The importance of forest structure for carbon fluxes of the Amazon rainforest

Precise descriptions of forest productivity, biomass, and structure are essential for understanding ecosystem responses to climatic and anthropogenic changes. However, relations between these components are complex, in particular for tropical forests. We developed an approach to simulate carbon dynamics in the Amazon rainforest including around 410 billion individual trees within 7.8 million km2. We integrated canopy height observations from space-borne LIDAR in order to quantify spatial variations in forest state and structure reflecting small-scale to large-scale natural and anthropogenic disturbances. Under current conditions, we identified the Amazon rainforest as a carbon sink, gaining 0.56 GtC per year. This carbon sink is driven by an estimated mean gross primary productivity (GPP) of 25.1 tC ha−1 a−1, and a mean woody aboveground net primary productivity (wANPP) of 4.2 tC ha−1 a−1. We found that successional states play an important role for the relations between productivity and biomass. Forests in early to intermediate successional states are the most productive, and woody above-ground carbon use efficiencies are non-linear. Simulated values can be compared to observed carbon fluxes at various spatial resolutions (>40 m). Notably, we found that our GPP corresponds to the values derived from MODIS. For NPP, spatial differences can be observed due to the consideration of forest successional states in our approach. We conclude that forest structure has a substantial impact on productivity and biomass. It is an essential factor that should be taken into account when estimating current carbon budgets or analyzing climate change scenarios for the Amazon rainforest

Can timber provision from Amazonian production forests be sustainable?

Around 30 Mm3 of sawlogs are extracted annually by selective logging of natural production forests in Amazonia, Earth's most extensive tropical forest. Decisions concerning the management of these production forests will be of major importance for Amazonian forests' fate. To date, no regional assessment of selective logging sustainability supports decision-making. Based on data from 3500 ha of forest inventory plots, our modelling results show that the average periodic harvests of 20 m3 ha−1 will not recover by the end of a standard 30 year cutting cycle. Timber recovery within a cutting cycle is enhanced by commercial acceptance of more species and with the adoption of longer cutting cycles and lower logging intensities. Recovery rates are faster in Western Amazonia than on the Guiana Shield. Our simulations suggest that regardless of cutting cycle duration and logging intensities, selectively logged forests are unlikely to meet timber demands over the long term as timber stocks are predicted to steadily decline. There is thus an urgent need to develop an integrated forest resource management policy that combines active management of production forests with the restoration of degraded and secondary forests for timber production. Without better management, reduced timber harvests and continued timber production declines are unavoidable

Simulating carbon stocks and fluxes of the Amazon rainforest: a journey across temporal and spatial scales

Global forests cover approximately 30% of land’s surface storing around 45% of above-ground terrestrial carbon. This carbon storage is constantly endangered by anthropogenic activities. Especially, tropical regions like the Amazon rainforest suffer from deforestation taking a great share in global CO2 emissions. In addition, forest dynamics are affected by climatic change like more frequent drought events. Quantifying the impact and feedback mechanisms of such climatic and anthropogenic changes on the global carbon cycle is still a great challenge. In this thesis, we developed a regionalization scheme to apply a forest gap model on the entire Amazon rainforest. Such a forest model has the advantage that it calculates forest growth at the individual tree level. It considers different successional states, that evolve form natural forest dynamics and disturbances, including information on tree height and species. The regionalized forest model thereby allows for integrating forest structure and species compositions into large-scale carbon analyses. The approach is independent of spatial scale and the simulation results can be linked to measurements from field inventory, eddy covariance, and remote sensing at local to continental scales. In a first study (chapter 2), we tested the capability of the forest model FORMIND to simulate gross primary production (GPP), respiration, and net ecosystem exchange (NEE) at daily and yearly time scales. The forest model was applied to spruce forests in Germany in order to analyze how the variability in environmental factors affects simulated carbon fluxes. Simulation results were compared to 6 years of eddy covariance (EC) data at a daily scale. The analysis shows that the forest model described the seasonal cycle of the carbon fluxes correctly, but estimated GPP differed from the observed data on days with extreme climatic conditions. Based on these findings, we developed two new parameterizations. One resulted from a numerical calibration against EC data. The other parameterization resulted from a method where EC data is filtered to extract the limiting factors for productivity. Thereby, new parameter values and even a new function for the temperature limitation of photosynthesis were found. The adopted forest model was then tested successfully at another spruce forest for cross validation. In general, the forest model reproduced the observed carbon fluxes of a forest ecosystem quite well. Although the overall performance of the calibrated model version was best, the filtering approach showed that calibrated parameter values did not necessarily correctly display the individual functional relations. The study has shown that the concept of simulating forest dynamics at the individual tree level is a valuable approach for simulating the NEE, GPP, and respiration of forest ecosystems. The focus of the second study (chapter 3) lied on the simulation of forest structure and above-ground biomass in the Amazon region with the forest model FORMIND. Estimating the spatial variation of biomass in the Amazon rainforest is challenging and, hence, a source of substantial uncertainty in the assessment of the global carbon cycle. On the one hand, estimates need to consider small-scale variations of forest structures due to natural tree mortality. On the other hand, it requires large-scale information on the state of the forest that can be detected by remote sensing. We, here, introduced a novel method that considered both aspects by linking the forest model and a wall-to-wall canopy height map derived from LIDAR remote sensing. The forest model was applied to estimate above-ground biomass stocks across the Amazon rainforest. This allowed for the direct comparison of simulated and observed canopy heights from remote sensing. The comparison enabled the detection of disturbed forest states from which we derived a biomass map of the Amazon rainforest at 0.16 ha resolution. Simulated biomass varied between 20 and 490 t(dry mass) ha-1 across 7.8 Mio km² of the Amazon rainforest (elevation < 1000 m). That equals a total above-ground biomass stock of 76 GtC with a strong spatial variation (coefficient of variation = 63%). The estimated biomass values fit estimates, that had been observed in 114 field inventories, well (deviation of only 15%). Beside biomass, the forest model allowed for estimating additional forest attributes such as basal area and stem density. The linkage of a forest model with a canopy height map allows for capturing forest structures at the individual to large scale. The approach is flexible and can also be combined with measurements of future satellite missions like ESA Biomass or GEDI. Hence, the study sets a basis for large-scale analyses of the heterogeneous structure of tropical forests and their carbon cycle. In a third study (chapter 4), we analyzed the interactions of productivity, biomass, and forest structure that are essential for understanding ecosystem’s response to climatic and anthropogenic changes. We here applied the forest model on the Amazon rainforest, combined simulation results with remotely-sensed data as in chapter 3, and additionally simulated ecosystem carbon fluxes. We found that the successional state of a forest has a strong influence on mean annual net ecosystem productivity (NEP), woody above-ground net primary production (wANPP), and net ecosystem productivity (NEP). These relations were used to derive maps of carbon fluxes at 0.16 ha resolutions (current state of the Amazon rainforest under spatial heterogenic environmental conditions). The Amazon was estimated to be a sink of atmospheric carbon with a mean NEP of 0.73 tC ha-1 a-1. Mean wANPP equals 4.16 tC ha-1 a-1 and GPP 25.2 tC ha-1 a-1. We found that forests in intermediate successional states are the most productive. Under current conditions, the Amazon rainforest takes up 0.59 PgC per year. This third study shows that forest structure and species compositions substantially influence productivity and biomass, and should not be neglected when estimating current carbon budgets or climate change scenarios for the Amazon rainforest. The findings of this thesis set a fundament for future analyses on carbon storage and fluxes of forests. Simulating at the tree level has the potential to investigate carbon dynamics from individual to continental scales. The regionalized forest model allows for the integration of different types of remotely sensed data in order to improve the spatial accuracy of estimates. The insights, we have gained from the eddy covariance study (chapter 2), help to investigate carbon dynamics of forests at continental scale also under changing climate. In combination with the regionalization approach (chapter 3 and 4), the findings of this thesis may be used to complement studies on drought events in forests and to understand feedback mechanisms caused by anthropogenic disturbances

Consequences of a Reduced Number of Plant Functional Types for the Simulation of Forest Productivity

Tropical forests represent an important pool in the global carbon cycle. Their biomass stocks and carbon fluxes are variable in space and time, which is a challenge for accurate measurements. Forest models are therefore used to investigate these complex forest dynamics. The challenge of considering the high species diversity of tropical forests is often addressed by grouping species into plant functional types (PFTs). We investigated how reduced numbers of PFTs affect the prediction of productivity (GPP, NPP) and other carbon fluxes derived from forest simulations. We therefore parameterized a forest gap model for a specific study site with just one PFT (comparable to global vegetation models) on the one hand, and two versions with a higher amount of PFTs, on the other hand. For an old-growth forest, aboveground biomass and basal area can be reproduced very well with all parameterizations. However, the absence of pioneer tree species in the parameterizations with just one PFT leads to a reduction in estimated gross primary production by 60% and an increase of estimated net ecosystem exchange by 50%. These findings may have consequences for productivity estimates of forests at regional and continental scales. Models with a reduced number of PFTs are limited in simulating forest succession, in particular regarding the forest growth after disturbances or transient dynamics. We conclude that a higher amount of species groups increases the accuracy of forest succession simulations. We suggest using at a minimum three PFTs with at least one species group representing pioneer tree species

The importance of forest structure for carbon fluxes of the Amazon rainforest

Precise descriptions of forest productivity, biomass, and structure are essential for understanding ecosystem responses to climatic and anthropogenic changes. However, relations between these components are complex, in particular for tropical forests.[br/] We developed an approach to simulate carbon dynamics in the Amazon rainforest including around 410 billion individual trees within 7.8 million km(2). We integrated canopy height observations from space-borne LIDAR in order to quantify spatial variations in forest state and structure reflecting small-scale to large-scale natural and anthropogenic disturbances. Under current conditions, we identified the Amazon rainforest as a carbon sink, gaining 0.56 GtC per year. This carbon sink is driven by an estimated mean gross primary productivity (GPP) of 25.1 tC ha(-1) a(-1), and a mean woody aboveground net primary productivity (wANPP) of 4.2 tC ha(-1) a(-1). We found that successional states play an important role for the relations between productivity and biomass. Forests in early to intermediate successional states are the most productive, and woody above-ground carbon use efficiencies are non-linear. Simulated values can be compared to observed carbon fluxes at various spatial resolutions (> 40 m). Notably, we found that our GPP corresponds to the values derived from MODIS. For NPP, spatial differences can be observed due to the consideration of forest successional states in our approach. We conclude that forest structure has a substantial impact on productivity and biomass. It is an essential factor that should be taken into account when estimating current carbon budgets or analyzing climate change scenarios for the Amazon rainforest

Upscaling in socio-environmental systems modelling: Current challenges, promising strategies and insights from ecology

Sustainability challenges in socio-environmental systems (SES) are inherently multiscale, with global-level changes emerging from socio-environmental processes that operate across different spatial, temporal, and organisational scales. Models of SES therefore need to incorporate multiple scales, which requires sound methodologies for transferring information between scales. Due to the increasing global connectivity of SES, upscaling – increasing the extent or decreasing the resolution of a modelling study – is becoming progressively more important. However, upscaling in SES models has received less attention than in other fields (e.g., ecology or hydrology) and therefore remains a pressing challenge. To advance the understanding of upscaling in SES, we take three steps. First, we review existing upscaling approaches in SES as well as other disciplines. Second, we identify four main challenges that are particularly relevant to upscaling in SES: 1) heterogeneity, 2) interactions, 3) learning and adaptation, and 4) emergent phenomena. Third, we present an approach that facilitates the transfer of existing upscaling methods to SES, using two good practice examples from ecology. To describe and compare these methods, we propose a scheme of five general upscaling strategies. This scheme builds upon and unifies existing schemes and provides a standardised way to classify and represent existing as well as new upscaling methods. We demonstrate how the scheme can help to transparently present upscaling methods and uncover scaling assumptions, as well as to identify limits for the transfer of upscaling methods. We finish by pointing out research avenues on upscaling in SES to address the identified upscaling challenges

Tackling unresolved questions in forest ecology: The past and future role of simulation models

Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches - species distribution models, individual-based forest models, and dynamic global vegetation models - as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change