Use of near-infrared reflectance spectroscopy to quantify diet mixing in a generalist marine herbivore

Abstract

The diet of individual animals in the field is governed by behavioural preference as well as factors intrinsic and extrinsic to the organism that may constrain their ability to obtain preferred foods. Accurate measurements of individual diets are essential to understanding how consumers can impact communities, but can be difficult to obtain. We aim to determine whether near-infrared reflectance spectroscopy (NIRS) can be used to quantify the degree of diet mixing in the diet of a generalist herbivore, the marine gastropod Lunella torquatus. NIRS successfully classified five algal diets and could quantify the proportions of these species in two-species combinations. We then assessed whether NIRS could predict dietary composition post-digestion by contrasting faecal material from single-species and mixed diets. Partial least squares methods were used to develop prediction models that effectively discriminated among species and, for most species, effectively predicted the proportion of a given species in all possible two-species mixtures. NIRS thus has the potential to provide a non-invasive method for assessing the realised diets of free-living herbivores.

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References

  1. Allen E, Crawley MJ (2011) Contrasting effects of insect and molluscan herbivores on plant diversity in a long-term field experiment. Ecol Lett 14:1246–1253

    Article  Google Scholar 

  2. Andre J, Lawler IR (2003) Near infrared spectroscopy as a rapid and inexpensive means of dietary analysis for a marine herbivore, dugong Dugong dugon. Mar Ecol Prog Ser 257:259–266

    Article  Google Scholar 

  3. Araújo MS, Bolnick DI, Layman CA (2011) The ecological causes of individual specialisation. Ecol Lett 14:948–958. doi:10.1111/j.1461-0248.2011.01662.x

    Article  Google Scholar 

  4. Asner PM, Martin RE (2009) Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Front Ecol Environ 7:269–276. doi:10.1890/070152

    Article  Google Scholar 

  5. Atkinson MD, Jervis AP, Sangha RS (1997) Discrimination between Betula pendula, Betula pubescens, and their hybrids using near-infrared reflectance spectroscopy. Can J Forest Res 27:1896–1900. doi:10.1139/x97-141

    Article  Google Scholar 

  6. Bain KF, Vergés A, Poore AGB (2013) Using near infra red reflectance spectroscopy (NIRS) to quantify tissue composition in the seagrass Posidonia australis. Aquat Bot 111:66–70. doi:10.1016/j.aquabot.2013.05.012

    CAS  Article  Google Scholar 

  7. Baker R, Buckland A, Sheaves M (2014) Fish gut content analysis: robust measures of diet composition. Fish Fisheries 15:170–177. doi:10.1111/faf.12026

    Article  Google Scholar 

  8. Barnes R, Dhanoa M, Lister SJ (1989) Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 43:772–777

    CAS  Article  Google Scholar 

  9. Baumgartner FA, Pavia H, Toth GB (2014) Individual specialization to non-optimal hosts in a polyphagous marine invertebrate herbivore. PLoS One. doi:10.1371/journal.pone.0102752

    Google Scholar 

  10. Behmer ST, Simpson SJ, Raubenheimer D (2002) Herbivore foraging in chemically heterogeneous environments: nutrients and secondary metabolites. Ecology 83:2489–2501. doi:10.1890/0012-9658%282002%29083%5B2489:HFICHE%5D2.0.CO;2

    Article  Google Scholar 

  11. Belovsky GE, Jordan PA (1978) The time-energy budget of a moose. Theor Popul Biol 14:76–104. doi:10.1016/0040-5809(78)90006-0

    CAS  Article  Google Scholar 

  12. Blankenship L, Yayanos A (2005) Universal primers and PCR of gut contents to study marine invertebrate diets. Mol Ecol 14:891–899

    CAS  Article  Google Scholar 

  13. Bolnick DI, Svanbäck R, Fordyce JA, Yang LH, Davis JM, Hulsey CD, Forister ML (2003) The ecology of individuals: incidence and implications of individual specialization. Am Nat 161:1–28. doi:10.1086/343878

    Article  Google Scholar 

  14. Bowen WD, Iverson SJ (2012) Methods of estimating marine mammal diets: a review of validation experiments and sources of bias and uncertainty. Mar Mammal Sci 29:719–754. doi:10.1111/j.1748-7692.2012.00604.x

    Google Scholar 

  15. Brendelberger H (1997) Coprophagy: a supplementary food source for two freshwater gastropods? Freshwater Biol 38:145–157

    Article  Google Scholar 

  16. Brett MT, Müller-Navarra DC, Persson J (2009) Crustacean zooplankton fatty acid composition. In: Kainz M, Brett MT, Arts MT (eds) Lipids in aquat ecosystems. Springer, New York, pp 115–146

    Google Scholar 

  17. Bromaghin JF, Rode KD, Budge SM, Thiemann GW (2015) Distance measures and optimization spaces in quantitative fatty acid signature analysis. Ecol Evol 5:1249–1262. doi:10.1002/ece3.1429

    Article  Google Scholar 

  18. Budge SM, Penney SN, Lall SP (2012) Estimating diets of Atlantic salmon (Salmo salar) using fatty acid signature analyses; validation with controlled feeding studies. Can J Fish Aquat Sci 69:1033–1046

    CAS  Article  Google Scholar 

  19. Chakravarti LJ, Cotton PA (2014) The effects of a competitor on the foraging behaviour of the shore crab Carcinus maenas. PLoS One 9(4):e93546

    Article  Google Scholar 

  20. Chataigner F, Surault F, Huyghe C, Julier B (2010) Determination of botanical composition in multispecies forage mixtures by near infrared reflectance spectroscopy. In: Huyghe C (ed) Sustainable use of genetic diversity in forage and turf breeding. Springer, Netherlands, pp 199–203. doi:10.1007/978-90-481-8706-5_28

    Google Scholar 

  21. Chesson J (1983) The estimation and analysis of preference and its relationship to foraging models. Ecology 64:1297–1304. doi:10.2307/1937838

    Article  Google Scholar 

  22. Coates DB, Dixon RM (2007) Faecal near infrared reflectance spectroscopy (F.NIRS) measurements of non-grass proportions in the diet of cattle grazing tropical rangelands. Rangeland J 29:51–63

    Article  Google Scholar 

  23. Coleman SW, Barton FE, Meyer RD (1985) The use of near-infrared reflectance spectroscopy to predict species composition of forage mixtures. Crop Sci 25:834–837

    Article  Google Scholar 

  24. Coleman SW, Christiansen S, Shenk JS (1990) Prediction of botanical composition using NIRS calibrations developed from botanically pure samples. Crop Sci 30:202–207. doi:10.1071/rj07011

    Article  Google Scholar 

  25. Crawley KR, Hyndes GA, Vanderklift MA, Revill AT, Nichols PD (2009) Allochthonous brown algae are the primary food source for consumers in a temperate, coastal environment. Mar Ecol Prog Ser 376:33–44

    Article  Google Scholar 

  26. Duffy JE (2002) Biodiversity and ecosystem function: the consumer connection. Oikos 99:201–219

    Article  Google Scholar 

  27. Ettinger-Epstein P, Kingsford MJ (2008) Effects of the El Niño southern oscillation on Turbo torquatus (Gastropoda) and their kelp habitat. Austral Ecol 33:594–606. doi:10.1034/j.1600-0706.2002.990201.x

    Article  Google Scholar 

  28. Filzmoser P, Maronna R, Werner M (2008) Outlier identification in high dimensions. Comput Stat Data Anal 52:1694–1711. doi:10.1016/j.csda.2007.05.018

    Article  Google Scholar 

  29. Foale S, Day R (1992) Recognizability of algae ingested by abalone. Mar Freshwater Res 43:1331–1338

    Article  Google Scholar 

  30. Foley WJ, Mcilwee A, Lawler I, Aragones L, Woolnough AP, Berding N (1998) Ecological applications of near infrared reflectance spectroscopy–a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia 116:293–305

    Article  Google Scholar 

  31. Fox LR, Morrow PA (1981) Specialization: species property or local phenomenon? Science 211:887–893. doi:10.1126/science.211.4485.887

    CAS  Article  Google Scholar 

  32. Freeland WJ, Janzen DH (1974) Strategies in herbivory by mammals: the role of plant secondary compounds. Am Nat 108:269–289. doi:10.2307/2459891

    CAS  Article  Google Scholar 

  33. Galloway AW, Britton-Simmons KH, Duggins DO, Gabrielson PW, Brett MT (2012) Fatty acid signatures differentiate marine macrophytes at ordinal and family ranks. J Phycol 48:956–965

    Article  Google Scholar 

  34. Galloway AWE, Eisenlord ME, Dether MN, Holtgrieve GW, Brett MT (2014) Quantitative estimates of isopod resource utilization using a Bayesian fatty acid mixing model. Mar Ecol Prog Ser 507:219–232. doi:10.3354/meps10860

    Article  Google Scholar 

  35. Galloway AWE, Brett MT, Holtgrieve GW et al (2015) A fatty acid based Bayesian approach for inferring diet in aquatic consumers. PLoS One 10:e0129723. doi:10.1371/journal.pone.0129723

    Article  Google Scholar 

  36. Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17

    CAS  Article  Google Scholar 

  37. Geladi P, Macdougall D, Martens H (1985) Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl Spectros 39:491–500

    Article  Google Scholar 

  38. Glasser T, Landau S, Ungar ED, Perevolotsky A, Dvash L, Muklada H, Kababya D, Walker JW (2008) A fecal near-infrared reflectance spectroscopy-aided methodology to determine goat dietary composition in a Mediterranean shrubland. J Anim Sci 86:1345–1356. doi:10.2527/jas.2006-817

    CAS  Article  Google Scholar 

  39. Hay KB, Millers KA, Poore AGB, Lovelock CE (2010) The use of near infrared reflectance spectrometry for characterization of brown algal tissue. J Phycol 46:937–946. doi:10.1111/j.1529-8817.2010.00890.x

    Article  Google Scholar 

  40. Heroldova M, Cizmar D, Tkadlec E (2010) Predicting rodent impact in crop fields by near-infrared reflectance spectroscopy analysis of their diet preferences. Crop Prot 29:773–776. doi:10.1016/j.cropro.2010.02.009

    Article  Google Scholar 

  41. Holechek JL, Vavra M, Pieper RD (1982) Botanical composition determination of range herbivore diets: a review. J Range Manag 35:309–315

    Article  Google Scholar 

  42. Huntly N (1995) How important are consumer species to ecosystem functioning? In: Jones C, Lawton J (eds) Linking species and ecosystems. Springer, Boston, MA, pp 72–83. doi:10.1007/978-1-4615-1773-3_8

    Google Scholar 

  43. Hyslop EJ (1980) Stomach contents analysis: a review of methods and their application. J Fish Biol 17:411–429. doi:10.1111/j.1095-8649.1980.tb02775.x

    Article  Google Scholar 

  44. Iverson SJ, Field C, Don Bowen W, Blanchard W (2004) Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol Monogr 74:211–235. doi:10.1890/02-4105

    Article  Google Scholar 

  45. Jean PO, Bradley RL, Giroux MA, Tremblay JP, Côté SD (2014) Near infrared spectroscopy and fecal chemistry as predictors of the diet composition of White-tailed Deer. Rangeland Ecol Manag 67:154–159. doi:10.2111/REM-D-13-00112.1

    Article  Google Scholar 

  46. Kaneko H, Lawler IR (2006) Can near infrared spectroscopy be used to improve assessment of marine mammal diets via fecal analysis? Mar Mammal Sci 22:261–275

    Article  Google Scholar 

  47. Kelly JR, Scheibling RE (2012) Fatty acids as dietary tracers in benthic food webs. Mar Ecol Prog Ser 446:1–22. doi:10.3354/meps09559

    CAS  Article  Google Scholar 

  48. Kilar JA, Lou RM (1984) Ecological and behavioral studies of the decorator crab Microphrys bicornutus Latreille (Decapoda: Brachyura): A test of optimum foraging theory. J Exp Marine Biol Ecol 74:157–167. doi:10.1016/0022-0981(84)90083-2

    Article  Google Scholar 

  49. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26

    Article  Google Scholar 

  50. Lawler I, Aragones L, Berding N, Marsh H, Foley W (2006) Near-infrared reflectance spectroscopy is a rapid cost-effective predictor of seagrass nutrients. J Chem Ecol 32:1353–1365. doi:10.1007/s10886-006-9088-x

    CAS  Article  Google Scholar 

  51. Lefcheck JS, Whalen MA, Davenport TM, Stone JP, Duffy JE (2012) Physiological effects of diet mixing on consumer fitness: a meta-analysis. Ecology 94:565–572. doi:10.1890/12-0192.1

    Article  Google Scholar 

  52. Legler ND, Johnson TB, Heath DD, Ludsin SA (2010) Water temperature and prey size effects on the rate of digestion of larval and early juvenile fish. Trans Am Fish Soc 139:868–875. doi:10.1577/T09-212.1

    Article  Google Scholar 

  53. Lei P, Bauhus J (2010) Use of near-infrared reflectance spectroscopy to predict species composition in tree fine-root mixtures. Plant Soil 333:93–103. doi:10.1007/s11104-010-0325-2

    CAS  Article  Google Scholar 

  54. Li W, Xing L, Cai Y, Qu H (2011) Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy. Vib Spectrosc 55:58–64. doi:10.1016/j.vibspec.2010.07.004

    CAS  Article  Google Scholar 

  55. Martens H, Jensen S, Geladi P (1983) Multivariate linearity transformation for near-infrared reflectance spectrometry. Proceedings of the Nordic symposium on applied statistics. Stokkand Forlag Publishers, Stavanger, pp 205–234

    Google Scholar 

  56. Mceachern MB, Eagles-Smith CA, Efferson CM, Van Vuren DH (2006) Evidence for local specialization in a generalist mammalian herbivore Neotoma fuscipes. Oikos 113:440–448. doi:10.1111/j.2006.0030-1299.14176.x

    Article  Google Scholar 

  57. Mcilwee AM, Lawler IR, Cork SJ, Foley WJ (2001) Coping with chemical complexity in mammal-plant interactions: near infrared spectroscopy as a predictor of Eucalyptus foliar nutrients and of the feeding rates of folivorous marsupials. Oecologia 128:539–548. doi:10.2307/4223040

    Article  Google Scholar 

  58. Mevik BH, Wehrens R (2007) The pls package: principal component and partial least squares regression in R. J Stat Softw 18:1–24

    Article  Google Scholar 

  59. Milinski M, Heller R (1978) Influence of a predator on the optimal foraging behaviour of sticklebacks (Gaterosteus aculeatus L.). Nature 275:642–644. doi:10.1038/275642a0

    Article  Google Scholar 

  60. Naes T, Isaksson T, Fearn T, Davies T (2002) A user friendly guide to multivariate calibration and classification. NIR publications, Chichester

    Google Scholar 

  61. Nejstgaard JC, Frischer ME, Simonelli P, Troedsson C, Brakel M, Adiyaman F, Sazhin AF, Artigas LF (2008) Quantitative PCR to estimate copepod feeding. Mar Biol 153:565–577. doi:10.1007/s00227-007-0830-x

    CAS  Article  Google Scholar 

  62. Newsome SD, Tinker MT, Monson DH, Oftedal OT, Ralls K, Staedler MM, Fogel ML, Estes JA (2009) Using stable isotopes to investigate individual diet specialization in California sea otters (Enhydra lutris nereis). Ecology 90:961–974. doi:10.1890/07-1812.1

    Article  Google Scholar 

  63. Oksanen J, Guillaume Blanchet F, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Henry M, Stevens H, Wagner H (2015) vegan: Community Ecology Package. R package version 2.2-1

  64. Phillips DL (2001) Mixing models in analyses of diet using multiple stable isotopes: a critique. Oecologia 127:166–170. doi:10.2307/4222912

    CAS  Article  Google Scholar 

  65. Phillips DL, Gregg JW (2001) Uncertainty in source partitioning using stable isotopes. Oecologia 127:171–179. doi:10.1007/s004420000578

    CAS  Article  Google Scholar 

  66. Polz MF, Cavanaugh CM (1998) Bias in template-to-product ratios in multitemplate PCR. Appl Environ Microbiol 64:3724–3730

    CAS  Google Scholar 

  67. Pompanon F, Deagle BE, Symondson WO, Brown DS, Jarman SN, Taberlet P (2012) Who is eating what: diet assessment using next generation sequencing. Mol Ecol 1:1931–1950. doi:10.1111/j.1365-294X.2011.05403.x

    Article  Google Scholar 

  68. Poore AGB, Hill NA (2006) Sources of variation in herbivore preference: among-individual and past diet effects on amphipod host choice. Mar Biol 149:1403–1410. doi:10.1007/s00227-006-0307-3

    Article  Google Scholar 

  69. Poore AGB, Steinberg PD (1999) Preference-performance relationships and effects of host plant choice in an herbivorous marine amphipod. Ecol Monogr 69:443–464

    Google Scholar 

  70. Poore AGB, Campbell AH, Coleman RA, Edgar GJ, Jormalainen V, Reynolds PL, Sotka EE, Stachowicz JJ, Taylor RB, Vanderklift MA (2012) Global patterns in the impact of marine herbivores on benthic primary producers. Ecol Lett 15:912–922

    Article  Google Scholar 

  71. Rinnan Å, Berg FVD, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. Trend Anal Chem 28:1201–1222. doi:10.1016/j.trac.2009.07.007

    CAS  Article  Google Scholar 

  72. Roumet C, Picon-Cochard C, Dawson LA, Joffre R, Mayes R, Blanchard A, Brewer MJ (2006) Quantifying species composition in root mixtures using two methods: near-infrared reflectance spectroscopy and plant wax markers. New Phytolt 170:631–638. doi:10.1111/j.1469-8137.2006.01698.x

    CAS  Article  Google Scholar 

  73. Shipley LA, Forbey JS, Moore BD (2009) Revisiting the dietary niche: when is a mammalian herbivore a specialist? Integr Comp Biol 49:274–290. doi:10.1093/icb/icp051

    Article  Google Scholar 

  74. Sih A, Crowley P, Mcpeek M, Petranka J, Strohmeier K (1985) Predation competition and prey communities: a review of field experiments. Annu Rev Ecol Syst 16:269–311. doi:10.1146/annurev.es.16.110185.001413

    Article  Google Scholar 

  75. Smoothey AF (2013) Habitat-associations of Turban snails on intertidal and subtidal rocky reefs. PLoS One. doi:10.1371/journal.pone.0061257

    Google Scholar 

  76. Sotka EE, Hay ME (2002) Geographic variation among herbivore populations in tolerance for a chemically rich seaweed. Ecology 83:2721–2735

    Article  Google Scholar 

  77. Stevens A, Ramirez-Lopez L (2013) An introduction to the prospectr package. R package, Vignette R package version 0.1.3

  78. Stuth J, Jama A, Tolleson D (2003) Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy. Field Crops Res 84:45–56. doi:10.1016/S0378-4290(03)00140-0

    Article  Google Scholar 

  79. Svanbäck R, Eklöv P, Fransson R, Holmgren K (2008) Intraspecific competition drives multiple species resource polymorphism in fish communities. Oikos 117:114–124

    Article  Google Scholar 

  80. Taipale S, Strandberg U, Peltomaa E, Galloway AW, Ojala A, Brett MT (2013) Fatty acid composition as biomarkers of freshwater microalgae: analysis of 37 strains of microalgae in 22 genera and in seven classes. Aquat Microb Ecol 71:165–178

    Article  Google Scholar 

  81. Traugott M, Pázmándi C, Kaufmann R, Juen A (2007) Evaluating 15 N/14 N and 13C/12C isotope ratio analysis to investigate trophic relationships of elaterid larvae (Coleoptera: Elateridae). Soil Biol Biochem 39:1023–1030. doi:10.1016/j.soilbio.2006.11.012

    CAS  Article  Google Scholar 

  82. Trowbridge CD (1991) Diet specialization limits herbivorous sea slug’s capacity to switch among food species. Ecology 72:1880–1888. doi:10.2307/1940985

    Article  Google Scholar 

  83. Valentini A, Miquel C, Nawaz MA, Bellemain E, Coissac E, Pompanon F, Gielly L, Cruaud C, Nascetti G, Wincker P (2009) New perspectives in diet analysis based on DNA barcoding and parallel pyrosequencing: the trnL approach. Mol Ecol Resourc 9:51–60

    CAS  Article  Google Scholar 

  84. Vander Zanden HB, Bjorndal KA, Reich KJ, Bolten AB (2010) Individual specialists in a generalist population: results from a long-term stable isotope series. Biol Lett. doi:10.1098/rsbl.2010.0124

    Google Scholar 

  85. Wachendorf M, Ingwersen B, Taube F (1999) Prediction of the clover content of red clover-and white clover-grass mixtures by near-infrared reflectance spectroscopy. Grass Forage Sci 54:87–90

    Article  Google Scholar 

  86. Walker JW, Mccoy SD, Launchbaugh KL (2002) Calibrating fecal NIRS equations for predicting botanical composition of diets. J Range Manage 55:374–382

    Article  Google Scholar 

  87. Ward DW, Davis AR (2002) Reproduction of the turban shell Turbo torquatus Gmelin 1791 (Mollusca: Gastropoda) in New South Wales Australia. Mar Freshwater Res 53:85–91. doi:10.1071/MF00066

    Article  Google Scholar 

  88. Wernberg T, White M, Vanderklift MA (2008) Population structure of turbinid gastropods on wave-exposed subtidal reefs: effects of density body size and algae on grazing behaviour. Mar Ecol Prog Ser 362:169–179. doi:10.3354/meps07416

    Article  Google Scholar 

  89. Wiedower EE, Kouba AJ, Vance CK, Hansen RL, Stuth JW, Tolleson DR (2012) Fecal near infrared spectroscopy to discriminate physiological status in Giant Pandas. PLoS One 7(6):e38908. doi:10.1371/journal.pone.0038908

    CAS  Article  Google Scholar 

  90. Wilby A, Shachak M (2000) Harvester ant response to spatial and temporal heterogeneity in seed availability: pattern in the process of granivory. Oecologia 125:495–503. doi:10.1007/s004420000478

    Article  Google Scholar 

  91. Woo KJ, Elliott KH, Davidson M, Gaston AJ, Davoren GK (2008) Individual specialization in diet by a generalist marine predator reflects specialization in foraging behaviour. J Anim Ecol 77:1082–1091. doi:10.1111/j.1365-2656.2008.01429.x

    Article  Google Scholar 

  92. Workman JJ (2007) NIR Spectroscpoy calibration basics. In: Burns DA, Ciurczak EW (eds) Handbook of near-infrared analysis, 3rd edn. CRC Press, New York, pp 123–150

    Google Scholar 

  93. Wright JT, De Nys R, Poore AGB, Steinberg PD (2004) Chemical defence in a marine alga: heritability and the potential for selection by herbivores. Ecology 85:2946–2959. doi:10.1890/03-4041

    Article  Google Scholar 

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Acknowledgments

We thank Brendan Lanham and Damon Bolton for helping with field collections and the two reviewers for the helpful comments that improved this manuscript. KB was supported by an Australian Postgraduate Award.

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Correspondence to Keryn F. Bain.

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Reviewed by J. Forbey and an undisclosed expert.

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Bain, K.F., Poore, A.G.B. Use of near-infrared reflectance spectroscopy to quantify diet mixing in a generalist marine herbivore. Mar Biol 163, 79 (2016). https://doi.org/10.1007/s00227-016-2852-8

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Keywords

  • Faecal Sample
  • Algal Species
  • Faecal Material
  • Mixed Diet
  • Algal Material