DOI: 10.5061/dryad.xgxd254dk
title: Data for: Parasitoids indicate major climate-induced shifts in Arctic communities

description: Climatic impacts are especially pronounced in the Arctic, which as a
 region is warming twice as fast as the rest of the globe. Here, we
 investigate how mean climatic conditions and rates of climatic change
 impact parasitoid insect communities in 16 localities across the Arctic.
 We focus on parasitoids in a wide-spread habitat, Dryas heathlands, and
 describe parasitoid community composition in terms of larval host use
 (i.e. parasitoid use of herbivorous Lepidoptera versus pollinating
 Diptera) and functional groups (i.e. parasitoids adhering to an idiobiont
 versus koinobiont lifestyle). Of the latter, we expect idiobionts to be
 generally associated with poorer tolerance to cold temperatures. To
 further test our findings, we assess whether similar climatic variables
 are associated with host abundances in a 22-year time series from
 Northeast Greenland. We find that sites which have experienced a
 temperature rise in summer while retaining cold winters to be dominated by
 parasitoids of Lepidoptera, with the pattern reversed among the
 parasitoids of Diptera. The rate of summer temperature rise is further
 associated with higher levels of herbivory, suggesting higher availability
 of lepidopteran hosts and changes in ecosystem functioning. We also detect
 a matching signal over time, as higher summer temperatures, coupled with
 cold early winter soils, are related to high herbivory by lepidopteran
 larvae, and to declines in the abundance of dipteran pollinators.
 Collectively, our results suggest that in parts of the warming Arctic,
 Dryas is being simultaneously exposed to increased herbivory and reduced
 pollination. Our findings point to potential drastic and rapid
 consequences of climate change on multitrophic-level community structure
 and on ecosystem functioning and highlight the value of collaborative
 systematic sampling effort.
description: 1. Description of methods used for collection/generation of
 data:  This dataset comprises of parasitoids caught in
 2016 at 19 Arctic and Sub-Arctic localities during two consecutive
 six-day-long trapping periods aimed to take place during the flowering
 peak of the mountain avens (Dryas spp.).  Each location had three to four
 trapping sites (A-D) in Dryas heath type habitats, each with ten 5cm by
 4.5cm white sticky traps cut out from sticky board (Barrettine
 Environmental, UK [product no longer available]). Sticky traps were
 embedded in growths of Dryas spp. The parasitoids were
 subsequently picked of off the sticky traps, their whole DNA was extracted
 and half of their Cytochrome Oxidase I barcode region was amplified using
 Primers B-F and HCO. The processing of samples was done in three parts
 (Data1, Data2, Data2) with slightly different methodology. See the
 supplementary information of the recommended publication for more details.
 Datasets were sequenced at the Helsinki Functional Genomics Unit (FuGU) in
 two separate MiSeq v3 2x300bp runs (Data1 and Data2). Additionally, a set
 of samples from a specific site (Zackenberg) were sequenced as part of
 larger set at the FIMM Technology Centre in a HiSeq2500 2x250bp run
 (Data3). Additionally, Dryas flower count, flowering
 phenology and flower damage by insect herbivores was recorded at the
 start, after a week (day 6) and in the end (day 12). These counts were
 done in 3 to 5 1/4 square meter monitoring plots per trapping site.
 Microclimate was recorded at one trapping site per locality using Lascar
 EL-USB-2 tempeerature and air humidity loggers under a small white plastic
 dome at ~ 10 cm height. 2. Methods for processing the
 data:  Initially, paired-end reads were merged and
 trimmed for quality using 32-bit usearch version 11 (Edgar 2010) with the
 command ‘fastq_mergepairs’. Primers were removed using software cutadapt
 version 1.14 (Martin 2011) with 15% mismatch rate. The reads were then
 collapsed into unique sequences (singletons removed) with command
 ‘fastx_uniques’. The subsequent clustering steps differed slightly for
 different data sets, due to the origin of the data (MiSeq vs. HiSeq2500),
 as follows. For Data1 and Data2, the newly-collapsed unique sequences were
 cleaned of chimeras using command ‘uchime_denovo’ and clustered into 96%
 OTUs (OTU = Operational Taxonomical Unit) using command ‘cluster_size’
 using USEARCH. The choice of 96% clustering threshold was based on
 empirical optimization, considering both the rapid genetic divergence in
 CO1, as well as potential sequencing errors. For Data3, the unique
 sequences were denoised (i.e., chimeras were removed) and reads were
 clustered into ZOTUs (= ‘zero-radius OTU’) with command ‘unoise3’ using
 USEARCH version 11. These ZOTUs do not practically differ from traditional
 clustering of OTUs (which are based on pre-set percentage threshold), but
 according to Edgar and Flyvbjerg (2015), the UNOISE algorithm performs
 better for certain heterogenous data sets in (i) removing chimeras, (ii)
 PhiX sequences and (iii) Illumina artefacts. Then OTUs and ZOTUs were
 mapped back to the original trimmed reads with command ‘usearch_global’
 (‘search_exact’ for Data3) to establish the total number of reads in each
 sample using 64-bit software vsearch (Rognes et al. 2016). Overall, we
 were able to map 25,673,920 reads (Data1: 4,261,291; Data2: 12,095,141;
 Data3: 9,317,488) to our original samples. These reads were subject to
 further filtering: from each sample, each OTU/ZOTU with less reads than 2%
 of the total reads in that sample were discarded, which also cleared most
 of the extraction and PCR negative controls. Finally, samples producing
 less than 37 reads (a threshold chosen by analysing the data as a whole)
 were removed from the subsequent analysis. The
 taxonomic assignations were initially done independently for each dataset
 (using identical criteria), but the final assignations were carried out
 using the whole, combined (Data1+Data2+Data3) dataset. The OTUs/ZOTUs were
 initially identified into genus-level using the RDP classifier with a very
 recently constructed COI-RDP database v3.2 (with 60% probability threshold
 for genus-level assignation) following Porter and Hajibabaei (2018). In
 cases where the database was clearly insufficient to reach a genus-level
 assignation, we used local BLAST against all the retrieved COI sequences
 in BOLD (Altschul et al. 1990; Ratnasingham and Hebert 2007) and chose the
 most probable match. Taxonomic information for remaining hits was
 retrieved manually from BOLD using BIN code (from earlier steps) or the
 actual OTU/ZOTU sequence. Finally, identifications were checked against
 our preliminary identification notes taken at the beginning of DNA
 extraction, and potentially false assignations (due to for example
 contamination in certain steps, or clear errors in the database) were
 either removed or assigned to the likely correct out/ZOTU.
 As the end result of all the bioinformatic steps, we arrived at a
 list of 460 parasitoid taxa (listed in OTU_info.csv Those dataset specific
 OTUs which were collapsed to one in the merging of the datasets are also
 listed in this file).
description: The included README_PanArcticParasitods.txt file contains
 detailed metadata on all included variables. There are
 some missing data e.g.: In the in situ microclimate
 measurements misising values are denoted with, NA In
 the parasitoid data, samples which failed to to produce parasitoid reads
 are marked with NA. In the Dryas data, two localities
 have their data data missing, and are not included in this table, where as
 one site Kobbejford had no <em>Dryas</em> and has counts of
 Loiseleuria procumbens as a phenological indicator in stead. This is
 marked in the datapoint notes. Concerning OTUs: Our
 OTUs are based on only a half of the CO1 barcode region and we used a
 quite wide clustering treshold of 96%. Also the clustering was done
 globally on a dataset containing closely related species from different
 continents. While this works well for the intended purpose of looking at
 functional group dominance at site level, the taxonomic resolution is
 inadequate for applications requiring true species level information. In
 such occasiton we encourage contacting the corresponding author. The DNA
 extracts are stored at the Department of Agricultural Sciences of the
 University of Helsinki, Finland.