BIOMOD – a platform for ensemble forecasting of species distributions

Abstract

BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package.Species distribution models (SDM, Guisan and Thuiller 2005) are being used in nearly all branches of life and environmental sciences. A quick search in ISI Web of Science (18/02/08) using ''species distribution models'' OR ''niche models'' OR ''habitat models'' OR ''bioclimatic models'' highlights 21 973 papers, 74% of which published in the past 10 yr, in fields as varied as environmental sciences (53% of the records), zoology (15%), marine and freshwater biology (15%), life sciences and biomedicine (9%), biodiversity and conservation (8%), evolutionary biology (8%), fisheries (6%), forestry (6%), oceanography (5%), genetics and heredity (5%), amongst others. Advancement of knowledge in these fields is now intertwined with technical innovation in species distribution modelling and dependent on the existence of suitable software for fitting models and examining results. One difficulty with the use of species distribution models is that the number of techniques available is large and is increasing steadily, making it difficult for ''non-aficionados'' to select the most appropriate methodology for their needs (Elith et al. 2006, Heikkinen et al. 2006. Recent analyses have also demonstrated that discrepancies between different techniques can be very large, making the choice of the appropriate model even more difficult. This is particularly true when models are used to project distributions of species into independent situations, which is the example of projections of species distributions under future climate change scenarios (Thuiller 2004, Pearson et al. 2006. A possible solution to account for this inter-model variability is to fit ensembles of forecasts by simulating across more than one set of initial conditions, model classes, model parameters, and boundary conditions (for a review see Araújo and New 2007) and analyse the resulting range of uncertainties with bounding box, consensus and probabilistic methodologies rather than lining up with a single modelling outcome New 2007, Thuiller 2007). BIOMOD offers such a platform for ensemble forecasting (Fig. 1) using freeware and open-source R software (R Development Core Team 2008). It overcomes some of the limitations of existing software (e.g. being able to fit and compare different models) and incorporates several features for testing models (e.g. k-fold cross validation) ...

Predicting the future of forests in the Mediterranean under climate change, with niche- and process-based models: CO2 matters!

“…a form of 'stacking') ensures accuracy-based discrimination between models. Further information on this procedure may be found in Thuiller et al (2009).…”

“…We performed the projections using nine different and widely used niche-based modeling techniques, within the BIOMOD computational framework (Thuiller, 2003;Thuiller et al, 2009), as outlined in Table 1.…”

“…The relative importance of each variable is calculated based on the correlation between standard prediction and the prediction with a randomized variable, therefore estimating the influence of the randomized variable in the modeling (see Thuiller et al, 2009 for further details).…”

Climate change alters the optimal wind-dependent flight routes of an avian migrant

“…0.8 and TSS . 0.7 to generate an ensemble model following a weighted (proportional) average approach [23]. We then projected the niche model to the values of explanatory variables averaged for the present (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013), the middle (2046-2055) and the end (2091-2100) of this century.…”

VisTrails SAHM: visualization and workflow management for species habitat modeling

“…Th e fi eld of species distribution or habitat niche modeling is contributing to this understanding. With an increasing availability of both ecological data (Graham et al 2004) and software packages to fi t ecological niche models (Phillips et al 2006, Franklin 2009, Th uiller et al 2009, Guo and Liu 2010, Peterson et al 2011 as well as new tools to evaluate model performance (Allouche et al 2006, Phillips and Elith 2010, Warren et al 2010), researchers and land managers now have an unprecedented opportunity to explore many parameters and iterations for any given habitat niche modeling exercise. Each niche modeling technique has multiple parameters and options that can be adjusted and choices for input and output data.…”