Abstract
To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A
separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size.
Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.
separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size.
Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.
| Original language | English |
|---|---|
| Pages (from-to) | 517-526 |
| Number of pages | 9 |
| Journal | International Journal of Knowledge-Based and Intelligent Engineering Systems |
| DOIs | |
| Publication status | Published - 8 Sept 2010 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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