, and euphausiids) and three types of nutrients (such as nitrate, ammonium, and silicate), dissolved and particulate matter. The model simulated primary hydrographic parameters, such as temperature, salinity, and plankton biomass, which were validated using bio‐Argos and ship observations. The model examines the nitrogen and silicate limitation over primary and secondary production. We found that at the surface, nitrate is the potential limiting nutrient with variable intensity for the entire season over East and West BoB; at intermediate levels of 65–105 m, silicate is the limiting nutrient. Further, we also quantified the nitrogen (nitrate and ammonium) and silicate contribution for plankton photosynthesis by conducting a few experiments. Key Points: The new production is responsible for 85 ± 1% (86 ± 1%) of living biomass in the East Bay of Bengal (BoB) (West BoB)The silicate limits the diatom production by 46% ± 22% (45% ± 27%) of the actual amount of diatom in the East BoB (West BoB)From surface to 60 m depth, severe nitrate limitation seen in East BoB, and equal limitation of nitrate and silicate in West BoB from June to September [ABSTRACT FROM AUTHOR]

Animals occupying higher trophic levels can have disproportionately large influence on ecosystem structure and functioning, owning to intricate behavioral responses to their environment, but the effects of behavioral adaptations on aquatic ecosystem dynamics are underrepresented, especially in model studies. Here, we explore how adaptive behavior of fish can affect the dynamics of aquatics ecosystems. We frame fish behavior in the context of the central trade‐off between feeding and predation, calculating the optimal level of feeding determined by ambient food availability and predation risk. To explore whole‐ecosystem consequences of fish behavior, we embed our behavioral model within the Water Ecosystems Tool (WET), a contemporary end‐to‐end aquatic ecosystem model. The principle of optimality provides a robust and mechanistic framework for representing animal behavior that is relevant for complex models, and can provide a stabilizing effect on model dynamics. The model predicts an emergent functional response similar to Holling type III, but with richer dynamics and a more rigorous theoretical foundation. We show how adaptive fish behavior works to stabilize food web dynamics compared to a control model with no optimal behavior, and how changing the strength of the underlying trade‐off has profound effects on trophic control and food web structure. Furthermore, we demonstrate how including fish behavior allows for an overall more realistic response of the model system to environmental perturbation in the form of nutrient enhancement. We discuss the structuring effects of behavioral adaptations in real ecosystems, and how approaches like this one may benefit aquatic ecological modeling. Our study further highlights how a mechanistic approach based on concepts from theoretical ecology can be successfully implemented in complex operational models resulting in improved dynamics and descriptive power. [ABSTRACT FROM AUTHOR]

Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single‐species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost‐benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single‐species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost‐benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits. [ABSTRACT FROM AUTHOR]

The two phases of El‐Niño‐Southern Oscillation (ENSO) influence both regional and global terrestrial vegetation productivity on inter‐annual scales. However, the major drivers for the regional vegetation productivity and their controlling strengths during different phases of ENSO remain unclear. We herein disentangled the impacts of two phases of ENSO on regional carbon cycle using multiple data sets. We found that soil moisture predominantly accounts for ∼40% of the variability in regional vegetation productivity during ENSO events. Our results showed that the satellite‐derived vegetation productivity proxies, gross primary productivity from data‐driven models (FLUXCOM) and observation‐constrained ecosystem model (Carbon Cycle Data Assimilation System) generally agree in depicting the contribution of soil moisture and air temperature in modulating regional vegetation productivity. However, the ensemble of weakly constrained ecosystem models exhibits non‐negligible discrepancies in the roles of vapor pressure deficit and radiation over extra‐tropics. This study highlights the significance of water in regulating regional vegetation productivity during ENSO. Plain Language Summary: ENSO, a significant climate phenomenon, profoundly influences ecosystem productivity from regional to global scales. Yet, accurately pinpointing the attributions of different climate factors that control Gross Primary Productivity (GPP) during ENSO events remains challenging, partly due to large uncertainties in existing GPP data. Through extensive analysis using various data sets, we found that soil moisture plays a dominant role in controlling GPP across most continents. This implies that current terrestrial biosphere models might underestimate the importance of soil moisture in driving productivity during ENSO events. Addressing this gap in understanding is crucial for refining and properly constraining the related processes in terrestrial biosphere models. Key Points: Soil moisture predominantly governs vegetation productivity changes during El‐Niño‐Southern Oscillation (ENSO) eventsSatellite‐derived vegetation indices consistently support the predominant influence of water on vegetation productivityENSO triggers varying effects on vegetation productivity across different regions, with distinct impacts during El Niño and La Niña [ABSTRACT FROM AUTHOR]

Variation in the recruitment of salmon is often found to be correlated with marine climate indices, but mechanisms behind environment–recruitment relationships remain unclear and correlations often break down over time. We used an ecosystem modeling approach to explore bottom‐up and top‐down mechanisms linking a variable environment to salmon recruitment variations. Our ecosystem model incorporates a regional ocean circulation submodel for hydrodynamics, a nutrient‐phytoplankton‐zooplankton submodel for producing planktonic prey fields, and an individual‐based model (IBM) representing juvenile Chinook salmon (Oncorhynchus tshawytscha), combined with observations of foraging distributions and diet of a seabird predator. The salmon IBM consists of modules, including a juvenile salmon growth module based on temperature and salmon–prey availability, a behavior‐based movement module, and a juvenile salmon predation mortality module based on juvenile salmon size distribution and predator–prey interaction probability. Seabird–salmon interactions depend on spatial overlap and juvenile salmon size, whereby salmon that grow past the size range of the prey distribution of the predator will escape predation. We used a 21‐year historical simulation to explore interannual variability in juvenile Chinook salmon growth and predation‐mediated survival under a range of ocean conditions for sized‐based mortality scenarios. We based a series of increasingly complex predation scenarios on seabird observational data to explore variability in predation mortality on juvenile Chinook salmon. We initially included information about the predator spatial distribution, then added population size, and finally the predator's diet percentage made up of juvenile salmon. Model agreement improves with added predator complexity, especially during periods when predator abundance is high. Overall, our model found that when the fraction of juvenile salmon in seabird diet increased relative to alternate prey (e.g., Northern anchovy Engraulis mordax, and juvenile rockfish Sebastes spp.), there was a concomitant decrease in salmon cohort survival during their first year at sea. [ABSTRACT FROM AUTHOR]

Downscaling physical forcing from global climate models is both time consuming and labor demanding and can delay or limit the physical forcing available for regional marine ecosystem modelers. Earlier studies have shown that downscaled physics is necessary for capturing the dynamics of primary production and lower trophic levels; however, it is not clear how higher trophic levels respond to the coarse resolution physics of global models. Here, we apply the Nordic and Barents Seas Atlantis ecosystem model (NoBa) to study the consequences of using physical forcing from global climate models versus using that from regional models. The study is therefore (i) a comparison between a regional model and its driving global model to investigate the extent to which a global climate model can be used for regional ecosystem predictions and (ii) a study of the impact of future climate change in the Nordic and Barents Seas. We found that few higher trophic level species were affected by using forcing from a global versus a regional model, and there was a general agreement in future biomass trends and distribution patterns. However, the slight difference in temperature between the models dramatically impacted Northeast Arctic cod (Gadus morhua), which highlights how species projection uncertainty could arise from poor physical representation of the physical forcing, in addition to uncertainty in the ecosystem model parameterization. [ABSTRACT FROM AUTHOR]

Nitrification regulates the potential for nitrogen (N) loss from ecosystems because it converts ammonium to nitrate, which is susceptible to leaching and gaseous emissions. Crops can suppress the microbes that perform nitrification by exuding nitrification‐inhibiting compounds from their roots and taking up available ammonium. However, the effect of nitrification suppression on agroecosystem N losses remains poorly characterized, and a lack of temporal synchrony between nitrification, N losses, and nitrification suppression by plants could limit the effect of nitrification suppression. We used the DayCent‐CABBI model to evaluate the effectiveness of the suppression of nitrification by sorghum to reduce N2O emissions and nitrate leaching in an energy sorghum/soybean rotation at the Energy Farm in Urbana‐Champaign, IL. We simulated nitrification suppression at the measured levels (MeasNS) and at the maximum measured level applied to the entire growing season (MaxNS), and we also explored ways to better utilize nitrification suppression by altering the timing of urea–ammonium–nitrate fertilizer applications. Model experiments showed that most nitrification occurred immediately after fertilizer was applied, whereas nitrification suppression began to ramp up more than a month after planting. On an annual basis, MeasNS experiments showed a 1%–2% reduction in annual N2O emissions relative to no nitrification suppression (NoNS), and MaxNS experiments showed a 4%–9% reduction in annual N2O emissions relative to NoNS. Both nitrification suppression levels showed <1% reduction in nitrate leaching. Altering the timing of fertilizer applications to better synchronize nitrification suppression with high soil ammonium levels had mixed effects on annual N2O emissions and nitrate leaching and sometimes resulted in increased N losses. The timing of simulated N2O emissions shifted with the timing of fertilization, and N2O emissions from denitrification increased when N2O emissions from nitrification decreased. Increasing N retention during the non‐growing season may be more effective than growing‐season nitrification suppression for reducing annual N losses in the rainfed Midwest. [ABSTRACT FROM AUTHOR]

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi‐empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi‐arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big‐leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations. Plain Language Summary: Earth system models are an essential tool for understanding the consequences of changing climate conditions on forest ecosystems. Models are rapidly incorporating more realistic representations of how drought impacts ecosystem carbon and water cycling. These advancements need to be thoroughly evaluated to ensure that the models adequately capture the plant functional response to drought stress. Here we merge underutilized measurements with new analytical tools to evaluate several model representations of plant response to drought. These tools allow us to both better understand relationships among drought stress and ecosystem response, as well as quantify model accuracy. We find that models generally perform similarly under unstressed conditions, but performance diverges under drought. Key Points: We evaluate several model formulations for coupling plant hydraulic and stomatal response using functional performance metricsInformation flows from soil water potential and vapor pressure deficit to transpiration reflect structural differences among modelsConsiderable biases in modeled canopy temperature propagate to a 5% offset in cumulative growing season transpiration [ABSTRACT FROM AUTHOR]

Although zooplankton play a substantial role in the biological carbon pump and serve as a crucial link between primary producers and higher trophic level consumers, the skillful representation of zooplankton is not often a focus of ocean biogeochemical models. Systematic evaluations of zooplankton in models could improve their representation, but so far, ocean biogeochemical skill assessment of Earth system model (ESM) ensembles have not included zooplankton. Here we use a recently developed global, observationally based map of mesozooplankton biomass to assess the skill of mesozooplankton in six CMIP6 ESMs. We also employ a biome‐based assessment of the ability of these models to reproduce the observed relationship between mesozooplankton biomass and surface chlorophyll. The combined analysis found that most models were able to reasonably simulate the large regional variations in mesozooplankton biomass at the global scale. Additionally, three of the ESMs simulated a mesozooplankton‐chlorophyll relationship within the observational bounds, which we used as an emergent constraint on future mesozooplankton projections. We highlight where differences in model structure and parameters may give rise to varied mesozooplankton distributions under historic and future conditions, and the resultant wide ensemble spread in projected changes in mesozooplankton biomass. Despite differences, the strength of the mesozooplankton‐chlorophyll relationships across all models was related to the projected changes in mesozooplankton biomass globally and in regional biomes. These results suggest that improved observations of mesozooplankton and their relationship to chlorophyll will better constrain projections of climate change impacts on these important animals. Plain Language Summary: Zooplankton are marine animals that have a key role in transferring carbon from the atmosphere deeper into the ocean. They also serve as a crucial link in food chains between microscopic marine plants (phytoplankton) and predators like fish and whales. Researchers have created mathematical representations (models) of the linked processes of the oceans, atmosphere, and land, most of which include zooplankton. Yet how well these models represent zooplankton has not been adequately tested. We compared observations of zooplankton biomass to model estimates. We explored if these models reproduce the observed relationship between zooplankton biomass and chlorophyll concentration, which is useful for assessing how well the models represent predator‐prey relationships. Five of six models had similar patterns and comparable average biomasses across the global ocean as the observations. The historic relationship in three models fell within the observed relationship. The strength of the relationships across all models was related to how much zooplankton biomass will decrease with climate change. To improve the representation of zooplankton in models, we need better observations of the relationships between organisms. This would advance estimates of carbon transfer to the deep sea and carbon available to fish, and how they will change with climate change. Key Points: On the global scale, five of six models that include mesozooplankton perform moderately well with respect to the observationsWe identify an emergent constraint using the mesozooplankton versus chlorophyll relationship that can help constrain zooplankton projectionsMore attention needs to be paid to prey preferences, food web structure and temperature sensitivity in addition to existing key parameters [ABSTRACT FROM AUTHOR]

Cat removal is already underway, but there is concern that a decrease in the cat population could release predation pressure on rats, potentially leading to increased rat predation on red-tailed tropicbirds ( I Phaethon rubricauda i ) (Baker et al., 2020; Han et al., 2020). The metabolic demands of rats and cats (which included the mass of rats and cats) and the mass and energy of adult birds influenced the estimates of cat equivalence most strongly. For example, if reducing cat populations by 50% costs twice as much as reducing rats by 50%, but each cat is worth 77 rats, then our cat equivalence metric suggests that removing cats would be the most cost-effective strategy, but if cat equivalence is 30 then removing rats would be more effective. [Extracted from the article]

Oxygen availability is decreasing in many lakes and reservoirs worldwide, raising the urgency for understanding how anoxia (low oxygen) affects coupled biogeochemical cycling, which has major implications for water quality, food webs, and ecosystem functioning. Although the increasing magnitude and prevalence of anoxia has been documented in freshwaters globally, the challenges of disentangling oxygen and temperature responses have hindered assessment of the effects of anoxia on carbon, nitrogen, and phosphorus concentrations, stoichiometry (chemical ratios), and retention in freshwaters. The consequences of anoxia are likely severe and may be irreversible, necessitating ecosystem‐scale experimental investigation of decreasing freshwater oxygen availability. To address this gap, we devised and conducted REDOX (the Reservoir Ecosystem Dynamic Oxygenation eXperiment), an unprecedented, 7‐year experiment in which we manipulated and modeled bottom‐water (hypolimnetic) oxygen availability at the whole‐ecosystem scale in a eutrophic reservoir. Seven years of data reveal that anoxia significantly increased hypolimnetic carbon, nitrogen, and phosphorus concentrations and altered elemental stoichiometry by factors of 2–5× relative to oxic periods. Importantly, prolonged summer anoxia increased nitrogen export from the reservoir by six‐fold and changed the reservoir from a net sink to a net source of phosphorus and organic carbon downstream. While low oxygen in freshwaters is thought of as a response to land use and climate change, results from REDOX demonstrate that low oxygen can also be a driver of major changes to freshwater biogeochemical cycling, which may serve as an intensifying feedback that increases anoxia in downstream waterbodies. Consequently, as climate and land use change continue to increase the prevalence of anoxia in lakes and reservoirs globally, it is likely that anoxia will have major effects on freshwater carbon, nitrogen, and phosphorus budgets as well as water quality and ecosystem functioning. [ABSTRACT FROM AUTHOR]

Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environmental changes do not accurately represent the size, distribution and flux of C from the soil. Here, we modified the daily Century (DAYCENT) biogeochemical model by tuning decomposition rates of conceptual SOC pools to match measurable C fraction data, followed by historical and future simulations of SOC dynamics. Results showed that simulations using fraction‐constrained DAYCENT (DCfrac) led to better initialization of SOC stocks and distribution compared to default/SOC‐only‐constrained DAYCENT (DCdef) at long‐term research sites. Regional simulation using DCfrac demonstrated higher SOC stocks for both croplands (34.86 vs. 26.17 MgC ha−1) and grasslands (54.05 vs. 40.82 MgC ha−1) compared to DCdef for the contemporary period (2001–2005 average), which better matched observationally constrained data‐driven maps of current SOC distributions. Projection of SOC dynamics in response to land cover change under a high warming climate showed average absolute SOC loss of 8.44 and 10.43 MgC ha−1 for grasslands and croplands, respectively, using DCfrac whereas, SOC losses were 6.55 and 7.85 MgC ha−1 for grasslands and croplands, respectively, using DCdef. The projected SOC loss using DCfrac was 33% and 29% higher for croplands and grasslands compared to DCdef. Our modeling study demonstrates that initializing SOC pools with measurable C fraction data led to more accurate representation of SOC stocks and distribution of SOC into individual carbon pools resulting in the prediction of greater sensitivity to agricultural intensification and warming. Plain Language Summary: We aim to improve the representation of soil organic carbon (SOC) dynamics in the earth system model by matching the conceptual soil pools with carbon fraction data. We found large divergence in SOC stocks with higher absolute and relative losses under historical and projected climate and land use using the fraction‐constrained compared to the default/SOC‐only‐constrained model. This implies that the conceptual soil pools parameterized to match with carbon fraction data can better simulate SOC dynamics now and into the future. Key Points: The fraction‐constrained (optimized) model led to better initialization and distribution of soil organic carbon (SOC) stocks compared to the default modelThe fraction‐constrained (optimized) model led to larger absolute and relative losses of SOC compared to the default model during 1895–2005Under high warming, projected SOC losses were 33% for croplands and 29% for grasslands using the optimized compared to the default model [ABSTRACT FROM AUTHOR]

The terrestrial net ecosystem productivity (NEP) has increased during the past three decades, but the mechanisms responsible are still unclear. We analyzed 17 years (2001–2017) of eddy‐covariance measurements of NEP, evapotranspiration (ET) and light and water use efficiency from a boreal coniferous forest in Southern Finland for trends and inter‐annual variability (IAV). The forest was a mean annual carbon sink (252 [±42] gC m‐2a‐1), and NEP increased at rate +6.4–7.0 gC m‐2a‐1 (or ca. +2.5% a‐1) during the period. This was attributed to the increasing gross‐primary productivity GPP and occurred without detectable change in ET. The start of annual carbon uptake period was advanced by 0.7 d a‐1, and increase in GPP and NEP outside the main growing season contributed ca. one‐third and one‐fourth of the annual trend, respectively. Meteorological factors were responsible for the IAV of fluxes but did not explain the long‐term trends. The growing season GPP trend was strongest in ample light during the peak growing season. Using a multi‐layer ecosystem model, we showed that direct CO2 fertilization effect diminishes when moving from leaf to ecosystem, and only 30–40% of the observed ecosystem GPP increase could be attributed to CO2. The increasing trend in leaf‐area index (LAI), stimulated by forest thinning in 2002, was the main driver of the enhanced GPP and NEP of the mid‐rotation managed forest. It also compensated for the decrease of mean leaf stomatal conductance with increasing CO2 and LAI, explaining the apparent proportionality between observed GPP and CO2 trends. The results emphasize that attributing trends to their physical and physiological drivers is challenged by strong IAV, and uncertainty of LAI and species composition changes due to the dynamic flux footprint. The results enlighten the underlying mechanisms responsible for the increasing terrestrial carbon uptake in the boreal zone. [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]

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]

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]

Summary: Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high‐humidity ecosystems.We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC.The analysis indicates that while CWC is strongly correlated with VOD (R2 = 0.62 across all sites), LWs accounts for 61–76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near‐linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter‐annual, time scales.Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements. See also the Commentary on this article by Gerlien‐Safdi, 231: 5–7. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high‐resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED‐2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the eastern Amazon as initial conditions to ED‐2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥66%) experienced water stress with declines in ET (up to 34%) and GPP (up to 35%) and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multiyear droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are driven by not only climate and deforestation but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems. Plain Language Summary: In the Amazon, timber extraction and forest fires ignited by people are the chief causes of damages that we call forest degradation. Degradation is as widespread as deforestation and changes how forests behave. Degraded forests may pump less water to the atmosphere and absorb less carbon dioxide from the atmosphere. To understand the differences in behavior between degraded and intact forests, we used high‐resolution scanning laser data collected from aircraft flights over regions in the Amazon where we knew if and when forests were degraded. Then, we provided these data to a computer program that calculates the exchange of water and carbon between the forest and the atmosphere. We found that, during the dry season, degraded forests are 6.5°C warmer, pump one‐third less water (i.e., 400,000 L ha−1 month−1), absorb one‐third less carbon (i.e., 1 tonC ha−1 month−1), and show higher fire risk than intact forests. To our surprise, when the Amazon is hit by severe droughts, intact forests start to behave like degraded forests, because all forests run out of water and become hot. Our results are important because they show that forest degradation caused by people can have large impacts on dry‐season climate and favor more fire, especially during typical, nondrought years. Key Points: Airborne lidar can be used to inform about degradation‐driven changes in structure to vegetation modelsForest degradation typically depletes evapotranspiration and productivity and increases flammabilityExtreme droughts reduce functional differences between degraded and intact tropical forests [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

Abstract: A nutrient mass balance and a three‐dimensional, coupled hydrodynamic‐ecological model, calibrated and validated for Lake St. Clair with observations from 2009 and 2010, were integrated to estimate monthly lake‐scale nutrient loss rates, and to calculate 3 monthly transport time scales: flushing time, water age, and water residence time. While nutrient loss rates had statistically significant relationships with all transport time scale measures, water age had the strongest explanatory power, with water age and nutrient loss rates both smaller in spring and fall and larger in summer. We show that Lake St. Clair is seasonally divided into two discrete regions of contrasting water age and productivity. The north‐western region is dominated by oligotrophic waters from the St. Clair River, and south‐eastern region is dominated by the nutrient enriched, more productive waters from the Thames‐Sydenham River complex. The spatial and temporal variations in local transport scales and nutrient loss rates, coupled with strong seasonal variations in discharge and nutrient loads from the major tributaries, suggest the need for different load reduction strategies for different tributaries. [ABSTRACT FROM AUTHOR]

For the 21st century, carbon cycle models typically project an increase of terrestrial carbon with increasing atmospheric CO2 and a decrease with the accompanying climate change. However, these estimates are poorly constrained, primarily because they typically rely on a limited number of emission and climate scenarios. Here we explore a wide range of combinations of CO2 rise and climate change and assess their likelihood with the climate change responses obtained from climate models. Our results demonstrate that the terrestrial carbon uptake depends critically on the climate sensitivity of individual climate models, representing a large uncertainty of model estimates. In our simulations, the terrestrial biosphere is unlikely to become a strong source of carbon with any likely combination of CO2 and climate change in the absence of land use change, but the fraction of the emissions taken up by the terrestrial biosphere will decrease drastically with higher emissions. [ABSTRACT FROM AUTHOR]

Abstract: Protected areas (PAs) cover about 22% of the conterminous United States. Understanding their role on historical land use and land cover change (LULCC) and on the carbon cycle is essential to provide guidance for environmental policies. In this study, we compiled historical LULCC and PAs data to explore these interactions within the terrestrial ecosystem model (TEM). We found that intensive LULCC occurred in the conterminous United States from 1700 to 2005. More than 3 million km2 of forest, grassland and shrublands were converted into agricultural lands, which caused 10,607 Tg C release from land ecosystems to atmosphere. PAs had experienced little LULCC as they were generally established in the 20th century after most of the agricultural expansion had occurred. PAs initially acted as a carbon source due to land use legacies, but their accumulated carbon budget switched to a carbon sink in the 1960s, sequestering an estimated 1,642 Tg C over 1700–2005, or 13.4% of carbon losses in non‐PAs. We also find that PAs maintain larger carbon stocks and continue sequestering carbon in recent years (2001–2005), but at a lower rate due to increased heterotrophic respiration as well as lower productivity associated to aging ecosystems. It is essential to continue efforts to maintain resilient, biodiverse ecosystems and avoid large‐scale disturbances that would release large amounts of carbon in PAs. [ABSTRACT FROM AUTHOR]

Abstract: Microtopographic variation that develops among features (troughs, rims, and centers) within polygonal landforms of coastal arctic tundra strongly affects movement of surface water and snow and thereby affects soil water contents (θ) and active layer depth (ALD). Spatial variation in ALD among these features may exceed interannual variation in ALD caused by changes in climate and so needs to be represented in projections of changes in arctic ALD. In this study, increases in near‐surface θ with decreasing surface elevation among polygon features at the Barrow Experimental Observatory (BEO) were modeled from topographic effects on redistribution of surface water and snow and from lateral water exchange with a subsurface water table during a model run from 1981 to 2015. These increases in θ caused increases in thermal conductivity that in turn caused increases in soil heat fluxes and hence in ALD of up to 15 cm with lower versus higher surface elevation which were consistent with increases measured at BEO. The modeled effects of θ caused interannual variation in maximum ALD that compared well with measurements from 1985 to 2015 at the Barrow Circumpolar Active Layer Monitoring (CALM) site (R2 = 0.61, RMSE = 0.03 m). For higher polygon features, interannual variation in ALD was more closely associated with annual precipitation than mean annual temperature, indicating that soil wetting from increases in precipitation may hasten permafrost degradation beyond that caused by soil warming from increases in air temperature. This degradation may be more rapid if increases in precipitation cause sustained wetting in higher features. [ABSTRACT FROM AUTHOR]

The response of ecosystems to different magnitudes of climate warming and corresponding precipitation changes during the last few decades may provide an important reference for predicting the magnitude and trajectory of net primary productivity ( NPP) in the future. In this study, a process-based ecosystem model, Carbon Exchange between Vegetation, Soil and Atmosphere ( CEVSA), was used to investigate the response of NPP to warming at both national and subregional scales during 1961-2010. The results suggest that a 1.3°C increase in temperature stimulated the positive changing trend in NPP at national scale during the past 50 years. Regardless of the magnitude of temperature increase, warming enhanced the increase in NPP; however, the positive trend of NPP decreased when warming exceeded 2°C. The largest increase in NPP was found in regions where temperature increased by 1-2°C, and this rate of increase also contributed the most to the total increase in NPP in China's terrestrial ecosystems. Decreasing precipitation depressed the positive trend in NPP that was stimulated by warming. In northern China, warming depressed the increasing trend of NPP and warming that was accompanied by decreasing precipitation led to negative changing trends in NPP in large parts of northern China, especially when warming exceeded 2°C. However, warming stimulated the increase in NPP until warming was greater than 2°C, and decreased precipitation helped to increase the NPP in southern China. [ABSTRACT FROM AUTHOR]

A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere-ocean general circulation models ( AOGCMs; CCSM4, Had GEM2- CC, MIROC5, and MRI- CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways ( RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model Pn ET- BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO2 effects on vegetation (growth enhancement and increases in water use efficiency ( WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce-fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO2 effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change. [ABSTRACT FROM AUTHOR]

The importance of multispecies models for understanding complex ecological processes and interactions is beginning to be realized. Recent developments, such as those by Lahoz-Monfort et al. (2011), have enabled synchrony in demographic parameters across multiple species to be explored. Species in a similar environment would be expected to be subject to similar exogenous factors, although their response to each of these factors may be quite different. The ability to group species together according to how they respond to a particular measured covariate may be of particular interest to ecologists. We fit a multispecies model to two sets of similar species of garden bird monitored under the British Trust for Ornithology's Garden Bird Feeding Survey. Posterior model probabilities were estimated using the reversible jump algorithm to compare posterior support for competing models with different species sharing different subsets of regression coefficients. There was frequently good agreement between species with small asynchronous random-effect components and those with posterior support for models with shared regression coefficients; however, this was not always the case. When groups of species were less correlated, greater uncertainty was found in whether regression coefficients should be shared or not. The methods outlined in this study can test additional hypotheses about the similarities or synchrony across multiple species that share the same environment. Through the use of posterior model probabilities, estimated using the reversible jump algorithm, we can detect multispecies responses in relation to measured covariates across any combination of species and covariates under consideration. The method can account for synchrony across species in relation to measured covariates, as well as unexplained variation accounted for using random effects. For more flexible, multiparameter distributions, the support for species-specific parameters can also be measured. [ABSTRACT FROM AUTHOR]

Earth observing systems are now routinely used to infer leaf area index ( LAI) given its significance in spatial aggregation of land surface fluxes. Whether LAI is an appropriate scaling parameter for daytime growing season energy budget, surface conductance ( G s), water- and light-use efficiency and surface-atmosphere coupling of European boreal coniferous forests was explored using eddy-covariance ( EC) energy and CO2 fluxes. The observed scaling relations were then explained using a biophysical multilayer soil-vegetation-atmosphere transfer model as well as by a bulk G s representation. The LAI variations significantly alter radiation regime, within-canopy microclimate, sink/source distributions of CO2, H2O and heat, and forest floor fluxes. The contribution of forest floor to ecosystem-scale energy exchange is shown to decrease asymptotically with increased LAI, as expected. Compared with other energy budget components, dry-canopy evapotranspiration ( ET) was reasonably 'conservative' over the studied LAI range 0.5-7.0 m2 m−2. Both ET and G s experienced a minimum in the LAI range 1-2 m2 m−2 caused by opposing nonproportional response of stomatally controlled transpiration and 'free' forest floor evaporation to changes in canopy density. The young forests had strongest coupling with the atmosphere while stomatal control of energy partitioning was strongest in relatively sparse ( LAI ~2 m2 m−2) pine stands growing on mineral soils. The data analysis and model results suggest that LAI may be an effective scaling parameter for net radiation and its partitioning but only in sparse stands ( LAI <3 m2 m−2). This finding emphasizes the significance of stand-replacing disturbances on the controls of surface energy exchange. In denser forests, any LAI dependency varies with physiological traits such as light-saturated water-use efficiency. The results suggest that incorporating species traits and site conditions are necessary when LAI is used in upscaling energy exchanges of boreal coniferous forests. [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]

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]

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]

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]

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]

The economic and ecological impacts of fish consumption by marine mammals, the associated interactions with commercial fish stocks, and the forage demands of these marine mammal populations are largely unknown. Consumption estimates are often either data deficient or not fully evaluated in a rigorous, quantitative manner. Although consumption estimates exist for the Northeast United States (NEUS) Large Marine Ecosystem, there is considerable uncertainty in those estimates. We examined consumption estimates for 12 marine mammal species inhabiting the regional ecosystem. We used sensitivity analyses to examine metabolically driven daily individual consumption rates, resulting in a suite of feasible parameter-pair ranges for each of three taxonomic groups: mysticetes, odontocetes, and pinnipeds. We expanded daily individual consumption to annual consumption based on abundance estimates of marine mammals found on the NEUS continental shelf coupled with estimates of annual residence time for each species. To examine consumptive removals for specific prey, diet compositions were summarized into major prey categories, and predatory removals by marine mammal species as well as for total marine mammal consumption were estimated for each prey taxa. Bounds on consumption estimates for each marine mammal species were determined using Monte Carlo resampling simulations. Our results suggest that consumption for these 12 marine mammal species combined may be similar in magnitude to commercial fishery landings for small pelagic and groundfish prey groups. Consumption by marine mammals warrants consideration both as a source of mortality in assessments of prey stocks, and to determine marine mammal forage demands in ecosystem assessment models. The approach that we present represents a rigorous, quantitative method to scope the bounds of the biomass that marine mammals are expected to consume, and is appropriate for use in other ecosystems where the interaction between marine mammals and commercial fisheries is thought to be prominent. [ABSTRACT FROM AUTHOR]

Understanding ecosystem responses to global and local anthropogenic impacts is paramount to predicting future ecosystem states. We used an ecosystem modeling approach to investigate the independent and cumulative effects of fishing, marine protection, and ocean acidification on a coastal ecosystem. To quantify the effects of ocean acidification at the ecosystem level, we used information from the peer-reviewed literature on the effects of ocean acidification. Using an Ecopath with Ecosim ecosystem model for the Wellington south coast, including the Taputeranga Marine Reserve (MR), New Zealand, we predicted ecosystem responses under 4 scenarios: ocean acidification + fishing; ocean acidification + MR (no fishing); no ocean acidification + fishing; no ocean acidification + MR for the year 2050. Fishing had a larger effect on trophic group biomasses and trophic structure than ocean acidification, whereas the effects of ocean acidification were only large in the absence of fishing. Mortality by fishing had large, negative effects on trophic group biomasses. These effects were similar regardless of the presence of ocean acidification. Ocean acidification was predicted to indirectly benefit certain species in the MR scenario. This was because lobster ( Jasus edwardsii) only recovered to 58% of the MR biomass in the ocean acidification + MR scenario, a situation that benefited the trophic groups lobsters prey on. Most trophic groups responded antagonistically to the interactive effects of ocean acidification and marine protection (46%; reduced response); however, many groups responded synergistically (33%; amplified response). Conservation and fisheries management strategies need to account for the reduced recovery potential of some exploited species under ocean acidification, nonadditive interactions of multiple factors, and indirect responses of species to ocean acidification caused by declines in calcareous predators. [ABSTRACT FROM AUTHOR]

The timing of fine root production and turnover strongly influences both the seasonal potential for soil resource acquisition among competing root systems and the plant fluxes of root carbon into soil pools. However, basic patterns and variability in the rates and timing or fine root production and turnover are generally unknown among perennial plants species. We address this shortfall using a heuristic model relating root phenology to turnover together with three years of minirhizotron observations of root dynamics in 12 temperate tree species grown in a common garden. We specifically investigated how the amount and the timing of root production differ among species and how they impact estimates of fine root turnover. Across the 12 species, there was wide variation in the timing of root production with some species producing a single root flush in early summer and others producing roots either more uniformly over the growing season or in multiple pulses. Additionally, the pattern and timing of root production appeared to be consistent across years for some species but varied in others. Root turnover rate was related to total root production (P < 0.001) as species with greater root production typically had faster root turnover rates. We also found that, within species, annual root production varied up to a threefold increase between years, which led to large interannual differences in turnover rate. Results from the heuristic model indicated that shifting the pattern or timing of root production can impact estimates of root turnover rates for root populations with life spans less than one year while estimates of root turnover rate for longer lived roots were unaffected by changes in root phenology. Overall, we suggest that more detailed observations of root phenology and production will improve fidelity of root turnover estimates. Future efforts should link patterns of root phenology and production with whole-plant life history traits and variation in annual and seasonal climate. [ABSTRACT FROM AUTHOR]

Marine mammals are an important part of ecosystems, and their trophic role and potential impact have been increasingly studied. One key question is how these large animals interact with fisheries or compete for similar resources. Consequently, some models once used only for fisheries management are now including pinnipeds and cetaceans. However, fish and marine mammals do not share the same ecology and bioenergetics, and complex ecosystem models may not be the best way to assess the impact of pinnipeds or cetaceans in food webs. Indeed, simpler methods based on thermodynamics might give us reasonable answers with limited amounts of data. Here, we present an assessment of two different approaches to assess the trophic role of marine mammals in the northern Gulf of St. Lawrence (Canada): mixed trophic impacts ( MTI) based on ecosystem modeling and surface index ( SI) impact based on bioenergetics. Our results show that while modeling represents a good way of getting a holistic view of the role of marine mammals in ecosystems, trophic impact estimates based on fundamental thermodynamics principles can also give us answers requiring less data. The body surface area approach presented here might provide a practical tool for ecologists, who are not necessarily ecosystem modelers, to study this issue. [ABSTRACT FROM AUTHOR]

Shellfish carrying capacity is determined by the interaction of a cultured species with its ecosystem, which is strongly infiuenced by hydrodynamics. Water circulation controls the exchange of matter between farms and the adjacent areas, which in turn estabhshes the nutrient supply that supports phytoplankton populations. The complexity of water circulation makes necessary the use of hydrodynamic models with detailed spatial resolution in carrying capacity estimations. This detailed spatial resolution also allows for the study of processes that depend on specific spatial arrangements, e.g., the most suitable location to place farms, which is crucial for marine spatial planning, and consequently for decision support systems. In the present study, a fully spatial physical-biogeochemical model has been combined with scenario building and optimization techniques as a proof of concept of the use of ecosystem modeling as an objective tool to inform marine spatial planning. The object of this exercise was to generate objective knowledge based on an ecosystem approach to establish new mussel aquaculture areas in a Norwegian fjord. Scenario building was used to determine the best location of a pump that can be used to bring nutrient-rich deep waters to the euphotic layer, increasing primary production, and consequently, carrying capacity for mussel cultivation. In addition, an optimization tool, parameter estimation (PEST), was applied to the optimal location and mussel standing stock biomass that maximize production, according to a preestablished carrying capacity criterion. Optimization tools allow us to make rational and transparent decisions to solve a well-defined question, decisions that are essential for policy makers. The outcomes of combining ecosystem models with scenario building and optimization facilitate planning based on an ecosystem approach, highlighting the capabilities of ecosystem modeling as a tool for marine spatial planning [ABSTRACT FROM AUTHOR]