DNA sequence data processing
Our DNA sequence data processing is detailed in Bessey et al. (2021), it directly follows the procedure described at https://pythonhosted.org/OBITools/wolves.html, and we briefly outline those procedures here again. Data generated by Illumina sequencing were processed using OBITools (https://pythonhosted.org/OBITools/) command ‘ngsfilter’ to assign each sequence record to the corresponding sample based on tag and primer. Then ‘obiuniq’ was used to dereplicate reads into unique sequences. Reads less than 190 bp and with counts less than 10 were discarded. Denoising was performed using ‘obiclean’ to retain only sequences with no variants containing a count greater than 5% of their own. Sequences were assigned to taxa using ‘ecotag’ and a result table was generated using ‘obiannotate’. Our reference database was built in silico using our universal fish primer assay on 03/08/2021. Only fish species with identities ≥ 90% and whose sequence variants could be assigned to at least family (and lower) were included. All variants were assigned a single name (eg. to family, genus or species) and directly compared to the known species in the mesocosm (Table 2). For example, an assignment to genus could be compared to the species of that genus which are known to inhabit the mesocosm.
Statistics
A Box-Cox transformation normalized the data (Shapiro-Wilks Test), which allowed for the use of parametric statistics. We used an analysis of variance on the linear model fit of mean Cq value by material, followed by a Tukey Honest Significant Difference to compare materials. We also used an analysis of variance on the linear model fit between mean Cq value and submersion duration for each material. A linear model fit of mean Cq values by material and submersion duration, and their interactions, produced the same results. These statistics were likewise used to determine differences in the number of species detected between materials and submersion intervals. We fit a smoothing spline to the interval data for a visual estimation of how mean Cq values and species detections varied with time. All statistics and graphics were produced using R (version 2.14.0; R Development Core Team 2011) and graphics were edited in Inkscape (https://inkscape.org/).