Assessing Habitat-Suitability Models With A Virtual Species At Khao Nan National Park, Thailand

This study examined a habitat-suitability assessment method namely the Ecological Niche Factor Analysis (ENFA). A virtual species was created and then dispatched in a geographic information system model of a real landscape in three historic scenarios: (1) spreading, (2) equilibrium, and (3) overabun...

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Main Authors: W. Srisang, K. Jaroensutasinee, M. Jaroensutasinee
Format: Text
Language:English
Published: Zenodo 2007
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.1070071
https://zenodo.org/record/1070071
id ftdatacite:10.5281/zenodo.1070071
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institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic Habitat-Suitability Models
Ecological niche factoranalysis
Climatic factors
Geographic information system.
spellingShingle Habitat-Suitability Models
Ecological niche factoranalysis
Climatic factors
Geographic information system.
W. Srisang
K. Jaroensutasinee
M. Jaroensutasinee
Assessing Habitat-Suitability Models With A Virtual Species At Khao Nan National Park, Thailand
topic_facet Habitat-Suitability Models
Ecological niche factoranalysis
Climatic factors
Geographic information system.
description This study examined a habitat-suitability assessment method namely the Ecological Niche Factor Analysis (ENFA). A virtual species was created and then dispatched in a geographic information system model of a real landscape in three historic scenarios: (1) spreading, (2) equilibrium, and (3) overabundance. In each scenario, the virtual species was sampled and these simulated data sets were used as inputs for the ENFA to reconstruct the habitat suitability model. The 'equilibrium' scenario gives the highest quantity and quality among three scenarios. ENFA was sensitive to the distribution scenarios but not sensitive to sample sizes. The use of a virtual species proved to be a very efficient method, allowing one to fully control the quality of the input data as well as to accurately evaluate the predictive power of the analyses. : {"references": ["L. Palma, P. Beja, and M. Rodgrigues, ''The use of sighting data to analyse Iberian lynx habitat and distribution,'' J. Appl. Ecol., vol.36, pp. 812-824, 1999.", "J. A. Sanchez-Zapata, J. F. Calvo, ''Raptor distribution in relation to landscape composition in semi-arid Mediterranean habitats,'' J. Appl. Ecol., vol. 36, pp. 254-262, 1999.", "D. J. Mladenoff, R. C. Haight, T. A. Sickley, and A. P. Wydeven, ''Causes and implications of species restoration in altered ecosystems. A spatial landscape projection of wolf population recovery,'' Bioscience, vol. 47, pp. 21-23, 1997.", "U. Breitenmoser, F. Zimmermann, P. Olsson, A. Ryser, C. Angst, A. Jobin, and C. Breitenmoser-W\u251c\u255drsten, Beurteilung des Kantons St. Gallen als Habitat f\u251c\u255dr den Luchs, KORA, Bern, 1999.", "H. R. Ak\u251c\u00baakaya, and J. L. Atwood, ''A habitat-based metapopulation model of the California Gnatcatcher,'' Conservat. Biol., vol. 11, pp. 422-434, 1997.", "H. R. Ak\u251c\u00baakaya, M. A. McCarthy, and J. L. Pearce, ''Linking landscape data with population viability analysis: management options for the helmeted honeyeater Lichenostomus melanops cassidix,'' Biol. Conservat., vol. 73, pp. 169-176, 1995.", "G. Le Lay, P. Clergeau and L. Hubert-Moy, ''Computerized map of risk to manage wildlife species in urban areas,'' Environ. Manage., vol. 27, pp. 451-461, 2001", "A. Guisan and N. E. Zimmermann, ''Predictive habitat distribution models in ecology,'' Ecol. Model., vol. 135, pp. 147-186, 2000.", "S. Lek, M. Delacoste, P. Baran, I. Dimopoulos, J. Lauga, and S. Aulagnier, ''Application of neural networks to modelling nonlinear relationships in ecology,'' Ecol. Model., vol. 90, pp. 39-52, 1996. [10] S. Manel, J. M. Dias, S. T. Buckton, and S. J. Ormerod, ''Alternative methods for predicting species distribution: an illustration with Himalayan river birds,'' J. Appl. Ecol., vol. 36, pp. 734-747, 1999. [11] S. L. \u251c\u00fbzesmi and U. \u251c\u00fbzesmi, ''An artificial neural network approach to spatial habitat modelling with interspecific interaction.'' Ecol. Model., vol. 116, pp. 15-31, 1999. [12] A. H. Hirzel, J. Hausser, D. Chessel, and N. Perrin, ''Ecological-niche factor analysis: how to compute habitat suitability maps without absence data?'' Ecology, vol. 83(7), pp. 2027-2036, 2002. [13] G. E. Hutchinson, Concluding remarks, Cold Spring Harbor Symposium, Quantitative Biol., vol. 22, pp. 415-427, 1957. [14] J. Hausser, Mammif?res de la Suisse: R\u00e9partition * Biologie * Ecologie. Commission des M\u00e9moires de l'Acad\u00e9mie Suisse des Sciences Naturelles, Birkh\u251c\u00f1user Verlag, Basel, 1995. [15] R. M. Chefaoui, J. Hortal, and J. M. Lobo, ''Potential distribution modelling, niche characterization and conservation status assessment using GIS tools: a case study of Iberian Copris species,'' Biol. Conservat., vol. 122, pp. 327-338, 2005. [16] A. H. Hirzel, J. Hausser, and N. Perrin, (2004) Biomapper 3.1, Lab. of Conservation Biology, Department of Ecology and Evolution, University of Lausanne. Available: http://www.unil.ch/biomapper [17] S. Wolfram, The Mathematica Book, 5th ed., Wolfram Media, 2003. [18] R. R. Sokal, and F. J. Rohlf, Biometry: The Principles and Practice of Statistics in Biological Research. New York: W. H. Freeman, 1981. [19] L. Legendre and P. Legendre, Numerical Ecology, 2nd English ed., Amsterdam, The Netherlands: Elsevier Science BV, 1998. [20] G. V. Glass and K. D. Hopkins, Statistical methods in education and psychology, 2nd ed., London, UK: Prentice Hall, 1984. [21] Thailand National Biodiversity Database System. (2006). Available: http://www.nbids.org [22] A. H. Fielding and J. F. Bell, ''A review of methods for the assessment of prediction errors in conservation presence/absence models,'' Environ. Conservat., vol. 24, pp. 38-49, 1997. [23] F. Mespl\u00e9 , M. Troussellier, C. Casellas, and P. Legendre, ''Evaluation of simple statistical criteria to qualify a simulation,'' Ecol. Model., vol. 88, pp. 9-18, 1996. [24] A. H. Hirzel, V. Helfer, and F. Metral, ''Assessing habitat-suitability models with a virtual species,'' Ecol. Model., vol. 145, pp. 111-121, 2001. [25] J. R. Alldredge, and J. T. Ratti, ''Further comparison of some statistical techniques for analysis of resource selection,'' J. Wildl. Manag., vol. 56, pp. 1-9, 1992."]}
format Text
author W. Srisang
K. Jaroensutasinee
M. Jaroensutasinee
author_facet W. Srisang
K. Jaroensutasinee
M. Jaroensutasinee
author_sort W. Srisang
title Assessing Habitat-Suitability Models With A Virtual Species At Khao Nan National Park, Thailand
title_short Assessing Habitat-Suitability Models With A Virtual Species At Khao Nan National Park, Thailand
title_full Assessing Habitat-Suitability Models With A Virtual Species At Khao Nan National Park, Thailand
title_fullStr Assessing Habitat-Suitability Models With A Virtual Species At Khao Nan National Park, Thailand
title_full_unstemmed Assessing Habitat-Suitability Models With A Virtual Species At Khao Nan National Park, Thailand
title_sort assessing habitat-suitability models with a virtual species at khao nan national park, thailand
publisher Zenodo
publishDate 2007
url https://dx.doi.org/10.5281/zenodo.1070071
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geographic Atwood
Baran
Haight
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genre Luchs
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genre_facet Luchs
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info:eu-repo/semantics/openAccess
op_rightsnorm CC-BY
op_doi https://doi.org/10.5281/zenodo.1070071
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spelling ftdatacite:10.5281/zenodo.1070071 2023-05-15T18:50:16+02:00 Assessing Habitat-Suitability Models With A Virtual Species At Khao Nan National Park, Thailand W. Srisang K. Jaroensutasinee M. Jaroensutasinee 2007 https://dx.doi.org/10.5281/zenodo.1070071 https://zenodo.org/record/1070071 en eng Zenodo https://dx.doi.org/10.5281/zenodo.1070070 Open Access Creative Commons Attribution 4.0 https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess CC-BY Habitat-Suitability Models Ecological niche factoranalysis Climatic factors Geographic information system. Text Journal article article-journal ScholarlyArticle 2007 ftdatacite https://doi.org/10.5281/zenodo.1070071 https://doi.org/10.5281/zenodo.1070070 2021-11-05T12:55:41Z This study examined a habitat-suitability assessment method namely the Ecological Niche Factor Analysis (ENFA). A virtual species was created and then dispatched in a geographic information system model of a real landscape in three historic scenarios: (1) spreading, (2) equilibrium, and (3) overabundance. In each scenario, the virtual species was sampled and these simulated data sets were used as inputs for the ENFA to reconstruct the habitat suitability model. The 'equilibrium' scenario gives the highest quantity and quality among three scenarios. ENFA was sensitive to the distribution scenarios but not sensitive to sample sizes. The use of a virtual species proved to be a very efficient method, allowing one to fully control the quality of the input data as well as to accurately evaluate the predictive power of the analyses. : {"references": ["L. Palma, P. Beja, and M. Rodgrigues, ''The use of sighting data to analyse Iberian lynx habitat and distribution,'' J. Appl. Ecol., vol.36, pp. 812-824, 1999.", "J. A. Sanchez-Zapata, J. F. Calvo, ''Raptor distribution in relation to landscape composition in semi-arid Mediterranean habitats,'' J. Appl. Ecol., vol. 36, pp. 254-262, 1999.", "D. J. Mladenoff, R. C. Haight, T. A. Sickley, and A. P. Wydeven, ''Causes and implications of species restoration in altered ecosystems. A spatial landscape projection of wolf population recovery,'' Bioscience, vol. 47, pp. 21-23, 1997.", "U. Breitenmoser, F. Zimmermann, P. Olsson, A. Ryser, C. Angst, A. Jobin, and C. Breitenmoser-W\u251c\u255drsten, Beurteilung des Kantons St. Gallen als Habitat f\u251c\u255dr den Luchs, KORA, Bern, 1999.", "H. R. Ak\u251c\u00baakaya, and J. L. Atwood, ''A habitat-based metapopulation model of the California Gnatcatcher,'' Conservat. Biol., vol. 11, pp. 422-434, 1997.", "H. R. Ak\u251c\u00baakaya, M. A. McCarthy, and J. L. Pearce, ''Linking landscape data with population viability analysis: management options for the helmeted honeyeater Lichenostomus melanops cassidix,'' Biol. Conservat., vol. 73, pp. 169-176, 1995.", "G. Le Lay, P. Clergeau and L. Hubert-Moy, ''Computerized map of risk to manage wildlife species in urban areas,'' Environ. Manage., vol. 27, pp. 451-461, 2001", "A. Guisan and N. E. Zimmermann, ''Predictive habitat distribution models in ecology,'' Ecol. Model., vol. 135, pp. 147-186, 2000.", "S. Lek, M. Delacoste, P. Baran, I. Dimopoulos, J. Lauga, and S. Aulagnier, ''Application of neural networks to modelling nonlinear relationships in ecology,'' Ecol. Model., vol. 90, pp. 39-52, 1996. [10] S. Manel, J. M. Dias, S. T. Buckton, and S. J. Ormerod, ''Alternative methods for predicting species distribution: an illustration with Himalayan river birds,'' J. Appl. Ecol., vol. 36, pp. 734-747, 1999. [11] S. L. \u251c\u00fbzesmi and U. \u251c\u00fbzesmi, ''An artificial neural network approach to spatial habitat modelling with interspecific interaction.'' Ecol. Model., vol. 116, pp. 15-31, 1999. [12] A. H. Hirzel, J. Hausser, D. Chessel, and N. Perrin, ''Ecological-niche factor analysis: how to compute habitat suitability maps without absence data?'' Ecology, vol. 83(7), pp. 2027-2036, 2002. [13] G. E. Hutchinson, Concluding remarks, Cold Spring Harbor Symposium, Quantitative Biol., vol. 22, pp. 415-427, 1957. [14] J. Hausser, Mammif?res de la Suisse: R\u00e9partition * Biologie * Ecologie. Commission des M\u00e9moires de l'Acad\u00e9mie Suisse des Sciences Naturelles, Birkh\u251c\u00f1user Verlag, Basel, 1995. [15] R. M. Chefaoui, J. Hortal, and J. M. Lobo, ''Potential distribution modelling, niche characterization and conservation status assessment using GIS tools: a case study of Iberian Copris species,'' Biol. Conservat., vol. 122, pp. 327-338, 2005. [16] A. H. Hirzel, J. Hausser, and N. Perrin, (2004) Biomapper 3.1, Lab. of Conservation Biology, Department of Ecology and Evolution, University of Lausanne. Available: http://www.unil.ch/biomapper [17] S. Wolfram, The Mathematica Book, 5th ed., Wolfram Media, 2003. [18] R. R. Sokal, and F. J. Rohlf, Biometry: The Principles and Practice of Statistics in Biological Research. New York: W. H. Freeman, 1981. [19] L. Legendre and P. Legendre, Numerical Ecology, 2nd English ed., Amsterdam, The Netherlands: Elsevier Science BV, 1998. [20] G. V. Glass and K. D. Hopkins, Statistical methods in education and psychology, 2nd ed., London, UK: Prentice Hall, 1984. [21] Thailand National Biodiversity Database System. (2006). Available: http://www.nbids.org [22] A. H. Fielding and J. F. Bell, ''A review of methods for the assessment of prediction errors in conservation presence/absence models,'' Environ. Conservat., vol. 24, pp. 38-49, 1997. [23] F. Mespl\u00e9 , M. Troussellier, C. Casellas, and P. Legendre, ''Evaluation of simple statistical criteria to qualify a simulation,'' Ecol. Model., vol. 88, pp. 9-18, 1996. [24] A. H. Hirzel, V. Helfer, and F. Metral, ''Assessing habitat-suitability models with a virtual species,'' Ecol. Model., vol. 145, pp. 111-121, 2001. [25] J. R. Alldredge, and J. T. Ratti, ''Further comparison of some statistical techniques for analysis of resource selection,'' J. Wildl. Manag., vol. 56, pp. 1-9, 1992."]} Text Luchs Lynx DataCite Metadata Store (German National Library of Science and Technology) Atwood ENVELOPE(-142.283,-142.283,-77.267,-77.267) Baran ENVELOPE(11.212,11.212,64.951,64.951) Haight ENVELOPE(-81.800,-81.800,50.800,50.800) McCarthy ENVELOPE(66.543,66.543,-70.404,-70.404)