Data from: Who’s for dinner? High-throughput sequencing reveals bat diet differentiation in a biodiversity hotspot where prey taxonomy is largely undescribed

Effective management and conservation of biodiversity requires understanding of predator–prey relationships to ensure the continued existence of both predator and prey populations. Gathering dietary data from predatory species, such as insectivorous bats, often presents logistical challenges, further exacerbated in biodiversity hot spots because prey items are highly speciose, yet their taxonomy is largely undescribed. We used high-throughput sequencing (HTS) and bioinformatic analyses to phylogenetically group DNA sequences into molecular operational taxonomic units (MOTUs) to examine predator–prey dynamics of three sympatric insectivorous bat species in the biodiversity hotspot of south-western Australia. We could only assign between 4% and 20% of MOTUs to known genera or species, depending on the method used, underscoring the importance of examining dietary diversity irrespective of taxonomic knowledge in areas lacking a comprehensive genetic reference database. MOTU analysis confirmed that resource partitioning occurred, with dietary divergence positively related to the ecomorphological divergence of the three bat species. We predicted that bat species' diets would converge during times of high energetic requirements, that is, the maternity season for females and the mating season for males. There was an interactive effect of season on female, but not male, bat species' diets, although small sample sizes may have limited our findings. Contrary to our predictions, females of two ecomorphologically similar species showed dietary convergence during the mating season rather than the maternity season. HTS-based approaches can help elucidate complex predator–prey relationships in highly speciose regions, which should facilitate the conservation of biodiversity in genetically uncharacterized areas, such as biodiversity hotspots.,Table 1_withMOTUTaxonomic assignment of MOTUs through the BOLD online identification engine using two different methods: the neighbour-joining hierarchical tree-based ‘strict’ (S) and the sequence similarity ‘best match’ (BM) approach (Ross et al. 2008). Only sequences with >98% similarity were considered as a possible match: >98% for a “genus” match and >99% for a “species” match. For the BM approach, only matches with sampling sites in Australia were considered; * indicates sampling sites in south-western WA while ** indicates sampling sites in WA but outside of the south-west. Species highlighted in grey are thought to use hearing based defences against the echolocation calls of bats. This table is a modified version of Table 1 and includes each associated MOTU.TableS1_Bat_Species_TraitsTable listing bat species traits.TableS2_Bdiversityβ-diversity results for all MOTUs and subset of MOTUs.TableS3_SitesTable with geographic coordinates of data collection locations.TableS4_CNV_unweighted_unifracInput table used for analyses within R – unweighted unifrac distance matrix.TableS5_Species_AccumulationInput table used for analyses within R – prey (species) accumulation data.TableS5_ALL_SpAccumPlot.csvTableS6_Chao1_PD_RarefactionInput table used for analyses within R – Chao1 and PD rarefaction.TableS6_CHAO_PD_Rarefaction.csvAppendixS1_Molecular methodsDetailed molecular methods for DNA extraction and amplification and HTS sequencing.Appendix S1_Molecular methods.pdfAppendixS2_Aligned_sequencesAlignment of representative sequences generated for this study.AppendixS2_rep_set_aligned.fastaAppendixS3_R_scriptScripts used for analyses in R.AppendixS4_QIIME_scriptScripts used for analyses in QIIME.AppendixS5_MapMapping file for QIIME and R analyses.AppendixS6_1MOTUbatList of MOTUs found in only one sample.AppendixS7_DNA SequencesTwo fasta files containing all arthropod DNA sequences (seqs.fna) and the representative DNA sequences for each Molecular Operational Taxonomic Unit (MOTU) (rep_set.fna).DNA_sequences.zip,