title: An approach to determination of optimal species pool of periphytic microfauna in colonization surveys for marine bioassessment.
abstract: To determine an optimal species pool for periphytic microfauna colonization surveys, a multivariate approach was used to identify the influential species from a raw dataset of periphytic microfauna. Samples were collected at two depths of 1 and 3 m in coastal waters of the Yellow Sea using a glass slide method. From the full 77-species dataset, a 23-species subset with sufficient information of the whole community was identified. The small subset maintained sufficient information of colonization pattern of entire raw communities (correlation coefficient >0.95). The colonization curves based the small subset well fitted the MacArthur-Wilson and logistic model equations in both species composition and individual abundance, respectively. Compared to the functional parameters based on the full dataset, the colonization rates (G) were significantly high and the time reaching 90% equilibrium species number (T 90) significantly low (P < 0.05), while the growth rates (r) and the time reaching 50 % maximum abundance (T 50) showed no significant changes (P > 0.05) at both depths, respectively. The species richness, diversity, and evenness represented significantly closed linear relationships between the subset and the full dataset. The results suggest that the small subset might be used as a robust optimal species pool for colonization-based bioassessment surveys and allows developing a time-efficient protocol for marine monitoring programs using of periphytic microfauna.
authors: College of Marine Life Science, Ocean University of China, Qingdao, 266003, China.
authors: Guangjian
authors: G
authors: Xu
authors: Xiaoxiao
authors: X
authors: Zhong
authors: Yangfan
authors: Y
authors: Wang
authors: Henglong
authors: H
authors: Xu
mesh_terms: D059001:Aquatic Organisms / Q000502:physiology*; D044822:Biodiversity; D002681:China; D016798:Ciliophora / Q000502:physiology*; D003247:Conservation of Natural Resources / Q000379:methods*
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