Mapping distribution of woody plant species richness from field rapid assessment and machine learning
Journal
Taiwania
Journal Volume
69
Journal Issue
1
Date Issued
2024-01-01
Author(s)
Perng, Bo Hao
Su, Sheng Hsin
Anderson-Teixeira, Kristina J.
Bourg, Norman A.
Burslem, David F.R.P.
Castaño, Nicolas
Duque, Álvaro
Ediriweera, Sisira
Gunatilleke, Nimal
Lutz, James A.
McShea, William J.
Sabri, Mohamad Danial M.D.
Novotny, Vojtech
O'brien, Michael J.
Reynolds, Glen
Weiblen, George D.
Zuleta, Daniel
Abstract
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.
Subjects
artificial neural network | forest planning | rapid biodiversity assessment | sustainable forest management
Type
journal article