元轉錄組學是一種高通量測序方法，藉由通過隨機測序特定環境條件下樣本的 RNA序列（信使 RNA [mRNA] 和核醣體 RNA [rRNA]）獲取群落的轉錄組信息（第 1 章）。在後生動物群落研究中(如浮游動物研究)使用元轉錄組學並不常見也沒有得到嚴格的驗證。為了解決這個問題,根據生態學代謝理論 (metabolic theory of ecology)和生長數率假設 (growth rate hypothesis),我們提出了一個理論框架來驗證 RNA序列豐度（mRNA 和 rRNA）的異速增長（allometric scaling）。納入影響RNA 產生的因子作為群落多樣性的指標，例如來自元轉錄組學的 RNA 序列讀數通過下一代測序技術可以為代謝率、能量通量和含高磷的RNA 流動提供理論基礎建立模型（第2 章)。基於 PCR 的方法, 我們使用基因組 (gDNA) ,互補 DNA (cDNA) 擴增子以及形態學來估計模擬群落的物種多樣性和組成, 測試並比較了利用元轉錄組學作為表徵浮游動物群落的方法。結果顯示元轉錄組學提供了更好的物種豐富度和組成估計且與利用形態學估計的結果相似（第 3 章）。最後，於翡翠水庫採集的樣本做進一步測試，結果顯示物種多樣性的估計在生物和技術重複之間是一致的。利用元轉錄組學可以檢測數量較少的分類群，並同時解決形態分析所需的繁重工作和分類學專業知識（第 4 章）。在模擬群落和野外樣本中, 利用RNA 序列讀數整合異速增生有助於提升 RNA 序列讀數與物種數量之間的相關性。總體而言，這項研究為在群落生態學研究中使用元轉錄組學提供了一個定量模型，並展示了其作為監測浮游動物群落多樣性的工具的優勢（第 5 章）。

Metatranscriptomics is a high-throughput sequencing method that allows direct access to community transcriptomic information through random sequencing of RNA (messenger RNA [mRNA] and ribosome RNA [rRNA]) transcripts from samples in specific environmental conditions (Chapter 1). Using metatranscriptomics in studying metazoan communities, like zooplankton research, has been uncommon and not rigorously validated. To address this, we first provide a theoretical framework to integrate the metabolic basis of RNA abundance (mRNA and rRNA) according to the assumptions of the metabolic theory of ecology and growth-rate hypothesis. Considering physiological factors affecting RNA production in molecular tools being used to characterize community diversity, such as RNA transcript reads from metatranscriptomics, could provide a theoretical baseline to model metabolic rate, energy flux, and turnover of phosphorus-rich RNA through next-generation sequencing technology (Chapter 2). Then, we tested and compared metatranscriptomics with PCR-based methods using genomic (gDNA) and complementary DNA (cDNA) amplicons, and morphology-based data for characterizing zooplankton mock communities. Metatranscriptomics provided better species richness and composition estimates that resembled those derived from morphological data (Chapter 3). Lastly, metatranscriptomics was further tested using field-collected samples (Feitsui reservoir), with the results showing consistent species diversity estimates among biological and technical replicates. Metatranscriptomics allowed the detection of less dominant taxa while addressing issues on laborious work and lack of taxonomic expertise needed in morphological analysis (Chapter 4). Moreover, integrating allometric scaling helped improve the predictive models on transcript reads and species biomass both in mock communities and field-collected samples. Overall, this study offers a theoretical framework that could extend the use of metatranscriptomics in characterizing community samples while demonstrating its advantages as an effective tool for monitoring the diversity of metazoan communities (Chapter 5).

Allan, J.D. (1976). Life history patterns in zooplankton. The American Naturalist, 110, 165–180 DOI 10.1086/283056.
Allen, A. P., & Gillooly, J. F. (2009). Towards integration of ecological stoichiometry and the metabolic theory of ecology to better understand nutrient cycling. Ecology Letters, 12, 369–384. doi:10.1111/j.1461–0248.2009.01302.x
Andújar, C., Creedy, T.J., Arribas, P., López, H., Salces‐Castellano, A., Pérez‐Delgado, A.J., Vogler, A.P., & Emerson, B.C. (2021). Validated removal of nuclear pseudogenes and sequencing artefacts from mitochondrial metabarcode data. Molecular Ecology Resources, https://doi.org/10.1111/1755–0998.13337
Arendt, J.D. (1997) Adaptive intrinsic growth rates: an integration across taxa. The Quarterly Review of Biology, 72, 149–177.
Banse, K. (1995). Zooplankton: pivotal role in the control of ocean production: I. Biomass and production. ICES Journal of Marine Science, 52, 265–277. doi/10.1016/1054-3139(95)80043-3.
Bashiardes, S., Zilberman–Schapira, G., Elinav, E. (2016). Use of metatranscriptomics in microbiome research. Bioinformatics and Biology Insights, 10, 19–25. doi: 10.4137/BBI.S34610
Bikel, S., Valdez–Lara, A., Cornejo–Granados, F., Rico, K., Canizales–Quinteros, S., Soberón, X., et al. (2015). Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems–level understanding of human microbiome. Computational and Structural Biotechnology Journal, 13, 390–401. doi: 10.1016/j.csbj.2015.06.001
Bensasson, D., Feldman, M. W., & Petrov, D. A. (2003). Rates of DNA duplication and mitochondrial DNA insertion in the human genome. Journal of Molecular Evolution, 57(3), 343–354. doi:10.1007/s00239–003–2485–7
Bensasson, D., Zhang, D., Hartl, D. L., & Hewitt G. M. (2011). Mitochondrial pseudogenes: Evolution’s misplaced witnesses. Trends in Ecology & Evolution, 16, 314–321
Berg, C., Dupont, C. L., Asplund–Samuelsson, J., Celepli, N. A., Eiler, A., Allen, A. E., et al. (2018). Dissection of microbial community functions during a cyanobacterial bloom in the baltic sea via metatranscriptomics. Frontiers in Marine Science, 5, 55. doi: 10.3389/fmars.2018.00055
Bista, I., Carvalho, G. R., Tang, M., Walsh, K., Zhou, X., Hajibabaei, M., & Creer, S. (2018). Performance of amplicon and shotgun sequencing for accurate biomass estimation in invertebrate community samples. Molecular Ecology Resources. https://doi.org/10.1111/1755–0998.12888
Braukmann, T. W. A., Ivanova, N. V., Prosser, S. W. J., Elbrecht, V., Ratnasingham, S. D. S., de Waard, J., … Hebert, P. D. N. (2019). Metabarcoding a diverse arthropod mock community. Molecular Ecology Resources, 19, 711–727. doi:10.1111/1755–0998.13008
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V., & West, G. B. (2004). Toward a metabolic theory of ecology. Ecology, 85(7), 1771–1789. https://doi.org/10.1890/03–9000
Bucklin, A., Divito, K. R., Smolina, I., Choquet, M., Questel, J. M., Hoarau, G. and O’neill, R. J. (2018). Population genomics of marine zooplankton. In Rajora, O. M. and Oleksiak, M. (eds.), Population genomics: Marine organisms, Springer, pp. 61–102.
Bullejos, F.J., Carrillo, P., Gorokhova, E., Medina–Sánchez, J.M., Villar–Argaiz, M. (2014) Nucleic Acid Content in Crustacean Zooplankton: Bridging Metabolic and Stoichiometric Predictions. PLoS ONE, 9(1): e86493.
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W. Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High–resolution sample inference from Illumina amplicon data. Nature Methods, 13, 581–583. doi:10.1038/nmeth.3869
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: Architecture and applications. BMC Bioinformatics, 10, 421. doi:10.1186/1471–2105–10–421
Chang, C. W., Shiah, F. K., Wu, J. T., Miki, T., & Hsieh, C. H. (2014). The role of food availability and phytoplankton community dynamics in the seasonal succession of the zooplankton community in a subtropical reservoir. Limnologica, 46, 131–138. doi:10.1016/j.limno.2014.01.002
Chiang, S., and Du, N. (1979). Freshwater Cladocera. Academica Sinica, Peking.
Chou, W. S., Lee, T. C., Lin, J. Y., and Yu, S. L. (2007). Phosphorus load reduction goals for Feitsui Reservoir watershed, Taiwan. Environmental Monitoring and Assessment, 131(1–3), 395–408.
Clarke, A. (2003). Costs and consequences of evolutionary temperature adaptation. Trends in Ecology and Evolution, 18, 573– 581.
Collura, R. V., Auerbach, M. R., & Stewart, C. B. (1996). A quick, direct method that can differentiate expressed mitochondrial genes from their nuclear pseudogenes. Current Biology, 6, 1337–1339. doi:10.1016/S0960–9822(02)70720–3
Creedy, T. J., Norman, H., Tang, C. Q., Qing Chin, K., Andújar, C., Arribas, P., … Vogler, A. P. (2019). A validated workflow for rapid taxonomic assignment and monitoring of a national fauna of bees (Apiformes) using high throughput DNA barcoding. Molecular Ecology Resources, 20(1), 40–53. doi:10.1111/1755–0998.13056
Cristescu, M. E. (2014). From barcoding single individuals to metabarcoding biological communities: Towards an integrative approach to the study of global biodiversity. Trends in Ecology & Evolution, 29, 566–571.
Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., Gingeras, T.R. (2013). STAR: ultrafast universal RNA–seq aligner. Bioinformatics, 29(1): 15–21. doi: 10.1093/bioinformatics/bts635
Dumont, H. J., van de Velde, I., & Dumont, S. (1975). The dry weight estimate of biomass in a selection of Cladocera, Copepoda, and Rotifera from the plankton, periphyton, and benthos of continental waters. Oecologia, 19(1), 75–97.
Dumont, H.J., & Tundisi, J.G. (1984). Tropical zooplankton. Hydrobiologia, 113:1–332
Dussart, B., Defaye, D., (2001). Introduction to the Copepoda. SPB Academic Publishing,Amsterdam.
Elbrecht, V., & Leese, F. (2015). Can DNA–based ecosystem assessments quantify species abundance? Testing primer bias and biomass–sequence relationships with an innovative metabarcoding protocol. PLOS ONE, 10, e0130324. doi:10.1371/journal.pone.0130324
Elbrecht, V., Braukmann, T., Ivanova, N. V., Prosser, S., Hajibabaei, M., Wright, M., Zakharov, E. V., Hebert, P., & Steinke, D. (2019). Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ, 7, e7745. https://doi.org/10.7717/peerj.7745
Elser, J. J., Sterner, R. W., Gorokhova, E., Fagan, W. F., Markow, T. A., Cotner, J. B., Harrison, J.F., Hobbie, S.E., Odell, G.M., Weider, L.W. (2000). Biological stoichiometry from genes to ecosystems. Ecology Letters. 3, 540–550. doi: 10.1046/j.1461–0248.2000.00185.x
Emms, D.M., Kelly, S. (2019). OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biology, 20(238), https://doi.org/10.1186/s13059–019–1832–y
Fernando, C.H. (1994). Zooplankton, fish and fisheries in tropical freshwaters. Hydrobiologia, 272, 105–123.
Fernando C.H. (2002). A guide to tropical freshwater zooplankton – identification, ecology and impact on fisheries. Leiden (Netherlands): Backhuys Publishers
Freedman, A. H., Clamp, M., & Sackton, T. B. (2020). Error, noise and bias in de novo transcriptome assemblies. Molecular Ecology Resources, 21, 18–29. https://doi.org/10.1111/1755–0998.13156
Frøslev, T. G., Kjøller, R., Bruun, H. H., Ejrnæs, R., Brunbjerg, A. K., Pietroni, C., & Hansen, A. J. (2017). Algorithm for post–clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nature Communication, 8, 1188. doi:10.1038/s41467–017–01312–x
Glazier, D.S. (2005). Beyond the ‘3/4-power law’: variation in the intra- and interspecific scaling of metabolic rate in animals. Biological Review, 80, 611– 662.
Glazier, D. S. (2014). Metabolic scaling in complex systems. Systems, 2, 451–540.
Gregory, T.R., Nicol, J.A, Tamm, H., Kullman, B., Kullman, K., Leitch, I.J., Murray, B.G., Kapraun, D.F., Greilhuber, J., Bennett, M.D. (2007). Eukaryotic genome size databases, Nucleic Acids Research, 35(1), 332–338.
Gillooly, J.F., Brown, J.H., West, G.B., Savage, V.M. & Charnov, E.L. (2001). Effects of size and temperature on metabolic rate. Science, 293, 2248–2251.
Gillooly, J.F., Allen, A.P., West, G.B., and Brown, J.H. (2005a). The rate of DNA evolution: Effects of body size and temperature on the molecular clock. Proceedings of the National Academy of Sciences of the United States of America, 102(1), 140–145. doi: 10.1073/pnas.0407735101
Gillooly J. F., Allen A. P., Brown J. H., Elser, J.E., del Rio, C.M., Savage, V.M., West, G.B., Woodruff, W.H., and Woods, H.A. (2005b). The metabolic basis of whole–organism RNA and phosphorus stoichiometry, Proceedings of the National Academy of Sciences of the United States of America, 102, 11923–11927.
Gittleman, J. (2011). Allometry. In Encyclopædia Britannica (Online): Encyclopædia Britannica, inc. Retrieved from https://www.britannica.com/science/allometry.
GLNPO (Great Lakes National Program Office) (2016) Standard operating procedure for zooplankton analysis. U. S. EPA Great Lakes National Program Office, method LG403. Revision 07.
Gorsky, G., Ohman, M.D., Picheral, M., Gasparini, S., Stemmann, L., Romagnan, J.B., Cawood, A., Pesant, S., García-Comas, C., Prejger, F. (2010). Digital zooplankton image analysis using the ZooScan integrated system, Journal of Plankton Research, 32(3). https://doi.org/10.1093/plankt/fbp124
Gorokhova, E., Dowling, T.E., Weider, L.J., Crease, T.J., & Elser, J.J. (2002). Functional and ecological significance of rDNA intergenic spacer variation in a clonal organism under divergent selection for production rate. Proceedings of the Royal Society B: Biological Sciences, 269: 2373–2379.
Gorokhova, E. (2005). Effects of preservation and storage of microcrustaceans in RNAlater on RNA and DNA degradation. Limnology and Oceanography: Methods, 3(2), 143–148.
Grabherr, M.G., Haas, B.J., Yassour, M., Levin, J.Z., Thompson, D.A., Amit, I., Adiconis, X., Fan, L., Raychowdhury, R., Zeng, Q., Chen, Z., Mauceli, E., Hacohen, N., Gnirke, A., Rhind, N., di Palma, F., Birren, B.W., Nusbaum, C., Lindblad–Toh, K., Friedman, N., Regev, A. (2011). Full–length transcriptome assembly from RNA–Seq data without a reference genome. Biotechnology, 29, 644–652.
Grimaldi, G., and Di Nocera, P.O. (1988). Multiple repeated units in Drosophila melanogaster ribosomal DNA spacer stimulate rRNA precursor transcription. Proceedings of the National Academy of Sciences of the United States of America, 85, 5502–5506.
Hlaing, T., Willoughby, T. L., Somboon, P., Socheat, D., Setha, T., Min, S., … Walton, C. (2009). Mitochondrial pseudogenes in the nuclear genome of Aedes aegypti mosquitoes: Implications for past and future population genetic studies. BMC Genetics, 10, 11. doi:10.1186/1471–2156–10–11
Hsieh, C., Ma, K. H., & Chao, A. (2020). iNEXT: Interpolation and extrapolation for species diversity. R package version 2.0.20. Retrieved from http://chao.stat.nthu.edu.tw/wordpress/software_download/
Hölzer, M. (2020). A decade of de novo transcriptome assembly: Are we there yet? Molecular Ecology Resources, 1–3.
Ikeda, T. (1985). Metabolic rates of epipelagic marine zooplankton as a function of body mass and temperature. Marine Biology, 85, 1–11. https://doi.org/10.1007/BF00396409
Kennedy, S. R., Prost, S., Overcast, I., Rominger, A. J., Gillespie, R. G., & Krehenwinkel, H. (2020). High-throughput sequencing for community analysis: The promise of DNA barcoding to uncover diversity, relatedness, abundances, and interactions in spider communities. Development Genes and Evolution, 230, 185-201. doi:10.1007/s00427- 020-00652-x
Killen, S.S., Atkinson, D., & Glazier, D.S. (2010). The intraspecific scaling of metabolic rate with body mass in fishes depends on lifestyle and temperature. Ecol. Lett., 13, 184–193.
Kong, W.L., Miki, T., Lin, Y.Y., Makino, W., Urabe, J., Gu, S.H., Machida, R.J. (2019). Nuclear and mitochondrial ribosomal ratio as an index of animal growth rate. Limnology and Oceanography Methods, 17, 11, 575–584.
Kozłowski, J., Konarzewski, M., and Gawelczyk, A. T. (2003). Cell size as a link between noncoding DNA and metabolic rate scaling. Proceedings of the National Academy of Sciences of the United States of America, 100, 24, 14080–14085. https://doi.org/10.1073/pnas.2334605100
Kuo, Y. M., and Wu, J. T. (2016). Phytoplankton dynamics of a subtropical reservoir controlled by the complex interplay among hydrological, abiotic, and biotic variables. Environmental Monitoring and Assessment, 188(689), 1–14.
Krehenwinkel, H., Wolf, M., Lim, J. Y., Simison W. B., & Gillespie R. G. (2017). Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Scientific Reports, 7, 17668. doi:10.1038/s41598–017–17333–x
Kuosmanen A, Norri T, Mäkinen V. (2018). Evaluating approaches to find exon chains based on long reads. Brief Bioinformatics, 19, 04–14.
Lampe, R. H., Wang, S., Cassar, N. and Marchetti, A. (2019) Strategies among phytoplankton in response to alleviation of nutrient stress in a subtropical gyre. ISME J, 13, 2984–2997.
Langmead, B., & Salzberg, S. (2012). Fast gapped–read alignment with Bowtie 2. Nature Methods, 9, 357–359. doi:10.1038/nmeth.1923
Leasi, F., Sevigny, J. L., Laflamme, E. M., Artois, T., Curini-Galletti, M., de Jesus Navarrete, A., …. Thomas, W. K. (2018). Biodiversity estimates and ecological interpretations of meiofaunal communities are biased by the taxonomic approach. Communications Biology, 1, 112. doi:10.1038/s42003-018-0119-2
Le Bourg, B., Cornet-Barthaux, V., Pagano, M., Blanchot, J. (2015). FlowCAM as a tool for studying small (80–1000 µm) metazooplankton communities. Journal of Plankton Research, (37)4, 666–670. https://doi.org/10.1093/plankt/fbv025
Lee C.D., and Tu B.P. (2017). Metabolic influences on RNA biology and translation. Critical Reviews in Biochemistry and Molecular Biology, 52:176–184. doi: 10.1080/10409238.2017.1283294
Leray, M., Ho, S. L., Lin, I. J., & Machida, R.J. (2018). MIDORI server: A webserver for taxonomic assignment of unknown metazoan mitochondrial–encoded sequences using a curated database. Bioinformatics, 34, 3753–3754, doi:10.1093/bioinformatics/bty454
Leray, M., & Knowlton, N. (2015). DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity. Proceedings of the National Academy of Sciences of the United States of America, 112, 2076–2081. doi:10.1073/pnas.1424997112
Leray, M., Knowlton, N., Ho, S. L., Nguyen, B. N., & Machida, R. J. (2019). GenBank is a reliable resource for 21st–century biodiversity research. Proceedings of the National Academy of Sciences of the United States of America, 116, 22651–22656. doi:10.1073/pnas.1911714116
Leray, M., Yang, J. Y., Meyer, C., Mills, S.C., Agudelo, N., Ranwez, V., … Machida, R. J. (2013). A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Frontiers in Zoology, 10, 34. doi:10.1186/1742–9994–10–34
Lenz, P.H., Lieberman, B., Cieslak, M.C., Roncalli, V, & Hartline, D.K. (2021). Transcriptomics and metatranscriptomics in zooplankton: wave of the future? Journal of Plankton Research, 43(1), 3–9.
Leung, H.C., Yiu, S.M., Parkinson, J., Chin, F.Y. (2013). IDBA–MT: de novo assembler for metatranscriptomic data generated from next–generation sequencing technology. Journal of Computational Biology, 20(7), 540–50. doi: 10.1089/cmb.2013.0042. PMID: 23829653
Li, B., & Dewey, C. N. (2011). RSEM: Accurate transcript quantification from RNA–Seq data with or without a reference genome. BMC Bioinformatics, 12, 323. doi:10.1186/1471–2105–12–323
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools, Bioinformatics, 25(16), 2078–2079.
Lopez, M.L.D., Pascual, J.A., Dela Paz, E.S., Rizo, E.Z., Tordesillas, D., Guinto, S.K., Dumont, H., Mamaril, A.C., & Papa, R.D.S. (2017). Annotated checklist and insular distribution of freshwater microcrustaceans (Copepoda: Calanoida & Cyclopoida; Cladocera: Anomopoda & Ctenopoda) in the Philippines. The Raffles Bulletin of Zoology, 65:623–654
Lopez, M.L.D., Lin, Y.Y., Sato, M., Hsieh, C.H., Shiah, F.K., and Machida, R.J. (2021a). Using metatranscriptomics to estimate the diversity and composition of zooplankton communities. Molecular Ecology Resources.
Lopez, M.L.D, Lin, Y.Y., Schneider, S.Q., Machida, R.J. (2021b). Allometric scaling of RNA abundance from genes to communities. bioRxiv. doi: https://doi.org/10.1101/2021.10.03.462954
Lubzens, E. (1987). Raising rotifers for use in aquaculture. In: Rotifer symposium IV. Dordrecht: Springer, 245–255.
Luikart, G., Ryman, N., Tallmon, D. A., Schwartz, M. K., & Allendorf, F. W. (2010). Estimation of census and effective population sizes: The increasing usefulness of DNA–based approaches. Conservation Genetics, 11(2), 355–373.
Machida, R. J., Hashiguchi, Y., Nishida, M., & Nishida, S. (2009). Zooplankton diversity analysis through single-gene sequencing of a community sample. BMC Genomics, 10, 438. doi:10.1186/1471-2164-10-438
Machida, R.J, Kurihara, H., Nakajima, R., Sakamaki, T., Lin, Y.Y., Furusawa, K. (2021) Comparative analysis of zooplankton diversities and compositions estimated from complement DNA and genomic DNA amplicons, metatranscriptomics, and morphological identifications, ICES Journal of Marine Science, fsab084.
Machida, R. J., Leray, M., Ho, S., & Knowlton, N. (2017). Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Scientific Data, 4, 170027. doi:10.1038/sdata.2017.27
Machida, R. J., & Lin Y. Y. (2017). Occurrence of mitochondrial CO1 pseudogenes in Neocalanus plumchrus (Crustacea: Copepoda): Hybridization indicated by recombined nuclear mitochondrial pseudogenes. PLOS ONE, 12(2), e0172710.
Martin, M. (2011). Cutadapt removes adapter sequences from high–throughput sequencing reads. EMBnet Journal, 17(1).
Madeira, F., Park, Y.M., Lee, J., Buso, N., Gur, T., Madhusoodanan, N., Basutkar, P., Tivey, A.R.N., Potter, P.C., Finn, R.D., Lopez, R., (2019). The EMBL–EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Research, 47(W1), 636–W641.
Meredith, C., Hoffman, J., Trebitz, A., Pilgrim, E., Okum, S., Martinson, J., Cameron, E.S. (2021) Evaluating the performance of DNA metabarcoding for assessment of zooplankton communities in Western Lake Superior using multiple markers. Metabarcoding and Metagenomics, 5: e64735.
Molik, D.C., Pfrender, M.E., & Emrich, S.J. (2020). Uncovering effects from the structure of metabarcode sequences for metagenetic and microbiome analysis. Methods and Protocol, 3(22), doi:10.3390/mps3010022
Moniruzzaman, M., Wurch, L. L., Alexander, H., Dyhrman, S. T., Gobler, C. J., and Wilhelm, S. W. (2017). Virus–host relationships of marine single–celled eukaryotes resolved from metatranscriptomics. Nature Communication, 8, 16054. doi: 10.1038/ncomms16054
Oliveira, S.M.D., Häkkinen, A., Lloyd–Price, J., Tran, H., Kandavalli, V., Ribeiro, A.S. (2016) Temperature–Dependent Model of Multi–step Transcription Initiation in Escherichia coli Based on Live Single–Cell Measurements. PLoS Computational Biology, 12(10): e1005174. https://doi.org/10.1371/journal.pcbi.1005174
Oksanen, J., Blanchet F. G, Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. (2019). Vegan: Community Ecology Package. R package version 2.5–6. Retrieved from https://CRAN.R–project.org/package=vega
Okuda, N., Sakai, Y., Fukumori, K., Yang, S.M., Hsieh, C.H., Shiah, F.K. (2017). Food web properties of the recently constructed, deep subtropical Fei-Tsui Reservoir in comparison with the ancient Lake Biwa. Hydrobiologia, 802, 199–210.
Peimbert, M., and Alcaraz, L.D. (2016) A Hitchhiker’s guide to metatranscriptomics. In: Field guidelinesfor genetic experimental designs in high–throughput sequencing. Springer, Cham, 313–342.
Peng, Y., Leung, H.C.M., Yiu, S.M., & Chin, F.Y.L. (2012). IDBA–UD: a de novo assembler for single–cell and metagenomic sequencing data with highly uneven depth, Bioinformatics, 28(11), 1420–1428.
Pérez‐Portela, R., & Riesgo, A. (2013). Optimizing preservation protocols to extract high‐quality RNA from different tissues of echinoderms for next‐generation sequencing. Molecular Ecology Resources, 13, 884–889.
Perna, N. T., & Kocher, T. D. (1996). Mitochondrial DNA: Molecular fossils in the nucleus. Current Biology, 6, 128–129.
Pertea, M., Pertea, G., Antonescu, C., Chang, T.C., Mendell, J.T., and Salzberg, S.L. (2015). StringTie enables improved reconstruction of a transcriptome from RNA–seq reads. Nature Biotechnology, 33, 290–295.
Peters, R. H. (1983). The ecological implications of body size. Cambridge: University Press
Piredda, R., Claverie, J., Decelle, J., de Vargas C., Dunthorn M., Edvardsen B., Eikrem W., … Zingone A. (2018). Diatom diversity through HTS-metabarcoding in coastal European seas. Scientific Reports, 8, 18059.
Piccolin, F., Pitzschler, L., Biscontin, A., Kawaguchi, S. and Meyer, B. (2020) Circadian regulation of diel vertical migration (DVM) and metabolism in Antarctic krill Euphausia superba. Scientific Reports, 10, 1–11.
Piñol, J., Mir, G., Gomez‐Polo, P., & Agustí, N. (2014). Universal and blocking primer mismatches limit the use of high‐throughput DNA sequencing for the quantitative metabarcoding of arthropods. Molecular Ecology, 15, 819–830.
Piñol, J., Senar, M. A. & Symondson, W. O. (2019). The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Molecular Ecology, 28, 407–419.
Piper, A.M., Batovska, J., Cogan, N.I.O., Weiss J., Cunningham, J.P., Rodoni, B.R., Blacket, M.J. (2019). Prospects and challenges of implementing DNA metabarcoding for high–throughput insect surveillance, GigaScience, 8(8), giz092.
Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., et al. (2010). A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 464, 59–65. doi: 10.1038/nature08821
Ranwez, V., Harispe, S., Delsuc, F., & Douzery, E. J. P. (2011). MACSE: Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons. PLoS ONE 6(9), e22594. doi:10.1371/journal.pone.0022594
Reeder, R.H., and Dunaway, M. (1983). Spacer regulation of Xenopus ribosomal gene transcription. Competition Oocytes. Cell, 35, 449 – 456.
R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R–project.org/
Richly, E., & Leister, D. (2004). NUMTs in sequenced eukaryotic genomes. Molecular Biology and Evolution, 21(6), 1081–1084. doi:10.1093/molbev/msh110
Rognes. T., Flouri, T., Nichols, B., Quince, C., & Mahé, F. (2016). VSEARCH: a versatile open–source tool for metagenomics. PeerJ, 4, e2584. doi:10.7717/peerj.2584
Romero, I.G., Pai, A.A., Tung, J., and Gilad, Y. (2014). RNA–seq: impact of RNA degradation on transcript quantification. BMC Biology, 12, 42.
Roncalli, V., Cieslak, M. C. and Lenz, P. H. (2016) Transcriptomic responses of the calanoid copepod Calanus finmarchicus to the saxitoxin producing dinoflagellate Alexandrium fundyense. Scientific Reports, 6, 25708.
Rondon, M. R., August, P. R., Bettermann, A. D., Brady, S. F., Grossman, T. H., Liles, M. R., et al. (2000). Cloning the soil metagenome: a strategy for accessing the genetic and functional diversity of uncultured microorganisms. Applied Environmental Microbiology, 66, 2541–2547.
Rozas, J., Ferrer–Mata, A., Sánchez–DelBarrio, J. C., Guirao–Rico, S., Librado, P., Ramos–Onsins, S. E., & Sánchez–Gracia, A. (2017). DnaSP 6: DNA sequence polymorphism analysis of large datasets. Molecular Biology and Evolution, 34, 3299–3302.
Santoferrara, L.S. (2019). Current practice in plankton metabarcoding: optimization and error management, Journal of Plankton Research, 41(5), 571–582.
Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., … Weber, C.F. (2009). Introducing Mothur: Open–source, platform–independent, community–supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75, 7537–7541.
Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., … Ragg, T. (2006) The RIN: An RNA integrity number for assigning integrity values to RNA measurements. BMC Molecular Biology, 7, 3.
Semmouri, I., de Schamphelaerea, K. A. C., Mees, J., Janssen, C. R., & Asselman, J. (2019). Evaluating the potential of direct RNA nanopore sequencing: Metatranscriptomics highlights possible seasonal differences in a marine pelagic crustacean zooplankton community. Marine Environmental Research, 153, 104836.
Seppey, M., Manni, M., Zdobnov, E.M. (2019). BUSCO: Assessing genome assembly and annotation completeness. In: Kollmar M. (ed.) Gene Prediction. Methods in Molecular Biology, vol 1962. Humana, New York, NY. 2019 doi.org/10.1007/978–1–4939–9173–0_14. PMID:31020564
Shakya, M., Lo, C.C., and Chain, P.S.G. (2019) Advances and Challenges in Metatranscriptomic Analysis. Frontiers in Genetics, 10:904. doi: 10.3389/fgene.2019.00904
Shen, J., Song, D. (1979). Freshwater Copepoda. Academia Sinica, Peking.
Shilova, I.N., Magasin, J.D., Mills, M.M., Robidart, J.C., Turk–Kubo, K.A., Zehr, J.P. (2020) Phytoplankton transcriptomic and physiological responses to fixed nitrogen in the California current system. PLoS ONE, 15(4): e0231771. https://doi.org/10.1371/journal.pone.0231771
Shokralla, S., Gibson, J. F., Nikbakht, H., Janzen, D. H., Hallwachs, W., & Hajibabaei, M. (2014) Next–generation DNA barcoding: Using next–generation sequencing to enhance and accelerate DNA barcode capture from single specimens. Molecular Ecology Resources, 14(5), 892–901.
Sibly, R.M., Brown, J.H., and Kodric–Brown, A. (2012). Metabolic Ecology: a Scaling Approach. Oxford: Willey–Blackwell.
Song, H., Buhay, J. E., Whiting, M. F., & Crandall, K. A. (2008). Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proceedings of the National Academy of Sciences of the United States of America, 105, 13486–13491.
Stefanni, S., Stanković, D., Borme, D., de Olazabal, D., Juretić, T., Pallavicini, A., & Tirelli, V. (2018). Multi–marker metabarcoding approach to study mesozooplankton at basin scale. Scientific Reports, 8, 12085.
Sterner, R.W., and Elser, J.J. (2002). Ecological Stoichiometry: the Biology of Elements from Molecules to the Biosphere. Princeton, New Jersey: Princeton University Press.
Sun, Y., Shi, Y. L., Wang, H., Zhang, T., Yu, L. Y., Sun, H., & Zhang, Y. Q. (2018). Diversity of bacteria and the characteristics of actinobacteria community structure in Badain Jaran Desert and Tengger Desert of China. Frontiers in Microbiology, 9, 1068. doi:10.3389/fmicb.2018.01068
Triant, D.A., & Whitehead, A. (2009). Simultaneous extraction of high–quality RNA and DNA from small tissue samples. Journal of Heredity, 100(2), 246–250.
Valdes, C., & Capobianco, E. (2014). Methods to detect transcribed pseudogenes: RNA–Seq discovery allows learning through features. In L. Poliseno (Ed.), Pseudogenes. Methods in Molecular Biology (Methods and Protocols) (Vol. 1167, pp. 157–183). New York, NY: Humana Press
van der Loos, L. M., & Nijland, R. (2020). Biases in bulk: DNA metabarcoding of marine communities and the methodology involved. Molecular Ecology, 00, 1–19.
Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., et al. (2004). Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66–74.
Wang, Q., Garrity, G. M., Tiedje, J., & Cole, J. R. (2007). Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73, 5261–5267.
Watiroyram, S., & Sanoamuang, L. (2017). A new species of Mongolodiaptomus Kiefer, 1938 from northeast Thailand and a key to the species (Crustacea, Copepoda, Calanoida, Diaptomidae). ZooKeys, 710, 15–32).
White, C.R., & Kearney, M. R. (2014). Metabolic scaling in animals: Methods, empirical results, and theoretical explanations. Compr. Physiol., 4, 231–256.
Wilson, J. J., Sing, K. W., Lee, P. S., & Wee, A. K. S. (2016). Application of DNA barcodes in wildlife conservation in Tropical East Asia. Conservation Biology, 30, 982-989.
Yang, J., Zhang, X., Xie, Y., Song C., Zhang Y., Yu H., & Burton, G. A. (2017). Zooplankton community profiling in a eutrophic freshwater ecosystem–Lake Tai Basin by DNA metabarcoding. Scientific Reports, 7. doi:10.1038/s41598–017–01808–y
Yates, M. C., Glaser, D., Post, J., Cristescu, M. E., Fraser, D. J., & Derry, A. M. (2020). The relationship between eDNA particle concentration and organism abundance in nature is strengthened by allometric scaling. Molecular Ecology, 30, 3068– 3082.
Yates, M., Cristescu, M. E., & Derry, A. M. (2021a). Integrating physiology and environmental dynamics to operationalize environmental DNA (eDNA) as a means to monitor freshwater macro-organism abundance. Molecular Ecology, 00: 1–20.
Yates, M. C., Wilcox, T. M., McKelvey, K. S., Young, M. K., Schwartz, M. K., & Derry, A. M. (2021b). Allometric scaling of eDNA produc-tion in stream-dwelling brook trout (Salvelinus fontinalis) inferred from population size structure. Environmental DNA, 3(3), 553–560.
Zhang, G.K., Chain, F.J.J., Abbott, C.L., & Cristescu, M.E. (2018). Metabarcoding using multiplexed markers increases species detection in complex zooplankton communities. Evolutionary Applications, 11:1901–1914. doi: 10.1111/eva.12694
Zhang, Y., Lin, X., Shi, X., Lin, L., Luo, H., Li, L. and Lin, S. (2019) Metatranscriptomic signatures associated with phytoplankton regime shift from diatom dominance to a dinoflagellate bloom. Frontiers in Microbiology, 10, 590.
Zhao, S., Zhang, Y., Gamini, R., Zhang, B., and von Schack, D. (2018). Evaluation of two main RNA–seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion. Scientific Reports, 8, 4781. https://doi.org/10.1038/s41598–018–23226–4
Zischler, H., Geisert, H., von Haeseler, A., & Pääbo, S. (1995). A nuclear ‘fossil’ of the mitochondrial D–loop and the origin of modern humans. Nature, 378, 489–492.