Browsing by Autor "Manuel Gloor"

Now showing 1 - 7 of 7

Does the disturbance hypothesis explain the biomass increase in basin‐wide Amazon forest plot data?
(Wiley, 2009) Manuel Gloor; Oliver L. Phillips; Jon Lloyd; Simon L. Lewis; Yadvinder Malhi; Timothy R. Baker; Gabriela López‐González; J. Peacock; S. Almeida; A. C. ALVES De OLIVEIRA
Abstract Positive aboveground biomass trends have been reported from old‐growth forests across the Amazon basin and hypothesized to reflect a large‐scale response to exterior forcing. The result could, however, be an artefact due to a sampling bias induced by the nature of forest growth dynamics. Here, we characterize statistically the disturbance process in Amazon old‐growth forests as recorded in 135 forest plots of the RAINFOR network up to 2006, and other independent research programmes, and explore the consequences of sampling artefacts using a data‐based stochastic simulator. Over the observed range of annual aboveground biomass losses, standard statistical tests show that the distribution of biomass losses through mortality follow an exponential or near‐identical Weibull probability distribution and not a power law as assumed by others. The simulator was parameterized using both an exponential disturbance probability distribution as well as a mixed exponential–power law distribution to account for potential large‐scale blowdown events. In both cases, sampling biases turn out to be too small to explain the gains detected by the extended RAINFOR plot network. This result lends further support to the notion that currently observed biomass gains for intact forests across the Amazon are actually occurring over large scales at the current time, presumably as a response to climate change.
Hyperdominance in Amazonian forest carbon cycling
(Nature Portfolio, 2015) Sophie Fauset; Michelle Johnson; Manuel Gloor; Timothy R. Baker; Abel Monteagudo M.; Roel Brienen; Ted R. Feldpausch; Gabriela López‐González; Yadvinder Malhi; Hans ter Steege
Individual-Based Modeling of Amazon Forests Suggests That Climate Controls Productivity While Traits Control Demography
(Frontiers Media, 2019) Sophie Fauset; Manuel Gloor; Nikolaos M. Fyllas; Oliver L. Phillips; Gregory P. Asner; Timothy R. Baker; Lisa Patrick Bentley; Roel Brienen; Bradley Christoffersen; Jhon del Águila Pasquel
Climate, species composition, and soils are thought to control carbon cycling and forest structure in Amazonian forests. Here, we add a demographics scheme (tree recruitment, growth, and mortality) to a recently developed non-demographic model - the Trait-based Forest Simulator (TFS) – to explore the roles of climate and plant traits in controlling forest productivity and structure. We compared two sites with differing climates (seasonal versus aseasonal precipitation) and plant traits. Through an initial validation simulation, we assessed whether the model converges on observed forest properties (productivity, demographic and structural variables) using datasets of functional traits, structure, and climate to model the carbon cycle at the two sites. In a second set of simulations, we tested the relative importance of climate and plant traits for forest properties within the TFS framework using the climate from the two sites with hypothetical trait distributions representing two axes of functional variation (‘fast’ versus ‘slow’ leaf traits, and high versus low wood density). The adapted model with demographics reproduced observed variation in gross (GPP) and net (NPP) primary production, and respiration. However NPP and respiration at the level of plant organs (leaf, stem, and root) were poorly simulated. Mortality and recruitment rates were underestimated. The equilibrium forest structure differed from observations of stem numbers suggesting either that the forests are not currently at equilibrium or that mechanisms are missing from the model. Findings from the second set of simulations demonstrated that differences in productivity were driven by climate, rather than plant traits. Contrary to expectation, varying leaf traits had no influence on GPP. Drivers of simulated forest structure were complex, with a key role for wood density mediated by its link to tree mortality. Modelled mortality and recruitment rates were linked to plant traits alone, drought-related mortality was not accounted for. In future, model development should focus on improving allocation, mortality, organ respiration, simulation of understory trees and adding hydraulic traits. This type of model that incorporates diverse tree strategies, detailed forest structure and realistic physiology is necessary if we are to be able to simulate tropical forest responses to global change scenarios.
Markedly divergent estimates of <scp>A</scp> mazon forest carbon density from ground plots and satellites
(Wiley, 2014) Edward T. A. Mitchard; Ted R. Feldpausch; Roel J. W. Brienen; Gabriela López‐González; Abel Monteagudo; Timothy R. Baker; Simon L. Lewis; Jon Lloyd; Carlos Alberto Quesada; Manuel Gloor
Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.
Oxygen isotopes in tree rings show good coherence between species and sites in Bolivia
(Elsevier BV, 2015) Jessica C. A. Baker; Sarah F.P. Hunt; Santiago Clerici; Robert J. Newton; Simon H. Bottrell; Melanie J. Leng; Timothy H. Heaton; Gerhard Helle; Jaime Argollo; Manuel Gloor
A tree ring oxygen isotope (δ18OTR) chronology developed from one species (Cedrela odorata) growing in a single site has been shown to be a sensitive proxy for rainfall over the Amazon Basin, thus allowing reconstructions of precipitation in a region where meteorological records are short and scarce. Although these results suggest that there should be large-scale (> 100 km) spatial coherence of δ18OTR records in the Amazon, this has not been tested. Furthermore, it is of interest to investigate whether other, possibly longer-lived, species similarly record interannual variation of Amazon precipitation, and can be used to develop climate sensitive isotope chronologies. In this study, we measured δ18O in tree rings from seven lowland and one highland tree species from Bolivia. We found that cross-dating with δ18OTR gave more accurate tree ring dates than using ring width. Our “isotope cross-dating approach” is confirmed with radiocarbon “bomb-peak” dates, and has the potential to greatly facilitate development of δ18OTR records in the tropics, identify dating errors, and check annual ring formation in tropical trees. Six of the seven lowland species correlated significantly with C. odorata, showing that variation in δ18OTR has a coherent imprint across very different species, most likely arising from a dominant influence of source water δ18O on δ18OTR. In addition we show that δ18OTR series cohere over large distances, within and between species. Comparison of two C. odorata δ18OTR chronologies from sites several hundreds of kilometres apart showed a very strong correlation (r = 0.80, p < 0.001, 1901–2001), and a significant (but weaker) relationship was found between lowland C. odorata trees and a Polylepis tarapacana tree growing in the distant Altiplano (r = 0.39, p < 0.01, 1931–2001). This large-scale coherence of δ18OTR records is probably triggered by a strong spatial coherence in precipitation δ18O due to large-scale controls. These results highlight the strength of δ18OTR as a precipitation proxy, and open the way for temporal and spatial expansion of precipitation reconstructions in South America.
Tree height integrated into pantropical forest biomass estimates
(Copernicus Publications, 2012) Ted R. Feldpausch; Jon Lloyd; Simon L. Lewis; Roel Brienen; Manuel Gloor; Abel Monteagudo Mendoza; Gabriela López‐González; Lindsay F. Banin; Kamariah Abu Salim; Kofi Affum‐Baffoe
Abstract. Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.
Variation in stem mortality rates determines patterns of above‐ground biomass in <scp>A</scp>mazonian forests: implications for dynamic global vegetation models
(Wiley, 2016) Michelle Johnson; David Galbraith; Manuel Gloor; Hannes De Deurwaerder; Matthieu Guimberteau; Anja Rammig; Kirsten Thonicke; Hans Verbeeck; Celso von Randow; Abel Monteagudo
Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.