Assessing the safety of Pseudomonas putida introduction in the environment: an overview of ecotoxicological tests.

Risk assessment guidelines for the environmental release of microbial agents are performed in a tiered sequence which includes evaluation of exposure effects on non target organisms. However, it becomes important to verify whether environmental risk assessment from temperate studies is applicable to tropical countries, as Brazil. Pseudomonas putida is a bacteria showing potential to be used for environmental applications as bioremediation and plant disease control. This study investigates the effects of this bacteria exposure on rodents and aquatic organisms (Daphnia similes) that are recommended to be used as non-target organism in environmental risk assessments. Also, the microbial activity in three different soils under P. putida exposure was evaluated. Rats did not show clinical alterations, although the agent was recovered 16 h after the exposure in lung homogenates. The bacteria did not reduce significantly the reproduction and survival of D. similis. The soil enzymatic activities presented fluctuating values after inoculation with bacteria. The measurement of perturbations in soil biochemical characteristics is presented as an alternative way of monitoring the overall effects of the microbial agent to be introduced even in first stage (Tier I) of the risk assessment in tropical ecosystems.

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.