Should species distribution models use only native or exotic records of existence or both?

Abstract This study investigated the importance of the use of appropriate species distribution records in projecting potential distributions under climate change using comparative bioclimatic models and alternative sets of data (native and exotic) to project a species in a new environment. We built bioclimatic models for date palm ( Phoenix dactylifera L.), using the MaxEnt correlative model and the CLIMEX mechanistic niche model, and fitted the models using three training data sets: native data only, exotic data only and entire data. We compared the ability of the different data sets using the different modelling approaches to project suitable climate envelope for independent records of the species at a global scale. We found that the output of projected species distributions was closely related to the modelling approach as well as the specific categorized distribution of species data used (native data only, exotic data only and entire data).

[1]  Mark A. Davis,et al.  Experimental support for a resource-based mechanistic model of invasibility , 2001 .

[2]  D. Tilman,et al.  Invasion, competitive dominance, and resource use by exotic and native California grassland species , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[4]  Sunil Kumar,et al.  Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia , 2009 .

[5]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[6]  M. Araújo,et al.  Presence-absence versus presence-only modelling methods for predicting bird habitat suitability , 2004 .

[7]  S. Jain,et al.  Date Palm Biotechnology , 2011 .

[8]  Lalit Kumar,et al.  DISTRIBUTION OF DATE PALMS IN THE MIDDLE EAST BASED ON FUTURE CLIMATE SCENARIOS , 2014, Experimental Agriculture.

[9]  M. Tengberg.  Beginnings and early history of date palm garden cultivation in the Middle East , 2012 .

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

[11]  David R. B. Stockwell,et al.  Effects of sample size on accuracy of species distribution models , 2002 .

[12]  S. Jain,et al.  Date Palm Genetic Resources and Utilization , 2015, Springer Netherlands.

[13]  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.

[14]  R. S. Burton,et al.  Competition, seed limitation, disturbance, and reestablishment of California native annual forbs , 2003 .

[15]  D. Kriticos,et al.  A comparison of systems to analyze potential weed distributions. , 2001 .

[16]  N. Shayesteh,et al.  Some biological characteristics of the Batrachedra amydraula Meyrick (Lepidoptera: Batrachedridae) on main varieties of dry and semi-dry date palm of Iran , 2010 .

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

[18]  B. Baets,et al.  Modelling the distribution of the pan-continental invasive fish Pseudorasbora parva based on landscape features in the northern Kyushu Island, Japan , 2013 .

[19]  P. Reich,et al.  Effects of plant species richness on invasion dynamics, disease outbreaks, insect abundances and diversity. , 1999, Ecology letters.

[20]  C. Carroll.  Role of climatic niche models in focal-species-based conservation planning: Assessing potential effects of climate change on Northern Spotted Owl in the Pacific Northwest, USA , 2010 .

[21]  Darren J. Kriticos,et al.  The fundamental and realized niche of the Monterey Pine aphid, Essigella californica (Essig) (Hemiptera: Aphididae): implications for managing softwood plantations in Australia , 2004 .

[22]  R. Plant,et al.  Classification trees: An alternative non‐parametric approach for predicting species distributions , 2000 .

[23]  L. Kumar,et al.  Suitable regions for date palm cultivation in Iran are predicted to increase substantially under future climate change scenarios , 2013, The Journal of Agricultural Science.

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

[25]  Paul H. Williams,et al.  Dynamics of extinction and the selection of nature reserves , 2002, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[26]  L. Kumar,et al.  Future distributions of Fusarium oxysporum f. spp. in European, Middle Eastern and North African agricultural regions under climate change , 2014 .

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

[28]  L. Kumar,et al.  Climate Change Impacts on the Future Distribution of Date Palms: A Modeling Exercise Using CLIMEX , 2012, PloS one.

[29]  Patrick E. Osborne,et al.  Should data be partitioned spatially before building large-scale distribution models? , 2002 .

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

[31]  H. Azadi,et al.  Enhancing date palm processing, marketing and pest control through organic culture. , 2008 .

[32]  Dov F Sax,et al.  Species invasions and extinction: The future of native biodiversity on islands , 2008, Proceedings of the National Academy of Sciences.

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

[34]  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 .

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

[36]  F. Shabani,et al.  Future distribution of cotton and wheat in Australia under potential climate change , 2015, The Journal of Agricultural Science.

[37]  A. R. Mahmud,et al.  Predictive Modeling and Mapping of Malayan Sun Bear (Helarctos malayanus) Distribution Using Maximum Entropy , 2012, PloS one.

[38]  N. Reid,et al.  Climate Change and the Potential Distribution of an Invasive Shrub, Lantana camara L , 2012, PloS one.

[39]  T. Dawson,et al.  SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species , 2002 .

[40]  Dawn M. Kaufman,et al.  THE GEOGRAPHIC RANGE: Size, Shape, Boundaries, and Internal Structure , 1996 .

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

[42]  Predicting distributions of seven bitterling fishes in northern Kyushu, Japan , 2012, Ichthyological Research.

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

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

[45]  Lalit Kumar,et al.  Sensitivity Analysis of CLIMEX Parameters in Modeling Potential Distribution of Phoenix dactylifera L. , 2014, PloS one.

[46]  L. Kumar,et al.  Risk Levels of Invasive Fusarium oxysporum f. sp. in Areas Suitable for Date Palm (Phoenix dactylifera) Cultivation under Various Climate Change Projections , 2013, PloS one.

[47]  L. Kumar,et al.  Projecting date palm distribution in Iran under climate change using topography, physicochemical soil properties, soil taxonomy, land use, and climate data , 2014, Theoretical and Applied Climatology.

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

[49]  Mark P. Robertson,et al.  Predicting the potential distribution of an endangered cryptic subterranean mammal from few occurrence records , 2011 .

[50]  Marian Grendar.  What is the question that MaxEnt answers? A probabilsitic interpretation , 2001 .

[51]  A. Lehmann,et al.  Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns , 2002 .

[52]  Suresh Kumar,et al.  Medicinal plants of the Indian desert: Commiphora wightii (Arnott) Bhand , 1982 .

[53]  R. Pattison,et al.  Potential distribution of the invasive tree Triadica sebifera (Euphorbiaceae) in the United States: evaluating climex predictions with field trials , 2007 .

[54]  Antoine Guisan,et al.  Unifying niche shift studies: insights from biological invasions. , 2014, Trends in ecology & evolution.

[55]  Daniel Fernández,et al.  Estimation of spatial sampling effort based on presence-only data and accessibility , 2015 .