Utilizing field collected insects for next generation sequencing: Effects of sampling, storage, and DNA extraction methods

Abstract DNA sequencing technologies continue to advance the biological sciences, expanding opportunities for genomic studies of non‐model organisms for basic and applied questions. Despite these opportunities, many next generation sequencing protocols have been developed assuming a substantial quantity of high molecular weight DNA (>100 ng), which can be difficult to obtain for many study systems. In particular, the ability to sequence field‐collected specimens that exhibit varying levels of DNA degradation remains largely unexplored. In this study we investigate the influence of five traditional insect capture and curation methods on Double‐Digest Restriction Enzyme Associated DNA (ddRAD) sequencing success for three wild bee species. We sequenced a total of 105 specimens (between 7–13 specimens per species and treatment). We additionally investigated how different DNA quality metrics (including pre‐sequence concentration and contamination) predicted downstream sequencing success, and also compared two DNA extraction methods. We report successful library preparation for all specimens, with all treatments and extraction methods producing enough highly reliable loci for population genetic analyses. Although results varied between species, we found that specimens collected by net sampling directly into 100% EtOH, or by passive trapping followed by 100% EtOH storage before pinning tended to produce higher quality ddRAD assemblies, likely as a result of rapid specimen desiccation. Surprisingly, we found that specimens preserved in propylene glycol during field sampling exhibited lower‐quality assemblies. We provide recommendations for each treatment, extraction method, and DNA quality assessment, and further encourage researchers to consider utilizing a wider variety of specimens for genomic analyses.


| INTRODUC TI ON
The rapid expansion of molecular techniques throughout the last 60 years has revolutionized the field of biology and the study of wild populations across taxonomic and spatial scales. Development of various molecular markers and sequencing technologies have allowed for essential empirical tests of population genetics theory (Charlesworth & Charlesworth, 2017), improved resolution of phylogenies (Whelan, Liò, & Goldman, 2001), greater insight into behavioral and evolutionary ecology (Hughes, 1998;Woodard et al., 2015), and a more informed identification of genetic conservation units (DeSalle & Amato, 2004). Most recently, next generation sequencing (NGS) techniques have been employed to advance understanding of critical ecological and evolutionary processes for non-model species (Davey et al., 2011;Woodard et al., 2015). Specifically, the increased genome coverage provided by NGS markers and the recent advancement of assembly pipelines for non-model species have made NGS techniques especially suitable for the investigation of species where sample availability may be limited or where sample quality may be degraded. For example, one recent study used NGS techniques (shotgun sequencing) to compare museum specimens from an extinct mainland population of the Lord Howe Island stick insect, an extremely rare and evolutionarily distinct species, with a newly discovered extant island morph. These data confirmed that the two populations belong to the same species, providing evidence that the island individuals may be suitable for mainland reintroduction (Mikheyev et al., 2017). Given the large number of species that are ecologically critical yet understudied (e.g. Fisher, Knowlton, Brainard, & Caley, 2011;McKinney, 1999), and the increasing interest in the evolutionary ecology of non-model organisms (Ekblom & Galindo, 2011), NGS tools provide key opportunities for the exploration of basic biological questions and the development of speciesspecific conservation guidelines.
However, despite the great potential of NGS tools, many protocols have been developed assuming that a substantial quantity of high molecular weight DNA should be used for library preparation.
For example, in a recent review of NGS methods, approximately 80% of the studies cited used tissue that was either freshly sampled or preserved in EtOH (Andrews, Good, Miller, Luikart, & Hohenlohe, 2016). This pattern belies the difficulty of obtaining high quality DNA for many species, the greater availability of specimens that may have lower quality DNA, and the untapped potential of these samples to address key questions in ecology and evolution. Tremendous biological insight can be gained from specimens which may have degraded DNA due to environmental or storage conditions including road-killed specimens (Rusterholz, Ursenbacher, Coray, Weibel, & Baur, 2015;Say, Devillard, Léger, Pontier, & Ruette, 2012), shed substances including feces, feathers, or fur (Alda et al., 2013;Hans et al., 2015;Waits & Paetkau, 2005), as well as museum and herbarium specimens (Beck & Semple, 2015;Gilbert, Moore, Melchior, & Worobey, 2007;Sproul & Maddison, 2017). Recent work on ancient specimens has revealed great potential for NGS with very limited amounts of highly degraded DNA (Heintzman et al., 2015;Knapp & Hofreiter, 2010;Kosintsev et al., 2018), although most ancient studies focus on large vertebrate taxa (but see Heintzman, Elias, Moore, Paszkiewicz, & Barnes, 2014). Thus, there is a need for better understanding more taxonomically diverse groups, like insects, which represent an estimated 40% of the world's non-microbial biodiversity (Scheffers, Joppa, Pimm, & Laurance, 2012), but have limited markerbased resources and typically need to be dried and pinned before expert identification is possible (Wheeler & Miller, 2017), possibly leading to greater DNA degradation than in other taxa.
Specifically, molecular studies of insects can be challenging because many of the standard field sampling and lab preservation methods that are traditionally used in entomology collections are especially prone to DNA degradation problems. The most common ways of collecting insects often involve capturing insects in traps without preservative, or in soapy water, where they can remain for several days (e.g. pan, pitfall, and blue vane traps; LeBuhn, Griswold, Minckley, & Droege, 2003;Potts et al., 2005;Rubink, Murray, & Baum, 2003), likely leading to DNA degradation. Hand netting into a kill jar may expose specimens to chemicals that could also degrade DNA (e.g. ethyl acetate, Dillon, Austin, & Bartowsky, 1996). Trapping using a preservative such as propylene glycol has been shown to be an effective method for DNA preservation of several invertebrate species (Dillon et al., 1996;Ferro & Park, 2013), but the effects of propylene glycol on NGS results are not known. Although trapping into an EtOH preservative could potentially be done, EtOH evaporates rapidly and so it must be refilled, which can be a hinderance to sampling in high temperatures, over large geographic scales, or longer time periods; in contrast, trapping without preservative in the trap or with propylene glycol is common in low resource areas (e.g., desert and dry grasslands) as these traps may be set for several days or even months at a time (Rubink et al., 2003;Stephen & Rao, 2005;Sudan, 2016). While some studies suggest netting samples directly into EtOH for NGS (e.g. Moreau, Wray, Czekanski-Moir, & Rubin, 2013), this technique also presents major challenges as it is timeconsuming, with a capture rate that can be 3-17x lower than trapping ( Figure 1; Roulston, Smith, & Brewster, 2007;Stephen & Rao, 2007), and can only be used on field-identifiable species. In practice, species identification of many insect groups requires morphological trait assessment that can only be determined under magnification and after pinning and drying in order to visualize minute morphological features (Huber, 1998), some of which are compromised after longterm storage in EtOH (e.g. hair color and growth patterns; J. Neff, pers. comm.). However, specimen curation that forgoes EtOH storage and instead optimizes morphological identification and specimen cataloguing may lead to additional DNA degradation (Andersen & Mills, 2012;Gilbert, Moore, Melchior, & Worobey, 2007;Strange, Knoblett, & Griswold, 2009), that may compromise use in genetic or genomic studies.
Small DNA fragments do not automatically hinder many NGS applications, such as shotgun sequencing, as most applications are designed to sequence short fragments. Next generation sequencing has allowed protocols to be developed for sequencing highly degraded samples, such as environmental or ancient DNA (Enk et F I G U R E 1 Summary of methods and results including (a) Mean number of specimens caught per active researcher hour, (b) DNA quality, (c) Locus Recovery & Depth of Coverage, and (d) Recommendation of each method for ddRAD analyses. (a) Arrows are weighted by mean number of specimens per species (Bombus pensylvanicus (blue); Melissodes tepaneca (gold); Lasioglossum bardum (green)) captured per sampling method (1 font size = 1 bee caught per active hour; this was calculated from all specimens of these species in our collection (n = 737)). The field sampling methods include (top to bottom): hand netting into either EtOH (sample code "Net-EtOH") or an ethyl acetate kill jar ("Net-Dry"), blue vane trapping into either propylene glycol ("Vane-Gly") or no preservative ("Vane-Dry"), and pan trapping into soapy water ("Pan"). (b) Mean Concentration per treatment per species indicated by dots per tube, with one dot per one ng/µl post-cleanup DNA concentration above 10 ng/µl (i.e. 1 dot = 11 ng/µl, 2 dots = 12 ng/µl, etc), and Mean Contamination is indicated by black X's per tube, with one X per 0.30 value below 2.0, where a value of 2.0-2.2 has no X's and indicates "pure" DNA in the NanoDrop 260/230 index (the results for the 260/280 index are not displayed). (c) Because locus recovery and depth of coverage varied dramatically between species, differences are represented as standard deviations of the mean per species. Thus, each column represents Locus Recovery where the mean number of loci within a species is represented by three columns (measured as the logit transformed mean probability of loci occurring in another sample), plus or minus one bar for each 1/2 standard deviation away from the mean. Likewise, number of rows represents Mean Depth of Coverage where three rows is equal to the mean log transformed locus depth per species, plus or minus one bar for each 1/2 standard deviation away from the mean. For example, B. pensylvanicus specimens (blue bars) sampled with the Net-EtOH treatment showed average levels of locus recovery among all treatments for B. pensylvanicus (three columns) but higher-than-average depth of coverage among all treatments for B. pensylvanicus (four rows). In contrast, M. tepaneca specimens (yellow bars) sampled with the Vane-Dry treatment showed higher-than average levels of locus recovery (four columns), but lower-than-average depth (two rows). (d) Recommendations for use of method are based on active researcher time collecting specimens, DNA quality, and locus recovery & depth of coverage results for all species, where highly recommended methods have a black checkmark and lower quality methods (to be used with caution) are denoted with a caution symbol. All methods produced ddRAD data, but Net-Dry and Vane-Gly specimens are not highly recommended due to various negative factors summarized in columns a-c. *Asterisks indicate significant (p < .05) or marginally significant (p < .10) differences between treatments within a species for DNA quality. See text for significant differences between treatments for locus recovery and depth   Knapp & Hofreiter, 2010;Thomsen & Willerslev, 2015).
However, an increasingly popular NGS application for both molecular ecologists and non-specialist laboratories is the use of reduced representation DNA libraries (RRLs) which use restriction enzyme digestion of the genome allowing for the creation of relatively lowcost libraries for SNP discovery and genotyping across multiplexed samples (Davey et al., 2011). Because the RRL approach relies upon analyzing restriction enzyme cut fragments, it is possibly well-suited to analyze already-fragmented DNA, although different applications have variable tolerances to DNA degradation. One of the most popular techniques for the creation of RRLs is Double Digest Restriction Enzyme Associated DNA sequencing (ddRAD, Peterson, Weber, Kay, Fisher, & Hoekstra, 2012). The ddRAD method is often used in marker discovery (Jansson et al., 2016), whole genome association mapping (Barría et al., 2018;Wu et al., 2016), phylogenetics (DaCosta & Sorenson, 2016), and population genomics (Andrews et al., 2016;Agudelo et al., 2015). It is considered especially useful for non-model organisms as it does not require a reference genome, though its utility for degraded specimens is still debated. For example, ddRAD requires completely intact 5′ and 3′ overhangs, and so its use may be compromised if genomic DNA quality is very low (Puritz et al., 2014).
One past ddRAD study using whitefish specimens found that proportions of low-quality reads increased exponentially for specimens with high levels of degradation (extracted 96 hr post-euthanasia), while specimens with moderate degradation (12-48 hr post-euthanasia) produced similar results to the non-degraded DNA, including high levels of depth and numbers of polymorphic loci (Graham et al., 2015). Other studies have shown that degraded DNA from museum specimens (collected 50-100 years ago) can be successfully used for RAD procedures (Haponski, Lee, & Foighil,2017;Sproul & Maddison, 2017;Tin, Economo, & Mikheyev, 2014), but these studies have not examined the impacts of differing sampling techniques (e.g. netting vs. trapping), different DNA extraction methods, or use of different focal species on ddRAD sequencing success.
In this study we investigate the effects of capture method, preservation method, and DNA extraction method on ddRAD sequencing success for three wild bee species: Bombus pensylvanicus, Melissodes tepaneca, and Lasioglossum bardum. These species vary in size (large, medium, and small respectively), and are all common throughout much of North America. We assessed three main measures as proxies for evaluating ddRAD sequencing success: number of polymorphic loci, sequencing read depth, and levels of missing loci between treatments within a species. While number of loci is often utilized as a standard measurement of sequencing success (Graham et al., 2015), depth of coverage (number of sequence reads for a given locus) is also an important metric of sequence quality, as higher depth allows for greater detection of sequencing errors, heterozygous loci, and differences between individuals and populations (Maroso et al., 2018;Sims, Sudbery, Ilott, Heger, & Ponting, 2014).
Quantifying the amount of missing data is also an important aspect of assessing RAD success, as there is a finite number of sequencing reads spread across multiple individuals during sequencing, and the assembly of genomes, either de novo or mapped to a reference, depends on sequence similarity (low levels of missing data) between specimens (Catchen, Amores, & Hohenlohe, 2011). Overall, we predicted that different sampling and extraction treatments would affect both DNA quality and sequencing success for the three species.
Specifically, we expected that specimens that were netted directly into and stored in EtOH would have the highest levels of DNA quality and sequencing success including higher numbers of loci, higher locus depth, and less missing data among specimens. We also predicted that netted specimens which were frozen before pinning would perform better than trapped specimens. Lastly, we expected that trapped specimens which included propylene glycol as a preservative would have higher DNA quality and better sequencing results than those specimens trapped without a preservative.

| Insect sampling and storage
Specimens were captured using three standardized methods including hand netting, blue vane trapping (Stephen & Rao, 2007), and pan trapping (Roulston et al., 2007) at 39 sites across Texas during the summers of 2012-2014 (Table S1). Samples were collected for various projects that were primarily focused on sampling the entire bee community to answer questions related to community assembly and meta-population analyses without considering genetic preservation consequences (Ballare, Neff, Ruppel, & Jha, 2019;Cusser, Neff, & Jha, 2016;Ritchie, Ruppel, & Jha, 2016), as is likely the case for many entomology collections (J. Neff, A. Wild, personal communication). Sample numbers ranged from 7 to 13 specimens per treatment based on sample availability in our collection; this sample size is higher than most methods studies that test NGS protocols, which often only include one or two specimens per treatment (Heintzman et al., 2014;Graham et al., 2015;Sproul & Maddison, 2017; but see Vaudo, Fritz, & Lopez-Uribe, 2018). Hand-netted specimens were collected via two methods: either directly into 100% EtOH (hereafter "Net-EtOH") or into a jar using ethyl acetate vapor as a killing agent (hereafter "Net-Dry"). Blue vane-trapped specimens were also trapped via two methods: with no preservation agent or bait in the trap (hereafter "Vane-Dry") or using propylene glycol as a preservation agent (hereafter "Vane-Gly"). Propylene glycol is commonly used for short-and long-term trapping (in the latter, specimens may remain in traps for several months) to preserve insects for later morphological identification (Rubink et al., 2003;Sudan, 2016 Table 1 for specimen trapping and storage summary).

| DNA extraction and pre-sequence DNA quality quantification
All specimens were extracted in Spring 2016 using Qiagen ® DNeasy Blood and Tissue Kit using the standard protocol with a few minor modifications to maximize DNA yield. We extracted approximately 1 cm 3 tissue from each specimen, using thoracic tissue from B. pensylvanicus and M. tepaneca, and using the entire specimen for the smaller species L. bardum. We ground tissue using a MiniBeadBeater Total amount of DNA per extraction was quantified using a Qubit Fluorometer (Life Technologies), with a Quant-iT dsDNA HS assay Kit using 2 µl of sample. Qubit has been shown to be much more accurate in detecting dsDNA concentration and is less influenced by RNA contamination than spectroscopy (Simbolo et al., 2013). We additionally ran extracted DNA on a 2% agarose gel to assess levels of DNA fragmentation and weight. Individual samples were given two qualitative scores of "high" or "low" fragmentation and "high" or "low" DNA weight after visualizing the gels (Table S1, Figure S1).
DNA had "high" fragmentation if there was no visible band anywhere on the gel or if visible bands were of low molecular weight (~100-200 bp). DNA had a "high" weight score if there was a visible band above 1,500 bp that was substantially darker than any band of low Note: For B. pensylvanicus, the same specimen was divided in half and extracted using two different extraction methods.

| Library preparation and ddRAD sequencing
One hundred ng of DNA per sample after normalization using

| Statistical analysis
Raw sequence data were assembled into putative loci and called to SNP's using the STACKS pipeline (v. 2.0 beta 9). To determine the optimal parameter settings for the pipeline, we used the strategy detailed by Paris, Stevens, Catchen (2017). Briefly, we repeated the assembly across a grid of values of the "m", "M" and "n" parameters All further analyses were performed using R version 3.4.3.
Because DNA quality often varied between specimens even within a treatment, we first assessed the overall success of various treatments separate from DNA quality by considering the degree of sequence similarity between samples in each treatment. We measured this by finding all possible subsets of samples of a given size, and counting the number of shared polymorphic loci within each subset.
We calculated sequence similarity at three subset sizes increasing in  , 2015). As qualitative levels of fragmentation per sample were unobtainable for many specimens and preliminary analyses indicated that these were not significant in any of the exploratory models, this metric was removed from further analysis. In practice, RADseq assemblies are filtered to remove loci that occur in less than a given fraction of specimens (often 60% or 80%, e.g. Maroso et al., 2018), as these loci could be spurious or originate from exogenous material. To assess the sensitivity of our results to various filters, we refit the models to subsets of the data resulting from removing loci that occurred in less than 40%, 60% and 80% of specimens per species.
We assessed the explanatory power of overall treatment/quality effects using ANOVA, and conducted post-hoc comparisons between individual treatments using the simultaneous testing procedure implemented in the R package multcomp (Hothorn, Bretz, & Westfall, 2008). Any significant differences between treatments for DNA quality metrics were tested by ANOVA and post-hoc Tukey tests correcting for multiple comparisons.

| RE SULTS
After quality filtering and assembly steps were completed in STACKS, all species, treatments, and extraction methods showed a mean number of highly reliable loci that would be adequate for in-   Figure S4).

| Sequence similarity
We assessed the amount of sequence similarity ( with specific differences between treatments highlighted below.

| Net-EtOH
Net-EtOH specimens showed intermediate levels of sequence similarity as compared to the other treatments in B. pensylvanicus and M. tepaneca (Figure 2a,b), but sequence similarity was not significantly different in Net-EtOH from the other treatments in either species.

| Vane-Dry
In B. pensylvanicus, these specimens showed the highest level of sequence similarity (Figure 2a), although sequence similarity in F I G U R E 2 Box plots showing numbers of polymorphic loci retained between specimens (sequence similarity) when grouping by three random samples per species. Different letters indicate statistically significant differences (Dunn's, p < .05) between treatments in Lasioglossum bardum. There were no statistically significant differences in sequence similarity between treatments for either Bombus pensylvanicus or Melissodes tepaneca after correcting for multiple comparisons. Sample treatments are coded as in Table 1, and graphs are color-coded according to species as in Figure 1  In L. bardum, Vane-Dry specimens had significantly greater levels of similarity than Vane-Gly (p < .001), but not in the other two treatments.

| Pan
Pan-trapped specimens showed the second-highest level of sequence similarity for M. tepaneca (Figure 2b), although post-hoc tests did not reveal a significant difference in treatments for the threesample filter. Pan showed the highest level of sequence similarity in L. bardum (Figure 2c), and was significantly different from Vane-Gly (p < .001) and Net-Dry (p = .008).

| Treatment and DNA quality effects on locus recovery and depth
When considering how treatment affected ddRAD assembly qual-

| Net-EtOH
Net-EtOH specimens did not have significantly different locus recovery from the other two treatments in B. pensylvanicus (Figure 3a).
Net-EtOH specimens had higher locus recovery than Vane-Gly M.

| Net-Dry
Net-Dry specimens did not differ significantly from the other two treatments in locus recovery for B. pensylvanicus (Figure 3a).
However, despite somewhat higher levels of locus recovery, Net-Dry specimens tended to have lower levels of depth across all three species. Net-Dry specimens had significantly lower levels of depth than Net-EtOH for both B. pensylvanicus (as noted above) and M. tepaneca (z = −5.865, p < .001), but otherwise did not differ significantly from the other treatments in these species (Figure 3a

| Vane-Gly
Vane-Gly specimens had variable results between levels of locus recovery and depth of coverage, as well as between species. Vane-Gly specimens had the poorest locus recovery, significantly lower than all other treatments for both species in which the method was used (M. tepaneca and L. bardum [ Figure 3b,c; Tables S4 and   S5]). For M. tepaneca, Vane-Gly specimens also had significantly lower depth than Net-EtOH specimens (z = −4.660, p < .001), but otherwise did not differ significantly from the other three treatments ( Figure 3e). For L. bardum, however, Vane-Gly had the highest level of depth compared to the other three treatments but was only significantly higher in depth than Net-Dry specimens (z = 4.163, p < .001, Figure 3f).

| Vane-Dry
Vane-Dry specimens did not differ significantly in locus recovery or depth from the other two treatments used in B. pensylvanicus (Figure 3a,d). However, Vane-Dry specimens had the second-highest and highest levels of locus recovery for both M. tepaneca and L. bardum respectively. Vane-Dry specimens had significantly higher locus recovery than Vane-Gly (z = 6.351, p < .001) for M. tepaneca, and significantly higher locus recovery than Net-Dry (z = 4.622, p < .001), as well as Vane-Gly for L. bardum (z = 17.897, p < .001, Figure 3c).
Vane-Dry had significantly lower levels of depth than Net-EtOH for M. tepaneca (z = 6.649, p < .001), but was not significantly different in depth from the other three treatments (Figure 3e). Vane-Dry had significantly higher levels of depth than Net-Dry (z = 4.163, p < .001) in L. bardum, and did not differ from the other two treatments in depth ( Figure 3f).

| Pan
Pan-trapped specimens had significantly higher locus recovery than Vane-Gly for M. tepaneca (z = 4.861, p < .001, Figure 3b), and significantly higher locus recovery than Vane-Gly (13.283, p < .001) and Net-Dry (7.073, p < .001) for L. bardum (Figure 3c). Pan specimens did not differ significantly in locus recovery from the best treatment for either species. Pan specimens had the lowest level of average depth for M. tepaneca but did not differ significantly from any treatment other than Net-EtOH for M. tepaneca (z = −6.787, p < .001, Figure 3e). For L. bardum, Pan specimens showed significantly higher average depth than Net-Dry specimens (z = 2.654, p = .037) and did not differ significantly from the other treatments ( Figure 3f).

| DNA concentration
Higher sample DNA concentration was significantly associated with higher locus recovery and locus depth in B. pensylvanicus (loci: Vane-Gly (p = .097).

| D ISCUSS I ON
Our results reveal very few differences between the ddRAD sequencing success of bee samples collected across a variety of traditional entomological collection methods; this bolsters previous work showing that field-collected and traditionally curated samples of multiple non-model species can be utilized for population genetic analyses using the ddRAD protocol (Tin et al., 2014;Vaudo et al., 2018). While we documented lower DNA concentrations and higher contamination levels in many treatments as compared to typical fresh DNA extractions, this did not prohibit retrieval of thousands of polymorphic loci and high levels of locus depth. However, we show that storage and sampling methods can have significant effects on these metrics of ddRAD assembly success, where treatment effects were distinct between the DNA quality and locus recovery/depth metrics and also between species. Additionally, our analyses indicate that extraction method has only a small effect on ddRAD assembly quality, with only minor differences in overall loci and depth. Lastly, we show that DNA concentration alone is not always predictive of higher quality ddRAD assemblies, and that other measures of DNA quality such as Nanodrop indices may be particularly useful for predicting the likelihood of downstream success in ddRAD projects.
To begin with, in all treatments and across species, enough loci (≿4,000) with sufficient coverage (≿20×) were obtained to conduct highly informative population genetic studies (Andrews & Luikart, 2014;Willing et al., 2012). Specifically, we found that, between many collection and storage treatments, there was often no significant effect on numbers of loci and depth, which was also consistent between species of differing body size. This was especially encouraging for our smallest species tested (L. bardum) as the amount of genetic material extracted was expected to be quite low and thus potentially more sensitive to DNA degradation.
Interestingly, a study investigating genotyping success on museum curated spiders found that genotyping quality actually declined with increasing body size. Krehenwinkel and Pekar (2015) suggest that smaller specimens are likely more quickly preserved than larger specimens, which may lead to good DNA recovery. Our results may also reflect this finding, and are congruent with other studies that have shown successful NGS success of museum specimens of small insects (Sproul & Maddison, 2017), as well as those utilizing ddRAD to successfully sequence traditionally collected and curated bees (Vaudo et al., 2018). Vaudo et al. (2018) specifically found that specimen age did not affect number of polymorphic loci or coverage depth, and similarly showed that collection method had the most significant effects on overall ddRAD assembly quality.
While we acknowledge that the treatments in our study utilized different time and handling procedures, they represented standard techniques in insect collecting and curation (LeBuhn et al., 2003;Stephen & Rao, 2007), and we expected their utility for ddRAD to vary considerably. Specifically, we predicted that specimens that were netted directly into and stored in EtOH (Net-EtOH) would show the best results in terms of higher numbers of loci, greater av-  (Dillon et al., 1996), others have suggested that such degradation is likely due to samples remaining in a prolonged damp state after ethyl acetate exposure before being pinned (Quicke, Belshaw, & Lopez-Vaamonde, 1999). Quick desiccation of entomological specimens has been shown to preserve DNA similarly well to preservation in EtOH (Quicke et al., 1999), and it is likely that we are documenting related patterns in our ddRAD assemblies. The exception to this general pattern in our results was that L. bardum Net-Dry specimens showed low average locus depth (overall low numbers of reads), but high standardized depth (average depth per locus per million reads) than other treatments. Again, this result is likely due to the faster desiccation rates of smaller specimens better preserving DNA (as discussed above). Thus, Net-Dry L. bardum specimens had more reliable locus recovery per standard number of reads than the other larger species, even if overall locus depth was low in this treatment.
Surprisingly, in contrast to our predictions, we found that specimens collected into propylene glycol traps (Vane-Gly) produced lower quality ddRAD assemblies, and that both blue vane trapped specimens with no preservative (Vane-Dry) and pan-trapped specimens into soapy water (Pan) produced relatively high quality ddRAD assemblies. Our results were unexpected because past studies report glycol as an effective DNA preservative for marker-based studies (Dillon et al., 1996;Ferro & Park, 2013;Rubink et al., 2003).
It is possible that differences in glycol concentration (some studies utilized lower concentrations of glycol) or glycol type (Dillon et al. (1996) utilized ethylene rather than propylene glycol) contributed to the disparity between our results and past work. However, we believe this is unlikely given that both Ferro and Park (2013) and Rubink et al. (2003) (Quicke et al., 1999), and that drying specimens in the sun is similar to chemical methods (Post, Flook, & Millest, 1993). Our pan-trapped specimens, despite remaining in water for up to 24 hr, showed better-than-expected DNA quality and ddRAD assembly results. This is likely due to the fact that these specimens were stored in 100% EtOH immediately after the 24 hr of field time, typical of pan-trapping protocols (e.g. Grootaert, Pollet, Dekoninck, & Achterberg, 2010); acting to rapidly dehydrate the samples. Another study has shown that pan trapped bee specimens had similar high quality ddRAD results in comparison to other capture methods, also likely due to rapid desiccation via EtOH storage (Vaudo et al., 2018).
Interestingly, we also found that DNA concentration was not always a key predictor in ddRAD sequencing success. Specifically, we found that higher DNA concentration was significantly related to higher locus depth in B. pensylvanicus, but not in the other two species. This was somewhat surprising as a high concentration of high-molecular weight DNA has frequently been assumed to be necessary for high-quality ddRAD assemblies (Andrews et al., 2016;Puritz et al., 2014). It is possible that we did not see correlations between DNA concentration and sequencing success due to exogenous DNA in our samples, however, as we analyzed the data with various sample number filters that show similar patterns, we believe that the presence of exogenous sequences is not a major driver of our results. It is also possible that the Qubit measurement was not sensitive enough to detect a correlation between DNA concentration and sequencing success, and thus we suggest that future studies include analysis with a fragment analyzer and/or other DNA quantification methods (i.e. Picogreen ® ) to assess levels of fragmentation and DNA concentration after extraction. Regardless, our results support the idea that moderate levels of DNA degradation can still produce acceptable ddRAD results (also seen in Graham et al., 2015;Tin et al., 2014). We also found that 2.0 in DNAzol extracted specimens, but this did not cause major differences in ddRAD assembly quality in our study. While Qiagen kits are convenient and more time-efficient than other methods, they are also more expensive, costing roughly twice the price of DNAzol extractions per sample (Chen et al., 2010). DNAzol extractions have also been shown to be more effective than other low-cost extraction methods (e.g. Chelex, phenol-chloroform) for extracting high quality DNA from museum specimens (Junqueira, Lessinger, & Espin, 2002).
Therefore, we suggest that labs wishing to cut costs for DNA extraction and ddRAD sequencing can utilize the lower-cost DNAzol protocol for DNA extractions without sacrificing ddRAD sequence quality.
Finally, we found that in some cases, there appeared to be a This implies that greater fragmentation in template DNA could lead to higher depth for the smaller fragments, due to more overall molecules contained in the smaller fragment length distribution, but fewer overall loci amplified across different fragment sizes. Thus, our study supports this tradeoff, and we suggest that researchers be sure to utilize sampling treatments that balance high levels of both number of loci and sequencing depth. Some researchers suggest that depth is one of the most important factors to consider in NGS analyses, and that labs should not sacrifice depth for more loci or number of samples (Andrews & Luikart, 2014). This is because high levels of coverage allow for the reduction or elimination of missing data, which is more common in highly degraded samples (Mikheyev et al., 2017).
In conclusion, by testing traditional collection and storage protocols commonly used by entomologists, we show that many of these techniques can yield specimens suitable for ddRAD sequencing and analysis. Based on our results, we conclude that although netting directly into 100% EtOH produces high quality ddRAD assemblies, trapping methods that allow for quick desiccation and subsequent preservation of specimens can also be utilized for successful ddRAD sequencing. This allows researchers greater flexibility in utilizing trapping methods which may be more convenient if, for example, encounter rates are low for the species of interest, or if specimens are not feasibly captured via netting. Passive trapping may also be an advantage in some studies, because hand netting is often biased towards larger bodied insects, and is also dependent on the researcher's experience and skill (Roulston et al., 2007). We also show that pinned specimens can be used to build successful ddRAD assemblies, and thus we posit that museum collections hold great opportunity for ddRADbased research, especially considering the advent of new lowinvasive sampling methods where the physical specimen can be retained (Andersen & Mills, 2012;Thomsen et al., 2009;Vaudo et al., 2018). For example, ddRAD could be used to examine population genomic patterns in insect specimens collected over long periods of time, a process which may be key to better understanding global pollinator declines (Cameron et al., 2011). We suggest that specimens netted directly into EtOH, as well as pan trapped and blue vane trapped specimens without preservatives, should be first-choice specimens for conducting NGS projects. Once DNA is extracted, concentration as well as DNA purity measurements could be used to screen samples for likelihood of downstream assembly success. Overall, our results encourage the expansion of genetic monitoring via NGS to a wider variety of non-model species in order to address new and exciting research questions in species and specimens previously believed to be too rare and degraded to investigate.

ACK N OWLED G EM ENTS
We thank M. Lopez-Uribe and A. Vaudo for helpful initial discussions on preliminary results and analysis. We thank M. O'Connell and E.
Lichtenberg for comments on the manuscript, and R. Ruppel for help with specimen collection. We also thank four anonymous reviewers for helpful comments and suggestions for revisions to the final manuscript.
S.J. was funded by the National Science Foundation, DEB-0908661.

CO N FLI C T O F I NTE R E S T
None declared.

DATA AVA I L A B I L I T Y S TAT E M E N T
Sequence assembly files (.vcf and output from STACKS), code to fit models, and a .csv file of treatments, sample ID's, sequence ID's and DNA quality metrics is deposited on DRYAD, https ://doi. org/10.7291/D1CD4P. Raw sequence data is deposited on GenBank, accession numbers SAMN12836825-SAMN12836960.