Breakdown of Leaf Litter in a Neotropical Stream

We investigated the breakdown of 2 leaf species, Croton gossypifolius (Euphorbiaceae) and Clidemia sp. (Melastomataceae), in a 4th-order neotropical stream (Andean Mountains, southwestern Colombia) using leaf bags over a 6-wk period. We determined the initial leaf chemical composition and followed the change in content of organic matter, C, N, and ergosterol, the sporulation activity of aquatic hyphomy cetes, and the structure and composition of leaf-associated aquatic hy phomy cetes and macroinvertebrates. Both leaf species decomposed rapidly ( k 5 0.0651 and 0.0235/d, respec- tively); Croton lost 95% of its initial mass within 4 wk compared to 54% for Clidemia . These high rates were probably related to the stable and moderately high water tempera ture (19 8 C), favoring strong biological a ctivity. Up to 2300 and 1500 invertebrates per leaf bag were found on Croton and Clidemia leaves after 10 and 16 d, respectively. Shredders accounted for , 5% of the total numbers and biomass. F ungal biomass peaked at 8.4 and 9.6% of the detrital mass of the 2 leaf species, suggesting that fungi contributed cons iderably to leaf mass l oss. The difference in breakdown rates between leaf species was consistent with the earlier peaks in ergosterol and sporulation rate in Croton (10 d vs 16 d in Clidemia ) and the faster colonization of Croton by macroinvertebrates. The softer texture, lower tannin content, and hi gher N content were partly responsible for the faster breakdown of Croton leaves. The rapid breakdown of leaf litter, combined with a low infl uence by shredders, is in accordance with previous findings. The high fungal activity associated with rapid leaf breakdown appears to be characteristic of leaf processing in tropical streams

Supervised-Component versus PLS regression. The case of GLMMs with autoregressive random effect

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Component-based regularisation of multivariate generalised linear mixed models

International audienceWe address the component-based regularisation of a multivariate Generalised Linear Mixed Model (GLMM) in the framework of grouped data. A set Y of random responses is modelled with a multivariate GLMM, based on a set X of explanatory variables, a set A of additional explanatory variables, and random effects to introduce the within-group dependence of observations. Variables in X are assumed many and redundant so that regression demands regularisation. This is not the case for A, which contains few and selected variables. Regularisation is performed building an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in X. To estimate the model, we propose to maximise a criterion specific to the Supervised Component-based Generalised Linear Regression (SCGLR) within an adaptation of Schall's algorithm. This extension of SCGLR is tested on both simulated and real grouped data, and compared to ridge and LASSO regularisations. Supplementary material for this article is available online

Regularising Generalised Linear Mixed Models with an autoregressive random effect

International audienceWe address regularised versions of the Expectation-Maximisation (EM) algorithm for Generalised Linear Mixed Models (GLMM) in the context of panel data (measured on several individuals at different time-points). A random response y is modelled by a GLMM, using a set X of explanatory variables and two random effects. The first one introduces the dependence within individuals on which data is repeatedly collected while the second one embodies the serially correlated time-specific effect shared by all the individuals. Variables in X are assumed many and redundant, so that regression demands regularisation. In this context, we first propose a L2-penalised EM algorithm, and then a supervised component-based regularised EM algorithm as an alternative

Algorithme EM régularisé pour données longitudinales

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Régularisation dans les Modèles Linéaires Généralisés Mixtes avec effet aléatoire autorégressif

International audienceWe address regularised versions of the Expectation-Maximisation (EM) algorithm for Generalised Linear Mixed Models (GLMM) in the context of panel data (measured on several individuals at different time points). A random response y is modelled by a GLMM, using a set X of explanatory variables and two random effects. The first effect introduces the dependence within individuals on which data is repeatedly collected while the second embodies the serially correlated time-specific effect shared by all the individuals. Variables in X are assumed many and redundant, so that regression demands regularisation. In this context, we first propose a L2-penalised EM algorithm for low-dimensional data, and then a supervised component-based regularised EM algorithm for the high-dimensional case.Nous proposons des versions régularisées de l'algorithme Espérance-Maximisation (EM) permettant d'estimer un Modèle Linéaire Généralisé Mixte (GLMM) pour des données de panel (mesurées sur plusieurs individus à différentes dates). Une réponse aléatoire y est modélisée par un GLMM, au moyen d'un ensemble X de variables explicatives et de deux effets aléatoires. Le premier effet modélise la dépendance des mesures relatives à un même individu, tandis que le second représente l'effet temporel autocorrélé partagé par tous les individus. Les variables dans X sont supposées nombreuses et redondantes, si bien qu'il est nécessaire de régulariser la régression. Dans ce contexte, nous proposons d'abord un algorithme EM pénalisé en norme L2 pour des données de petite dimension, puis une version régularisée de l'algorithme EM, basée sur la construction de composantes supervisées, plutôt destinée à la grande dimension