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01 februari 2023, Item does not contain fulltext, OBJECTIVE: To assess the impact of milrinone administration on time spent on nitric oxide (iNO) in infants with acute pulmonary hypertension (aPH). We hypothesized that intravenous milrinone used in conjunction with iNO would reduce the time on iNO therapy and the time spent on invasive ventilation in infants ≥34 weeks gestation with a diagnosis of aPH. We aimed to assess the practicality of instituting the protocol and contributing to a sample size calculation for a definitive multicentre study. STUDY DESIGN: This was a multicentre, randomized, double-blind, two arm pilot study, with a balanced (1:1) allocation. Infants with a gestation ≥34 weeks and a birth weight ≥2000 grams aPH, an oxygenation index of ≥10, and commenced on iNO were eligible. Participants on iNO were assigned to either a milrinone infusion (intervention) or a normal saline infusion (placebo) for up to 35 h. The primary outcome was time on iNO and feasibility of conducting the protocol. RESULTS: The trial was terminated early after 4 years of enrollment due to poor recruitment. Four infants were allocated to the intervention arm and 5 to the placebo arm. The groups were well matched for baseline variables. No differences were seen in any of the primary or secondary outcomes. CONCLUSION: Conducting an interventional trial in the setting of acute pulmonary hypertension in infants is not feasible using our current approach. Future studies in this area require alternative trial design to improve recruitment as this topic remains understudied in the neonatal field. TRIAL REGISTRATION: www.isrctn.com ; ISRCTN:12949496; EudraCT Number:2014-002988-16.

01 februari 2023, Item does not contain fulltext, OBJECTIVE: To assess the impact of milrinone administration on time spent on nitric oxide (iNO) in infants with acute pulmonary hypertension (aPH). We hypothesized that intravenous milrinone used in conjunction with iNO would reduce the time on iNO therapy and the time spent on invasive ventilation in infants ≥34 weeks gestation with a diagnosis of aPH. We aimed to assess the practicality of instituting the protocol and contributing to a sample size calculation for a definitive multicentre study. STUDY DESIGN: This was a multicentre, randomized, double-blind, two arm pilot study, with a balanced (1:1) allocation. Infants with a gestation ≥34 weeks and a birth weight ≥2000 grams aPH, an oxygenation index of ≥10, and commenced on iNO were eligible. Participants on iNO were assigned to either a milrinone infusion (intervention) or a normal saline infusion (placebo) for up to 35 h. The primary outcome was time on iNO and feasibility of conducting the protocol. RESULTS: The trial was terminated early after 4 years of enrollment due to poor recruitment. Four infants were allocated to the intervention arm and 5 to the placebo arm. The groups were well matched for baseline variables. No differences were seen in any of the primary or secondary outcomes. CONCLUSION: Conducting an interventional trial in the setting of acute pulmonary hypertension in infants is not feasible using our current approach. Future studies in this area require alternative trial design to improve recruitment as this topic remains understudied in the neonatal field. TRIAL REGISTRATION: www.isrctn.com ; ISRCTN:12949496; EudraCT Number:2014-002988-16.

Seminal scientific papers positing that mycorrhizal fungal networks can distribute carbon (C) among plants have stimulated a popular narrative that overstory trees, or 'mother trees', support the growth of seedlings in this way. This narrative has far-reaching implications for our understanding of forest ecology and has been controversial in the scientific community. We review the current understanding of ectomycorrhizal C metabolism and observations on forest regeneration that make the mother tree narrative debatable. We then re-examine data and conclusions from publications that underlie the mother tree hypothesis. Isotopic labeling methods are uniquely suited for studying element fluxes through ecosystems, but the complexity of mycorrhizal symbiosis, low detection limits, and small carbon discrimination in biological processes can cause researchers to make important inferences based on miniscule shifts in isotopic abundance, which can be misleading. We conclude that evidence of a significant net C transfer via common mycorrhizal networks that benefits the recipients is still lacking. Furthermore, a role for fungi as a C pipeline between trees is difficult to reconcile with any adaptive advantages for the fungi. Finally, the hypothesis is neither supported by boreal forest regeneration patterns nor consistent with the understanding of physiological mechanisms controlling mycorrhizal symbiosis.

Climate change consists of synergistic changes in a wide range of environmental conditions, characterized by elevated CO2, higher mean temperatures, and higher climate variability. While elevated CO2 concentrations may potentially increase the productivity of some ecosystems, it has been argued that nutrient limitation, increased respiration, and increased mortality may dampen or even negate these productivity gains. The capacity of global forests to adjust to such synergistic environmental changes depends on their functional diversity and the ecosystem’s adaptive capacity. The Plant-FATE eco-evolutionary model describes vegetation responses to altered environmental conditions, including CO2 concentrations, temperature, and water limitation. It represents functional diversity by modelling species as points in trait space and incorporates ecosystem adaptations at three levels: 1) to model acclimation of plastic traits of individual plants, we leverage the power of eco-evolutionary optimality principles, 2) to model shifts in species composition via demographic changes and species immigration, we implement a trait-size-structured demographic vegetation model, and 3) to model the long-term genetic evolution of species, we have developed new evolutionary theory for trait-size-structured communities. First, we show that with just a few calibrated parameters, the Plant-FATE model accurately predicts the fluxes of CO2 and water, size distributions, and trait distributions for a tropical wet site in the Amazon Forest. Second, we show that under elevated CO2 our model predictions are broadly consistent with observations, namely: an increase in leaf area, productivity and biomass, and a decrease in stomatal conductance and photosynthetic capacity. Third, we show that CO2 and nutrient fertilization both drive changes in community composition towards fast life-histories, and that competition drives the system in a direction opposite to what is optimal for individual plants. Our

Climate change consists of synergistic changes in a wide range of environmental conditions, characterized by elevated CO2, higher mean temperatures, and higher climate variability. While elevated CO2 concentrations may potentially increase the productivity of some ecosystems, it has been argued that nutrient limitation, increased respiration, and increased mortality may dampen or even negate these productivity gains. The capacity of global forests to adjust to such synergistic environmental changes depends on their functional diversity and the ecosystem’s adaptive capacity. The Plant-FATE eco-evolutionary model describes vegetation responses to altered environmental conditions, including CO2 concentrations, temperature, and water limitation. It represents functional diversity by modelling species as points in trait space and incorporates ecosystem adaptations at three levels: 1) to model acclimation of plastic traits of individual plants, we leverage the power of eco-evolutionary optimality principles, 2) to model shifts in species composition via demographic changes and species immigration, we implement a trait-size-structured demographic vegetation model, and 3) to model the long-term genetic evolution of species, we have developed new evolutionary theory for trait-size-structured communities. First, we show that with just a few calibrated parameters, the Plant-FATE model accurately predicts the fluxes of CO2 and water, size distributions, and trait distributions for a tropical wet site in the Amazon Forest. Second, we show that under elevated CO2 our model predictions are broadly consistent with observations, namely: an increase in leaf area, productivity and biomass, and a decrease in stomatal conductance and photosynthetic capacity. Third, we show that CO2 and nutrient fertilization both drive changes in community composition towards fast life-histories, and that competition drives the system in a direction opposite to what is optimal for individual plants. Our

Climate change consists of synergistic changes in a wide range of environmental conditions, characterized by elevated CO2, higher mean temperatures, and higher climate variability. While elevated CO2 concentrations may potentially increase the productivity of some ecosystems, it has been argued that nutrient limitation, increased respiration, and increased mortality may dampen or even negate these productivity gains. The capacity of global forests to adjust to such synergistic environmental changes depends on their functional diversity and the ecosystem’s adaptive capacity. The Plant-FATE eco-evolutionary model describes vegetation responses to altered environmental conditions, including CO2 concentrations, temperature, and water limitation. It represents functional diversity by modelling species as points in trait space and incorporates ecosystem adaptations at three levels: 1) to model acclimation of plastic traits of individual plants, we leverage the power of eco-evolutionary optimality principles, 2) to model shifts in species composition via demographic changes and species immigration, we implement a trait-size-structured demographic vegetation model, and 3) to model the long-term genetic evolution of species, we have developed new evolutionary theory for trait-size-structured communities. First, we show that with just a few calibrated parameters, the Plant-FATE model accurately predicts the fluxes of CO2 and water, size distributions, and trait distributions for a tropical wet site in the Amazon Forest. Second, we show that under elevated CO2 our model predictions are broadly consistent with observations, namely: an increase in leaf area, productivity and biomass, and a decrease in stomatal conductance and photosynthetic capacity. Third, we show that CO2 and nutrient fertilization both drive changes in community composition towards fast life-histories, and that competition drives the system in a direction opposite to what is optimal for individual plants. Our

Data used in the paper "Optimal balancing of xylem efficiency and safety explains plant vulnerability to drought"

Seminal scientific papers positing that mycorrhizal fungal networks can distribute carbon (C) among plants have stimulated a popular narrative that overstory trees, or 'mother trees', support the growth of seedlings in this way. This narrative has far-reaching implications for our understanding of forest ecology and has been controversial in the scientific community. We review the current understanding of ectomycorrhizal C metabolism and observations on forest regeneration that make the mother tree narrative debatable. We then re-examine data and conclusions from publications that underlie the mother tree hypothesis. Isotopic labeling methods are uniquely suited for studying element fluxes through ecosystems, but the complexity of mycorrhizal symbiosis, low detection limits, and small carbon discrimination in biological processes can cause researchers to make important inferences based on miniscule shifts in isotopic abundance, which can be misleading. We conclude that evidence of a significant net C transfer via common mycorrhizal networks that benefits the recipients is still lacking. Furthermore, a role for fungi as a C pipeline between trees is difficult to reconcile with any adaptive advantages for the fungi. Finally, the hypothesis is neither supported by boreal forest regeneration patterns nor consistent with the understanding of physiological mechanisms controlling mycorrhizal symbiosis.

The resilience of biodiverse forests to climate change depends on an interplay of adaptive processes operating at multiple temporal and organizational scales. These include short-term acclimation of physiological processes like photosynthesis and respiration, mid-term changes in forest structure due to competition, and long-term changes in community composition arising from competitive exclusion and genetic trait evolution. To investigate the roles of diversity and adaptation for forest resilience, we present Plant-FATE, a parsimonious eco-evolutionary vegetation model. Tested with data from a hyperdiverse Amazonian terra-firme forest, our model accurately predicts multiple emergent ecosystem properties characterizing forest structure and function. Under elevated CO2 conditions, we predict an increase in productivity, leaf area, and aboveground biomass, with the magnitude of this increase declining in nutrient-deprived soils if trees allocate more carbon to the rhizosphere to overcome nutrient limitation. Furthermore, increased aboveground productivity leads to greater competition for light and drives a shift in community composition towards fast-growing but short-lived species characterized by lower wood densities. Such a transition reduces the carbon residence time of woody biomass, dampening carbon-sink strength and potentially rendering the Amazon Forest more vulnerable to future climatic extreme events.

The resilience of biodiverse forests to climate change depends on an interplay of adaptive processes operating at multiple temporal and organizational scales. These include short-term acclimation of physiological processes like photosynthesis and respiration, mid-term changes in forest structure due to competition, and long-term changes in community composition arising from competitive exclusion and genetic trait evolution. To investigate the roles of diversity and adaptation for forest resilience, we present Plant-FATE, a parsimonious eco-evolutionary vegetation model. Tested with data from a hyperdiverse Amazonian terra-firme forest, our model accurately predicts multiple emergent ecosystem properties characterizing forest structure and function. Under elevated CO2 conditions, we predict an increase in productivity, leaf area, and aboveground biomass, with the magnitude of this increase declining in nutrient-deprived soils if trees allocate more carbon to the rhizosphere to overcome nutrient limitation. Furthermore, increased aboveground productivity leads to greater competition for light and drives a shift in community composition towards fast-growing but short-lived species characterized by lower wood densities. Such a transition reduces the carbon residence time of woody biomass, dampening carbon-sink strength and potentially rendering the Amazon Forest more vulnerable to future climatic extreme events.

Seminal scientific papers positing that mycorrhizal fungal networks can distribute carbon (C) among plants have stimulated a popular narrative that overstory trees, or 'mother trees', support the growth of seedlings in this way. This narrative has far-reaching implications for our understanding of forest ecology and has been controversial in the scientific community. We review the current understanding of ectomycorrhizal C metabolism and observations on forest regeneration that make the mother tree narrative debatable. We then re-examine data and conclusions from publications that underlie the mother tree hypothesis. Isotopic labeling methods are uniquely suited for studying element fluxes through ecosystems, but the complexity of mycorrhizal symbiosis, low detection limits, and small carbon discrimination in biological processes can cause researchers to make important inferences based on miniscule shifts in isotopic abundance, which can be misleading. We conclude that evidence of a significant net C transfer via common mycorrhizal networks that benefits the recipients is still lacking. Furthermore, a role for fungi as a C pipeline between trees is difficult to reconcile with any adaptive advantages for the fungi. Finally, the hypothesis is neither supported by boreal forest regeneration patterns nor consistent with the understanding of physiological mechanisms controlling mycorrhizal symbiosis.

Seminal scientific papers positing that mycorrhizal fungal networks can distribute carbon (C) among plants have stimulated a popular narrative that overstory trees, or 'mother trees', support the growth of seedlings in this way. This narrative has far-reaching implications for our understanding of forest ecology and has been controversial in the scientific community. We review the current understanding of ectomycorrhizal C metabolism and observations on forest regeneration that make the mother tree narrative debatable. We then re-examine data and conclusions from publications that underlie the mother tree hypothesis. Isotopic labeling methods are uniquely suited for studying element fluxes through ecosystems, but the complexity of mycorrhizal symbiosis, low detection limits, and small carbon discrimination in biological processes can cause researchers to make important inferences based on miniscule shifts in isotopic abundance, which can be misleading. We conclude that evidence of a significant net C transfer via common mycorrhizal networks that benefits the recipients is still lacking. Furthermore, a role for fungi as a C pipeline between trees is difficult to reconcile with any adaptive advantages for the fungi. Finally, the hypothesis is neither supported by boreal forest regeneration patterns nor consistent with the understanding of physiological mechanisms controlling mycorrhizal symbiosis.

Reliable manipulation of soil organic matter (SOM) – a necessity for optimal land management – is constrained by our limited mechanistic understanding of SOM formation. Here we propose a novel mechanistic element that may contribute to SOM dynamics, supplementing existing frameworks, based on evolutionary-ecological rather than chemical or physical limitations to decomposition. We argue that decomposition of some substrates may be constrained by spatial competition from opportunists. We describe and test a mathematical model based on our framework, providing a proof-of-concept that substrate can, in principle, be spared decomposition and accumulate even when it is physically and chemically accessible. Our framework can help explain a variety of SOM dynamics, including priming and the suppression of decomposition by nitrogen addition, as well as the typical composition of SOM. An augmented mechanistic framework for understanding SOM dynamics can help guide targeted empirical study, which in turn can contribute to more optimised land management.

Reliable manipulation of soil organic matter (SOM) – a necessity for optimal land management – is constrained by our limited mechanistic understanding of SOM formation. Here we propose a novel mechanistic element that may contribute to SOM dynamics, supplementing existing frameworks, based on evolutionary-ecological rather than chemical or physical limitations to decomposition. We argue that decomposition of some substrates may be constrained by spatial competition from opportunists. We describe and test a mathematical model based on our framework, providing a proof-of-concept that substrate can, in principle, be spared decomposition and accumulate even when it is physically and chemically accessible. Our framework can help explain a variety of SOM dynamics, including priming and the suppression of decomposition by nitrogen addition, as well as the typical composition of SOM. An augmented mechanistic framework for understanding SOM dynamics can help guide targeted empirical study, which in turn can contribute to more optimised land management.

Mitigation of, and adaptation to, climate change can be addressed only through the collective action of multiple agents. The engagement of involved agents critically depends on their perception that the burdens and benefits of collective action are distributed fairly. Integrated Assessment Models (IAMs), which inform climate policies, focus on the minimization of costs and the maximization of overall utility, but they rarely pay sufficient attention to how costs and benefits are distributed among agents. Consequently, some agents may perceive the resultant model-based policy recommendations as unfair. In this paper, we propose how to adjust the objectives optimized within IAMs so as to derive policy recommendations that can plausibly be presented to agents as fair. We review approaches to aggregating the utilities of multiple agents into fairness-relevant social rankings of outcomes, analyze features of these rankings, and associate with them collections of properties that a model’s objective function must have to operationalize each of these rankings within the model. Moreover, for each considered ranking, we propose a selection of specific objective functions that can conveniently be used for generating this ranking in a model. Maximizing these objective functions within existing IAMs allows exploring and identifying climate polices to which multiple agents may be willing to commit.

Mitigation of, and adaptation to, climate change can be addressed only through the collective action of multiple agents. The engagement of involved agents critically depends on their perception that the burdens and benefits of collective action are distributed fairly. Integrated Assessment Models (IAMs), which inform climate policies, focus on the minimization of costs and the maximization of overall utility, but they rarely pay sufficient attention to how costs and benefits are distributed among agents. Consequently, some agents may perceive the resultant model-based policy recommendations as unfair. In this paper, we propose how to adjust the objectives optimized within IAMs so as to derive policy recommendations that can plausibly be presented to agents as fair. We review approaches to aggregating the utilities of multiple agents into fairness-relevant social rankings of outcomes, analyze features of these rankings, and associate with them collections of properties that a model’s objective function must have to operationalize each of these rankings within the model. Moreover, for each considered ranking, we propose a selection of specific objective functions that can conveniently be used for generating this ranking in a model. Maximizing these objective functions within existing IAMs allows exploring and identifying climate polices to which multiple agents may be willing to commit.

Mitigation of, and adaptation to, climate change can be addressed only through the collective action of multiple agents. The engagement of involved agents critically depends on their perception that the burdens and benefits of collective action are distributed fairly. Integrated Assessment Models (IAMs), which inform climate policies, focus on the minimization of costs and the maximization of overall utility, but they rarely pay sufficient attention to how costs and benefits are distributed among agents. Consequently, some agents may perceive the resultant model-based policy recommendations as unfair. In this paper, we propose how to adjust the objectives optimized within IAMs so as to derive policy recommendations that can plausibly be presented to agents as fair. We review approaches to aggregating the utilities of multiple agents into fairness-relevant social rankings of outcomes, analyze features of these rankings, and associate with them collections of properties that a model’s objective function must have to operationalize each of these rankings within the model. Moreover, for each considered ranking, we propose a selection of specific objective functions that can conveniently be used for generating this ranking in a model. Maximizing these objective functions within existing IAMs allows exploring and identifying climate polices to which multiple agents may be willing to commit.

Mitigation of, and adaptation to, climate change can be addressed only through the collective action of multiple agents. The engagement of involved agents critically depends on their perception that the burdens and benefits of collective action are distributed fairly. Integrated Assessment Models (IAMs), which inform climate policies, focus on the minimization of costs and the maximization of overall utility, but they rarely pay sufficient attention to how costs and benefits are distributed among agents. Consequently, some agents may perceive the resultant model-based policy recommendations as unfair. In this paper, we propose how to adjust the objectives optimized within IAMs so as to derive policy recommendations that can plausibly be presented to agents as fair. We review approaches to aggregating the utilities of multiple agents into fairness-relevant social rankings of outcomes, analyze features of these rankings, and associate with them collections of properties that a model’s objective function must have to operationalize each of these rankings within the model. Moreover, for each considered ranking, we propose a selection of specific objective functions that can conveniently be used for generating this ranking in a model. Maximizing these objective functions within existing IAMs allows exploring and identifying climate polices to which multiple agents may be willing to commit.

Mitigation of, and adaptation to, climate change can be addressed only through the collective action of multiple agents. The engagement of involved agents critically depends on their perception that the burdens and benefits of collective action are distributed fairly. Integrated Assessment Models (IAMs), which inform climate policies, focus on the minimization of costs and the maximization of overall utility, but they rarely pay sufficient attention to how costs and benefits are distributed among agents. Consequently, some agents may perceive the resultant model-based policy recommendations as unfair. In this paper, we propose how to adjust the objectives optimized within IAMs so as to derive policy recommendations that can plausibly be presented to agents as fair. We review approaches to aggregating the utilities of multiple agents into fairness-relevant social rankings of outcomes, analyze features of these rankings, and associate with them collections of properties that a model’s objective function must have to operationalize each of these rankings within the model. Moreover, for each considered ranking, we propose a selection of specific objective functions that can conveniently be used for generating this ranking in a model. Maximizing these objective functions within existing IAMs allows exploring and identifying climate polices to which multiple agents may be willing to commit.

Understanding how plant water uptake interacts with acquisition of soil nitrogen (N) and other nutrients is fundamental for predicting plant responses to a changing environment, but it is an area where models disagree. We present a novel isotopic labelling approach which reveals spatial patterns of water and N uptake, and their interaction, by trees. The stable isotopes 15 N and 2 H were applied to a small area of the forest floor in stands with high and low soil N availability. Uptake by surrounding trees was measured. The sensitivity of N acquisition to water uptake was quantified by statistical modelling. Trees in the high-N stand acquired twice as much 15 N as in the low-N stand and around half of their N uptake was dependent on water uptake (2 H enrichment). In contrast, in the low-N stand there was no positive effect of water uptake on N uptake. We conclude that tree N acquisition was only marginally dependent on water flux toward the root surface under low N conditions but under high-N conditions, the water-associated N uptake was substantial. The results suggest a fundamental shift in N acquisition strategy under high-N conditions.

Trees receive growth-limiting nitrogen from their ectomycorrhizal symbionts, but supplying the fungi with carbon can also cause nitrogen immobilization, which hampers tree growth. We present results from field and greenhouse experiments combined with mathematical modelling, showing that these are not conflicting outcomes. Mycorrhizal networks connect multiple trees, and we modulated C provision by strangling subsets of Pinus sylvestris trees, assuming that carbon supply to fungi was reduced proportionally to the strangled fraction. We conclude that trees gain additional nitrogen at the expense of their neighbours by supplying more carbon to the fungi. But this additional carbon supply aggravates nitrogen limitation via immobilization of the shared fungal biomass. We illustrate the evolutionary underpinnings of this situation by drawing on the analogous tragedy of the commons, where the shared mycorrhizal network is the commons, and explain how rising atmospheric CO2 may lead to greater nitrogen immobilization in the future.

Understanding how plant water uptake interacts with acquisition of soil nitrogen (N) and other nutrients is fundamental for predicting plant responses to a changing environment, but it is an area where models disagree. We present a novel isotopic labelling approach which reveals spatial patterns of water and N uptake, and their interaction, by trees. The stable isotopes 15 N and 2 H were applied to a small area of the forest floor in stands with high and low soil N availability. Uptake by surrounding trees was measured. The sensitivity of N acquisition to water uptake was quantified by statistical modelling. Trees in the high-N stand acquired twice as much 15 N as in the low-N stand and around half of their N uptake was dependent on water uptake (2 H enrichment). In contrast, in the low-N stand there was no positive effect of water uptake on N uptake. We conclude that tree N acquisition was only marginally dependent on water flux toward the root surface under low N conditions but under high-N conditions, the water-associated N uptake was substantial. The results suggest a fundamental shift in N acquisition strategy under high-N conditions.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generate parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration, and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Trees receive growth-limiting nitrogen from their ectomycorrhizal symbionts, but supplying the fungi with carbon can also cause nitrogen immobilization, which hampers tree growth. We present results from field and greenhouse experiments combined with mathematical modelling, showing that these are not conflicting outcomes. Mycorrhizal networks connect multiple trees, and we modulated C provision by strangling subsets of Pinus sylvestris trees, assuming that carbon supply to fungi was reduced proportionally to the strangled fraction. We conclude that trees gain additional nitrogen at the expense of their neighbours by supplying more carbon to the fungi. But this additional carbon supply aggravates nitrogen limitation via immobilization of the shared fungal biomass. We illustrate the evolutionary underpinnings of this situation by drawing on the analogous tragedy of the commons, where the shared mycorrhizal network is the commons, and explain how rising atmospheric CO2 may lead to greater nitrogen immobilization in the future.

Understanding how plant water uptake interacts with acquisition of soil nitrogen (N) and other nutrients is fundamental for predicting plant responses to a changing environment, but it is an area where models disagree. We present a novel isotopic labelling approach which reveals spatial patterns of water and N uptake, and their interaction, by trees. The stable isotopes 15 N and 2 H were applied to a small area of the forest floor in stands with high and low soil N availability. Uptake by surrounding trees was measured. The sensitivity of N acquisition to water uptake was quantified by statistical modelling. Trees in the high-N stand acquired twice as much 15 N as in the low-N stand and around half of their N uptake was dependent on water uptake (2 H enrichment). In contrast, in the low-N stand there was no positive effect of water uptake on N uptake. We conclude that tree N acquisition was only marginally dependent on water flux toward the root surface under low N conditions but under high-N conditions, the water-associated N uptake was substantial. The results suggest a fundamental shift in N acquisition strategy under high-N conditions.

Understanding how plant water uptake interacts with acquisition of soil nitrogen (N) and other nutrients is fundamental for predicting plant responses to a changing environment, but it is an area where models disagree. We present a novel isotopic labelling approach which reveals spatial patterns of water and N uptake, and their interaction, by trees. The stable isotopes 15 N and 2 H were applied to a small area of the forest floor in stands with high and low soil N availability. Uptake by surrounding trees was measured. The sensitivity of N acquisition to water uptake was quantified by statistical modelling. Trees in the high-N stand acquired twice as much 15 N as in the low-N stand and around half of their N uptake was dependent on water uptake (2 H enrichment). In contrast, in the low-N stand there was no positive effect of water uptake on N uptake. We conclude that tree N acquisition was only marginally dependent on water flux toward the root surface under low N conditions but under high-N conditions, the water-associated N uptake was substantial. The results suggest a fundamental shift in N acquisition strategy under high-N conditions.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generate parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration, and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Plant functional trait variation in tropical forests results from taxonomic differences in phylogeny and associated genetic differences. as well as, phenotypic plastic responses to the environment Accounting for different components driving plant functional trait variation is important for understanding the potential rate of change of ecosystems since trait acclimation via phenotypic plasticity is very fast compared to shifts in community composition and genetic adaptation. We here applied a statistical technique todecompose the relative role of phenotypic plasticity, genetic adaptation and phylogenetic constraints. We examined typically obtained plant functional traits, such as wood density, plant height, specific leaf area, leaf area, leaf thickness, leaf dry mass content, leaf nitrogen content and leaf phosphorus content. We assumed that genetic differences in plant functional traits between species increases with geographic distance, whereas trait variation due to plastic acclimation to the local environment is independent of spatial constraints. Results suggest that most of the observed trait variation could not be explained by a single component, thus indicating a limited potential to predict individual plant traits from commonly measured environmental variables. However, we found a non-uniform response between different plant tissues in accordance with the plant economic spectrum, such that leaf traits varied in response to canopy light regime and nutrient availability, whereas wood traits were related to topoedaphic factors and water availability. Our analysis furthermore indicated differences in the functional response between coexisting tropical tree species, such that endemic species with conservative ecological strategies appear especially prone to competitive exclusion under projected climate change.

Trees receive growth-limiting nitrogen from their ectomycorrhizal symbionts, but supplying the fungi with carbon can also cause nitrogen immobilization, which hampers tree growth. We present results from field and greenhouse experiments combined with mathematical modelling, showing that these are not conflicting outcomes. Mycorrhizal networks connect multiple trees, and we modulated C provision by strangling subsets of Pinus sylvestris trees, assuming that carbon supply to fungi was reduced proportionally to the strangled fraction. We conclude that trees gain additional nitrogen at the expense of their neighbours by supplying more carbon to the fungi. But this additional carbon supply aggravates nitrogen limitation via immobilization of the shared fungal biomass. We illustrate the evolutionary underpinnings of this situation by drawing on the analogous tragedy of the commons, where the shared mycorrhizal network is the commons, and explain how rising atmospheric CO2 may lead to greater nitrogen immobilization in the future.

Plant functional trait variation in tropical forests results from taxonomic differences in phylogeny and associated genetic differences. as well as, phenotypic plastic responses to the environment Accounting for different components driving plant functional trait variation is important for understanding the potential rate of change of ecosystems since trait acclimation via phenotypic plasticity is very fast compared to shifts in community composition and genetic adaptation. We here applied a statistical technique todecompose the relative role of phenotypic plasticity, genetic adaptation and phylogenetic constraints. We examined typically obtained plant functional traits, such as wood density, plant height, specific leaf area, leaf area, leaf thickness, leaf dry mass content, leaf nitrogen content and leaf phosphorus content. We assumed that genetic differences in plant functional traits between species increases with geographic distance, whereas trait variation due to plastic acclimation to the local environment is independent of spatial constraints. Results suggest that most of the observed trait variation could not be explained by a single component, thus indicating a limited potential to predict individual plant traits from commonly measured environmental variables. However, we found a non-uniform response between different plant tissues in accordance with the plant economic spectrum, such that leaf traits varied in response to canopy light regime and nutrient availability, whereas wood traits were related to topoedaphic factors and water availability. Our analysis furthermore indicated differences in the functional response between coexisting tropical tree species, such that endemic species with conservative ecological strategies appear especially prone to competitive exclusion under projected climate change.

Plant functional trait variation in tropical forests results from taxonomic differences in phylogeny and associated genetic differences. as well as, phenotypic plastic responses to the environment Accounting for different components driving plant functional trait variation is important for understanding the potential rate of change of ecosystems since trait acclimation via phenotypic plasticity is very fast compared to shifts in community composition and genetic adaptation. We here applied a statistical technique todecompose the relative role of phenotypic plasticity, genetic adaptation and phylogenetic constraints. We examined typically obtained plant functional traits, such as wood density, plant height, specific leaf area, leaf area, leaf thickness, leaf dry mass content, leaf nitrogen content and leaf phosphorus content. We assumed that genetic differences in plant functional traits between species increases with geographic distance, whereas trait variation due to plastic acclimation to the local environment is independent of spatial constraints. Results suggest that most of the observed trait variation could not be explained by a single component, thus indicating a limited potential to predict individual plant traits from commonly measured environmental variables. However, we found a non-uniform response between different plant tissues in accordance with the plant economic spectrum, such that leaf traits varied in response to canopy light regime and nutrient availability, whereas wood traits were related to topoedaphic factors and water availability. Our analysis furthermore indicated differences in the functional response between coexisting tropical tree species, such that endemic species with conservative ecological strategies appear especially prone to competitive exclusion under projected climate change.

For trees in forests, striving for light is matter of life and death, either by growing taller towards brighter conditions or by expanding the crown to capture more of the available light. Here we present a mechanistic model for the development path of stem height and crown size, accounting for light capture and growth, as well as mortality risk. We determine the optimal growth path among all possible trajectories using dynamic programming. The optimal growth path follows a sequence of distinct phases: (i) initial crown size expansion, (ii) stem height growth towards the canopy, (iii) final expansion of the crown in the canopy, and (iv) seed production without further increase in size. The transition points between these phases can be optimized by maximizing fitness, defined as expected life-time reproductive production. The results imply that to reach the canopy in an optimal way, trees must consider the full profile of expected increasing light levels towards the canopy. A short-sighted maximization of growth based on initial light conditions can result in arrested height growth, preventing the tree from reaching the canopy. The previous result can explain canopy stratification, and why canopy species often get stuck at certain size under a shading canopy. The model explains why trees with lower wood density have a larger diameter at a given tree height and grow taller than trees with higher wood density. The model can be used to implement plasticity in height versus diameter growth in individual based vegetation and forestry models.

For trees in forests, striving for light is matter of life and death, either by growing taller towards brighter conditions or by expanding the crown to capture more of the available light. Here we present a mechanistic model for the development path of stem height and crown size, accounting for light capture and growth, as well as mortality risk. We determine the optimal growth path among all possible trajectories using dynamic programming. The optimal growth path follows a sequence of distinct phases: (i) initial crown size expansion, (ii) stem height growth towards the canopy, (iii) final expansion of the crown in the canopy, and (iv) seed production without further increase in size. The transition points between these phases can be optimized by maximizing fitness, defined as expected life-time reproductive production. The results imply that to reach the canopy in an optimal way, trees must consider the full profile of expected increasing light levels towards the canopy. A short-sighted maximization of growth based on initial light conditions can result in arrested height growth, preventing the tree from reaching the canopy. The previous result can explain canopy stratification, and why canopy species often get stuck at certain size under a shading canopy. The model explains why trees with lower wood density have a larger diameter at a given tree height and grow taller than trees with higher wood density. The model can be used to implement plasticity in height versus diameter growth in individual based vegetation and forestry models.

Tropical rainforests harbor exceptionally high biodiversity and store large amounts of carbon in vegetation biomass. However, regional variation in plant species richness and vegetation carbon stock can be substantial, and may be related to the heterogeneity of topoedaphic properties. Therefore, aboveground vegetation carbon storage typically differs between geographic forest regions in association with the locally dominant plant functional group. A better understanding of the underlying factors controlling tropical forest diversity and vegetation carbon storage could be critical for predicting tropical carbon sink strength in response to projected climate change. Based on regionally replicated 1-ha forest inventory plots established in a region of high geomorphological heterogeneity we investigated how climatic and edaphic factors affect tropical forest diversity and vegetation carbon storage. Plant species richness (of all living stems >10 cm in diameter) ranged from 69 to 127 ha−1 and vegetation carbon storage ranged from 114 to 200 t ha−1. While plant species richness was controlled by climate and soil water availability, vegetation carbon storage was strongly related to wood density and soil phosphorus availability. Results suggest that local heterogeneity in resource availability and plant functional composition should be considered to improve projections of tropical forest ecosystem functioning under future scenarios.

The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale. Copyright 2020 Deckmyn et al.

Sweden has the world’s highest density of moose (Alces alces). Moose is not only a valuable game species; it also causes forest damages and traffic accidents. To avoid moose browsing, foresters respond by planting spruce (Picea abies) to an extent that reshapes the forest landscape with impacts on both production and biodiversity. To address this problem and maintain a healthy moose population in balance with the other interests, an adaptive management based on the knowledge and experiences of local hunters and landowners is advocated. However, the different stakeholders do not agree on what is an appropriate moose population, which leads to conflicts that are hard to resolve. A key problem is that it is very difficult to encompass and foresee long-term consequences of different options for moose hunting and forest management. This makes it challenging to form coherent strategies that integrate different sectorial interests at a national level. To address this issue, we have developed a systems analysis framework for integrated modeling of the moose population, forestry, and their interactions and consequences for biodiversity. We analyze the short and long-term consequences for multiple scenarios of moose hunting and forest management. Based on the results we elucidate and quantify the trade-offs and possible synergies between moose hunting and forest production. This analysis can be used to support better informed and more constructive discussions among the stakeholders in the Swedish forest sectors, and to support policies for long term sustainable forest and moose management.

Sweden has the world’s highest density of moose (Alces alces). Moose is not only a valuable game species; it also causes forest damages and traffic accidents. To avoid moose browsing, foresters respond by planting spruce (Picea abies) to an extent that reshapes the forest landscape with impacts on both production and biodiversity. To address this problem and maintain a healthy moose population in balance with the other interests, an adaptive management based on the knowledge and experiences of local hunters and landowners is advocated. However, the different stakeholders do not agree on what is an appropriate moose population, which leads to conflicts that are hard to resolve. A key problem is that it is very difficult to encompass and foresee long-term consequences of different options for moose hunting and forest management. This makes it challenging to form coherent strategies that integrate different sectorial interests at a national level. To address this issue, we have developed a systems analysis framework for integrated modeling of the moose population, forestry, and their interactions and consequences for biodiversity. We analyze the short and long-term consequences for multiple scenarios of moose hunting and forest management. Based on the results we elucidate and quantify the trade-offs and possible synergies between moose hunting and forest production. This analysis can be used to support better informed and more constructive discussions among the stakeholders in the Swedish forest sectors, and to support policies for long term sustainable forest and moose management.

Sweden has the world’s highest density of moose (Alces alces). Moose is not only a valuable game species; it also causes forest damages and traffic accidents. To avoid moose browsing, foresters respond by planting spruce (Picea abies) to an extent that reshapes the forest landscape with impacts on both production and biodiversity. To address this problem and maintain a healthy moose population in balance with the other interests, an adaptive management based on the knowledge and experiences of local hunters and landowners is advocated. However, the different stakeholders do not agree on what is an appropriate moose population, which leads to conflicts that are hard to resolve. A key problem is that it is very difficult to encompass and foresee long-term consequences of different options for moose hunting and forest management. This makes it challenging to form coherent strategies that integrate different sectorial interests at a national level. To address this issue, we have developed a systems analysis framework for integrated modeling of the moose population, forestry, and their interactions and consequences for biodiversity. We analyze the short and long-term consequences for multiple scenarios of moose hunting and forest management. Based on the results we elucidate and quantify the trade-offs and possible synergies between moose hunting and forest production. This analysis can be used to support better informed and more constructive discussions among the stakeholders in the Swedish forest sectors, and to support policies for long term sustainable forest and moose management.

The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.

The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.

The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale.

Based on Free Air Carbon Dioxide Enrichment (FACE) and other raised-CO2 experiments (eCO2), new hypotheses have been proposed to explain how the magnitude of the CO2 fertilization effect on biomass and biomass production depends primarily on soil nitrogen and phosphorus availability [1,2]. To test whether these hypotheses and measurements from eCO2 could explain the land carbon sink as independently determined from data and models, we combined a CO2 response curve for biomass production with a simple two-box model of biomass and soil to simulate the evolution of the land carbon sink during the past century. Results were compared to Dynamic Global Vegetation Model (DGVM) results, as reported by the Global Carbon Project, and to results from inversion studies based on atmospheric CO2 measurements. The interannual variability of the modelled land sink was realistic, dominated by the temperature dependence of heterotrophic respiration, and similar to DGVMs results. However, the magnitude of the derived land sink based on eCO2 results was smaller, and its geographical distribution was different to DGVMs average. Sensitivity tests showed that these findings were robust to reasonable variations of parameter values. The smaller sink is due to the smaller amount of vegetation biomass increment documented by eCO2 experiments in comparison with the mean predictions of DGVMs. A land sink closer to the observed one could be produced, however, when incorporating the hypothesis that nutrient-stressed plants export “excess” carbon (generated by increased photosynthesis, but unable to be used for growth) to the soil and that only a fraction of this excess carbon returns to the atmosphere. This hypothesis requires further exploration but hints at a reconciliation between DGVMs that explain the land carbon sink without nutrient limitations, with experimental findings of (sometimes severe) restrictions on CO2 fertilization due to nutrient stress.

Currently applied dynamic vegetation models do not realistically represent forest ecosystem processes and thus are not able to reproduce in-situ observations of forest ecosystem responses to drought. This is due to the fact that models typically rely on plant functional types to forecast the functional response of vegetation to climate change and to anthropogenic disturbance. However, recent observations of divergent ecosystem responses between topographic forest sites, differing in the availability of water and nutrients, indicate that we should no longer rely on this outdated concept but rather should explore new avenues of representing vegetation dynamics and associated climate change response in next-generation approaches. Global climate change scenarios forecast increasing severity of climate extremes in association with El Niño–Southern Oscillation (ENSO). Such climate anomalies have been shown to affect forest ecosystem processes such as net primary productivity, which is determined by climate (precipitation, temperature, and light) and soil fertility (geology and topography). However, more recently it has been suggested that the impact of such climate fluctuations on forest productivity was strongly related to local site characteristics, which determined the sensitivity of forest ecosystem processes to climate anomalies. We propose a novel approach integrating in-situ observations with remotely sensed estimates of forest aboveground productivity for parameterization of next-generation vegetation models capable of forecasting realistic forest ecosystem responses under future scenarios. Our approach considers local site characteristics associated with topography and disturbance history, both of which determine the sensitivity of forest aboveground productivity to projected climate anomalies. Our results therefore should have crucial implications for management and restoration of forest ecosystems and could be used to refine estimates of forest C sink-strength

Tropical rainforests harbor exceptionally high biodiversity and store large amounts of carbon in vegetation biomass. However, regional variation in plant species richness and vegetation carbon stock can be substantial, and may be related to the heterogeneity of topoedaphic properties. Therefore, aboveground vegetation carbon storage typically differs between geographic forest regions in association with the locally dominant plant functional group. A better understanding of the underlying factors controlling tropical forest diversity and vegetation carbon storage could be critical for predicting tropical carbon sink strength in response to projected climate change. Based on regionally replicated 1-ha forest inventory plots established in a region of high geomorphological heterogeneity we investigated how climatic and edaphic factors affect tropical forest diversity and vegetation carbon storage. Plant species richness (of all living stems >10 cm in diameter) ranged from 69 to 127 ha−1 and vegetation carbon storage ranged from 114 to 200 t ha−1. While plant species richness was controlled by climate and soil water availability, vegetation carbon storage was strongly related to wood density and soil phosphorus availability. Results suggest that local heterogeneity in resource availability and plant functional composition should be considered to improve projections of tropical forest ecosystem functioning under future scenarios.

Tropical rainforests harbor exceptionally high biodiversity and store large amounts of carbon in vegetation biomass. However, regional variation in plant species richness and vegetation carbon stock can be substantial, and may be related to the heterogeneity of topoedaphic properties. Therefore, aboveground vegetation carbon storage typically differs between geographic forest regions in association with the locally dominant plant functional group. A better understanding of the underlying factors controlling tropical forest diversity and vegetation carbon storage could be critical for predicting tropical carbon sink strength in response to projected climate change. Based on regionally replicated 1-ha forest inventory plots established in a region of high geomorphological heterogeneity we investigated how climatic and edaphic factors affect tropical forest diversity and vegetation carbon storage. Plant species richness (of all living stems >10 cm in diameter) ranged from 69 to 127 ha−1 and vegetation carbon storage ranged from 114 to 200 t ha−1. While plant species richness was controlled by climate and soil water availability, vegetation carbon storage was strongly related to wood density and soil phosphorus availability. Results suggest that local heterogeneity in resource availability and plant functional composition should be considered to improve projections of tropical forest ecosystem functioning under future scenarios.