Remote sensing enables the quantification of tropical deforestation with high spatial resolution. This in-depth mapping has led to substantial advances in the analysis of continent-wide fragmentation ...of tropical forests. Here we identified approximately 130 million forest fragments in three continents that show surprisingly similar power-law size and perimeter distributions as well as fractal dimensions. Power-law distributions have been observed in many natural phenomena such as wildfires, landslides and earthquakes. The principles of percolation theory provide one explanation for the observed patterns, and suggest that forest fragmentation is close to the critical point of percolation; simulation modelling also supports this hypothesis. The observed patterns emerge not only from random deforestation, which can be described by percolation theory, but also from a wide range of deforestation and forest-recovery regimes. Our models predict that additional forest loss will result in a large increase in the total number of forest fragments-at maximum by a factor of 33 over 50 years-as well as a decrease in their size, and that these consequences could be partly mitigated by reforestation and forest protection.
Aim: Estimating the current spatial variation of biomass in the Amazon rain forest is a challenge and remains a source of substantial uncertainty in the assessment of the global carbon cycle. Precise ...estimates need to consider small-scale variations of forest structures resulting from local disturbances, on the one hand, and require large-scale information on the state of the forest that can be detected by remote sensing, on the other hand. In this study, we introduce a novel method that links a forest gap model and a canopy height map to derive the biomass distribution of the Amazon rain forest. Location: Amazon rain forest. Methods: An individual-based forest model was applied to estimate the variation of aboveground biomass across the Amazon rain forest. The forest model simulated individual trees; hence, it allowed the direct comparison of simulated and observed canopy heights from remote sensing. The comparison enabled the detection of disturbed forest states and the derivation of a simulation-based biomass map at 0.16 has resolution. Results: Simulated biomass values ranged from 20 to 490 t (dry mass)/ha across 7.8 Mio km2 of Amazon rain forest. We estimated a total aboveground biomass stock of 76 GtC, with a coefficient of variation of 45%. We found mean differences of only 15% when comparing biomass values of the map with 114 field inventories. The forest model enables the derivation of additional estimates, such as basal area and stem density. Main conclusions: Linking a canopy height map with an individual-based forest model captures the spatial variation of biomass in the Amazon rain forest at high resolution. The study demonstrates how this linkage allows for quantifying the spatial variation in forest structure caused by tree-level to regional-scale disturbances. It thus provides a basis for large-scale analyses on the heterogeneous structure of tropical forests and their carbon cycle.
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.
Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a ...challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20-43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.
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.
Forest models can help understanding the processes that shape forest functioning, structure and diversity, since they can can simulate forest dynamics over spatio‐temporal scales unreachable by most empirical investigations. Here we describe the development of three widely applied but contrasting forest mo−delling approaches — species distribution models, individual‐based models and dynamic global vegetation models. We provide an overview of recent model applications and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.
•Consider species-richness by aggregation into plant functional types.•Model the impact of disturbances on forest dynamics.•Upscale carbon dynamics from the leaf to the global carbon budget ...level.•Process-based and individual-based philosophy is important for application.•FORMIND is generic and flexible to be applied to forest sites worldwide.
Forests worldwide are threatened by various environmental and anthropogenic hazards, especially tropical forests. Knowledge on the impacts of these hazards on forest structure and dynamics has been compiled in empirical studies. However, the results of these studies are often not sufficient for long-term projections and extrapolations to large spatial scales especially for unprecedented environmental conditions, which require both the identification and understanding of key underlying processes. Forest models bridge this gap by incorporating multiple ecological processes in a dynamic framework (i.e. including a realistic model structure) and addressing the complexity of forest ecosystems. Here, we describe the evolution of the individual-based and process-based forest gap model FORMIND and its application to tropical forests. At its core, the model includes physiological processes on tree level (photosynthesis, respiration, tree growth, mortality, regeneration, competition). During the past two decades, FORMIND has been used to address various scientific questions arising from different forest types by continuously extending the model structure. The model applications thus provided understanding in three main aspects: (1) the grouping of single tree species into plant functional types is a successful approach to reduce complexity in vegetation models, (2) structural realism was necessary to analyze impacts of natural and anthropogenic disturbances such as logging, fragmentation, or drought, and (3) complex ecological processes such as carbon fluxes in tropical forests – starting from the individual tree level up to the entire forest ecosystem – can be explored as a function of forest structure, species composition and disturbance regime. Overall, this review shows how the evolution of long-term modelling projects not only provides scientific understanding of forest ecosystems, but also provides benefits for ecological theory and empirical study design.
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.
Background： Capturing the response of forest ecosystems to inter-annual climate variability is a great challenge.In this study, we tested the capability of an individual-based forest gap model to ...display carbon fluxes at yearly and daily time scales.The forest model was applied to a spruce forest to simulate the gross primary production（GPP）, respiration and net ecosystem exchange（NEE）.We analyzed how the variability in climate affected simulated carbon fluxes at the scale of the forest model.Results： Six years were simulated at a daily time scale and compared to the observed eddy covariance（EC） data.In general, the seasonal cycle of the individual carbon fluxes was correctly described by the forest model.However, the estimated GPP differed from the observed data on the days of extreme climatic conditions.Two new parameterizations were developed： one resulting from a numerical calibration, and the other resulting from a filtering method.We suggest new parameter values and even a new function for the temperature limitation of photosynthesis.Conclusions： The forest model reproduced the observed carbon fluxes of a forest ecosystem quite wel.Of the three parameterizations, the calibrated model version performed best.However, the filtering approach showed that calibrated parameter values do not necessarily correctly display the individual functional relations.The concept of simulating forest dynamics at the individual base is a valuable tool for simulating the NEE, GPP and respiration of forest ecosystems.
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.
Comparing model output and observed data is an
important step for assessing model performance and quality of simulation
results. However, such comparisons are often hampered by differences in
spatial ...scales between local point observations and large-scale simulations
of grid cells or pixels. In this study, we propose a generic approach for a
pixel-to-point comparison and provide statistical measures accounting for the
uncertainty resulting from landscape variability and measurement errors in
ecosystem variables. The basic concept of our approach is to determine the
statistical properties of small-scale (within-pixel) variability and
observational errors, and to use this information to correct for their effect
when large-scale area averages (pixel) are compared to small-scale point
estimates. We demonstrate our approach by comparing simulated values of
aboveground biomass, woody productivity (woody net primary productivity, NPP)
and residence time of woody biomass from four dynamic global vegetation
models (DGVMs) with measured inventory data from permanent plots in the
Amazon rainforest, a region with the typical problem of low data
availability, potential scale mismatch and thus high model uncertainty. We
find that the DGVMs under- and overestimate aboveground biomass by 25 %
and up to 60 %, respectively. Our comparison metrics provide a
quantitative measure for model–data agreement and show moderate to good
agreement with the region-wide spatial biomass pattern detected by plot
observations. However, all four DGVMs overestimate woody productivity and
underestimate residence time of woody biomass even when accounting for the
large uncertainty range of the observational data. This is because DGVMs do
not represent the relation between productivity and residence time of woody
biomass correctly. Thus, the DGVMs may simulate the correct large-scale
patterns of biomass but for the wrong reasons. We conclude that more
information about the underlying processes driving biomass distribution are
necessary to improve DGVMs. Our approach provides robust statistical measures
for any pixel-to-point comparison, which is applicable for evaluation of
models and remote-sensing products.
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