Marine Biology

, 167:15 | Cite as

Assessing how body size affects the species-time relationship in a shallow marine benthic megafauna community exposed to a strong hypoxia disturbance

  • Fabio A. Labra
  • Eduardo Hernández-MirandaEmail author
  • Renato A. Quiñones
Original Paper


The species–area relationship (SAR) and the species–time relationship (STR) are two well-established macro-ecological patterns. Species richness has also been shown to follow a humped relationship with body size, suggesting that SAR and STR may also depend on body size. We test whether the pattern of increase in the number of species with time varies with species body size. We analyzed data on carnivorous and scavenger species collected in a shallow marine benthic megafauna community in Coliumo Bay, Chile. Body size data were tabulated into exponential body size classes using two binning strategies. Species-level binning represented all individuals of each species by their average body size. Individual-level binning tabulated all data into exponential body size classes, allowing species to potentially occupy multiple size classes. We determined empirical relationships between body size and species richness, as well as estimated temporal species accumulation curves (TSAC) and nested species–time relationships (NSTR). We also tested whether the increase of species richness within body size classes follows either a power-law relationship or a logarithmic function. We find that the number of species increases with the time span studied, both for a TSAC and in the NSTR, for both species-based and individual-based data, while also showing a unimodal relationship with body size for both species and individual-based data. Thus, the STR is a general pattern, which depends on body size, despite reproductive seasonal forcing and strong hypoxia disturbance in Coliumo Bay.



The author thanks EHM’s laboratory members for their assistance during field and laboratory activities. In addition, the authors would like to thank three reviewers for their valuable comments and suggestions to improve the quality of the manuscript.

Author contributions

FAL, EHM, and RQ conceived the idea; FAL and EHM assembled the database; FAL analyzed the data; FAL, EHM, and RQ wrote the paper. All authors contributed to the drafts and gave final approval for publication.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest and that all applicable guidelines for sampling of organisms have been followed. This study was funded by the Programa de Investigación Marina de Excelencia (PIMEX), of the Faculty of Natural and Oceanographic Sciences (University of Concepción, Chile). R. Quiñones and E. Hernández-Miranda received additional funding from INCAR (FONDAP Nº15110027). F. A. Labra received funding from PIA-CONICYT ACT-172037 Grant. E. Hernández-Miranda also received additional funding from FONDECYT 11100334, and 1130868 Grants.

Supplementary material

227_2019_3625_MOESM1_ESM.pdf (55 kb)
Supplementary material 1 (PDF 54 kb)


  1. Adler PB (2004) Neutral models fail to reproduce observed species-area and species-time relationships in Kansas grasslands. Ecology 85:1265–1272CrossRefGoogle Scholar
  2. Adler PB, Lauenroth WK (2003) The power of time: spatiotemporal scaling of species diversity. Ecol Lett 6:749–756CrossRefGoogle Scholar
  3. Adler PB, White EP, Lauenroth WK, Kaufman DM, Rassweiler A, Rusak JA (2005) Evidence for a general species–time–area relationship. Ecology 86:2032–2039CrossRefGoogle Scholar
  4. Allen AP, White EP (2003) Effects of range size on species–area relationships. Evol Ecol Res 5:493–499Google Scholar
  5. Arrhenius O (1921) Species and area. J Ecol 9:95–99CrossRefGoogle Scholar
  6. Azovsky AI (2011) Specie–area and species–sampling effort relationships: disentangling the effects. Ecography 34:18–30CrossRefGoogle Scholar
  7. Blackburn TM, Gaston KJ (2004) Macroecology: concepts and consequences: the 43rd Annual Symposium of the British Ecological Society Held at the University of Birmingham. Cambridge University Press, CambridgeGoogle Scholar
  8. Brooks TM, Pimm SL, Collar NJ (1997) Deforestation predicts the number of threatened birds in insular Southeast Asia. Conserv Biol 11:382–394CrossRefGoogle Scholar
  9. Chown SL, Gremmen NJM, Gaston KJ (1998) Ecological biogeography of southern ocean islands: species–area relationships, human impacts, and conservation. Am Nat 152:562–575PubMedCrossRefGoogle Scholar
  10. Colwell RK, Mao CX, Chang J (2004) Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85:2717–2727CrossRefGoogle Scholar
  11. Dengler J (2009) Which function describes the species-area relationship best? A review and empirical evaluation. J Biogeogr 36:728–744CrossRefGoogle Scholar
  12. Drakare S, Lennon JJ, Hillebrand H (2006) The imprint of the geographical, evolutionary and ecological context on species-area relationships. Ecol Lett 9:215–227PubMedCrossRefGoogle Scholar
  13. Erös T, Schmera D (2010) Spatio-temporal scaling of biodiversity and the species-time relationship in a stream fish assemblage. Freshw Biol 55:2391–2400CrossRefGoogle Scholar
  14. Fa DA, Fa JE (2002) Species diversity, abundance and body size in rocky-shore Mollusca: a twist in Siemann, Tilman and Haarstad’s parabola? J Molluscan Stud 68:95–100CrossRefGoogle Scholar
  15. Fisher RA, Corbet AS, Williams CB (1943) The relation between the number of species and the number of individuals in a random sample of an animal population. J Anim Ecol 1:42–58CrossRefGoogle Scholar
  16. Fridley JD, Peet RK, Van der Maarel E, Willems JH (2006) Integration of local and regional species–area relationships from space-time species accumulation. Am Nat 168:133–143PubMedCrossRefGoogle Scholar
  17. Gaston KJ, Blackburn TM (2000) Pattern and process in macroecology. Wiley, HobokenCrossRefGoogle Scholar
  18. Gotelli NJ, Colwell RK (2001) Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett 4:379–391CrossRefGoogle Scholar
  19. Harte J (2011) Maximum entropy and ecology: a theory of abundance, distribution, and energetics. OUP, OxfordCrossRefGoogle Scholar
  20. Häussermann V, Försterra G (eds) (2009) Fauna Marina Bentónica de la Patagonia Chilena. Nature in Focus, Santiago, p 1000Google Scholar
  21. Hernández-Miranda E, Palma AT, Ojeda FP (2003) Larval fish assemblages in nearshore coastal waters off central Chile: temporal and spatial patterns. Estuar Coast Shelf S 56:1075–1092CrossRefGoogle Scholar
  22. Hernández-Miranda E, Quiñones RA, Aedo G, Valenzuela A, Mermoud N, Román C, Yañez F (2010) A major fish stranding caused by a natural hypoxic event in a shallow bay of the eastern South Pacific Ocean. J Fish Biol 76:1543–1564PubMedCrossRefGoogle Scholar
  23. Hernández-Miranda E, Veas R, Labra FA, Salamanca M, Quiñones RA (2012a) Response of the epibenthic macrofaunal community to a strong upwelling-driven hypoxic event in a shallow bay of the southern Humboldt Current System. Mar Environ Res 79:16–28PubMedCrossRefGoogle Scholar
  24. Hernández-Miranda E, Quiñones RA, Aedo G, Díaz-Cabrera E, Cisterna J (2012b) The impact of a strong natural hypoxic event on the toadfish Aphos porosus in Coliumo Bay, south-central Chile. Rev Biol Mar Oceanogr 47:475–487CrossRefGoogle Scholar
  25. Hernández-Miranda E, Veas R, Anabalón V, Quiñones RA (2017) Short-term alteration of biotic and abiotic components of the pelagic system in a shallow bay produced by a strong natural hypoxia event. PLoS One 12(7):e0179023. CrossRefPubMedPubMedCentralGoogle Scholar
  26. Hurlbert AH (2006) Linking species–area and species–energy relationships in Drosophila microcosms. Ecol Lett 9:287–294PubMedCrossRefGoogle Scholar
  27. Kass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90:773–795CrossRefGoogle Scholar
  28. Labra FA, Hernández-Miranda E, Quinones RA (2015) Dynamic relationships between body size, species richness, abundance, and energy use in a shallow marine epibenthic faunal community. Ecol Evol 5:391–408PubMedCrossRefGoogle Scholar
  29. Laudien J, Rojo ME, Oliva ME, Arntz WE, Thatje S (2007) Sublittoral soft bottom communities and diversity of Mejillones Bay in northern Chile (Humboldt Current upwelling system). Helgol Mar Res 61:103–116CrossRefGoogle Scholar
  30. Lawton JH (1999) Are there general laws in ecology? Oikos 84:177–192CrossRefGoogle Scholar
  31. Leitner WA, Rosenzweig ML (1997) Nested species-area curves and stochastic sampling: a new theory. Oikos 79:503–512CrossRefGoogle Scholar
  32. Magurran AE (1988) Ecological diversity and its measurement. Princeton University Press, PrincetonCrossRefGoogle Scholar
  33. May RM (1988) How many species are there on earth? Science 241:1441–1449PubMedCrossRefGoogle Scholar
  34. McClain CR (2004) Connecting species richness, abundance and body size in deep-sea gastropods. Global Ecol Biogeogr 13:327–334CrossRefGoogle Scholar
  35. McGlinn DJ, Palmer MW (2009) Modeling the sampling effect in the species–time–area relationship. Ecology 90:836–846PubMedCrossRefGoogle Scholar
  36. Moreno CE, Halffter G (2000) Assessing the completeness of bat biodiversity inventories using species accumulation curves. J Appl Ecol 37:149–158CrossRefGoogle Scholar
  37. Morse DR, Lawton JH, Dodson MM, Williamson MH (1985) Fractal dimension of vegetation and the distribution of arthropod body lengths. Nature 314:731–733CrossRefGoogle Scholar
  38. Myers N, Mittermeier RA, Mittermeier CG, Da Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858CrossRefGoogle Scholar
  39. Palmer MW, White PS (1994) Scale dependence and the species–area relationship. Am Nat 144(5):717–740CrossRefGoogle Scholar
  40. Pimm SL, Askins RA (1995) Forest losses predict bird extinctions in eastern North America. Proc Natl Acad Sci USA 92:9343–9347PubMedCrossRefGoogle Scholar
  41. Plotkin JB, Potts MD, Leslie N, Manokaran N, La Frankie J, Ashton PS (2000) Species-area curves, spatial aggregation, and habitat specialization in tropical forests. J Theor Biol 207:81–99PubMedCrossRefGoogle Scholar
  42. Preston FW (1960) Time and space and the variation of species. Ecology 41:611–627CrossRefGoogle Scholar
  43. Retamal MA (2000) Los Decápodos de Chile. CD Rom ETI. Springer, BerlinGoogle Scholar
  44. Retamal M, Ferrada P (2016) Catálogo Ilustrado de Crustáceos Decápodos de la Patagonia Chilena. COPAS Sur-Austral (PFB-31). Universidad de Concepción, Concepción, p 96Google Scholar
  45. Reyes P, Hüne M (2012) Peces del Sur de Chile. Ocho Libros Editores, Chile, p 500Google Scholar
  46. Ricklefs RE (2004) A comprehensive framework for global patterns in biodiversity. Ecol Lett 7:1–15CrossRefGoogle Scholar
  47. Rosenzweig ML (1995) Species diversity in space and time. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  48. Sibly RM, Brown JH, Kodric-Brown A (2012) Metabolic ecology: a scaling approach. Wiley-Blackwell, ChichesterCrossRefGoogle Scholar
  49. Siemann E, Tilman D, Haarstad J (1996) Insect species diversity, abundance and body size relationships. Nature 380:704–706CrossRefGoogle Scholar
  50. Siemann E, Tilman D, Haarstad J (1999) Abundance, diversity and body size: patterns from a grassland arthropod community. J Anim Ecol 68:824–835CrossRefGoogle Scholar
  51. Soetaert K (2017) plot3D: plotting multi-dimensional data. R. package version 1.1.1. Accessed July 2019
  52. Storch D, Marquet P, Brown J (eds) (2007) Scaling biodiversity. Cambridge University Press, CambridgeGoogle Scholar
  53. R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed July 2019Google Scholar
  54. Ugland KI, Gray JS, Ellingsen KE (2003) The species-accumulation curve and estimation of species richness. J Anim Ecol 72:888–897CrossRefGoogle Scholar
  55. Ulrich W (2005) Predicting species numbers using species-area and endemics-area relations. Biodivers Conserv 14:3351–3362CrossRefGoogle Scholar
  56. Ulrich W, Buszko JÇ (2003) Species–area relationships of butterflies in Europe and species richness forecasting. Ecography 26:365–373CrossRefGoogle Scholar
  57. Ulrich W, Buszko JÇ (2004) Habitat reduction and patterns of species loss. Basic Appl Ecol 5:231–240CrossRefGoogle Scholar
  58. Ulrich W, Zalewski M, Hajdamowicz I, Stańska M, Ciurzycki W, Tykarski P (2013) Towards a general species–time–area–sampling effort relationship. Pol J Ecol 61:197–205Google Scholar
  59. Werner U, Buszko J (2005) Detecting biodiversity hotspots using species–area and endemics–area relationships: the case of butterflies. Biodivers Conserv 14:1977–1988CrossRefGoogle Scholar
  60. White EP (2004) Two-phase species–time relationships in North American land birds. Ecol Lett 7:329–336CrossRefGoogle Scholar
  61. White EP (2007) Spatiotemporal scaling of species richness: patterns, processes, and implications. In: Storch D, Marquet PA, Brown JH (eds) Scaling biodiversity. Cambridge University Press, Cambridge, pp 325–346CrossRefGoogle Scholar
  62. White EP, Adler PB, Lauenroth WK, Gill RA, Greenberg D, Kaufman DM, Rassweiler A, Rusak JA, Smith MD, Steinbeck J, Waide RB, Yao J (2006) A comparison of the species-time relationship across ecosystems and taxonomic groups. Oikos 112:185–195CrossRefGoogle Scholar
  63. Wickham H (2016) Ggplot2: elegant graphics for data analysis. Springer, New YorkCrossRefGoogle Scholar
  64. Wickham H (2017) Tidyverse: easily install and load the ‘Tidyverse’. R package version 1.2.1. Accessed July 2019
  65. Zagal CJ, Hermosilla C, Riedemann H (2007) Guía de Invertebrados Marinos del sur de Chile/Marine Invertebrates of Southern Chile. Editorial Fantástico Sur, Chile, p 264Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Centro de Investigación e Innovación para el Cambio Climático (CiiCC), Facultad de CienciasUniversidad Santo TomásSantiagoChile
  2. 2.Doctorado en Conservación y Gestión de la Biodiversidad, Facultad de CienciasUniversidad Santo TomásSantiagoChile
  3. 3.Programa de Investigación Marina de Excelencia (PIMEX), Facultad de Ciencias Naturales y OceanográficasUniversidad de ConcepciónConcepciónChile
  4. 4.Interdisciplinary Center for Aquaculture Research (INCAR)Universidad de ConcepciónConcepciónChile

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