Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees  1 Mg) and reduced the range of plot-level error (in %) from [−23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Copernicus Publications on behalf of the European Geosciences Union. The published article can be found at: http://www.biogeosciences.net

Diversity, taxonomy, and history of the tropical fern genus Didymoglossum Desv. (Hymenophyllaceae, Polypodiidae) in Africa

International audienceThe fern genus Didymoglossum (Hymenophyllaceae) is not so diverse in Africa with seven species at most. However, its local taxonomy is surprisingly still strongly debated, in particular within the Didymoglossum erosum complex interpreted either as a single polymorphic species or as a group of at least three distinct but morphologically very close taxa (D. erosum, Didymoglossum chamaedrys, and Didymoglossum benlii). Investigating these taxonomic issues and more generally the diversity of the genus in Africa and its origin, we conducted a complete anatomo–morphological analysis coupled with a molecular phylogenetic work based on rbcL. Our results support the recognition of all seven species, including Didymoglossum robinsonii that is likely distinct from the Neotropical Didymoglossum reptans to which the African populations were traditionally attributed. We here propose new characters and a novel key to distinguish the seven African species which also include Didymoglossum ballardianum, Didymoglossum lenormandii, and Didymoglossum liberiense. Once the taxonomy is clarified with respect to the distinct evolutionary lineages evidenced, the biogeographic history of the genus in Africa is discussed based on a divergence time estimation and the reconstruction of the ancestral geographic areas. These analyses reveal a Mesozoic (Cretaceous) vicariance event within Didymoglossum which is the second one hypothesized for the family Hymenophyllaceae

Data from: Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: a comparison with traditional destructive approach

1. Calibration of local, regional or global allometric equations to estimate biomass at the tree level constitutes a significant burden on projects aiming at reducing Carbon emissions from forest degradation and deforestation. The objective of this contribution is to assess the precision and accuracy of Terrestrial Laser Scanning (TLS) for estimating volumes and above-ground biomass (AGB) of the woody parts of tropical trees, and for the calibration of allometric models. 2. We used a destructive dataset of 61 trees, with diameters and AGB of up to 186.6 cm and 60 Mg respectively, which were scanned, felled and weighed in the semi-deciduous forests of eastern Cameroon. We present an operational approach based on available software allowing the retrieving of TLS volume with low bias and high accuracy for large tropical trees. Edition of the obtained models proved necessary, mainly to account for the complexity of buttressed parts of tree trunks, which were separately modelled through a meshing approach, and to bring a few corrections in the topology and geometry of branches, thanks to the amapstudio-scan software. 3. Over the entire dataset, TLS-derived volumes proved highly reliable for branches larger than 5 cm in diameter. The volumes of the remaining woody parts estimated for stumps, stems and crowns as well as for the whole tree proved very accurate (RMSE below 2.81% and R² above of .98) and unbiased. Once converted into AGB using mean local-specific wood density values, TLS estimates allowed calibrating a biomass allometric model with coefficients statistically undistinguishable from those of a model based on destructive data. The Unedited Quantitative Structure Model (QSM) however leads to systematic overestimations of woody volumes and subsequently to significantly different allometric parameters. 4. We can therefore conclude that a non-destructive TLS approach can now be used as an operational alternative to traditional destructive sampling to build the allometric equations, although attention must be paid to the quality of QSM model adjustments to avoid systematic bias

Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach

1.Calibration of local, regional or global allometric equations to estimate biomass at the tree level constitutes a significant burden on projects aiming at reducing Carbon emissions from forest degradation and deforestation. The objective of this contribution is to assess the precision and accuracy of Terrestrial Laser Scanning (TLS) for estimating volumes and aboveground biomass (AGB) of the woody parts of tropical trees, and for the calibration of allometric models. 2.We used a destructive dataset of 61 trees, with diameters and AGB of up to 186.6 cm and 60 Mg respectively, which were scanned, felled and weighed in the semi-deciduous forests of eastern Cameroon. We present an operational approach based on available software allowing to retrieve TLS volume with low bias and high accuracy for large tropical trees. Edition of the obtained models proved necessary, mainly to account for the complexity of buttressed parts of tree trunks, which were separately modelled through a meshing approach, and to bring a few corrections in the topology and geometry of branches, thanks to the AMAPStudio-Scan software. 3.Over the entire dataset, TLS derived volumes proved highly reliable for branches larger than 5 cm in diameter. The volumes of the remaining woody parts estimated for stumps, stems and crowns as well as for the whole tree proved very accurate (RMSE below 2.81% and R² above of 0.98) and unbiased. Once converted to AGB using mean local specific wood density values, TLS estimates allowed calibrating a biomass allometric model with coefficients statistically undistinguishable from those of a model based on destructive data. Un-edited Quantitative Structure Model (QSM) however lead to systematic overestimations of woody volumes and subsequently to significantly different allometric parameters. 4.We can therefore conclude that the non-destructive TLS approach can now be used as an operational alternative to traditional destructive sampling to build the allometric equations, although attention must be paid to the quality of QSM model adjustments to avoid systematic bia

Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data

With the improvement of remote sensing techniques for forest inventory application such as terrestrial LiDAR, tree volume can now be measured directly, without resorting to allometric equations. However, wood specific gravity (WSG) remains a crucial factor for converting these precise volume measurements into unbiased biomass estimates. In addition to this WSG values obtained from samples collected at the base of the tree (WSGBase) or from global repositories such as Dryad (WSGDryad) can be substantially biased relative to the overall tree value. Our aim was to assess and mitigate error propagation at tree and stand level using a pragmatic approach that could be generalized to National Forest Inventories or other carbon assessment efforts based on measured volumetric data. In the semi-deciduous forests of Eastern Cameroon, we destructively sampled 130 trees belonging to 15 species mostly represented by large trees (up to 45 Mg). We also used stand-level dendrometric parameters from 21 1-ha plots inventoried in the same area to propagate the tree-level bias at the plot level. A new descriptor, volume average-weighted WSG (WWSG) of the tree was computed by weighting the WSG of tree compartments by their relative volume prior to summing at tree level. As WWSG cannot be assessed non-destructively, linear models were adjusted to predict field WWSG and revealed that a combination of WSGDryad, diameter at breast height (DBH) and species stem morphology (Sm) were significant predictors explaining together 72% of WWSG variation. At tree level, estimating tree aboveground biomass using WSGBase and WSGDryad yielded overestimations of 10% and 7% respectively whereas predicted WWSG only produced an underestimation of less than 1%. At stand-level, WSGBase and WSGDryad gave an average simulated bias of 9% (S.D. = ±7) and 3% (S.D. = ±7) respectively whereas predicted WWSG reduced the bias by up to 0.1% (S.D. = ±8). We also observed that the stand-level bias obtained with WSGBase and WSGDryad decreased with total plot size and plot area. The systematic bias induced by WSGBase and WSGDryad for biomass estimations using measured volumes are clearly not negligible but yet generally overlooked. A simple corrective approach such as the one proposed with our predictive WWSG model is liable to improve the precision of remote sensing-based approaches for broader scale biomass estimations

Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas

International audienceTree crowns play a central role in stand dynamics. Remotely sensed canopy images have been shown to allow inferring stand structure and biomass which suggests that allometric scaling between stems and crowns may be tight, although insufficiently investigated to date. Here, we report the first broad-scale assessment of stem vs. crown scaling exponents using measurements of bole diameter (DBH), total height (H), and crown area (CA) made on 4148 trees belonging to 538 species in five biogeographic areas across the wet tropics. Allometries were fitted with power functions using ordinary least-squares regressions on log-transformed data. The inter-site variability and intra-site (sub-canopy vs. canopy trees) variability of the allometries were evaluated by comparing the scaling exponents. Our results indicated that, in contrast to both DBH-H and H-CA allometries, DBH-CA allometry shows no significant inter-site variation. This fairly invariant scaling calls for increased effort in documenting crown sizes as part of tree morphology. Stability in DBH-CA allometry, indeed, suggests that some universal constraints are sufficiently pervasive to restrict the exponent variation to a narrow range. In addition, our results point to inverse changes in the scaling exponent of the DBH-CA vs. DBH-H allometries when shifting from sub-canopy to canopy trees, suggesting a change in carbon allocation when a tree reaches direct light. These results pave the way for further advances in our understanding of niche partitioning in tree species, tropical forest dynamics, and to estimate AGB in tropical forests from remotely sensed images

Trees database (Momo et al)

Trees database (Momo et al

Genetic breaks caused by ancient forest fragmentation: phylogeography of Staudtia kamerunensis (Myristicaceae) reveals distinct clusters in the Congo Basin

Funder: Fondation Philippe Wiener - Maurice Anspach; doi: http://dx.doi.org/10.13039/501100003138Documenting species and population diversity is becoming increasingly important as the destruction and degradation of natural ecosystems are leading to a worldwide biodiversity loss. Despite the rapid development of genetic tools, many species remain undocumented and little is known about the diversity of individuals and populations, especially for tropical African plants. In this study, we aim to identify putative hidden species and/or differentiated populations in the tropical African tree Staudtia kamerunensis Warb. (Myristicaceae), a widespread species characterized by a high morphological diversity and a complex taxonomical history. Historical herbarium vouchers were sampled and leaf or cambium samples were collected in the field, dried in silica gel, and subsequently genotyped at 14 microsatellite loci (SSRs), as well as sequenced for two nuclear genes (At103, Agt1) and one plastid region (psbA-trnH). These genetic data were then analyzed using Bayesian clustering, population genetics, and the construction of haplowebs to assess genetic clustering patterns, the distribution of genetic diversity, and genetic differentiation among populations. Multiple genetically differentiated clusters were observed in parapatry throughout Central Africa. Genetic diversity was high and similar among these clusters, apart from the most differentiated populations in southeast Democratic Republic of the Congo (DR Congo), which showed lower levels of genetic diversity. The genetic breaks detected between S. kamerunensis populations are likely not indicative of hidden species but rather result from ancient rainforest fragmentation during cold and dry periods in the Pliocene and/or Pleistocene. The strong genetic divergence between populations in southeast DR Congo could be the result of an ongoing speciation linked to ecological niche differentiation

Genetic breaks caused by ancient forest fragmentation: phylogeography of Staudtia kamerunensis (Myristicaceae) reveals distinct clusters in the Congo Basin

Funder: Fondation Philippe Wiener - Maurice Anspach; doi: http://dx.doi.org/10.13039/501100003138AbstractDocumenting species and population diversity is becoming increasingly important as the destruction and degradation of natural ecosystems are leading to a worldwide biodiversity loss. Despite the rapid development of genetic tools, many species remain undocumented and little is known about the diversity of individuals and populations, especially for tropical African plants. In this study, we aim to identify putative hidden species and/or differentiated populations in the tropical African tree Staudtia kamerunensis Warb. (Myristicaceae), a widespread species characterized by a high morphological diversity and a complex taxonomical history. Historical herbarium vouchers were sampled and leaf or cambium samples were collected in the field, dried in silica gel, and subsequently genotyped at 14 microsatellite loci (SSRs), as well as sequenced for two nuclear genes (At103, Agt1) and one plastid region (psbA-trnH). These genetic data were then analyzed using Bayesian clustering, population genetics, and the construction of haplowebs to assess genetic clustering patterns, the distribution of genetic diversity, and genetic differentiation among populations. Multiple genetically differentiated clusters were observed in parapatry throughout Central Africa. Genetic diversity was high and similar among these clusters, apart from the most differentiated populations in southeast Democratic Republic of the Congo (DR Congo), which showed lower levels of genetic diversity. The genetic breaks detected between S. kamerunensis populations are likely not indicative of hidden species but rather result from ancient rainforest fragmentation during cold and dry periods in the Pliocene and/or Pleistocene. The strong genetic divergence between populations in southeast DR Congo could be the result of an ongoing speciation linked to ecological niche differentiation.</jats:p

A map of African humid tropical forest aboveground biomass derived from management inventories

International audienceForest biomass is key in Earth carbon cycle and climate system, and thus under intense scrutiny in the context of international climate change mitigation initiatives (e.g. REDD+). In tropical forests, the spatial distribution of aboveground biomass (AGB) remains, however, highly uncertain. There is increasing recognition that progress is strongly limited by the lack of field observations over large and remote areas. Here, we introduce the Congo basin Forests AGB (CoFor-AGB) dataset that contains AGB estimations and associated uncertainty for 59,857 1-km pixels aggregated from nearly 100,000 ha of in situ forest management inventories for the 2000 – early 2010s period in five central African countries. A comprehensive error propagation scheme suggests that the uncertainty on AGB estimations derived from c. 0.5-ha inventory plots (8.6–15.0%) is only moderately higher than the error obtained from scientific sampling plots (8.3%). CoFor-AGB provides the first large scale view of forest AGB spatial variation from field data in central Africa, the second largest continuous tropical forest domain of the world