Raphaël Pélissier

Improved allometric models to estimate the aboveground biomass of tropical trees

Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven-dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter-height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter-height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development.

Community ecology in the age of multivariate multiscale spatial analysis

Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes.

TREE SPATIAL PATTERNS IN THREE CONTRASTING PLOTS OF A SOUTHERN INDIAN TROPICAL MOIST EVERGREEN FOREST

In a primary dense moist evergreen forest of southern India, spatial patterns of trees ≥30 cm gbh were investigated from three contrasting 0.4-ha plots that differed in topography and amount of disturbance due to treefall. Exploratory data analysis is based on second-order neighbourhood and pair-correlation statistics used to describe the degree of clustering/regularity in patterns of all trees, and the degree of attraction/repulsion between young trees and adults. Stochastic simulations from the Markov point process models are then used to fit spatial interaction models. The results show that spatial patterns can be related to particular dynamic processes which depend on both exogenous and endogenous factors: on steep slopes disturbed by many treefalls, spatial pattern displays large clusters which can be interpreted as within-gap regeneration stages of various ages, while in areas undisturbed over a long period, interactions between young trees and adults give rise to spatial patterns consistent with substitution dynamic processes implying standing mortality rather than treefalls. Characterizing forest dynamics through spatial patterns of trees opens up the possibility of mapping structural units that might be considered as elementary functional patches of the forest mosaic.

Assessing aboveground tropical forest biomass using Google Earth canopy images

Reducing Emissions from Deforestation and Forest Degradation (REDD) in efforts to combat climate change requires participating countries to periodically assess their forest resources on a national scale. Such a process is particularly challenging in the tropics because of technical difficulties related to large aboveground forest biomass stocks, restricted availability of affordable, appropriate remote-sensing images, and a lack of accurate forest inventory data. In this paper, we apply the Fourier-based FOTO method of canopy texture analysis to Google Earths very-high-resolution images of the wet evergreen forests in the Western Ghats of India in order to (1) assess the predictive power of the method on aboveground biomass of tropical forests, (2) test the merits of free Google Earth images relative to their native commercial IKONOS counterparts and (3) highlight further research needs for affordable, accurate regional aboveground biomass estimations. We used the FOTO method to ordinate Fourier spectra of 1436 square canopy images (125 x 125 m) with respect to a canopy grain texture gradient (i.e., a combination of size distribution and spatial pattern of tree crowns), benchmarked against virtual canopy scenes simulated from a set of known forest structure parameters and a 3-D light interception model. We then used 15 1-ha ground plots to demonstrate that both texture gradients provided by Google Earth and IKONOS images strongly correlated with field-observed stand structure parameters such as the density of large trees, total basal area, and aboveground biomass estimated from a regional allometric model. Our results highlight the great potential of the FOTO method applied to Google Earth data for biomass retrieval because the texture-biomass relationship is only subject to 15% relative error, on average, and does not show obvious saturation trends at large biomass values. We also provide the first reliable map of tropical forest aboveground biomass predicted from free Google Earth images.

Seeing Central African forests through their largest trees

Large tropical trees and a few dominant species were recently identified as the main structuring elements of tropical forests. However, such result did not translate yet into quantitative approaches which are essential to understand, predict and monitor forest functions and composition over large, often poorly accessible territories. Here we show that the above-ground biomass (AGB) of the whole forest can be predicted from a few large trees and that the relationship is proved strikingly stable in 175 1-ha plots investigated across 8 sites spanning Central Africa. We designed a generic model predicting AGB with an error of 14% when based on only 5% of the stems, which points to universality in forest structural properties. For the first time in Africa, we identified some dominant species that disproportionally contribute to forest AGB with 1.5% of recorded species accounting for over 50% of the stock of AGB. Consequently, focusing on large trees and dominant species provides precise information on the whole forest stand. This offers new perspectives for understanding the functioning of tropical forests and opens new doors for the development of innovative monitoring strategies.

CONSISTENCY BETWEEN ORDINATION TECHNIQUES AND DIVERSITY MEASUREMENTS: TWO STRATEGIES FOR SPECIES OCCURRENCE DATA

Both the ordination of taxonomic tables and the measurements of species diversity aim to capture the prominent features of the species composition of a community. However, interrelations between ordination techniques and diversity measurements are sel- dom explicated and are mainly ignored by many field ecologists. This paper starts from the notion of the species occurrence table, which provides a unifying formulation for different kinds of taxonomic data. Here it is demonstrated that alternative species weightings can be used to equate the total inertia of a centered-by-species occurrence table with common diversity indices, such as species richness, Simpson diversity, or Shannon information. Such an equation defines two main ordination strategies related to two different but con- sistent measures of species diversity. The first places emphasis on scarce species and is based on Correspondence Analysis and species richness (CA-richness strategy). The second, in which abundant species are prominent, relies on Non-Symmetric Correspondence Anal- ysis and Simpson diversity (NSCA-Simpson strategy). Both strategies are suitable for mea- suring a. and f3 diversity by analyzing the centered-by-species occurrence table with respect to external environmental or instrumental variables. In this paper, these two strategies are applied to ecological data obtained in a Neotropical rainforest plot. The results are then discussed with respect to the intrinsic characteristics of the community under analysis, and also to the broad classes of floro-faunistic data used in ecology (i.e., data gathered from museum or herbarium collections, exhaustive inventories in a reference plot, or enumeration through species-by-releves tables). The approach en- compasses several well-known techniques such as Correspondence Analysis, Non-Sym- metric Correspondence Analysis, Canonical Correspondence Analysis, and Redundancy Analysis, and provides greater insight into interrelations between ordination methods and diversity studies.

Within-plot relationships between tree species occurrences and hydrological soil constraints: an example in French Guiana investigated through canonical correlation analysis

Spatial relationships between tree species and hydrological soilconstraints are analysed within a 10-ha rainforest plot at PistedeSt Elie in French Guiana. We used canonical correlation analysis to crossdirectly the occurrence-by-species table of 4 992 individuals (d.b.h. ≥10 cm) belonging to 120 species with qualitative soil variablesandquantitative spatial data. Firstly, the list of species occurrences wasconfronted to nine soildescriptors characterising a weathering sequence from the initial well-drainedferralitic cover to transformed hydromorphic soil conditions. This analysisrevealed that, apart from some specialised species restricted to the swampsthatexperience prolonged water saturation, the most abundant species can be orderedalong two intermingled gradients of tolerance limiting their niche amplitude: amain gradient of tolerance to prolonged water saturation that appears downslopeduring the weathering sequence; a second gradient of less importance,displayingthe species intolerant of prolonged water saturation according to theirtolerance to temporary confinement of the uphill transformed soil systems duetothe late appearance of a perched water-table. The results support thehypothesisthat at Piste de St Elie, the constraining soil conditions imposed by surfacewater saturation are more important determinants for tree zonation of many treespecies than water shortage. Secondly, the list of species occurrences wasconfronted to a spatialdata table built from a trend surface regression of the tree coordinates. Thisanalysis indicated that soil drainage is the main structuring factor of thelocal multispecies spatial pattern. After partialling out the soil effect, themultispecies pattern revealed a broader scale of heterogeneity that we supposedto be linked to endogenous factors resulting from population dynamics.Implications of the results are then discussed in the perspective offuture research on tree zonation, local diversity pattern and communitystructuring in tropical rainforests.

Regional variation in tropical forest tree species composition in the Central African Republic: an assessment based on inventories by forest companies

Understanding how species assemblages are structured in relation to environmental variation is a central issue in community ecology. However, factors that create regional variation in relative species abundances have been little studied due to the rarity of large-scale datasets. Here, we investigated a large dataset (30 180 0.5-ha plots spread over 1 600 000 ha) gathered from forest planning inventories in the semi-deciduous forest of the south western Central African Republic. We used Correspondence Analysis and Non-Symmetric Correspondence Analysis on Instrumental Variables to analyse variation in the abundance of 73 common tree species in relation to soil type, rainfall and proximity to villages. Together, environmental variables explained 10.3% of multi-species floristic variation among plots, and the regional spatial structure almost disappeared when the effects of these variables were removed. A Trend Surface Analysis using a third order polynomial function of the geographical coordinates of the plots explained 14.5% of the floristic variation and more than 75% of this variation was explained by environmental variables. Sandy soil was the most influential factor affecting floristic composition. Residual spatial variation not explained by the environmental variables probably reflects the natural and anthropogenic history of the vegetation.

Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India

Forest fires are a recurrent management problem in the Western Ghats of India. Although most fires occur during the dry season, information on the spatial distribution of fires is needed to improve fire prevention. We used the MODIS Hotspots database and Maxent algorithm to provide a quantitative understanding of the environmental controls regulating the spatial distribution of forest fires over the period 2003–07 in the entire Western Ghats and in two nested subregions with contrasting characteristics. We used hierarchical partitioning to assess the independent contributions of climate, topography and vegetation to the goodness-of-fit of models and to build the most parsimonious fire susceptibility model in each study area. Results show that although areas predicted as highly prone to forest fires were mainly localised on the eastern slopes of the Ghats, spatial predictions and model accuracies differed significantly between study areas. We suggest accordingly a two-step approach to identify: first, large fire-prone areas by paying special attention to the climatic conditions of the monsoon season before the fire season, which determine the fuels moisture content during the fire season; second, the most vulnerable sites within the fire-prone areas using local models mainly based on the type of vegetation.

Two-year tree growth patterns investigated from monthly girth records using dendrometer bands in a wet evergreen forest in India

With the aim of characterizing tree growth patterns, this paper re- examines the growth data of 100 selected trees belonging to 24 species that were recorded monthly in a 0.2-ha plot of a wet evergreen forest in the Western Ghats of India during the period 1980-82 using dendrometer bands. The mean growth profile, combining all of the selected trees, showed: (a) a significantly lower annual growth rate during the second year of survey which seemed to be negatively related to monsoon precipitation; (b) significant intra-annual growth variation clearly related to the regular alternation between a period of heavy rain and a quite long dry season of the monsoon climatic regime. Analysis of the variability of the indi- vidual smoothed growth profiles representing the 2-y trend of the growth data showed that: (a) the mean growth rate depended on a combination of an intrinsic endogenous variable (the structural class grouping species according to their max- imum size), a tree size variable (tree diameter at breast height, dbh) and a neigh- bourhood variable (the number of taller neighbours in a 10-m radius); (b) the sudden change in growth rate from one year to the other was not predictable using these variables. The amplitude of the seasonal variations, investigated from the detrended growth profiles, appeared to be dependent on a combination of tree dbh and the number of taller neighbours in a 10-m radius. A co-inertia analysis of the smoothed and the detrended growth profiles indicated that the trees with fast growth also exhibited high seasonal variation. It is suggested that fast growing trees are those with favourable crown positions, which are consequently subject to high transpiration rates due to radiation and wind exposure.