Predicting and mapping potential suitable habitat for threatened species especially amphibians is critical in monitoring and management of native habitats. Here we attempt ecological niche modelling (ENM) for the golden puddle frog Phrynobatrachus auritus.  ENM was generated for P. auritus using the Maxent algorithm, which uses the principle of maximum entropy on presence data to estimate a set of functions that relate environmental variables and habitat suitability in order to approximate the species’ niche and potential geographic distribution. Maxent seeks a marginal suitability function for each variable that matches the empirical data, and has a mean equal to that from the empirical data. The Maxent method was chosen because of the wide availability of presence data for many taxa, the paucity and questionable accuracy of absence data for habitat specialists such as the golden puddle frog, and comparative study has shown that the Maxent model outperforms other presence-only models such as GARP (Genetic Algorithm for Rule Set Production), including studies involving other highly mobile amphibian species.

Presence data for P. auritus were obtained from across the study region (Gabon and Cameroon) through field surveys and from the Global Biodiversity Information Facility (GBIF). Considerable time was spent cleaning the data (standardizing the coordinate reference system, eliminating duplicate records, etc.).  Environmental data consisted of 20 variables from the WorldClim dataset including elevation and 19 bioclimatic variables. The resulting bioclimatic model confirms that P. auritus is a lowland species. However, the scale of the environmental data is too coarse to accurately predict the species’ microclimate.  More time should also be devoted to data cleaning to avoid inconsistent data standards and formats, data redundancy, and in making more of the available occurrence data fit for use. Open access data with finer resolution would assist in drawing suitable microhabitats for this frog. Finally, more emphasis should be placed on increasing the capacity of young scientists in developing countries, such as Cameroon, to improve collection and digitization techniques, use of international data standards and formats, and developing algorithms for data cleaning and filtration in biodiversity informatics research.