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Process-based ecological models are essential tools to quantify and predict forest growth and carbon cycles under the background of climate change. The accurate description of phenology and tree growth processes enables an improved understanding and predictive modeling of forest dynamics. An individual tree-based carbon model, FORCCHN2 (Forest Ecosystem Carbon Budget Model for China version 2.0), used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. This model performed well in reducing uncertainty when predicting forest carbon fluxes. Here, we describe the framework in detail and provide the source code of FORCCHN2. We also present a dynamic-link library (DLL) package containing the latest version of FORCCHN2. This package has the advantage of using Fortran as an interface to make the model run fast on a daily step, and the package also allows users to call it with their preferred computer tools (e.g., MATLAB, R, Python). FORCCHN2 can be used directly to predict spring and autumn phenological dates, daily carbon fluxes (including photosynthesis, aboveground and belowground autotrophic respiration, and soil heterotrophic respiration), and biomass on plot, regional, and hemispheric scales. As case studies, we provide an example of FORCCHN2 running model validations in 78 forest sites and an example model application for the carbon dynamics of Northern Hemisphere forests. We demonstrate that FORCCHN2 can produce a reasonable agreement with flux observations. Given the potential importance of the application of this ecological model in many studies, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimations.

Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra- and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate-driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non-structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf-growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%-210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38 Pg C/year from forest ecosystems during 1985-2016. The results showed an increasing trend of GPP in North America (1.0 Pg C/decade). The inclusion of climate-driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis.

Background Gap models are individual-based models for forests. They simulate dynamic multispecies assemblages over multiple tree-generations and predict forest responses to altered environmental conditions. Their development emphases designation of the significant biological and ecological processes at appropriate time/space scales. Conceptually, they are with consistent with A.G. Tansley’s original definition of “the ecosystem”. Results An example microscale application inspects feedbacks among terrestrial vegetation change, air-quality changes from the vegetation’s release of volatile organic compounds (VOC), and climate change effects on ecosystem production of VOC’s. Gap models can allocate canopy photosynthate to the individual trees whose leaves form the vertical leaf-area profiles. VOC release depends strongly on leaf physiology by species of these trees. Leaf-level VOC emissions increase with climate-warming. Species composition change lowers the abundance of VOC-emitting taxa. In interactions among ecosystem functions and biosphere/atmosphere exchanges, community composition responses can outweigh physiological responses. This contradicts previous studies that emphasize the warming-induced impacts on leaf function. As a mesoscale example, the changes in climate (warming) on forests including pest-insect dynamics demonstrates changes on the both the tree and the insect populations. This is but one of many cases that involve using a gap model to simulate changes in spatial units typical of sampling plots and scaling these to landscape and regional levels. As this is the typical application scale for gap models, other examples are identified. The insect/climate-change can be scaled to regional consequences by simulating survey plots across a continental or subcontinental zone. Forest inventories at these scales are often conducted using independent survey plots distributed across a region. Model construction that mimics this sample design avoids the difficulties in modelling spatial interactions, but we also discuss simulation at these scales with contagion effects. Conclusions At the global-scale, successful simulations to date have used functional types of plants, rather than tree species. In a final application, the fine-scale predictions of a gap model are compared with data from micrometeorological eddy-covariance towers and then scaled-up to produce maps of global patterns of evapotranspiration, net primary production, gross primary production and respiration. New active-remote-sensing instruments provide opportunities to test these global predictions.

Forests provide important ecosystem services such as carbon sequestration. Forest landscapes are intrinsically heterogeneous—a problem for biomass and productivity assessment using remote sensing. Forest structure constitutes valuable additional information for the improved estimation of these variables. However, survey of forest structure by remote sensing remains a challenge which results mainly from the differences in forest structure metrics derived by using remote sensing compared to classical structural metrics from field data. To understand these differences, remote sensing measurements were linked with an individual-based forest model. Forest structure was analyzed by lidar remote sensing using metrics for the horizontal and vertical structures. To investigate the role of forest structure for biomass and productivity estimations in temperate forests, 25 lidar metrics of 375,000 simulated forest stands were analyzed. For the lidar-based metrics, top-of-canopy height arose as the best predictor for describing horizontal forest structure. The standard deviation of the vertical foliage profile was the best predictor for the vertical heterogeneity of a forest. Forest structure was also an important factor for the determination of forest biomass and aboveground wood productivity. In particular, horizontal structure was essential for forest biomass estimation. Predicting aboveground wood productivity must take into account both horizontal and vertical structures. In a case study based on these findings, forest structure, biomass and aboveground wood productivity are mapped for whole of Germany. The dominant type of forest in Germany is dense but less vertically structured forest stands. The total biomass of all German forests is 2.3 Gt, and the total aboveground woody productivity is 43 Mt/year. Future remote sensing missions will have the capability to provide information on forest structure (e.g., from lidar or radar). This will lead to more accurate assessments of forest biomass and productivity. These estimations can be used to evaluate forest ecosystems related to climate regulation and biodiversity protection.

Soil-atmosphere exchange significantly influences the global atmospheric abundances of carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O). These greenhouse gases (GHGs) have been extensively studied at the soil profile level and extrapolated to coarser scales (regional and global). However, finer scale studies of soil aggregation have not received much attention, even though elucidating the GHG activities at the full spectrum of scales rather than just coarse levels is essential for reducing the large uncertainties in the current atmospheric budgets of these gases. Through synthesizing relevant studies, we propose that aggregates, as relatively separate micro-environments embedded in a complex soil matrix, can be viewed as biogeochemical reactors of GHGs. Aggregate reactivity is determined by both aggregate size (which determines the reactor size) and the bulk soil environment including both biotic and abiotic factors (which further influence the reaction conditions). With a systematic, dynamic view of the soil system, implications of aggregate reactors for soil-atmosphere GHG exchange are determined by both an individual reactor's reactivity and dynamics in aggregate size distributions. Emerging evidence supports the contention that aggregate reactors significantly influence soil-atmosphere GHG exchange and may have global implications for carbon and nitrogen cycling. In the context of increasingly frequent and severe disturbances, we advocate more analyses of GHG activities at the aggregate scale. To complement data on aggregate reactors, we suggest developing bottom-up aggregate-based models (ABMs) that apply a trait-based approach and incorporate soil system heterogeneity.

Forests provide essential ecosystem services and hold approximately 45% of global terrestrial carbon. Estimates of the quantity and spatial distribution of global forest carbon are built on the assumption that regional- or national-scale allometry accurately captures growth form across the wide spectrum of plant size. Allometry is painstaking and costly to create: trees must be cut, dried, and weighed, over the span of months. This bottleneck has left most equations low in sample size and without large trees ( > 50 cm), which can contain over 40% of aboveground carbon. Terrestrial laser scanning (TLS) can potentially increase the range and sample size of allometric equations through non-destructive biomass estimation and must be evaluated in this context. We deployed TLS at the Center for Tropical Forest Science - Forest Global Earth Observatory (CTFS-ForestGEO) plot in Front Royal, Virginia and virtually reconstructed 329 trees with diameters up to 123 cm. Three-dimensional tree models were the basis for 22 local allometric relationships for comparison to the Jenkins et al. (2003) and Chojnacky et al. (2014) equations. Overall, TLS allometry had lower RMSE and predicted higher tree-level biomass compared to the equivalent national equations. We evaluated site-wide allometry for errors from insufficient sample size and diameter range. Allometric equations did not stabilize to a consistent set of parameters until 100–200 samples were reached and exclusion of large trees severely limited prediction accuracy. This work implies that current biomass equations may be inadequate and highlights TLS stem modeling as an appropriate method of non-destructive allometric equation development for updating allometry and reducing uncertainty in landscape-level biomass estimates.

Air quality is closely associated with climate change via the biosphere because plants release large quantities of volatile organic compounds (VOC) that mediate both gaseous pollutants and aerosol dynamics. Earlier studies, which considered only leaf physiology and simply scale up from leaf-level enhancements of emissions, suggest that climate warming enhances whole forest VOC emissions, and these increased VOC emissions aggravate ozone pollution and secondary organic aerosol formation. Using an individual-based forest VOC emissions model, UVAFME-VOC, that simulates system-level emissions by explicitly simulating forest community dynamics to the individual tree level, ecological competition among the individuals of differing size and age, and radiative transfer and leaf function through the canopy, we find that climate warming only sometimes stimulates isoprene emissions (the single largest source of non-methane hydrocarbon) in a southeastern U.S. forest. These complex patterns result from the combination of higher temperatures' stimulating emissions at the leaf level but decreasing the abundance of isoprene-emitting taxa at the community level by causing a decline in the abundance of isoprene-emitting species (Quercus spp.). This ecological effect eventually outweighs the physiological one, thus reducing overall emissions. Such reduced emissions have far-reaching implications for the climate-air-quality relationships that have been established on the paradigm of warming-enhancement VOC emissions from vegetation. This local scale modeling study suggests that community ecology rather than only individual physiology should be integrated into future studies of biosphere-climate-chemistry interactions.

Miombo woodlands are the most extensive dry forest type in southern Africa, covering ca. 1.9 million km2 across seven countries. Fire is a key ecosystem process that has structured miombo for the last 200,000 years. However, how fires affect the ecosystem's functioning is not well understood. In this study, we used the individual-based forest model called FORMIND to analyze the carbon balance in the miombo woodlands of Niassa Special Reserve (NSR), northern Mozambique. The 42.000 km2 NSR represents the most important conservation area in Mozambique (~31% of the total conservation area in the country) and of miombo woodlands worldwide. Long-term inventory data from 2004 to 2019 for NSR were used to calibrate FORMIND. The primary ecosystem processes of this model are tree growth, mortality, regeneration, and competition. Fire is set as one of the main factors that affect these processes, after the woodland reaches an equilibrium at 200 years of age. We also calculated the Net Present Value (NPV) of carbon credits resulting from altering the fire regime (e.g., reducing or eliminating fires). The FORMIND model successfully reproduced important characteristics of the woodlands (aboveground biomass, stem size distribution and basal area). NPV estimates of above-ground woody biomass carbon stocks were highly dependent on the woodland age. The maximum NPV estimates were generated for a 30-year project starting with 200 year old woodlands (the current forest age) at 192–1339 USD based on a realistic range of carbon values (i.e., 3–20 USD MgCO2e−1). While fire plays an important role in miombo woodlands by reducing stock and changing species composition, its effects on the capacity of the woodland to mitigate the effects of climate change varies depending on the age of stands. Our results show that FORMIND model reliably reproduce the field inventory data, thus can be used to improve carbon accounting standards. We recommend the development of a fire management system to sustain the miombo woodlands of NSR for multiple reasons. NSR is a globally significant protected area, but perhaps more importantly it could become a regional example for how to improve miombo woodland management. Given that miombo woodlands provide a myriad of ecosystem services to rural Africans, investing in improving fire management could increase the benefits to local communities. Altering fire regimes could improve habitat quality and promote greater resilience to climate change while sequestering carbon. In addition, local employment opportunities in fire management could be created via carbon financing from a carbon project. However, much more outreach and education will be needed to local and national stakeholders for fire management to be perceived more positively and realize the potential to generate multiple benefits for nature and people.

The growth of forests and the feedbacks between forests and environmental changes are central issues in the planetary carbon cycle, global climate change, and basic plant ecology. A challenge to understanding both growth and feedbacks from local to global scales is that many critical metabolic processes vary among species. An innovation in solving this challenge is the recognition that species can be lumped into “functional groups” based on metabolic similarity, and these functional groups can then be studied in computational models that simulate ecosystem function. Despite the vast resources devoted to functional group studies and the progress made by them, an important logical and biological question has not been formally addressed, “How do the groupings alter the results of modeling studies?” To what extent do modeling results depend on the choices made in aggregating taxa into functional groups. Here, we consider the effects of using different aggregation strategies in simulating the carbon dynamics of a deciduous forest. Understanding the impacts that aggregation strategy has on efforts to simulate regional-to-global-scale forest dynamics offers insights into both ecosystem regulation and model function and addresses this central problem in the study of carbon dynamics.

Climate change is affecting the growth and distribution of trees in the Chinese boreal forest. Such changes in China, the southern terminus of the extensive Eurasian boreal forests, reflect on the changes that could occur further north under a warming climate. Most studies have found that tree growth increases with increasing temperature and precipitation in boreal forests, but there is little observational evidence of the climate thresholds that might slow these growth rates at the more extreme temperatures which are predicted to occur under future global warming. Here, we examine growth responses of this dominant boreal tree species (Larix gmelinii) to climate based on the data from plantation sample plots across a broad region (40° 51'-52° 58'N, 118° 12'E-133° 42'E) in northeast China. From statistically significant fits to quadratic equations, temperature and precipitation are the important climatic factors determining tree growth in L. gmelinii plantations at two age classes (

Global estimates of forest aboveground biomass and carbon storage have major discrepancies linked to limitations in tree-level biomass estimates. Robust allometric equations can improve biomass estimates; however, destructive sampling to measure single-tree biomass is expensive, challenging, and prone to measurement error. We present a method to efficiently and non-destructively estimate single-tree biomass from terrestrial LiDAR scan data and test the approach on 21 destructively-sampled lodgepole pine ( Pinus contorta ) trees. The approach estimates branch and foliage volume using voxelization and estimates trunk volume using a method developed in this study called the Outer Hull Model (OHM). The OHM iteratively fits convex hulls, accurately handles noisy scan data, and fits the true shape of the trunk rather than forcing a cylindrical fit. Volume from the LiDAR scans is converted to biomass using density values from the literature and from field sampling to assess model sensitivity to density values. Whole-tree aboveground biomass estimates derived from the LiDAR scans were nearly unbiased and agreed strongly with destructive sampling data (R 2 = 0.98, RMSE = 20.4 kg). Estimation of the trunk component biomass (R 2 = 0.99, RMSE = 12.3 kg) was stronger than foliage and needle component estimates (R 2 = 0.54, RMSE = 21.4 kg). The approach presented in this study accurately and non-destructively estimated the aboveground biomass of needleleaf trees with minimal user input. The promising performance on coniferous trees advances efficient sampling of single-tree biomass.

Diffuse radiation can increase canopy light use efficiency (LUE). This creates the need to differentiate the effects of direct and diffuse radiation when simulating terrestrial gross primary production (GPP). Here, we present a novel GPP model, the diffuse-fraction-based two-leaf model (DTEC), which includes the leaf response to direct and diffuse radiation, and treats maximum LUE for shaded leaves (emsh defined as a power function of the diffuse fraction (Df)) and sunlit leaves (emsu defined as a constant) separately. An Amazonian rainforest site (KM67) was used to calibrate the model by simulating the linear relationship between monthly canopy LUE and Df . This showed a positive response of forest GPP to atmospheric diffuse radiation, and suggested that diffuse radiation was more limiting than global radiation and water availability for Amazon rainforest GPP on a monthly scale. Further evaluation at 20 independent AmeriFlux sites showed that the DTEC model, when driven by monthly meteorological data and MODIS leaf area index (LAI) products, explained 70% of the variability observed in monthly flux tower GPP. This exceeded the 51% accounted for by the MODIS 17A2 big-leaf GPP product. The DTEC model's explicit accounting for the impacts of diffuse radiation and soil water stress along with its parameterization for C4 and C3 plants was responsible for this difference. The evaluation of DTEC at Amazon rainforest sites demonstrated its potential to capture the unique seasonality of higher GPP during the diffuse radiation-dominated wet season. Our results highlight the importance of diffuse radiation in seasonal GPP simulation.

Siberia has experienced a pronounced warming over the past several decades, which has induced an increase in the extent of evergreen conifer forest. However, the potential slowing of the trend of increasing surface air temperature (SAT) has produced intense debate since the late 1990s. During this warming hiatus, the Siberian region experienced a significant cooling during the winter season around 10 times that of the Northern Hemisphere (NH) as a whole. This potentially stresses evergreen conifer forests because cooler winters can cause cold-temperature damage and, hence, increase the mortality of young evergreen conifer forests. In this study, the response of Siberian forest composition during the warming hiatus was investigated using satellite observations coupled with model simulations. Observations indicated that from 2001 to 2012, the apparent area of evergreen conifer forest has increased by 10%, while that of the deciduous conifer forest has decreased by 40%. The transition from deciduous to evergreen conifer forest usually occurs through mixed forest or woody savannas as a buffer. To verify the response of evergreen conifer forest, model experiments were performed using an individual-based forest model. Hysteresis of forest change seen in the model simulations indicates that changes in forest composition dynamics under temperature oscillations induced by internal climate variability may not reverse this composition change. As a result, the evergreen conifer forest expansion under climate warming is expected to be a continuing process despite the occurrence of a warming hiatus, exerting far-reaching implications for climate-change-induced albedo shifts in the Siberian forest.

A model is a simplification of our understanding of reality, but how much simplification is too much? We assessed model sensitivity for a new spatially-explicit individual-based gap model (IBM) SIBBORK to determine how spatial resolution (i.e., plot size) and explicit representation of light and space affect the simulated vegetation characteristics (e.g., structure and composition). The 3-dimensional (3-D) representation of light is the most computationally-expensive part of the simulation, so it is worthwhile to evaluate whether the time, resource and effort of spatial explicity improve predictive capabilities. In the simulation, gap dynamics occur at the plot-level, wherein a certain level of spatial homogeneity is assumed. Therefore, for the simulation of each ecosystem it is imperative to select the appropriate scale at which gap dynamics are simulated, i.e. the plot size. We conducted these sensitivity analyses and qualitatively compared model output to the descriptions of forest structure and composition at two different sites in central and southern Siberia. We systematically varied the plot size (100–900 m2) and found that a 100 m2 plot is most appropriate for the simulation of the central Siberian boreal forest ecosystem. Moreover, we found that the forest structure and composition simulated under the light environment generated using a 3-dimensional light ray tracing subroutine more closely resembled local and regional forest characteristics at both sites, whereas the growth and stand processes computed based on the simplified (1-D) light conditions approximated only large-scale (continental) average forest structure and biomass for the Eurasian boreal forest.

The carbon budget of forest ecosystems, an important component of the terrestrial carbon cycle, needs to be accurately quantified and predicted by ecological models. As a preamble to apply the model to estimate global carbon uptake by forest ecosystems, we used the CO2 flux measurements from 37 forest eddy-covariance sites to examine the individual tree-based FORCCHN model's performance globally. In these initial tests, the FORCCHN model simulated gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) with correlations of 0.72, 0.70 and 0.53, respectively, across all forest biomes. The model underestimated GPP and slightly overestimated ER across most of the eddy-covariance sites. An underestimation of NEP arose primarily from the lower GPP estimates. Model performance was better in capturing both the temporal changes and magnitude of carbon fluxes in deciduous broadleaf forest than in evergreen broadleaf forest, and it performed less well for sites in Mediterranean climate. We then applied the model to estimate the carbon fluxes of forest ecosystems on global scale over 1982-2011. This application of FORCCHN gave a total GPP of 59.41±5.67 and an ER of 57.21±5.32PgCyr-1 for global forest ecosystems during 1982-2011. The forest ecosystems over this same period contributed a large carbon storage, with total NEP being 2.20±0.64PgCyr-1. These values are comparable to and reinforce estimates reported in other studies. This analysis highlights individual tree-based model FORCCHN could be used to evaluate carbon fluxes of forest ecosystems on global scale.

Forests produce and emit abundant non-methane volatile hydrocarbon species (VOCs) influencing the atmosphere chemistry and climate. Over a half century of research has produced significant understandings of the biochemistry and eco-physiology of biogenic VOCs. However, VOCs production is highly species-specific, and the impact of changes in species composition and abundance on VOCs emissions is unobservable in the time scales usually seen in field experiments. Prior modelling efforts are based on cases with an aggregate representation of vegetation. Individual-based models of forests simulate the dynamics of complex forest ecosystems based on birth, growth, and death of the individual trees comprising a simulated forest. Here an individual-based forest VOCs emissions model—UVAFME-VOC v1.0—is developed from the state-of-the-art individual-based forest gap model, UVAFME, coupled with a canopy VOCs emission model. UVAFME-VOC v1.0 implementation for the temperate deciduous forest in the southeastern United States is tested by comparisons to independent data in the region. A model application tested the hypothesis that the historical collapse of American Chestnut ( Castanea dentate ) resulted in the dominance of oak trees ( Quercus spp.) and enhanced isoprene emissions. The model demonstrated a capability to simulate the forest compositional and structural dynamics and forest isoprene emission dynamics. The simulations show isoprene emissions depend heavily on forest successional stage and species composition, suggesting that environmental change can affect forest VOCs emissions primarily by influencing forest species composition. Prediction of isoprene emissions, of other phytogenic volatile organic compounds, and of impacts on atmospheric chemistry of various global change agents (e.g., warming, rising CO 2 , ozone elevation, and herbivory) should explicitly consider forest diversity change. This individual-based model could provide widespread applications in addressing these problems.

Spruce beetle (Dendroctonus rufipennis (Kirby)) outbreaks cause widespread mortality of Engelmann spruce (Picea engelmannii (Parry ex Engelm)) within the subalpine forests of the western United States. Early detection of infestations could allow forest managers to mitigate outbreaks or anticipate a response to tree mortality and the potential effects on ecosystem services of interest. However, the subtle changes in the foliage of infested spruce make early detection difficult. An experiment was conducted in southern Colorado to determine important wavelengths for detecting early-stage (i.e. recently infested) spruce beetle infestation in Engelmann spruce. Spectral reflectance from non-infested and recently infested spruce needles were obtained using the ASDi Field-Spec Pro spectroradiometer. After pre-processing, random forest analysis was used to identify hyperspectral bands and aggregations of hyperspectral bands corresponding to Landsat TM bands and vegetation indices that effectively discriminated between non-infested and infested trees. Results show that the shortwave infrared region of the electromagnetic spectrum was a key area for detecting early stages of spruce beetle infestation, likely due to the effects of beetle infestation on water transport within Engelmann spruce. The strong discriminability of bands in the shortwave infrared region indicates a potential for this spectral region to be used to detect early-stage spruce beetle infestation over larger areas using multispectral satellite imagery. In a preliminary trial, we found that a time series of reflectance in Landsat TM band 7 (shortwave infrared) was strongly correlated with the progression through time of a spruce beetle outbreak in southern Wyoming. These findings suggest that multispectral indicators of early-stage spruce beetle outbreak can be developed. These indicators are needed to better understand spatiotemporal dynamics of spruce beetle outbreaks, and can be used by forest managers to detect early stages of spruce beetle infestation and to potentially mitigate some spruce mortality.

Forest mortality is accelerating due to climate change and the largest trees may be at the greatest risk, threatening critical ecological, economic, and social benefits. Here, we combine high-resolution airborne LiDAR and optical data to track tree-level mortality rates for ~2 million trees in California over 8 years, showing that tree height is the strongest predictor of mortality during extreme drought. Large trees die at twice the rate of small trees and environmental gradients of temperature, water, and competition control the intensity of the height-mortality relationship. These findings suggest that future persistent drought may cause widespread mortality of the largest trees on Earth., Drought is intensifying due to climate change, impacting forests globally. Here, the authors track nearly 2 million trees through severe drought and show that tree height is the greatest predictor of mortality risk, suggesting that the tallest trees may be the most vulnerable.

Author(s): Wang, Bin; Brewer, Paul E; Shugart, Herman H; Lerdau, Manuel T; Allison, Steven D