Labeling Ecological Niche Models

Abstract

The ongoing biodiversity crisis is pushing ecologists and conservation biologists to develop models to foretell the effects of human-induced transformation of natural resources on the distribution of species, although ecology and biogeography still lacks a paradigmatic body of theory to fully understand the drivers of biodiversity patterns. Two decades of research on ecological niche models and species distributions have been characterized by technical development and discussions on a plethora of methods or algorithms to infer and predict species distributions. Here we suggest a metaphorical classification scheme for some of the most popular models based on their complexity, interpretability and suitability for specific applications in ecology and conservation biology. Our purpose is not to compare methods by their capacity to accurately predict the observed distribution of species, nor to criticize how they are commonly used in applied studies. Instead, we believe that a simple classification scheme can potentially highlight how some methods are more suited for specific applications in ecology and conservation biology. Envelope and distance-based models are grouped into the "fish bowl" category, for their transparency and simplicity. Statistical models are classified as "turbine" models, because of their hidden complexity and general applicability. Finally, machine-learning models are classified as "vault" models, for their high complexity and lack of interpretability of fit parameters. We conclude that the diversity of species distribution models used today is expected for a young research field, but the choice of modeling strategy depends on the purpose of the study. We provide some general guidelines for choosing models for studies of conservation planning and climate change mitigation and suggest models of intermediate complexity for conservation planning and forecast of climate change effects on biodiversity as they provide a good balance between interpretability, predictive power and robustness to model over-fit.

Species conservation under future climate change: the case of Bombus bellicosus, a potentially threatened South American bumblebee species

“…The later algorithm is simpler and usually needs presence-only data to predict the potential distribution of the targeted species. Meanwhile, Maxent and SVM are artificial intelligence algorithms that are more complex and tend to better predict the known/true distribution of the target species (Rangel and Loyola 2012).…”

Climatically-mediated landcover change: impacts on Brazilian territory

“…Currently, many ecological niche modeling algorithms are available and their predictions can vary greatly due to differences in their analytical approach (Araújo and New 2007). ENM can be divided into three general categories (bioclimatic envelope or distance models, statistical models, and machine-learning models), which vary according to complexity, generality and precision (for a review of the main methods, see Araújo and New 2007, Elith and Leathwick 2009, Rangel and Loyola 2012. To deal with the problem of prediction variation amongst models, many researchers consider areas of convergence among models, called ensemble forecasting, as the most probable distribution New 2007, Diniz-Filho et al 2009).…”

Range increase of a Neotropical orchid bee under future scenarios of climate change

“…The use of SDMs to predict the ranges of species is not a trivial task, and there are several issues regarding the selection of the algorithms to be used (Elith et al 2006;Elith and Leathwick 2009;Rangel and Loyola 2012); focus species abundances (Tôrres et al 2012;Martínez-Meyer et al 2013;Ureña-Aranda et al 2015), allocation of pseudoabsences during the evaluation of the models (VanDerWal et al 2009;Barbet-Massin et al 2012), thresholding the suitability matrices produced by the algorithms (Liu et al 2011), evaluation statistics (Allouche et al 2006;Lobo et al 2008;Muscarella et al 2014), but also occurrence biases (Kramer-Schadt et al 2013;Varela et al 2014). Habitat generalists, such as El.…”

“…One way to assess and estimate species' distributional responses to climate change despite a lack of comprehensive distributional data is to use species distribution models (SDMs; Guisan and Zimmermann 2000;Elith and Leathwick 2009;Rangel and Loyola 2012). These methods correlate known species occurrences with climatic data available for relevant areas to delimit the species' multidimensional range space.…”