Advertisement

Reviews in Fish Biology and Fisheries

, Volume 28, Issue 3, pp 625–636 | Cite as

Comparative analysis of feeding habits and dietary niche breadth in skates: the importance of body size, snout length, and depth

  • Santiago A. BarbiniEmail author
  • David E. Sabadin
  • Luis O. Lucifora
Research Paper

Abstract

Skates (Elasmobranchii, Rajiformes) are a morphologically conservative group of bentophagous chondrichthyan fishes with a high degree of endemism, that occur on marine soft bottoms. Subtle morphological aspects and bathymetric distribution are traits that vary among skate species that could have implications for their feeding ecology. We test how body size, snout length and bathymetric distribution influence the feeding habits and dietary niche breadth in skates using data on 71 species taken from the literature. We hypothesized that snout length has an effect on diet composition. We also hypothesized that dietary niche breadth increases with increasing depth range and decreases with increasing body size of skate species. Generalized additive models for location scale and shape were fitted with taxonomic level (genera nested within family) included as a random effect term in each model. A model selection approach to test the level of support for alternative models was applied. We found that skate species that forage on large prey have the largest body size and skate species with the smallest body size prey on small and medium-sized invertebrates. The results indicated that body size has an effect on feeding habits of skates, whereas an effect of snout length was not supported. Bathymetric variables have an effect on the diet of skates. Our prediction that dietary niche breadth increases with increasing depth range and decreases with increasing body size of skate species was supported in part: in a first phase the relationship between dietary niche breadth and body size is positive, then in a second phase, including species larger than 1000 mm total length, the relationship become negative.

Keywords

Elasmobranch fishes GAMLSS Levins’ standardized index Macroecology Rajiformes 

Notes

Acknowledgements

We thank Nicholas K. Dulvy and Daniel Barrios-O’Neill for their suggestions and constructive comments as referees. This study was supported by Fondo para la Investigación Científica y Tecnológica (FONCyT) PICT 2014-0819.

Supplementary material

11160_2018_9522_MOESM1_ESM.docx (244 kb)
Supplementary material 1 (DOCX 243 kb)

References

  1. Barbini SA, Lucifora LO (2016) Big fish (and a smallish skate) eat small fish: diet variation and trophic level of Sympterygia acuta, a medium-sized skate high in the food web. Mar Ecol 37:283–293CrossRefGoogle Scholar
  2. Begon ME, Harper JL, Townsend CR (2006) Ecology. From individuals to ecosystems, 4th edn. Blackwell Science, OxfordGoogle Scholar
  3. Belleggia M, Andrada N, Paglieri S, Cortés F, Massa AM, Figueroa DE, Bremec C (2016) Trophic ecology of yellownose skate Zearaja chilensis, a top predator in the south-western Atlantic Ocean. J Fish Biol 88:1070–1087CrossRefPubMedGoogle Scholar
  4. Bizzarro JJ, Robinson HJ, Rinewalt CS, Ebert DA (2007) Comparative feeding ecology of four sympatric skate species off central California, USA. Environ Biol Fish 80:197–220CrossRefGoogle Scholar
  5. Boyles JG, Storm JJ (2007) The perils of picky eating: dietary breadth is related to extinction risk in insectivorous bats. PLoS ONE 7:e672CrossRefGoogle Scholar
  6. Brandl R, Kristín A, Leisler B (1994) Dietary niche breadth in a local community of passerine birds: an analysis using phylogenetic contrasts. Oecologia 98:109–116CrossRefPubMedGoogle Scholar
  7. Brändle M, Prinzing A, Pfeifer R, Brandl R (2002) Dietary niche breadth for Central European birds: correlations with species-specific traits. Evol Ecol Res 4:643–657Google Scholar
  8. Brown JH (1984) On the relationship between abundance and distribution of species. Am Nat 124:255–279CrossRefGoogle Scholar
  9. Brown JH (1995) Macroecology. University of Chicago Press, ChicagoGoogle Scholar
  10. Cliff G, Dudley SFJ, Davis B (1989) Sharks caught in the protective gill nets off Natal, South Africa. 2. The great white shark Carcharodon carcharias (Linnaeus). S Afr J Mar Sci 8:131–144CrossRefGoogle Scholar
  11. Cortés E (1999) Standardized diet compositions and trophic levels of sharks. ICES J Mar Sci 56:707–717CrossRefGoogle Scholar
  12. Costa GC (2009) Predator size, prey size, and dietary niche breadth relationships in marine predators. Ecology 90:2014–2019CrossRefPubMedGoogle Scholar
  13. Costa GC, Vitt LJ, Pianka ER, Mesquita DO, Colli GR (2008) Optimal foraging constraints macroecological patterns: body size and dietary niche breadth in lizards. Global Ecol Biogeogr 17:570–677CrossRefGoogle Scholar
  14. Dean MN, Wilga CD, Summers AP (2005) Eating without hands or tongue: specialization, elaboration and the evolution of prey processing mechanisms in cartilaginous fishes. Biol Lett 1:357–361CrossRefPubMedPubMedCentralGoogle Scholar
  15. Díaz M (1994) Variability in seed size selection by granivorous passerines: effects of bird size, bird size variability, and ecological plasticity. Oecologia 99:1–6CrossRefPubMedGoogle Scholar
  16. Dulvy NK, Reynolds JD (2002) Predicting extinction in skates. Conserv Biol 16:440–450CrossRefGoogle Scholar
  17. Dulvy NK, Fowler SL, Musick JA et al (2014) Extinction risk and conservation of the world’s sharks and rays. ELife 3:e00590CrossRefPubMedPubMedCentralGoogle Scholar
  18. Ebert DA, Bizzarro JJ (2007) Standardized diet compositions and trophic levels of skates (Chondrichthyes, Rajiformes, Rajoidei). Environ Biol Fish 80:221–237CrossRefGoogle Scholar
  19. Ebert DA, Compagno LJV (2007) Biodiversity and systematics of skates (Chondrichthyes: Rajiformes: Rajoidei). Environ Biol Fish 80:111–124CrossRefGoogle Scholar
  20. Fitzgerald DB, Winemiller KO, Sabaj Perez MH, Sousa LM (2017) Using trophic structure to reveal patterns of trait-based community assembly across niche dimensions. Funct Ecol 31:1135–1144CrossRefGoogle Scholar
  21. Forman JS, Dunn MR (2012) Diet and scavenging habits of the smooth skate Dipturus innominatus. J Fish Biol 80:1546–1562CrossRefPubMedGoogle Scholar
  22. Franklin AB, Sheik TM, Anderson DR, Burnham KP (2001) Statistical model selection: an alternative to null hypothesis testing. In: Shenk TM, Franklin AM (eds) Modeling in natural resources management: development, interpretation, and application. Island Press, Washington, pp 75–90Google Scholar
  23. Gaston KJ, Blackburn TM, Lawton JH (1997) Interspecific abundance range size relationships: an appraisal of mechanisms. J Anim Ecol 66:579–601CrossRefGoogle Scholar
  24. Goodwin NB, Dulvy NK, Reynolds JD (2005) Macroecology of live-bearing in fishes: latitudinal and depth range comparisons with egg-laying relatives. Oikos 110:209–218CrossRefGoogle Scholar
  25. Hutchings JA, Myers RA, García VB, Lucifora LO, Kuparinen A (2012) Life-history correlates of extinction risk and recovery potential. Ecol Appl 22:1061–1067CrossRefPubMedGoogle Scholar
  26. Johnson JB, Omland KS (2004) Model selection in ecology and evolution. Trends Ecol Evol 19:101–108CrossRefPubMedGoogle Scholar
  27. Kadri H, Marouani S, Bradai NM, Bouaïn A (2014) Food habits of the brown ray Raja miraletus (Chondrichthyes: Rajidae) from the Gulf of Gabès (Tunisia). Mar Biol Res 10:426–434CrossRefGoogle Scholar
  28. Kajiura SM (2001) Head morphology and electrosensory pore distribution of carcharhinid and sphyrnid sharks. Environ Biol Fish 61:125–133CrossRefGoogle Scholar
  29. Kajiura SM, Holland KN (2002) Electroreception in juvenile scalloped hammerhead and sandbar shark. J Exp Biol 205:3609–3621PubMedGoogle Scholar
  30. Karpouzi VS, Stergiou KI (2003) The relationships between mouth size and shape and body length for 18 species of marine fishes and their trophic implications. J Fish Biol 62:1353–1365CrossRefGoogle Scholar
  31. Klaczko J, Sherratt E, Setz EZF (2016) Are diet preferences associated to skulls shape diversification in Xenodontidae snakes? PLoS ONE 11:e0148375CrossRefPubMedPubMedCentralGoogle Scholar
  32. Koen Alonso M, Crespo EA, Gracía NA, Pedraza SN, Mariotti PA, Berón Vega B, Mora NJ (2001) Food habits of Dipturus chilensis (Pisces: Rajidae) off Patagonia, Argentina. ICES J Mar Sci 58:288–297CrossRefGoogle Scholar
  33. Krebs CJ (1989) Ecological methodology. Harper Collins Publishers, New YorkGoogle Scholar
  34. Kyne PM, Courtney AJ, Bennett MB (2008) Aspects of reproduction and diet of the Australian endemic skate Dipturus polyommata (Ogilby) (Elasmobranchii: Rajidae), by-catch of a commercial prawn trawl fishery. J Fish Biol 72:61–77CrossRefGoogle Scholar
  35. Last PR, White WT, Pogonoski JJ, Gledhill DC (2008) New Australian skates (Batoidea: Rajoidei)-background and methodology. In: Last PR, White WT, Pogonoski JJ, Gledhill DC (eds) Descriptions of New Australian skates Batoidea: Rajoidei. CSIRO, HobartGoogle Scholar
  36. Last P, White W, de Carvalho M, Séret B, Stehmann M, Naylor G (2016) Rays of the world. CSIRO Publishing, ClaytonGoogle Scholar
  37. Layman CA, Winemiller KO, Arrington DA, Jepsen DB (2005) Body size and trophic position in a diverse tropical food web. Ecology 86:2530–2535CrossRefGoogle Scholar
  38. López-García J, Navia AF, Mejía-Falla PA, Rubio EA (2012) Feeding habits and trophic ecology of Dasyatis longa (Elasmobranchii: Myliobatiformes): sexual, temporal and ontogenetic shifts. J Fish Biol 80:1563–1579CrossRefPubMedGoogle Scholar
  39. Lucifora LO, Valero JL, Bremec CS, Lasta ML (2000) Feeding habits and prey selection by the skate Dipturus chilensis (Elasmobranchii: Rajidae) from the south-western Atlantic. J Mar Biol Assoc UK 80:953–954CrossRefGoogle Scholar
  40. Motta PJ (1988) Functional morphology of the feeding apparatus of ten species of Pacific butterflyfishes (Perciformes, Chaetodontidae): an ecomorphological approach. Environ Biol Fish 22:39–67CrossRefGoogle Scholar
  41. Motta PJ (2004) Prey capture behavior and feeding mechanics of elasmobranchs. In: Carrier JC, Musick JA, Heithaus MR (eds) Biology of sharks and their relatives. CRC Press, Boca Raton, pp 165–202CrossRefGoogle Scholar
  42. Mulas A, Bellodi A, Cannas R, Cau A, Cuccu D, Marongiu MF, Porcu C, Follesa MC (2015) Diet and feeding behaviour of longnosed skate Dipturus oxyrinchus. J Fish Biol 86:121–138CrossRefPubMedGoogle Scholar
  43. Orlov AM (1998) The diets and feeding habits of some deep-water benthic skates (Rajidae) in the Pacific waters off the Northern Kuril Islands and Southeastern Kamchatka. Alaska Fish Res Bull 5:1–17Google Scholar
  44. Pyron M (1999) Relationships between geographical range size, body size, local abundance, and habitat breadth in North American suckers and sunfishes. J Biogeogr 26:549–558CrossRefGoogle Scholar
  45. R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  46. Richards SA (2005) Testing ecological theory using the information-theoretic approach: examples and cautionary results. Ecology 86:2805–2814CrossRefGoogle Scholar
  47. Robinson HJ, Cailliet GM, Ebert DA (2007) Food habits of the longnose skate, Raja rhina (Jordan and Gilbert, 1880), in central California waters. Environ Biol Fish 80:165–179CrossRefGoogle Scholar
  48. Scharf FS, Juanes F, Rountree RA (2000) Predator size – prey size relationships of marine fish predators: interespecific variation and effects of ontogeny and body size trophic-niche breadth. Mar Ecol Prog Ser 208:229–248CrossRefGoogle Scholar
  49. Smith KF, Brown JH (2002) Patterns of diversity, depth range and body size among pelagic fishes along a gradient of depth. Global Ecol Biogeogr 11:313–322CrossRefGoogle Scholar
  50. Stasinopoulos DM, Rigby RA (2007) Generalized additive models for location scale and shape (GAMLSS) in R. J Stat Softw 23:1–46CrossRefGoogle Scholar
  51. Stasinopoulos DM, Rigby RA, Akantziliotou C (2008) Instructions on how to use the GAMLSS package in R. Second edition. Technical Report 01/08, STORM Research Centre, Metropolitan University, LondonGoogle Scholar
  52. Symonds ME, Moussalli A (2011) A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav Ecol Sociobiol 65:13–21CrossRefGoogle Scholar
  53. Tucker MA, Rogers TL (2014) Examining predator-prey body size, trophic level and body mass across marine and terrestrial mammals. P Roy Soc B 281:20142103CrossRefGoogle Scholar
  54. Vögler R, Milessi AC, Quiñones RA (2003) Trophic ecology of Squatina guggenheim on the continental shelf off Uruguay and northern Argentina. J Fish Biol 62:1254–1267CrossRefGoogle Scholar
  55. Wasserman SS, Mitter C (1978) The relationship of body size to breadth of diet in some Lepidoptera. Ecol Entomol 3:155–160CrossRefGoogle Scholar
  56. Wetherbee BM, Cortés E (2004) Food consumption and feeding habits. In: Carrier JC, Musick JA, Heithaus MR (eds) Biology of sharks and their relatives. CRC Press, Boca Raton, pp 225–246CrossRefGoogle Scholar
  57. Wilga CD, Motta PJ, Sanford CP (2007) Evolution and ecology of feeding in elasmobranchs. Integr Comp Biol 47:55–69CrossRefPubMedGoogle Scholar
  58. Wilga CD, Maia A, Nauwelaerts S, Lauder GV (2012) Prey handling using whole-body fluid dynamics in batoids. Zoology 115:47–57CrossRefPubMedGoogle Scholar
  59. Winemiller KO, Fitzgerald DB, Bower LM, Pianka ER (2015) Functional traits, convergence evolution, and periodic tables of niches. Ecol Lett 18:737–751CrossRefPubMedPubMedCentralGoogle Scholar
  60. Witmann JD, Roy K (2009) Marine macroecology. The University of Chicago Press, ChicagoCrossRefGoogle Scholar
  61. Wueringer EB, Squire Jnr L, Kajiura SM, Tibbets IR, Hart NS (2012) Electric field detection in sawfish and shovelnose rays. Plos ONE 7:e41605CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Santiago A. Barbini
    • 1
    Email author
  • David E. Sabadin
    • 1
  • Luis O. Lucifora
    • 2
  1. 1.Instituto de Investigaciones Marinas y CosterasUniversidad Nacional de Mar del Plata, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Mar del Plata, Buenos AiresArgentina
  2. 2.Instituto de Biología Subtropical - IguazúUniversidad Nacional de Misiones, CONICETPuerto IguazúArgentina

Personalised recommendations