More Web Proxy on the site http://driver.im/

article

openModeller: a generic approach to species' potential distribution modelling

Authors:

Mauro Enrique Souza Muñoz,

Renato Giovanni,

Marinez Ferreira Siqueira,

Ricardo Scachetti Pereira,

Dora Ann Canhos,

Vanderlei Perez CanhosAuthors Info & Claims

Geoinformatica, Volume 15, Issue 1

Pages 111 - 135

https://doi.org/10.1007/s10707-009-0090-7

Published: 01 January 2011 Publication History

Abstract

Species' potential distribution modelling is the process of building a representation of the fundamental ecological requirements for a species and extrapolating these requirements into a geographical region. The importance of being able to predict the distribution of species is currently highlighted by issues like global climate change, public health problems caused by disease vectors, anthropogenic impacts that can lead to massive species extinction, among other challenges. There are several computational approaches that can be used to generate potential distribution models, each achieving optimal results under different conditions. However, the existing software packages available for this purpose typically implement a single algorithm, and each software package presents a new learning curve to the user. Whenever new software is developed for species' potential distribution modelling, significant duplication of effort results because many feature requirements are shared between the different packages. Additionally, data preparation and comparison between algorithms becomes difficult when using separate software applications, since each application has different data input and output capabilities. This paper describes a generic approach for building a single computing framework capable of handling different data formats and multiple algorithms that can be used in potential distribution modelling. The ideas described in this paper have been implemented in a free and open source software package called openModeller. The main concepts of species' potential distribution modelling are also explained and an example use case illustrates potential distribution maps generated by the framework.

References

[1]

Peterson AT (2006) Uses and requirements of ecological niche models and related distributional models. Biodiversity Informatics 3:59-72.

[2]

Canhos VP, Souza S, De Giovanni R, Canhos DAL (2004) Global Biodiversity Informatics: setting the scene for a "new world" of ecological forecasting. Biodiversity Informatics 1:1.

[3]

Yesson C, Brewer PW, Sutton T, Caithness N, Pahwa JS, Burgess M, Gray WA, White RJ, Jones AC, Bisby FA, Culham A (2007) How global is the Global Biodiversity Information Facility. PLoS ONE 2(11):e1124.

[4]

Anderson RP, Lew D, Peterson AT (2003) Evaluating predictive models of species' distributions: criteria for selecting optimal models. Ecol Model 162:211-232.

[5]

Peterson AT, Papes M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30(4):550-560.

[6]

Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann AL, Li J, Lohman LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JMcC, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129-151.

[7]

Manel S, Dias JM, Buckton ST, Ormerod SJ (1999) Alternative methods for predicting species distribution: an illustration with Himalayan river birds. J Appl Ecol 36:734-747.

[8]

Johnson CJ, Gillingham MP (2005) An evaluation of mapped species distribution models used for conservation planning. Environ Conserv 32:117-128.

[9]

Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231-259.

[10]

Carpenter G, Gillison AN, Winter J (1993) DOMAIN: a flexible modeling procedure for mapping potential distributions of animals and plants. Biodivers Conserv 2:667-680.

[11]

Scachetti-Pereira R (2002) Desktop GARP, http://www.nhm.ku.edu/desktopgarp, October, 24, 2007.

[12]

Thuiller W (2003) BIOMOD--optimizing prediction of species distributions and projecting potential future shifts under global change. Glob Chang Biol 9:1353-1362.

[13]

Garzón MB, Blazek R, Neteler M, de Dios RS, Ollero HS, Furlanello C (2006) Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecol Model 97:383-393.

[14]

MacArthur RH (1972) Geographical ecology: patterns in the distribution of species. Harper and Row, New York.

[15]

Hutchinson GE (1957) Concluding remarks. Cold Spring Harb Symp Quant Biol 22:415-442.

[16]

Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species' distributional areas. Biodiversity Informatics, https://journals.ku.edu/index.php/jbi/article/view/4

[17]

Anderson RP, Laverde M, Peterson AT (2002) Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocketmice. Oikos 93:3-16.

[18]

Ferrier S, Drielsma M, Manion G, Watson G (2002) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Biodivers Conserv 11(12):2309-2338.

[19]

Kearney M, Porter W (2009) Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. Ecol Lett 12(4):334-350.

[20]

Beaman R, Conn B (2003) Automated geoparsing and georeferencing of Malesian collection locality data. Telopea 10:43-52.

[21]

Guralnick RP, Hill AW, Lane M (2007) Towards a collaborative, global infrastructure for biodiversity assessment. Ecol Lett 10(8):663-672.

[22]

Longley PA, Goodchild MF, Maguire DJ, Rhind DW (2005) Geographic information systems and science, 2nd edn. John Wiley & Sons, Chichester, 517 p.

[23]

Nix HA (1986) A biogeographic analysis of Australian elapid snakes. In: Longmore R (ed) Atlas of Australian elapid snakes. Australian Flora and Fauna Series 8:4-15.

[24]

Stockwell DRB, Noble IR (1992) Induction of sets of rules from animal distribution data: a robust and informative method of analysis. Math Comput Simul 33:385-390.

Digital Library

[25]

Mladenoff DJ, Sickley TA, Haight RG, Wydeven AP (1995) A regional landscape analysis and prediction of favorable greywolf habitat in the northern Great Lakes region. Conserv Biol 9:279-294.

[26]

Bian L, West E (1997) GIS modeling of Elk calving habitat in a prairie environment with statistics. Photogramm Eng Remote Sensing 63:161-167.

[27]

Frescino TS, Edwards TC, Moisen GG (2001) Modeling spatially explicit forest structural attributes using generalized additive models. J Veg Sci 12:15-26.

[28]

Kelly NM, Fonseca M, Whitfield P (2001) Predictive mapping for management and conservation of seagrass beds in North Carolina. Aquatic Conservation: Marine and Freshwater Ecosystems 11(6):437-451.

[29]

Guisan A, Edwards TC, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89-100.

[30]

Felicísimo AM, Francés E, Fernández JM, González-Díez A, Varas J (2002) Modeling the potential distribution of forests with a GIS. Photogramm Eng Remote Sensing 68:455-462.

[31]

Fonseca MS, Whitfield PE, Kelly NM, Bell SS (2002) Statistical modeling of seagrass landscape pattern and associated ecological attributes in relation to hydrodynamic gradients. Ecol Appl 12(1):218-237.

[32]

Livingston SA, Todd CS, Krohn WB, Owen RB (1990) Habitat models for nesting bald eagles in Maine. J Wildl Manage 54(4):644-653.

[33]

Fielding AH, Haworth PF (1995) Testing the generality of bird-habitat models. Conserv Biol 9(6):1466-1481.

[34]

Pearson RG, Dawson TP, Berry PM, Harrison PA (2002) SPECIES: a spatial evaluation of climate impact on the envelope of species. Ecol Model 154(3):289-300.

[35]

Guo Q, Kelly M, Graham CH (2005) Support vector machines for predicting distribution of sudden oak death in California. Ecol Model 182(1):75-90.

[36]

Leathwick JR, Elith J, Francis MP, Hastie T, Taylor P (2006) Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Mar Ecol Prog Ser 321:267-281.

[37]

Kaschner K, Ready JS, Agbayani E, Rius J, Kesner-Reyes K, Eastwood PD, South AB, Kullander SO, Rees T, Close CH, Watson R, Pauly D, Froese R (2007) AquaMaps: predicted range maps for aquatic species, http://www.aquamaps.org, December, 2007.

[38]

Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81-106.

[39]

Yesson C, Culham A (2006) Phyloclimatic modelling: combining phylogenetics and bioclimatic modelling. Syst Biol 55(5):788-802.

[40]

Yesson C, Culham A (2006) A phyloclimatic study of cyclamen. BMC Evol Biol 6:72.

[41]

Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York.

[42]

Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge.

[43]

Peterson AT, Vieglais DA, Navarro-Sigüenza AG, Silva M (2003) A global distributed biodiversity information network: building the world museum. Bull Br Ornithol Club 123A:186-196.

[44]

Soberón J, Peterson AT (2004) Biodiversity informatics: managing and applying primary biodiversity data. Philos Trans R Soc Lond, B 359:689-698.

[45]

Stein BR, Wieczorek JR (2004) Mammals of the world: MaNIS as an example of data integration in a distributed network environment. Biodiversity Informatics.

[46]

Robertson MP, Caithness N, Villet MH (2001) A PCA-based modelling technique for predicting environmental suitability for organisms from presence records. Divers Distrib 7:15-27.

[47]

Durigan G, Baitello JB, Franco GADC, Siqueira MF (2004) Plantas do cerrado paulista: imagens de uma paisagem ameaçada. Páginas & Letras Editora e Gráfica, São Paulo, 475 p.

[48]

Ratter JA, Bridgewater S, Ribeiro JF, Dias TAB, Silva MR (2000) Distribuição das espécies lenhosas da fitofisionomia cerrado sentido restrito nos estados compreendidos no bioma cerrado. Bol Herb Ezechias Paulo Heringer 5:5-43.

[49]

Durigan G, Siqueira MF, Franco GADC, Bridgewater S, Ratter JA (2003) The vegetation of priority areas for cerrado conservation in São Paulo state, Brazil. Edinb J Bot 60:217-241.

[50]

Ratter JA, Bridgewater S, Atkinson R, Ribeiro JF (1996) Analysis of the floristic composition of the Brazilian cerrado vegetation II: comparison of the woody vegetation of 98 areas. Edinb J Bot 53:153-180.

[51]

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965-1978.

[52]

Chapman AD (2005) Principles of data quality, version 1.0 report for the Global Biodiversity Information Facility. Copenhagen, Denmark, pp1-58, http://www2.gbif.org/DataQuality.pdf

[53]

Guralnick RP, Wieczorek JR, Beaman R, Hijmans RJ, the BioGeomancer Working Group (2006) Biogeomancer: automated georeferencing to map the world's biodiversity data. PLoS Biol 4(11):e381.

[54]

Wheeler QD, Raven PH, Wilson EO (2004) Taxonomy: impediment or expedient? Science 303:285.

[55]

Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264-323.

Digital Library

[56]

Michie D, Spiegelhalter DJ, Taylor CC (1994) Machine learning, neural and statistical classification. Ellis Horwood, New York.

[57]

Stockwell DRB, Peterson AT (2002) Predicting species occurrences: issues of accuracy and scale, controlling bias in biodiversity data. Island, Washington, pp 537-546.

[58]

Johnson CM, Johnson LB, Richards C, Beasley V (2002) Predicting species occurrences: issues of accuracy and scale, predicting the occurrence of amphibians: an assessment of multiple-scale models. Island, Washington, pp 157-170.

[59]

Chapman AD, Muñoz MES, Koch I (2005) Environmental information: placing biodiversity phenomena in an ecological and environmental context. Biodiversity Informatics, https://journals.ku.edu/index.php/ jbi/article/view/5

[60]

Hartkamp AD, De Beurs K, Stein A, White JW (1999) Interpolation techniques for climate variables, NRG-GIS Series 99-01, CIMMYT, Mexico D.F.

[61]

Bannerman BS (1999) Positional accuracy, error and uncertainty in spatial information. Geoinnovations, Howard Springs, NT, Australia. http://www.geoinnovations.com.au/posacc/default.htm. Accessed 12 Jun 2009.

Cited By

Shaffer TKremer-Herman NThain DFurlani T(2019)Flexible Partitioning of Scientific Workflows Using the JX Workflow LanguagePractice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)10.1145/3332186.3338100(1-8)Online publication date: 28-Jul-2019
https://dl.acm.org/doi/10.1145/3332186.3338100
Passos FRebello V(2016)An Autonomic Parallel Strategy forźtheźProjection of Ecological Niche Models inźHeterogeneous Computational EnvironmentsProceedings of the 22nd International Conference on Euro-Par 2016: Parallel Processing - Volume 983310.1007/978-3-319-43659-3_27(363-375)Online publication date: 24-Aug-2016
https://dl.acm.org/doi/10.1007/978-3-319-43659-3_27
Candela LCastelli DCoro GPagano PSinibaldi F(2016)Species distribution modeling in the cloudConcurrency and Computation: Practice & Experience10.1002/cpe.303028:4(1056-1079)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1002/cpe.3030
Show More Cited By

openModeller: a generic approach to species' potential distribution modelling
1. Applied computing
  1. Physical sciences and engineering

Recommendations

Comparing machine learning classifiers in potential distribution modelling

Research highlights We employ Machine Learning techniques in species' potential distribution modelling. The distribution of 35 species from Latin America was modelled by classifiers. The best performance in potential distribution modeling was achieved ...
Potential Distribution Modelling Using Machine Learning
IEA/AIE '08: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence

Potential distribution modelling has been widely used to predict and to understand the geographical distribution of species. These models are generally produced by retrieving the environmental conditions where the species is known to be present or ...
Decomposition of the maximum entropy niche function - A step beyond modelling species distribution

Some algorithms used to model species' distributions are often only considered as black boxes; coefficients of the underlying niche functions are often not interpreted ecologically. Here, we focus on the maximum entropy approach that is commonly and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Geoinformatica

Geoinformatica Volume 15, Issue 1

January 2011

216 pages

ISSN:1384-6175

Issue’s Table of Contents

Copyright © Copyright © 2011 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2011

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shaffer TKremer-Herman NThain DFurlani T(2019)Flexible Partitioning of Scientific Workflows Using the JX Workflow LanguagePractice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)10.1145/3332186.3338100(1-8)Online publication date: 28-Jul-2019
https://dl.acm.org/doi/10.1145/3332186.3338100
Passos FRebello V(2016)An Autonomic Parallel Strategy forźtheźProjection of Ecological Niche Models inźHeterogeneous Computational EnvironmentsProceedings of the 22nd International Conference on Euro-Par 2016: Parallel Processing - Volume 983310.1007/978-3-319-43659-3_27(363-375)Online publication date: 24-Aug-2016
https://dl.acm.org/doi/10.1007/978-3-319-43659-3_27
Candela LCastelli DCoro GPagano PSinibaldi F(2016)Species distribution modeling in the cloudConcurrency and Computation: Practice & Experience10.1002/cpe.303028:4(1056-1079)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1002/cpe.3030
Silva ABorba CCorrêa P(2015)Metadata for models generated by openmodellerProceedings of the 2015 International Conference on Dublin Core and Metadata Applications10.5555/2907896.2907926(218-219)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.5555/2907896.2907926
Silva ABorba CCorrêa P(2015)Metadata for models generated by open modellerProceedings of the 2015 International Conference on Dublin Core and Metadata Applications10.5555/2907896.2907925(216-219)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.5555/2907896.2907925
Fiore SMancini MElia DNassisi PBrasileiro FBlanquer INapoli CSalapura VFranke HHou R(2015)Big data analytics for climate change and biodiversity in the EUBrazilCC federated cloud infrastructureProceedings of the 12th ACM International Conference on Computing Frontiers10.1145/2742854.2747282(1-8)Online publication date: 6-May-2015
https://dl.acm.org/doi/10.1145/2742854.2747282
Santos NKatz JMoyle PViers J(2014)A programmable information system for management and analysis of aquatic species range data in CaliforniaEnvironmental Modelling & Software10.5555/2772077.277221653:C(13-26)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.5555/2772077.2772216
Nativi SMazzetti PGeller G(2013)Environmental model access and interoperabilityEnvironmental Modelling & Software10.5555/2773794.277382839:C(214-228)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.5555/2773794.2773828
Holl SGarijo DBelhajjame KZimmermann ODe Giovanni RObst MGoble CMontagnat JTaylor I(2013)On specifying and sharing scientific workflow optimization results using research objectsProceedings of the 8th Workshop on Workflows in Support of Large-Scale Science10.1145/2534248.2534251(28-37)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1145/2534248.2534251
Brooking CHunter J(2013)Short communicationEnvironmental Modelling & Software10.1016/j.envsoft.2013.01.01143(163-168)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1016/j.envsoft.2013.01.011
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents