Predicting distributions of invasive species

This chapter aims to inform a practitioner about current methods for predicting potential distributions of invasive species. It mostly addresses single species models, covering the conceptual bases, touching on mechanistic models, and then focusing on methods using species distribution records and environmental data to predict distributions. The commentary in this last section is oriented towards key issues that arise in fitting, and predicting with, these models (which include CLIMEX, MaxEnt and other regression methods). In other words, it is more about the process of thinking about the data and the modelling problem (which is a challenging one) than it is about one technique versus another. The discussion helps clarify the necessary steps and expertise for predicting distributions. Some researchers are optimistic that correlative models will predict with high precision; while that may be true for some species at some scales of evaluation, I believe that the issues discussed in this chapter show that substantial errors are reasonably likely. I am hopeful that ongoing developments will produce models better suited to the task and tools to help practitioners to better understand predictions and their uncertainties.

[1]  J. Andrew Royle,et al.  Hierarchical Spatiotemporal Matrix Models for Characterizing Invasions , 2007, Biometrics.

[2]  N. Ray,et al.  Subjective uncertainties in habitat suitability maps , 2006 .

[3]  M. Austin,et al.  Predictive vegetation modeling for conservation: impact of error propagation from digital elevation data. , 2007, Ecological applications : a publication of the Ecological Society of America.

[4]  H. Lee,et al.  Predictions for an invaded world: A strategy to predict the distribution of native and non-indigenous species at multiple scales , 2008 .

[5]  T. Hastie,et al.  Bias correction in species distribution models: pooling survey and collection data for multiple species , 2014, Methods in ecology and evolution.

[6]  Simon Ferrier,et al.  Evaluating the predictive performance of habitat models developed using logistic regression , 2000 .

[7]  K. Mellert,et al.  Species distribution models as a tool for forest management planning under climate change: risk evaluation of Abies alba in Bavaria , 2011 .

[8]  C. Graham,et al.  The ability of climate envelope models to predict the effect of climate change on species distributions , 2006 .

[9]  W. L. Chadderton,et al.  Dispersal, disturbance and the contrasting biogeographies of New Zealand’s diadromous and non‐diadromous fish species , 2008 .

[10]  Holger R. Maier,et al.  Future research challenges for incorporation of uncertainty in environmental and ecological decision-making , 2008 .

[11]  ENVIRONMENTAL DATASET FOR MARINE SPECIES DISTRIBUTION MODELING , 2014 .

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

[13]  Robert M. Dorazio,et al.  Accounting for imperfect detection and survey bias in statistical analysis of presence‐only data , 2014 .

[14]  Martin Wassen,et al.  Prediction of plant species distribution in lowland river valleys in Belgium: modelling species response to site conditions , 2002, Biodiversity & Conservation.

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

[16]  Jane Elith,et al.  Comparing species abundance models , 2006 .

[17]  D. Rödder,et al.  Explanative power of variables used in species distribution modelling: an issue of general model transferability or niche shift in the invasive Greenhouse frog (Eleutherodactylus planirostris) , 2010, Naturwissenschaften.

[18]  Robert P. Anderson,et al.  Harnessing the world's biodiversity data: promise and peril in ecological niche modeling of species distributions , 2012, Annals of the New York Academy of Sciences.

[19]  Bruce L. Webber,et al.  Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models , 2014 .

[20]  J. Overpeck,et al.  Responses of plant populations and communities to environmental changes of the late Quaternary , 2000, Paleobiology.

[21]  Alberto Jiménez-Valverde,et al.  The uncertain nature of absences and their importance in species distribution modelling , 2010 .

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

[23]  Sunil J Rao,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .

[24]  S. Cunningham,et al.  Predicting the economic impact of an invasive species on an ecosystem service. , 2007, Ecological applications : a publication of the Ecological Society of America.

[25]  D. Kriticos,et al.  The effects of climate data precision on fitting and projecting species niche models , 2010 .

[26]  J. Drake,et al.  Profiling ecosystem vulnerability to invasion by zebra mussels with support vector machines , 2009, Theoretical Ecology.

[27]  Myron P. Zalucki,et al.  The effect of data sources and quality on the predictive capacity of CLIMEX models: An assessment of Teleonemia scrupulosa and Octotoma scabripennis for the biocontrol of Lantana camara in Australia , 2010 .

[28]  T. Hastie,et al.  Finite-Sample Equivalence in Statistical Models for Presence-Only Data. , 2012, The annals of applied statistics.

[29]  S. Ferrier,et al.  Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling , 2004, Biodiversity & Conservation.

[30]  V. Fensterer Statistical methods in niche modelling for the spatial prediction of forest tree species , 2010 .

[31]  Philip E. Hulme,et al.  Biological invasions: winning the science battles but losing the conservation war? , 2003, Oryx.

[32]  M. Araújo,et al.  Reducing uncertainty in projections of extinction risk from climate change , 2005 .

[33]  Daniel Sol,et al.  TEASIng apart alien species risk assessments: a framework for best practices. , 2012, Ecology letters.

[34]  Wilfried Thuiller,et al.  Predicting potential distributions of invasive species: where to go from here? , 2010 .

[35]  Ingolf Kühn,et al.  Relating geographical variation in pollination types to environmental and spatial factors using novel statistical methods. , 2006, The New phytologist.

[36]  C. Margules,et al.  Nature Conservation: Cost Effective Biological Surveys and Data Analysis , 1990 .

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

[38]  P. Legendre Spatial Autocorrelation: Trouble or New Paradigm? , 1993 .

[39]  G. Inglis,et al.  Using habitat suitability index and particle dispersion models for early detection of marine invaders. , 2006, Ecological applications : a publication of the Ecological Society of America.

[40]  Jane Elith,et al.  What do we gain from simplicity versus complexity in species distribution models , 2014 .

[41]  V. Funk,et al.  Systematic data in biodiversity studies: use it or lose it. , 2002, Systematic biology.

[42]  S. Reddy,et al.  Geographical sampling bias and its implications for conservation priorities in Africa , 2003 .

[43]  Steven J. Phillips,et al.  Point process models for presence‐only analysis , 2015 .

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

[45]  Chris J. Johnson,et al.  Sensitivity of species-distribution models to error, bias, and model design: An application to resource selection functions for woodland caribou , 2008 .

[46]  M. Sykes,et al.  Predicting global change impacts on plant species' distributions: Future challenges , 2008 .

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

[48]  M. Luoto,et al.  Modelling the spatial distribution of a threatened butterfly: Impacts of scale and statistical technique , 2007 .

[49]  M. Angilletta,et al.  Can mechanism inform species' distribution models? , 2010, Ecology letters.

[50]  Jorge Soberón,et al.  Prediction of potential areas of species distributions based on presence-only data , 2005, Environmental and Ecological Statistics.

[51]  A. Peterson,et al.  New developments in museum-based informatics and applications in biodiversity analysis. , 2004, Trends in ecology & evolution.

[52]  Jane Elith,et al.  Uncertainty Analysis for Regional‐Scale Reserve Selection , 2006, Conservation biology : the journal of the Society for Conservation Biology.

[53]  S. Barry,et al.  Predicting establishment success for introduced freshwater fishes: a role for climate matching , 2010, Biological Invasions.

[54]  A. Hirzel,et al.  Habitat suitability modelling and niche theory , 2008 .

[55]  THE DISTRIBUTION OF THE ALFALFA WEEVIL (PHY- TONOMUS POSTICUS GYLL. A STUDY IN PHYSICAL ECOLOGY , 2010 .

[56]  Kalle Ruokolainen,et al.  Analysing botanical collecting effort in Amazonia and correcting for it in species range estimation , 2007 .

[57]  A. Peterson Uses and requirements of ecological niche models and related distributional models , 2006 .

[58]  A. Peterson Predicting the Geography of Species’ Invasions via Ecological Niche Modeling , 2003, The Quarterly Review of Biology.

[59]  A. Peterson,et al.  Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula , 2009, Biological Invasions.

[60]  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 .

[61]  J. Busby BIOCLIM - a bioclimate analysis and prediction system , 1991 .

[62]  H. Pulliam On the relationship between niche and distribution , 2000 .

[63]  S. Cherry,et al.  USE AND INTERPRETATION OF LOGISTIC REGRESSION IN HABITAT-SELECTION STUDIES , 2004 .

[64]  Brendan A. Wintle,et al.  PRECISION AND BIAS OF METHODS FOR ESTIMATING POINT SURVEY DETECTION PROBABILITIES , 2004 .

[65]  D. Richardson,et al.  Home away from home — objective mapping of high‐risk source areas for plant introductions , 2007 .

[66]  Susan P. Worner,et al.  Prediction of Global Distribution of Insect Pest Species in Relation to Climate by Using an Ecological Informatics Method , 2006 .

[67]  M. Austin,et al.  Improving species distribution models for climate change studies: variable selection and scale , 2011 .

[68]  Yakov Ben-Haim,et al.  Robustness of Risk Maps and Survey Networks to Knowledge Gaps About a New Invasive Pest , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[69]  Gordon H. Rodda,et al.  Challenges in Identifying Sites Climatically Matched to the Native Ranges of Animal Invaders , 2011, PloS one.

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

[71]  Jorge Soberón,et al.  Niches and distributional areas: Concepts, methods, and assumptions , 2009, Proceedings of the National Academy of Sciences.

[72]  A. Peterson,et al.  Use of niche models in invasive species risk assessments , 2011, Biological Invasions.

[73]  Brean W. Duncan,et al.  SETTING RELIABILITY BOUNDS ON HABITAT SUITABILITY INDICES , 2001 .

[74]  M. Kearney,et al.  microclim: Global estimates of hourly microclimate based on long-term monthly climate averages , 2014, Scientific Data.

[75]  Hans-Joachim Klemmt,et al.  Hypothesis‐driven species distribution models for tree species in the Bavarian Alps , 2011 .

[76]  Isabelle Chuine,et al.  Phenology is a major determinant of tree species range , 2001 .

[77]  M. Kearney,et al.  Modelling species distributions without using species distributions: the cane toad in Australia under current and future climates , 2008 .

[78]  T. Dawson,et al.  Model‐based uncertainty in species range prediction , 2006 .

[79]  R. Mac Nally,et al.  Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models , 2000 .

[80]  R. W. Sutherst,et al.  A computerised system for matching climates in ecology , 1985 .

[81]  Reuben P. Keller,et al.  Bioeconomics of invasive species : integrating ecology, economics, policy, and management , 2009 .

[82]  C. Dormann,et al.  Components of uncertainty in species distribution analysis: a case study of the Great Grey Shrike. , 2008, Ecology.

[83]  Alberto Jiménez-Valverde,et al.  Not as good as they seem: the importance of concepts in species distribution modelling , 2008 .

[84]  Mark New,et al.  Ensemble forecasting of species distributions. , 2007, Trends in ecology & evolution.

[85]  Markus Neteler,et al.  Landscape complexity and spatial scale influence the relationship between remotely sensed spectral diversity and survey-based plant species richness , 2011 .

[86]  D. Maitre,et al.  Developing an approach to defining the potential distributions of invasive plant species: a case study of Hakea species in South Africa , 2008 .

[87]  M. Zappa,et al.  Are niche‐based species distribution models transferable in space? , 2006 .

[88]  Antoine Guisan,et al.  Climatic extremes improve predictions of spatial patterns of tree species , 2009, Proceedings of the National Academy of Sciences.

[89]  J. Elith,et al.  Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models , 2009 .

[90]  J. Lobo,et al.  Historical bias in biodiversity inventories affects the observed environmental niche of the species , 2008 .

[91]  J. Blackard,et al.  Journal of Applied , 2006 .

[92]  C. Capinha,et al.  Assessing the environmental requirements of invaders using ensembles of distribution models , 2011 .

[93]  J. Leathwick,et al.  COMPETITIVE INTERACTIONS BETWEEN TREE SPECIES IN NEW ZEALAND'S OLD‐GROWTH INDIGENOUS FORESTS , 2001 .

[94]  Steven J. Phillips,et al.  Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. , 2009, Ecological applications : a publication of the Ecological Society of America.

[95]  R. Baker,et al.  The role of climatic mapping in predicting the potential geographical distribution of non-indigenous pests under current and future climates , 2000 .

[96]  W. Thuiller,et al.  Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. , 2009, Ecology.

[97]  Trevor H. Booth,et al.  Niche analysis and tree species introduction , 1988 .

[98]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[99]  R. Crozier,et al.  Combined modelling of distribution and niche in invasion biology: a case study of two invasive Tetramorium ant species , 2008 .

[100]  P. Edwards,et al.  Limits to the niche and range margins of alien species. , 2010 .

[101]  J. Elith,et al.  Taxonomic uncertainty and decision making for biosecurity: spatial models for myrtle/guava rust , 2012, Australasian Plant Pathology.

[102]  M. Kearney,et al.  MAPPING THE FUNDAMENTAL NICHE: PHYSIOLOGY, CLIMATE, AND THE DISTRIBUTION OF A NOCTURNAL LIZARD , 2004 .

[103]  M. McCarthy,et al.  Optimal Marking of Threatened Species to Balance Benefits of Information with Impacts of Marking , 2008, Conservation biology : the journal of the Society for Conservation Biology.

[104]  Antoine Guisan,et al.  Predicting current and future biological invasions: both native and invaded ranges matter , 2008, Biology Letters.

[105]  F. Harrell General Aspects of Fitting Regression Models , 2015 .

[106]  Mark P. Robertson,et al.  Getting the most out of atlas data , 2010 .

[107]  Jennifer A. Miller,et al.  Mapping Species Distributions: Spatial Inference and Prediction , 2010 .

[108]  J. Ditomaso,et al.  Herbarium records, actual distribution, and critical attributes of invasive plants: genus Crotalaria in Taiwan , 2005 .

[109]  M. Kearney,et al.  Habitat, environment and niche: what are we modelling? , 2006 .

[110]  Tracy M. Rout,et al.  Prevent, search or destroy? A partially observable model for invasive species management , 2014 .

[111]  L. Kumar,et al.  Sensitivity Analysis of CLIMEX Parameters in Modelling Potential Distribution of Lantana camara L. , 2012, PloS one.

[112]  H. Possingham,et al.  Spatial conservation prioritization: Quantitative methods and computational tools , 2009, Environmental Conservation.

[113]  A. Guisan,et al.  An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data , 2004 .

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

[115]  Laura J. Pollock,et al.  Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM) , 2014 .

[116]  B. Faivre,et al.  Spatial dynamics of an invasive bird species assessed using robust design occupancy analysis: the case of the Eurasian collared dove (Streptopelia decaocto) in France , 2007 .

[117]  K. J. Gutzwiller,et al.  BIRD-LANDSCAPE RELATIONS IN THE CHIHUAHUAN DESERT: COPING WITH UNCERTAINTIES ABOUT PREDICTIVE MODELS , 2001 .

[118]  Thiago F. Rangel,et al.  Towards an integrated computational tool for spatial analysis in macroecology and biogeography , 2006 .

[119]  Jane Elith,et al.  Error and uncertainty in habitat models , 2006 .

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

[121]  Jane Elith,et al.  Logistic Methods for Resource Selection Functions and Presence-Only Species Distribution Models , 2011, AAAI.

[122]  A. Peterson,et al.  The crucial role of the accessible area in ecological niche modeling and species distribution modeling , 2011 .

[123]  Trevor Hastie,et al.  Statistical Models for Presence-Only Data: Finite-Sample Equivalence and Addressing Observer Bias , 2012 .

[124]  Richard Baker,et al.  The EPPO prioritization process for invasive alien plants , 2010 .

[125]  Harold A. Mooney,et al.  Bioinvasions and Globalization: Ecology, Economics, Management, and Policy , 2010 .

[126]  D. Ackerly Community Assembly, Niche Conservatism, and Adaptive Evolution in Changing Environments , 2003, International Journal of Plant Sciences.

[127]  Harold A. Mooney,et al.  Climate change and species' distributions: an alien future? , 2010 .

[128]  Annika Kangas,et al.  Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis , 2004 .

[129]  Robert K. Colwell,et al.  Hutchinson's duality: The once and future niche , 2009, Proceedings of the National Academy of Sciences.

[130]  S. B. McDowell,et al.  Atlas of elapid snakes of Australia , 1987 .

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

[132]  B. Schröder Challenges of species distribution modeling belowground , 2008 .

[133]  D. Richardson,et al.  Inferring Process from Pattern in Plant Invasions: A Semimechanistic Model Incorporating Propagule Pressure and Environmental Factors , 2003, The American Naturalist.

[134]  Edward J. Rykiel,et al.  Testing ecological models: the meaning of validation , 1996 .

[135]  R. Sutherst,et al.  Prediction of species geographical ranges , 2003 .

[136]  A. Peterson,et al.  Environmental data sets matter in ecological niche modelling: an example with Solenopsis invicta and Solenopsis richteri. , 2007 .

[137]  Brendan A. Wintle,et al.  Correlative and mechanistic models of species distribution provide congruent forecasts under climate change , 2010 .

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

[139]  M. Robertson,et al.  Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance , 2010 .

[140]  Helen M. Regan,et al.  Mapping epistemic uncertainties and vague concepts in predictions of species distribution , 2002 .

[141]  P. Herman,et al.  Macrobenthic species response surfaces along estuarine gradients: prediction by logistic regression , 2002 .

[142]  Otso Ovaskainen,et al.  Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. , 2010, Ecology.

[143]  Maggi Kelly,et al.  Support vector machines for predicting distribution of Sudden Oak Death in California , 2005 .

[144]  M. Austin Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .

[145]  Bruce McCune,et al.  Non-parametric habitat models with automatic interactions , 2006 .

[146]  Harris David,et al.  A statistical explanation of MaxEnt for ecologists , 2013 .

[147]  M. Kearney,et al.  Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. , 2009, Ecology letters.

[148]  M. Lechowicz,et al.  Contemporary perspectives on the niche that can improve models of species range shifts under climate change , 2008, Biology Letters.

[149]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[150]  J. C. de Almeida,et al.  Concluding Remarks , 2015, Clinical practice and epidemiology in mental health : CP & EMH.

[151]  O. Reichman,et al.  Physiology on a Landscape Scale: Plant-Animal Interactions1 , 2002, Integrative and comparative biology.

[152]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[153]  Catherine S. Jarnevich,et al.  Ensemble Habitat Mapping of Invasive Plant Species , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[154]  R. Pearson Species’ Distribution Modeling for Conservation Educators and Practitioners , 2010 .

[155]  Gordon H. Rodda,et al.  What parts of the US mainland are climatically suitable for invasive alien pythons spreading from Everglades National Park? , 2009, Biological Invasions.

[156]  E. Fleishman,et al.  Modeling and Predicting Species Occurrence Using Broad‐Scale Environmental Variables: an Example with Butterflies of the Great Basin , 2001 .

[157]  R. Hallett,et al.  Potential distribution and relative abundance of swede midge, Contarinia nasturtii, an invasive pest in Canada , 2006 .

[158]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[159]  Darren J. Kriticos,et al.  CLIMEX Version 3: User's Guide , 2007 .

[160]  D. Richardson,et al.  Niche‐based modelling as a tool for predicting the risk of alien plant invasions at a global scale , 2005, Global change biology.

[161]  S. Hartley,et al.  Quantifying uncertainty in the potential distribution of an invasive species: climate and the Argentine ant. , 2006, Ecology letters.

[162]  W. Thuiller BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change , 2003 .

[163]  D. Kriticos,et al.  Managing invasive weeds under climate change: considering the current and potential future distribution of Buddleja davidii , 2011 .

[164]  Stefan Leyk,et al.  A Conceptual Framework for Uncertainty Investigation in Map‐based Land Cover Change Modelling , 2005, Trans. GIS.

[165]  Niklaus E. Zimmermann,et al.  Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods , 2006 .

[166]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[167]  A. Hahs,et al.  A dispersal-constrained habitat suitability model for predicting invasion of alpine vegetation. , 2008, Ecological applications : a publication of the Ecological Society of America.

[168]  T. Hastie,et al.  Presence‐Only Data and the EM Algorithm , 2009, Biometrics.

[169]  Philip E. Hulme,et al.  Herbarium records identify the role of long‐distance spread in the spatial distribution of alien plants in New Zealand , 2010 .

[170]  T. B. Sutton,et al.  NAPPFAST: An Internet System for the Weather-Based Mapping of Plant Pathogens. , 2007, Plant disease.

[171]  R. Huey,et al.  Using invasive species to study evolution. Case studies with Drosophila and salmon , 2005 .

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

[173]  A. O. Nicholls,et al.  Measurement of the realized qualitative niche: environmental niches of five Eucalyptus species , 1990 .

[174]  David I. Warton,et al.  Model-Based Control of Observer Bias for the Analysis of Presence-Only Data in Ecology , 2013, PloS one.

[175]  B. Manly,et al.  Resource selection by animals: statistical design and analysis for field studies. , 1994 .

[176]  M. Austin Species distribution models and ecological theory: A critical assessment and some possible new approaches , 2007 .

[177]  L. Belbin,et al.  Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory , 2006 .

[178]  M. Kearney,et al.  Correlation and process in species distribution models: bridging a dichotomy , 2012 .

[179]  Robert P. Anderson,et al.  Ecological Niches and Geographic Distributions , 2011 .

[180]  J. Stachowicz,et al.  Species Invasions: Insights into Ecology, Evolution, and Biogeography , 2005 .

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

[182]  Shashi Shekhar,et al.  Availability of Spatial Data Mining Techniques , 2009 .

[183]  D. Warton,et al.  Correction note: Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology , 2010, 1011.3319.

[184]  P. Hulme,et al.  Mixed messages from multiple information sources on invasive species: a case of too much of a good thing? , 2011 .