Orbit

年度 2018
全部作者 Lam TY, Hsu Y-H, Yang T-R, Kershaw JA, Su S-H
作者類別 First Author,Corresponding Author
論文名稱 Sampling with Probability Proportional to Prediction: Rethinking Rapid Plant Diversity Assessment
期刊名稱 Forestry
卷數 91
期數 1
起頁 17
迄頁 26
期刊等級 SCI
總頁數 10
發表日期 2018-01-01
摘要 Rapid biodiversity assessment (RBA) methods are regularly applied to assess plant species richness. One approach is developing sampling designs that integrate expert knowledge. 3P sampling does so by selecting samples with probability proportional to prediction (3P). Higher effort is allocated to areas with high species richness based on predictions made on the ground. 3P sampling for RBA was simulated considering two major factors: knowledge of plant species and types of rapid assessment. Two large census forest plots over 25 ha in size were used. Results showed that sampling error of 3P sampling for RBA was relatively low and could be improved by changing methods of prediction. Sampling was more efficient and accurate when predictions were made with knowledge about abundant species instead of random species. When such prediction was made, knowing only three quarters of the total species richness in a forest performed as well as full knowledge. Randomly walking around in an area and predicting also increased efficiency and accuracy compared to standing stationary at an assessment point. This was counterintuitive to the common practices of establishing ground plots for assessment. Our findings propose that 3P sampling for RBA is workable through engaging local communities in an assessment, which could be cost-effective. Finally, the procedure laid out in this study is the first unequal probability sampling design proposed for RBA.
語言 英文
參考連結 https://academic.oup.com/forestry/article/91/1/17/4371595?guestAccessKey=2ba49b15-87f1-4d00-9841-60fb1857abd7
所屬計畫案 MOST 104-2313-B-002-002