Advanced search
1 file | 2.31 MB

Do absence data matter when modelling fish habitat preference using a genetic Takagi-Sugeno fuzzy model?

Shinji Fukuda (UGent) and Bernard De Baets (UGent)
Author
Organization
Abstract
Information on species distributions is of key importance when designing management plans for a target species or ecosystem. This paper illustrates the effects of absence data on fish habitat prediction and habitat preference evaluation using a genetic Takagi-Sugeno fuzzy model. Three independent data sets were prepared from a series of fish habitat surveys conducted in an agricultural canal in Japan. To quantify the effects of absence data, two kinds of abundance data (entire data and presence data) were used for developing a fuzzy habitat preference model (FHPM). As a result, habitat preference curves (HPCs) obtained from presence data resulted in similar HPCs between the three data sets, while those obtained from entire data slightly differed according to the data sets. The higher generalization ability of the FHPMs obtained from presence data supports the usefulness of presence data for better extracting the habitat preference information of a target species from field observation data.
Keywords
preference modelling, Species distribution model, data characteristics, predictive performance, transferability, genetic fuzzy systems, SPECIES DISTRIBUTION MODELS, MEDAKA ORYZIAS-LATIPES, IMBALANCED DATA-SETS, CLASSIFICATION SYSTEMS, SALMO-SALAR, TRANSFERABILITY, PREDICTION, CRITERIA, OPTIMIZATION

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 2.31 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Fukuda, Shinji, and Bernard De Baets. 2012. “Do Absence Data Matter When Modelling Fish Habitat Preference Using a Genetic Takagi-Sugeno Fuzzy Model?” International Journal of Uncertainty Fuzziness and Knowledge-based Systems 20 (suppl. 2): 233–245.
APA
Fukuda, S., & De Baets, B. (2012). Do absence data matter when modelling fish habitat preference using a genetic Takagi-Sugeno fuzzy model? INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 20(suppl. 2), 233–245.
Vancouver
1.
Fukuda S, De Baets B. Do absence data matter when modelling fish habitat preference using a genetic Takagi-Sugeno fuzzy model? INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS. 2012;20(suppl. 2):233–45.
MLA
Fukuda, Shinji, and Bernard De Baets. “Do Absence Data Matter When Modelling Fish Habitat Preference Using a Genetic Takagi-Sugeno Fuzzy Model?” INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 20.suppl. 2 (2012): 233–245. Print.
@article{2998065,
  abstract     = {Information on species distributions is of key importance when designing management plans for a target species or ecosystem. This paper illustrates the effects of absence data on fish habitat prediction and habitat preference evaluation using a genetic Takagi-Sugeno fuzzy model. Three independent data sets were prepared from a series of fish habitat surveys conducted in an agricultural canal in Japan. To quantify the effects of absence data, two kinds of abundance data (entire data and presence data) were used for developing a fuzzy habitat preference model (FHPM). As a result, habitat preference curves (HPCs) obtained from presence data resulted in similar HPCs between the three data sets, while those obtained from entire data slightly differed according to the data sets. The higher generalization ability of the FHPMs obtained from presence data supports the usefulness of presence data for better extracting the habitat preference information of a target species from field observation data.},
  author       = {Fukuda, Shinji and De Baets, Bernard},
  issn         = {0218-4885},
  journal      = {INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS},
  language     = {eng},
  number       = {suppl. 2},
  pages        = {233--245},
  title        = {Do absence data matter when modelling fish habitat preference using a genetic Takagi-Sugeno fuzzy model?},
  url          = {http://dx.doi.org/10.1142/S0218488512400223},
  volume       = {20},
  year         = {2012},
}

Altmetric
View in Altmetric
Web of Science
Times cited: