Taiwania

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

本研究旨在探究快速评估的知识、方法和努力程度对物种丰富度空间分布制图准确性和一致性的影响。研究通过模拟实验，在热带、亚热带和温带地区的九个25-50公顷的样地中，首先将每个森林场地划分为互不重叠的单元格。在所有单元格内进行快速评估，以生成对潜在物种丰富度的全面代理覆盖。对单元格进行子采样进行清查，在这些清查单元格中识别所有植物个体至物种水平。利用包含快速评估和清查信息的清查单元格构建人工神经网络模型，该模型随后预测未清查单元格的物种丰富度。结果表明，知识水平并未提高物种丰富度制图的准确性和一致性。快速评估的努力和方法对准确性和一致性产生了显著影响。将快速评估的努力从10个植物个体增加到40个植物个体，分别可以提高准确性和一致性至2.2%和2.8%。横断面调查分别降低了准确性和一致性至0.5%和0.8%。本研究表明，了解森林中至少一半的物种对快速评估是充分的。建议每个单元格进行快速评估时至少有20个植物个体。最后，由熟悉其森林的当地社区进行快速评估，从而进一步支持可持续森林管理。