Research ArticleECOLOGY

Global pattern of phytoplankton diversity driven by temperature and environmental variability

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Science Advances  15 May 2019:
Vol. 5, no. 5, eaau6253
DOI: 10.1126/sciadv.aau6253
  • Fig. 1 Global patterns of monthly phytoplankton species richness and species turnover.

    (A) Annual mean of monthly species richness and (B) month-to-month species turnover projected by SDMs. Latitudinal gradients of (C) richness and (D) turnover. Colored lines (regressions with local polynomial fitting) indicate the means per degree latitude from three different SDM algorithms used (red shading denotes ±1 SD from 1000 Monte Carlo runs that used varying predictors for GAM). Poleward of the thin horizontal lines shown in (C) and (D), the model results cover only <12 or <9 months, respectively.

  • Fig. 2 Relationships between species richness and temperature or latitude.

    (A) The natural logarithm of the annual mean of monthly phytoplankton richness is shown as a function of sea temperature (k, Boltzmann’s constant; T, temperature in kelvin). Filled and open circles indicate areas where the model results cover 12 or less than 12 months, respectively. Trend lines are shown separately for each hemisphere (regressions with local polynomial fitting). The solid black line represents the linear fit to richness, and the dashed black line indicates the slope expected from metabolic theory (−0.32). The map inset visualizes richness deviations from the linear fit. The relative area of three different thermal regimes (separated by thin vertical lines) is given at the bottom of the figure. Observed thermal (B) and latitudinal (C) ranges of individual species are displayed by gray horizontal bars (minimum to maximum, dots for median) and ordered from wide-ranging (bottom) to narrow-ranging (top). The x axis in (C) is reversed for comparison with (B). Red lines show the expected richness based on the overlapping ranges, and blue lines depict the species’ average range size (±1 SD, blue shading) at any particular x value. Lines are shown for areas with higher confidence.

  • Fig. 3 Latitudinal trends in phytoplankton richness and selected environmental variables.

    (A) Annual mean of monthly species richness (black line) and sea temperature (red line). Shadings indicate the annual amplitude of monthly richness (gray) and temperature (red). (B) Departure of richness (black line) from the linear fit (see Fig. 2A) versus species turnover (blue line) by latitude. Shadings denote ±1 SD from Monte Carlo runs. (C) Net primary production (NPP; green), sea surface wind stress (orange), and mixed-layer depth (slate blue). Shadings indicate the annual amplitude (minimum to maximum of monthly patterns) for each variable.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/5/eaau6253/DC1

    Supplementary Materials and Methods

    Fig. S1. Distribution of phytoplankton presence observations in space and time.

    Fig. S2. SDM performance for the three statistical algorithms used.

    Fig. S3. Sensitivity of global species richness patterns to methodological choices.

    Fig. S4. Latitudinal species richness gradients derived from the observational raw data.

    Fig. S5. Species richness–temperature relationships derived from the observational raw data.

    Fig. S6. Species ranges for key environmental factors.

    Table S1. Fraction of equatorial species recorded at higher latitudes.

    Table S2. Single variable model skill for predicting species distributions and global richness.

    Table S3. Contribution of sources to the phytoplankton dataset.

    Table S4. Statistics on data collected and species modeled within major taxon groups.

    Data file S1. Monthly species richness diagnosed at global scale, 1° spatial resolution.

    References (6165)

  • Supplementary Materials

    The PDF file includes:

    • Supplementary Materials and Methods
    • Fig. S1. Distribution of phytoplankton presence observations in space and time.
    • Fig. S2. SDM performance for the three statistical algorithms used.
    • Fig. S3. Sensitivity of global species richness patterns to methodological choices.
    • Fig. S4. Latitudinal species richness gradients derived from the observational raw data.
    • Fig. S5. Species richness–temperature relationships derived from the observational raw data.
    • Fig. S6. Species ranges for key environmental factors.
    • Table S1. Fraction of equatorial species recorded at higher latitudes.
    • Table S2. Single variable model skill for predicting species distributions and global richness.
    • Table S3. Contribution of sources to the phytoplankton dataset.
    • Table S4. Statistics on data collected and species modeled within major taxon groups.
    • References (6165)

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    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (.nc format). Monthly species richness diagnosed at global scale, 1° spatial resolution.

    Files in this Data Supplement:

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