Statistical analysis
All statistical analyses were performed using R version 4.3.2
(https://www.R-project.org/). To evaluate sampling completeness and
compare the detection efficiency between bio-sampler species or genetic
markers, we generated species accumulation curves using the random
method with 100 permutations and estimated the expected species richness
using Chao2 (Chao et al., 2014), with the vegan package version
2.6 (Oksanen et Blanchet, 2017). Following Deagle et al. (2019), dDNA
metabarcoding data were summarized asĀ : (1) the Frequency of Occurrence
(FOO), defined as the percentage of samples in which a fish species was
present, and (2) the Relative Read Abundance (RRA), calculated as the
average proportion of total reads assigned to the species.
Fish assemblages recovered through dDNA, WFD surveys, and deep inventory
were compared using multidimensional scaling (MDS) based on Jaccard
dissimilarity matrices. To assess potential size-related detection
biases across monitoring methods, we compared the body length
distributions of detected species using Wilcoxon rank-sum tests. For
each species, we used the mean body length recorded from historical WFD
surveys conducted in French Guiana. Additionally, to determine whether
detection probability via dDNA metabarcoding was influenced by species
abundance, we compared the catch abundances between species detected and
not detected by metabarcoding using Wilcoxon rank-sum tests. Finally, we
also tested whether species exclusively detected through metabarcoding
exhibited different frequencies of occurrence compared to species
detected by both metabarcoding and traditional surveys.