Arthropod communities recovered by metabarcoding and sampling
blocks
The estimation of species richness in any ecological setting, and
especially in forested environments, can be challenging due to the
rarity of some species, differences in detection probabilities, and the
field effort necessary to collect enough samples or species to ensure
meaningful coverage
(Andújar et al., 2017;
Arribas et al., 2016; Creedy et al., 2019). In our study, we used
pitfall traps in sampling blocks, maximizing the probability of
detecting arthropod species by sampling intensively at multiple sites in
one mountain, covering eight orders of arthropods from haplotypes to
communities. Our sampling method and size sorting step allowed the
recovery of eight major arthropod orders, congruently with other
metabarcoding analyses (Elbrecht et al., 2017; Elbrecht et al., 2018;
Creedy et al., 2019). According to our rarefaction curves, our sampling
detected different taxa per sample (Figure S3), demonstrating the
utility of cMBC and our sampling design to study a region of high
biological diversity and ecological complexity. It is difficult to
compare our results against morphological studies in Nevado de Toluca
because there are no complete checklists of arthropods for Mexican
highlands. For instance, out of 29 dung beetle species found by a recent
survey of four sky-islands, more than 10% were new species
(Arriaga-Jiménez et al., 2018). Comparing our results against other cMBC
studies shows that more OTUs (913 at 3% CL) of different orders were
found at a tropical forest canopy (Creedy et al., 2019) than what was
found here for a tropical conifer forest floor (476 OTUs at 3% CL).
Additionally, the metabarcoding pipeline we followed (Arribas et al.,
2020) allowed us to analyze diversity patterns for each order separately
and allowed us to consider multi-hierarchical levels into community
assembly, from haplotypes to 7.5% CL lineages. Our metabarcoding
approach coupled with pitfall traps allowed for the automated
identification of 1,277 ASV from 42 bulk samples, which contained whole
organisms (or part of them). This allowed to calculate community
composition and turnover without the bias introduced by traditional
taxonomy (Creedy et al.,2019), including small taxa (< 0.5
mm), such as Collembola, and specimens that break easily, such as
Diptera (Figure 3). Thus, combining metabarcoding and pitfall traps
sampling, allows to perform large scale community composition analyses
in tropical mountains.