Automated DNA‐based plant identification for large‐scale biodiversity assessment

Abstract

Rapid degradation of tropical forests urges to improve our efficiency in large‐scale biodiversity assessment. DNA barcoding can assist greatly in this task, but commonly used phenetic approaches for DNA‐based identifications rely on the existence of comprehensive reference databases, which are infeasible for hyperdiverse tropical ecosystems. Alternatively, phylogenetic methods are more robust to sparse taxon sampling but time‐consuming, while multiple alignment of species‐diagnostic, typically length‐variable, markers can be problematic across divergent taxa. We advocate the combination of phylogenetic and phenetic methods for taxonomic assignment of DNA‐barcode sequences against incomplete reference databases such as GenBank, and we developed a pipeline to implement this approach on large‐scale plant diversity projects. The pipeline workflow includes several steps: database construction and curation, query sequence clustering, sequence retrieval, distance calculation, multiple alignment and phylogenetic inference. We describe the strategies used to establish these steps and the optimization of parameters to fit the selected psbA‐trnH marker. We tested the pipeline using infertile plant samples and herbivore diet sequences from the highly threatened Nicaraguan seasonally dry forest and exploiting a valuable purpose‐built resource: a partial local reference database of plant psbA‐trnH. The selected methodology proved efficient and reliable for high‐throughput taxonomic assignment, and our results corroborate the advantage of applying ‘strict’ tree‐based criteria to avoid false positives. The pipeline tools are distributed as the scripts suite ‘BAGpipe’ (pipeline for Biodiversity Assessment using GenBank data), which can be readily adjusted to the purposes of other projects and applied to sequence‐based identification for any marker or taxon.

Evaluation of bias on the assessment of diet breadth of herbivorous insects using molecular methods

“…As in our first study using BAGpipe (Papadopoulou et al ., ), some inferences were compromised by seemingly erroneous taxonomic metadata in GenBank or by unresolved nomenclature discrepancies. These problems were detected after running the analysis and obtaining taxonomically lax identifications suggestive of very divergent taxonomic ranks included in the same query group.…”

“…() without modifications. Some samples yielded PCR products directly with universal primers, some required reamplification with custom internal primers (Papadopoulou et al ., ), and some generated multiple bands, which were isolated from 1.5% agarose gels, reamplified with internal primers and sequenced. All PCR products were sequenced in both directions using their corresponding PCR primers and the BigDye Terminator 3.1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA).…”

“…DNA extractions were used as template for PCR amplification of chloroplast psbA‐trnH intergenic spacer as a host‐plant marker, and following the same protocol as in Papadopoulou et al . () without modifications. Some samples yielded PCR products directly with universal primers, some required reamplification with custom internal primers (Papadopoulou et al ., ), and some generated multiple bands, which were isolated from 1.5% agarose gels, reamplified with internal primers and sequenced.…”

“…In the case of trophic associations, these methods rely heavily on PCR and sequencing of diagnostic markers amplified typically from degraded organic material present in the gut or feces of the animals of interest (Symondson, 2002;King et al, 2008;Pompanon et al, 2012;Zarzoso-Lacoste et al, 2013). Phenetic or phylogenetic sequence comparisons against a reference database that includes taxonomically relevant information provide the required identification of the food source (e.g., Valentini et al, 2009;García-Robledo et al, 2013;Gibson et al, 2014;Papadopoulou et al, 2015). This approach has found great applicability in ecological studies of invertebrates that are difficult to study with traditional methodologies, particularly in insects of cryptic ecology.…”

Integrative Profiling of Bee Communities from Habitats of Tropical Southern Yunnan (China)

“…For maximizing congruence, we used the Hubert and Arabie adjusted Rand Index (HA Rand Index), calculated in R 55 using the Clues package 56 . To further assist in identification of errors during the iterative stage, broad-level (genus) taxonomic assignments were inferred with two-gene DNA barcoding, using the BAGpipe and SAP pipelines 57, 58 querying the reference data mined earlier. Taxonomic iterations examined unexpected groupings, and various sources of error were corrected (sequence error, sequence label error, algorithmic shortcomings, specimen misidentification, labeling or data input error).…”