openModeller: a generic approach to species’ potential distribution modelling

Abstract

Species' potential distribution modelling is the process of building a representation of the fundamental ecological requirements for a species and extrapolating these requirements into a geographical region. The importance of being able to predict the distribution of species is currently highlighted by issues like global climate change, public health problems caused by disease vectors, anthropogenic impacts that can lead to massive species extinction, among other challenges. There are several computational approaches that can be used to generate potential distribution models, each achieving optimal results under different conditions. However, the existing software packages available for this purpose typically implement a single algorithm, and each software package presents a new learning curve to the user. Whenever new software is developed for species' potential distribution modelling, significant duplication of effort results because many feature requirements are shared between the different packages. Additionally, data preparation and comparison between algorithms becomes difficult when using separate software applications, since each application has different data input and output capabilities. This paper describes a generic approach for building a single computing framework capable of handling different data formats and multiple algorithms that can be used in potential distribution modelling. The ideas described in this paper have been implemented in a free and open source software package called openModeller. The main concepts of species' potential distribution modelling are also explained and an example use case illustrates potential distribution maps generated by the framework. Geoinformatica (2011) 15:111-135

Implementing new northernmost records to modelling the distribution of Hypsiboas caingua (Anura: Hylidae) in South America

“…BIOCLIM characterizes sites that are located within the environmental hyperspace occupied by a species, in which the potential climatic domain is the multidimensional envelope that encompasses all recorded locations of the species (Nix, 1986). OM-GARP is a version of GARP (Anderson et al, 2003) implemented in openModeller 1.1.0 software (Muñoz et al, 2011), which uses a genetic algorithm to select a set of rules that best predicts the species' distributions (Stockwell and Peters, 1999). Support Vector Machines (SVM) are a class of nonprobabilistic statistical pattern recognition that estimate the boundary of the set from which a collection of observations is drawn, minimizing errors of empirical classifications and maximizing the geometric boundaries (De Marco-Junior and Siqueira, 2009;Giovanelli et al, 2010).…”

“…The SDM of each model was evaluated by the receiver operating characteristic (ROC) curve, whose area under the curve (AUC) ranges from 0.5 (random prediction) to 1 (sufficiently discriminatory prediction) (Elith and Burgman, 2002). Finally, two consensus maps were generated in openModeller 1.1.0 (Muñoz et al, 2011) in order to consider, based on the three SDM methodologies, the most probable occurrence area for H. caingua in South America. The first consensus map considered a probability of 50% of occurrence considering all SDM methods, while the second map considered a more restrictive threshold of 70%.…”

“…The first consensus map considered a probability of 50% of occurrence considering all SDM methods, while the second map considered a more restrictive threshold of 70%. All SDM methods were performed in openModeller 1.1.0 (Muñoz et al, 2011). …”

Ecological niche differentiation and taxonomic distinction between Eremias strauchi strauchi and Eremias strauchi kopetdaghica (Squamata: Lacertidae) on the Iranian Plateau based on ecological niche modeling

“…In addition, we used a factor (i.e., slope) that represents the topographic effect on the species presence. Distribution points of both subspecies and climatic layers were employed by Openmodeller v. 1.0.7 (Muñoz et al 2011) to reach the number of correlated variables. Bivariate-correlation Pearson coefficients were used to identify variable pairs with correlations > 0.75, which were subsequently removed from the analysis.…”

Dimorphandra wilsonii Rizzini (Fabaceae): distribution, habitat and conservation status

“…We used seven bioclimatic variables extracted from the Worldclim database at a resolution of 1 km 2 (Hijmans et al 2005). The algorithms we used were maximum entropy (Phillips et al 2006), environmental distance, and the genetic algorithm for rule-set prediction (Stockwell & Peters 1999), the last two available on the openModeller platform (Muñoz et al 2009); to interpret the predictive maps, we applied a threshold rule to the training data and scheduled new field searches for validation.Throughout the study period, because of new occurrences Dimorphandra wilsonii and at the suggestion of collaborators, as well as because of the introduction of SDM, the search area was extended to other mesoregions of the state, including the: northwestern, western, Triângulo Mineiro/Alto Paranaíba, southern, northern, and Jequitinhonha regions. In all of those regions, we made incursions and applied the local population approach.…”