Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models

Aim Correlative species distribution models (SDMs) often involve some degree of projection into novel covariate space (i.e. extrapolation), because calibration data may not encompass the entire space of interest. Most methods for identifying extrapolation focus on the range of each model covariate individually. However, extrapolation can occur that is well within the range of univariate variation, but which exhibits novel combinations between covariates. Our objective was to develop a tool that can detect, distinguish and quantify these two types of novelties: novel univariate range and novel combinations of covariates. Location Global, Australia, South Africa. Methods We developed a new multivariate statistical tool, based on the Mahalanobis distance, which measures the similarity between the reference and projection domains by accounting for both the deviation from the mean and the correlation between variables. The method also provides an assessment tool for the detection of the most influential covariates leading to dissimilarity. As an example application, we modelled an Australian shrub (Acacia cyclops) widely introduced to other countries and compared reference data, global distribution data and both types of model extrapolation against the projection globally and in South Africa. Results The new tool successfully detected and quantified the degree of dissimilarity for points that were either outside the univariate range or formed novel covariate combinations (correlations) but were still within the univariate range of covariates. For A. cyclops, more than half of the points (6617 of 10,785) from the global projection space that were found to lie within the univariate range of reference data exhibited distorted correlations. Not all the climate covariates used for modelling contributed to novelty equally over the geographical space of the model projection. Main conclusions Identifying non-analogous environments is a critical component of model interrogation. Our extrapolation detection (ExDet) tool can be used as a quantitative method for exploring novelty and interpreting the projections from correlative SDMs and is available for free download as stand-alone software from http://www.climond.org/exdet.

[1]  E. Matthysen,et al.  Predicting the potential distribution of invasive ring-necked parakeets Psittacula krameri in northern Belgium using an ecological niche modelling approach , 2009, Biological Invasions.

[2]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[3]  D. L. Le Maitre,et al.  Comment on “Climatic Niche Shifts Are Rare Among Terrestrial Plant Invaders” , 2012, Science.

[4]  John E. Kutzbach,et al.  Projected distributions of novel and disappearing climates by 2100 AD , 2006, Proceedings of the National Academy of Sciences.

[5]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[6]  Damaris Zurell,et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .

[7]  M. Araújo,et al.  Validation of species–climate impact models under climate change , 2005 .

[8]  A. Lowe,et al.  A case for incorporating phylogeography and landscape genetics into species distribution modelling approaches to improve climate adaptation and conservation planning , 2010 .

[9]  D. Kriticos,et al.  Pest Risk Maps for Invasive Alien Species: A Roadmap for Improvement , 2010 .

[10]  V. E. Shelford,et al.  Some Concepts of Bioecology , 1931 .

[11]  A. Peterson,et al.  Evidence of climatic niche shift during biological invasion. , 2007, Ecology letters.

[12]  C. A. Howell,et al.  Re-Shuffling of Species with Climate Disruption: A No-Analog Future for California Birds? , 2009, PloS one.

[13]  Bruce L. Webber,et al.  Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models , 2011 .

[14]  C. Dormann.  Promising the future? Global change projections of species distributions , 2007 .

[15]  C. Thatcher,et al.  Identifying Suitable Sites for Florida Panther Reintroduction , 2006 .

[16]  P. Rousseeuw,et al.  Unmasking Multivariate Outliers and Leverage Points , 1990 .

[17]  Steven J. Phillips,et al.  The art of modelling range‐shifting species , 2010 .

[18]  Michelle R. Leishman,et al.  Evidence for climatic niche and biome shifts between native and novel ranges in plant species introduced to Australia , 2010 .

[19]  W. Hargrove,et al.  The projection of species distribution models and the problem of non-analog climate , 2009, Biodiversity and Conservation.

[20]  J. Lawton,et al.  Making mistakes when predicting shifts in species range in response to global warming , 1998, Nature.

[21]  Jon C. Lovett,et al.  Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty , 2008 .

[22]  D. Strayer,et al.  Usefulness of Bioclimatic Models for Studying Climate Change and Invasive Species , 2008, Annals of the New York Academy of Sciences.

[23]  Myron P. Zalucki,et al.  Predicting invasions in Australia by a Neotropical shrub under climate change: the challenge of novel climates and parameter estimation , 2009 .

[24]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[25]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[26]  M. Araújo,et al.  Five (or so) challenges for species distribution modelling , 2006 .

[27]  D. Richardson,et al.  Trees and shrubs as invasive alien species – a global review , 2011 .

[28]  Antoine Guisan,et al.  Are niche-based species distribution models transferable in space? , 2006 .

[29]  J. Busby.  A biogeoclimatic analysis of Nothofagus cunninghamii (Hook.) Oerst. in southeastern Australia , 1986 .

[30]  Bruce L. Webber,et al.  CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling , 2012 .

[31]  Ross K Meentemeyer,et al.  Early detection of emerging forest disease using dispersal estimation and ecological niche modeling. , 2008, Ecological applications : a publication of the Ecological Society of America.

[32]  Hugh P Possingham,et al.  Modeling Species' Distributions to Improve Conservation in Semiurban Landscapes: Koala Case Study , 2006, Conservation biology : the journal of the Society for Conservation Biology.

[33]  R. Kadmon,et al.  Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance , 2003 .

[34]  S. Jackson,et al.  Novel climates, no‐analog communities, and ecological surprises , 2007 .

[35]  S. Lavorel,et al.  Biodiversity conservation: Uncertainty in predictions of extinction risk , 2004, Nature.

[36]  Damaris Zurell,et al.  Predicting to new environments: tools for visualizing model behaviour and impacts on mapped distributions , 2012 .

[37]  R. W. Sutherst,et al.  Modelling non-equilibrium distributions of invasive species: a tale of two modelling paradigms , 2009, Biological Invasions.

[38]  J. Franklin.  Moving beyond static species distribution models in support of conservation biogeography , 2010 .

[39]  R. Meentemeyer,et al.  Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions? , 2009 .

[40]  Darren J. Kriticos,et al.  Essential elements of discourse for advancing the modelling of species' current and potential distributions , 2013 .

[41]  Helge Bruelheide,et al.  Predicting the spread of an invasive plant: combining experiments and ecological niche model , 2008 .

[42]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[43]  M. Graham.  CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION , 2003 .

[44]  M. Araújo,et al.  Uses and misuses of bioclimatic envelope modeling. , 2012, Ecology.

[45]  D. Richardson,et al.  Predicting the subspecific identity of invasive species using distribution models: Acacia saligna as an example , 2011 .

[46]  Kimberly G. Smith,et al.  A multivariate model of female black bear habitat use for a geographic information system , 1993 .

[47]  S. Lavorel,et al.  Effects of restricting environmental range of data to project current and future species distributions , 2004 .

[48]  P. Pearman,et al.  Integrating species distribution models (SDMs) and phylogeography for two species of Alpine Primula , 2012, Ecology and evolution.

[49]  A. Peterson,et al.  Species Distribution Modeling and Ecological Niche Modeling: Getting the Concepts Right , 2012 .

[51]  Robert P. Freckleton,et al.  Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error , 2010, Behavioral Ecology and Sociobiology.

[52]  C. Ricotta,et al.  Accounting for uncertainty when mapping species distributions: The need for maps of ignorance , 2011 .

[53]  A. Townsend Peterson,et al.  Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent , 2007 .