Toxicokinetic-toxicodynamic (TKTD) models, as the General Unified Threshold model of Survival (GUTS), provide a consistent process-based framework compared to classical dose-response models to analyze both time and concentration-dependent data sets. However, the extent to which GUTS models (Stochastic Death (SD) and Individual Tolerance (IT)) lead to a better fitting than classical dose-response model at a given target time (TT) has poorly been investigated. Our paper highlights that GUTS estimates are generally more conservative and have a reduced uncertainty through smaller credible intervals for the studied data sets than classical TT approaches. Also, GUTS models enable estimating any x% lethal concentration at any time (LC), and provide biological information on the internal processes occurring during the experiments. While both GUTS-SD and GUTS-IT models outcompete classical TT approaches, choosing one preferentially to the other is still challenging. Indeed, the estimates of survival rate over time and LC are very close between both models, but our study also points out that the joint posterior distributions of SD model parameters are sometimes bimodal, while two parameters of the IT model seems strongly correlated. Therefore, the selection between these two models has to be supported by the experimental design and the biological objectives, and this paper provides some insights to drive this choice.

Severe constraints on grasslands productivity, ecosystem functions, goods and services are expected to result from projected warming and drought scenarios under climate change. Negative effects on vegetation can be mediated via soil fertility and water holding capacity, though specific mechanisms are fairly complex to generalise. In field drought experiments, it can be difficult to disentangle a drought effect per se from potential confounding effects related to vegetation or soil type, both varying along with climate. Furthermore, there is the need to distinguish the long-term responses of vegetation and soil to gradual climate shift from responses to extreme and stochastic climatic events. Here we address these limitations by means of a factorial experiment using a single dominant grassland species (the perennial ryegrass Lolium perenne L.) grown as a phytometer on two soils types with contrasted physicochemical characteristics, placed at two elevation sites along a climatic gradient, and exposed to early or late-season drought during the plant growing season. Warmer site conditions and reduced precipitation along the elevational gradient affected biogeochemistry and plant productivity more than the drought treatments alone, despite the similar magnitude in volumetric soil moisture reduction. Soil type, as defined here by its organic matter content (SOM), modulated the drought response in relation to local site climatic conditions and, through changes in microbial biomass and activity, determined the seasonal above and belowground productivity of L. perenne. More specifically, our combined uni-and multivariate analyses demonstrate that microbes in a loamy soil with low SOM are strongly responsive to change in climate, as indicated by a simultaneous increase in their C,N,P pools at high elevation with cooler temperatures and wetter soils. Contrastingly, microbes in a clay-loam soil with high SOM are mainly sensitive to temperature, as indicated by a strong increase in microbial biomass under warmer temperatures at low elevation and a