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).