Ecology

1.Hebert, P. D., Penton, E. H., Burns, J. M., Janzen, D. H. & Hallwachs, W. T. species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proc. Natl. Acad. Sci. U.S.A. 101, 14812–14817. https://doi.org/10.1073/pnas.0406166101 (2004).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
2.Saez, A. G. & Lozano, E. Body doubles. Nature 433, 111. https://doi.org/10.1038/433111a (2005).CAS 
Article 
PubMed 
ADS 

Google Scholar 
3.Vyskočilová, S., Tay, W. T., van Brunschot, S., Seal, S. & Colvin, J. An integrative approach to discovering cryptic species within the Bemisia tabaci whitefly species complex. Sci. Rep. 8, 10886. https://doi.org/10.1038/s41598-018-29305-w (2018).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
4.Liu, S. S. et al. Asymmetric mating interactions drive widespread invasion and displacement in a whitefly. Science 318, 1769–1772. https://doi.org/10.1126/science.1149887 (2007).CAS 
Article 
PubMed 
ADS 

Google Scholar 
5.Vyskocilova, S., Seal, S. & Colvin, J. Relative polyphagy of “Mediterranean” cryptic Bemisia tabaci whitefly species and global pest status implications. J. Pest Sci. 92, 1071–1088. https://doi.org/10.1007/s10340-019-01113-9 (2019).Article 

Google Scholar 
6.Behere, G. T., Tay, W. T., Russell, D. A. & Batterham, P. Molecular markers to discriminate among four pest species of Helicoverpa (Lepidoptera: Noctuidae). Bull. Entomol. Res. 98, 599–603. https://doi.org/10.1017/S0007485308005956 (2008).CAS 
Article 
PubMed 

Google Scholar 
7.Elfekih, S., Tay, W. T., Gordon, K., Court, L. N. & De Barro, P. J. Standardized molecular diagnostic tool for the identification of cryptic species within the Bemisia tabaci complex. Pest Manag. Sci. 74, 170–173. https://doi.org/10.1002/ps.4676 (2018).CAS 
Article 
PubMed 

Google Scholar 
8.Walsh, T. K. et al. Mitochondrial DNA genomes of five major Helicoverpa pest species from the Old and New Worlds (Lepidoptera: Noctuidae). Ecol. Evol. 9, 2933–2944. https://doi.org/10.1002/ece3.4971 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Anderson, C. J., Tay, W. T., McGaughran, A., Gordon, K. & Walsh, T. K. Population structure and gene flow in the global pest, Helicoverpa armigera. Mol. Ecol. 25, 5296–5311. https://doi.org/10.1111/mec.13841 (2016).CAS 
Article 
PubMed 

Google Scholar 
10.Elfekih, S. et al. Genome-wide analyses of the Bemisia tabaci species complex reveal contrasting patterns of admixture and complex demographic histories. PLoS ONE 13, e0190555. https://doi.org/10.1371/journal.pone.0190555 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Anderson, C. J. et al. Hybridization and gene flow in the mega-pest lineage of moth, Helicoverpa. Proc. Natl. Acad. Sci. U.S.A. 115, 5034–5039. https://doi.org/10.1073/pnas.1718831115 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.FAOSTAT. http://www.fao.org/faostat/en/#data/QC/visualize (2017).13.Legg, J. P. et al. Spatio-temporal patterns of genetic change amongst populations of cassava Bemisia tabaci whiteflies driving virus pandemics in East and Central Africa. Virus Res. 186, 61–75. https://doi.org/10.1016/j.virusres.2013.11.018 (2014).CAS 
Article 
PubMed 

Google Scholar 
14.Patil, B. L. & Fauquet, C. M. Cassava mosaic geminiviruses: Actual knowledge and perspectives. Mol. Plant Pathol. 10, 685–701. https://doi.org/10.1111/j.1364-3703.2009.00559.x (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
15.Macfadyen, S. et al. Cassava whitefly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) in East African farming landscapes: A review of the factors determining abundance. Bull. Entomol. Res. 108, 565–582. https://doi.org/10.1017/S0007485318000032 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Minato, N. et al. Surveillance for Sri Lankan cassava mosaic virus (SLCMV) in Cambodia and Vietnam one year after its initial detection in a single plantation in 2015. PLoS ONE 14, e0212780. https://doi.org/10.1371/journal.pone.0212780 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Wang, H. L. et al. First Report of Sri Lankan cassava mosaic virus Infecting Cassava in Cambodia. Plant Dis. 100, 1029–1029. https://doi.org/10.1094/Pdis-10-15-1228-Pdn (2016).Article 

Google Scholar 
18.De Barro, P. J., Liu, S. S., Boykin, L. M. & Dinsdale, A. B. Bemisia tabaci: A statement of species status. Annu. Rev. Entomol. 56, 1–19. https://doi.org/10.1146/annurev-ento-112408-085504 (2011).CAS 
Article 
PubMed 

Google Scholar 
19.Hopkinson, J. et al. Insecticide resistance status of Bemisia tabaci MEAM1 (Hemiptera: Aleyrodidae) in Australian cotton production valleys. Austral Entomol. 59, 202–214 (2020).Article 

Google Scholar 
20.Hadjistylli, M., Roderick, G. K. & Gauthier, N. First report of the Sub-Saharan Africa 2 species of the Bemisia tabaci complex in the Southern France. Phytoparasitica 43, 679–687. https://doi.org/10.1007/s12600-015-0480-3 (2015).Article 

Google Scholar 
21.Lee, W., Park, J., Lee, G. S., Lee, S. & Akimoto, S. Taxonomic status of the Bemisia tabaci complex (Hemiptera: Aleyrodidae) and reassessment of the number of its constituent species. PLoS ONE 8, e63817. https://doi.org/10.1371/journal.pone.0063817 (2013).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
22.Mugerwa, H. et al. African ancestry of New World, Bemisia tabaci-whitefly species. Sci. Rep. 8, 2734. https://doi.org/10.1038/s41598-018-20956-3 (2018).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
23.Martin, J. H. An identification guide to common whitefly pest species of the world (Homopt Aleyrodidae). Int. J. Pest Manag. 334, 298–322. https://doi.org/10.1080/09670878709371174 (1987).Article 

Google Scholar 
24.Martin, J. H. & Mound, L. A. An annotated check list of the world’s whiteflies (Insecta: Hemiptera: Aleyrodidae). Zootaxa 1492, 1–84 (2007).Article 

Google Scholar 
25.Mound, L. A. Host-correlated variation in Bemisia tabaci (Gennadius). Proc. R. Entomol. Soc. Lond. A38, 171–180 (1963).ADS 

Google Scholar 
26.Tay, W. T. et al. Novel molecular approach to define pest species status and tritrophic interactions from historical Bemisia specimens. Sci. Rep. https://doi.org/10.1038/s41598-017-00528-7 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Tay, W. T., Evans, G. A., Boykin, L. M. & De Barro, P. J. Will the real Bemisia tabaciplease stand up?. PLoS ONE https://doi.org/10.1371/journal.pone.0050550 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Dinsdale, A., Cook, L., Riginos, C., Buckley, Y. M. & De Barro, P. Refined global analysis of Bemisia tabaci (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae) mitochondrial cytochrome oxidase 1 to identify species level genetic boundaries. Ann. Entomol. Soc. Am. 103, 196–208. https://doi.org/10.1603/An09061 (2010).Article 

Google Scholar 
29.Kunz, D., Tay, W. T., Elfekih, S., Gordon, K. H. J. & De Barro, P. J. Take out the rubbish – Removing NUMTs and pseudogenes from the Bemisia tabacicryptic species mtCOI database. bioRxiv. https://doi.org/10.1101/724765 (2019).Article 

Google Scholar 
30.Wongnikong, W., van Brunschot, S. L., Hereward, J. P., De Barro, P. J. & Walter, G. H. Testing mate recognition through reciprocal crosses of two native populations of the whitefly Bemisia tabaci (Gennadius) in Australia. Bull. Entomol. Res. 110, 328–339. https://doi.org/10.1017/S0007485319000683 (2020).CAS 
Article 
PubMed 

Google Scholar 
31.Mugerwa, H., Wang, H.-L., Sseruwagi, P., Seal, S. & Colvin, J. Whole-genome single nucleotide polymorphism and mating compatibility studies reveal the presence of distinct species in sub-Saharan Africa Bemisia tabaci whiteflies. Insect Sci. https://doi.org/10.1111/1744-7917.12881 (2020).Article 
PubMed 

Google Scholar 
32.Delatte, H. et al. A new silverleaf-inducing biotype Ms of Bemisia tabaci (Hemiptera: Aleyrodidae) indigenous to the islands of the south-west Indian Ocean. Bull. Entomol. Res. 95, 29–35. https://doi.org/10.1079/Ber2004337 (2005).CAS 
Article 
PubMed 

Google Scholar 
33.Boykin, L. M., Savill, A. & De Barro, P. Updated mtCOI reference dataset for the Bemisia tabaci species complex. F1000Research 6, 1835. https://doi.org/10.12688/f1000research.12858.1 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Liu, S. S., Colvin, J. & De Barro, P. J. Species concepts as applied to the whitefly Bemisia tabaci systematics: How many species are there?. J Integr Agr 11, 176–186. https://doi.org/10.1016/S2095-3119(12)60002-1 (2012).Article 

Google Scholar 
35.Tay, W. T. et al. The trouble with MEAM2: Implications of pseudogenes on species delimitation in the globally invasive Bemisia tabaci (Hemiptera: Aleyrodidae) cryptic species complex. Genome Biol. Evol. 9, 2732–2738. https://doi.org/10.1093/gbe/evx173 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Kunz, D. et al. Draft mitochondrial DNA genome of a 1920 Barbados cryptic Bemisia tabaci “New World” species (Hemiptera: Aleyrodidae). Mitochondrial DNA B 4, 1183–1184. https://doi.org/10.1080/23802359.2019.1591197 (2019).Article 

Google Scholar 
37.Paini, D. R. et al. Global threat to agriculture from invasive species. Proc. Natl. Acad. Sci. U.S.A. 113, 7575–7579. https://doi.org/10.1073/pnas.1602205113 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Wosula, E. N., Chen, W. B., Fei, Z. J. & Legg, J. P. Unravelling the genetic diversity among cassava Bemisia tabaci whiteflies using NextRAD sequencing. Genome Biol. Evol. 9, 2958–2973. https://doi.org/10.1093/gbe/evx219 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Thresh, J. M., Fargette, D. & Otim-Nape, G. W. Effects of African cassava mosaic geminivirus on the yield of cassava. Trop. Sci. 34, 26–42 (1994).
Google Scholar 
40.Legg, J. et al. A global alliance declaring war on cassava viruses in Africa. Food Secur. 6, 231–248. https://doi.org/10.1007/s12571-014-0340-x (2014).Article 

Google Scholar 
41.Legg, J. P. et al. Biology and management of Bemisia whitefly vectors of cassava virus pandemics in Africa. Pest Manag. Sci. 70, 1446–1453. https://doi.org/10.1002/ps.3793 (2014).CAS 
Article 
PubMed 

Google Scholar 
42.Berry, S. D. et al. Molecular evidence for five distinct Bemisia tabaci (Homoptera: Aleyrodidae) geographic haplotypes associated with cassava plants in sub-Saharan Africa. Ann. Entomol. Soc. Am. 97, 852–859. https://doi.org/10.1603/0013-8746(2004)097[0852:Meffdb]2.0.Co;2 (2004).CAS 
Article 

Google Scholar 
43.Mugerwa, H., Rey, M. E. C., Tairo, F., Ndunguru, J. & Sseruwagi, P. Two sub-Saharan Africa 1 populations of Bemisia tabaci exhibit distinct biological differences in fecundity and survivorship on cassava. Crop Prot. 117, 7–14. https://doi.org/10.1016/j.cropro.2018.11.011 (2019).Article 

Google Scholar 
44.Ghosh, S., Bouvaine, S. & Maruthi, M. N. Prevalence and genetic diversity of endosymbiotic bacteria infecting cassava whiteflies in Africa. BMC Microbiol. https://doi.org/10.1186/s12866-015-0425-5 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population-structure. Evolution 38, 1358–1370. https://doi.org/10.2307/2408641 (1984).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
46.Ghosh, S., Bouvaine, S., Richardson, S. C. W., Ghanim, M. & Maruthi, M. N. Fitness costs associated with infections of secondary endosymbionts in the cassava whitefly species Bemisia tabaci. J. Pest Sci. 91, 17–28. https://doi.org/10.1007/s10340-017-0910-8 (2018).Article 

Google Scholar 
47.Elfekih, S. et al. Evolutionary genomics of Bemisia tabaci and characterization of its endosymbiont metacommunities using nextRAD sequencing. International Plant and Animal Genome Asia, Singapore 23–25 July 2015 (2015).48.Elfekih, S. et al. Genome-wide SNPs Decipher Global Incursion pathways in the Bemisia tabaci species complex. International Plant and Animal Genome Conferences San Diego, 9–13 January 2016 (2016).49.Elfekih, S. et al. Genome-wide scans unravel fine-scale invasion routes in the Bemisia tabaci species complex. 2nd International Whitefly Symposium, Arusha, Tanzania. p38, 14–19 February 2016 (2016).50.Boykin, L. M., Bell, C. D., Evans, G., Small, I. & De Barro, P. J. Is agriculture driving the diversification of the Bemisia tabaci species complex (Hemiptera: Sternorrhyncha: Aleyrodidae)? Dating, diversification and biogeographic evidence revealed. BMC Evol. Biol. 13, 228. https://doi.org/10.1186/1471-2148-13-228 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Boykin, L. M. et al. Review and guide to a future naming system of African Bemisia tabaci species. Syst. Entomol. 43, 427–433. https://doi.org/10.1111/syen.12294 (2018).Article 

Google Scholar 
52.Hebert, P. D. N., Cywinska, A., Ball, S. L. & DeWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. B Biol. Sci. 270, 313–321. https://doi.org/10.1098/rspb.2002.2218 (2003).CAS 
Article 

Google Scholar 
53.Hanemaaijer, M. J. et al. Mitochondrial genomes of Anophelesarabiensis, An. gambiae and An. coluzzii show no clear species division [version 2; peer review: 2 approved]. F1000Research 7, 347. https://doi.org/10.12688/f1000research.13807.2 (2019).Article 
PubMed Central 

Google Scholar 
54.Tabachnick, W. J. Culicoides variipennis and bluetongue-virus epidemiology in the United States. Annu. Rev. Entomol. 41, 23–43. https://doi.org/10.1146/annurev.en.41.010196.000323 (1996).CAS 
Article 
PubMed 

Google Scholar 
55.Legg, J. P., French, R., Rogan, D., Okao-Okuja, G. & Brown, J. K. A distinct Bemisia tabaci (Gennadius) (Hemiptera: Sternorrhyncha: Aleyrodidae) genotype cluster is associated with the epidemic of severe cassava mosaic virus disease in Uganda. Mol. Ecol. 11, 1219–1229. https://doi.org/10.1046/j.1365-294X.2002.01514.x (2002).CAS 
Article 
PubMed 

Google Scholar 
56.Colvin, J., Omongo, C. A., Maruthi, M. N., Otim-Nape, G. W. & Thresh, J. M. Dual begomovirus infections and high Bemisia tabaci populations: Two factors driving the spread of a cassava mosaic disease pandemic. Plant Pathol. 53, 577–584. https://doi.org/10.1111/j.1365-3059.2004.01062.x (2004).Article 

Google Scholar 
57.Polston, J. E., De Barro, P. & Boykin, L. M. Transmission specificities of plant viruses with the newly identified species of the Bemisia tabaci species complex. Pest Manag. Sci. 70, 1547–1552. https://doi.org/10.1002/ps.3738 (2014).CAS 
Article 
PubMed 

Google Scholar 
58.Ally, H. M. et al. What has changed in the outbreaking populations of the severe crop pest whitefly species in cassava in two decades?. Sci. Rep. https://doi.org/10.1038/s41598-019-50259-0 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Kalyebi, A. et al. Within-season changes in land use impact pest abundance in smallholder African cassava production systems. Insects (2021) (Revised Submitted).60.Kalyebi, A. et al. African cassava whitefly, Bemisia tabaci, cassava colonization preferences and control implications. PLoS ONE 13, e0204862. https://doi.org/10.1371/journal.pone.0204862 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Macfadyen, S. et al. Landscape factors and how they influence whitefly pests in cassava fields across East Africa. Landsc. Ecol. 36, 45–67. https://doi.org/10.1007/s10980-020-01099-1 (2021).Article 

Google Scholar 
62.Tay, W. T. et al. A high-throughput amplicon sequencing approach for population-wide species diversity and composition survey. bioRxiv https://doi.org/10.1101/2020.10.12.336545 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
63.Manani, D. M., Ateka, E. M., Nyanjom, S. R. G. & Boykin, L. M. Phylogenetic relationships among whiteflies in the Bemisia tabaci(Gennadius) species complex from major cassava growing areas in Kenya. Insects https://doi.org/10.3390/insects8010025 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Gottelli, D., Marino, J., Sillero-Zubiri, C. & Funk, S. M. The effect of the last glacial age on speciation and population genetic structure of the endangered Ethiopian wolf (Canis simensis). Mol. Ecol. 13, 2275–2286. https://doi.org/10.1111/j.1365-294X.2004.02226.x (2004).CAS 
Article 
PubMed 

Google Scholar 
65.Sezonlin, M. et al. Phylogeography and population genetics of the maize stalk borer Busseola fusca (Lepidoptera, Noctuidae) in sub-Saharan Africa. Mol. Ecol. 15, 407–420. https://doi.org/10.1111/j.1365-294X.2005.02761.x (2006).CAS 
Article 
PubMed 

Google Scholar 
66.Lehmann, T. et al. The rift valley complex as a barrier to gene flow for Anopheles gambiae in Kenya. J. Hered. 90, 613–621. https://doi.org/10.1093/jhered/90.6.613 (1999).CAS 
Article 
PubMed 

Google Scholar 
67.Schmidt, H. et al. Transcontinental dispersal of Anopheles gambiae occurred from West African origin via serial founder events. Commun. Biol. 2, 473. https://doi.org/10.1038/s42003-019-0717-7 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
68.Mairal, M. et al. Geographic barriers and Pleistocene climate change shaped patterns of genetic variation in the Eastern Afromontane biodiversity hotspot. Sci. Rep. 7, 45749. https://doi.org/10.1038/srep45749 (2017).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
69.Freilich, X. et al. Comparative Phylogeography of Ethiopian anurans: Impact of the Great Rift Valley and Pleistocene climate change. BMC Evol. Biol. 16, 206. https://doi.org/10.1186/s12862-016-0774-1 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
70.Huhndorf, M. H., Peterhans, J. C. K. & Loew, S. S. Comparative phylogeography of three endemic rodents from the Albertine Rift, east central Africa. Mol. Ecol. 16, 663–674. https://doi.org/10.1111/j.1365-294X.2007.03153.x (2007).Article 
PubMed 

Google Scholar 
71.Matsubayashi, K. W., Ohshima, I. & Nosil, P. Ecological speciation in phytophagous insects. Entomol. Exp. Appl. 134, 1–27. https://doi.org/10.1111/j.1570-7458.2009.00916.x (2010).Article 

Google Scholar 
72.Malka, O. et al. Species-complex diversification and host-plant associations in Bemisia tabaci: A plant-defence, detoxification perspective revealed by RNA-Seq analyses. Mol. Ecol. 27, 4241–4256. https://doi.org/10.1111/mec.14865 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
73.Chen, W. B. et al. The draft genome of whitefly Bemisia tabaciMEAM1, a global crop pest, provides novel insights into virus transmission, host adaptation, and insecticide resistance. BMC Biol. https://doi.org/10.1186/s12915-016-0321-y (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Xie, W. et al. The invasive MED/Q Bemisia tabaci genome: A tale of gene loss and gene gain. BMC Genomics 19, 68. https://doi.org/10.1186/s12864-018-4448-9 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
75.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
76.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
77.Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595. https://doi.org/10.1093/bioinformatics/btp698 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
78.Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079. https://doi.org/10.1093/bioinformatics/btp352 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
79.Eaton, D. A. R. PyRAD: Assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics 30, 1844–1849. https://doi.org/10.1093/bioinformatics/btu121 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
80.Zheng, X. W. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328. https://doi.org/10.1093/bioinformatics/bts606 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
81.Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664. https://doi.org/10.1101/gr.094052.109 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
82.Stamatakis, A. RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690. https://doi.org/10.1093/bioinformatics/btl446 (2006).CAS 
Article 
PubMed 

Google Scholar 
83.Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. https://doi.org/10.1371/journal.pgen.1002967 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
84.Decker, J. E. et al. Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle. PLoS Genet. 10, e1004254. https://doi.org/10.1371/journal.pgen.1004254 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
85.Gompert, Z. et al. Admixture and the organization of genetic diversity in a butterfly species complex revealed through common and rare genetic variants. Mol. Ecol. 23, 4555–4573. https://doi.org/10.1111/mec.12811 (2014).Article 
PubMed 

Google Scholar  More

1.Barnosky, A. D. & Lindsey, E. L. Timing of Quaternary megafaunal extinction in South America in relation to human arrival and climate change. Quat. Int. 217, 10–29 (2010).Article 

Google Scholar 
2.Broughton, J. M. & Weitzel, E. M. Population reconstructions for humans and megafauna suggest mixed causes for North American Pleistocene extinctions. Nat. Commun. 9, 1–12 (2018).CAS 
Article 

Google Scholar 
3.Haynes, G. in The Encyclopedia of the Anthropocene 1 (eds. DellaSala, D. A., & Goldstein, M. I.) 219–226 (Springer, 2018).4.Wolfe, A. L. & Broughton, J. M. A foraging theory perspective on the associational critique of North American Pleistocene overkill. J. Archaeol. Sci. 119, 105162 (2020).Article 

Google Scholar 
5.Rothschild, B. M. & Laub, R. Hyperdisease in the late Pleistocene: validation of an early 20th century hypothesis. Naturwissenschaften 93, 557–564 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Borrero, L. A. in American megafaunal extinctions at the end of the Pleistocene (ed. Haynes, G.) 145–168 (Springer, 2009).7.Cione, A. L., Tonni, E. P., & Soibelzon, L. in American megafaunal extinctions at the end of the Pleistocene (ed. Haynes, G.) 125–144 (Springer, 2009).8.Lima-Ribeiro, M. S., Nogués-Bravo, D., Terribile, L. C., Batra, P. & Diniz-Filho, J. A. F. Climate and humans set the place and time of Proboscidean extinction in late Quaternary of South America. Palaeogeogr. Palaeoclimatol. Palaeoecol. 392, 546–556 (2013).Article 

Google Scholar 
9.Grayson, D. K. & Meltzer, D. J. A requiem for North American overkill. J. Archaeol. Sci. 30, 585–593 (2003).Article 

Google Scholar 
10.Firestone, R. B. et al. Evidence for an extraterrestrial impact 12,900 years ago that contributed to the megafaunal extinctions and the Younger Dryas cooling. PNAS 104, 16016–16021 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Waters, M. R. & Stafford, T. W. in Paleoamerican Odyssey (eds. Graf, K. E., Ketron, C. V. & Waters, M.) 543–562 (Texas A&M University Press, 2013).12.Politis, G., Prado, J. L., & Beukens, R. P. in Ancient Peoples and Landscapes (ed. Johnson, E.) 187–205 (Tech University Press, 1995).13.Martin, P. S. The Discovery of America: The first Americans may have swept the Western Hemisphere and decimated its fauna within 1000 years. Science 179, 969–974 (1973).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Fiedel, S. J. in Paleoamerican origins: beyond Clovis (eds. Bonnichsen, R., Lepper, B. T., Stanford, D. & Waters M. R.) 97–102 (Texas A&M University Press, 2005).15.Surovell, T. A., Pelton, S. R., Anderson-Sprecher, R. & Myers, A. D. Test of Martin’s overkill hypothesis using radiocarbon dates on extinct megafauna. PNAS 113, 886–891 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Pedro, J. B. et al. Southern Ocean deep convection as a driver of Antarctic warming events. Geophys. Res. Lett. 43, 2192–2199 (2016).ADS 
Article 

Google Scholar 
17.Politis, G. in Clovis. Origins and Adaptations (eds Bonnichsen, R. & Turnmire, K.) 287–301 (Texas A&M University Press, 1991).18.Nami, H. G. Fishtailed projectile points in the Americas: Remarks and hypotheses on the peopling of northern South America and beyond. Quat. Int., in press (2020).19.Waters, M. R., Amorosi, T. & Stafford, T. W. Redating Fell’s cave, Chile and the chronological placement of the Fishtail projectile point. Am. Antiq. 80, 376–386 (2015).Article 

Google Scholar 
20.Loponte, D., Carbonera, M. & Silvestre, R. Fishtail projectile points from South America: the Brazilian record. Archaeol. Discov. 3, 85 (2015).Article 

Google Scholar 
21.Nami, H. G. & Yataco Capcha, J. Further Data on Fell Points from the Southern Cone of South America. PaleoAmerica 6, 379–386 (2020).Article 

Google Scholar 
22.Weitzel, C., Mazzia, N. & Flegenheimer, N. Assessing Fishtail points distribution in the southern Cone. Quat. Int. 473, 161–172 (2018).Article 

Google Scholar 
23.Martínez, G., Gutiérrez, M. A. & Tonni, E. P. Paleoenvironments and faunal extinctions: analysis of the archaeological assemblages at the Paso Otero locality (Argentina) during the Late Pleistocene–Early Holocene. Quat. Int 299, 53–63 (2013).Article 

Google Scholar 
24.Prates, L., Politis, G. G. & Perez, S. I. Rapid radiation of humans in South America after the last glacial maximum: a radiocarbon-based study. PLoS ONE 15, e0236023 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of late Pleistocene extinctions on the continents. Science 306, 70–75 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Buchanan, B., Collard, M., Hamilton, M. J. & O’Brien, M. J. Points and prey: a quantitative test of the hypothesis that prey size influences early Paleoindian projectile point form. J. Archaeol. Sci. 38, 852–864 (2011).Article 

Google Scholar 
27.Timpson, A. et al. Reconstructing regional population fluctuations in the European Neolithic using radiocarbon dates: a new case-study using an improved method. J. Archaeol. Sci. 52, 549–557 (2014).Article 

Google Scholar 
28.Crema, E. R., Habu, J., Kobayashi, K. & Madella, M. Summed probability distribution of 14C dates suggests regional divergences in the population dynamics of the Jomon period in eastern Japan. PLoS ONE 11, e0154809 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
30.Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Ann. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).Article 

Google Scholar 
31.d’Amen, M. et al. Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models. J. Biogeog. 42, 1255–1266 (2015).Article 

Google Scholar 
32.McCulloch, R. D. et al. Climatic inferences from glacial and palaeoecological evidence at the last glacial termination, southern South America. J. Quat. Sci.: Published Quat. Res. Assoc. 15, 409–417 (2000).Article 

Google Scholar 
33.Moreno, P. I., Villa-Martínez, R., Cárdenas, M. L. & Sagredo, E. A. Deglacial changes of the southern margin of the southern westerly winds revealed by terrestrial records from SW Patagonia (52 S). Quat. Sci. Rev. 41, 1–21 (2012).ADS 
Article 

Google Scholar 
34.Barnosky, A. D. et al. Variable impact of late-Quaternary megafaunal extinction in causing ecological state shifts in North and South America. PNAS 113, 856–861 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Politis, G. & Messineo, P. The Campo Laborde site: New evidence for the Holocene survival of Pleistocene megafauna in the Argentine Pampas. Quat. Int. 191, 98–114 (2008).Article 

Google Scholar 
36.Politis, G. G., Messineo, P. G., Stafford, T. W. & Lindsey, E. L. Campo Laborde: a Late Pleistocene giant ground sloth kill and butchering site in the Pampas. Sci. Adv. 5, eaau4546 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open‐source release of Maxent. Ecography 40, 887–893 (2017).Article 

Google Scholar 
38.Fordham, D. A. et al. PaleoView: a tool for generating continuous climate projections spanning the last 21 000 years at regional and global scales. Ecography 40, 1348–1358 (2017).Article 

Google Scholar 
39.Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 1–9 (2018).ADS 
Article 
CAS 

Google Scholar 
40.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org (2020).41.Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Tonni, E. P., Carlini, A. A., Yané, G. J. S. & Figini, A. J. Cronología radiocarbónica y condiciones climáticas en la “Cueva del Milodón” (sur de Chile) durante el Pleistoceno Tardío. Ameghiniana 40, 609–615 (2003).
Google Scholar 
43.Tonni, E. P. & Carlini, A. A. Neogene vertebrates from Argentine Patagonia: their relationship with the most significant climatic changes. Dev. Quat. Sci. 11, 269–283 (2008).
Google Scholar 
44.Schmitt, S., Pouteau, R., Justeau, D., de Boissieu, F. & Birnbaum, P. ssdm: an r package to predict distribution of species richness and composition based on stacked species distribution models. Methods Ecol. Evol. 8, 1795–1803 (2017).Article 

Google Scholar 
45.Mothé, D. et al. An artifact embedded in an extinct proboscidean sheds new light on human-megafaunal interactions in the quaternary of South America. Quat. Sci. Rev. 229, 106125 (2020).Article 

Google Scholar 
46.Jaimes, A. Condiciones tafonómicas, huesos modificados y comportamiento humano en los sitios de matanza de El Vano (Tradición El Jobo) y Lange/Ferguson (Tradición Clovis). Bol. Antropol. Am. 41, 159–184 (2005).
Google Scholar 
47.Moreno, P. I. et al. Renewed glacial activity during the Antarctic Cold reversal and persistence of cold conditions until 11.5 ka in SW Patagonia. Geology 37, 375–378 (2009).ADS 
CAS 
Article 

Google Scholar 
48.Obase, T. & Abe‐Ouchi, A. Abrupt Bølling‐Allerød warming simulated under gradual forcing of the last deglaciation. Geophys. Res. Lett. 46, 11397–11405 (2019).ADS 
Article 

Google Scholar 
49.de Porras, M. E. et al. Environmental and climatic changes in central Chilean Patagonia since the late glacial (Mallín El Embudo, 44 S). Clim 10, 1063–1078 (2014).ADS 

Google Scholar 
50.Mendelová, M., Hein, A. S., Rodes, A., Smedley, R. K. & Xu, S. Glacier expansion in central Patagonia during the Antarctic Cold Reversal followed by retreat and stabilisation during the Younger Dryas. Quat. Sci. Rev. 227, 106047 (2020).Article 

Google Scholar 
51.Villavicencio, N. A. et al. Combination of humans, climate, and vegetation change triggered Late Quaternary megafauna extinction in the Última Esperanza region, southern Patagonia, Chile. Ecography 39, 125–140 (2016).Article 

Google Scholar 
52.Prieto, A. R. Vegetational History of the Late glacial-holocene transition in the grasslands of Eastern Argentina. Palaeogr. Palaeoclimatol. Palaeoecol. 157, 167–188 (2000).ADS 
Article 

Google Scholar 
53.Miotti, L., Tonni, E. & Marchionni, L. What happened when the Pleistocene megafauna became extinct? Quat. Int. 473, 173–189 (2018).Article 

Google Scholar 
54.Méndez, C. et al. J. L. Human effects in Holocene fire dynamics of central Western Patagonia (~ 44° S, Chile). Front. Ecol. Evol. 4, 100 (2016).Article 

Google Scholar 
55.Pires, M. M. et al. Pleistocene megafaunal interaction networks became more vulnerable after human arrival. Proc. R. Soc. Lon. [Biol.] 282, 20151367 (2015).
Google Scholar 
56.Pires, M. et al. Before, during and after megafaunal extinctions: human impact on Pleistocene-Holocene trophic networks in South Patagonia. Quat. Sci. Rev. 250, 106296 (2020).Article 

Google Scholar 
57.Haynes, G. & Klimowicz, J. Recent elephant-carcass utilization as a basis for interpreting mammoth exploitation. Quat. Int. 359, 19–37 (2015).Article 

Google Scholar 
58.Surovell, T. A. & Grund, B. S. The associational critique of quaternary overkill and why it is largely irrelevant to the extinction debate. Am. Antiq. 77, 672–687 (2012).Article 

Google Scholar 
59.Rindel, D. D., Moscardi, B. F., & Perez, S. I. The distribution of the guanaco (Lama guanicoe) in Patagonia during Late Pleistocene–Holocene and its importance for prehistoric human diet. Holocene, https://doi.org/10.1177/0959683620981689 (2020).60.Metcalf, J. L. et al. Synergistic roles of climate warming and human occupation in Patagonian megafaunal extinctions during the Last Deglaciation. Sci. Adv. 2, e1501682 (2016).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Villavicencio, N. A., Corcoran, D. & Marquet, P. A. Assessing the causes behind the late quaternary extinction of Hhorses in South america using species distribution models. Fron. Ecol. Evol. 7, 226 (2019).Article 

Google Scholar 
62.Prado, J. L., & Alberdi, M. T. Fossil Horses of South America. Phylogeny, Systemics and Ecology. (Springer, 2017).63.Varela, L. & Fariña, R. A. Co-occurrence of mylodontid sloths and insights on their potential distributions during the late Pleistocene. Quat. Res. 85, 66–74 (2016).Article 

Google Scholar 
64.R-Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2020).65.Menegaz, A. N., Goin, F. J. & Jaureguizar, E. O. Análisis morfológico y morfométrico multivariado de los representantes fósiles y vivientes del género Lama (Artiodactyla, Camelidae). Sus implicancias sistemáticas, biogeográficas, ecológicas y biocronológicas. Ameghiniana 26, 153–172 (1989).
Google Scholar 
66.Scherer, C. S. Os Camelidae Lamini (Mammalia, Artiodactyla) do plesistoceno da América do Sul: aspectos taxonômicos e filogenéticos. Unpublished PhD Thesis, Universidade Federal do Rio Grande do Sul. (2009).67.Weinstock, J. et al. The Late Pleistocene distribution of vicuñas (Vicugna vicugna) and the “extinction” of the gracile llama (“Lama gracilis”): new molecular data. Quat. Sci. Rev. 28, 1369–1373 (2009).ADS 
Article 

Google Scholar 
68.Mothé, D., Avilla, L. S. & Cozzuol, M. A. The south American gomphotheres (Mammalia, Proboscidea, Gomphotheriidae): taxonomy, phylogeny, and biogeography. J. Mamm. Evol. 20, 23–32 (2013).Article 

Google Scholar 
69.Prado, J. L. & Alberdi, M. T. Global evolution of equidae and gomphotheriidae from South America. Integ. Zool. 9, 434–443 (2014).Article 

Google Scholar  More

1.Garlapati, D., Charankumar, B., Ramu, K., Madeswaran, P. & Ramana Murthy, M. V. A review on the applications and recent advances in environmental DNA (eDNA) metagenomics. Rev. Environ. Sci. Biotechnol. 18, 389–411 (2019).CAS 
Article 

Google Scholar 
2.Rees, H. C., Maddison, B. C., Middleditch, D. J., Patmore, J. R. M. & Gough, K. C. REVIEW: The detection of aquatic animal species using environmental DNA—a review of eDNA as a survey tool in ecology. J. Appl. Ecol. 51, 1450–1459 (2014).CAS 
Article 

Google Scholar 
3.Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).Article 

Google Scholar 
4.Thomsen, P. F. & Willerslev, E. Environmental DNA—an emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).Article 

Google Scholar 
5.Knudsen, S. W. et al. Species-specific detection and quantification of environmental DNA from marine fishes in the Baltic Sea. J. Exp. Mar. Biol. Ecol. 510, 31–45 (2019).CAS 
Article 

Google Scholar 
6.Salter, I., Joensen, M., Kristiansen, R., Steingrund, P. & Vestergaard, P. Environmental DNA concentrations are correlated with regional biomass of Atlantic cod in oceanic waters. Commun. Biol. 2, 1–9 (2019).Article 
CAS 

Google Scholar 
7.Stoeckle, M. Y. et al. Trawl and eDNA assessment of marine fish diversity, seasonality, and relative abundance in coastal New Jersey, USA. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsaa225 (2020).Article 

Google Scholar 
8.Maruyama, A., Nakamura, K., Yamanaka, H., Kondoh, M. & Minamoto, T. The release rate of environmental DNA from juvenile and adult fish. PLoS ONE 9, e114639 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Jane, S. F. et al. Distance, flow and PCR inhibition: eDNA dynamics in two headwater streams. Mol. Ecol. Resour. 15, 216–227 (2015).CAS 
PubMed 
Article 

Google Scholar 
10.Yoshitake, K. et al. HaCeD-Seq: a novel method for reliable and easy estimation about the fish population using haplotype count from eDNA. Mar. Biotechnol. N. Y. 21, 813–820 (2019).CAS 
Article 

Google Scholar 
11.Kinde, I., Wu, J., Papadopoulos, N., Kinzler, K. W. & Vogelstein, B. Detection and quantification of rare mutations with massively parallel sequencing. Proc. Natl. Acad. Sci. 108, 9530–9535 (2011).ADS 
PubMed 
Article 

Google Scholar 
12.Casbon, J. A., Osborne, R. J., Brenner, S. & Lichtenstein, C. P. A method for counting PCR template molecules with application to next-generation sequencing. Nucleic Acids Res. 39, e81–e81 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2012).CAS 
Article 

Google Scholar 
14.Vander Heiden, J. A. et al. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930–1932 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Shugay, M. et al. MAGERI: Computational pipeline for molecular-barcoded targeted resequencing. PLOS Comput. Biol. 13, e1005480 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Clement, K., Farouni, R., Bauer, D. E. & Pinello, L. AmpUMI: design and analysis of unique molecular identifiers for deep amplicon sequencing. Bioinformatics 34, i202–i210 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Kumar, G., Kocour, M. & Kunal, S. P. Mitochondrial DNA variation and phylogenetic relationships among five tuna species based on sequencing of D-loop region. Mitochondrial DNA Part A 27, 1976–1980 (2016).CAS 
Article 

Google Scholar 
18.Nomura, S. et al. Genetic population structure of the Pacific bluefin tuna Thunnus orientalis and the yellowfin tuna Thunnus albacares in the North Pacific Ocean. Fish. Sci. 80, 1193–1204 (2014).CAS 
Article 

Google Scholar 
19.Gleiss, A. C., Schallert, R. J., Dale, J. J., Wilson, S. G. & Block, B. A. Direct measurement of swimming and diving kinematics of giant Atlantic bluefin tuna (Thunnus thynnus). R. Soc. Open Sci. 6, 190203 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Juan-Jordá, M. J., Mosqueira, I., Freire, J. & Dulvy, N. K. The conservation and management of tunas and their relatives: setting life history research priorities. PLoS ONE 8, e70405 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Shibata, M. et al. Transcriptomic features associated with energy production in the muscles of Pacific bluefin tuna and Pacific cod. Biosci. Biotechnol. Biochem. 80, 1114–1124 (2016).CAS 
PubMed 
Article 

Google Scholar 
22.Swanson, D., Block, R. & Mousa, S. A. Omega-3 fatty acids EPA and DHA: health benefits throughout life. Adv. Nutr. Bethesda Md 3, 1–7 (2012).CAS 
Article 

Google Scholar 
23.Collette, B. B. et al. Conservation. High value and long life–double jeopardy for tunas and billfishes. Science 333, 291–292 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
24.Kumai, H. & Miyashita, S. Life cycle of the Pacific bluefin tuna is completed under reared condition. Nippon Suisan Gakkaishi Jpn. 69, 124–127 (2003).Article 

Google Scholar 
25.Miyashita, S. et al. Maturation and spawning of cultured bluefin tuna, Thunnus thynnus. Suisanzoushoku Jpn. 48, 475–488 (2000).
Google Scholar 
26.Cho, J. et al. Production performance of Pacific bluefin tuna Thunnus orientalis larvae and juveniles fed commercial diets and effects of switching diets. Aquac. Sci. 64, 359–370 (2016).CAS 

Google Scholar 
27.Tsuji, S. et al. Environmental DNA analysis shows high potential as a tool for estimating intraspecific genetic diversity in a wild fish population. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13165 (2020).Article 
PubMed 

Google Scholar 
28.Tsuji, S. et al. Evaluating intraspecific genetic diversity using environmental DNA and denoising approach: a case study using tank water. Environ. DNA 2, 42–52 (2020).Article 

Google Scholar 
29.Ppyun, H. et al. Improved PCR performance and fidelity of double mutant Neq A523R/N540R DNA polymerase. Enzym. Microb. Technol. 82, 197–204 (2016).CAS 
Article 

Google Scholar 
30.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

Google Scholar 
31.Myers, R. A. & Worm, B. Rapid worldwide depletion of predatory fish communities. Nature 423, 280–283 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
32.Worm, B. et al. Rebuilding global fisheries. Science 325, 578–585 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
33.Bravington, M., Grewe, P. & Davies, C. Fishery-independent estimate of spawning biomass of southern bluefin tuna through identification of close-kin using genetic markers. FRDC Report2007034CSIRO Aust. (2014).34.Bravington, M. V., Grewe, P. M. & Davies, C. R. Absolute abundance of southern bluefin tuna estimated by close-kin mark-recapture. Nat. Commun. 7, 13162 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Song, N., Jia, N., Yanagimoto, T., Lin, L. & Gao, T. Genetic differentiation of Trachurus japonicus from the Northwestern Pacific based on the mitochondrial DNA control region. Mitochondrial DNA 24, 705–712 (2013).CAS 
PubMed 
Article 

Google Scholar 
36.Zhu, Y., Cheng, Q. & Rogers, S. M. Genetic structure of Scomber japonicus (Perciformes: Scombridae) along the coast of China revealed by complete mitochondrial cytochrome b sequences. Mitochondrial DNA Part DNA Mapp. Seq. Anal. 27, 3828–3836 (2016).CAS 
Article 

Google Scholar 
37.Tzeng, T.-D. Population structure and historical demography of the spotted mackerel (Scomber australasicus) off Taiwan inferred from mitochondrial control region sequencing. Zool. Stud. 8, 656–663 (2007).
Google Scholar 
38.Ichinokawa, M., Okamura, H. & Kurota, H. The status of Japanese fisheries relative to fisheries around the world. ICES J. Mar. Sci. 74, 1277–1287 (2017).Article 

Google Scholar 
39.Pikitch, E. K. et al. Ecosystem-based fishery management. Science 305, 346–347 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Yates, M. C., Fraser, D. J. & Derry, A. M. Meta-analysis supports further refinement of eDNA for monitoring aquatic species-specific abundance in nature. Environ. DNA 1, 5–13 (2019).Article 

Google Scholar 
41.Lacoursière Roussel, A., Rosabal, M. & Bernatchez, L. Estimating fish abundance and biomass from eDNA concentrations: variability among capture methods and environmental conditions. Mol. Ecol. Resour. 16, 1401–1414 (2016).PubMed 
Article 
CAS 

Google Scholar 
42.Yamamoto, S. et al. Environmental DNA as a ‘Snapshot’ of fish distribution: a case study of Japanese jack mackerel in Maizuru bay, Sea of Japan. PLoS ONE 11, e0149786 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Untergasser, A. et al. Primer3–new capabilities and interfaces. Nucleic Acids Res. 40, e115 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Sekino, M. & Yamashita, H. Mitochondrial DNA barcoding for Okinawan oysters: a cryptic population of the Portuguese oyster Crassostrea angulata in Japanese waters. Fish. Sci. 79, 61–76 (2013).CAS 
Article 

Google Scholar 
45.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Minamoto, T., Naka, T., Moji, K. & Maruyama, A. Techniques for the practical collection of environmental DNA: filter selection, preservation, and extraction. Limnology 17, 23–32 (2016).CAS 
Article 

Google Scholar 
47.Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

Population dynamicsThe abundance of A. omorii peaked in April 2016, March 2017, and April–May 2018, ranging from 2.99 × 104 to 6.73 × 104 individuals m−3 (Fig. 3, Table 1). Anakubo and Murano28 reported that the abundance of A. omorii, including individuals from the nauplius to adult stage, peaked at 5.34 × 104 m−3 in April 1981 in Tokyo Bay. Itoh and Aoki18 surveyed A. omorii in Tokyo Bay from March 1990 to November 1992 and found that abundance peaked at 2.13 × 104 individuals m−3 in March. Tachibana et al.40 reported that abundance peaked at 2.60 × 104 individuals m−3 in April in 2007. The peak abundance in Tokyo Bay apparently occurs between March and May; this pattern was also observed in the Seto Inland Sea (Table 1). According to Kasahara et al.15, the peak period of abundance of adults and later-stage copepodites in the Seto Inland Sea occurred in mid-May at  > 3.5 × 104 individuals m−3. The abundance of adults and later-stage copepodites significantly decreased in July. The planktonic population disappeared from the water column in mid-August and recovered in November, when the water temperature was  50 g wet weight m−3). Hence summer jellyfish bloom probably has a strong negative impact on the abundance of A. omorii. Unfortunately, we do not have enough information on jellyfish abundance in Onagawa Bay, effect of jellyfish should be considered for the better understanding of A. omorii dynamics in future.A. omorii abundance became zero only from September to November 2016 in the 3 years of the present study. However, in other years, A. omorii appeared at low abundance in summer, as reported in previous studies conducted in Tokyo Bay 16,18,28,40. The period of complete disappearance was short, and low densities were recorded throughout the year in most studies (Table 1), indicating that the population is maintained in the planktonic stages even under unfavorable conditions in Tokyo Bay.EPR and egg type dynamicsSEPR peaked in winter (January or February) and May in both years at both stations (Fig. 8a). The second peaks were caused by increase in unhatched egg production, except for station CB in 2017 (Figs. 7b,c and 8b,c). Increase in unhatched egg production occurred when the surface water temperature exceeded 18 °C (Fig. 2a) and day length increased to  > 14 h, similar to that reported by Uye5. From January to May 1982, 80% of eggs that were newly spawned by A. omorii females collected in Fukuyama Harbor of the Seto Inland Sea were subitaneous; resting eggs appeared in June when the surface water temperature exceeded 17.5 °C. In the present study, on June 9, 32% of the total eggs produced were resting and did not hatch within 14 days but hatched after being reincubated at 15 °C for 2 weeks. Uye5 also demonstrated the effect of photoperiod on egg production by A. omorii via laboratory experiments under two temperature conditions (15 °C and 20 °C ± 1 °C). Approximately half were resting eggs under l4L–10D photoperiodic conditions at both water temperatures, indicating that photoperiod is important for shift to resting eggs. Many copepods have dormancy strategies at the thermal limits of species distribution46. In Tokyo Bay, the photoperiod exceeds 14 h in mid-May, when the water temperature usually exceeds 18 °C (Fig. 2a). Therefore, higher water temperatures and increased day length periods are synchronized in early summer in Tokyo Bay and may serve as cues for diapause egg production.Unhatched PEPR exceeded subitaneous PEPR from May to June (Fig. 9), when abundance drastically decreased to  45%) (Fig. 6) in the late period of A. omorii appearance (in May at station CB and in early June at station F3). However, in early June at station CB and late June at station F3, when the abundance sharply decreased, the proportion of females producing subitaneous eggs increased, whereas those producing quiescent eggs decreased from the previous month (Fig. 11). Uye5 reported a similar result; the proportion of diapause eggs to total eggs produced by A. omorii in the Seto Inland Sea peaked on June 9 and then reduced by half by June 30. Quiescent eggs have been defined as subitaneous eggs with arrested development that remain in a quiescent stage in unsuitable environmental conditions49. Uye5 defined diapause eggs as eggs that did not hatch during 2 weeks at in situ water temperatures but hatched within 2 weeks when the incubation temperature decreased to 15 °C. These eggs may be classified as quiescent eggs in the present study. The results of Uye5 and the present study suggest that not all produced eggs of this species shift from subitaneous to quiescent eggs at higher water temperatures.As mentioned in the previous subsection, a planktonic population of A. omorii has been found in mid-summer at low abundance in Tokyo Bay16,18,28,40 (Fig. 3). Similar results were reported in Maizuru Bay50. Itoh et al.45 investigated the vertical distribution of copepods at 1-m depth intervals at station F3 in Tokyo Bay in mid-summer, when hypoxia develops near the bottom, and showed that A. omorii population had a sharp peak, with densities exceeding 1.5 × 103 individuals m−3, at 8 m at a water temperature of 18 °C and just above the hypoxic zone45. This suggests that A. omorii maintains a planktonic stage even at low density in mid-summer, whereas most of the population estivates by forming resting eggs in bottom sediments. These mid-summer populations are presumably hatched from subitaneous eggs spawned in mid-July (Figs. 7, 10). Uye5 also reported that more than half of the eggs were still subitaneous in late July in the Seto Inland Sea. Therefore, we tentatively think that this phenomenon is a bet-hedging strategy of A. omorii in an unfavorable and uncertain environment. In contrast, Ueda50 stated that the increase in subitaneous EPR in summer was due to immature development of this species. Thus, these remaining populations may not contribute to the autumnal development of the population. To understand how A. omorii survive in mid-summer, more detailed field investigations are warranted, including egg and nauplii dynamics in the water column, egg hatching process from sediments, and differences in endogenous factors in individual females producing subitaneous and diapause eggs in summer.Information on A. omorii’s delayed-hatching eggs is strictly limited. Delayed-hatching eggs are eggs hatching over a wide time span regardless of environmental conditions4,11. Takayama and Toda4 defined the unhatched eggs of A. japonica hatching during 72 h–50 days as “delayed-hatching eggs.” Thus, delayed-hatching eggs may have been included in the quiescent and diapause eggs defined in this study. Our results showed that no eggs hatched between 48–96 h and 7 days in the experiment at in situ water temperature; many quiescent eggs hatched within a few days after reincubation at lower water temperatures (Figs. 10, 11). Therefore, delayed-hatching eggs may not have been produced in the present study.Effects of water temperature on the production of subitaneous and diapause eggsMultiple regression analysis revealed that subitaneous SEPR negatively correlated with bottom water temperature, inconsistent with the results of Uye3. EPR and copepod growth generally increase with increased water temperature51. Uye3 reported that EPR of A. omorii also increased with water temperature; they developed a simple model equation describing the fecundity of A. omorii in Onagawa Bay via a laboratory experiment:$${text{F}} = 0.000{331 }left( {{text{T}} + {12}.0} right)^{{{3}.{25}}} {text{SW}}_{{text{f}}} /left( {0.{47}0 + {text{S}}} right),$$
where F is daily fecundity (eggs female−1 day−1), T is water temperature (°C), S is chlorophyll a concentration (µg L−1), and Wf is female carbon content (µg). The fecundity predicted by the above described model was similar to the observed EPR of this species in Onagawa Bay3.Many studies have used Uye’s equation to estimate A. omorii egg production. Kang et al.52 reported A. omorii’s EPR in Ilkwang Bay to be 22–57 eggs female−1 day−1, which was higher than that in the present study (1.6–18.7 eggs female−1 day−1) (Fig. 7a). Liang and Uye17 estimated A. omorii’s EPR in the Seto Inland Sea by two methods: the above described model (estimated incubation fecundity)3 and based on the number of eggs remaining in the water column and the adult female population (egg-ratio fecundity)53. In the Seto Inland Sea, the estimated incubation fecundity was 26–60 eggs female−1 day−1 and the egg-ratio fecundity was 0.5–25 eggs female−1 day−1; the estimated incubation fecundity was always greater than the egg-ratio fecundity17.Suspension-feeding copepods may ingest their own eggs and nauplii. In the Seto Inland Sea, possible egg predators were the dominant copepods Centropages abdominalis and A. omorii54. Based on their abundance (0.2–39 predators L−1) and assuming a predator clearance rate of 50 mL d−1, C. abdominalis and A. omorii could remove 1–86% of eggs in the water column per day. Liang and Uye17 noted that their predators were abundant when the abundance of surviving eggs in the water column was low; therefore, they tentatively concluded that the difference between the two estimates was due to egg loss by predation, including cannibalism. However, it is unlikely that fecundity reached its highest value ( > 50 eggs female−1 day−1) in mid-July when the population disappeared from the water column17. At that time, the water temperature was 25 °C, which also does not support the increase in fecundity observed by Liang and Uye17.The model equation of Uye3 was derived from Onagawa Bay, where the average water temperature is 7.7–21.9°C55. In laboratory experiments using A. omorii from Onagawa Bay, EPR decreased when the water temperature exceeded 22.5°C3. In Uye’s equation3, the decrease in egg production above 22.5 °C was not foreseen, whereas water temperature exceeded 22.5 °C in the Seto Inland Sea17, Ikkwang Bay52, and Tokyo Bay (Fig. 2). Thus, Uye’s model equation3 is not applicable to these warm environments.Based on the temperature regime, seasonal population dynamics and egg types produced are divided into two types: no resting egg production in the colder Onagawa Bay and resting egg production in the warmer Tokyo Bay and Seto Inland Sea. As mentioned above, A. omorii in Onagawa Bay exists throughout the year, even in summer14 and hardly produces diapause eggs5,7. However, the population almost disappears in late summer in Tokyo Bay16,18,28,40 and the Seto Inland Sea15,17. Furthermore, in these warm coastal waters, A. omorii produced diapause eggs just before copepodite disappearance from the water column. Therefore, a separate equation for estimating egg production should be developed, depending on the temperature regime of the habitat.Recent climate change, particularly global warming, may affect A. omorii’s egg production. In Tokyo Bay, between 1955 and 2015, the water temperature increased by 1.0 °C and 0.94 °C at the surface and bottom layers, respectively, in winter and autumn56,57. In summer, the water temperature at both the surface and bottom layers decreased, probably due to strengthened estuary circulation56,57. Considering the response of A. omorii to water temperature, the increase in winter temperature might reduce subitaneous egg production, resulting in delayed population increase. In contrast, the decrease in summer temperature might lead to reduced diapause egg production per amount of body carbon. The long-term trends of water temperature might have different effects on each egg type production and alter the dynamics of A. omorii egg production.Effects of phytoplankton composition on the production of subitaneous and diapause eggsThe EPR of A. omorii may be saturated at low (1–2 µg L−1) chlorophyll a concentrations3,19. However, multiple regression analysis revealed that small diatoms stimulate subitaneous SEPR (Figs. 8, 12). The EPR at station CB was quite high ( > 14 eggs female−1 day−1) in January and February 2018, when the diatoms comprised Skeletonema and Chaetoceros. In contrast, at station F3, EPR drastically decreased from 18.7 ± 6.3 eggs female−1 day−1 in January to 8.4 ± 4.6 eggs female−1 day−1 in February 2018. The EPR at station F3 in February was significantly lower than that at station CB (Tukey’s post hoc test, p  95%) at station CB in January and February 2018, suggesting that small diatoms ingestion enhances A. omorii’s egg production.It is also likely that small nanoflagellates have an adverse effect on egg production. At station F3, the proportion of nanoflagellates to total phytoplankton carbon biomass was high ( > 93%) in February and March (Fig. 12), when EPR was quite low ( More

1.Coon, K. L., Brown, M. R. & Strand, M. R. Mosquitoes host communities of bacteria that are essential for development but vary greatly between local habitats. Mol. Ecol. 25, 5806–5826 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Coon, K. L., Brown, M. R. & Strand, M. R. Gut bacteria differentially affect egg production in the anautogenous mosquito Aedes aegypti and facultatively autogenous mosquito Aedes atropalpus (Diptera: Culicidae). Parasit. Vectors. https://doi.org/10.1186/s13071-016-1660-9 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
3.Minard, G., Mavingui, P. & Moro, C. Diversity and function of bacterial microbiota in the mosquito holobiont. Parasit. Vectors 6, 146 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Strand, M. R. Composition and functional roles of the gut microbiota in mosquitoes. Curr. Opin. Insect Sci. 28, 59–65 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Minard, G. et al. French invasive Asian tiger mosquito populations harbor reduced bacterial microbiota and genetic diversity compared to Vietnamese autochthonous relatives. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00970 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Boissière, A. et al. Midgut microbiota of the malaria mosquito vector Anopheles gambiae and interactions with Plasmodium falciparum infection. PLoS Pathog. 8, e1002742 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
7.Osei-Poku, J., Mbogo, C. M., Palmer, W. J. & Jiggins, F. M. Deep sequencing reveals extensive variation in the gut microbiota of wild mosquitoes from Kenya. Mol. Ecol. 21, 5138–5150 (2012).CAS 
PubMed 
Article 

Google Scholar 
8.Muturi, E. J. et al. Culex pipiens and Culex restuans mosquitoes harbor distinct microbiota dominated by few bacterial taxa. Parasit. Vectors 9, 18 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Muturi, E. J., Ramirez, J. L., Rooney, A. P. & Kim, C.-H. Comparative analysis of gut microbiota of mosquito communities in central Illinois. PLoS Negl. Trop. Dis. 11, e0005377 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Coon, K. L., Vogel, K. J., Brown, M. R. & Strand, M. R. Mosquitoes rely on their gut microbiota for development. Mol. Ecol. 23, 2727–2739 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Ramirez, J. L. et al. Chromobacterium Csp_P reduces malaria and dengue infection in vector mosquitoes and has entomopathogenic and in vitro anti-pathogen activities. PLoS Pathog. 10, e1004398 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Gaio, A. D. O. et al. Contribution of midgut bacteria to blood digestion and egg production in Aedes aegypti (diptera: Culicidae) (L.). Parasit. Vectors 4, 105 (2011).PubMed Central 
Article 
PubMed 

Google Scholar 
13.Gonzalez-Ceron, L., Santillan, F., Rodriguez, M. H., Mendez, D. & Hernandez-Avila, J. E. Bacteria in midguts of field-collected Anopheles albimanus block Plasmodium vivax sporogonic development. J. Med. Entomol. 40, 371–374 (2003).PubMed 
Article 

Google Scholar 
14.Tchioffo, M. T. et al. Modulation of malaria infection in Anopheles gambiae mosquitoes exposed to natural midgut bacteria. PLoS ONE. https://doi.org/10.1371/annotation/d8908395-a526-428c-b9ed-4430aaf8f7d7 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Dutra, H. L. C. et al. Wolbachia blocks currently circulating Zika virus isolates in Brazilian Aedes aegypti mosquitoes. Cell Host Microbe 19, 771–774 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Gimonneau, G. et al. Composition of Anopheles coluzzii and Anopheles gambiae microbiota from larval to adult stages. Infect. Genet. Evol. 28, 715–724 (2014).PubMed 
Article 

Google Scholar 
17.Rani, A., Sharma, A., Rajagopal, R., Adak, T. & Bhatnagar, R. K. Bacterial diversity analysis of larvae and adult midgut microflora using culture-dependent and culture-independent methods in lab-reared and field-collected Anopheles stephensi—An Asian malarial vector. BMC Microbiol. 9, 96 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Muturi, E. J., Bara, J. J., Rooney, A. P. & Hansen, A. K. Midgut fungal and bacterial microbiota of Aedes triseriatus and Aedes japonicus shift in response to La Crosse virus infection. Mol. Ecol. 25, 4075–4090 (2016).CAS 
PubMed 
Article 

Google Scholar 
19.Muturi, E. J. et al. Mosquito microbiota cluster by host sampling location. Parasit. Vectors 11, 468 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Wang, X. et al. Bacterial microbiota assemblage in Aedes albopictus mosquitoes and its impacts on larval development. Mol. Ecol. 27, 2972–2985 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Wang, Y., Gilbreath, T. M., Kukutla, P., Yan, G. & Xu, J. Dynamic gut microbiome across life history of the malaria mosquito Anopheles gambiae in Kenya. PLoS ONE 6, e24767 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
22.Akorli, J., Namaali, P. A., Ametsi, G. W., Egyirifa, R. K. & Pels, N. A. P. Generational conservation of composition and diversity of field-acquired midgut microbiota in Anopheles gambiae (sensu lato) during colonization in the laboratory. Parasit. Vectors 12, 27 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Akorli, J. et al. Seasonality and locality affect the diversity of Anopheles gambiae and Anopheles coluzzii midgut microbiota from Ghana. PLoS ONE 11, e0157529 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Lindh, J. M., Borg-Karlson, A. K. & Faye, I. Transstadial and horizontal transfer of bacteria within a colony of Anopheles gambiae (Diptera: Culicidae) and oviposition response to bacteria-containing water. Acta Trop. 107, 242–250 (2008).CAS 
PubMed 
Article 

Google Scholar 
25.Duguma, D. et al. Developmental succession of the microbiome of Culex mosquitoes. BMC Microbiol. 15, 140 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Chavshin, A. R. et al. Malpighian tubules are important determinants of Pseudomonas transstadial transmission and longtime persistence in Anopheles stephensi. Parasit. Vectors 8, 36 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Duguma, D. et al. Bacterial communities associated with Culex mosquito larvae and two emergent aquatic plants of bioremediation importance. PLoS ONE 8, 1–11 (2013).Article 
CAS 

Google Scholar 
29.Dickson, L. B. et al. Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector. Sci. Adv. 3, e1700585 (2017).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
30.Minard, G. et al. Shared larval rearing environment, sex, female size and genetic diversity shape Ae. albopictus bacterial microbiota. PLoS ONE 13, e0194521 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Muturi, E. J., Dunlap, C., Ramirez, J. L., Rooney, A. P. & Kim, C. H. Host blood-meal source has a strong impact on gut microbiota of Aedes aegypti. FEMS Microbiol. Ecol. 95, 213 (2018).
Google Scholar 
32.Villegas, L. E. M. et al. Zika virus infection modulates the bacterial diversity associated with Aedes aegypti as revealed by metagenomic analysis. PLoS ONE 13, e0190352 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Tchioffo, M. T. et al. Dynamics of bacterial community composition in the malaria mosquito’s epithelia. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01500 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Buck, M. et al. Bacterial associations reveal spatial population dynamics in Anopheles gambiae mosquitoes. Sci. Rep. https://doi.org/10.1038/srep22806 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Yee, D. A. Tires as habitats for mosquitoes: A review of studies within the eastern United States. J. Med. Entomol. 45, 581–593 (2008).PubMed 

Google Scholar 
36.Silver, J. B. Sampling the larval population. In Mosquito Ecology: Field Sampling Methods Vol. 165 (ed. Service, M. W.) 137–338 (Springer, Berlin, 2008).
Google Scholar 
37.Fish, D. & Carpenter, S. R. Leaf litter and larval mosquito dynamics in tree-hole ecosystems. Ecology 63, 283–288 (1982).Article 

Google Scholar 
38.Murrell, E. G. et al. Detritus type alters the outcome of interspecific competition between Aedes aegypti and Aedes albopictus (Diptera: Culicidae). J. Med. Entomol. 45, 375–383 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Gardner, A. M. et al. Terrestrial vegetation and aquatic chemistry influence larval mosquito abundance in catch basins, Chicago, USA. Parasit. Vectors 6, 9 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Walker, E. D., Lawson, D. L., Merritt, R. W., Morgan, W. T. & Klug, M. J. Nutrient dynamics, bacterial populations, and mosquito productivity in tree hole ecosystems and microcosms. Ecology 72, 1529–1546 (1991).Article 

Google Scholar 
41.Beier, J., Patricoski, C., Travis, M. & Kranzfelder, J. Influence of water chemical and environmental parameters on larval mosquito dynamics in tires. Environ. Entomol. 12, 434–438 (1983).Article 

Google Scholar 
42.Kaufman, M. G. et al. Stable isotope analysis reveals detrital resource base sources of the tree hole mosquito, Aedes triseriatus. Ecol. Entomol. 35, 586–593 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
43.Muturi, E. J., Orindi, B. O. & Kim, C. H. Effect of leaf type and pesticide exposure on abundance of bacterial taxa in mosquito larval habitats. PLoS ONE 8, e71812 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
44.Muturi, E. J., Allan, B. F. & Ricci, J. Influence of leaf detritus type on production and longevity of container-breeding mosquitoes. Environ. Entomol. 41, 1062–1068 (2012).PubMed 
Article 

Google Scholar 
45.Walker, E. D., Merritt, R. W., Kaufman, M. G., Ayres, M. P. & Riedel, M. H. Effects of variation in quality of leaf detritus on growth of the eastern tree-hole mosquito, Aedes triseriatus (Diptera: Culicidae). Can. J. Zool. 75, 706–718 (1997).Article 

Google Scholar 
46.Gardner, A. M., Allan, B. F., Frisbie, L. A. & Muturi, E. J. Asymmetric effects of native and exotic invasive shrubs on ecology of the West Nile virus vector Culex pipiens (Diptera: Culicidae). Parasit. Vectors 8, 329 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Gardner, A. M., Muturi, E. J. & Allan, B. F. Discovery and exploitation of a natural ecological trap for a mosquito disease vector. Proc. Biol. Sci. 285, 20181962 (2018).PubMed 
PubMed Central 

Google Scholar 
48.Yee, D. A. & Juliano, S. A. Consequences of detritus type in an aquatic microsystem: Effects on water quality, micro-organisms and performance of the dominant consumer. Freshw. Biol. 51, 448–459 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
49.Carpenter, S. R. Stemflow chemistry: Effects on population dynamics of detritivorous mosquitoes in tree-hole ecosystems. Oecologia 53, 1 (1982).PubMed 
Article 
ADS 

Google Scholar 
50.Ramírez, A., Pringle, C. M. & Molina, L. Effects of stream phosphorus levels on microbial respiration. Freshw. Biol. 48, 88–97 (2003).Article 

Google Scholar 
51.Peyton, E. L., Campbell, S. R., Candeletti, T. M., Romanoski, M. & Crans, W. J. Aedes (Finlaya) japonius japonicus (Theobald), a new introduction into the United States. J. Am. Mosq. Control Assoc. 15, 238–241 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Takashima, I. & Rosen, L. Horizontal and vertical transmission of Japanese encephalitis virus by Aedes japonicus (Diptera: Culicidae). J. Med. Entomol. 26, 454–458 (1989).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Scott, J. J. The Ecology of the Exotic Mosquito Ochlerotatus (Finlaya) Japonicus japonicus (Theobald 1901) (Diptera: Culicidae) and an Examination of Its Role in the West Nile Virus Cycle in New Jersey (The State University of New Jersey, 2003).
Google Scholar 
54.Harris, M. C. et al. La crosse virus in Aedes japonicus japonicus mosquitoes in the Appalachian Region, United States. Emerg. Infect. Dis. 21, 646–649 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Yang, F. et al. Cache Valley virus in Aedes japonicus japonicus mosquitoes, Appalachian region, United States. Emerg. Infect. Dis. 24, 553–557 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Barker, C. M., Paulson, S. L., Cantrell, S. & Davis, B. S. Habitat preferences and phenology of Ochlerotatus triseriatus and Aedes albopictus (Diptera: Culicidae) in southwestern Virginia. J. Med. Entomol. 40, 403–410 (2003).CAS 
PubMed 
Article 

Google Scholar 
57.Kaufman, M. G. & Fonseca, D. M. Invasion biology of Aedes japonicus japonicus (Diptera: Culicidae). Annu. Rev. Entomol. 59, 31–49 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Kling, L. J., Juliano, S. A. & Yee, D. A. Larval mosquito communities in discarded vehicle tires in a forested and unforested site: Detritus type, amount, and water nutrient differences. J. Vector Ecol. 32, 207–217 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Kaufman, M., Goodfriend, W., Kohler-Garrigan, A., Walker, E. & Klug, M. Soluble nutrient effects on microbial communities and mosquito production in Ochlerotatus triseriatus habitats. Aquat. Microb. Ecol. 29, 73–88 (2002).Article 

Google Scholar 
60.Potempa, J. & Pike, R. N. Bacterial peptidases. In Concepts in Bacterial Virulence Vol. 12 (eds Russell, W. & Herwald, H.) 132–180 (KARGER, 2004).
Google Scholar 
61.Willis, A. D. Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10, 2407 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
62.Chiu, C. H., Wang, Y. T., Walther, B. A. & Chao, A. An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics 70, 671–682 (2014).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
63.Dinsdale, E. A. et al. Multivariate analysis of functional metagenomes. Front. Genet. 4, 1–25 (2013).CAS 
Article 

Google Scholar 
64.Duguma, D., Hall, M. W., Smartt, C. T. & Neufeld, J. D. Temporal variations of microbiota associated with the immature stages of two Florida Culex mosquito vectors. Microb. Ecol. 74, 979–989 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Merritt, R. W., Dadd, R. H. & Walker, E. D. Feeding behavior, natural food, and nutritional relationships of larval mosquitoes. Annu. Rev. Entomol. 37, 349–374 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Yee, D. A., Kesavaraju, B. & Juliano, S. A. Larval feeding behavior of three co-occurring species of container mosquitoes. J. Vector Ecol. 29, 315–322 (2004).PubMed 
PubMed Central 

Google Scholar 
67.O’Donnell, D. L. et al. Comparison of larval foraging behavior of Aedes albopictus and Aedes japonicus (Diptera: Culicidae). J. Med. Entomol. 44, 984–989 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Dillon, R. J. & Dillon, V. M. The gut bacteria of insects: Nonpathogenic interactions. Annu. Rev. Entomol. 49, 71–92 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Pan, X. et al. Wolbachia induces reactive oxygen species (ROS)-dependent activation of the Toll pathway to control dengue virus in the mosquito Aedes aegypti. Proc. Natl. Acad. Sci. 109, 13–14 (2012).CAS 
Article 

Google Scholar 
70.Andrews, E. S., Crain, P. R., Fu, Y., Howe, D. K. & Dobson, S. L. Reactive oxygen species production and Brugia pahangi survivorship in Aedes polynesiensis with artificial Wolbachia infection types. PLoS Pathog. 8, 1003075 (2012).Article 
CAS 

Google Scholar 
71.Pei, D. et al. The waaL gene mutation compromised the inhabitation of Enterobacter sp. Ag1 in the mosquito gut environment. Parasit. Vectors. https://doi.org/10.1186/s13071-015-1049-1 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
72.Pang, X. et al. Mosquito C-type lectins maintain gut microbiome homeostasis. Nat. Microbiol. https://doi.org/10.1038/nmicrobiol.2016.23 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
73.Strand, M. R. The gut microbiota of mosquitoes: Diversity and function. Arthropod. Vector Controll. Dis. Transm. 1, 185–199 (2017).Article 

Google Scholar 
74.Dada, N. et al. Comparative assessment of the bacterial communities associated with Aedes aegypti larvae and water from domestic water storage containers. Parasit. Vectors 7, 391 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
75.Dillon, R. & Charnley, K. Mutualism between the desert locust Schistocerca gregaria and its gut microbiota. Res. Microbiol. 153, 503–509 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Sprockett, D., Fukami, T. & Relman, D. A. Role of priority effects in the early-life assembly of the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 15, 197–205 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Minard, G. et al. Pyrosequencing 16S rRNA genes of bacteria associated with wild tiger mosquito Aedes albopictus: A pilot study. Front. Cell. Infect. Microbiol. 4, 1–9 (2014).Article 
CAS 

Google Scholar 
78.Husseneder, C., Berestecky, J. M. & Grace, J. K. Changes in composition of culturable bacteria community in the gut of the formosan subterranean termite depending on rearing conditions of the host. Ann. Entomol. Soc. Am. 102, 498–507 (2009).Article 

Google Scholar 
79.Kim, C.-H., Lampman, R. L. & Muturi, E. J. Bacterial communities and midgut microbiota associated with mosquito populations from waste tires in east-central Illinois. J. Med. Entomol. 52, 63–75 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Krajacich, B. J. et al. Investigation of the seasonal microbiome of Anopheles coluzzii mosquitoes in Mali. PLoS ONE 13, e0194899 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Willems, A., De Ley, J., Gillis, M. & Kersters, K. Comamonadaceae, a new family encompassing the acidovorans rRNA complex, including Variovorax paradoxus gen. nov., comb. nov., for Alcaligenes paradoxus (Davis 1969). Int. J. Syst. Bacteriol. 41, 445–450 (1991).Article 

Google Scholar 
82.Xu, Y. et al. Bacterial community structure in tree hole habitats of Ochlerotatus triseriatus: Influences of larval feeding. J. Am. Mosq. Control Assoc. 24, 219–227 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
83.Nilsson, L. K. J., Sharma, A., Bhatnagar, R. K., Bertilsson, S. & Terenius, O. Presence of Aedes and Anopheles mosquito larvae is correlated to bacteria found in domestic water-storage containers. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiy058 (2018).Article 
PubMed 

Google Scholar 
84.Muturi, E. J., Donthu, R. K., Fields, C. J., Moise, I. K. & Kim, C.-H. Effect of pesticides on microbial communities in container aquatic habitats. Sci. Rep. 7, 44565 (2017).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
85.Duguma, D., Hall, M. W., Smartt, C. T. & Neufeld, J. D. Effects of organic amendments on microbiota ssociated with the Culex nigripalpus mosquito vector of the Saint Louis encephalitis and West Nile viruses. mSphere 2, e00387 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Muturi, E. J., Ramirez, J. L., Rooney, A. P. & Kim, C. H. Comparative analysis of gut microbiota of Culex restuans (Diptera: Culicidae) females from different parents. J. Med. Entomol. 55, 163–171 (2018).CAS 
PubMed 
Article 

Google Scholar 
87.Yadav, K. K. et al. Diversity of cultivable midgut microbiota at different stages of the Asian tiger mosquito, Aedes albopictus from Tezpur, India. PLoS ONE 11, e0167409 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
88.Kasalický, V. et al. Aerobic anoxygenic photosynthesis is commonly present within the genus Limnohabitans. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02116-17 (2018).Article 
PubMed 

Google Scholar 
89.Jezberová, J. et al. The Limnohabitans genus harbors generalistic and opportunistic subtypes: Evidence from spatiotemporal succession in a canyon-shaped reservoir. Appl. Environ. Microbiol. 83, 1–15 (2017).Article 

Google Scholar 
90.Briones, A. M., Shililu, J., Githure, J., Novak, R. & Raskin, L. Thorsellia anophelis is the dominant bacterium in a Kenyan population of adult Anopheles gambiae mosquitoes. ISME J. 2, 74–82 (2008).CAS 
PubMed 
Article 

Google Scholar 
91.Yee, D. A., Allgood, D., Kneitel, J. M. & Kuehn, K. A. Constitutive differences between natural and artificial container mosquito habitats: Vector communities, resources, microorganisms, and habitat parameters. J. Med. Entomol. 49, 482–491 (2012).CAS 
PubMed 
Article 

Google Scholar 
92.Kitching, R. Food Webs and Container Habitats. The Natural History and Ecology of Phytolemata (Cambridge University Press, 2000).
Google Scholar 
93.Daugherty, M. P., Alto, B. W. & Juliano, S. A. Invertebrate carcasses as a resource for competing Aedes albopictus and Aedes aegypti (Diptera: Culicidae). J. Med. Entomol. 37, 364–372 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
94.Yee, D. A., Kesavaraju, B. & Juliano, S. A. Direct and indirect effects of animal detritus on growth, survival, and mass of invasive container mosquito Aedes albopictus (Diptera: Culicidae). J. Med. Entomol. 44, 580–588 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Kaufman, M. G., Walker, E. D., Smith, T. W., Merritt, R. W. & Klug, M. J. Effects of larval mosquitoes (Aedes triseriatus) and stemflow on microbial community dynamics in container habitats. Appl. Environ. Microbiol. 65, 2661–2673 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Kaufman, M. G., Bland, S. N., Worthen, M. E., Walker, E. D. & Klug, M. J. Bacterial and fungal biomass responses to feeding by larval Aedes triseriatus (Diptera: Culicidae). J. Med. Entomol. 38, 711–719 (2001).CAS 
PubMed 
Article 

Google Scholar 
97.Walker, E. D. & Merritt, R. W. The significance of leaf detritus to mosquito (Diptera: Culicidae) productivity from treeholes. Environ. Entomol. 17, 199–206 (1988).Article 

Google Scholar 
98.Gusmão, D. S. et al. Culture-dependent and culture-independent characterization of microorganisms associated with Aedes aegypti (Diptera: Culicidae) (L.) and dynamics of bacterial colonization in the midgut. Acta Trop. 115, 275–281 (2010).PubMed 
Article 

Google Scholar 
99.Favia, G. et al. Bacteria of the genus Asaia stably associate with Anopheles stephensi, an Asian malarial mosquito vector. Proc. Natl. Acad. Sci. 104, 9047–9051 (2007).CAS 
PubMed 
Article 
ADS 

Google Scholar 
100.Favia, G. et al. Bacteria of the genus Asaia: A potential paratransgenic weapon against malaria. In Transgenesis and Management of Vector-Borne Diseases (ed. Aksoy, S.) 50–58 (Landes Biosciences and Springer, 2008).
Google Scholar 
101.Damiani, C. et al. Mosquito-bacteria symbiosis: The case of Anopheles gambiae and Asaia. Microb. Ecol. 60, 644–654 (2010).PubMed 
Article 

Google Scholar 
102.van Tol, S. & Dimopoulos, G. Influences of the mosquito microbiota on vector competence. In Advances in Insect Physiology Vol. 51 (ed. Shankland, K.) 249–291 (Elsevier, 2016).
Google Scholar 
103.Guégan, M. et al. The mosquito holobiont: Fresh insight into mosquito-microbiota interactions. Microbiome 6, 49 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
104.Lampman, R. L. & Novak, R. J. Oviposition preferences of Culex pipiens and Culex restuans for infusion-baited traps. J. Am. Mosq. Control Assoc. 12, 23–32 (1996).CAS 
PubMed 

Google Scholar 
105.Ross, H. H. & Horsfall, W. R. A Synopsis of the mosquitoes of Illinois. Illinois Natural History Survey Biological Notes Vol. 52 (1965).106.Farajollahi, A. & Price, D. C. A rapid identification guide for larvae of the most common North American container-inhabiting Aedes species of medical importance. J. Am. Mosq. Control Assoc. 29, 203–221 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
107.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
108.Muyzer, G., de Waal, E. & Uitterlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
109.Yang, B., Wang, Y. & Qian, P.-Y. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinform. 17, 135 (2016).Article 
CAS 

Google Scholar 
110.Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
111.DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
112.R core team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2017).113.Rstudio Team. RStudio: Integrated Development for R (2016).114.Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
115.McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
116.Hughes, J. B., Hellmann, J. J., Ricketts, T. H. & Bohannan, B. J. Counting the uncountable: Statistical approaches to estimating microbial diversity. Appl. Environ. Microbiol. 67, 4399–4406 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
117.Olszewski, T. D. A unified mathematical framework for the measurement of richness and evenness within and among multiple communities. Oikos 104, 377–387 (2004).Article 

Google Scholar 
118.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–856 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
119.Anderson, M. J. & Walsh, D. C. I. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).Article 

Google Scholar 
120.Oksanen, J. et al. Community ecology package: ordination, diversity and dissimilarities. Mol. Biol. Evol. 33, 1–282. https://doi.org/10.1093/molbev/msv334 (2018).CAS 
Article 

Google Scholar 
121.Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
122.Giacalone, M., Panarello, D. & Mattera, R. Multicollinearity in regression: An efficiency comparison between Lp-norm and least squares estimators. Qual. Quant. 52, 1831–1859 (2018).Article 

Google Scholar 
123.Friendly, M. & Fox, J. Visualizing generalized canonical discriminant and canonical correlation analysis. R package version 0.8-0 (2017). More

The illumination system often is considered the key setting in most image acquisitions30. Thus, we first studied this system under three different settings, namely 2Pt_Rf, RRf_P and Trans due to their practical and scientific merits. The schematic ray diagrams showing the lighting conditions and their experimental set-ups have already been illustrated (Fig. 1). The 2Pt_Rf system is one of the most-used illumination systems in the field of entomology and food-filth detection, for it allows the operator to view a wide area of samples, even when they are dark and thick9. Beetle elytra (and other external surfaces of beetles) are coated with natural waxy substances and sometimes also with setae (hair-like features), which scatter reflected light, causing glare spots in their images31. Due to its reflective nature, this system, produced significant glare spots, often overshadowing the actual patterns of the elytra as seen on the top rows of Fig. 2a,b, and rendering it the least advantageous for our application.Figure 2Comparing three different illumination systems. Images of elytra under three different lighting conditions (top: 2Pt_Rf, middle: RRf_P and bottom: Trans), for two different families (a) Anobiidae, with species L. serricorne (1st column) and S. paniceum (2nd column) and (b) Tenebrionidae with species G. Cornutus (3rd column) and T. castaneum (4th column). All images were captured at 50x magnification, with the scale bars being 250 µm. The glare in images due to 2Pt_Rf illumination is evident. The RRf_P and Trans systems provide a much better alternative that significantly reduces glare, which obstruct elytral patterns. The extent of glare has been quantified as (c) percentage of elytra area obstructed due to glare, (d) number of glare spots (i.e. artifacts) due to setae when compared against three different illumination systems for two different families, which further highlights the advantages of RRf_P and Trans systems.Full size imageThe use of 2Pt_Rf light produced so much glare that it obstructed about 25% of the pattern area (Fig. 2c). Moreover, the dorsal setae produced a large number of tiny glare spots that could easily be confused for feature points (Fig. 2d), and led to misidentifications in previous studies21,22,23,24. Furthermore, we observed that the position of the glare spots did not remain constant, and varied with the angle of incident light and/or the ambient lighting conditions (Supplementary Figure S1). Such randomness in image illumination (i.e., brightness and contrast) makes this setting very inconsistent, (e.g., highly dependent on random, external factors) and rendering it ineffective for automated pattern analysis, particularly in developing computer models for pattern recognition27,28.The RRf_P system was a significant improvement over the 2Pt_Rf system, as it allowed us to remove many of the illumination inconsistencies (Fig. 2a,b, the middle row). The use of a ring light instead of two light bulbs provided a more uniform illumination, and the polarized filter helped minimize glare spots from the images by imaging in circular polarized light (also minimizing the incoherent scattering of light, responsible for glare)32. However, upon closer observation, we found that the illumination system could not completely remove all the glare spots, especially for the species with dorsal setae. The orientation of the polarizing filter can be adjusted to remove random scattering of light only from one particular plane and/or angle. Since dorsal setae lie at a slightly different angle to the elytra surface, it was not possible to simultaneously remove glare spots originating from both elytra and dorsal setae. However, this setting was found to be excellent in imaging whole insects and/or other thick, dark or shiny objects that may have been challenging when using the 2Pt_Rf setting33.Compared to RRf_P and 2Pt_Rf, the Trans system was a much simpler and more economical set-up that produced excellent glare-free images (Fig. 2a,b, the bottom row). The images of elytra revealed patterns clearly as they did not suffer from any reflective glares (Fig. 2c,d). Unlike the RRf_P system, images captured in Trans remained mostly unaffected by the polarizing filter or its orientation (Supplementary Figure S2). This makes Trans least susceptible to variations such as those in operating personnel and ambient lighting conditions; and it remained relatively unaffected by the change in intensity of the transmitted beam, (i.e., due to the camera’s automatic intensity balance setting). Methods that enable consistent acquisition of data are highly desirable for any automated processing as they help avoid any batch effect or unknown error34,35. In this regard, the Trans system provides an effective, yet simple solution for consistently capturing clear, high-quality images.We also captured images under the combined illumination of RRf_P and Trans systems. This combined lighting system, however, fell short by revealing both surface and internal features with equal precision, and its images more closely resembled the RRf_P-based images (Supplementary Figure S3). It was not possible to balance or equalize the illumination intensities of the two (i.e., reflected and transmitted) light beams. The ring light was higher in illumination intensity compared to the transmitted beam, which also underwent sample adsorption, possibly resulting from the camera’s greater expose to reflected light, producing images similar to those captured using RRf_P.The key to discerning between similar patterns lies in acquiring images that enable the visualization of fine pattern details. This would permit maximum information to be extracted from each image and could help identify a species with a much larger set of characteristic features. To ensure visualization of the most patterns, both sides (D and V) of elytra were imaged. We noted that both of these illumination systems were excellent in revealing the pattern details, as one can clearly observe differences in patterns for D and V sides of the same elytron. The lack of glare spots made distinguishing between beetles belonging to the same family relatively easy, as their pattern features such as color, design, and setae are quite different from each other when observed through either the RRf_P or Trans illumination systems (Supplementary Figure S4).Differences between species were subtler for beetles belonging to the same genus however. For instance, rectangular patterns are only slightly lighter with marginally thicker ridge lines between T. castaneum and T. confusum (both genus Tribolium), irrespective of the illumination system used (Fig. 3a,b). Differences also were found to be subtle between O. mercator and O. surinamensis (genus Oryzaephilus) (Supplementary Figure S4e). For the same species, the variation in elytra patterns due to two different illumination settings (RRf_P and Trans) could only be perceived during careful observation of the images (refer to first row for D & second row for V side in images Fig. 3a,b). This difference, though minute, originated from the variation in the imaging mechanism and deserves comprehensive interpretation. The RRf_P system uses a light beam that reflects back from the specimen surface to capture an image, which allows better visualization of surface features. On the contrary, the Trans system uses a transmitted light beam that traverses through the specimen allowing better visualization of internal structures, in our case this is the cytoskeletal pattern on each elytron. That may explain why the images captured through the Trans system did not appear significantly different between the D and V sides, as they did for RRf_P lighting (Fig. 3a,b).Figure 3Acquired patterns on either side of elytra dorsal (D) & ventral (V) sides for maximum visualization of the patterns and to compare the two different illumination systems, RRf_P and Trans, for (a) T. castenium, (b) T. confusum both belonging to the genus Tribolium; and are well known for their remarkably similar appearance; and (c) in visualizing surface features in O. surinamensis (OS), L. serricorne (LS), and T. confusum (TCo). Although, both systems seemed equally good for visualizing elytral patterns, the Trans system was found to be the least affected by minor surface features such as setae, thin surface coatings or occasional adherents, making it more advantageous for the imaging application.Full size imageIn an ideal scenario the RRf_P system should be used for imaging surface features such as setae, contours and the like, and the Trans system for such internal structures as ridges and bulges. In practice however we observed that the RRf_P system is more susceptible to dorsal setae or artifacts such as contaminants or adherents (Fig. 3c). In several of the elytron samples, especially in the case of deeply concave samples such as L. serricorne, we found thin adherent coatings (possibly from the medium in which the insect lived) on the V side of the elytra persisted even after thorough ultrasonic cleaning. We also observed occasional dust-like adherent on either side of the elytra, all of which masked the actual elytral patterns. Additionally, we foresee the loss of non-structural surface features such as setae in real-world samples that undergo aggressive sample preparation steps prior to analysis. The internal structures of elytra are made of well crosslinked chitin (or its modified version), which is known to have excellent mechanical and structural properties16,36. Thus, capturing images through the Trans system is advantageous in this regard, as it captures the internal structures which usually remain unaffected by minor surface adhesions, contamination or even vigorous sample preparation steps.Having studied the illumination systems, we focused on another important imaging parameter, namely, magnification. Figure 4b shows images of elytra from O. mercator and O. surinamensis at magnifications 20 x to 160 x under the Trans system. It is obvious that the patterns are visibly better and more prominent at higher magnifications. Moreover, the size and shape of an elytron varies from one species to another (Fig. 4a,c). For the seven beetle species examined in this study, their sizes varied from ~ 5 mm2 for genus Oryzaephilus to ~ 13 mm2 for genus Tribolium. At these sizes, 50 x to 100 x magnification seemed a good choice, as it enabled us to capture most of the elytra (Fig. 4b,d). Imaging at higher magnifications (such as 160 x) allowed significantly better visualization of individual patterns. On the other hand, it only revealed parts of the elytral patterns, with a large portion remaining out of the field of view, and thus unanalyzed.Figure 4Magnification considerations: (a) Elytra images captured at 50 ×  magnification, showing their overall appearance; (b) Elytra images of T. castaneum (TCa) and T. confusum (TCo) at various magnifications showing area of elytra captured in each frame/magnification, (c) variation in average elytra sizes (of 10 images per species) for the seven different species used in this study, (d) proportion (area imaged/total area) of elytra captured at various magnifications, (e) number of images required (elytra size/frame size) to visually represent the whole elytra and (f) change in frame area (area captured in an image) by magnification. Even though there is a variation in elytral sizes, collectively they can be represented effectively at a magnification of 100 x.Full size imageTo remedy this inconvenience, many images must be captured in order to visually represent the entire elytra (about five or so in some cases as shown in Fig. 4e). This would increase overlapping errors (i.e. same parts being imaged in multiple images) without significantly increasing the pattern information. It also makes the process more susceptible to vibrations, thus increasing the likelihood of introducing undesirable artifacts. Increasing the magnification also significantly reduces the frame area. The average size of pantry beetle elytra is approximately 10 mm2, which is similar in dimension to the area of the image frame at 100 x magnification (Fig. 4f). Therefore, images captured at this magnification (i.e. 100 x), would reveal all or most of the elytra structure for a majority of the pantry beetles (Fig. 5a). We thus concluded that this a good starting point for a more detailed investigation.Figure 5Elytra and their patterns at various magnifications; (a) elytral images of species O. mercator (OM) and O. surinamensis (OS) at magnifications 20x to 160x; (b) elytra patterns captured for six different species at 100x magnification under the Trans illumination system; and (c) various elytra patterns for the same six species at higher display resolution, which highlights that this setup (Trans at 100x) is capable of resolving the finest pattern details required to distinguish species belonging to the same genus or family from one another.Full size imageLike the sizes of elytra, morphology of the patterns also varied widely across family and genera, and even within the same genera, which could clearly be visible at 100 x magnification across all the species (Fig. 5b). For instance, the central bulges were more circular in shape and measured on average ~ 100 µm diameter for T. confusum compared to a less circular bulge shape that measured less than ~ 50 µm in diameter for T. castaneum. Interestingly, L. serricorne completely lacked any such bulges (a feature we observed on very few pantry beetles) and had distances of less than 20 µm between the seatal pits (hair roots). Such small feature size could not be resolved adequately at 50 x magnification (even at ~ 2400 × 1900 dpi image resolution). Imaging at 100 x magnification (and at ~ 2400 × 1900 dpi resolution) allowed us to visualize the patterns clearly enough to visibly distinguish one elytra pattern from another (Fig. 5c). We therefore, chose this magnification as optimal, as it struck a reasonable balance between appropriate field of view and adequate resolution of detailed elytral patterns. At this point we also began to concur that magnification of 100 x under Trans illumination yielded high quality elytra images that allowed one to visualize fine patterns in beetle elytra in a consistent manner. It could be the optimal imaging condition, as images captured this way enabled us to visually differentiate one species from another, at least for the set of beetle species under investigation.After optimizing the illumination and magnification settings (which were the hardware-based parameters), we focused our attention to digital parameters for improving the image quality. Amongst these parameters, sharpness, distortion and aberration were important for their significance in image processing. They also contribute to the visual clarity, by revealing the finest elytral patterns required for distinguishing one species from another. The corresponding FFT images of the images obtained using conventional (low resolution 2Pt_Rf), 2Pt-Rf and Trans settings (Supplementary Figure S5) highlights the difference in the pattern clarity. The central vertical line represents the horizonal groves in the pattern and the smaller spots on both sides possibly represent the rectangular box like features patterns. Their distinct nature for the FFT image, indicates that the pattern (obtained using the Trans setting) is clear due to the good image quality. On the contrary, the FFT patterns are increasingly diffused (due to glare and scattering, less sharpness and clarity) for 2Pt-Rf setting and lower resolution. The FFT patterns from species belong to different families shows variation. The FFTs from those belong to the same genus and family, shows some similarity (due to the similarity in their elytral patterns) but are not identical, further bolsters the superior image quality of Trans setting that allows distinction of one species from another.Image resolution, another of the image quality parameter, was kept at 2592 × 1944 dpi (~ 5-megapixel, image size: 14 MB per image). This was found to be adequate for our applications, as higher resolution (greater than 10 megapixel) increased the images size significantly without providing any additional information. This may also cause difficulty in handling and storage for a repository aimed to contain about 2000 images. Fortunately, most modern cameras, including the one we used, comes with well-documented, user-guided software interface that allows convenient optimization of the ‘soft’ parameters enabling easy capturing of good quality images. So, it was less arduous for us to obtain good quality elytral images of once the illumination and magnification settings were worked out. A step-wise details on how to capture a good quality image has been elaborated (Supplementary Information-Step-Wise Imaging Method) for consistency and reproducibility.Our ultimate objective is to obtain large number of high-quality elytral images for species identification through elytral pattern recognition using artificial intelligence (AI) based machine learning methods. However, developing such methods require both time and high-performing computational capability, which could be expensive. It may be prudent to run a ‘quick-check’ to observe if improving the image quality indeed showed any promise in improving species-level identification. Therefore, as a proof of concept, we used ImageJ to analyze the elytral patterns based on their difference in sizes and shapes. For this test, we analyzed elytra images of G. cornutus, T. castaneum and T. confusum, all three belonging to the same family Tenebrionidae, thus showing very similar elytral patterns (Fig. 6a). The processed images yield corresponding pattern outlines whose size and shape have been quantified by the area within the outlines and their circularity (area/perimeter2, 0 for line and 1 for circle) (Fig. 6b). It can be noted that their distributions created three different bell curves for the three species analyzed. The shape distributions did show three peaks having good overlap, probably indicating the similarity in pattern shapes for species belonging to the same family (Fig. 6c). The size distribution curves however are further apart for the species belonging to the same family but different genus and quite close for those of the same genus (Fig. 6d). This species wise classification using rudimentary size analysis of elytra patterns indicated that improving image quality may indeed help improve species-level identification.Figure 6Analysis of elytra patterns for 3 different species, namely G. cornutus (GC), T. confusum (TCo), & T. castaneum (TCa) belonging to the same family Tenebrionidae. (a) optical images captured in Trans illumination at 100x magnification showing elytra patterns, with the scale bars being 250 µm; (b) the corresponding binary image, that shows only the dominant patterns. The log-normal distribution of (c) ‘circularity’ (shape) and (d) ‘size’ of the patterns for the three different species. It can be noted that the shape of the patterns are similar (close ‘circularity’ distribution) for the species belonging to the same family. But the ‘size’ distribution varied for three species with TCo & TCa belonging to the same genus Tribolium, much closer compared to the other family member GC. The indexed numbers show the mean and SD of the distribution, with the size values bearing units of  10–3 mm2.Full size imageAny optimization technique must be tested for its robustness before being extended to a much wider range of samples. Thus, we wanted to observe whether the Trans setting at a 100x magnification could be used for other species of beetles. For this setting to work the transmitted light must pass through the elytra, which may seem difficult if the elytron is too dark and/or thick. Thus, we tested this system to image elytral patterns on rainbow scrap beetle (Phanaeus vindex), black dung beetle (Onitis aygulus) and click beetle (Orthostethus infuscatus), (none of which are pantry beetles), for they have some of the biggest, darkest and thickest elytral known to entomologists (Fig. 7a,b)36. It was indeed possible to capture elytra patterns for all three species using this setup. For comparison, images also were obtained using the RRf_P setting further highlighting the advantage of imaging in transmitted light, Trans remains least influenced by the presence of surface features such as pigmentation, excessive setae and the like as shown in the bottom row images in Fig. 7a–c. Since the setup worked well for some of the biggest and darkest elytra samples, presumably it could be used for imaging any species of pantry beetle (or other class of beetles) which are roughly 10 times smaller in size and have far thinner elytra.Figure 7Control study to evaluate imaging capability when using Trans at 100x magnification, which was employed to image (a) Rainbow Scrap Beetle (Phanaeus vindex); (b) Black Dung Beetle (Onitis aygulus) and (c) Click Beetle (Orthostethus infuscatus), which possess some of the thickest and darkest known elytra in beetles. For comparison, the bottom row imaged using the RRf_P system, shows colored pigmentation, waxy coating and excessive setae. The top row represents images captured using the Trans system showing only the cytoskeletal structure, and which remain mostly unaffected by nonuniform surface features.Full size imageHaving tested the Trans system at 100x magnification on thicker and darker elytral samples, we moved forward in capturing elytral images of other pantry beetles to further expand the robustness of this system. Figure 8 shows representative images of six more species, listed in Table 1, by their family and genus names. We observed that this system (100 x magnification with the Trans setting) yielded excellent images for most of the beetles. The only exception was Zabrotes subfasciatus (Z. subfasciatus- commonly known as Mexican bean weevil of family Chrysomelidae). Its dark dorsal setae with white stripes, combined with a deeply concave shape probably made auto-focusing difficult during image acquisition. This could have contributed to slightly imperfect stacking of multi-layer images resulting in a poorer 3D montage construction. Images of the same species obtained through the RRf_P system also were of poorer quality, leading us to conclude that elytra with surface setae of contrasting colors combined with deep concave shapes are more difficult to image compared to elytra with more uniform color, flatter shape and clear patterns. This, however, may not pose a significant challenge, as in a real-world scenario, elytra often lose both their dorsal setae and concave shape due to food processing steps and/or fragmentation.Figure 8Extending this imaging method to other species of food contaminating beetles. Elytra images of species belonging to (a) two completely different families; (b,c) of same genus. Sitophilus granarius and Sitophilus oryzae of genus Sitophilus; T. freemani and T. madens of genus Tribolium. The optimized imaging technique works well for imaging elytra of most species of pantry beetles. However, species with variegated dorsal setae and deep concave shapes are most difficult to image properly. The images are at 100x magnification, with the scale bars being 250 µm; the insets show the magnified patters with scale bar being 100 µm.Full size imageWe currently are extending this imaging method to a total of 40 different species and plan to create a publicly available database of these images. Such database is meant to serve the food safety and entomology communities by providing good quality images for referencing and taxonomical applications. The optimization of parameters required for machine learning methods for species identification through pattern recognition are quite different from those in image acquisition and is far beyond the scope of this manuscript. But this study is aimed to serve as a forerunner for such study as it provides the means to obtain a large set of good-quality, noise-free images for developing a good computational model, which has been one of the primary challenges in the field of AI and machine leaning. We hope that a publicly available database of images will encourage data scientists to develop state-of-the art species identification algorithms, thus eventually contribute to our long-term goal of efficient automated species identification of food contaminating beetles. We believe this work lays a solid foundation for such an exhaustive study, which would require more resources and would help predict the correct species of pantry pests with greater accuracy. We hope such efforts ultimately would expedite the entire process of taxonomical analysis and help better manage contamination scenarios or catalog ecological systems, in the foreseeable future. More

1.Bik, H. M. et al. Sequencing our way towards understanding global eukaryotic biodiversity. Trends Ecol. Evol. 27(4), 233–243 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26(21), 5872–5895 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Porter, T. M. & Hajibabaei, M. Scaling up: A guide to high-throughput genomic approaches for biodiversity analysis. Mol. Ecol. 27(2), 313–338 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Arulandhu, A. J. et al. Development and validation of a multi-locus DNA metabarcoding method to identify endangered species in complex samples. GigaScience 6(10), gix080 (2017).Article 

Google Scholar 
5.Raclariu, A. C., Heinrich, M., Ichim, M. C. & de Boer, H. Benefits and limitations of DNA barcoding and metabarcoding in herbal product authentication. Phytochem. Anal. 29(2), 123–128 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Staats, M. et al. Advances in DNA metabarcoding for food and wildlife forensic species identification. Anal. Bioanal. Chem. 408(17), 4615–4630 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Comtet, T., Sandionigi, A., Viard, F. & Casiraghi, M. DNA (meta)barcoding of biological invasions: A powerful tool to elucidate invasion processes and help managing aliens. Biol. Invasions 17(3), 905–922 (2015).Article 

Google Scholar 
8.Piper, A. M. et al. Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. GigaScience 8(8), giz092 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Tedersoo, L., Drenkhan, R., Anslan, S., Morales-Rodriguez, C. & Cleary, M. High-throughput identification and diagnostics of pathogens and pests: Overview and practical recommendations. Mol. Ecol. Resour. 19(1), 47–76 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Andújar, C. et al. Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill. Mol. Ecol. 27(1), 146–166 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
11.Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J. & Leese, F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol. Evol. 8(10), 1265–1275 (2017).Article 

Google Scholar 
12.Brown, E. A., Chain, F. J. J., Zhan, A., MacIsaac, H. J. & Cristescu, M. E. Early detection of aquatic invaders using metabarcoding reveals a high number of non-indigenous species in Canadian ports. Divers. Distrib. 22(10), 1045–1059 (2016).Article 

Google Scholar 
13.Hebert, P. D. N., Ratnasingham, S. & deWaard, J. R. Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. B Biol. Sci. 270(Suppl 1), S96–S99 (2003).CAS 

Google Scholar 
14.Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270(15), 313–321 (2003).CAS 
Article 

Google Scholar 
15.Clarke, L. J., Soubrier, J., Weyrich, L. S. & Cooper, A. Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14(6), 1160–1170 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Yu, D. W. et al. Biodiversity soup: Metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods Ecol. Evol. 3(4), 613–623 (2012).Article 

Google Scholar 
17.Brandon-Mong, G.-J. et al. DNA metabarcoding of insects and allies: An evaluation of primers and pipelines. Bull. Entomol. Res. 105(6), 717–727 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Freeland, J. R. The importance of molecular markers and primer design when characterizing biodiversity from environmental DNA. Genome 60(4), 358–374 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
19.Marquina, D., Andersson, A. F. & Ronquist, F. New mitochondrial primers for metabarcoding of insects, designed and evaluated using in silico methods. Mol. Ecol. Resour. 19(1), 90–104 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Epanchin-Niell, R. S., Haight, R. G., Berec, L., Kean, J. M. & Liebhold, A. M. Optimal surveillance and eradication of invasive species in heterogeneous landscapes. Ecol. Lett. 15(8), 803–812 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Batovska, J. et al. Effective mosquito and arbovirus surveillance using metabarcoding. Mol. Ecol. Resour. 18, 32–40 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.Liebhold, A. M. et al. Eradication of invading insect populations: From concepts to applications. Annu. Rev. Entomol. 61, 335–352 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Lamb, P. D. et al. How quantitative is metabarcoding: A meta-analytical approach. Mol. Ecol. 28(2), 420–430 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10(7), e0130324 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Krehenwinkel, H. et al. Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Sci. Rep. 7(1), 17668 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Piñol, J., Senar, M. A. & Symondson, W. O. C. The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Mol. Ecol. 28(2), 407–419 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
27.Ashfaq, M. & Hebert, P. D. N. DNA barcodes for bio-surveillance: Regulated and economically important arthropod plant pests. Genome 59(11), 933–945 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14(2), 306–323 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
29.Hauck, L. L., Weitemier, K. A., Penaluna, B. E., Garcia, T. S. & Cronn, R. Casting a broader net: Using microfluidic metagenomics to capture aquatic biodiversity data from diverse taxonomic targets. Environ. DNA 1(3), 251–267 (2019).Article 

Google Scholar 
30.Zhang, G. K., Chain, F. J. J., Abbott, C. L. & Cristescu, M. E. Metabarcoding using multiplexed markers increases species detection in complex zooplankton communities. Evol. Appl. 11(10), 1901–1914 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Costello, M. et al. Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms. BMC Genomics 19(1), 332 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.MacConaill, L. E. et al. Unique, dual-indexed sequencing adapters with UMIs effectively eliminate index cross-talk and significantly improve sensitivity of massively parallel sequencing. BMC Genomics 19(1), 30 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Bengtsson-Palme, J. et al. Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics 16(18), 2454–2460 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Shen, Y.-Y., Chen, X. & Murphy, R. W. Assessing DNA barcoding as a tool for species identification and data quality control. PLoS ONE 8(2), e57125 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Kozlov, A. M., Zhang, J., Yilmaz, P., Glöckner, F. O. & Stamatakis, A. Phylogeny-aware identification and correction of taxonomically mislabeled sequences. Nucleic Acids Res. 44(11), 5022–5033 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Simmons, M., Tucker, A., Chadderton, W. L., Jerde, C. L. & Mahon, A. R. Active and passive environmental DNA surveillance of aquatic invasive species. Can. J. Fish. Aquat. Sci. 73(1), 76–83 (2015).Article 
CAS 

Google Scholar 
37.Olmos, A. et al. High-throughput sequencing technologies for plant pest diagnosis: Challenges and opportunities. EPPO Bull. 48(2), 219–224 (2018).Article 

Google Scholar 
38.Darling, J. A., Pochon, X., Abbott, C. L., Inglis, G. J. & Zaiko, A. The risks of using molecular biodiversity data for incidental detection of species of concern. Divers. Distrib. 26(9), 1116–1121 (2020).Article 

Google Scholar 
39.Carew, M. E., Coleman, R. A. & Hoffmann, A. A. Can non-destructive DNA extraction of bulk invertebrate samples be used for metabarcoding?. PeerJ 6, e4980 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Ji, Y. et al. SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes. Mol. Ecol. Resour. 20(1), 256–267 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Nielsen, M., Gilbert, M. T. P., Pape, T. & Bohmann, K. A simplified DNA extraction protocol for unsorted bulk arthropod samples that maintains exoskeletal integrity. Environ. DNA 1(2), 144–154 (2019).Article 

Google Scholar 
42.Martins, F. M. S. et al. Have the cake and eat it: Optimizing nondestructive DNA metabarcoding of macroinvertebrate samples for freshwater biomonitoring. Mol. Ecol. Resour. 19(4), 863–876 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Zizka, V. M. A., Leese, F., Peinert, B. & Geiger, M. F. DNA metabarcoding from sample fixative as a quick and voucher-preserving biodiversity assessment method. Genome 62(3), 122–136 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
44.Martoni, F., Valenzuela, I. & Blacket, M. J. Non-destructive DNA extractions from fly larvae (Diptera: Muscidae) enable molecular identification of species and enhance morphological features. Austral. Entomol. 58(4), 848–856 (2019).Article 

Google Scholar 
45.Plant Health Australia. Tomato-potato psyllid (2019). Retrieved 10 April, 2019 from http://www.planthealthaustralia.com.au/pests/tomatopotato-psyllid/.46.Yazdani, M. et al. First detection of Russian wheat aphid Diuraphis noxia Kurdjumov (Hemiptera: Aphididae) in Australia: A major threat to cereal production. Austral. Entomol. 57(4), 410–417 (2018).Article 

Google Scholar 
47.Pirtle, E., Maino, J., Lye, J., Umina, P., Heddle, T. & van Helden, M. Managing Russian wheat aphid risk—early season considerations. Centre for Environmental Stress and Adaptation Research (CESAR) (2019). Retrieved February 7, 2020 from http://www.cesaraustralia.com/assets/Uploads/PDFs/RWA-portal/Russian-wheat-aphid-green-bridge-surveillence-update-May-2019.pdf.48.Wilson, C., Rowbottom, R., Walker, P., Allen, G., Tegg, R. & Quarrell, S. Surveillance of tomato potato psyllid in the Eastern States and South Australia. Horticulture Innovation Australia (2018). Retrieved February 7, 2020 from https://ausveg.com.au/app/uploads/technical-insights/MT16016.pdf.49.Blackman, R. L. & Eastop, V. F. Aphids on the world’s crops: an identification and information guide. Aphids Worlds Crops Identif. Inf. Guide 2nd edn (2000).50.Kent, D. & Taylor, G. Two new species of Acizzia Crawford (Hemiptera: Psyllidae) from the Solanaceae with a potential new economic pest of eggplant, Solanum melongena. Aust. J. Entomol. 49(1), 73–81 (2010).Article 

Google Scholar 
51.Subcommittee on Plant Health Diagnostic Standards (SPHDS). Diagnostic protocol for the detection of the Tomato Potato Psyllid, Bactericera cockerelli (Šulc). Department of Agriculture, Australia (2017). Retrieved December 8, 2019 from https://www.plantbiosecuritydiagnostics.net.au/app/uploads/2018/11/NDP-20-Tomato-potato-psyllid-Bactericera-cockerelli-V1.2.pdf.52.Farrow, R. & Greenslade, P. Description of a robust interception trap for collecting airborne arthropods in climatically challenging regions. Antarct. Sci. 25(5), 657–662 (2013).ADS 
Article 

Google Scholar 
53.Ferro, M. L. & Park, J.-S. Effect of propylene glycol concentration on mid-term DNA preservation of Coleoptera. Coleopt. Bull. 67(4), 581–586 (2013).Article 

Google Scholar 
54.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
55.Martoni, F. Biodiversity, evolution and microbiome of the New Zealand Psylloidea (Hemiptera: Sternorrhyncha) (2017).56.Ouvrard, D., Campbell, B. C., Bourgoin, T. & Chan, K. L. 18S rRNA secondary structure and phylogenetic position of Peloridiidae (Insecta, hemiptera). Mol. Phylogenet. Evol. 16(3), 403–417 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28(12), 1647–1649 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73(16), 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Ratnasingham, S. & Hebert, P. D. N. BOLD: The barcode of life data system (http://www.barcodinglife.org). Mol. Ecol. Notes 7(3), 355–364 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 37(Database issue), D26–D31 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Chamberlain, S. bold: Interface to Bold Systems API. R package version 0.5.0 (2017). https://github.com/ropensci/bold.62.Winter, D. J. rentrez: An R package for the NCBI eUtils API. R J. 9(2), 520–526 (2017).Article 

Google Scholar 
63.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2014). http://www.R-project.org/.64.Sherrill-Mix, S. taxonomizr: Functions to Work with NCBI Accessions and Taxonomy. R package version 0.5.2 (2018). https://rdrr.io/cran/taxonomizr/.65.Mercier, C., Boyer, F., Bonin, A. & Coissac, E. SUMATRA and SUMACLUST: fast and exact comparison and clustering of sequences (2013). http://metabarcoding.org.66.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Bushnell, B. BBMap short read aligner, and other bioinformatic tools (2017). https://sourceforge.net/projects/bbmap/.68.Ranwez, V., Douzery, E. J. P., Cambon, C., Chantret, N. & Delsuc, F. MACSE v2: Toolkit for the alignment of coding sequences accounting for frameshifts and stop codons. Mol. Biol. Evol. 35(10), 2582–2584 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Saitoh, S. et al. A quantitative protocol for DNA metabarcoding of springtails (Collembola). Genome 59(9), 705–723 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Wilcox, T. M. et al. Capture enrichment of aquatic environmental DNA: A first proof of concept. Mol. Ecol. Resour. 18(6), 1392–1401 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4), e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).
Google Scholar 
73.Walsh, P. S., Metzger, D. A. & Higuchi, R. Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10(4), 506–513 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35(6), 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.ABRS. Australian Faunal Directory. Australian Biological Resources Study, Canberra (2009). Retrieved October 30, 2019 from https://biodiversity.org.au/afd/mainchecklist.76.Bista, I. et al. Performance of amplicon and shotgun sequencing for accurate biomass estimation in invertebrate community samples. Mol. Ecol. Resour. 18, 1020–1103 (2018).CAS 
Article 

Google Scholar 
77.Illumina. Effects of index misassignment on multiplexing and downstream analysis [White paper] (2017). Retrieved November 25, 2019 from https://www.illumina.com/content/dam/illumina-marketing/documents/products/whitepapers/index-hopping-white-paper-770-2017-004.pdf.78.Minich, J. J. et al. Quantifying and understanding well-to-well contamination in microbiome research. mSystems 4(4), e00186-19 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Galan, M. et al. Metabarcoding for the parallel identification of several hundred predators and their prey: Application to bat species diet analysis. Mol. Ecol. Resour. 18(3), 474–489 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Palmer, J. M., Jusino, M. A., Banik, M. T. & Lindner, D. L. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, e4925 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Meusnier, I. et al. A universal DNA mini-barcode for biodiversity analysis. BMC Genomics 9, 214 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Elbrecht, V. & Steinke, D. Scaling up DNA metabarcoding for freshwater macrozoobenthos monitoring. Freshw. Biol. 64(2), 380–387 (2019).CAS 

Google Scholar 
83.Larsson, A. J. M., Stanley, G., Sinha, R., Weissman, I. L. & Sandberg, R. Computational correction of index switching in multiplexed sequencing libraries. Nat. Methods 15(5), 305–307 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
84.Gibbons, S. M., Duvallet, C. & Alm, E. J. Correcting for batch effects in case–control microbiome studies. PLoS Comput. Biol. 14(4), 1006102 (2018).ADS 
Article 
CAS 

Google Scholar 
85.Yeh, Y.-C., Needham, D. M., Sieradzki, E. T. & Fuhrman, J. A. Taxon disappearance from microbiome analysis reinforces the value of mock communities as a standard in every sequencing run. mSystems 3(3), e00023-18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
86.McLaren, M. R., Willis, A. D. & Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments. eLife 8, e46923 https://doi.org/10.7554/eLife.46923 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
87.Thomas, A. C., Deagle, B. E., Eveson, J. P., Harsch, C. H. & Trites, A. W. Quantitative DNA metabarcoding: Improved estimates of species proportional biomass using correction factors derived from control material. Mol. Ecol. Resour. 16(3), 714–726 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Dowle, E. J., Pochon, X., Banks, C. & J., Shearer, K., and Wood, S.A. ,. Targeted gene enrichment and high-throughput sequencing for environmental biomonitoring: A case study using freshwater macroinvertebrates. Mol. Ecol. Resour. 16(5), 1240–1254 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Peñalba, J. V. et al. Sequence capture using PCR-generated probes: A cost-effective method of targeted high-throughput sequencing for nonmodel organisms. Mol. Ecol. Resour. 14(5), 1000–1010 (2014).PubMed 
PubMed Central 

Google Scholar 
90.Liu, S. et al. Mitochondrial capture enriches mito-DNA 100 fold, enabling PCR-free mitogenomics biodiversity analysis. Mol. Ecol. Resour. 16(2), 470–479 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Blackman, R. L. & Eastop, V. F. Aphids on the World’s Herbaceous Plants and Shrubs, 2 Volume Set (Wiley, 2008).
Google Scholar 
92.Edgar, R. C. Taxonomy annotation and guide tree errors in 16S rRNA databases. PeerJ 6, e5030 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
93.Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47(D1), D259–D264 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Tang, C. Q. et al. The widely used small subunit 18S rDNA molecule greatly underestimates true diversity in biodiversity surveys of the meiofauna. Proc. Natl. Acad. Sci. U.S.A. 109(40), 16208–16212 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Wangensteen, O. S., Palacín, C., Guardiola, M. & Turon, X. DNA metabarcoding of littoral hard-bottom communities: High diversity and database gaps revealed by two molecular markers. PeerJ 6, e4705 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
96.Porter, T. M. & Hajibabaei, M. Automated high throughput animal CO1 metabarcode classification. Sci. Rep. 8(1), 4226 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
97.Porter, T. M. et al. Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier. Mol. Ecol. Resour. 14(5), 929–942 (2014).CAS 
PubMed Central 
Article 

Google Scholar 
98.Edgar, R. C. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. bioRxiv https://doi.org/10.1101/2020.05.12.088096 (2016).Article 

Google Scholar  More

Åkesson M, Liberg O, Sand H, Wabakken P, Bensch S, Flagstad Ø (2016) Genetic rescue in a severely inbred wolf population. Mol Ecol 25:4745–4756PubMed 
PubMed Central 
Article 

Google Scholar 
Andersen LW, Harms V, Caniglia R, Czarnomska SD, Fabbri E, Jędrzejewska B et al. (2015) Long-distance dispersal of a wolf, Canis lupus, in northwestern Europe. Mamm Res 60:163–168Article 

Google Scholar 
Ansorge H, Kluth G, Hahne S (2006) Feeding ecology of wolves Canis lupus returning to Germany. Acta Theriol 51:99–106Article 

Google Scholar 
Beugin M-P, Gayet T, Pontier D, Devillard S, Jombart T (2018) A fast likelihood solution to the genetic clustering problem. Methods Ecol Evol 9:1006–1016PubMed 
PubMed Central 
Article 

Google Scholar 
Caniglia R, Fabbri E, Galaverni M, Milanesi P, Randi E (2014) Noninvasive sampling and genetic variability, pack structure, and dynamics in an expanding wolf population. J Mammal 95:41–59Article 

Google Scholar 
Caniglia R, Fabbri E, Mastrogiuseppe L, Randi E (2013) Who is who? Identification of livestock predators using forensic genetic approaches. Forensic Sci Int Genet 7:397–404CAS 
PubMed 
Article 

Google Scholar 
Carter NH, Linnell JDC (2016) Co-adaptation is key to coexisting with large carnivores. Trends Ecol Evol 31:575–578PubMed 
Article 

Google Scholar 
Chapron G, Kaczensky P, Linnell JDC, von Arx M, Huber D, Andrén H et al. (2014) Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346:1517–1519CAS 
PubMed 
Article 

Google Scholar 
Ciucci P, Reggioni W, Maiorano L, Boitani L (2009) Long-distance dispersal of a rescued wolf from the northern Apennines to the Western Alps. J Wildl Manag 73:1300–1306Article 

Google Scholar 
Curran JM, Tvedebrink T (2013) DNAtools: tools for empirical testing of DNA match probabilities. R packageCzarnomska SD, Jędrzejewska B, Borowik T, Niedziałkowska M, Stronen AV, Nowak S et al. (2013) Concordant mitochondrial and microsatellite DNA structuring between Polish lowland and Carpathian Mountain wolves. Conserv Genet 14:573–588Article 

Google Scholar 
DBBW (2017) Dokumentations- und Beratungsstelle des Bundes zum Thema Wolf. Wölfe in Deutschland—Statusbericht 2015/2016. p 1–28Di Marco M, Boitani L, Mallon D, Hoffmann M, Iacucci A, Meijaard E et al. (2014) A retrospective evaluation of the global decline of carnivores and ungulates. Conserv Biol 28:1109–1118PubMed 
Article 

Google Scholar 
Dufresnes C, Miquel C, Remollino N, Biollaz F, Salamin N, Taberlet P et al. (2018) Howling from the past: historical phylogeography and diversity losses in European grey wolves. Proc R Soc B 285:20181148PubMed 
Article 
CAS 

Google Scholar 
Excoffier L, Foll M, Petit RJ (2009) Genetic consequences of range expansions. Annu Rev Ecol Evol Syst 40:481–501Article 

Google Scholar 
Fabbri E, Miquel C, Lucchini V, Santini A, Caniglia R, Duchamp C et al. (2007) From the Apennines to the Alps: colonization genetics of the naturally expanding Italian wolf (Canis lupus) population. Mol Ecol 16:1661–1671CAS 
PubMed 
Article 

Google Scholar 
Francisco LV, Langston AA, Mellersh CS, Neal CL, Ostrander EA (1996) A class of highly polymorphic tetranucleotide repeats for canine genetic mapping. Mamm Genome 7:359–362CAS 
PubMed 
Article 

Google Scholar 
Fredholm M, Winterø AK (1995) Variation of short tandem repeats within and between species belonging to the Canidae family. Mamm Genome 6:11–18CAS 
PubMed 
Article 

Google Scholar 
Geffen E, Kam M, Hefner R, Hersteinsson P, Angerbjörn A, Dalèn L et al. (2011) Kin encounter rate and inbreeding avoidance in canids. Mol Ecol 20:5348–5358PubMed 
Article 

Google Scholar 
Gese ME, Mech LD (1991) Dispersal of wolves (Canis lupus) in northeastern Minnesota, 1969–1989. Can J Zool 69:2946–2955Article 

Google Scholar 
Gorjanc G, Henderson DA (2007) GeneticsPed: pedigree and genetic relationship functions. R package version 1.40.0Goudet J (2005) hierfstat, a package for r to compute and test hierarchical F-statistics. Mol Ecol Notes 5:184–186Article 

Google Scholar 
Goudet J, Perrin N, Waser P (2002) Tests for sex-biased dispersal using bi-parentally inherited genetic markers. Mol Ecol 11:1103–1114CAS 
PubMed 
Article 

Google Scholar 
Granroth-Wilding H, Primmer C, Lindqvist M, Poutanen J, Thalmann O, Aspi J et al. (2017) Non-invasive genetic monitoring involving citizen science enables reconstruction of current pack dynamics in a re-establishing wolf population. BMC Ecol 17:44PubMed 
PubMed Central 
Article 

Google Scholar 
Harmoinen J, von Thaden A, Aspi J, Kvist L, Cocchiararo B, Jarausch A et al. (2020) Reliable wolf-dog hybrid detection in Europe using a reduced SNP panel developed for non-invasively collected samples, PREPRINT (Version 1). Research Square. https://doi.org/10.21203/rs.3.rs-113866/v1Hausknecht R, Gula R, Pirga B, Kuehn R (2007) Urine— a source for noninvasive genetic monitoring in wildlife. Mol Ecol Notes 7:208–212CAS 
Article 

Google Scholar 
Hedrick PW, Peterson RO, Vucetich LM, Adams JR, Vucetich JA (2014) Genetic rescue in Isle Royale wolves: genetic analysis and the collapse of the population. Conserv Genet 15:1111–1121Article 

Google Scholar 
Hijmans RJ, Williams E, Vennes C (2017) geosphere: spherical trigonometry. R package version 1:5–7Hindrikson M, Remm J, Pilot M, Godinho R, Stronen AV, Baltrūnaité L et al. (2017) Wolf population genetics in Europe: a systematic review, meta-analysis and suggestions for conservation and management. Biol Rev Camb Philos Soc 92:1601–1629PubMed 
Article 

Google Scholar 
Hulva P, Černá Bolfíková B, Woznicová V, Jindřichová M, Benešová M, Mysłajek RW et al. (2018) Wolves at the crossroad: Fission-fusion range biogeography in the Western Carpathians and Central Europe. Divers Distrib 24:179–192Article 

Google Scholar 
Jimenez MD, Bangs EE, Boyd DK, Smith DW, Becker SA, Ausband DE et al. (2017) Wolf dispersal in the Rocky Mountains, Western United States: 1993-2008. J Wildl Manag 81:581–592Article 

Google Scholar 
Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405CAS 
PubMed 
Article 

Google Scholar 
Jones AG, Small CM, Paczolt KA, Ratterman NL (2010) A practical guide to methods of parentage analysis. Mol Ecol Resour 10:6–30PubMed 
Article 

Google Scholar 
Jones OR, Wang J (2010) COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol Ecol Resour 10:551–555PubMed 
Article 

Google Scholar 
Kaczensky P, Kluth G, Knauer F, Rauer G, Reinhardt I, Wotschikowsky U (2009) Monitoring of large carnivores in Germany. BfN-Skripten 251:1–99
Google Scholar 
Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol 16:1099–1106Article 

Google Scholar 
Kardos M, Åkesson M, Fountain T, Flagstad Ø, Liberg O, Olason P et al. (2018) Genomic consequences of intensive inbreeding in an isolated wolf population. Nat Ecol Evol 2:124–131PubMed 
Article 

Google Scholar 
Koch E, Schweizer RM, Schweizer TM, Stahler DR, Smith DW, Wayne RK et al. (2019) De novo mutation rate estimation in wolves of known pedigree. Mol Biol Evol 36:2536–2547CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
Kojola I, Aspi J, Hakala A, Heikkinen S, Ilmoni C, Ronkainen S (2006) Dispersal in an expanding wolf population in Finland. J Mammal 87:281–286Article 

Google Scholar 
Kramer-Schadt S, Wenzler M, Gras P, Knauer F (2020) Habitatmodellierung und Abschätzung der potenziellen Anzahl von Wolfsterritorien in Deutschland. BfN-Skripten 556:1–30
Google Scholar 
Lesniak I, Heckmann I, Heitlinger E, Szentiks CA, Nowak C, Harms V et al. (2017) Population expansion and individual age affect endoparasite richness and diversity in a recolonising large carnivore population. Sci Rep 7:41730CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liberg O, Andrén H, Pedersen H-C, Sand H, Sejberg D, Wabakken P et al. (2005) Severe inbreeding depression in a wild wolf (Canis lupus) population. Biol Lett 1:17–20CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Libiseller C, Grimvall A (2002) Performance of partial Mann–Kendall tests for trend detection in the presence of covariates. Environmetrics 13:71–84CAS 
Article 

Google Scholar 
Mech LD, Boitani L (2003) Wolf social ecology. In: Mech LD, Boitani L (eds) Wolves: behavior, ecology, and conservation. University of Chicago Press, Chicago & London, p 1–34Meuwissen THE, Luo Z (1992) Computing inbreeding coefficients in large populations. Genet Sel Evol 24:305–313PubMed Central 
Article 
PubMed 

Google Scholar 
Neff MW, Broman KW, Mellersh CS, Ray K, Acland GM, Aguirre GD et al. (1999) A second-generation genetic linkage map of the domestic dog, Canis familiaris. Genetics 151:803–820CAS 
PubMed 
PubMed Central 

Google Scholar 
Nowak S, Mysłajek RW (2016) Wolf recovery and population dynamics in Western Poland, 2001–2012. Mamm Res 61:83–98Article 

Google Scholar 
Parreira BR, Chikhi L (2015) On some genetic consequences of social structure, mating systems, dispersal, and sampling. Proc Natl Acad Sci U S A 112:E3318–E3326CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Resour 6:288–295Article 

Google Scholar 
Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Petit RJ (2011) Early insights into the genetic consequences of range expansions. Heredity 106:203–204CAS 
PubMed 
Article 

Google Scholar 
Pilot M, Branicki W, Jędrzejewski W, Goszczyński J, Jędrzejewska B, Dykyy I et al. (2010) Phylogeographic history of grey wolves in Europe. BMC Evol Biol 10:104PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pohlert T (2018) trend: non-parametric trend tests and change-point detection. R package version 1.1.0R Core Team (2017) R: a language and environment for statistical computing. Vienna, AustriaRaymond M, Rousset F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248–249Article 

Google Scholar 
Ražen N, Brugnoli A, Castagna C, Groff C, Kaczensky P, Kljun F et al. (2016) Long-distance dispersal connects Dinaric-Balkan and Alpine grey wolf (Canis lupus) populations. Eur J Wildl Res 62:137–142Article 

Google Scholar 
Reinhardt I, Kaczensky P, Knauer F, Rauer G, Kluth G, Wölfl S et al. (2015) Monitoring von Wolf, Luchs und Bär in Deutschland. BfN-Skripten 413:1–96
Google Scholar 
Reinhardt I, Kluth G (2007) Leben mit Wölfen. Leitfaden für den Umgang mit einer konfliktträchtigen Tierart in Deutschland. BfN-Skripten 201:1–180
Google Scholar 
Reinhardt I, Kluth G, Blum C, Möslinger H, Harms V (2014) Wölfe in der Lausitz. Statusbericht für das Monitoringjahr 2013/2014. https://www.wolf.sachsen.de/download/Statusbericht_Sachsen_2013_2014.pdf. (Mar 2, 2020)Reinhardt I, Kluth G, Nowak C, Szentiks CA, Krone O, Ansorge H et al. (2019) Military training areas facilitate the recolonization of wolves in Germany. Conserv Lett 10:e12635
Google Scholar 
Ripple WJ, Estes JA, Beschta RL, Wilmers CC, Ritchie EG, Hebblewhite M et al. (2014) Status and ecological effects of the world’s largest carnivores. Science 343:1241484PubMed 
Article 
CAS 

Google Scholar 
Robinson JA, Räikkönen J, Vucetich LM, Vucetich JA, Peterson RO, Lohmueller KE et al. (2019) Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction. Sci Adv 5:eaau0757PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson SP, Simmons LW, Kennington WJ (2013) Estimating relatedness and inbreeding using molecular markers and pedigrees: the effect of demographic history. Mol Ecol 22:5779–5792CAS 
PubMed 
Article 

Google Scholar 
Rousset F (2008) Genepop’007: a complete reimplementation of the Genepop software for Windows and Linux. Mol Ecol Resour 8:103–106PubMed 
Article 

Google Scholar 
Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464Article 

Google Scholar 
Seddon JM (2005) Canid-specific primers for molecular sexing using tissue or non-invasive samples. Conserv Genet 6:147–149Article 

Google Scholar 
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389Article 

Google Scholar 
Shibuya H, Collins BK, Huang TH-M, Johnson GS (1994) A polymorphic (AGGAAT), tandem repeat in an intron of the canine von Willebrand factor gene. Anim Genet 25:122CAS 
PubMed 
Article 

Google Scholar 
Smith D, Meier T, Geffen E, Mech LD, Burch JW, Adams LG et al. (1997) Is incest common in gray wolf packs? Behav Ecol 8:384–391Article 

Google Scholar 
Sugg DW, Chesser RK, Dobson FS, Hoogland JL (1996) Population genetics meets behavioural ecology. Trends Ecol Evol 11:338–342CAS 
PubMed 
Article 

Google Scholar 
Szewczyk M, Nowak S, Niedźwiecka N, Hulva P, Špinkytė-Bačkaitienė R, Demjanovičová K et al. (2019) Dynamic range expansion leads to establishment of a new, genetically distinct wolf population in Central Europe. Sci Rep 9:481Article 
CAS 

Google Scholar 
Szpiech ZA, Jakobsson M, Rosenberg NA (2008) ADZE: a rarefaction approach for counting alleles private to combinations of populations. Bioinformatics 24:2498–2504CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tvedebrink T, Eriksen PS, Curran JM, Mogensen HS, Morling N (2012) Analysis of matches and partial-matches in a Danish STR data set. Forensic Sci Int Genet 6:387–392CAS 
PubMed 
Article 

Google Scholar 
van Eeden LM, Crowther MS, Dickman CR, Macdonald DW, Ripple WJ, Ritchie EG et al. (2018) Managing conflict between large carnivores and livestock. Conserv Biol 32:26–34PubMed 
Article 

Google Scholar 
van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535–538Article 
CAS 

Google Scholar 
vonHoldt BM, Stahler DR, Smith DW, Earl DA, Pollinger JP, Wayne RK (2008) The genealogy and genetic viability of reintroduced Yellowstone grey wolves. Mol Ecol 17:252–274PubMed 
Article 

Google Scholar 
Wabakken P, Sand H, Kojola I, Zimmermann B, Arnemo JMON, Pedersen HC et al. (2007) Multistage, long-range natal dispersal by a Global Positioning System-Collared Scandinavian Wolf. J Wildl Manag 71:1631–1634Article 

Google Scholar 
Wabakken P, Sand H, Liberg O, Bjärvall A (2001) The recovery, distribution, and population dynamics of wolves on the Scandinavian peninsula, 1978-1998. Can J Zool 79:710–725Article 

Google Scholar 
Waits LP, Luikart G, Taberlet P (2001) Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol Ecol 10:249–256CAS 
PubMed 
Article 

Google Scholar 
Walling CA, Pemberton JM, Hadfield JD, Kruuk LEB (2010) Comparing parentage inference software: reanalysis of a red deer pedigree. Mol Ecol 19:1914–1928PubMed 
Article 

Google Scholar 
Watson JEM, Shanahan DF, Di Marco M, Allan J, Laurance WF, Sanderson EW et al. (2016) Catastrophic declines in wilderness areas undermine global environment targets. Curr Biol 26:2929–2934CAS 
PubMed 
Article 

Google Scholar  More