Spatio-temporal temperature variations in MarkSim multimodel data and their impact on voltinism of fruit fly, Bactrocera species on mango.

Fruit flies are the most serious economic insect pests of mango in India and other parts of the world. Under future climate change, shifts in temperature will be a key driver of ecosystem function especially in terms of insect pest dynamics. In this study, we predicted the voltinism of the three economically important fruit fly species viz., Bactrocera dorsalis (Hendel), Bactrocera correcta (Bezzi) and Bactrocera zonata (Saunders) of mango from 10 geographical locations in India using well established degree day approaches. Daily minimum and maximum temperature data were generated by using seven General Circulation Models (GCMs) along with their ensemble, in conjunction with the four representative concentration pathways (RCPs) scenarios (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) and three time periods (2020, 2050 and 2080) generated from MarkSim® DSSAT weather file generator. Historical data from 1969-2005 of these 10 locations were considered as baseline period. Under future predicted climates, model outputs indicates that all three fruit fly species will produce higher number of generations (1-2 additional generations) with 15-24% reduced generation time over the baseline period. The increased voltinism of fruit fly species due to increased temperature may lead to ≃5% higher infestation of mango fruits in India by the year 2050. Analysis of variance revealed that 'geographical locations' explained 77% of the total variation in voltinism followed by 'time periods' (11%). Such increase in the voltinism of fruit flies and the consequent increases in the infestation of mango fruits are likely to have significant negative impacts on mango protection and production.

Collapsing mechanistic models: an application to dose selection for proof of concept of a selective irreversible antagonist.

When data fail to support fully mechanistic models, alternative modeling strategies must be pursued. Simpler, more empirical models or the fixing of various rate constants are necessary to avoid over-parameterization. Fitting empirical models can dilute information, limit interpretation, and cloud inference. Fixing rate constants requires external, relevant, and reliable information on the mechanism and can introduce subjectivity as well as complicate determining the validity of model extrapolation. Furthermore, both these methods ignore the possibility that failure of the data to support the mechanistic model could contain information about the pharmacodynamic process. If the pathway has processes with "fast" dynamics, these steps could collapse yielding parametrically simpler classes of models. The collapsed models would retain the mechanistic interpretation of the full model, which is crucial for performing substantive inference, while reducing the number of parameters to be estimated. These concepts are illustrated through their manifestations on the dose-effect relationship and ensuing dose selection for a proof of concept study. Specifically, a mechanistic model for a selective irreversible antagonist was posited and candidate classes of models were derived utilizing "fast dynamics" assumptions. Model assessment determined the rate-limiting step facilitating pertinent inference with respect to the mechanism. For comparison, inference using a more empirical modeling strategy is also presented. A general solution for the collapse of the typical PK-PD model differential equations is provided in Appendix A.

A method of obtaining starting values of k(in) and k(out) for the indirect response models.

A method based on the multivariate technique known as principal component analysis is proposed to obtain starting values for the rate constants of indirect response models. The method is not iterative and only requires standard deviation calculations for two quantities, which are simple functions of the measured pharmacodynamic response. An algorithm is provided which can be implemented in a spreadsheet. The methodology is justified theoretically herein, nevertheless, two examples are provided to illustrate the method and demonstrate its viability.