Mapping distribution of woody plant species richness from field rapid assessment and machine learning

Bo Hao Perng, Tzeng Yih Lam, Su Ting Cheng, Sheng Hsin Su, Kristina J. Anderson-Teixeira, Norman A. Bourg, David F.R.P. Burslem, Nicolas Castaño, Álvaro Duque, Sisira Ediriweera, Nimal Gunatilleke, James A. Lutz, William J. McShea, Mohamad Danial M.D. Sabri, Vojtech Novotny, Michael J. O'brien, Glen Reynolds, George D. Weiblen, Daniel Zuleta

Research output: Contribution to journalArticlepeer-review


Sustainable forest management needs information on spatial distribution of species richness. The objectives of this study were to understand whether knowledge, method, and effort of a rapid assessment affected accuracy and consistency in mapping species richness. A simulation study was carried out with nine 25–50 ha census plots located in tropical, subtropical, and temperate zones. Each forest site was first tessellated into non-overlapping cells. Rapid assessment was conducted in all cells to generate a complete coverage of proxies of the underlying species richness. Cells were subsampled for census, where all plant individuals were identified to species in these census cells. An artificial neural network model was built using the census cells that contain rapid assessment and census information. The model then predicted species richness of cells that were not censused. Results showed that knowledge level did not improve the accuracy and consistency in mapping species richness. Rapid assessment effort and method significantly affected the accuracy and consistency. Increasing rapid assessment effort from 10 to 40 plant individuals could improve the accuracy and consistency up to 2.2% and 2.8%, respectively. Transect reduced accuracy and consistency by up to 0.5% and 0.8%, respectively. This study suggests that knowing at least half of the species in a forest is sufficient for a rapid assessment. At least 20 plant individuals per cell is recommended for rapid assessment. Lastly, a rapid assessment could be carried out by local communities that are familiar with their forests; thus, further supporting sustainable forest management.

Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
Issue number1
StatePublished - 2024

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  • artificial neural network
  • forest planning
  • rapid biodiversity assessment
  • sustainable forest management


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