{"paper_id":"27b55094-0b75-45fe-9935-af7697b8272d","body_text":"PREPRINT\nAuthor-formatted, not peer-reviewed document posted on 28/05/2024\nDOI: https://doi.org/10.3897/arphapreprints.e128447\nComparing the efficiency of DNA extraction protocols\nacross a multinational environmental DNA initiative\nLauren Rodriguez,  Lorenzo De Bonis, Jack McKee, James A McKenna, Teddy Urvois, Eleonora Barbaccia, \nEileen Dillane, Caterina Lanfredi, Helene Hjellnes, Armelle Jung,  Enrico Villa, Arianna Azzellino, Jon-Ivar \nWestgaard, Erwan Quéméré,  Bettina Thalinger\n\n \nTitle 1 \n 2 \nComparing the efficiency of DNA extraction protocols across a multinational 3 \nenvironmental DNA initiative 4 \n 5 \nRunning head 6 \n 7 \nEfficiency of DNA extraction in a multinational eDNA study 8 \n 9 \nAbstract 10 \n 11 \nThe comparability of methods applied to environmental DNA (eDNA) samples across 12 \nlaboratories remains a significant challenge for international biodiversity monitoring 13 \nprojects. Inconsistently performing practices can jeopardize the reliability of data that is 14 \nessential for effective conservation strategies across geographic regions and focal 15 \nspecies. To address potential discrepancies among four international partner 16 \nlaboratories who are part of a collaborative eDNA initiative, a ring test was conducted to 17 \ncompare extraction efficiencies based on 39 eDNA samples. Each laboratory 18 \ncontributed eight to eleven eDNA samples collected from five locations throughout the 19 \nNorth-East Atlantic and Mediterranean Sea near marine megafauna (whales, dolphins, 20 \nand sharks). After lysis, aliquots were exchanged between laboratories then 21 \nindependently extracted using each facility’s preferred method. Extracts were then 22 \nreturned to their respective laboratories of origin for measurements of total DNA 23 \nconcentration as well as quantitative PCRs using species-specific assays designed for 24 \neach associated target species. Our findings revealed similar concentrations of total 25 \nDNA, yet a significant deviation in extraction performance for targeted qPCR reactions 26 \nby one laboratory. Overall, detection success differed based on the target taxa with 27 \nsharks being less often detected (and at lower concentrations) than whales and 28 \ndolphins. Significant interaction effects were found between combinations of 29 \nlaboratories and species, suggesting that particular extraction protocols may be most 30 \nefficient for specific environmental conditions. Our study serves as a foundational step 31 \ntowards establishing rigorous, reproducible eDNA practices that are crucial for the 32 \nsuccess of multinational environmental monitoring projects to enable the direct 33 \ncomparison of results. 34 \n 35 \nKey Words  36 \n 37 \nassay, cetaceans, DNA extraction, eDNA, Limit of Detection, optimization, ring test38 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \nIntroduction  1 \n 2 \nAdvances in molecular technologies have revolutionized the collective perception and 3 \ncapabilities of assessing biodiversity. Over the past fifteen years, environmental DNA 4 \n(eDNA) has burgeoned as a noteworthy tool for monitoring the diversity of a system 5 \n(Beng and Corlett, 2020; Rourke et al., 2022). Through the collection, extraction, and 6 \nanalysis of trace amounts of genetic material shed by organisms into their environment, 7 \nresearchers can now detect the presence of species in environmental samples such as 8 \nsediment, water, snow, or air (Ficetola et al., 2008; Lynggaard et al., 2022; Miya, 2022). 9 \nNotably, the inherent attributes of eDNA-based approaches make them particularly 10 \nsuitable for the investigation of rare and/or protected species, due to their noninvasive 11 \nnature which removes the necessity for direct animal contact (Foote et al. 2012; Baker, 12 \nScott 2015; (Juhel et al., 2021; Rojahn et al., 2021). Although eDNA methods are 13 \nincreasingly used by ecologists, especially for such studies on elusive species, their 14 \nintegration into large-scale routine management and decision-making processes 15 \nremains limited. A prominent obstacle is the need for rigorous international standards 16 \nand optimized protocols, which could make applications of eDNA monitoring more 17 \nreliable and comparable across initiatives. 18 \n 19 \nConsidering the highly sensitive nature of methodological choices, efforts to 20 \noptimize and standardize sampling and analysis methods, especially within the 21 \nframework of large international projects, are crucial. The multifarious nature of 22 \nbiological systems, coupled with the rapid evolution of technology, present significant 23 \nchallenges to standardization efforts (Thomsen and Willerslev, 2015; Goldberg et al., 24 \n2016; Bruce et al., 2021; Buxton et al., 2021; Thalinger et al., 2021). Variability in the 25 \ntechniques for sample collection and processing can lead to discrepancies in data 26 \ninterpretation and conclusions, undermining the reproducibility of research findings 27 \n(Katano et al., 2017; Bruce et al., 2021; Buxton et al., 2021). This issue is further 28 \nmagnified in international projects in which variations in technical expertise, resources 29 \n(e.g., field or laboratory equipment), and regulatory environments across participating 30 \nlaboratories can exacerbate inter-institutional inconsistencies. To address these 31 \nchallenges, several guidelines have been published, making a first attempt to 32 \nsummarize best practices in eDNA research from preliminary sampling to post hoc 33 \nbioinformatic processing (Loeza-Quintana et al., 2020; Minamoto et al., 2021; Morisette 34 \net al., 2021; Blancher et al., 2022, Bruce et al. 2021). Furthermore, working groups 35 \nconsisting of eDNA specialists are being established internationally to monitor and 36 \nassess current methods and applications of eDNA research. Examples include a 37 \nsubgroup of the European Committee for Standardization (CEN/TC 230/WG 28; 38 \nhttps://www.cencenelec.eu/), the USA Government eDNA Working Group (GeDWG; 39 \nusgs.gov), and the international eDNA Society (ednasociety.org).  40 \n 41 \nAmong processing steps such as field sampling and target DNA amplification, 42 \neDNA-based data is subject to the protocol with which the genetic material was 43 \nextracted from the environmental sample. DNA extraction encompasses a series of 44 \nintricate steps, including cellular lysis, DNA isolation, protein and contaminant washing, 45 \nand final elution of high-quality DNA (Knebelsberger and Stöger, 2012; Barbosa et al., 46 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n3 \n2016). The widespread adoption of commercial kits, replete with the necessary reagents 47 \nfor extracting DNA from a variety of mediums (e.g., tissue, water, soil), have become 48 \ncommon practice throughout recent years as they provide easily accessible, 49 \nstreamlined, and reproducible protocol for retrieving genetic material from a sample 50 \n(Lear et al., 2018; Pearman et al., 2020). Some of the most widely used and 51 \nrecommended commercially available extraction kits are Qiagen’s (Venlo, The 52 \nNetherlands) DNeasy kit, which is recommended by the official manual for eDNA 53 \nresearch published by the eDNA Society (Ficetola et al., 2008; Lear et al., 2018; Tsuji et 54 \nal., 2019; Minamoto et al., 2021), and the PowerWater DNA Isolation Kit (Mobio, Hilden, 55 \nGermany). According to a review of eDNA extraction approaches by Kumar et al., 2019, 56 \na distinguishing feature of some kits (such as the PowerWater kit) lies in its built-in PCR 57 \ninhibitor removal step, which can also be conducted after extraction is carried out (e.g., 58 \nwith Zymo OneStep PCR Inhibitor Removal Kit). This is highly relevant for environments 59 \nwith high levels of suspended particulate matter or poor water quality, which are likely 60 \nthe source of PCR inhibitors such as humic acids, fulvic acids, and polysaccharides 61 \n(Kuhn et al., 2017; Lear et al., 2018). However, inhibitor removal also introduces the 62 \npotential of losing target DNA due to increased agitation of the lysate (McKee, Spear 63 \nand Pierson, 2015; Goldberg et al., 2016) and the inclusion of this step does not 64 \nguarantee superior extracts. Consequently, the efficacy of the chosen extraction and 65 \npotential inhibitor-removal approach is contingent upon its compatibility with the specific 66 \ntaxonomic, geographical, and environmental attributes of the study, warranting 67 \nmeticulous consideration.  68 \n 69 \nFollowing extraction, total DNA concentration in an extract can be measured via 70 \nspectrophotometry or fluorometry (Brunker, 2020; García-Alegría et al., 2020). 71 \nMeanwhile, targeted approaches, such as quantitative PCR (qPCR) and droplet digital 72 \nPCR (ddPCR) can be used to ascertain the presence and abundance of a specific 73 \nspecies within the sampled environment by discerning particular genetic traces of 74 \ninterest amidst a heterogeneous sample, in which the quantity of target DNA is likely 75 \npresent at very low concentrations (Goldberg et al., 2016; Hunter et al., 2017). qPCR 76 \nmethods (with assays either using an intercalating dye or a fluorescently labeled probe 77 \nfor quantification) are the most widely used technique for attaining species-specific 78 \ndetections (Thalinger et al. 2021). However, its success depends upon precise assay 79 \ndesign entailing selectivity that precludes the amplification of nontarget taxa co-existing 80 \nwith the focal species. Moreover, a rigorous validation regimen spanning in silico, in 81 \nvitro, and in situ evaluations is needed to forestall spurious reactions (primer dimers, 82 \nhairpins, etc.) and enhance the applicability to eDNA samples collected from the field. 83 \nThese testing protocols have recently been presented by Thalinger et al. (2021) as a 5-84 \nlevel validation scale, beginning at Level 1 with simple in silico and nontarget tissue 85 \ntesting to Level 5 with statistical testing of an assay’s detection probability as well as 86 \nmodeling with ecological and physical factors which may influence the rate of perception 87 \nof a sample’s DNA (Garafutdinov, Galimova and Sakhabutdinova, 2020; Klymus et al., 88 \n2020).  89 \n 90 \nThe Limit of Detection (LOD) and Limit of Quantification (LOQ) are two metrics 91 \nwhich describe the sensitivity and quantitative precision of DNA assays. The LOD 92 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n4 \ndelineates the lowest amount of DNA that can be consistently detected, while the LOQ 93 \nspecifies the minimum level at which DNA can not only be detected, but also quantified 94 \nwith acceptable accuracy and precision (Hunter et al., 2017; Klymus et al. 2020, 95 \nThalinger et al., 2021). However, caution must be exercised when accounting for these 96 \nmetrics during data analysis, particularly when interpreting positive amplifications which 97 \nfall below an assay’s Limit of Detection, as highlighted by Klymus et al. (2020). Taking a 98 \nconservative approach by excluding data below LOD may result in the loss of actual 99 \ndetections of the target species. Conversely, the LOD can serve as a comparative 100 \nbenchmark for inter-laboratory processing of the same sample.  101 \n 102 \nProfoundly influencing an assay’s LOD and LOQ is the design of qPCR primers 103 \n(and often a fluorescently labeled probe), a process which is now often supplemented 104 \nby advanced machine learning or automation (Kronenberger et al., 2022; Allison et al., 105 \n2023). The careful design process ensures the sensitivity and specificity of qPCR, which 106 \nis vital for distinguishing low abundance targets (Wilcox et al., 2013; Rees et al., 2014). 107 \nFurthermore, the choice of PCR chemistry and cycling conditions is equally critical for 108 \noptimizing assay performance (Klymus et al., 2020; Langlois et al., 2021). In summary, 109 \nthe analytical workflow of eDNA extraction and analysis involves intricate considerations 110 \nranging from the means of obtaining DNA from a sample (e.g., the selected extraction 111 \nkit) to the development and refinement of a species-specific assay. Although this is only 112 \npart of the eDNA workflow, the manifold options available for DNA extraction and target 113 \nDNA amplification already make comparative tests a requirement before direct data 114 \ncomparisons and applications.  115 \n 116 \nIn the context of international efforts to enhance the detection rates of marine 117 \nmegafauna DNA from environmental samples, a key focus has consistently relied on 118 \nrefining of both field sampling and laboratory protocols. This endeavor led to the 119 \ninitiation of a comparative study (i.e., a ring test) involving four laboratories from 120 \ndifferent countries who are all working together in an international research project titled 121 \neWHALE, which aims to study marine megafauna across the North-East Atlantic and 122 \nMediterranean Sea using eDNA-based methods. The four laboratories are: University of 123 \nInnsbruck (UIBK; Austria), the National Research Institute for Agriculture, Food, and the 124 \nEnvironment (INRAE; France), University College Cork (UCC; Ireland) and the Institute 125 \nfor Marine Research (IMR; Norway), each relying upon specialized molecular 126 \ntechniques, particularly eDNA extraction methods. Our aim was to compare the 127 \nefficiency of extraction protocols for a variety of eDNA samples collected around various 128 \nmarine megafauna species. Additionally, three single-species qPCR assays, which can 129 \nbe utilized by other eDNA specialists in future studies, were developed to specifically 130 \namplify sperm whale (Physeter macrocephalus), porbeagle shark (Lamna nasus) and 131 \nbasking shark (Cetorhinus maximus) DNA from environmental samples. We aimed to 132 \nevaluate the efficacy of laboratory-specific extraction techniques by comparing both 133 \ntotal DNA yield and target species DNA yield. This evaluation is crucial for filling the 134 \nexisting gap in the standardization of eDNA monitoring methodologies across various 135 \ninstitutions, namely for the purpose of assessing mobile species with spatial ranges 136 \nbeyond country borders.  137 \n 138 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n5 \nMethods 139 \n 140 \nField Sampling 141 \n 142 \nIn summer 2023, water samples were collected from different regions throughout the 143 \nNorth-East Atlantic and Mediterranean Sea by researchers, students, and partners who 144 \nwere trained in eDNA sampling (Fig. 1; Supplementary File 1). Samples were filtered 145 \nfrom the surface of the water column through different commercially available 146 \nenvironmental DNA filters: Smith-Root (Vancouver, USA), Sylphium (Sylphium 147 \nmolecular ecology, Groningen, The Netherlands), and Sterivex™ (Millipore®; Merck 148 \nChemicals and Life Science GesmbH, Darmstadt, Germany; Table 1). At the end of 149 \nfiltration, all filters were dried by running the pump for an additional 30 s to 1 min outside 150 \nof the water or pushing air through the filter with a syringe. Storage buffer consisting of 151 \nTES buffer (0.1 M TRIS, 10 mM EDTA, 2% sodium dodecyl sulfate; pH 8) and 152 \nproteinase K (20 mg/mL) in a ratio of 190:1 was added to each filter (1.5-3 mL 153 \ndepending on the filter type, see below) except from Smith-Root filters. Between 154 \nsamples, the tubing was rinsed three times with household bleach and three times with 155 \ntap water (marine species-DNA-free) to prevent cross-contamination.  156 \n 157 \nIn the Mediterranean Sea, samples (n=68) were collected in volumes of either 2, 158 \n5, or 10 L with a bucket, with 17 samples collected in close proximity to sperm whales 159 \n(Physeter macrocephalus; Fig. 1). Nine water samples were immediately filtered 160 \nthrough self-preserving Smith-Root filter capsules (1.2 µm filter pore size) using a 161 \nperistaltic pump (Solinst; Model 410; Thomas et al., 2019). Filter capsules were stored 162 \nat 4 °C on board then in a facility in the harbor of San Remo (Italy) following the cruise 163 \nuntil a subset (n=6) was shipped to UIBK in October 2023 for subsequent analysis.  164 \n 165 \nIn the North-Eastern Atlantic Ocean waters around the Azores islands of Faial 166 \nand Pico (Fig. 1), researchers aboard CW Azores whale watching cruises 167 \n(cwazores.com) used a bucket to collect 10 L of water (n=42 samples) from sperm 168 \nwhale flukeprints, which were immediately filtered through Sylphium filter capsules (0.8 169 \nµm filter pore size; ID: SYL010-08-20) using a peristaltic pump (ID: 12.34.SB; 170 \nEijkelkamp, Giesbeek, The Netherlands). All filters were filled with 1.5 mL of storage 171 \nbuffer which included an Internal Positive Control (IPC), an artificial fragment of DNA 172 \nused for quality control, from Sinsoma GmbH (https://www.sinsoma.com/en/). eDNA 173 \nfilters were stored at the University of the Azores in a -20 °C freezer until being 174 \ntransported to UIBK in July 2023 for subsequent analysis (n=5 used in this study).  175 \n 176 \nIn the Shannon Estuary, 9 water samples were collected with a 12 L bucket from 177 \nthe fluke prints of bottlenose dolphins (Tursiops truncatus). One short-beaked common 178 \ndolphin (Delphinus delphis) sample was collected in the same manner off the South-179 \nWest Coast of Ireland (near Baltimore, Cape Clear Island; Fig. 1). From these water 180 \nsamples, between 1.5 and 2 L were filtered through Sterivex-HV filter capsules (0.45 µm 181 \npore size; Merck Millipore ID: SVHV010RS) using 50 mL disposable syringes. 182 \nAfterwards, 1.5 mL of storage buffer were added. The filter capsules were stored in a 183 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n6 \ncooler on ice before being transferred to a -20 °C freezer upon return to the laboratories 184 \nat UCC’s North Mall Campus.  185 \n 186 \nIn the French National Nature Reserve of the Seven Islands in Brittany (Fig. 1) a 187 \ntotal of 10 water samples targeting porbeagle sharks (Lamna nasus) eDNA were 188 \ncollected between June and September 2023. Five water samples were collected in 189 \n5.75 L containers and fully filtered through Sylphium capsules (0.8 µm filter pore size; 190 \nID: SYL010-08-20) using a suction pump. The other five water samples were directly 191 \nfiltered from the water using the same type of capsules and the same pump for 5 192 \nminutes. Once the filters were pumped dry, 3 mL of storage buffer were added. The 193 \nfilters were then stored at -20 °C until analysis at INRAE. 194 \n  195 \nIn the Norwegian Sea by the Lofoten Islands (Fig. 1), 8 surface water samples 196 \nwere collected targeting basking sharks (Cetorhinus maximus). Each 5 L sample was 197 \nfiltered through Sterivex-HV filter capsules (0.45 µm pore size) using a peristaltic pump. 198 \nA 50 mL syringe was used to push air through the filters before 1.5 mL storage buffer 199 \nwas added. Filters were stored at -20 °C until further analysis at IMR. 200 \n 201 \n 202 \nFigure 1. Locations in which eDNA samples analyzed for this ring test were collected. 203 \nPoints are colored according to the target species. Cartography was created using 204 \nQGIS v 3.34.3 using ESRI basemap services (Esri, DeLorme, HERE, MapmyIndia). 205 \n 206 \nSample lysis and extraction  207 \n 208 \nAll filters were incubated for 3 h at 56 °C. Prior to incubation, each Smith-Root filter was 209 \nremoved from its housing (using DNA-free forceps) then soaked with 400 µL of storage 210 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n7 \nbuffer. After incubation, each Smith-Root filter was transferred into a plastic inlet placed 211 \ninside the original reaction tube and centrifuged at 18,626 g for 10 min to separate the 212 \nlysate from the filter. For Sylphium filter capsules, lysis buffer was removed using a 3 213 \nmL or 6 mL syringe, resulting in 1 to 1.4 mL lysate per sample for UIBK and 2.25 to 6 214 \nmL for INRAE. Lysis buffer was removed from Sterivex filters using a 2 mL syringe 215 \nresulting in 0.5 to 2.0 mL of lysate per sample.  216 \n 217 \nEach laboratory received a 250 µL aliquot of lysate per eDNA sample. Samples 218 \nwith less than 1 mL lysate were diluted with TES buffer to 1 mL total volume before 219 \naliquoting. At UIBK, an extraction IPC (IPC-L: approximately 5,000 copies per sample; 220 \nSinsoma GmbH) was added to each aliquot. Generally, lysates were stored at -80 °C 221 \nprior to shipping (packaged with ice in styrofoam containers) between project partners in 222 \nfall 2023. We opted for overnight shipping whenever possible, but in some cases, 223 \nlysates took 1-2 days to arrive at their final destination. Once eDNA lysates arrived at 224 \ntheir destination, they were stored at -80°C or -20°C prior to further analysis.  225 \n 226 \nTable 1. Overview of eDNA filters, assays, and analysis techniques per participating 227 \nlaboratory.  228 \nParameter UIBK INRAE UCC IMR \nFiltration \ntechnique \nPeristaltic pump Suction pump Syringes Peristaltic \npump \neDNA filter  \nSmith-Root \n(n=6), Sylphium \n(n=5) \nSylphium (n=10) Sterivex (n=10) Sterivex \n(n=8) \nFilter pore \nsize  \n1.2 μm \n0.8 µm \n0.8 µm 0.45 µm 0.45 µm \nFilter \nmaterial \nPolyethersulfon\ne (PES) \nPolyethersulfone \n(PES) \nPVDF PVDF \nTarget \nspecies \nSperm whale \n(Physeter \nmacrocephalus) \nPorbeagle shark \n(Lamna nasus) \nBottlenose \ndolphin (Tursiops \ntruncatus) and \nCommon dolphin \n(Delphinus \ndelphis) \nBasking \nshark \n(Cetorhinus \nmaximus) \nExtraction \nmethod \nQiagen \nBioSprint® 96 \nWorkstation \nusing the \nBiosprint 96 \nMacherey-Nagel \nNucleoSpin \nTissue Kit**  \nQiagen DNeasy \nBlood and Tissue \nKit** \nQiagen \nDNeasy \nBlood and \nTissue Kit** \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n8 \ntissue protocol \n** \nSanger \nsequencing \nEurofins \nGenomics \nGermany \nGmbH \n(Ebersberg, \nGermany) \nGenoScreen \n(Lille, France) \nEurofins \nGenomics \nGermany GmbH \n(Ebersberg, \nGermany) \nUniversity \nHospital of \nNorth \nNorway \n(Tromsø, \nNorway) \n** Modifications made to protocol (see main text for details) 229 \n 230 \nExtraction protocol per laboratory  231 \n 232 \nEach project partner employed a DNA extraction protocol that is commonly used in the 233 \nfacility for high numbers of eDNA samples, each including slight modifications from the 234 \noriginal manufacturer’s protocols (Table 1). All extractions were performed in dedicated 235 \nlaboratory spaces with proper ventilation and cleaning procedures adhering to the 236 \nprocessing of eDNA samples (e.g., surface cleaning with bleach, sterilized DNA-free 237 \ngloves and protective wear; Thalinger et al., 2021; Hymus, 2016). Additionally, PCR 238 \npreparation was conducted in separate rooms with appropriate PCR-dedicated 239 \nworkbenches that are disinfected by UV light at least once per working day. 240 \n 241 \nAt UIBK, DNA extraction was performed with the BioSprint 96 instrument 242 \n(QIAGEN; Venlo, The Netherlands) using the BioSprint 96 DNA blood Kit (ID: 940057; 243 \nQIAGEN) in accordance with the manufacturer's instructions except for using 100 µL of 244 \nTE buffer instead of AE buffer for elution (Supplementary Material 1, DOI: 245 \ndx.doi.org/10.17504/protocols.io.q26g71p83gwz/v1). In total, 39 lysates were extracted in 246 \none Biosprint run with one extraction control containing only elution buffer. 247 \n 248 \nAt INRAE, DNA extraction of lysates (n=39 eDNA, 1 control) was performed 249 \nusing the Macherey-Nagel NucleoSpin Tissue kit (ID: 740952.50, Düren, Germany) 250 \naccording to the manufacturer’s recommended protocol with the addition of 25 µL 251 \nproteinase K at the lysis step. To maximize DNA yield, the Buffer BE was heated at 70 252 \n°C and elution was repeated twice with the same 100 μL of Buffer BE with 3-minute 253 \nincubation time (Detailed extraction protocol can be found here: DOI: 254 \ndx.doi.org/10.17504/protocols.io.4r3l2q2yql1y/v1, Private link for reviewers: 255 \nhttps://www.protocols.io/private/7FDA25FB19BE11EFAE230A58A9FEAC02 to be 256 \nremoved before publication.  257 \n 258 \nAt UCC, lysates were incubated at 56 °C for 1 hr prior to extraction. DNA 259 \nextraction of lysates (n=39 eDNA, 2 controls) was performed using the Qiagen DNeasy 260 \nBlood and Tissue Kit (ID: 69504; QIAGEN) with 100 µL elution volume in the final step.   261 \n 262 \nAt IMR, DNA extraction of lysates (n=39 eDNA, 4 controls) was performed using 263 \nthe Qiagen DNeasy Blood and Tissue Kit (ID: 69504, QIAGEN). A QiaVAC 24 Plus 264 \nvacuum system (ID: 19413, QIAGEN) was used instead of centrifugation for spin 265 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n9 \ncolumn steps, with a final elution of 100 µL in a centrifuge. Detailed extraction protocols 266 \nfor UCC and IMR using the Qiagen DNeasy Blood and Tissue Kit can be found here: 267 \nDOI: dx.doi.org/10.17504/protocols.io.n92ld8m2ov5b/v1, Private link for reviewers: 268 \nhttps://www.protocols.io/private/565B501A19BA11EFAE230A58A9FEAC02 to be 269 \nremoved before publication. 270 \n 271 \nPer extract, 4 aliquots (25µL each) were generated and sent back to their 272 \nlaboratory of origin for further analysis using the same shipping conditions as before. 273 \n 274 \nTotal DNA quantification 275 \n  276 \nEach project partner measured the total DNA and the target DNA of extracts from their 277 \noriginal lysates (e.g., UIBK measured the extracts generated from the 11 sperm whale 278 \neDNA samples for extracts created at all participating laboratories: UIBK, INRAE, UCC 279 \nand IMR). Total DNA concentrations (ng/µL) per extract were measured via a Qubit™ 280 \nfluorometer using the Qubit dsDNA High Sensitivity (HS) Assay Kit (Life Technologies, 281 \nCarlsbad, California, US; ID: Q32851). Qubit standards and DNA sample tubes were 282 \nprepared using low-bind tubes (ID: Q32856; Thermo Fisher Scientific, Waltham, MA, 283 \nUSA) and 5 µL of extract (protocol: https://dx.doi.org/10.17504/protocols.io.bc6vize6). 284 \nAll tubes were measured in triplicate. 285 \n 286 \nSpecies-specific eDNA quantification 287 \n 288 \nAssay development and validation 289 \n 290 \nTargeted qPCR TaqMan MGB assays were developed for this study in order to amplify 291 \nDNA from the species of interest for field samples contributed by UIBK, INRAE, and 292 \nIMR. Primarily, full mitochondrial sequences from target and nontarget species (i.e., 293 \nclosely related and/or co-occurring species) were obtained from publicly available 294 \nrepositories (GenBank database at the National Center for Biotechnology Information 295 \n(NCBI), https://www.ncbi.nlm.nih.gov/genbank/). Sequences were aligned with Clustal 296 \nOmega (Sievers et al., 2011), and preliminary species-specific qPCR assays were 297 \nselected using assayID, a publicly available software tool 298 \n(https://github.com/jammc313/assayID). This program scans the inputted mitochondrial 299 \nsequence alignment file using Primer3 (Koressaar and Remm, 2007; Untergasser et al., 300 \n2012) to design primer/probe sets for previously defined windows across the full 301 \nmitogenome. Given a DNA sequence template, Primer3 generates primer/probe sets 302 \noptimized for various parameters that are critical to assay performance. This includes 303 \nprimer/probe length, melting temperatures (Tm), GC content, and avoidance of 304 \nsecondary structure formations, among others. The software is designed to maximize 305 \nspecificity and efficiency in amplification, minimizing potential issues such as 306 \ndimerization or hairpin formation that can impair the qPCR assay's accuracy and 307 \nsensitivity. Sequence diversity and distance metrics are calculated for the regions 308 \ncovered by the designed assays, including measures of target species genetic diversity 309 \nand distance measures between target and nontarget sequences (e.g., Shannon 310 \nEntropy, sequence similarity, nucleotide divergence). The assays are then ranked 311 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n10 \naccording to their specificity and sensitivity. An ideal assay will target a region that has a 312 \ncombination of a low genetic diversity for the target species sequences, and high 313 \ngenetic distance to nontarget species sequences. A multivariate statistical method: 314 \nTechnique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is finally 315 \nused to rank designed assays and identify those with optimal specificity and sensitivity. 316 \nAssays with the highest rankings from this program were selected for further manual 317 \ndevelopment and in silico testing (described below per lab).  318 \n 319 \nThree unique species-specific TaqMan assays were ultimately optimized for the 320 \nspecies of interest from UIBK. INRAE, and IMR (Table 2; Supplementary File 2). At 321 \nUIBK, a sperm whale qPCR assay was designed targeting the Cytochrome B (CYTB) 322 \nregion of the mitochondrial genome. Binding regions and primer lengths were manually 323 \nadapted to enhance specificity (i.e., sufficient mismatches with nontarget taxa), 324 \nadhering to standard recommendations for TaqMan assays (Applied Biosystems Primer 325 \nExpress v3.0.1; Life Technologies, Foster City, CA, USA) and minimizing the 326 \noccurrence of secondary structures using BioEdit v7 (Hall, 2004), Primer3 (Untergasser 327 \net al., 2012), Primer Premiere (PREMIER Biosoft), and Primer Express 3.0.1 (Applied 328 \nBiosystems). The probe was labeled with 6-FAM and MGB-Q530 quencher (5’ and 3,’ 329 \nrespectively, Table 2). 330 \n 331 \nAt INRAE, the assayID program identified a total of 295 primer/probe 332 \ncombinations for porbeagle sharks. A total of 25 combinations that met the criteria of a 333 \nwindow size of 150-180 bp, no hairpin, oligo not ending with G and no “GGGG” string in 334 \nthe oligos were retained. They were BLASTed (https://blast.ncbi.nlm.nih.gov/Blast.cgi) 335 \nto check for specificity with porbeagle sequences, and results matching with other 336 \nspecies or with mismatches with porbeagle sequences were excluded. The best 337 \ncandidate targeted the ND1, and to improve its specificity, the last bp was manually 338 \nremoved from the probe. The probe was labeled with 6-FAM and a BHQ-1 quencher (5’ 339 \nand 3’, respectively; Table 2). 340 \n 341 \nAt UCC, 8 primer pairs were selected, 3 from the above-mentioned assayID 342 \nprogram, 2 created using IDT PrimerQuest Tool, and 3 from existing literature 343 \n(Stoeckle, Mishu and Charlop-Powers, 2018; Greiner-Ferris, 2020). The specificity and 344 \nefficiency of these primers was initially tested in vitro (via conventional PCR and gel 345 \nelectrophoresis) using DNA extracts of bottlenose dolphin (5 ng/µL, 0.5 ng/µL and 0.05 346 \nng/µL), short-beaked common dolphin, harbour porpoise (Phocoena phocoena), killer 347 \nwhale (Orcinus orca), long-finned pilot whale (Globicephala melas), sperm whale, fin 348 \nwhale (Balaenoptera physalus) and grey seal (Halichoerus grypus). The primers 349 \ndesigned by Greiner-Ferris (2020), targeting the displacement loop (D-loop) region of 350 \nthe mitochondria, were selected because they were the most specific to the target 351 \nspecies (Table 2; Supplementary File 2). The last base pair at the 3' end of the reverse 352 \nprimer was removed so that the primers would better amplify the bottlenose dolphin 353 \nhaplotypes found in the study area (Nykänen et al., 2019). A putative probe for TaqMan 354 \nchemistry was initially designed for this modified version of the primer pair using the IDT 355 \nPrimerQuest™ Tool. The probe/primer assay was then extensively tested using 356 \nstandard dilutions of tissue-derived bottlenose dolphin DNA, but failed to detect target 357 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n11 \nDNA beyond 0.01 ng/µL. The primers were tested using SYBRgreen mastermix (without 358 \nthe use of a probe) in which it was possible to detect target DNA beyond 0.001 ng/µL. 359 \nThus, it was decided that all subsequent runs would be on SYBRgreen based chemistry 360 \nusing the aforementioned primer pair.  361 \n 362 \nAt IMR, the assayID program resulted in several potential assays for Cetorhinus 363 \nmaximus. Further in silico testing for target species specificity and tendency to form 364 \nsecondary structures using Primer-BLAST (Ye et al., 2012) and Integrated DNA 365 \nTechnologies OligoAnalyzer Tool (Integrated DNA Technologies, 2023), resulted in the 366 \nselection of the best performing assay targeting part of the ND5 region. The probe was 367 \nlabeled with 6-FAM and NFQ-MGB quencher (5’ and 3,’ respectively, Table 2). 368 \n 369 \nThe specificity for all assays presented herein was verified in silico via Primer-370 \nBLAST (Ye et al., 2012), with standard settings and the nr database. No amplification of 371 \nclosely related or co-occurring species was found for UIBK, IMR, and INRAE assays. 372 \nThe assay for UCC amplified all Delphinidae species, including the target species 373 \nbottlenose dolphin and common dolphin. DNA extracts from target and nontarget 374 \nspecies were used for in vitro testing of all selected assays’ specificity. Tissue samples 375 \nused for in vitro testing of species-specific assays were dried in a fume hood and then 376 \nextracted using either the Qiagen DNeasy Blood and Tissue Kit (ID: 69504, QIAGEN) or 377 \nthe Macherey-Nagel NucleoSpin Tissue Kit (ID: 740952.50, Düren, Germany) following 378 \nthe manufacturer’s instructions. 379 \n 380 \nUpon optimization of cycling conditions (Supplementary Material 2), the Limit of 381 \nDetection (LOD) and Limit of Quantification (LOQ) were calculated for each assay 382 \nfollowing the definitions of Klymus et al., 2020 using measurements from triplicate 383 \nstandard curves (per qPCR plate) of serial dilutions of target DNA from known 384 \nconcentrations (ng/µL; Table 2). Ultimately, there was insufficient statistical power to 385 \ncalculate each assay’s LOQ in accordance with the defined calculation method (Klymus 386 \net al., 2020).  387 \n 388 \nTable 2. Assays used in the current study for the amplification of target species DNA.  389 \nInstitute Target \nSpecies Gene  Name \nForward (5’-3’) \n \nReverse (5’-3’) \n \nMGB Probe (5’-3’) \nFragment \nlength \n(bp) \nOptimal \nAnnealing \nTemp. \nAssay \nLOD** \n \nUIBK \nSperm whale \n(Physeter \nmacrocephalu\ns) \nCYTB \nPhy-cat-S939 * \nPhy-cat-A939 * \nP030_Phy-cat * \nCCTACCACACAAT\nCAAAGACACC \n \nGGTTTGATGTGT\nGTTGGGGTAT \n \nTAGTGGATTTGCT\nGGGGTGTA \n144 61 0.0001 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n12 \nINRAE \nPorbeagle \nshark (Lamna \nnasus) \nND1 \nLnND1-F209 * \nLnND1-R380 * \nLnND1-P242 * \nTCAGCATCTTCCC\nCTTTCCT \n \nATCCGGAGCCCA\nAGATAGTG \n \nCCCACAATGGCT\nCTTACACTGGCC\nCTCCT \n172 60 0.0005\n26  \nUCC \nBottlenose \ndolphin \n(Tursiops \ntruncatus) \nand Common \ndolphin \n(Delphinus \ndelphis) \nD-loop \nTtDloopF \nTtDloopR \n \nCACACGTGCATG\nCTAATATTTAG \n \nGAGTGACCATAG\nGATATAATGGAG \n159 60 0.0000\n1  \nIMR \nBasking shark \n(Cetorhinus \nmaximus) \nND5 \nCetoMaxND5F\n_01 * \nCetoMaxND5R\n_01 * \nCetoMaxND5P\n_01 * \nAGTTTCCGCCCT\nACTCCATT \n \nGCTGCGGTAAAG\nAGGGTAGT \n \nAGTCGTTGCCGG\nCGTCTTCCTGCTA \n144 60 0.0001 \n* Created for this study 390 \n** As described by the discrete method presented in Klymus et al., 2021 391 \n 392 \nqPCR  393 \n 394 \nEach participating laboratory performed qPCR with triplicates of each eDNA extract and 395 \ntriplicate serial dilutions of known concentration (6-8 points of tenfold dilution starting at 396 \neither 1 or 0.526 ng/µL) on each plate. 397 \n 398 \nAt UIBK, qPCRs were carried out using a qTOWER3G (Jena, Germany). Primary 399 \nqPCRs sought to amplify IPC-F and IPC-L to assess the amount of DNA that is lost 1) 400 \nduring transportation and storage of eDNA filters and 2) during extraction. Triplicate 401 \ndilution series of IPC-F (1:10, 15 copies/µL, 6 points) and IPC-L (1:10, 1,000 copies/µL, 402 \n6 points) as well as two No Template Controls (NTC; i.e., nontarget extracts) and nine 403 \nNegative Controls (NC) were included on each plate. The 10 µL reactions for both IPCs 404 \ncontained 5 µL 2x TaqMan Environmental MM (ID: 4396838, EMM, Life Technologies), 405 \n1.0 µL of Primer mix and 0.4 µL of Probes mix (Sinsoma GmbH), 0.6 µL nuclease-free 406 \nwater, and 3 µL eDNA extract or positive control or NTC. Optimized thermocycling 407 \nconditions were 1) an enzyme activation step at 95 °C for 10 min, 2) a denaturation step 408 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n13 \n(40 cycles) at 95 °C for 15 s, and 3) a combined annealing and extension step (40 409 \ncycles) at 60 °C for 90 s.  410 \n 411 \nFor the sperm whale assay (UIBK), 10 µL reactions contained 5 µL 2x TaqMan 412 \nEnvironmental Master Mix, 1 µL primer/probe mix (0.4µM per primer and 0.2µM probe), 413 \n1 µL nuclease-free water, and 3 µL eDNA extract or NTC. Triplicate dilution series of 414 \nsperm whale DNA (1:10, 1 ng/µL, 6 points as well as two NTC and nine NC were 415 \nincluded on each plate. Optimized thermocycling conditions were 1) an enzyme 416 \nactivation step at 95 °C for 10 min, 2) a denaturation step (40 cycles) at 95 °C for 15s, 417 \nand 3) a combined annealing and extension step (40 cycles) at 61°C for 90s.  418 \nAt INRAE, qPCRs were carried out using a BioRad CFX96 (Bio-Rad Laboratories, 419 \nHercules, CA). Triplicate dilution series of porbeagle shark DNA (1:10, 0.526 ng/µL, 6 420 \npoints) as well as one NTC and one NC were included on each plate. The 20 µL 421 \nreactions contained 10 µL 2x TaqMan Environmental MM (ID: 4396838, EMM, Life 422 \nTechnologies), 1.0 µL of Primer mix and 1 µL of probe, 4 µL nuclease-free water, and 3 423 \nµL eDNA extract or positive control or NTC. Optimized thermocycling conditions were 1) 424 \nan enzyme activation step at 95 °C for 10 min, 2) a denaturation step (49 cycles) at 95 425 \n°C for 30 s, and 3) a combined annealing and extension step (49 cycles) at 60°C for 426 \n00:50.  427 \n 428 \nAt UCC, qPCRs were run on Applied Biosystems 7500 Real-Time PCR System 429 \n(Foster City, CA). Triplicate dilution series of bottlenose dolphin DNA (1:10 starting at 1 430 \nng/µL, 6 points) were used as standards on each plate as well as one negative control. 431 \nAn internal PCR positive control, Kavlick IPC was used to test for possible inhibitors in 432 \nthe extracted eDNA samples in a separate run (Kavlick, 2018). 10 µL reactions 433 \ncontained 5 µL 2x SYBR™ Green PCR Master Mix (ID: 4309155, Applied 434 \nBiosystems™), 0.8 µL of Primer mix (0.4 µL forward and reverse primer, 2.2 µL 435 \nnuclease-free water, and 2 µL eDNA extract or positive control or NTC. Optimized 436 \nthermocycling conditions were 1) an enzyme activation step at 95 °C for 10:00, 2) a 437 \ndenaturation step (40 cycles) at 95 °C for 00:15, and 3) a combined annealing and 438 \nextension step (40 cycles) at 60°C for 1 min.  439 \n 440 \nAt IMR, qPCRs were carried out using an Applied Biosystems QuantStudio Flex 441 \n6 Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). Triplicate dilution 442 \nseries of basking shark DNA (1:10, 1 ng/µL, 8 points) were used as standards as well 443 \nas one NC. The 10 µL reactions contained 5 µL 2x PerfeCTa qPCR Master Mix (ID: 444 \n101419-220, QuantaBio), 0.5 µL 150 nM TaqMan Custom gene expression assay, 1.3 445 \nµL nuclease-free water, 1 µL ThermoFisher IPC Exo Mix, 0.2 µL ThermoFisher IPC Exo 446 \nDNA, and 2 µL eDNA extract or positive control or NTC. Optimized thermocycling 447 \nconditions were 1) an enzyme activation step at 95 °C for 10 min, 2) a denaturation step 448 \n(55 cycles) at 95 °C for 15 s, and 3) a combined annealing and extension step (55 449 \ncycles) at 60°C for 1 min.  450 \n 451 \nSequencing 452 \n 453 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n14 \nFollowing positive amplification of target species DNA using qPCR, amplifications were 454 \nverified via Sanger sequencing of both the forward and reverse strands. At UIBK, 455 \npositive qPCR amplifications of target DNA were purified using an enzymatic treatment 456 \nkit (ExoSAP-IT® Express PCR Product Cleanup Reagent; Affymetrix-USB Corporation, 457 \nSanta Clara, California, USA) then sent to Eurofins Genomics Germany GmbH 458 \n(Ebersberg, Germany). At INRAE, the target fragments from eDNA products yielding 459 \npositive qPCR amplifications were re-amplified using end-point PCR (Supplementary 460 \nMaterial 3) and bands corresponding to the expected size on a 2% agarose gel were 461 \nextracted and purified using the NucleoSpin Gel and PCR Clean-up (Macherey-Nagel, 462 \nDünel, Germany) before being sequenced by GenoScreen (Lille, France). At UCC, 463 \nqPCR products yielding amplifications were run on a 2% agarose gel at 100 V for 1 h. 464 \nBands which corresponded to the expected length of the target species fragment (200 465 \nbp) were extracted and purified using the QIAquick Gel Extraction Kit (Qiagen, Venlo, 466 \nThe Netherlands). The products were sent to Eurofins Genomics Germany GmbH 467 \n(Ebersberg, Germany). At IMR, positive qPCR amplifications of target DNA were 468 \npurified using ExoSAP-IT®, Sanger sequencing reactions were performed using 469 \nBigDye™ Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems™ 4404310), and 470 \nsequencing products were sent to University Hospital of North Norway (Tromsø, 471 \nNorway). 472 \n 473 \nResulting sequences were trimmed to remove low quality ends using Bioedit 7.7 474 \n(Informer Technologies), AliView 1.28 (Uppsala University), Geneious 7.1.9 (Biomatters, 475 \nAuckland, New Zealand) and UGENE 50.0 (Unipro). The taxonomic classification of all 476 \nDNA sequences obtained from sequencing was determined using BLAST (NCBI). 477 \n 478 \nStatistical analysis 479 \n 480 \nTotal DNA measurements (via Qubit fluorometer) were tested for statistically significant 481 \ndifferences between laboratories using a Generalized Linear Mixed Model (GLMM) in 482 \nSPSS (v 28; IBM Corp, Armonk, NY, USA) with the following formula: 483 \n 484 \nYijk= µ + πi + αj + παij + εijk      485 \n1) 486 \n 487 \nIn which Υijk is the DNA concentration (in ng/µL) of the extract for the k-th observation in 488 \nthe j-th laboratory from the i-th source, µ is the general mean, πi is the random effect of 489 \nthe i-th source in which samples originated from (i.e., the different target species), αj  is 490 \nthe fixed effect of the j-th laboratory performing the eDNA extraction, παij is the random 491 \neffect of the i-th source in which sample extracts originated from the j-th laboratory, and 492 \nεijk is the error term.  493 \n 494 \nFor qPCR amplifications, statistical tests were carried out on two datasets: one 495 \nwith all positive amplifications of the target species (hereafter “all-inclusive dataset”) and 496 \none with a subset of positive amplifications that were at or below an assay’s Limit of 497 \nDetection following the definition provided in Klymus et al. (2021) in which at least 95% 498 \nof PCR replicates for a given standard DNA concentration is amplified (hereafter 499 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n15 \n“conservative dataset”). qPCR amplifications were tested for significant differences 500 \nbetween detection probability (based on a binary variable for target species 501 \namplification) using Pearson’s Chi-Square tests. Furthermore, Chi-Square was also 502 \napplied to test for significant deviations in the detection probability by species (i.e., 503 \nbasking shark, dolphin species, porbeagle shark, sperm whale). The detection 504 \nsensitivity of positive amplifications (defined henceforth by the Ct values, a proxy for the 505 \nDNA concentration within an extract; measured in triplicate per extract) were analyzed 506 \nusing a Kruskal-Wallis test with a post-hoc Dunn’s test to identify which extraction 507 \ntechnique had a positive or negative effect on amplification strength (i.e., high or low Ct 508 \nvalues). High Ct values correspond to lower concentrations of target DNA within an 509 \nextract since it takes a higher cycle number (Ct) for the DNA to be detected by the 510 \ncycler. Statistical significance was defined for all tests at a p-value < 0.05.  511 \n 512 \nFigures were created in R (v 4.3.1; R Core Team, 2023) using ggplot2 (v 3.4.4; 513 \nWickham et al., 2024), dplyr (v 1.1.3, Wickham et al., 2023) reshape2 (v 1.4.4; 514 \nWickham, 2020) and viridis (v 0.6.5; Garnier et al., 2024). All code that was used to 515 \ngenerate figures and statistical results can be found at https://github.com/eWHALE-516 \nDNA. All data used for this publication can be found in the Supplementary Files: 517 \nQubit_Data.csv and qPCR_Data.csv.  518 \n 519 \nResults 520 \n 521 \nTotal DNA quantification  522 \n 523 \nTotal DNA (ng/µL) measurements for all samples did not differ significantly between 524 \nlaboratories (GLMM p > 0.05; Fig. 2). The sample source (i.e., the random factor) and 525 \nthe sample source*laboratory (i.e., target species and laboratory performing the 526 \nextraction) interaction were both found significant (p < 0.05), with sample source having 527 \nthe largest effect size (Table 3). Initially, the model also considered the effect of sample 528 \ntransportation as a binary variable to assess potential DNA degradation during shipping. 529 \nHowever, this factor did not significantly influence the results and led to a higher AICc, 530 \nindicating a less efficient model fit (Bolker et al., 2009). 531 \n 532 \nTable 3. Results from GLMM regarding the effects of extraction protocol on samples 533 \nfrom each partner. Laboratory x Sample source indicates the interaction term between 534 \nlaboratory-specific extraction protocol and the source from which samples were sent 535 \nfrom (i.e., different target species).  536 \nModel Variable Sum of Squares DF Mean Square F Sig. \nIntercept \nHypothesis 3742.60 1 3742.60 2.38 0.220 \nError 4712.87 3 1570.94     \nLaboratory \nHypothesis 128.43 3 42.81 2.03 0.180 \nError 190.18 9.01 21.10     \nHypothesis 4733.47 3 1577.82 74.50 < 0.05** \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n16 \nSample \nSource Error 190.65 9 21.18     \nLaboratory x \nSample \nSource \nHypothesis 190.70 9 21.19 7.24 < 0.05** \nError 1453.83 497 2.93     \n* Sum of Squares indicates the total variation in the data accounting for the presence of 537 \nother factors included in the model. Degrees of Freedom (DF) indicates the number of 538 \nobservations of this parameter minus the number of estimated parameters. The Mean 539 \nSquare is the average variation explained by random effects. F is a statistic taking into 540 \naccount the mean square of the effect and the mean square of residuals. Significance 541 \nvalue estimates the statistical significance of model parameters to the overall fit of the 542 \nmodel.  543 \n** Indicates statistical significance 544 \n 545 \n 546 \nFigure 2. Total DNA concentration per eDNA sample (x-axis) and extract (ng/µL) 547 \nmeasured in triplicate with a Qubit fluorometer. Individual points represent replicate 548 \nmeasurements (n=3) per extract. Note the variation of y-axis range per plot. For an 549 \nexpanded version of this graph, showing the exact measurements in triplicate per 550 \nlaboratory per sample as their own box plots, see Supplementary Fig. 1.   551 \n 552 \nSpecies-specific DNA quantification 553 \n 554 \nOut of 468 qPCR reactions across all target species (not including standards, NCs or 555 \nNTCs), 115 successfully amplified their respective target species DNA during qPCR (all-556 \ninclusive dataset; Fig. 3, Supplementary Material 5). Out of those detections, 79 557 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n17 \nexhibited DNA concentrations at or below the LOD (conservative dataset; Fig. 3; 558 \nSupplementary Material 5).  559 \n 560 \nAcross all 39 water samples, 28 yielded at least one positive qPCR reaction (out 561 \nof triplicate measurements) for the associated target species in the all-inclusive dataset. 562 \nFor UIBK samples, the target species (sperm whale) was detected in 7 out of 11 563 \nsamples: 4 samples with one PCR replicate from UIBK and UCC extracts and 3 564 \nsamples with at least 2 PCR replicates from UIBK, INRAE, UCC, and/or IMR extracts. In 565 \ntotal, 43 out of 132 reactions (11 samples, 4 extracts each, measured in triplicate 566 \nqPCRs) amplified sperm whale DNA. For INRAE samples, the target species 567 \n(porbeagle shark) was detected in 8 out of 10 samples: 4 samples with one PCR 568 \nreplicate from UIBK, INRAE, and UCC extracts and 4 samples with at least 2 replicates 569 \nfrom UIBK and UCC extracts. In total, 13 out of 120 reactions (10 samples, 4 extracts 570 \neach, measured in triplicate qPCRs) amplified porbeagle shark DNA. For UCC samples, 571 \nthe target species (dolphin species) was detected in all 10 samples: 3 with one PCR 572 \nreplicate from INRAE, UCC, and IMR extracts and 7 with at least 2 PCR replicates from 573 \nUIBK, INRAE, and/or UCC extracts. In total, 56 out of 120 reactions (10 samples, 4 574 \nextracts each, measured in triplicate qPCRs) amplified dolphin DNA. For IMR, the target 575 \nspecies (basking shark) was detected in 3 out of 8 samples: all detections were only 576 \nfrom one PCR replicate from either UIBK or UCC extracts. In total, 3 out of 96 reactions 577 \n(8 samples, 4 extracts each, measured in triplicate qPCRs) amplified basking shark 578 \nDNA. There were no amplifications of NC or NTC.  579 \n 580 \n 581 \nFigure 3. Species-specific detections with standard (i.e., target extract) dilution series of 582 \nknown DNA concentrations (ng/µL) for each lab. The LOD for each assay is denoted as 583 \na dashed blue line. Only positive detections below or at the assay’s LOD (i.e., below the 584 \nblue line) were included in the conservative dataset.  585 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n18 \n 586 \nChi-Square Tests of detection probability between extraction methods for all 587 \nqPCR reactions indicated that IMR detected target species significantly less than other 588 \nlabs for both datasets (all-inclusive dataset: Chi-Square=21.155, df=3, p < 0.05, Fig. 4A; 589 \nconservative dataset: Chi-Square=14.302, df=3, p < 0.05, Fig. 4B). Detection probability 590 \ndid not differ significantly between UIBK, INRAE, and UCC (Fig. 3; see Supplementary 591 \nMaterial 5 for further details).  592 \n 593 \nA)  594 \nB)  595 \nFigure 4. Results of qPCRs organized by the laboratory performing the extraction 596 \n(triplicate reactions; x-axis) and the eDNA sample number and target species (y-axis). 597 \nTile color represents the Ct value; reactions without amplification were left blank. Panel 598 \nA shows the all-inclusive dataset: all 115 detections, whereas panel B shows the 599 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n19 \nconservative dataset: 79 positive detections that were at or below the respective 600 \nassay’s LOD. For alternative visualization, see Supplementary Fig. 2. 601 \n 602 \nAcross all positive amplifications, extracts generated by INRAE had significantly 603 \nlower DNA concentrations (i.e., higher Ct values) than the other partner laboratories for 604 \nboth datasets (all-inclusive dataset: Chi-Square=12.215, df=3, p < 0.05; conservative 605 \ndataset: Chi-Square=24.322, df=3, p < 0.05; Fig. 3 & 4). Pairwise comparisons of the 606 \nextracts (Table 4) generated in different laboratories via Dunn’s Multiple Comparison 607 \nTest revealed a significant difference in DNA concentrations (Ct values) between UCC 608 \nand UIBK for the all-inclusive dataset (Z-score=2.730, p < 0.05), likely due to the 609 \nvariation in detections of porbeagle shark above the assay’s LOD, resulting in UCC 610 \nhaving higher Ct values on average than UIBK (Fig. 4A). Additionally, Dunn’s Multiple 611 \nComparison Test identified a significant difference between the pairwise Ct values of 612 \nUIBK and INRAE for both datasets (Table 4), with UIBK having an average Ct value 613 \n3.189 lower than INRAE (i.e., higher target DNA concentration) for the 10 samples in 614 \nwhich both laboratories had positive amplifications (based on the all-inclusive dataset).  615 \n 616 \nTable 4. Pairwise comparisons of triplicate Ct values by laboratory-specific extraction 617 \nmethod. Columns 2-3 represent the all-inclusive dataset with all 115 detections, 618 \nwhereas columns 4-5 represent the conservative dataset with only the 79 detections 619 \nthat were at or below the respective assay’s LOD.  620 \nSignificance values have been adjusted by the Bonferroni correction for multiple tests 621 \n** Indicates significantly different Ct values per sample between extraction methods 622 \n 623 \nAt UIBK, 9 (triplicates from 3 samples) out of 144 reactions (11 water samples 624 \nextracted by four partners and analyzed in triplicate in PCR) did not detect the 625 \nextraction IPC (IPC-L), suggesting either inhibition in these samples or human error 626 \n(e.g., from pipetting errors). Therefore, these extracts were further assessed for 627 \ninhibition by spiking 0.5 µL into a species-specific qPCR targeting Ichthyosaura 628 \nalpestris. All amplifications of the spiked reactions showed detections with similar Ct 629 \nvalues, indicating a lack of inhibition. At UCC, 5 out of 80 reactions (10 water samples 630 \nextracted by four partners and analyzed in duplicate in PCR) did not detect the IPC. At 631 \nInstitute-Institute All-inclusive dataset Conservative dataset   \n(Detections at or below LOD) \nSample 1-Sample 2 Z-score Adjusted P-value Z-score Adjusted P-value \nUCC-UIBK 2.730 0.019** 1.796 0.217 \nUCC-IMR -0.457 1.000 0.267 1.00 \nUCC-INRAE 0.801 1.000 3.092 0.006** \nUIBK-IMR 1.439 0.451 1.458 0.434 \nUIBK-INRAE 3.250 0.003** 4.907 2.77e-06** \nIMR-INRAE -1.004 0.946 -1.854 0.191 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n20 \nIMR, 132 out of 138 qPCR reactions amplified the IPC added to the PCR master mix, 632 \nthe remaining 6 reactions contained an IPC Block (Non-Amplification Controls) and did 633 \nnot amplify.  634 \n 635 \nSanger Sequencing 636 \n 637 \nFor all positive PCR products sent for sequencing by UIBK, at least 1 qPCR replicate 638 \n(per DNA extract) was successfully Sanger sequenced and matched to the target 639 \nspecies (Physeter macrocephalus) using the Basic Local Alignment Search Tool 640 \n(BLAST; https://blast.ncbi.nlm.nih.gov/Blast.cgi). There were two instances in which the 641 \nsequence was also >97% identical to another species (Table 5). From the extracts 642 \nsequenced after qPCR by INRAE, two sequences were matched to the target species 643 \n(Lamna nasus) using BLAST. At UCC, out of the 26 PCR products sent for sequencing 644 \n13 resulted in low-quality reads (less than 50 bp) and the remaining 13 matched (>98% 645 \nidentity) to the target species (Tursiops truncatus) using BLAST. All PCR products with 646 \na visible band were successfully matched to the target species, whereas PCR products 647 \nthat had no visible band on agarose gels yielded low-quality reads which could not be 648 \npositively matched with any species. Of the 4 qPCR products sent for Sanger 649 \nsequencing from IMR, all returned low-quality reads with no significant hits in BLAST. 650 \n 651 \nTable 5. Detections of target species DNA via Sanger sequencing of positive PCR 652 \nproducts.  653 \nDetections UIBK INRAE UCC IMR \nNumber of PCR products sent \nin for Sanger Sequencing \n48 7 26 4 \nNumber of PCR products \nmatching to target species (> \n97%)  \n32 2 13 0 \nNumber of PCR products \nunable to be matched to any \nspecies* \n15 1  13  4 \nNumber of PCR products \nmatching to a nontarget \nspecies (>97%)  \n2 (Drosophila \nspp., \nScotophilus \nheathii)  \nNA NA NA \n* Due to sequencing error or insufficient quantity of DNA for sequencing.  654 \n 655 \nDiscussion 656 \n 657 \nWe aimed to evaluate the efficacy of laboratory-specific extraction techniques by 658 \ncomparing both total DNA yield and target species DNA yield from extracts generated 659 \nfrom the same lysate by different laboratories. Our findings confirm that variations in 660 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n21 \nDNA extraction methodologies significantly influenced the detection of targeted marine 661 \nmegafauna species in eDNA samples. Despite the general uniformity of total DNA 662 \nconcentrations of extracts generated from the same eDNA sample, this ring test 663 \nidentified significant disparities in the detections of target-specific DNA in extracts 664 \ngenerated by IMR using their modified Qiagen DNeasy Blood and Tissue Kit extraction 665 \nprotocol. Three novel species-specific TaqMan assays were designed to amplify marine 666 \nmegafauna species. Out of all positive qPCR replicates utilizing these assays (which 667 \nwere sent in for Sanger sequencing), 55% were successfully sequenced with at least 668 \none positive verification for all extracts in which target DNA was detected. Notable 669 \ndiscrepancies in detections were observed between combinations of laboratory-specific 670 \nextraction protocols and target species, suggesting that the effectiveness of a particular 671 \nextraction technique was highly dependent on the sample type (e.g., the type of filter 672 \nused). Regardless, there were significantly less detections of shark species than marine 673 \nmammals overall, indicating species-specific variation in eDNA samples. This study 674 \nexemplifies the importance of assessing gaps in the reliability of eDNA analysis 675 \nprotocols post field collection across multiple laboratories. 676 \n 677 \nLysates extracted by IMR with the modified Qiagen DNeasy Blood and Tissue kit 678 \nyielded significantly lower detection rates for all target species for both datasets (all-679 \ninclusive and conservative regarding the assay’s LOD), albeit extraction protocols being 680 \nalmost identical between IMR and UCC, whose DNA extracts in total had the highest 681 \nnumber of positives in PCR (41 out of 115 reactions vs IMR 12 out of 115 reactions). 682 \nUpon reflection, two differences in the DNeasy protocol were found between IMR and 683 \nUCC. The first occurred prior to extraction in which lysates were incubated for an 684 \nadditional hour before any subsequent work at UCC. This warming potentially improved 685 \nthe binding of DNA to silica membranes (i.e., reduced the chance of clogging) by 686 \npreventing the precipitation of AL buffer (Lear et al., 2018). The other difference stems 687 \nfrom a vacuum being used at IMR during the DNA purification step for the purpose of 688 \ndrawing the sample and binding buffer through the spin column which the DNA binds to 689 \nthe membrane while other cellular components are washed away. The vacuum 690 \ntechnique versus the commonly used centrifugation protocol (which is also employed by 691 \nINRAE’s NucleoSpin protocol) may vary in their extraction performance due to several 692 \nfactors: incomplete binding of DNA resulting from insufficient vacuum pressure, 693 \ninefficient washing of contaminants by the vacuum, and/or higher saturation of the 694 \nextraction column resulting in lower DNA yields. The difference between IMR and 695 \nUCC’s detection probability of target DNA could not be directly attributed to any of these 696 \nfactors, but this finding demonstrates the effect of protocol modifications for downstream 697 \nanalyses. Concerning differences induced by the mechanisms used for DNA binding 698 \nand separation of lysate components, the extraction robot used at UIBK has been 699 \nshown to be more robust in attaining the amount of total DNA within samples as the use 700 \nof paramagnetic beads avoids these steps altogether (Wallinger et al., 2017). 701 \nAccordingly, UIBK extracts detected the target species across multiple replicates with 702 \nhigher concentrations of target DNA than other labs (i.e., lower Ct values; average Ct 703 \nacross all positive amplifications: UIBK=32.54, INRAE=34.35, UCC=34.82, IMR=33.21).  704 \n 705 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n22 \nAll qPCR assays provided herein met Level 3 of the validation scale presented 706 \nby Thalinger et al. (2021) in which the target organism was successfully detected from 707 \nan environmental sample (Supplementary Material 4). In all cases, the specifics of DNA 708 \nextraction and concentration of eDNA from the environmental sample were reported. 709 \nThe assays almost satisfied Level 4 of the validation scale in which the LOD has to be 710 \ncalculated and in vitro qPCRs on co-occurring nontarget species have to be carried out, 711 \nyet failed to meet the expectation of extensive field testing as this was a preliminary 712 \nstudy on a subset of samples. For qPCR assays targeting a particular species of 713 \ninterest, the assayID program can be used as a preliminary means to identify candidate 714 \nprimer/probes. Further testing with manual in silico techniques and software is 715 \nrecommended for all automated selections prior to ordering a costly hydrolysis probe. 716 \nThe optimized assays were shown to be highly effective at detecting target species 717 \nDNA down to a low concentration as denoted by their LODs (0.0001 ng/µL for sperm 718 \nwhale, porbeagle shark, and basking shark, 0.00001 ng/µL for dolphins; Klymus et al., 719 \n2020). Therefore, non-detections of target species throughout the course of this study 720 \nare likely due to the lack of (or extremely low concentrations of) quantifiable target DNA 721 \nin the extract (Eichmiller, Miller and Sorensen, 2016; Hunter et al., 2017). The LOD of 722 \nUCC’s SYBRgreen assay was one dilution point higher than the LOD of the TaqMan 723 \n(hydrolysis probe-dependent) assays. We hypothesize that this is due to the efficiency 724 \nat which target DNA can hybridize to the primer pair, whereas the primer pair + probe 725 \nassay for TaqMan-based qPCR is highly specific and may not bind to all target DNA 726 \nwithin an extract, which is suggested by other comparative studies (Cao and Shockey, 727 \n2012; Zhang et al., 2015).  728 \n 729 \nDetection rates across all qPCRs varied exceptionally depending on the species: 730 \nsperm whales and dolphins were detected in 43 and 56 qPCR replicates, respectively, 731 \nwhereas shark species were only detected in three (in the case of basking shark) and 732 \n13 (porbeagle shark) qPCR replicates and were completely absent from the 733 \nconservative dataset. This is justified from previous environmental DNA work with shark 734 \nspecies, which report low concentrations of eDNA from elasmobranch (sharks and rays) 735 \ntaxa (Dunn et al., 2023). In contrast, extracts generated by all partner laboratories 736 \namplified sperm whale DNA across almost all replicates for three separate eDNA 737 \nsamples and dolphin DNA in eight samples for more than one qPCR replicate. Marine 738 \nmammals notably lose sloughed skin and dispel fecal matter while resting at the surface 739 \nof the water column in between feeding events (Whitehead et al., 1990; Konrad et al., 740 \n2018). Therefore, the genetic material that was collected during sampling events near 741 \nsurfacing individuals likely provided sufficient quantities of eDNA to be collected, 742 \nfiltered, and extracted. Not only does the behavior of each species affect their ability to 743 \nbe detected by eDNA, but differences in field sampling may have also factored into 744 \ndetection probability. We attribute the low frequency of detections for IMR’s assay 745 \n(basking shark) in both the all-inclusive and conservative datasets to field sampling at 746 \ntoo-great distances (spatially and temporally) from the target species in order to identify 747 \nsignificant concentrations of eDNA. Also, water volumes of samples taken near sharks 748 \n(both species) were 5 liters, whereas sperm whale samples were all 10 liters. Lysate 749 \nvolumes from INRAE Sylphium filters were higher (up to 6 mL in some instances 750 \ncompared to 1.5 mL from UIBK Sylphium filters) due to improper drying of the filter, 751 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n23 \nwhich may have consequently diluted the eDNA to a point in which it was no longer 752 \ndetectable. Though only two liters of water were collected near dolphins, their tendency 753 \nto travel closely in groups at the water’s surface likely enhanced the amount of eDNA 754 \npresent (Shane, Wells and Würsig, 1986; Acevedo-Gutiérrez and Parker, 2000; Gridley 755 \net al., 2017). Therefore, while the extraction protocols did impact the capability of 756 \npartner laboratories to detect different species, there are many other factors to account 757 \nfor during further ecological applications of these detections amongst others.  758 \n 759 \nThere was a significant difference observed between combinations of laboratory-760 \nspecific extraction and the subsequent target species. This implies that the nature of the 761 \nsample (e.g., different environmental parameters at the sampling site) could have 762 \naffected the efficiency of DNA extraction following its collection from the field. Such a 763 \ntheory is consistent with the findings of the Lear et al., 2018 review concerning eDNA 764 \nextraction, storage, amplification, and sequencing methods. Thus, while particular 765 \nextraction techniques may perform well with certain sample types (e.g., samples 766 \nincluding PCR inhibiting substances), they may not be universally applicable across all 767 \neDNA samples, introducing significant discrepancies especially in the context of inter-768 \nlaboratory comparisons. For example, INRAE yielded lower total DNA measurements 769 \nthan other laboratories for their own eDNA samples (Sylphium filters, target species: 770 \nporbeagle shark) as well as UIBK eDNA samples (Sylphium and Smith-Root filters, 771 \ntarget species: sperm whale), yet similar measurements to other labs for UCC samples 772 \n(target species: dolphin species, Sterivex filters) and IMR samples (target species: 773 \nbasking shark, Sterivex filters). This suggests that the NucleoSpin Tissue Kit may be 774 \nmost effective with DNA lysed from Sterivex filters, which is in accordance with Tsuji et 775 \nal. (2019) who present that each commercial DNA extraction kit has shown dependence 776 \non a combination of the eDNA collection method and the condition of water samples 777 \n(e.g., the degree of inhibition). For samples in which inhibition (compounds which may 778 \ndisrupt PCRs) may be expected (e.g., in turbid marine environments), inhibitor-removal 779 \nkits and/or additional extraction protocol steps are often utilized to enhance the 780 \ndetection of target DNA (Rees et al., 2014). For the purposes of this study, however, all 781 \npartner laboratories agreed to avoid inhibitor removal steps. Ultimately, the 782 \nimplementation of various Internal Positive Controls corroborated the lack of inhibition 783 \nacross all generated DNA extracts, confirming our choice to not risk the loss of target 784 \nDNA via an additional inhibition removal protocol. The use of high-quality PCR 785 \nchemistry could have also influenced the detection success of each qPCR assay (Beng 786 \nand Corlett, 2020; Thalinger et al., 2021), but this effect was not specifically tested in 787 \nthe current study. However, external factors, namely the effect of shipment of eDNA 788 \nlysate and extracts, were included in preliminary analyses but did not show any 789 \nsignificant effect on the resulting concentrations of DNA per extract. 790 \n 791 \nA total of 36 positive amplifications fell below the assays’ LOD and were 792 \nexcluded from one of the two datasets used for statistical testing. Although the results of 793 \nstatistical tests between the full dataset (with all positive amplifications regardless of the 794 \nassay’s LOD) and the subsetted dataset showed the same result - IMR’s DNA extracts 795 \namplified the target species significantly less than the other lab’s extracts - the detection 796 \nrate of each lab’s DNA extracts decreased by approximately 20% (41% in the case of 797 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n24 \nIMR) in the subsetted dataset. The analysis of eDNA detections above or below an 798 \nassay’s LOD is an important factor to consider for future interpretations of eDNA 799 \nsamples in marine ecosystems in which genomic traces are expected to be far lower 800 \nthan the desired confidence of detection (Paul, Jeffrey and DeFlaun, 1987; Collins et al., 801 \n2018). On that note, almost all positive detections which fell above the assay’s LOD 802 \nwere positively sequenced to match the target species’ DNA, proving that the above-803 \nLOD amplifications were true positives of the target species. However, for the purpose 804 \nof a cross-laboratory comparison, the dataset provided in the conservative dataset 805 \n(amplifications at or below the LOD), is most appropriate as stated by Klymus et al., 806 \n2020. Ultimately, the DNA was extracted from the same environmental sample and 807 \nshould theoretically be the same across independent laboratories.  808 \n 809 \nConclusions 810 \n 811 \nOverall, this study demonstrates a comprehensive effort to evaluate the consistency 812 \nand accuracy of DNA extraction, quantification and species detection across four 813 \ndifferent laboratories. Our findings support the general reproducibility of eDNA analyses 814 \nacross eWHALE partners, despite some disparities in one lab’s extraction performance. 815 \nThis analysis demonstrates both the variability introduced by different target species, 816 \nsampling protocols and extraction methods as well as the critical importance of 817 \ninterpreting data within the context of a given assay’s Limit of Detection. As a result of 818 \nthis study, adjustments were made to improve IMR’s extraction protocol, thereby 819 \nenhancing its effectiveness. This research demonstrates the necessity of conducting 820 \npreliminary validation tests for research projects involving multiple laboratories, ensuring 821 \nreliable and comparable results. Our work represents a significant step towards the 822 \nsuccessful implementation of standardized protocols, promoting consistent performance 823 \nacross an international environmental DNA initiative.  824 \n 825 \nAcknowledgements 826 \n 827 \nThis research was conducted within the eWHALE project funded by 1) FWF Project no. 828 \nI 6389 (UIBK), 2) ANR-22-EBIP-0011 (INRAE), 3) EPA Research Programme 2030; 829 \nProject code “2022-NE-1170 eWHALE”; Project reference “R21568” (UCC), 4) 830 \nResearch Council of Norway via the Sharks on the Move project RCN #326879 (IMR), 831 \n5) M2.2/eWHALE/001/2023 via the Fundo Regional para a Ciência e Tecnologia – 832 \nFRCT, Governo Regional dos Açores. Porbeagle shark samples were collected under 833 \npermit number 708/2023 (delivered 6 June 2023). Basking shark eDNA samples were 834 \ncollected onboard “Rind” provided by the Directorate of Fisheries.  835 \n 836 \nWe thank Dania Tesei, Michael Costello, Emer Rogan, Allen Whitaker, Oriol 837 \nGiralt Paradell, Jasmine Stavenow, Des Requins et des Hommes, Anne-Laure Besnard, 838 \nLoïc Baulier, Ingrid Bruvold, and Antonia Klöcker for their dedicated efforts to facilitate 839 \nsample collection and transportation. We thank Tanja Hanebrekke and Daniela Sint for 840 \ntheir diligent work in the laboratory.   841 \nAuthor-formatted, not peer-reviewed document posted on 28/05/2024. DOI:  https://doi.org/10.3897/arphapreprints.e128447\n\n \n25 \nReferences 842 \nAcevedo-Gutiérrez A, Parker N (2000) Surface Behavior of Bottlenose Dolphins Is 843 \nRelated to Spatial Arrangement of Prey. 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