Mixture of Generalized Linear Regression Models for Species-Rich Ecosystems

Understanding how climate change could impact population dynamics is of primary importance for species conservation. In species-rich ecosystems with many rare species, the small population sizes hinder a good fit of species-specific models. We propose a mixture of regression models with variable selection allowing the simultaneous clustering of species into groups according to vital rate information (recruitment, growth, and mortality) and the identification of group-specific explicative environmental variables. We illustrate the effectiveness of the method on data from a tropical rain forest in the Central African Republic and demonstrate the accuracy of the model in successfully reproducing stand dynamics and classifying tree species into well-differentiated groups with clear ecological interpretations. (Résumé d'auteur

Incorporating environmental variability in matrix models predictions for highly diverse rainforests

In matrix models that describe forest dynamics, the uncertainty on model predictions is directly related to the precision of estimation of the transition parameters of the model (growth, recruitment and mortality rates). The two main sources of variability in parameter estimates are sampling and environmental variability. Sampling variability depends on the amount of available observations. As tropical rainforests have many rare species, most species-specific parameter estimates have huge errors. A solution to this problem is to group species into functional group to increase the number of available observations. Environmental variability is related to the spatio-temporal variations of transition parameters due to environmental fluctuations. This kind of variability is not yet considered in the models used by forest managers. We address rainfall variability in forest dynamic predictions. Species were grouped according to their response to drought. The functional species classification and the relation between transition parameters and climatic covariates for each species group has been simultaneously fitted using cluster-wise regression. Data come from permanent sample plots (25 years monitoring) located in the Central African Republic. We predict stand dynamics and we compare and discuss predictions with and without rainfall variability. (Résumé d'auteur

twoe: An R package for modelling tropical forest dynamics from permanent sample plots using a hierarchical Bayesian approach to capture species diversity

Permanent sample plots are commonly used in tropical forest ecology for conservation and management purposes. Longitudinal tree data can be used to estimate species demographic functions which can then be implemented in forest dynamics simulators to help decision. For tropical forests, with many species being rare species with few observations, the classical modelling approach assumes a restricted number of functional groups (pioneer, light-demanding and shade-tolerant species groups when light partitioning is supposed to be the main mechanism driving community, which is a frequent assumption). Although this simplified approach is convenient in practice, it relies on strong assumptions: i) that species can be grouped, ii) regarding a limited number of criteria, and it suffers from several pitfalls both on the theoretical and applied side. First, because of the principle of competitive exclusion and of the multidimensionality of the species niche, the functional group approach is likely to be biased and to lead to unrealistic simulations. Second, using functional groups impede conservation planning at the species level which should be the advised approach especially when considering rare species. In this study, we present the "twoe" (2e) software, available as a R package, which allows i) formatting the permanent sample plot data for demographic analysis, ii) estimating the parameters of growth, mortality and recruitment functions including a competition effect, iii) simulating forest dynamics with a forecast of the basal area (possibly carbon) and of the community composition. The modelling approach in the twoe software includes species random effects in a hierarchical Bayesian framework, allowing an independant dynamics for each species. The twoe software includes original MCMC algorithms to handle variable time-interval between census for the mortality and recruitment processes, is easy of use and product usefull objects (table with parameter estimates for each species, graphics) for ecological interpretations. (Résumé d'auteur

Prediction of a multivariate spatial random field with continuous, count and ordianl outcomes

As most georeferenced data sets are multivariate and concern variables of different kinds, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non-Gaussian variables and the dependence modelling between processes. The aim of this paper is to present a new approach that permits simultaneous modelling of Gaussian, count and ordinal spatial processes. We consider a hierarchical model implemented within a Bayesian framework. The method used for Gaussian and count variables is based on the generalized linear model. Ordinal variable is taken into account through a generalization of the ordinal probit model. We use the moving average approach of Ver Hoef and Barry to model the dependencies between the processes. (Résumé d'auteur

A new tool to calculate roadless space in forest landscapes, applied in the Congo basin

New global strategies for road building require innovative tools to analyze linear patterns and their spatial distribution and to evaluate their environmental impacts. Roads not only present physical barriers to wildlife but also provide access for human and biological invasions. In tropical regions especially, forest degradation has been associated with roads built for selective logging into formerly intact forest landscapes. To quantify to what extent ecosystems are influenced by roads, it is important not only to know road length density but also their location in a landscape unit. The concept of roadless space is based on distance to the nearest road from any point. We present the computation of this distribution using the Empty-Space-Function, a general statistical mathematical tool based on stochastic geometry and random sets theory. We demonstrate the applicability of this well-defined probability function to calculate roadless space based on vector road data. In a Congo Basin case study we compared the temporal development of road networks inside different logging concessions over time. We hypothesized that roadless space decreases, even when the rate of wood volume harvest remains constant. Based on LANDSAT time series covering the last 29 years, we assessed accessible roads in relation with the river network and calculated the roadless space at different points in time. As expected, roadless space decreased continuously throughout most concessions, despite a drop in total annual harvest volume after 2008 and independent of forest certification schemes. We recommend that measures to reduce impacts of selective logging should not only be based on the extraction of timber, but should also include the total area impacted by roads. The Empty-Space-Function provides a rigorous mathematical description and a straightforward way to assess intact forest landscapes and is therefore highly applicable to road impact evaluation in conservation science

Modélisation statistique multivariée pour l'écologie et la génétique

Le devenir des forêts est désormais l'une des préoccupations majeures du 20ième siècle. Celles-ci sont justifiées par l'importance que revêtent les forêts pour de multiples acteurs et à de multiples échelles. L'enjeu consiste aujourd'hui à conserver la biodiversité des forêts tropicales et à les gérer durablement, c'est-à-dire à exploiter leurs ressources en préservant à long terme leurs fonctions écologiques, économiques et sociales. Protéger et gérer durablement un écosystème dans son ensemble conduit à le considérer non plus comme un ensemble indépendant de processus biologiques mais comme un ensemble de processus interdépendants. Analyser, comprendre ou encore prédire le future de ces écosystèmes nécessite certaines précautions et des méthodes d'analyses adéquates doivent être employées. C'est ce que je me suis efforcé de faire au cours de ma carrière et ce mémoire, d'habilitation à diriger des recherches, présente les travaux que j'ai été amenés à développer. Il est important de souligner que ce sont les questions biologiques qui ont motivé mes recherches en statistique. Il m'est donc apparu naturel que ce soit au travers des applications que je devais présenter mes activités de recherches en bio-statistiques. La première partie donne un rapide aperçu du contexte biologique et mathématique. La seconde présente plus en détail quatre résultats qui me semblent majeurs et qui traitent de la prise en compte des dépendances spatiales, de la richesse spécifique des écosystèmes tropicaux ou encore des questions de prédictions. La dernière partie présente les stratégies à long terme que je souhaiterais mettre en place pour mener à bien et fédérer les recherches et répondre ainsi à l'objectif commun : la préservation des écosystèmes forestiers compatible avec le développement des populations humaines. (Résumé d'auteur

Spatial and temporal variability of plant-available soil water in Congo Basin and its relationship with tree species distributions

Regional-scale patterns of tropical rainforest tree composition can be due to climate (rainfall, dry season length), geology and/or soil properties (chemical fertility, available water). In Amazonia, soil fertility and dry season length appears to be the main factor to explain this pattern. However, in the Congo Basin, geology has been proposed to explain the pattern of some commercial timber species. Since the geological substrates of this area have similar chemical properties, we hypothesized that this pattern could be explained by the plant-available soil water (PAW). We used a soil water balance model similar to RisQue in the Congo Basin over the period from 2000 to 2010, with a decade time step, and with a spatial resolution of 8 km. The input parameters of this model were the maximum plant-available soil water (PAWmax), rainfall and evapotranspiration. The output parameter was the maximum number of successive decades when PAW was null, named extreme drought index (EDI). Finally we carried out a map of EDI at Congo Basin scale that we compared with maps of the spatial pattern of 31 commercial species. We showed that Arenosols, as expected, but also other soils like Ferralsols, have the lowest PAWmax of the Congo Basin. We evidenced no or low correlations between the map of EDI and maps of the spatial pattern of each of the 31 commercial species. Other factors, not taken into account in this study, might explain this result like the water table level and variable forest rooting depth in function of soil type. (Résumé d'auteur

Thinning after selective logging facilitates floristic composition recovery in a tropical rain forests of Central Africa

In the Congo Basin, where most timber species are light-demanding, low logging intensities commonly implemented (1-2 trees harvested ha-1) do not provide sufficient canopy gaps to ensure species regeneration. The regeneration of light-demanding timber species may therefore benefit from more intensive logging, or from post-harvest treatments such as thinning using poison girdling that increases light penetration. Little is known of the impact of post-harvest treatments on the floristic composition of tropical moist forests. This study therefore aimed to assess the effects of low and high selective logging, followed or not by thinning, on the floristic composition of a tropical moist forest in the Central African Republic, from 7 to 23 years after logging. We analyzed abundance data for 110 tree genera recorded every year for 14 years in 25 1-ha permanent subplots and we compared floristic composition recovery between thinned and unthinned subplots, using unlogged subplots as a reference characterizing the pre-logging floristic composition. We discuss the results and their potential implication for forest management. (Résumé d'auteur