Guillaume Cornu

Geological substrates shape tree species and trait distributions in African moist forests.

Background Understanding the factors that shape the distribution of tropical tree species at large scales is a central issue in ecology, conservation and forest management. The aims of this study were to (i) assess the importance of environmental factors relative to historical factors for tree species distributions in the semi-evergreen forests of the northern Congo basin; and to (ii) identify potential mechanisms explaining distribution patterns through a trait-based approach. Methodology/Principal Findings We analyzed the distribution patterns of 31 common tree species in an area of more than 700,000 km2 spanning the borders of Cameroon, the Central African Republic, and the Republic of Congo using forest inventory data from 56,445 0.5-ha plots. Spatial variation of environmental (climate, topography and geology) and historical factors (human disturbance) were quantified from maps and satellite records. Four key functional traits (leaf phenology, shade tolerance, wood density, and maximum growth rate) were extracted from the literature. The geological substrate was of major importance for the distribution of the focal species, while climate and past human disturbances had a significant but lesser impact. Species distribution patterns were significantly related to functional traits. Species associated with sandy soils typical of sandstone and alluvium were characterized by slow growth rates, shade tolerance, evergreen leaves, and high wood density, traits allowing persistence on resource-poor soils. In contrast, fast-growing pioneer species rarely occurred on sandy soils, except for Lophira alata. Conclusions/Significance The results indicate strong environmental filtering due to differential soil resource availability across geological substrates. Additionally, long-term human disturbances in resource-rich areas may have accentuated the observed patterns of species and trait distributions. Trait differences across geological substrates imply pronounced differences in population and ecosystem processes, and call for different conservation and management strategies.

Vegetation structure and greenness in Central Africa from Modis multi-temporal data

African forests within the Congo Basin are generally mapped at a regional scale as broad-leaved evergreen forests, with the main distinction being between terra-firme and swamp forest types. At the same time, commercial forest inventories, as well as national maps, have highlighted a strong spatial heterogeneity of forest types. A detailed vegetation map generated using consistent methods is needed to inform decision makers about spatial forest organization and their relationships with environmental drivers in the context of global change. We propose a multi-temporal remotely sensed data approach to characterize vegetation types using vegetation index annual profiles. The classifications identified 22 vegetation types (six savannas, two swamp forests, 14 forest types) improving existing vegetation maps. Among forest types, we showed strong variations in stand structure and deciduousness, identifying (i) two blocks of dense evergreen forests located in the western part of the study area and in the central part on sandy soils; (ii) semi-deciduous forests are located in the Sangha River interval which has experienced past fragmentation and human activities. For all vegetation types enhanced vegetation index profiles were highly seasonal and strongly correlated to rainfall and to a lesser extent, to light regimes. These results are of importance to predict spatial variations of carbon stocks and fluxes, because evergreen/deciduous forests (i) have contrasted annual dynamics of photosynthetic activity and foliar water content and (ii) differ in community dynamics and ecosystem processes.

Relationships between demography and gene flow and their importance for the conservation of tree populations in tropical forests under selective felling regimes

Determining how tropical tree populations subject to selective felling (logging) pressure may be conserved is a crucial issue for forest management and studying this issue requires a comprehensive understanding of the relationships between population demography and gene flow. We used a simulation model, SELVA, to study (1) the relative impact of demographic factors (juvenile mortality, felling regime) and genetic factors (selfing, number and location of fathers, mating success) on long-term genetic diversity; and (2) the impact of different felling regimes on population size versus genetic diversity. Impact was measured by means of model sensitivity analyses. Juvenile mortality had the highest impact on the number of alleles and genotypes, and on the genetic distance between the original and final populations. Selfing had the greatest impact on observed heterozygote frequency and fixation index. Other factors and interactions had only minor effects. Overall, felling had a greater impact on population size than on genetic diversity. Interestingly, populations under relatively low felling pressure even had a somewhat lower fixation index than undisturbed populations (no felling). We conclude that demographic processes such as juvenile mortality should be modelled thoroughly to obtain reliable long-term predictions of genetic diversity. Mortality in selfed and outcrossed progenies should be modelled explicitly by taking inbreeding depression into account. The modelling of selfing based on population rate appeared to be oversimplifying and should account for inter-tree variation. Forest management should pay particular attention to the regeneration capacities of felled species.

Mapping ecosystem services at the regional scale: the validity of an upscaling approach

ABSTRACT Mapping ecosystem services (ES) over large scales is important for environmental monitoring but is often prohibitively expensive and difficult. We test a hybrid, low-cost method of mapping ES indicators over large scales in Pará State, Brazil. Four ES indicators (vegetation carbon stocks, biodiversity index, soil chemical quality index and rates of water infiltration into soil) were measured in the field and then summarized spatially for regional land-cover classes derived from satellite imagery. The regionally mapped ES values correlated strongly with independent and local measures of ES. For example, regional estimates of the vegetation carbon stocks are strongly correlated with actual measures derived from field samples and validation data (significant anova test – p-value = 4.51e−9) and differed on average by only 20 Mg/ha from the field data. Our spatially-nested approach provides reliable and accurate maps of ES at both local and regional scales. Local maps account for the specificities of an area while regional maps provide an accurate generalization of an ES’ state. Such up-scaling methods infuse large-scale ES maps with localized data and enable the estimation of uncertainty of at regional scales. Our approach is first step towards the spatial characterization of ES at large and potentially global scales.

Supervised Component Generalized Linear Regression with Multiple Explanatory Blocks: THEME-SCGLR

We address component-based regularization of a multivariate Generalized Linear Model (GLM). A set of random responses Y is assumed to depend, through a GLM, on a set X of explanatory variables, as well as on a set T of additional covariates. X is partitioned into R conceptually homogeneous blocks X1, …, X R , viewed as explanatory themes. Variables in each X r are assumed many and redundant. Thus, generalized linear regression demands regularization with respect to each X r . By contrast, variables in T are assumed selected so as to demand no regularization. Regularization is performed searching each X r for an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in X r . We propose a very general criterion to measure structural relevance (SR) of a component in a block, and show how to take SR into account within a Fisher-scoring-type algorithm in order to estimate the model. We show how to deal with mixed-type explanatory variables. The method, named THEME-SCGLR, is tested on simulated data, and then applied to rainforest data in order to model the abundance of tree-species.