Incorporating knowledge uncertainty into species distribution modelling

Aidin Niamir (Corresponding Author), A.K. Skidmore, Antonio-román Muñoz, A.G. Toxopeus, Raimundo Real

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Monitoring progress towards global goals and biodiversity targets require reliable descriptions of species distributions over time and space. Current gaps in accessible information on species distributions urges the need for integrating all available data and knowledge sources, and intensifying cooperations to more effectively support global environmental governance. For many areas and species groups, experts can constitute a valuable source of information to fill the gaps by offering their knowledge on species-environment interactions. However, expert knowledge is always subject to uncertainty, and incorporating that into species distribution mapping poses a challenge. We propose the use of the dempster–shafer theory of evidence (DST) as a novel approach in this field to extract expert knowledge, to incorporate the associated uncertainty into the procedure, and to produce reliable species distribution maps. We applied DST to model the distribution of two species of eagle in Spain. We invited experts to fill in an online questionnaire and express their beliefs on the habitat of the species by assigning probability values for given environmental variables, along with their confidence in expressing the beliefs. We then calculated evidential functions, and combined them using Dempster’s rules of combination to map the species distribution based on the experts’ knowledge. We evaluated the performances of our proposed approach using the atlas of Spanish breeding birds as an independent test dataset, and further compared the results with the outcome of an ensemble of conventional SDMs. Purely based on expert knowledge, the DST approach yielded similar results as the data driven SDMs ensemble. Our proposed approach offers a strong and practical alternative for species distribution modelling when species occurrence data are not accessible, or reliable, or both. The particular strengths of the proposed approach are that it explicitly accounts for and aggregates knowledge uncertainty, and it capitalizes on the range of data sources usually considered by an expert.
Original languageEnglish
Pages (from-to)571-588
Number of pages18
JournalBiodiversity and conservation
Volume28
Issue number3
Early online date3 Dec 2018
DOIs
Publication statusPublished - 15 Mar 2019

Fingerprint

uncertainty
biogeography
expert opinion
modeling
environmental governance
eagles
information sources
space and time
distribution
questionnaires
Spain
species occurrence
biodiversity
environmental factors
monitoring
birds
atlas
breeding
extracts
habitats

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID
  • Belief
  • Evidential functions
  • Bonelli’s eagle
  • Expert knowledge
  • Short-toed eagle
  • Dempster–Shafer theory of evidence
  • Accessible species information
  • Spain
  • UT-Hybrid-D

Cite this

Niamir, Aidin ; Skidmore, A.K. ; Muñoz, Antonio-román ; Toxopeus, A.G. ; Real, Raimundo. / Incorporating knowledge uncertainty into species distribution modelling. In: Biodiversity and conservation. 2019 ; Vol. 28, No. 3. pp. 571-588.
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abstract = "Monitoring progress towards global goals and biodiversity targets require reliable descriptions of species distributions over time and space. Current gaps in accessible information on species distributions urges the need for integrating all available data and knowledge sources, and intensifying cooperations to more effectively support global environmental governance. For many areas and species groups, experts can constitute a valuable source of information to fill the gaps by offering their knowledge on species-environment interactions. However, expert knowledge is always subject to uncertainty, and incorporating that into species distribution mapping poses a challenge. We propose the use of the dempster–shafer theory of evidence (DST) as a novel approach in this field to extract expert knowledge, to incorporate the associated uncertainty into the procedure, and to produce reliable species distribution maps. We applied DST to model the distribution of two species of eagle in Spain. We invited experts to fill in an online questionnaire and express their beliefs on the habitat of the species by assigning probability values for given environmental variables, along with their confidence in expressing the beliefs. We then calculated evidential functions, and combined them using Dempster’s rules of combination to map the species distribution based on the experts’ knowledge. We evaluated the performances of our proposed approach using the atlas of Spanish breeding birds as an independent test dataset, and further compared the results with the outcome of an ensemble of conventional SDMs. Purely based on expert knowledge, the DST approach yielded similar results as the data driven SDMs ensemble. Our proposed approach offers a strong and practical alternative for species distribution modelling when species occurrence data are not accessible, or reliable, or both. The particular strengths of the proposed approach are that it explicitly accounts for and aggregates knowledge uncertainty, and it capitalizes on the range of data sources usually considered by an expert.",
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Incorporating knowledge uncertainty into species distribution modelling. / Niamir, Aidin (Corresponding Author); Skidmore, A.K.; Muñoz, Antonio-román; Toxopeus, A.G.; Real, Raimundo.

In: Biodiversity and conservation, Vol. 28, No. 3, 15.03.2019, p. 571-588.

Research output: Contribution to journalArticleAcademicpeer-review

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