In the early 20th century,numerous western botanists,often referred to as‘plant hunters’,embarked on ambitious expeditions to China,playing a crucial role in the study of botany and botanical diversity.Despite their...In the early 20th century,numerous western botanists,often referred to as‘plant hunters’,embarked on ambitious expeditions to China,playing a crucial role in the study of botany and botanical diversity.Despite their contributions,comprehensive assessments of their explorations are lacking.To bridge this gap,this article focuses on the work of Joseph Charles Francis Rock,a notable figure in that era.Our work revisits Rock’s botanical expeditions within the broader context of botanical diversity conservation.It outlines his historical experiences in collecting plants in China and enumerates the species composition and phenotypic traits of the plants he collected.Additionally,it also analyzes the spatial distribution of the species,the completeness of his collection and the α-and β-diversity of the plants he collected.Our findings reveal that Rock led four major botanical expeditions in China between 1922 and 1933,amassing a total of 28184 sheets and 16608 numbers across 204 families,1081 genera and 4231 species.His focus was predominantly on ornamental species,which exhibit a variety of flower colors and inflorescences.His collection work spanned 5 provinces,35 cities and 72 counties,with a notable concentration in the Hengduan Mountains,a current biodiversity hotspot.This study not only reconstructs Rock’s botanical legacy but also offers valuable historical data and fresh analytical insights for understanding contemporary plant diversity.It contributes to the ongoing discourse on the importance of preserving plant diversity as a cornerstone of environmental sustainability.展开更多
Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the fi...Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a k-step ACS based on Horvitz-Thompson （HT） estimator was developed and an unbiased estimator was derived. The k-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz （ACS-HH） and ACS- HT estimators, and k-step ACS-HT estimator. The effectiveness of using different k- step sizes was also compared. The results showed that k-step ACS^HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species, k-step ACS without replacement was slightly more effective than that with replacement. In k-step ACS, the variance estimate of one-step ACS is much larger than other k-step ACS （k 〉 1）, but it is smaller than ACS. This implies that k-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.展开更多
文摘In the early 20th century,numerous western botanists,often referred to as‘plant hunters’,embarked on ambitious expeditions to China,playing a crucial role in the study of botany and botanical diversity.Despite their contributions,comprehensive assessments of their explorations are lacking.To bridge this gap,this article focuses on the work of Joseph Charles Francis Rock,a notable figure in that era.Our work revisits Rock’s botanical expeditions within the broader context of botanical diversity conservation.It outlines his historical experiences in collecting plants in China and enumerates the species composition and phenotypic traits of the plants he collected.Additionally,it also analyzes the spatial distribution of the species,the completeness of his collection and the α-and β-diversity of the plants he collected.Our findings reveal that Rock led four major botanical expeditions in China between 1922 and 1933,amassing a total of 28184 sheets and 16608 numbers across 204 families,1081 genera and 4231 species.His focus was predominantly on ornamental species,which exhibit a variety of flower colors and inflorescences.His collection work spanned 5 provinces,35 cities and 72 counties,with a notable concentration in the Hengduan Mountains,a current biodiversity hotspot.This study not only reconstructs Rock’s botanical legacy but also offers valuable historical data and fresh analytical insights for understanding contemporary plant diversity.It contributes to the ongoing discourse on the importance of preserving plant diversity as a cornerstone of environmental sustainability.
文摘Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a k-step ACS based on Horvitz-Thompson （HT） estimator was developed and an unbiased estimator was derived. The k-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz （ACS-HH） and ACS- HT estimators, and k-step ACS-HT estimator. The effectiveness of using different k- step sizes was also compared. The results showed that k-step ACS^HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species, k-step ACS without replacement was slightly more effective than that with replacement. In k-step ACS, the variance estimate of one-step ACS is much larger than other k-step ACS （k 〉 1）, but it is smaller than ACS. This implies that k-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.
