Accurate modelling of drought-induced mortality is challenging. A steady-state model is presented integrating xylem and phloem transport, leaf-level gas exchange and plant carbohydrate consumption ...during drought development.
A Bayesian analysis of parameter uncertainty based on expert knowledge and a literature review is carried out. The model is tested by combining six data compilations covering 170 species using information on sensitivities of xylem conductivity, stomatal conductance and leaf turgor to water potential.
The possible modes of plant failure at steady state are identified (i.e. carbon (C) starvation, hydraulic failure and phloem transport failure). Carbon starvation occurs primarily in the parameter space of isohydric stomatal control, whereas hydraulic failure is prevalent in the space of xylem susceptibility to embolism. Relative to C starvation, phloem transport failure occurs under conditions of low sensitivity of photosynthesis and high sensitivity of growth to plant water status.
These three failure modes are possible extremes along two axes of physiological vulnerabilities, one characterized by the balance of water supply and demand and the other by the balance between carbohydrate sources and sinks. Because the expression of physiological vulnerabilities is coordinated, we argue that different failure modes should occur with roughly equal likelihood, consistent with predictions using optimality theory.
Key message
Tree structure equations derived from pipe model theory (PMT) are well-suited to estimate biomass allocation in Scots pine (
Pinus sylvestris
L.) and Norway spruce (
Picea abies
L. ...Karst.). However, age dependence of parameters should be accounted for when applying the equations.
Context
Pipe model theory-based (PMT-based) structure equations have been incorporated in many process-based models. However, more data concerning old-growth trees is needed to test the reliability and generality of the structure equations.
Aims
This study (1) tested the age independence of the PMT-based structure equations and (2) provided general information about the stability of tree structure with age.
Methods
A total of 162 Scots pine and 163 Norway spruce trees in four age groups were analysed to test the age effect on the parameters of structure equations using a linear mixed model. Biomass of stem, branch and foliage was estimated from destructive measurements, and with other tree dimensions, they were used to present the tree growth patterns.
Results
(1) Stem biomass proportion increased with age, while branch and foliage biomass proportion decreased; biomass allocation and most tree variables became steady after maturing. (2) PMT-based structure equations were well-suited to Scots pine and Norway spruce in all age groups; however, age dependence was detected in the parameters of these equations, except for the branch-related equations in Scots pine and stem form coefficient below the crown base in both species.
Conclusion
Our study (1) provides information applicable to predictions of growth and biomass allocation in old boreal stands and (2) suggests taking age effect into account when structure equations are implemented in forest growth models.
Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based ...studies have aimed to derive growth–mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth–mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth–mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth–mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth–mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth–mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.
Forests regulate climate, as carbon, water and nutrient fluxes are modified by physiological processes of vegetation and soil. Forests also provide renewable raw material, food, and recreational ...possibilities. Rapid climate warming projected for the boreal zone may change the provision of these ecosystem services. We demonstrate model based estimates of present and future ecosystem services related to carbon cycling of boreal forests. The services were derived from biophysical variables calculated by two dynamic models. Future changes in the biophysical variables were driven by climate change scenarios obtained as results of a sample of global climate models downscaled for Finland, assuming three future pathways of radiative forcing. We introduce continuous monitoring on phenology to be used in model parametrization through a webcam network with automated image processing features. In our analysis, climate change impacts on key boreal forest ecosystem services are both beneficial and detrimental. Our results indicate an increase in annual forest growth of about 60% and an increase in annual carbon sink of roughly 40% from the reference period (1981-2010) to the end of the century. The vegetation active period was projected to start about 3 weeks earlier and end ten days later by the end of the century compared to currently. We found a risk for increasing drought, and a decrease in the number of soil frost days. Our results show a considerable uncertainty in future provision of boreal forest ecosystem services.
The pressure to increase forest and land carbon stocks simultaneously with increasing forest based biomass harvest for energy and materials emphasizes the need for dedicated analyses of impacts and ...possible trade-offs between these different mitigation options including also forest related biophysical factors, surface albedo and the formation of biogenic Secondary Organic Aerosols (SOA). We analyzed the change in global radiative forcing (RF) due to changes in these climatic agents as affected by the change in state of Finnish forests under increased or decreased harvest scenarios from a baseline. We also included avoided emissions due to wood material and energy substitution. Increasing harvests from baseline (65% of Current Annual Increment) decreased the total carbon sink (carbon in trees, soil and harvested wood products) at least for 50 years. When we coupled this change in carbon with other biosphere responses, surface albedo and aerosols, decreasing harvests from the baseline produced the largest cooling effect during 50 years. Accounting also for the avoided emissions due to increased wood use, the RF responses of the two lowest harvest scenarios were within uncertainty range. Our results show that the effects of forest management on SOA formation should be included in the analyses trying to deduce the net climate impact of forest use. The inclusion of the rarely considered SOA effects enforces the view that the lower the harvest, the more climatic cooling boreal forests provide. These results should act as a caution mark for policy makers who are emphasizing the increased utilization of forest biomass for short-living products and bioenergy as an efficient measure to mitigate climate change.
•A forest growth model was calibrated for Scots pine, Norway spruce and Silver birch.•We assimilated into the model multiple source of data collected over decades.•Parametric and output uncertainties ...were reduced by means of Bayesian calibration.•Results showed how quality of data used for calibration affects model performance.•We identified model weaknesses and subroutines that need to be improved.
Policy-relevant forest models must be environment and management sensitive and provide unbiased estimates of predicted variables over their intended areas of application. While empirical models derive their structure and parameters from representative data sets, process-based model (PBM) parameters should be evaluated in ranges that have a biological meaning independently of output data. At the same time PBMs should be calibrated against observations in order to obtain unbiased estimates and an understanding of their predictive capability. By means of model data assimilation, we Bayesian calibrated a forest model (PREBAS) using an extensive dataset that covered a wide range of climatic conditions, species composition and management practices. PREBAS was calibrated for three species in Finland: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies L. H. Karst.) and Silver birch (Betula pendula L.). Data assimilation was strongly effective in reducing the uncertainty of PREBAS parameters and predictions. A country-generic calibration showed robust performances in predicting forest variables and the results were consistent with yield tables and national forest statistics. The posterior predictive uncertainty of the model was mainly influenced by the uncertainty of the structural and measurement error.
Applications of ecosystem flux models on large geographical scales are often limited by model complexity and data availability. Here we calibrated and evaluated a semi‐empirical ecosystem flux model, ...PREdict Light‐use efficiency, Evapotranspiration and Soil water (PRELES), for various forest types and climate conditions, based on eddy covariance data from 55 sites. A Bayesian approach was adopted for model calibration and uncertainty quantification. We applied the site‐specific calibrations and multisite calibrations to nine plant functional types (PFTs) to obtain the site‐specific and PFT‐specific parameter vectors for PRELES. A systematically designed cross‐validation was implemented to evaluate calibration strategies and the risks in extrapolation. The combination of plant physiological traits and climate patterns generated significant variation in vegetation responses and model parameters across but not within PFTs, implying that applying the model without PFT‐specific parameters is risky. But within PFT, the multisite calibrations performed as accurately as the site‐specific calibrations in predicting gross primary production (GPP) and evapotranspiration (ET). Moreover, the variations among sites within one PFT could be effectively simulated by simply adjusting the parameter of potential light‐use efficiency (LUE), implying significant convergence of simulated vegetation processes within PFT. The hierarchical modelling of PRELES provides a compromise between satellite‐driven LUE and physiologically oriented approaches for extrapolating the geographical variation of ecosystem productivity. Although measurement errors of eddy covariance and remotely sensed data propagated a substantial proportion of uncertainty or potential biases, the results illustrated that PRELES could reliably capture daily variations of GPP and ET for contrasting forest types on large geographical scales if PFT‐specific parameterizations were applied.
A semi‐empirical ecosystem flux model, PREdict Light‐use efficiency, Evapotranspiration and Soil water, was calibrated and evaluated for various forest types and climate conditions. The variations among sites within one plant functional type (PFT) could be effectively simulated by simply adjusting the parameter of potential light‐use efficiency, implying significant convergence of simulated vegetation processes within PFT.
Uncertainties are essential, yet often neglected, information for evaluating the reliability in forest carbon balance projections used in national and regional policy planning. We analysed ...uncertainties in the forest net biome exchange (NBE) and carbon stocks under multiple management and climate scenarios with a process-based ecosystem model. Sampled forest initial state values, model parameters, harvest levels and global climate models (GCMs) served as inputs in Monte Carlo simulations, which covered forests of the 18 regions of mainland Finland over the period 2015–2050. Under individual scenarios, the results revealed time- and region-dependent variability in the magnitude of uncertainty and mean values of the NBE projections. The main sources of uncertainty varied with time, by region and by the amount of harvested wood. Combinations of uncertainties in the representative concentration pathways scenarios, GCMs, forest initial values and model parameters were the main sources of uncertainty at the beginning, while the harvest scenarios dominated by the end of the simulation period, combined with GCMs and climate scenarios especially in the north. Our regionally explicit uncertainty analysis was found a useful approach to reveal the variability in the regional potentials to reach a policy related, future target level of NBE, which is important information when planning realistic and regionally fair national policy actions.
•We predict GPP and water balance of Finnish forests by simple ecosystem model PRELES.•We show how different sources of uncertainty propagates to ecological impacts.•Global Circulation Model (GCM) ...variability was the major source of uncertainty until 2060.•We need to improve our mechanistic understanding of long-term CO2 fertilization effect on GPP.•A thorough assessment of uncertainties in the projections of the impacts is important for drawing robust conclusions.
We are bound to large uncertainties when considering impacts of climate change on forest productivity. Studies formally acknowledging and determining the relative importance of different sources of this uncertainty are still scarce, although the choice of the climate scenario, and e.g. the assumption of the CO2 effects on tree water use can easily result in contradicting conclusions of future forest productivity. In a large scale, forest productivity is primarily driven by two large fluxes, gross primary production (GPP), which is the source for all carbon in forest ecosystems, and heterotrophic respiration. Here we show how uncertainty of GPP projections of Finnish boreal forests divides between input, mechanistic and parametric uncertainty. We used the simple semi-empirical stand GPP and water balance model PRELES with an ensemble of downscaled global circulation model (GCM) projections for the 21st century under different emissions and forcing scenarios (both RCP and SRES). We also evaluated the sensitivity of assumptions of the relationships between atmospheric CO2 concentration (Ca), photosynthesis and water use of trees. Even mean changes in climate projections of different meteorological variables for Finland were so high that it is likely that the primary productivity of forests will increase by the end of the century. The scale of productivity change largely depends on the long-term Ca fertilization effect on GPP and transpiration. However, GCM variability was the major source of uncertainty until 2060, after which emission scenario/pathway became the dominant factor. Large uncertainties with a wide range of projections can make it more difficult to draw ecologically meaningful conclusions especially on the local to regional scales, yet a thorough assessment of uncertainties is important for drawing robust conclusions.
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