Insights into assessing environmental quality status using potential surrogates of biofilm-dwelling ciliate fauna in coastal waters

Abstract

To determine potential surrogates of biofilm-dwelling ciliate fauna for assessing water quality, a dataset of coastal waters of the Yellow Sea was studied. Samples were collected monthly at a depth of 1 m from four sampling stations during a 1-year cycle (August 2011-July 2012), northern China. The ciliate communities at low taxonomic resolutions up to family level were significantly correlated with the full ciliate fauna at species-level resolution and represented a significant spatial variation in relation to the environmental changes. The presence/absence resolutions up to genus level maintained sufficient information of the ciliate species-abundance matrix. The vagile and sessile ciliate assemblages were significantly correlated with the full ciliate communities but only the latter maintained sufficient information with a significant spatial variation in response to the environmental stress. Correlation analyses showed that the taxon richness indices of potential surrogates at genus- and family-level resolutions can explain >95 % of the variance in that of the full species pool. Thus, it is suggested that the taxonomic resolutions up to family level may be a robust time-efficient surrogate of whole periphytic ciliate communities for assessing water quality status and would allow improving sampling strategies of large spatial scale monitoring programs, using biofilm-dwelling ciliates, in coastal waters.

Colonization features of marine biofilm-dwelling protozoa in Chinese coastal waters of the Yellow Sea

“…The colonization dynamics of the periphytic protozoa, for example, were similar in the spring and autumn (S eq = 29/23; G = 0.301/0.296; T 90% = 7.650/7.779) differed significantly in the summer and winter (S eq = 32/121; G = 0.708/0.005; T 90% = 3.252/479.705). This was probably due to differences in the water conditions (e.g., water temperature) and food supply among the four seasons as stated by Abdullah Al et al (2019) and Zhang et al (2015). Otherwise, these findings provide clear evidence that the colonization process of periphytic ciliates and other protozoa may differ under different seasonal conditions.…”

“…Some studies on community structures and community patterns of marine ciliates and other protozoa have been conducted (Duong et al 2007;Gong et al 2005;Railkin 1995;Xu et al 2012a, b;Zhang et al 2013). Few studies, however, have investigated seasonal variations of their colonization dynamics, although such data are important to both biomonitoring programs and microbial ecological research (Xu et al 2012b;Zhang et al 2015).…”

Periphytic Biofilm Formation on Natural and Artificial Substrates: Comparison of Microbial Compositions, Interactions, and Functions

“…As for the beta diversity, the difference of microbial community structure between natural and artificial substrates was analysed by principal coordinate analysis (PCoA; Gower and Legendre, 1986 ; Legendre and Legendre, 1998 ) and non-metric multidimensional scaling (NMDS) analysis using weighted UniFrac distance matrix ( Shepard, 1962 ) in R (version 3.6.3). Statistical comparison between different biofilm samples was deduced by permutational multivariate ANOVA (PERMANOVA; Anderson, 2005 ; Zhang et al, 2015 ).…”

Calidad estacionaria del agua ante el costo ambiental sostenible relativo con agregación de biomarcadores: Bahía de Puno, lago Titicaca, Pperú

“…Aunque, no basta determinar las fluctuaciones físicoquímicas de calidad del agua para reconocer la salud ambiental de los ecosistemas se requiere del mismo modo, el uso de indicadores biológicos que permitan el monitoreo de vigilancia (Aguirre et al, 2012;Argota & Iannacone, 2017) donde, la optimización y celeridad en todo programa de monitoreo como estrategia relevante requiere, un programa de monitoreo biológico que incorpore pruebas sencillas, dinámicas y confiables fundamentalmente en los taxones superiores como los peces y que permitan disminuir la razón numérica sobre los índices de calidad ambiental en los sistemas acuáticos (Zhang et al, 2015;Mazón, 2016). De la misma manera, analizarse biomarcadores ícticos en el tiempo posibilita informaciones cuantitativas (Hamza, 2014;Çiftçi et al, 2015;AbdAllah, 2017) y por ende; sobre los efectos del costo ambiental (Dixit et al, 2015).…”