BioDinamica: a toolkit for analyses of biodiversity and biogeography on the Dinamica-EGO modelling platform.

Ubirajara Oliveira,Britaldo Soares-Filho,Rômulo Fernandes Machado Leitão,Hermann O Rodrigues

doi:10.7717/peerj.7213

Abstract

Biogeography and macroecology are at the heart of the debate on ecology and evolution. We have developed the BioDinamica package, a suite of user-friendly graphical programs for analysing spatial patterns of biogeography and macroecology. BioDinamica includes analyses of beta-diversity, species richness, endemicity, phylo-diversity, species distribution models, predictive models of biodiversity patterns, and several tools for spatial biodiversity analysis. BioDinamica consists of a sub-library of Dinamica-EGO operators developed by integrating EGO native functions with R scripts. The BioDinamica operators can be assembled to create complex analytical and simulation models through the EGO graphical programming interface. In addition, we make available “Wizard” tutorials for end users. BioDinamica can be downloaded free of charge from the Dinamica EGO submodel store. The tools made available in BioDinamica not only facilitate complex biodiversity analyses, they also help develop state-of-the-art spatial models for biogeography and macroecology studies.

Highlights

Biogeographical and macroecological studies have multiplied largely over the last decade (Ladle et al, 2015)
Given the growing interest in spatial analyses in biogeographic and macroecology, we have developed a set of user-friendly tools embedded in the Dinamica-EGO software (Soares-Filho, Rodrigues & Follador, 2013)
In addition to species distribution models (SDM), we have developed a set of ancillary tools for pre-processing and post-processing SDM inputs and outputs

Summary

INTRODUCTION

Biogeographical and macroecological studies have multiplied largely over the last decade (Ladle et al, 2015). Biogeography software to date do not encompass a wide set of relevant analyses Some promising methods, such as Generalized Dissimilarity Model (GDM), for instance, (Ferrier et al, 2007) is eleven years old, but still little used (e.g., Ferrier et al, 2012; Carnaval et al, 2014; Rosauer et al, 2014), possibly because it is only available as a R package. Other methods, such as the Geographical Interpolation of Endemism—GIE (Oliveira, Brescovit & Santos, 2015), which identifies areas of endemism without the use of grid cells as sample units, have been barely used due to the absence of a friendly software— performing GIE requires a series of GIS standalone procedures.

SURVEY METHODOLOGY

CONCLUSIONS