https://scholars.lib.ntu.edu.tw/handle/123456789/631760
DC Field | Value | Language |
---|---|---|
dc.contributor.author | CHUN-HUO CHIU | en_US |
dc.date.accessioned | 2023-06-05T02:51:02Z | - |
dc.date.available | 2023-06-05T02:51:02Z | - |
dc.date.issued | 2022-11-01 | - |
dc.identifier.issn | 2041-210X | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/631760 | - |
dc.description.abstract | Individual-based abundance data and sample-based incidence data are the two most widely used survey data formats to assess the species diversity in a target area, where the sample-based incidence data are more available and efficient for estimating species richness. For species individual with spatial aggregation, individual-unit-based random sampling scheme is difficult to implement, and quadrat-unit-based sampling scheme is more available to implement and more likely to fit the model assumption of random sampling. In addition, sample-based incidence data, without recording the number of individuals of a species and only recording the binary presence or absence of a species in the sampled unit, could considerably reduce the survey loading in the field. In this study, according to sample-based incidence data and based on a beta-binomial model assumption, instead of using the maximum likelihood method, I used the moment method to derive the richness estimator. The proposed richness estimation method provides a lower bound estimator of species richness for beta-binomial models, in which the new method only uses the number of singletons, doubletons and tripletons in the sample to estimate undetected richness. I evaluated the proposed estimator using simulated datasets generated from various species abundance models. For highly heterogeneous communities, the simulation results indicate that the proposed estimator could provide a more stable, less biased estimate and a more accurate 95% confidence interval of true richness compared to other traditional parametric-based estimators. I also applied the proposed approach to real datasets for assessment and comparison with traditional estimators. The newly proposed richness estimator provides different information and conclusions from other estimators. | en_US |
dc.publisher | WILEY | en_US |
dc.relation.ispartof | Methods in Ecology and Evolution | en_US |
dc.subject | beta-binomial model; doubletons; sample-based incidence data; singletons; tripletons | en_US |
dc.title | Incidence-data-based species richness estimation via a Beta-Binomial model | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1111/2041-210X.13979 | - |
dc.identifier.scopus | 2-s2.0-85137840573 | - |
dc.identifier.isi | WOS:000853430800001 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85137840573 | - |
dc.relation.pages | 2546 | en_US |
dc.relation.journalvolume | 13 | en_US |
dc.relation.journalissue | 11 | en_US |
dc.relation.pageend | 2558 | en_US |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | journal article | - |
item.grantfulltext | none | - |
crisitem.author.dept | Agronomy | - |
crisitem.author.orcid | 0000-0002-7096-2278 | - |
crisitem.author.parentorg | College of Bioresources and Agriculture | - |
Appears in Collections: | 農藝學系 |
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