Abstract
The process of moving from experimental data to modeling and characterizing the dynamics and interactions in natural processes is a challenging task. This paper proposes an interactive platform for fitting data derived from experiments to mathematical expressions and carrying out spatial visualization. The platform is designed using a component-based software architectural approach, implemented in R and the Java programming languages. It uses experimental data as input for model fitting, then applies the obtained model at the landscape level via a spatial temperature grid data to yield regional and continental maps. Different modules and functionalities of the tool are presented with a case study, in which the tool is used to establish a temperature-dependent virulence model and map the potential zone of efficacy of a fungal-based biopesticide. The decision support system (DSS) was developed in generic form, and it can be used by anyone interested in fitting mathematical equations to experimental data collected following the described protocol and, depending on the type of investigation, it offers the possibility of projecting the model at the landscape level.
Highlights
The ability to make reliable predictions from data through mathematical and computational concepts is fundamental in scientific research
Temperature was selected as the key variable due to the paramount role it plays in the development, survival, reproduction, and mortality of Entomopathogenic Fungi (EPF) and insects
The LM algorithm combines two approaches; it operates like the gradient descent method when the equation parameters are far from their optimal values, while it performs like the Gauss–Newton method when the parameter values are close to their optimum (Nelles, 2001)
Summary
The ability to make reliable predictions from data through mathematical and computational concepts is fundamental in scientific research. Some group of scientists like biologists and entomologists are not always equipped with the necessary knowledge of calculus allowing them to perform certain types of analysis. This justifies why the various algorithms developed for fitting data to mathematical equations are not used by many. Among the techniques for fitting data to mathematical equations, nonlinear regression represents one of the most used approaches [2] It is a very helpful process in engineering, agricultural, and natural science, and it is used to capture and understand the underling relationships among variables (dependent and independent) of interest described by mathematical expressions
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