A case for an active eukaryotic marine biosphere during the Proterozoic era.

The microfossil record demonstrates the presence of eukaryotic organisms in the marine ecosystem by about 1,700 million years ago (Ma). Despite this, steranes, a biomarker indicator of eukaryotic organisms, do not appear in the rock record until about 780 Ma in what is known as the "rise of algae." Before this, it is argued that eukaryotes were minor ecosystem members, with prokaryotes dominating both primary production and ecosystem dynamics. In this view, the rise of algae was possibly sparked by increased nutrient availability supplying the higher nutrient requirements of eukaryotic algae. Here, we challenge this view. We use a size-based ecosystem model to show that the size distribution of preserved eukaryotic microfossils from 1,700 Ma and onward required an active eukaryote ecosystem complete with phototrophy, osmotrophy, phagotrophy, and mixotrophy. Model results suggest that eukaryotes accounted for one-half or more of the living biomass, with eukaryotic algae contributing to about one-half of total marine primary production. These ecosystems lived with deep-water phosphate levels of at least 10% of modern levels. The general lack of steranes in the pre-780-Ma rock record could be a result of poor preservation.

A gap in nitrous oxide emission reporting complicates long-term climate mitigation.

Nitrous oxide (N2O) is an important greenhouse gas (GHG) that also contributes to depletion of ozone in the stratosphere. Agricultural soils account for about 60% of anthropogenic N2O emissions. Most national GHG reporting to the United Nations Framework Convention on Climate Change assumes nitrogen (N) additions drive emissions during the growing season, but soil freezing and thawing during spring is also an important driver in cold climates. We show that both atmospheric inversions and newly implemented bottom-up modeling approaches exhibit large N2O pulses in the northcentral region of the United States during early spring and this increases annual N2O emissions from croplands and grasslands reported in the national GHG inventory by 6 to 16%. Considering this, emission accounting in cold climate regions is very likely underestimated in most national reporting frameworks. Current commitments related to the Paris Agreement and COP26 emphasize reductions of carbon compounds. Assuming these targets are met, the importance of accurately accounting and mitigating N2O increases once CO2 and CH4 are phased out. Hence, the N2O emission underestimate introduces additional risks into meeting long-term climate goals.

Cost-benefit analysis of ecosystem modeling to support fisheries management.

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.

Progress and opportunities in advancing near-term forecasting of freshwater quality.

Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels.

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

Valuing bioeconomic impacts of regulation on nutrient loads mediated by invasive mussels in Lake Michigan.

Current calls to correct the perturbed ecosystems and nutrient imbalances of the Laurentian Great Lakes focus on setting target nutrient levels that will be met by environmental and economic regulations to reduce the flow of excess nutrients into the lakes. While these standard types of regulations have been successful in the past, it is unclear whether they will achieve similar ecological and economic successes now amid possible ecosystem regime shifts triggered by invasive mussels. We compute the bioeconomic costs and benefits of hypothetical regulations designed to target nutrient loads in present-day Lake Michigan through agricultural operations, which are known to be nonpoint source polluters of the aquatic ecosystem network. Our results show how reducing nutrient loads may exacerbate spatially disparate ecosystem issues that generate overall economic losses. Conversely, and counterintuitively, allowing for more nutrient loading could generate economic gains over our assessment timeframe. We determine that implementation of standard nutrient target policies are costly and likely inadequate for current dreissenid-affected aquatic ecosystems experiencing nutrient imbalances because they disregard the interactive role of invasive mussels in nutrient cycling.

Putting the Pacific marine heatwave into perspective: The response of larval fish off southern California to unprecedented warming in 2014-2016 relative to the previous 65 years.

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.

Anoxia decreases the magnitude of the carbon, nitrogen, and phosphorus sink in freshwaters.

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.

Does growing atmospheric CO2 explain increasing carbon sink in a boreal coniferous forest?

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.

EcoDiet: A hierarchical Bayesian model to combine stomach, biotracer, and literature data into diet matrix estimation.

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.

Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem.

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.

Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change.

While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends.

Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content.

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.

Climate-induced decrease in biomass flow in marine food webs may severely affect predators and ecosystem production.

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.

Model parameterization to represent processes at unresolved scales and changing properties of evolving systems.

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.

Predicting ecosystem state changes in shallow lakes using an aquatic ecosystem model: Lake Hinge, Denmark, an example.

In recent years, considerable efforts have been made to restore turbid, phytoplankton-dominated shallow lakes to a clear-water state with high coverage of submerged macrophytes. Various dynamic lake models with simplified physical representations of vertical gradients, such as PCLake, have been used to predict external nutrient load thresholds for such nonlinear regime shifts. However, recent observational studies have questioned the concept of regime shifts by emphasizing that gradual changes are more common than sudden shifts. We investigated if regime shifts would be more gradual if the models account for depth-dependent heterogeneity of the system by including the possibility of vertical gradients in the water column and sediment layers for the entire depth. Hence, bifurcation analysis was undertaken using the 1D hydrodynamic model GOTM, accounting for vertical gradients, coupled to the aquatic ecosystem model PCLake, which is implemented in the framework for aquatic biogeochemical modeling (FABM). First, the model was calibrated and validated against a comprehensive data set covering two consecutive 7-yr periods from Lake Hinge, a shallow, eutrophic Danish lake. The autocalibration program Auto-Calibration Python (ACPy) was applied to achieve a more comprehensive adjustment of model parameters. The model simulations showed excellent agreement with observed data for water temperature, total nitrogen, and nitrate and good agreement for ammonium, total phosphorus, phosphate, and chlorophyll a concentrations. Zooplankton and macrophyte coverage were adequately simulated for the purpose of this study, and in general the GOTM-FABM-PCLake model simulations performed well compared with other model studies. In contrast to previous model studies ignoring depth heterogeneity, our bifurcation analysis revealed that the spatial extent and depth limitation of macrophytes as well as phytoplankton chlorophyll-a responded more gradually over time to a reduction in the external phosphorus load, albeit some hysteresis effects still appeared. In a management perspective, our study emphasizes the need to include depth heterogeneity in the model structure to more correctly determine at which external nutrient load a given lake changes ecosystem state to a clear-water condition.

Interaction between top-down and bottom-up control in marine food webs.

Climate change and resource exploitation have been shown to modify the importance of bottom-up and top-down forces in ecosystems. However, the resulting pattern of trophic control in complex food webs is an emergent property of the system and thus unintuitive. We develop a statistical nondeterministic model, capable of modeling complex patterns of trophic control for the heavily impacted North Sea ecosystem. The model is driven solely by fishing mortality and climatic variables and based on time-series data covering >40 y for six plankton and eight fish groups along with one bird group (>20 y). Simulations show the outstanding importance of top-down exploitation pressure for the dynamics of fish populations. Whereas fishing effects on predators indirectly altered plankton abundance, bottom-up climatic processes dominate plankton dynamics. Importantly, we show planktivorous fish to have a central role in the North Sea food web initiating complex cascading effects across and between trophic levels. Our linked model integrates bottom-up and top-down effects and is able to simulate complex long-term changes in ecosystem components under a combination of stressor scenarios. Our results suggest that in marine ecosystems, pathways for bottom-up and top-down forces are not necessarily mutually exclusive and together can lead to the emergence of complex patterns of control.

On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls.

Contents Summary 693 I. Introduction 693 II. Comparison of various definitions and measurement techniques of minimum conductance 694 III. Cuticular conductance 695 IV. Contribution of stomata 696 V. Environmental and ecological variation in minimum conductance 696 VI. Use of minimum conductance in models 698 VII. Conclusions 703 Acknowledgements 703 References 703 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.

Tundra landscape heterogeneity, not interannual variability, controls the decadal regional carbon balance in the Western Russian Arctic.

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.

The role of protected areas in land use/land cover change and the carbon cycle in the conterminous United States.

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.