DOI: 10.5061/dryad.37b53
title: Data from: Plant diversity accurately predicts insect diversity in two tropical landscapes

description: Plant diversity surely determines arthropod diversity, but only moderate
 correlations between arthropod and plant species richness had been
 observed until Basset et al. (Science, 338, 2012 and 1481) finally
 undertook an unprecedentedly comprehensive sampling of a tropical forest
 and demonstrated that plant species richness could indeed accurately
 predict arthropod species richness. We now require a high-throughput
 pipeline to operationalize this result so that we can (i) test competing
 explanations for tropical arthropod megadiversity, (ii) improve estimates
 of global eukaryotic species diversity, and (iii) use plant and arthropod
 communities as efficient proxies for each other, thus improving the
 efficiency of conservation planning and of detecting forest degradation
 and recovery. We therefore applied metabarcoding to Malaise-trap samples
 across two tropical landscapes in China. We demonstrate that plant species
 richness can accurately predict arthropod (mostly insect) species richness
 and that plant and insect community compositions are highly correlated,
 even in landscapes that are large, heterogeneous and anthropogenically
 modified. Finally, we review how metabarcoding makes feasible highly
 replicated tests of the major competing explanations for tropical
 megadiversity.
description: Raw Short Reads of
 Insects in MengsongThe data is for insect
 samples from Malaise traps in Mengsong, Xishuangbanna, Yunnan Province,
 China. Samples were prepared by using one leg from all specimens equal to
 or larger than a large fly (~5 mm length) and whole bodies of everything
 smaller. Following the protocol of Ji et al. (2013), samples were
 homogenized, and DNA was extracted, quality-checked, PCR-amplified with
 indexed, degenerate primers for the standard mtCOI barcode region, and
 gel-purified. The PCR products were A-amplicon-sequenced on a Roche GS FLX
 at the Kunming Institute of Zoology. The 28 Mengsong samples were
 sequenced on one whole run (four 1/4 regions, November-December 2010: wet
 season) and two 1/4 regions (May-June 2011: dry season), producing 519 865
 and 253 025 raw reads, respectively.submission_fastq_MS.tar.gzRaw Short Reads of Insects in Yinggeling National Nature ReserveThe data is for insect samples from Malaise traps in Yinggeling National Nature Reserve, Hainan Province, China. Samples were prepared by using one leg from all specimens equal to or larger than a large fly (~5 mm length) and whole bodies of everything smaller. Following the protocol of Ji et al. (2013), samples were homogenized, and DNA was extracted, quality-checked, PCR-amplified with indexed, degenerate primers for the standard mtCOI barcode region, and gel-purified. The PCR products were A-amplicon-sequenced on a Roche GS FLX at the Kunming Institute of Zoology. The 21 Yinggeling samples were sequenced on two 1/8 regions (one 1/8 region shared with other samples), producing 40 261 raw reads.submission_fastq_YGL.tar.gz454 sequencer datasets of MengsongThe datasets are output of 454 sequencer, and input of the bioinformatic analyses.454sequencer_dataset_MS.tar.gz454 sequencer datasets of YinggelingThe datasets are output of 454 sequencer, and input of the bioinformatic analyses.454sequencer_dataset_YGL.tar.gzBioinformatic scripts of MengsongCommand history (command_history_mengsong.txt) and related files, e.g. map files, reference data, programs written by perl language, etc.bioinformatic_script_MS.zipBioinformatic scripts of YinggelingCommand history (command_history_yinggeling.txt) and related files, e.g. map files, reference data, programs written by perl language, etc.bioinformatic_script_YGL.zipR scriptsThe file includes all R scripts used, and is of R Markdown format. You may need to open it in RStudio.Plants_accurately_predict_insects.RmdEnvironmental variables in MengsongThe data includes environmental variables in Mengsong. The row names are IDs of survey plots (i.e. 7, which corresponds to PLOT007 in the data of raw short reads). Abbreviations for column names are: HAB = habitat types (RF: regenerating forest, OL: open lands, MF: mature forest), UTM_E and UTM_N = geographic coordinates of each survey plot, F_NR = the survey plots inside the forests of Bulong Nature Reserve? (TRUE: yes, FALSE: no)MSenv.csvEnvironmental variables in YinggelingThe data includes environmental variables in Yinggeling. The row names are IDs of survey plots. Abbreviations for column names are: UTM_E and UTM_N = geographic coordinates of each survey plot, Time = the time when survey plots were established and trees were surveyed (2009 or 2011).YGLenv.csvPlant community composition in MengsongPlant community composition of each survey plot in Mengsong. The rows are survey plots and columns are plant species. This is an input file for R.MS_Plant_output.csvTree community composition in YinggelingTree community composition of each survey plot in Yinggeling. The rows are survey plots and columns are plant species. This is an input file for R.YGL_treedensity.csvTree community composition of survey plots in the forests of Bulong Nature ReserveThe dataset is a subset of plant community composition data in Mengsong. For cross-site predictions, we used only the Mengsong plots (n = 16) located within the forest of Bulong Nature Reserve (~60 km2) and only included trees &gt; 5cm DBH in each plot, to maximum comparability between the two landscapes. The rows are survey plots and columns are tree species. This is an input file for R.MS_Tree_output_5cm.csvThe Operational Taxonomic Unit (OTU) representative sequences in MengsongAfter denoising and deconvoluting, we clustered the reads into 97%-similarity Operational Taxonomic Units (OTUs), which should approximate or somewhat underestimate biological species. We further assigned taxonomic information to the OTUs. The data here shows the OTU representative sequences in Mengsong.MSall_CROP97_DNACLUST_otus_r0.cluster.fastaThe Operational Taxonomic Unit (OTU) representative sequences in YinggelingAfter denoising and deconvoluting, we clustered the reads into 97%-similarity Operational Taxonomic Units (OTUs), which should approximate or somewhat underestimate biological species. We further assigned taxonomic information to the OTUs. The data here shows the OTU representative sequences in Yinggeling.YGL_CROP97_otus_z5k.cluster.fastaThe Operational Taxonomic Unit (OTU) table with assigned taxonomies in MengsongArthropod community composition of each survey plot in both seasons of Mengsong. MS1 = November-December 2010 (the end of wet season), MS2 = May-June 2011 (the end of dry season). The rows are OTUs, and columns are survey plots together with taxonomies of each OTU. The formats of the dataset are slightly modified for input to R.MBCar_MS.rarThe Operational Taxonomic Unit (OTU) table with assigned taxonomies in YinggelingArthropod community composition of each survey plot in Yinggeling. The rows are OTUs, and columns are survey plots together with taxonomies of each OTU. The formats of the dataset are slightly modified for input to R.MBCar_YGL.csv