Research ArticleMARINE BIODIVERSITY

Abundance and local-scale processes contribute to multi-phyla gradients in global marine diversity

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Science Advances  18 Oct 2017:
Vol. 3, no. 10, e1700419
DOI: 10.1126/sciadv.1700419
  • Fig. 1 Patterns of global marine diversity.

    Geographical distribution of ecoregion richness (species richness per ecoregion), local richness (rarefied estimates of total richness for sites <12 km from each other), and site richness (mean species richness per transect) for vertebrates, invertebrates, and all taxa. Legends indicate upper bounds for species richness bins. Ecoregion totals are predictively modeled using random forest procedures, with data trained using observed data from 82 ecoregions and 16 environmental covariates for which data were globally available.

  • Fig. 2 Global patterns of site richness per 50-m transect for 10 investigated marine animal classes.

    Data are smoothed using random forest predictions. Crustacea includes only species in orders Decapoda and Stomatopoda because other orders did not surpass the 2.5-cm minimum size requirement. Legends indicate the upper bounds for species richness bins.

  • Fig. 3 Latitudinal trends in abundance and richness.

    Bivariate plots relating ecoregion richness (A to C), local richness (D to F), site richness (G to I), and site abundance (J to L) to absolute latitude for all taxa, vertebrates, and invertebrates. Latitude was calculated as the mean latitude of sites investigated within ecoregion. Abundance y axes are shown as log scale. Best-fit polynomial curves to the third order were assessed using Bayesian information criterion (BIC) and R2 values; order of polynomial is shown in parentheses.

  • Fig. 4 Most likely causal network showing hypothesized links between environmental drivers and diversity across scales.

    Width of arrows indicates the magnitude of standardized linear coefficients in SEMs. Black arrows are positive coefficients, and red arrows are negative coefficients. Dashed arrow between local and ecoregion richness for all taxa indicates a link at the margins of significance (P = 0.066), albeit with a moderately high linear coefficient value (2.1). Model fit is good in all cases, indicated by nonsignificant χ2 values.

  • Fig. 5 Proposed model of global marine diversity.

    At the site scale, temperature and nutrients influence abundance, which affects site richness, which in turn strongly influences local richness. Fishes control abundances of large mobile invertebrates through predation, generating a negative relationship between vertebrate and invertebrate richness at the site scale. At the ecoregion scale, species richness is influenced by local richness, the extent of coral reef, and biogeographic factors. [Top and bottom photos by G.J.E. and middle photo by R.D.S.-S. (University of Tasmania)].

  • Table 1 Hypotheses proposed to explain latitudinal biodiversity gradients.

    Associated predictor metrics tested in this study and transformations applied in linear models are also listed. SST, sea surface temperature; Chl, chlorophyll; NO3, nitrate; PAR, photosynthetically active radiation; sqrt, square root.

    HypothesisMetricMeasurement
    Temperature
    Species richness increases with temperature (76), perhaps due
    to increased metabolic rates leading to faster generation times
    and hence increased rates of mutation, evolution, and accumulation
    of species (30). Alternatively, niche conservatism may constrain
    thermal tolerances of most species to warm conditions (13, 31, 77),
    or temperature may indirectly link to diversity through its influence
    on productivity.
    SST (log)Mean SST of sites, transformed to units
    of energy (−1/kT, where k is the Boltzmann
    constant and T is in Kelvin) (30).
    Primary productivity
    Light irradiance and nutrients drive primary productivity,
    which, at low to moderate levels at least, increases abundance
    and species richness (32, 33).
    Chl (log)
    NO3 (log)
    PAR (sqrt)
    Satellite derived mean Chl, NO3, and PAR
    measurements per 5–arc min (9.2 km) grid
    cells compiled in Bio-ORACLE (ocean rasters
    for analysis of climate and environment) (71).
    Area
    Species richness increases with geographic area, independent
    of habitat diversity, in part through increased persistence of
    animals with large range requirements (34).
    Shelf (sqrt)Total continental shelf area (water depths,
    <200 m), from NOAA (National Oceanic
    and Atmospheric Administration) NGDC
    (National Geophysical Data Center) (78).
    Coral (log x + 1)Total area of coral reef within ecoregion,
    assessed through “Reefs at Risk” (79).
    Natural disturbance regime
    Species richness decreases with, or peaks at intermediate
    levels of, environmental disturbance.
    CyclonePercentage of sites surveyed in an ecoregion
    with a cyclone of at least category 3 passing
    within 50 km in the past 10 years, from IBTrACS
    (International Best Track Archive for Climate
    Stewardship) (80).
    SST range (log)Difference between annual mean monthly
    maximum and minimum SST, averaged
    across years, from Bio-ORACLE (71).
    Climatic stability
    Species richness is highest at locations with landscapes
    little affected by recent cataclysmic geological and
    climatic events and thus have avoided major extinctions.
    Stability (log)Inverse of SD of mean water temperature
    since the last glacial maximum, estimated
    for three time periods: 0 ky (thousand years),
    8 ky, and 21 ky before present, from
    Paleo-MARSPEC (Ocean Climate Layers
    for Marine Spatial Ecology) (81).
    Fragmentation and connectivity
    Species richness varies with rate of speciation, which is
    dependent on frequency of vicariant events, including
    breaks in population connectivity through isolation of
    island populations, coastal fragmentation, and changing
    dispersal pathways into adjoining regions.
    Coast (log)Total length of coastline (km) within
    the ecoregion.
    ConnectNumber of abutting coastal ecoregions.
    Islands (log)Number of separated coastline features on
    map of the ecoregion.
    Human disturbance
    Species richness is negatively affected by human activities.
    Pop (log x + 10)Estimated population density, calculated
    by fitting a smoothly tapered surface to
    each settlement point on a year 2000
    world population density grid (82), using
    the quadratic kernel function described
    in the study of Silverman (83). Populations
    were screened for a density greater than
    1000 people per 0.04° cell, and the
    search radius was set at 3.959° (28).
  • Table 2 Predictors of richness and abundance identified by GLMs.

    Mean values per ecoregion provide replicate sample data in models. Marginal R2 values (% contribution) are shown for predictors that explain >5% of variation in best models identified using Akaike information criteria (AIC). Predictors trending in the opposite direction to associated latitudinal hypothesis are shown in italics; predictors with direction consistent with hypothesis are in bold. Additional details are provided in table S1. na, not assessed; −, poor model fit.

    TaxonMean abundanceSite richnessSite richness (N-corrected)Local richnessEcoregion richness
    All taxa+NO3 (6)+SST (56)
    +Coral (7)
    +SST (60)
    +Coral (10)
    +Coral (48) −NO3 (7)
    SST range (6)
    +Coral (42)
    NO3 (15)
    Vertebrates+SST (68)+SST (85)NO3 (21)
    +Coral (10)
    +SST (73)NO3 (45)
    +Coral (15)
    InvertebratesCoral (33)
    +Chl (6)
    SST (33) +Chl (6)
    −Shelf (6)
    Stability (9)+Coast (8)
    +Shelf (7)
    +Coral (20) +Shelf (8)
    NO3 (6)
    Actinopterygii+SST (68)+SST (85)+Coral (25)
    NO3 (7)
    na+Coral (44)
    NO3 (16)
    AsteroideaSST (37)
    +Chl (14)
    SST (41)
    +Chl (9)
    na+Connect (7)
    Echinoidea+NO3 (23)
    Coral (8)
    +NO3 (9)
    Island (9)
    Shelf (14)na+Coral (9)
    GastropodaCoral (34)SST (28)na+Coral (19) +Chl (7)
    CrustaceaStability (31)
    +NO3 (8)
    Stability (42)Stability (9)
    +Coral (9)
    na+Coral (16)
    +SST range (6)

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/10/e1700419/DC1

    table S1. Results of GLMs.

    table S2. Pearson’s correlation matrix relating transformed predictor metrics.

    fig. S1. Map of investigated shallow reef sites.

    fig. S2. Species richness relationships between scales and major taxa.

    fig. S3. Species richness relationships between ecoregion and site scales for major taxa.

    fig. S4. Local richness versus abundance.

    fig. S5. Reversal of causal network showing hypothesized links between environmental drivers and diversity across scales, with effects propagating downward from regional diversity.

  • Supplementary Materials

    This PDF file includes:

    • table S1. Results of GLMs.
    • table S2. Pearson’s correlation matrix relating transformed predictor metrics.
    • fig. S1. Map of investigated shallow reef sites.
    • fig. S2. Species richness relationships between scales and major taxa.
    • fig. S3. Species richness relationships between ecoregion and site scales for major taxa.
    • fig. S4. Local richness versus abundance.
    • fig. S5. Reversal of causal network showing hypothesized links between environmental drivers and diversity across scales, with effects propagating downward from regional diversity.

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