2.6 | Zooplankton diversity estimation and statistical analysis
The mitochondrial CO1 fasta files prepared for each size-fractionated sample were clustered using VSEARCH (Rognes et al. 2016: vsearch –cluster_fast CO1.contig.fasta –strand both -id 0.90 –uc out.uc –centroids out.cent.fasta). Sequence read numbers, which each constitute an OTU with <10% intergenetic distance, was estimated from .uc files generated as outfiles of the VSEARCH (standard UNIX commands were used for this process). Hill unified diversity numbers (D) were calculated using the obtained read values (Hill, 1973), in essence, calculating diversity as the effective number of OTUs, by considering a variable (q) which is based on the gradual increase in the weight given to abundance (Table S2). When q is 0 (0D) only presence is accounted for and abundances are disregarded, favouring rare species; Shannon’s entropy is represented by the equivalent 1D, and represents the effective number of common species in a community; 2D is the equivalent to Simpson’s concentration, and accounts only for abundance, neglecting occurrence, thus favouring numerically dominant species (Jost 2006). iNEXT (Hsieh et al., 2016: list_data, q = c(0,1,2), datatype = ’abundance’) within R Core Team (2020) was used for the estimation of extrapolated species alpha diversities (α). Boxplots were produced using ggplot2 V. 3.3.6 using the extrapolated diversity numbers (qD) as the dependent variable and the four environmental factors -Transect, Time, Station and Size Fraction - as independent variables (Methods S4).
Hypothesis testing was also performed by using the OTU table data available in Supplementary Table S3. Considering the large variance and a large number of samples in our data, we transformed the abundance data into presence and absence data. Next, we used the command andonis2 from Vegan v.2.6-2 (Oksanen et al., 2019; adonis2(abundance ~ Area + time + Station + Fraction, data, by= ”margin”)), to run a PERMANOVA (Methods S4).