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Virtual species are simplified models of real species that codify the response of those species to the climatic conditions. Virtual species have been used to quantify the response of species to climatic changes, to predict potential shifts in species’ geographic ranges and to test the methods used to predict the geographic ranges of species (Ecological Niche Models). Today, there are different methods used to construct virtual species for biogeographic analysis. All of those methods combine partial suitabilities across variables (temperature, precipitation, etc.) to create one multi-variable habitat suitability index. The normal procedure for combining partial suitabilities is to sum or multiply the individual layers. However, this procedure might yield misleading results. In this paper, we run several analyses that indicate that those methods underestimate the role of the limitation factors (factors with unsuitable conditions that should decrease the habitat suitability index to zero). To solve this problem we programmed an algorithm—the Niche Limitation algorithm (NicheLim). NicheLim uses the same philosophy as the BIOCLIM model: species have independent physiological tolerances to the environmental variables. This means that we must first transform each continuous layer into a presence-absence variable, and then combine them. Here, we discuss the current framework for constructing virtual species in biogeography and its main drawbacks. We then explain the improvements of using NicheLim to create our virtual species and test biogeographic hypotheses. [ABSTRACT FROM AUTHOR]

• We review the use of Sequential Montel Carlo (SMC) algorithms for model calibration. • We present a new SMC algorithm and benchmark it against a state-of-the-art MCMC. • The advantage of SMC algorithms over MCMC algorithms is that they can be parallelized. • With appropriate settings to avoid conversion problems, our algorithm can outperform MCMCs for model runtimes >20 ms on multi-core systems. • We provide our algorithm as an r package. Bayesian inference has become an important framework for calibrating complex ecological and environmental models. Markov-Chain Monte Carlo (MCMC) algorithms are the methodological backbone of this framework, but they are not easily parallelizable and can thus not make optimal use of modern computer architectures. A possible solution is the use of Sequential Monte Carlo (SMC) algorithms. Currently, SMCs are used mainly for Bayesian state updating, for example in weather forecasting, and are thought to be less efficient for parameter calibration than MCMCs. Unlike MCMCs, however, SMCs are easily parallelizable. Thus, SMCs may become an interesting alternative when modelers have access to parallel computing environments. The purpose of this paper is to provide an introduction to SMC algorithms for Bayesian model calibration, and to explore the trade-off between efficiency and parallelizability for MCMC and SMC algorithms. To that end, we discuss different SMC variants, and benchmark them against a state-of-the-art MCMC algorithm by calibrating three ecological models of increasing complexity. Our results show that, with appropriately chosen settings, SMCs can be faster than state-of-the-art MCMC algorithms when a sufficiently large number of parallel cores are available and when the model runtime is large compared to communication overhead for parallelization (on our hardware, a model runtime of 20 ms was enough to favor SMC algorithms). Efficient SMC settings were characterized by a balanced mix of SMC filtering and MCMC mutation steps, suggesting that mixing MCMC and SMC principles may be ideal for creating efficient and parallelizable calibration algorithms. The algorithms used in this study are provided within the BayesianTools R package for Bayesian inference with complex ecological models. [ABSTRACT FROM AUTHOR]

• Configuration settings in SDM algorithms can strongly affect modelled distribution. • SDM algorithms vary widely in their sensitivity to altered configuration settings. • Algorithm sensitivity to configuration settings shows some dependence on the species being modelled • CTA, SRE, ANN and FDA showed strong sensitivity to configuration settings, while MaxEnt and GLM showed less sensitivity. • It is important to carefully configure SDMs and document these settings for reliable provenance and scientific robustness. In pursuit of a more robust provenance in the field of species distribution modelling, an extensive literature search was undertaken to find the typical default values, and the range of values, for configuration settings of a large number of the most commonly used statistical algorithms available for constructing species distribution models (SDM), as implemented in the R script packages (such as Dismo and Biomod2) or other species distribution modelling programs like MaxEnt. We found that documentation of SDM algorithm configuration option settings in the SDM literature is, overall, very uncommon, and the justifications for these settings were minimal, when present. Such settings were often the R default values, or were the result of trial and error. This is potentially concerning since: (i) it detracts from the robustness of the provenance for such SDM studies; (ii) a lack of documentation of configuration option settings in a paper prevents the replication of an experiment, which contravenes one of the main tenets of the scientific method; (iii) inappropriate or uninformed configuration option settings are particularly concerning if they represent a poorly understood ecological variable or process, and if the algorithm is sensitive to such settings, this could result in erroneous and/or unrealistic SDMs. Therefore, this study sets out to comprehensively test the sensitivity of eight widely used SDM algorithms to variation in configuration options settings: MaxEnt, Artificial Neural Network (ANN), Generalized Linear Model (GLM), Generalized Additive Model (GAM), Multivariate Adaptive Regression Splines (MARS), Flexible Discriminant Analysis (FDA), Surface Range Envelope (SRE) and Classification tree analysis (CTA). A process of expert elicitation was used to derive a range of appropriate values with which to test the sensitivity of our algorithms. We chose to use species occurrence records for two species - Koala (P hascolartos cinereus) and Thorny Devil (Moloch horridus) - in order to investigate how algorithm sensitivity depends on the species being modelled. Results were assessed by comparing the modelled distribution of the control SDM (default settings) to the modelled distribution from each sensitivity test SDM (i.e. non-default configuration settings). This was done using the visual and statistical measures of predictive performance available in the Biodiversity and Climate Change Virtual Laboratory (BCCVL), including the area under the (receiver operating characteristic) curve. The aim of our study was to be able to draw conclusions as to how the sensitivity of SDM algorithms to their configuration option settings may detract from the reliability of SDM results, given the often unjustified and unscrutinized use of the default settings, and generally infrequent and largely perfunctory attendance to this issue in most of the published SDM literature. Our results indicate that all of the algorithms tested showed sensitivity to alternative (non-default) values for some of their configuration settings and that often this sensitivity is species-dependent. Therefore we can conclude that the choice of configuration settings in these widely used SDM algorithms can have a large impact on the resulting projected distribution. This has important ramifications for decision-making and policy outcomes wherever SDMs are used to inform species and biodiversity management plans and policy settings. This study demonstrates that assigning suitable values for these settings is a very important consideration and as such should always be published along with the model. Documenting all configuration settings is necessary to increase the scientific robustness, transparency and reproducibility of species distribution modelling studies. [ABSTRACT FROM AUTHOR]