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\"Completely updated and expanded new edition of this widely cited book, Modelling Forest Growth and Yield, 2nd Edition synthesizes current scientific literature, provides insights in how models are constructed, gives suggestions for future developments, and outlines keys for successful implementation of models.The book describes current modeling approaches for predicting forest growth and yield and explores the components that comprise the various modeling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model\"--

1. Tropical forests play an important role in the global carbon cycle, but the drivers of net forest biomass change (i.e. net carbon sequestration) are poorly understood. Here, we evaluate how abiotic factors (soil conditions and disturbance) and biotic factors (forest structure, diversity and community trait composition) shape three important demographic processes (biomass recruitment, growth and mortality) and how these underlie net biomass change. 2. To test this, we evaluated 9 years of biomass dynamics using 48 1-ha plots in a Bolivian tropical moist forest, and measured for the most abundant species eight functional traits that are important for plant carbon gain and loss. Demographic processes were related to the abiotic and biotic factors using structural equation models. 3. Variation in net biomass change across plots was mostly due to stand-level mortality, but mortality itself could not be predicted at this scale. Contrary to expectations, we found that species richness and trait composition – which is an indicator for the mass-ratio theory – had little effect on the demographic processes. Biomass recruitment (i.e. the biomass growth by recruiting trees) increased with higher resource availability (i.e. water and light) and with high species richness, probably because of increased resource use efficiency. Biomass growth of larger, established trees increased with higher sand content, which may facilitate root growth of larger trees to deeper soil layers. 4. In sum, diversity and mass-ratio are of limited importance for the productivity of this forest. Instead, in this moist tropical forest with a marked dry season, demographic processes are most strongly determined by soil texture, soil water availability and forest structure. Only by simultaneously evaluating multiple abiotic and biotic drivers of demographic processes, better insights can be gained into mechanisms playing a role in the carbon sequestration potential of tropical forests and natural systems in general.

We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an ‘extra’ day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.

• Land surface models (LSMs) typically use empirical functions to represent vegetation responses to soil drought. These functions largely neglect recent advances in plant ecophysiology that link xylem hydraulic functioning with stomatal responses to climate. • We developed an analytical stomatal optimization model based on xylem hydraulics (SOX) to predict plant responses to drought. Coupling SOX to the Joint UK Land Environment Simulator (JULES) LSM, we conducted a global evaluation of SOX against leaf- and ecosystem-level observations. • SOX simulates leaf stomatal conductance responses to climate for woody plants more accurately and parsimoniously than the existing JULES stomatal conductance model. An ecosystem-level evaluation at 70 eddy flux sites shows that SOX decreases the sensitivity of gross primary productivity (GPP) to soil moisture, which improves the model agreement with observations and increases the predicted annual GPP by 30% in relation to JULES. SOX decreases JULES root-mean-square error in GPP by up to 45% in evergreen tropical forests, and can simulate realistic patterns of canopy water potential and soil water dynamics at the studied sites. • SOX provides a parsimonious way to incorporate recent advances in plant hydraulics and optimality theory into LSMs, and an alternative to empirical stress factors.

Primary (budburst, foliage and shoot) growth and secondary (cambium and xylem) growth of plants play a vital role in sequestering atmospheric carbon. However, their potential relationships have never been mathematically quantified and the underlying physiological mechanisms are unclear. We monitored primary and secondary growth in Picea mariana and Abies balsamea on a weekly basis from 2010 to 2013 at four sites over an altitudinal gradient (25–900 m) in the eastern Canadian boreal forest. We determined the timings of onset and termination through the fitted functions and their first derivative. We quantified the potential relationships between primary growth and secondary growth using the mixed‐effects model. We found that xylem formation of boreal conifers can be modeled as a function of cambium activity, bud phenology, and shoot and needle growth, as well as species‐ and site‐specific factors. Our model reveals that there may be an optimal mechanism to simultaneously allocate the photosynthetic products and stored nonstructural carbon to growth of different organs at different times in the growing season. This mathematical link can bridge phenological modeling, forest ecosystem productivity and carbon cycle modeling, which will certainly contribute to an improved prediction of ecosystem productivity and carbon equilibrium.

Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site ’Hohes Holz’ (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was ∼ 16 % higher in 2018 ( ∼ 424 g C m - 2 ) and ∼ 25 % lower in 2019 ( ∼ 274 g C m - 2 ) compared to pre-drought years ( ∼ 367 g C m - 2 ). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly 50 % . We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.

1. The role of niche complementarity for driving the positive biodiversity-ecosystem productivity relationship has been widely recognized, but there is scant evidence regarding the role of tree canopy structure on this relationship. Litterfall productivity is proportional to forest net primary productivity in natural forests, and we hypothesized that litterfall productivity would increase with tree species diversity via increased tree crown complementarity. 2. We investigated annual litterfall productivity, species diversity, tree crown architecture, soil moisture content, soil carbon content and stand age across 28 subtropical forest plots in eastern Zhejiang province, China. Simple linear regression was used to examine bivariate relationships among rarified species richness, crown complementarity, total crown volume, soil moisture content, soil carbon content, stand age and litterfall productivity. Structural equation modelling was employed to quantify the direct and indirect effects of species richness on litterfall productivity through tree crown complementarity. 3. Litterfall productivity increased with rarefied species richness via increasing crown complementarity rather than total crown volume. Species richness, crown complementarity and litterfall productivity increased with soil moisture content, while crown complementarity and litterfall productivity increased with soil carbon content. Neither species richness nor crown complementarity increased with stand age, even though litterfall productivity increased with stand age. 4. Synthesis. Our study provides evidence for the strong role of tree crown assembly in shaping ecosystem functions in complex natural forests. Our findings suggest that crown spatial complementarity among trees operates mechanistically to drive the positive tree species diversity-litterfall productivity relationship in subtropical forests. We argue that community and/or ecosystem ecology would benefit from more attention to crown variability among coexisting tree species.

A meta-analysis and field data show that frequent fires in savannas and broadleaf forests decrease soil carbon and nitrogen over many decades; modelling shows that nitrogen loss drives carbon loss by reducing net primary productivity. Soil degradation fuelled by fire The patterns of naturally occurring fires have been altered, both spatially and temporally, as a result of climate and land-use changes. The long-term effects of fire frequency on soil carbon and nutrient storage and the resulting potential limitations on plant productivity remain poorly understood. On the basis of a meta-analysis and an independent dataset of additional field sites, this paper finds that frequent burning leads to soil carbon and nitrogen losses that emerge over decadal timescales. Furthermore, the authors use a model to suggest that the decadal losses of soil nitrogen as a result of more frequent burning could decrease the amount of carbon sequestered by net primary productivity. Fire frequency is changing globally and is projected to affect the global carbon cycle and climate 1 , 2 , 3 . However, uncertainty about how ecosystems respond to decadal changes in fire frequency makes it difficult to predict the effects of altered fire regimes on the carbon cycle; for instance, we do not fully understand the long-term effects of fire on soil carbon and nutrient storage, or whether fire-driven nutrient losses limit plant productivity 4 , 5 . Here we analyse data from 48 sites in savanna grasslands, broadleaf forests and needleleaf forests spanning up to 65 years, during which time the frequency of fires was altered at each site. We find that frequently burned plots experienced a decline in surface soil carbon and nitrogen that was non-saturating through time, having 36 per cent (±13 per cent) less carbon and 38 per cent (±16 per cent) less nitrogen after 64 years than plots that were protected from fire. Fire-driven carbon and nitrogen losses were substantial in savanna grasslands and broadleaf forests, but not in temperate and boreal needleleaf forests. We also observe comparable soil carbon and nitrogen losses in an independent field dataset and in dynamic model simulations of global vegetation. The model study predicts that the long-term losses of soil nitrogen that result from more frequent burning may in turn decrease the carbon that is sequestered by net primary productivity by about 20 per cent of the total carbon that is emitted from burning biomass over the same period. Furthermore, we estimate that the effects of changes in fire frequency on ecosystem carbon storage may be 30 per cent too low if they do not include multidecadal changes in soil carbon, especially in drier savanna grasslands. Future changes in fire frequency may shift ecosystem carbon storage by changing soil carbon pools and nitrogen limitations on plant growth, altering the carbon sink capacity of frequently burning savanna grasslands and broadleaf forests.

The future trajectory of atmospheric CO₂ concentration depends on the development of the terrestrial carbon sink, which in turn is influenced by forest dynamics under changing environmental conditions. An in-depth understanding of model sensitivities and uncertainties in non-steady-state conditions is necessary for reliable and robust projections of forest development and under scenarios of global warming and CO₂ enrichment. Here, we systematically assessed if a biogeochemical process-based model (3D-CMCC-CNR), which embeds similarities with many other vegetation models, applied in simulating net primary productivity (NPP) and standing woody biomass (SWB), maintained a consistent sensitivity to its 55 input parameters through time, during forest ageing and structuring as well as under climate change scenarios. Overall, the model applied at three contrasting European forests showed low sensitivity to the majority of its parameters. Interestingly, model sensitivity to parameters varied through the course of >100 yr of simulations. In particular, the model showed a large responsiveness to the allometric parameters used for initialize forest carbon and nitrogen pools early in forest simulation (i.e., for NPP up to ∼37%, 256 g C·m−2·yr−1 and for SWB up to ∼90%, 65 Mg C/ha, when compared to standard simulation), with this sensitivity decreasing sharply during forest development. At medium to longer time scales, and under climate change scenarios, the model became increasingly more sensitive to additional and/or different parameters controlling biomass accumulation and autotrophic respiration (i.e., for NPP up to ∼30%, 167 g C·m−2·yr−1 and for SWB up to ∼24%, 64 Mg C/ha, when compared to standard simulation). Interestingly, model outputs were shown to be more sensitive to parameters and processes controlling stand development rather than to climate change (i.e., warming and changes in atmospheric CO₂ concentration) itself although model sensitivities were generally higher under climate change scenarios. Our results suggest the need for sensitivity and uncertainty analyses that cover multiple temporal scales along forest developmental stages to better assess the potential of future forests to act as a global terrestrial carbon sink.

Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early- and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.