NDLI: Efficient sampling of plant diversity in arid deserts using non-parametric estimators

Content Provider	Springer Nature Link
Author	Liu, Zunchi Liu, Tong Yu, Hang Han, Zhiquan Wang, Tao Liu, Huafeng
Copyright Year	2017
Abstract	Surveying plant diversity in arid desert areas is extremely difficult because of the harsh climate, hostile terrain, lack of roads, and insecurity, which is why it is particularly important to improve the sampling efficiency, but few relevant studies have been done. The performance of non-parametric estimators was assessed with first-hand field data to determine (a) the threshold of the proportion of uniques (number of species that occur in exactly one plot divided by the number of species sampled) that involves the least sampling effort and (b) the method of locating plots to obtain a more reliable estimate of species richness. The study area (Gurbantunggut desert, China) was divided into five sub-regions based on variation in physical environment and vegetation. The following common correction factors were selected: ACE, Chao1, Bootstrap, Chao2, ICE, Jack1, and Jack2. The estimates for each sub-region (partition) and for the entire region (without partition), the threshold of proportion of uniques, and the method of determining sampling locations (including prior sampling of plots that show large differences in habitats) were compared in terms of their ability to predict the number of species more accurately. We found that ACE and Chao1 (which use abundance data) showed more biased estimates than the other factors (incidence data), and best estimator is Jack1. Species richness was significantly underestimated for the region, but the non-parametric estimators could estimate the species richness for each sub-region reliably. Sampling locations affected the performance of non-parametric estimators significantly. The threshold of minimum sampling was 15% and that of uniques was 30%; the two were able to limit the bias within 5 and 10%, respectively. It is concluded that the non-parametric estimators can estimate the plant diversity of arid deserts reliably from the data on incidence. The study area (on the scale of a region) should be partitioned to improve the performance of the non-parametric estimators. The plots with larger differences in habitats should be sampled more extensively based on the threshold of the proportion of uniques.
Starting Page	1225
Ending Page	1242
Page Count	18
File Format	PDF
ISSN	09603115
Journal	Biodiversity and Conservation
Volume Number	26
Issue Number	5
e-ISSN	15729710
Language	English
Publisher	Springer Netherlands
Publisher Date	2017-01-27
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Arid desert areas Minimum sampling effort Non-parametric estimators Partition Plot locations Species richness Biodiversity Ecology Conservation Biology/Ecology Climate Change/Climate Change Impacts
Content Type	Text
Resource Type	Article
Subject	Ecology Ecology, Evolution, Behavior and Systematics Nature and Landscape Conservation

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