Éric Parent

Deep soil carbon dynamics are driven more by soil type than by climate: a worldwide meta-analysis of radiocarbon profiles.

The response of soil carbon dynamics to climate and land-use change will affect both the future climate and the quality of ecosystems. Deep soil carbon (>20xa0cm) is the primary component of the soil carbon pool, but the dynamics of deep soil carbon remain poorly understood. Therefore, radiocarbon activity (Δ14C), which is a function of the age of carbon, may help to understand the rates of soil carbon biodegradation and stabilization. We analyzed the published 14C contents in 122 profiles of mineral soil that were well distributed in most of the large world biomes, except for the boreal zone. With a multivariate extension of a linear mixed-effects model whose inference was based on the parallel combination of two algorithms, the expectation-maximization (EM) and the Metropolis-Hasting algorithms, we expressed soil Δ14C profiles as a four-parameter function of depth. The four-parameter model produced insightful predictions of soil Δ14C as dependent on depth, soil type, climate, vegetation, land-use and date of sampling (R2=0.68). Further analysis with the model showed that the age of topsoil carbon was primarily affected by climate and cultivation. By contrast, the age of deep soil carbon was affected more by soil taxa than by climate and thus illustrated the strong dependence of soil carbon dynamics on other pedologic traits such as clay content and mineralogy.

Compound Poisson‐gamma vs. delta‐gamma to handle zero‐inflated continuous data under a variable sampling volume

Ecological data such as biomasses often present a high proportion of zeros with possible skewed positive values. The Delta-Gamma (DG) approach, which models separately the presence-absence and the positive biomass, is commonly used in ecology. A less commonly known alternative is the compound Poisson-gamma (CPG) approach, which essentially mimics the process of capturing clusters of biomass during a sampling event. Regardless of the approach, the effort involved in obtaining a sample (henceforth called the sampling volume, but could also include swept areas, sampling durations, etc.), which can potentially be quite variable between samples, needs to be taken into account when modelling the resulting sample biomass. This is achieved empirically for the DG approach (using a generalized linear model with sampling volume as a covariate), and theoretically for the CPG approach (by scaling a parameter of the model). In this study, the consequences of this disparity between approaches were explored first using theoretical arguments, then using simulations and finally by applying the approaches to catch data from a commercial groundfish trawl fishery. The simulation study results point out that the DG approach can lead to poor estimates when far from standard idealized sampling assumptions. On the contrary, the CPG approach is much more robust to variable sampling conditions, confirming theoretical predictions. These results were confirmed by the case study for which model performances were weaker for the DG. Given the results, care must be taken when choosing an approach for dealing with zero-inflated continuous data. The DG approach, which is easily implemented using standard statistical softwares, works well when the sampling volume variability is small. However, better results were obtained with the CPG model when dealing with variable sampling volumes.

Towards using a full spectrum of early clinical trial data: a retrospective analysis to compare potential longitudinal categorical models for molecular targeted therapies in oncology

Following the pattern of phase I clinical trials for cytotoxic drugs, dose-finding clinical trials in oncology of molecularly targeted agents (MTA) aim at determining the maximum tolerated dose (MTD). In classical phase I clinical trials, MTD is generally defined by the number of patients with short-term major treatment toxicities (usually called dose-limiting toxicities, DLT), occurring during the first cycle of study treatment (e.g. within the first 3weeks of treatment). However, S. Postel-Vinay (2011) highlighted that half of grade 3 to 4 toxicities, usually considered as DLT, occur after the first cycle of MTA treatment. In addition, MTAs could induce other moderate (e.g. grade 2) toxicities which could be taken into account depending on their clinical importance, chronic nature and duration. Ignoring these late toxicities may lead to an underestimation of the drug toxicity and to wrong dose recommendations for phase II and III clinical trials. Some methods have been proposed, such as the time-to-event continuous reassessment method (Cheung 2000 and Mauguen 2011), to take into account the late toxicities. We suggest approaches based on longitudinal models (Doussau 2013). We compare several models for longitudinal data, such as transitional or marginal models, to take into account all relevant toxicities occurring during the entire length of the patient treatment (and not just the events within a predefined short-term time-window). These models allow the statistician to benefit from a larger amount of safety data which could potentially improve that accuracy in MTD assessment.