properties in addition to the number of nodes, links, and connectance [64]. The importance of developing barcode libraries for food webs crosses into other ecological disciplines and issues facing society. Increasing public awareness of the biodiversity crisis has placed a new set of demands upon resource managers. One obvious aspect of this looming issue is to delineate diversity. Barcoding techniques play a clear and obvious role here as they are allowing us to quantify diversity efficiently and accurately. As such, they promise to be an important tool for biodiversity policy. A second more fundamental aspect of biodiversity remains to be addressed in resource management. That is, how this diversity is actually connected on the landscape. It is these connections (e.g., herbivory, parasitism rates), not diversity itself, which ultimately dictates how these ecosystems function [65]). The DNA database we have created here will be important for revealing the mechanisms of how ecological systems work – a fact made more poignant by the cascade of economic consequences contingent upon budworm outbreaks. For instance, we anticipate that this library of DNA barcodes will permit the development of microarrays that permit the rapid field identification of species involved in the budworm food web – a tool desired by the monitoring agencies responsible for the management of the forests of Quebec and New Brunswick. Barcoding, when combined with long-term, ecologically relevant collections, promises the first rapid and efficient tool capable of delineating species interactions with unparalleled precision and speed. This type of research therefore promises to lead resource managers from a strictly population- based approach to a unified conceptual attack (i.e., from population to food web) on the major environmental issues facing society. The application of barcoding techniques within scientif- ically vigorous long-term monitoring campaigns will change the face of community ecology. Materials and Methods Field For full description of the field methodologies and the collection localities see the Supporting Information file in [9]. In brief, six herbivore species (Choristoneura fumiferana (Clem), Choristoneura rosaceana (Harris), Acleris variana (Fernald), Epinota radicana (Hein- rich), Coleotechnites piceaella (Kearfott), and Coleotechnites atrupictella (Deitz)) were sampled and reared from three forest plots that varied in degree of heterogeneity (i.e., % of plot basal area comprised of balsam fir (Abies balsamea)). The six herbivores selected were the species most frequently sampled during twenty plot-years of field collections. Although other herbivores were present (particularly in the most heterogeneous plot) these were rare. The supporting information files in [9] list all other cases where we could not associate parasitoids with their hosts. From these herbivore species, approximately 100 different parasitoid species were reared, identified morphologically with the best keys and collaboration with the appropriate taxonomic expertise at the Canadian National Collection of Insects in Ottawa, Ontario, Canada. Specimens were selected for DNA barcoding from as broad a series of representative hosts as was evident from the rearing program. While New Brunswick has historically treated areas of budworm outbreak with pesticides, the three plots sampled in this work were not subject to such pesticide treatments. Molecular DNA extracts were prepared from single legs using a glass fibre protocol [66]. Extracts were re-suspended in 30 ml of dH2O, and a 658-bp region near the 5’ terminus of the COI gene was amplified Cryptic Species in a Food Web using primers (LepF1–LepR1) following standard protocols [15,16,17]. Composite sequences were generated using internal primers when initial amplification was not successful[15,16,17]. Primer information for individual sequences can be retrieved from BOLD using the Process IDs detailed in Table S1, but primers are as detailed in [15]. Sequence divergences were calculated using the K2P distance model and a NJ tree of K2P distance was created to provide a graphic representation of the among-species divergenc- es. Full details of methodology are as in [15,16,17]. All sequence data are available on BOLD (www.barcodinglife.org) in the public project: Spruce Budworm food web parasitoids and hosts [ASSPP]. All collection information, BOLD, and GenBank accessions for all sequences are listed in Table S1. MOTU Each individual was assigned to a barcode cluster using barcode divergences of approximately 2% in BOLD. Annotations to these MOTU assignments were considered based on data from alternative markers, and/or ecological information (see Figure S1). MOTU identification results were compared with the species delineated by a comprehensive morphological study. When barcode analyses suggested morphologically cryptic mitochondrial diversity we amplified portions of the rRNA gene region 1) for a portion of the large subunit (LSU or 28S – the variable D2 region) – 593 individuals, or 2), the internal transcribed spacer region 1 (80 individuals) or 2 (93 individuals) in addition to the CO1 barcode region. rDNA sequences facilitated our interpretation of morphologically cryptic and geographically sympatric deep mtDNA splits (correlated splitting within the independent rDNA sequence supports the hypothesis of morphologically cryptic species, while the lack of a split suggests mtDNA variation within a species). Within the variable D2 region of 28S, the forward primer corresponds to positions 3549–3568 in Drosophila melanogaster reference sequence (Genbank M21017) while the ITS1 sequence was generated using primers where the forward primer corre- sponds to positions 1822–1843 in the same reference sequence. GenBank accessions for all complementary marker sequences are in Table S1. The ITS2 region has been proposed as, and discussed as, a potential, ‘‘holy grail’’ for species determination [32,34]. We compared the resolution of this gene region for species determination to morphologically defined or barcode defined species. To do so, we used the pipeline for ITS2 and complementary base pair determination described in Shuctlz et al [50]. Briefly, sequences were amplified using previously published primers [67] with the forward primer corresponds to positions 2805–2830 in D. melanogaster reference sequence (Genbank M21017). These were cleaned and trimmed using Sequencher (v4.5) and then imported into the ITS2 Database [68]. Here, if no significant matches were found in a structure search we input the sequences into 4SALE [69] where the program RNAFold was used to predict secondary structures. From these secondary structures, predictions were made regarding the presence of compensatory base changes (CBCs). These matrices were compared on a pairwise basis to other genetic discontinuities observed. PD Neighbor-joining trees were constructed with MEGA (v4 [70]) using pairwise deletion and p-distance on all CO1 sequences greater than 200 bp (as measured from the 5’ end of the barcoding region to ensure overlap) and being produced by species parasitizing budworm (and not restricted to other related PLoS ONE | www.plosone.org 10 July 2011 | Volume 6 | Issue 7 | e14424