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Integrating abundance and functional traits reveals new global hotspots of fish diversity

Nature volume 501, pages 539–542 (2013)Cite this article

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Abstract

Species richness has dominated our view of global biodiversity patterns for centuries1,2. The dominance of this paradigm is reflected in the focus by ecologists and conservation managers on richness and associated occurrence-based measures for understanding drivers of broad-scale diversity patterns and as a biological basis for management3,4. However, this is changing rapidly, as it is now recognized that not only the number of species but the species present, their phenotypes and the number of individuals of each species are critical in determining the nature and strength of the relationships between species diversity and a range of ecological functions (such as biomass production and nutrient cycling)5. Integrating these measures should provide a more relevant representation of global biodiversity patterns in terms of ecological functions than that provided by simple species counts. Here we provide comparisons of a traditional global biodiversity distribution measure based on richness with metrics that incorporate species abundances and functional traits. We use data from standardized quantitative surveys of 2,473 marine reef fish species at 1,844 sites, spanning 133 degrees of latitude from all ocean basins, to identify new diversity hotspots in some temperate regions and the tropical eastern Pacific Ocean. These relate to high diversity of functional traits amongst individuals in the community (calculated using Rao’s Q6), and differ from previously reported patterns in functional diversity and richness for terrestrial animals, which emphasize species-rich tropical regions only7,8. There is a global trend for greater evenness in the number of individuals of each species, across the reef fish species observed at sites (‘community evenness’), at higher latitudes. This contributes to the distribution of functional diversity hotspots and contrasts with well-known latitudinal gradients in richness2,4. Our findings suggest that the contribution of species diversity to a range of ecosystem functions varies over large scales, and imply that in tropical regions, which have higher numbers of species, each species contributes proportionally less to community-level ecological processes on average than species in temperate regions. Metrics of ecological function usefully complement metrics of species diversity in conservation management, including when identifying planning priorities and when tracking changes to biodiversity values.

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Figure 1: Global fish diversity patterns predicted from quantitative diver censuses at 1,844 sites.
Figure 2: The species diversity–functional diversity relationship for reef fishes differs between temperate and tropical sites.

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Acknowledgements

We thank the many Reef Life Survey (RLS) divers who participated in data collection and provide ongoing expertize and commitment to the program, University of Tasmania staff including T. Cooper, M. Davey, N. Barrett, J. Berkhout and E. Oh, and T. Bird for assistance running models and checking code. Development of the RLS data set was supported by the former Commonwealth Environment Research Facilities Program, and analyses were supported by the Australian Research Council, Institute for Marine and Antarctic Studies, and the Marine Biodiversity Hub, a collaborative partnership supported through the Australian Government’s National Environmental Research Program. Additional funding and support for field surveys was provided by grants from the National Geographic Society, Conservation International, Wildlife Conservation Society Indonesia, The Winston Churchill Memorial Trust, and ASSEMBLE Marine.

Author information

Authors and Affiliations

  1. Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 49, Hobart, Tasmania 7001, Australia,

    Rick D. Stuart-Smith, Amanda E. Bates, Russell J. Thomson, Jemina F. Stuart-Smith, Nicole A. Hill, German A. Soler & Graham J. Edgar

  2. Department of Biological Sciences, Virginia Institute of Marine Science, P.O. Box 1346, Gloucester Point, Virginia 23062-1346, USA,

    Jonathan S. Lefcheck & J. Emmett Duffy

  3. School of Plant Science, University of Tasmania, Private Bag 55, Hobart, Tasmania 7001, Australia,

    Susan C. Baker

  4. Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, Stockholm SE-114 19, Sweden,

    Stuart J. Kininmonth

  5. Dipartimento di Scienze Biologiche, Geologiche ed Ambientali, Università di Bologna, Via San Alberto 163, Ravenna 48123, Italy,

    Laura Airoldi & Elisabeth M. A. Strain

  6. Hopkins Marine Station, Stanford University, Pacific Grove, 93950, California, USA

    Laura Airoldi

  7. Natural Products and Agrobiology Institute (IPNA-CSIC), Avda Astrofísico Francisco Sanchez 3, La Laguna 38206, Tenerife, Spain,

    Mikel A. Becerro

  8. Wildlife Conservation Society, Indonesia Marine Program, Jalan Atletik no. 8, Bogor, Jawa Barat 16151, Indonesia,

    Stuart J. Campbell

  9. School of the Environment, University of Dundee, Perth Road, Dundee DD1 4HN, Scotland, UK,

    Terence P. Dawson

  10. Estación Costera de Investigaciones Marinas & Center for Marine Conservation, Pontificia Universidad Católica de Chile, Casilla 114-D, Santiago, Chile,

    Sergio A. Navarrete

  11. School of Biological Sciences, Institute of Marine Sciences, University of Portsmouth, Ferry Road, Eastney, Portsmouth PO4 9LY, UK,

    Trevor J. Willis

Authors
  1. Rick D. Stuart-Smith
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Contributions

R.D.S.-S., J.S.L., G.J.E., S.C.B. and A.E.B. conceived the idea, G.J.E., R.D.S.-S., J.F.S.-S., S.C.B., S.J.K., G.A.S., E.M.A.S. and many others collected the data. R.D.S.-S., A.E.B., J.E.D., G.J.E., and J.S.L. drafted the paper, with substantial input from S.C.B., R.J.T., J.F.S.-S., N.A.H., S.J.K., L.A., M.A.B., S.J.C., T.P.D., S.A.N., G.A.S., E.M.A.S. and T.J.W. A.E.B., R.J.T. and J.S.L. analysed the data and S.J.K. prepared the maps.

Corresponding author

Correspondence to Rick D. Stuart-Smith.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Global relationships between different functional diversity measures.

a, b, Abundance-weighted functional diversity (Rao’s Q expressed as effective numbers; see Methods) for reef fishes provides different information to functional richness expressed as the number of functional groups (functional group richness; FGR) (a) and the volume of multidimensional trait space filled by the community (convex hull volume) (b). Kendall’s Tau correlation coefficients, r = 0.21 and 0.11, respectively. Points represent individual reef sites surveyed.

Extended Data Figure 2 Details of global fish surveys.

a, b, The fish fauna was quantitatively surveyed at 1,844 rocky and coral reef sites in 11 Marine Ecoregions of the World realms by visual census (a). Note that many sites are overlapping or hidden behind symbols for other sites. Tropical realms possessed much higher average fish abundance and species densities (b).

Extended Data Figure 3 The accuracy importance of the thirteen predictor variables for each of the random forest models.

Models were for species density (a), species evenness (b), functional group richness (c) and functional diversity (d). Explanations and units for variables are provided in Extended Data Table 4.

Extended Data Figure 4 Scatter plots comparing global predictions from random forest models used for mapping in Fig. 1 with those based on a training set including only sites >5 km apart.

Models were for species density (a), functional group richness (b), species evenness (c) and functional diversity (d). Predictions were compared for all global ocean grid cells where the depth was less than 20 m.

Extended Data Table 1 Functional traits of reef fishes used in estimation of functional diversity.
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Extended Data Table 2 Linear mixed effects model summary table for functional diversity versus species diversity shown in Fig. 2.
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Extended Data Table 3 Contributions of individual traits to global patterns in functional diversity.
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Extended Data Table 4 Environmental and geographic variables used in random forest models.
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Extended Data Table 5 Transformations and correlations of observed to predicted diversity values from random forest models.
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Extended Data Table 6 Spatial autocorrelation measured by Moran’s I for diversity metrics calculated from the raw data at reef sites and residuals from the four random forest models used to predict diversity values for global maps
Full size table

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Stuart-Smith, R., Bates, A., Lefcheck, J. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013). https://doi.org/10.1038/nature12529

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  • DOI: https://doi.org/10.1038/nature12529

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