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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Spatially-explicit estimation of geographical representation in large-scale species distribution datasets
PLoS ONE, Volume 9, No. 1, Article e85306, Year 2014
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Description
Much ecological research relies on existing multispecies distribution datasets. Such datasets, however, can vary considerably in quality, extent, resolution or taxonomic coverage. We provide a framework for a spatially-explicit evaluation of geographical representation within large-scale species distribution datasets, using the comparison of an occurrence atlas with a range atlas dataset as a working example. Specifically, we compared occurrence maps for 3773 taxa from the widelyused Atlas Florae Europaeae (AFE) with digitised range maps for 2049 taxa of the lesser-known Atlas of North European Vascular Plants. We calculated the level of agreement at a 50-km spatial resolution using average latitudinal and longitudinal species range, and area of occupancy. Agreement in species distribution was calculated and mapped using Jaccard similarity index and a reduced major axis (RMA) regression analysis of species richness between the entire atlases (5221 taxa in total) and between co-occurring species (601 taxa). We found no difference in distribution ranges or in the area of occupancy frequency distribution, indicating that atlases were sufficiently overlapping for a valid comparison. The similarity index map showed high levels of agreement for central, western, and northern Europe. The RMA regression confirmed that geographical representation of AFE was low in areas with a sparse data recording history (e.g., Russia, Belarus and the Ukraine). For co-occurring species in south-eastern Europe, however, the Atlas of North European Vascular Plants showed remarkably higher richness estimations. Geographical representation of atlas data can be much more heterogeneous than often assumed. Level of agreement between datasets can be used to evaluate geographical representation within datasets. Merging atlases into a single dataset is worthwhile in spite of methodological differences, and helps to fill gaps in our knowledge of species distribution ranges. Species distribution dataset mergers, such as the one exemplified here, can serve as a baseline towards comprehensive species distribution datasets. Copyright: © 2014 Kalwij et al.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC3893194/bin/pone.0085306.s001.docx
https://efashare.b-cdn.net/share/pmc/articles/PMC3893194/bin/pone.0085306.s002.docx
https://efashare.b-cdn.net/share/pmc/articles/PMC3893194/bin/pone.0085306.s003.zip
Authors & Co-Authors
Kalwij, Jesse M.
Estonia, Tartu
Ökoloogia ja Maateaduste Instituut
South Africa, Johannesburg
University of Johannesburg
Robertson, Mark P.
South Africa, Pretoria
University of Pretoria
Ronk, Argo
Estonia, Tartu
Ökoloogia ja Maateaduste Instituut
Zobel, Martin
Estonia, Tartu
Ökoloogia ja Maateaduste Instituut
Pärtel, Meelis
Estonia, Tartu
Ökoloogia ja Maateaduste Instituut
Statistics
Citations: 22
Authors: 5
Affiliations: 3
Identifiers
Doi:
10.1371/journal.pone.0085306
e-ISSN:
19326203
Research Areas
Health System And Policy
Study Design
Cohort Study
Study Approach
Quantitative