Marine Biology

, 163:79 | Cite as

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

Original paper

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.

Supplementary material

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Supplementary material 1 (PDF 572 kb)

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 14:1246–1253CrossRefGoogle 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 257:259–266CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  8. Barnes R, Dhanoa M, Lister SJ (1989) Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl Spectrosc 43:772–777CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  12. Blankenship L, Yayanos A (2005) Universal primers and PCR of gut contents to study marine invertebrate diets. Mol Ecol 14:891–899CrossRefGoogle 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 CrossRefGoogle 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 38:145–157CrossRefGoogle 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 115–146CrossRefGoogle 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 CrossRefGoogle 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 69:1033–1046CrossRefGoogle 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):e93546CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 29:51–63CrossRefGoogle 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 25:834–837CrossRefGoogle 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 CrossRefGoogle 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 376:33–44CrossRefGoogle Scholar
  26. Duffy JE (2002) Biodiversity and ecosystem function: the consumer connection. Oikos 99:201–219CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  29. Foale S, Day R (1992) Recognizability of algae ingested by abalone. Mar Freshwater Res 43:1331–1338CrossRefGoogle 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 116:293–305CrossRefGoogle Scholar
  31. Fox LR, Morrow PA (1981) Specialization: species property or local phenomenon? Science 211:887–893. doi:10.1126/science.211.4485.887 CrossRefGoogle 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 CrossRefGoogle 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 48:956–965CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  36. Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17CrossRefGoogle Scholar
  37. Geladi P, Macdougall D, Martens H (1985) Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl Spectros 39:491–500CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  41. Holechek JL, Vavra M, Pieper RD (1982) Botanical composition determination of range herbivore diets: a review. J Range Manag 35:309–315CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 22:261–275CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  49. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 205–234Google 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  58. Mevik BH, Wehrens R (2007) The pls package: principal component and partial least squares regression in R. J Stat Softw 18:1–24CrossRefGoogle 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 CrossRefGoogle Scholar
  60. Naes T, Isaksson T, Fearn T, Davies T (2002) A user friendly guide to multivariate calibration and classification. NIR publications, ChichesterGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 2.2-1Google Scholar
  64. Phillips DL (2001) Mixing models in analyses of diet using multiple stable isotopes: a critique. Oecologia 127:166–170. doi:10.2307/4222912 CrossRefGoogle Scholar
  65. Phillips DL, Gregg JW (2001) Uncertainty in source partitioning using stable isotopes. Oecologia 127:171–179. doi:10.1007/s004420000578 CrossRefGoogle Scholar
  66. Polz MF, Cavanaugh CM (1998) Bias in template-to-product ratios in multitemplate PCR. Appl Environ Microbiol 64:3724–3730Google 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 CrossRefGoogle 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 CrossRefGoogle Scholar
  69. Poore AGB, Steinberg PD (1999) Preference-performance relationships and effects of host plant choice in an herbivorous marine amphipod. Ecol Monogr 69:443–464Google 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 15:912–922CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 83:2721–2735CrossRefGoogle Scholar
  77. Stevens A, Ramirez-Lopez L (2013) An introduction to the prospectr package. R package, Vignette R package version 0.1.3Google Scholar
  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 CrossRefGoogle Scholar
  79. Svanbäck R, Eklöv P, Fransson R, Holmgren K (2008) Intraspecific competition drives multiple species resource polymorphism in fish communities. Oikos 117:114–124CrossRefGoogle 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 71:165–178CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 9:51–60CrossRefGoogle 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 54:87–90CrossRefGoogle Scholar
  86. Walker JW, Mccoy SD, Launchbaugh KL (2002) Calibrating fecal NIRS equations for predicting botanical composition of diets. J Range Manage 55:374–382CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 123–150Google 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 CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Evolution and Ecology Research Centre, School of Biological and Earth and Environmental ScienceUniversity of New South WalesSydneyAustralia

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