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Adrianzen M.U.,Jardin Botanico de Missouri | Adrianzen M.U.,International University Menendez Pelayo
Revista de Biologia Tropical | Year: 2015

Amazonian forests are a vast storehouse of biodiversity and function as carbon sinks from biomass that accumulates in various tree species. In these forests, the taxa with the greatest contribution of biomass cannot be precisely defined, and the representative distribution of Myristicaceae in the Peruvian Amazon was the starting point for designing the present study, which aimed to quantify the biomass contribution of this family. For this, I analyzed the databases that corresponded to 38 sample units that were previously collected and that were provided by the Team Network and RAINFOR organizations. The analysis consisted in the estimation of biomass using pre-established allometric equations, Kruskal-Wallis sample comparisons, interpolation-analysis maps, and nonparametric multidimensional scaling (NMDS). The results showed that Myristicaceae is the fourth most important biomass contributor with 376.97Mg/ha (9.92Mg/ha in average), mainly due to its abundance. Additionally, the family shows a noticeable habitat preference for certain soil conditions in the physiographic units, such is the case of Virola pavonis in “varillales”, within “floodplain”, or Iryanthera tessmannii and Virola loretensis in sewage flooded areas or “igapó” specifically, and the preference of Virola elongata and Virola surinamensis for white water flooded areas or “várzea” edaphic conditions of the physiographic units taken in the study. © 2015, Universidad de Costa Rica. All Rights Reserved. Source


Rejou-Mechain M.,CNRS Biological Evolution and Diversity Laboratory | Rejou-Mechain M.,French Institute of Pondicherry | Tymen B.,CNRS Biological Evolution and Diversity Laboratory | Blanc L.,CIRAD ES | And 7 more authors.
Remote Sensing of Environment | Year: 2015

In recent years, LiDAR technology has provided accurate forest aboveground biomass (AGB) maps in several forest ecosystems, including tropical forests. However, its ability to accurately map forest AGB changes in high-biomass tropical forests has seldom been investigated. Here, we assess the ability of repeated LiDAR acquisitions to map AGB stocks and changes in an old-growth Neotropical forest of French Guiana. Using two similar aerial small-footprint LiDAR campaigns over a four year interval, spanning ca. 20km2, and concomitant ground sampling, we constructed a model relating median canopy height and AGB at a 0.25-ha and 1-ha resolution. This model had an error of 14% at a 1-ha resolution (RSE=54.7Mgha-1) and of 23% at a 0.25-ha resolution (RSE=86.5Mgha-1). This uncertainty is comparable with values previously reported in other tropical forests and confirms that aerial LiDAR is an efficient technology for AGB mapping in high-biomass tropical forests. Our map predicts a mean AGB of 340Mgha-1 within the landscape. We also created an AGB change map, and compared it with ground-based AGB change estimates. The correlation was weak but significant only at the 0.25-ha resolution. One interpretation is that large natural tree-fall gaps that drive AGB changes in a naturally regenerating forest can be picked up at fine spatial scale but are veiled at coarser spatial resolution. Overall, both field-based and LiDAR-based estimates did not reveal a detectable increase in AGB stock over the study period, a trend observed in almost all forest types of our study area. Small footprint LiDAR is a powerful tool to dissect the fine-scale variability of AGB and to detect the main ecological controls underpinning forest biomass variability both in space and time. © 2015 Elsevier Inc. Source


Pallqui N.C.,National University San Antonio Abad del Cusco | Monteagudo A.,National University San Antonio Abad del Cusco | Phillips O.L.,University of Leeds | Lopez-Gonzalez G.,University of Leeds | And 4 more authors.
Revista Peruana de Biologia | Year: 2014

In this study we evaluated the floristic composition and changes in stored biomass and dynamics over time in 9 permanent plots monitored by RAINFOR (Amazon Forest Inventory Network) and located in the lowland Amazon rainforest of the Tambopata National Reserve. Data were acquired in the field using the standardized methodology of RAINFOR. The biomass was estimated using the equation for tropical moist forests of Chave et al. (2005). Biomass dynamics were analyzed, in three separated periods from 2003 to 2011. 64 families, 219 genera and 531 species were recorded. The tree floristic composition is very similar in all plots except for one swamp plot, although but it is also evident that two slightly different forest communities exist in the rest of landscape, apparently related to the age of the ancient river terraces in the area. Mortality and recruitment of individuals averaged 2.12 ± 0.52% and 1.92 ± 0.49%, respectively. The turnover rate is 2.02% per year. Aboveground biomass stored in these forests averages 296.2 ± 33.9 t ha-1. The biomass dynamics show a total net gain of 1.96, 1.69 and -1.23 t ha-1 for period respectively. Prior to the drought of 2010 a change in biomass was found 1.88 t ha-1 yr-1 and post drought was -0.18 t ha-1 yr-1 on average, though the difference is not significant. Demographic analysis suggests a dynamic equilibrium in the plots. The negative balance of biomass observed for the period 2008 - 2011 may be due to the drought of 2010, in which half of the monitored plots experienced negative net biomass change due to mortality of individuals selectively affecting the floristic composition. © Los autores. Source


Espirito-Santo F.D.B.,Jet Propulsion Laboratory | Espirito-Santo F.D.B.,University of New Hampshire | Gloor M.,University of Leeds | Keller M.,University of New Hampshire | And 22 more authors.
Nature Communications | Year: 2014

Forest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we characterize the frequency distribution of disturbance events in natural forests from 0.01 ha to 2,651 ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of ~1.7 Pg C y -1 over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of ~0.2 Pg C y-1, and that the largest-scale disturbances as a result of blow-downs only account for losses of ~0.004 Pg C y-1. Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink. © 2014 Macmillan Publishers Limited. All rights reserved. Source


Talbot J.,University of Leeds | Lewis S.L.,University of Leeds | Lewis S.L.,University College London | Lopez-Gonzalez G.,University of Leeds | And 24 more authors.
Forest Ecology and Management | Year: 2014

Forest inventory plots are widely used to estimate biomass carbon storage and its change over time. While there has been much debate and exploration of the analytical methods for calculating biomass, the methods used to determine rates of wood production have not been evaluated to the same degree. This affects assessment of ecosystem fluxes and may have wider implications if inventory data are used to parameterise biospheric models, or scaled to large areas in assessments of carbon sequestration. Here we use a dataset of 35 long-term Amazonian forest inventory plots to test different methods of calculating wood production rates. These address potential biases associated with three issues that routinely impact the interpretation of tree measurement data: (1) changes in the point of measurement (POM) of stem diameter as trees grow over time; (2) unequal length of time between censuses; and (3) the treatment of trees that pass the minimum diameter threshold ("recruits"). We derive corrections that control for changing POM height, that account for the unobserved growth of trees that die within census intervals, and that explore different assumptions regarding the growth of recruits during the previous census interval. For our dataset we find that annual aboveground coarse wood production (AGWP; in Mgha-1year-1 of dry matter) is underestimated on average by 9.2% if corrections are not made to control for changes in POM height. Failure to control for the length of sampling intervals results in a mean underestimation of 2.7% in annual AGWP in our plots for a mean interval length of 3.6years. Different methods for treating recruits result in mean differences of up to 8.1% in AGWP. In general, the greater the length of time a plot is sampled for and the greater the time elapsed between censuses, the greater the tendency to underestimate wood production. We recommend that POM changes, census interval length, and the contribution of recruits should all be accounted for when estimating productivity rates, and suggest methods for doing this. © 2014 Elsevier B.V. Source

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