The art of modelling range-shifting species

Abstract

Summary1. Species are shifting their ranges at an unprecedented rate through human transportation and environmental change. Correlative species distribution models (SDMs) are frequently applied for predicting potential future distributions of range-shifting species, despite these models' assumptions that species are at equilibrium with the environments used to train (fit) the models, and that the training data are representative of conditions to which the models are predicted. Here we explore modelling approaches that aim to minimize extrapolation errors and assess predictions against prior biological knowledge. Our aim was to promote methods appropriate to range-shifting species. 2. We use an invasive species, the cane toad in Australia, as an example, predicting potential distributions under both current and climate change scenarios. We use four SDM methods, and trial weighting schemes and choice of background samples appropriate for species in a state of spread. We also test two methods for including information from a mechanistic model. Throughout, we explore graphical techniques for understanding model behaviour and reliability, including the extent of extrapolation. 3. Predictions varied with modelling method and data treatment, particularly with regard to the use and treatment of absence data. Models that performed similarly under current climatic conditions deviated widely when transferred to a novel climatic scenario. 4.The results highlight problems with using SDMs for extrapolation, and demonstrate the need for methods and tools to understand models and predictions. We have made progress in this direction and have implemented exploratory techniques as new options in the free modelling software, MaxEnt. Our results also show that deliberately controlling the fit of models and integrating information from mechanistic models can enhance the reliability of correlative predictions of species in non-equilibrium and novel settings. 5. Implications. The biodiversity of many regions in the world is experiencing novel threats created by species invasions and climate change. Predictions of future species distributions are required for management, but there are acknowledged problems with many current methods, and relatively few advances in techniques for understanding or overcoming these. The methods presented in this manuscript and made accessible in MaxEnt provide a forward step.

Potential for spread of the white-nose fungus (Pseudogymnoascus destructans) in the Americas: use of Maxent and NicheA to assure strict model transference

“…Considering that the current distribution of P. destructans in North America and Europe is entirely north of latitude 23.5°N (the Tropic of Cancer). In order to avoid projection onto entirely novel conditions (Elith et al, 2010;Owens et al, 2013), we explored the invasive potential of the agent in continental areas south of latitude 23.5°S (the Tropic of Capricorn) including portions of Argentina, Brazil, Chile, Paraguay, and Uruguay. We developed and explored six maps representing the following niche model projections: North America, Europe, North America and Europe pooled, North America projected to South America, Europe projected to South America and North America and Europe pooled projected to South America.…”

“…South America) was assessed using mobility-oriented parity (MOP) according to Owens et al (2013), which refines the earlier multivariate environmental similarity surface (MESS) metrics implemented within Maxent for identifying areas of simple transfer (i.e. interpretation is acceptable) versus areas where extrapolation is occurring and interpretation should involve considerable caution (Elith et al, 2010).…”

VisTrails SAHM: visualization and workflow management for species habitat modeling

“…Th e Parameter Exploration tool could be used to specify a list of geospatial fi les to produce models with varying spatial extent or resolutions, or both (explained in more detail in the user guide and associated tutorial). Th e output optionally also includes MESS (multivariate environmental similarity surface) maps following Elith et al (2010) to illustrate levels of extrapolation,the importance of which is demonstrated in Stohlgren et al (2011).…”

“…View the spatial model output in a GIS window along with the occurrence data. Outputs include probability of occurrence map, binary map, smoothed residual surface, multivariate environmental similarity surfaces maps (MESS maps), and most dissimilar variable maps (MOD maps; Elith et al 2010). …”

Adequabilidade ambiental dos biomas brasileiros à ocorrência do lobo-guará (<i>Chrysocyon brachyurus</i>) e efeitos da composição da paisagem em sua ecologia espacial, atividade e movimentação

“…A conversão dos habitats naturais em lavouras e pastagens tem sido apontado como a ameaça mais significativa para as populações DEMATTEO, 2015 (PETERSON 2006;ELITH et al, 2010). O produto gerado apresenta resultados estatísticos e gráficos acerca de relações da biodiversidade com atributos ambientais diversos e fatores antrópicos (LEEMANS, 1999;ELITH et al, 2010).…”