Research ArticleECOLOGY

The commonness of rarity: Global and future distribution of rarity across land plants

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Science Advances  27 Nov 2019:
Vol. 5, no. 11, eaaz0414
DOI: 10.1126/sciadv.aaz0414
  • Fig. 1 Computational workflow for creating gSADs.

    TNRS, Taxonomic Name Resolution Service; GNRS, Geographic Name Resolution Service.

  • Fig. 2 The gSAD for all plant species.

    (A) Schematic illustration of the predicted gSAD based on expectations from theory (see main text) (28). In the inset, we list several differing predictions for the shape of the gSAD. (B) Two estimates of the gSAD for all land plant species. The first distribution (green) is the observed number of observations per species for all species found in ecological plots. Each data point represents the total number of individuals observed for a given species. The second distribution (red) is all botanical specimens collected within 100 km of each plot. The third distribution (light purple) is all botanical specimens per species. Each distribution is strongly modal at the lowest abundance, showing that most species have only been observed a very small number of times and only a few species are common. The distributions are shown on log10-transformed axes. Comparing the shape of the distributions of the competing fits of differing proposed gSAD distributions allows us to test differing hypotheses for the origin of the gSAD.

  • Fig. 3 Does using the number of observations in botanical datasets provide a reliable measure of rarity?

    Assessments of rarity by taxonomic specialists at the Missouri Botanical Garden and the New York Botanical Garden for a random sample of 300 species with three observations or fewer in the BIEN database. Most species (72.7%) identified as “rare” based on the number of unique occurrences within the BIEN database are also recognized as rare by experts. Approximately 7.3% of these species appear to be incorrectly characterized as rare, as they are recognized by experts as abundant or having large ranges. The apparent scarcity of approximately 7.5% of these taxa may reflect recent taxonomic splits or old names no longer used. Moreover, 10.3% are non-native species (which may or may not be rare). In sum, we estimate that between 72 and 90% of plant taxa (recognized as rare + recent name + unresolved + old name) identified by BIEN as being rare would be recognized as rare by other measures.

  • Fig. 4 Where are rare species distributed geographically?

    Plotting the geographic coordinates for all the observations for species with three observations or fewer at a coarse, 1° resolution reveals several patterns. The sampling background is shown (grey cells are areas with no georeferenced botanical sampling records, while yellow cells indicate regions with observation records but no rare species). Colored cells correspond to areas with rare species (species with three observations or fewer) rarified to the sampling intensity using the Margalef index (see the Supplementary Materials). Areas with a proportionally high number of rare species are dark brown (“hotspots of rarity”), while areas with relatively low numbers of rare species are yellow to orange. Areas with a high number of rare species tend to be clustered in a small number of locations including mountainous tropical and subtropical regions including New Guinea, Indonesia, southwestern China, Madagascar, the Andes (in Ecuador, Columbia, and Peru), Central America (Costa Rica and Panama), and southern Mexico. In addition, several notable temperate zone locations including the Fynbos in South Africa and southwest Australia, Northern Iran/Georgia/Turkey, and the Iberian Peninsula.

  • Fig. 5 Regions that currently have high numbers of rare species are also characterized by higher human impact and will experience faster rates of future climate change.

    (A) Density plot of human footprint index in areas with rare species (light gray) and the global map (dark gray). Areas with rare species have, on average, human footprint values of 8.5 ± 5.8, which is ~1.6 times higher (P < 0.001, Wilcoxon test) human impact than on the globe on average (5.2 ± 5.8). (B) Density plot of the ratio of future climate (temperature) velocity versus historical climate velocity. On average, areas with rare species will experience ~200 (±58) times greater rates of temperature velocity than those same areas experienced historically and will experience ~1.2 times greater (P < 0.001, Wilcoxon test) rates of temperature velocity change than the globe will experience on average (170 ± 77). (C) Global variation in the human footprint index. Areas with high human footprint are in brown. Areas with low human footprint are dark green. (D) Global map of the ratio between future (baseline climate to late century, 1960–1990 to 2060–2080, under RCP8.5) and historical rates of temperature change [LGM to baseline climate (~21 ka ago to 1960–1990)]. Future temperatures will increase across the globe. However, in comparison with historical rates of climate change, some areas will experience relatively faster (ratio values greater than 1; yellow to red values) or slower (ratio values less than 1; green to blue values) rates of change. Note that many of the regions of rarity hotspots are found in regions that will be experiencing relatively faster rates of climate change compared to historical rates of change.

  • Fig. 6 What will happen to rare species diversity with climate change?

    (A) The predicted change in Margalef SAR rarity index under climate change from the autoregressive models (SAR). The rarity indices are log-transformed. Large decreases in climate suitability for rare species are in red to orange, whereas smaller reductions in climate suitability are given in green to blue colors. Note the large decreases in climate suitability for rare species in the Andes and Mesoamerica, African highlands, New Guinea, southwestern China, Indonesia, Nepal, and New Zealand. (B) The diagonal 1:1 line (red) represents situations of no difference between the predicted current and future rarity index from SAR and OLS models. All points in the scatter plot are below the diagonal line, indicating a reduction of rare species diversity across all the areas where they currently occur.

  • Table 1 Three different measures of goodness of fit (r2 or percentage of variance explained in the cumulative distribution function, χ2 on log2 bins, and Akaike’s information criterion) are shown for six different species abundance models [see (40)].

    All distributions shown have two parameters except the log-series and power distributions, which have one. Distributions were fitted for the number of observations per species across all species found (i) within ecological plots only and (ii) across all datasets within the BIEN database. Sampling species found only in plots standardizes for sampling influences, as all individuals within ecological plots are sampled and identified to species. Thus, the species abundance distribution from ecological plots is expected to more accurately describe the species abundance distribution. As predicted by the CLT, the Poisson lognormal distribution provides the best fit to both gSADs. Nonetheless, Pareto and truncated Pareto also all fit well. The log-series distribution, predicted by the k-niche model and neutral theory, falls behind these distributions across the different goodness-of-fit measures. AIC, Akaike’s information criterion; CDF, cumulative distribution function.

    ModelPlot data onlyAll data
    CDF r2χ2 log2AIC∆AICCDF r2χ2 log2AIC∆AIC
    Zipf-Mandelbrot0.92954,188139,82225,8480.44773,884,9477,402,206330,9517
    Weibull0.9991.6 × 1010127,11113,1370.9993.01 × 10104,269,287176,598
    Log series0.9911.57 × 1013120,08261090.9995.08 × 10134,119,05726,368
    Pareto0.9995.69 × 1013115,24412700.9991.46 × 10134,110,90018,211
    Poisson lognormal0.999490113,97400.99929664,092,6890
    Pareto with finite
    sample
    exponential
    adjustment
    0.999563114,0961220.998100,5584,203,550110,861
  • Table 2 Parameter fits for each of the fitted statistical distributions.

    The estimated slope values, β, of the gSAD are given in bold by fits of the Pareto and Truncated Pareto distributions. Note that the estimated slope values differ from −1.0 expected from the unified neutral theory of biodiversity. Instead, the observed fitted slope, β, is steeper than expected from neutral theory (with fitted exponents more negative than −1.0). The steeper exponents indicate that of all of the observed plant species on Earth, proportionally more of them are rare and that there are more rare species than expected by demographic and evolutionary neutral processes. Thus, the processes creating and maintaining rare species on Earth generate proportionally more rare species.

    ModelPlot dataAll data
    Zipf-Mandelbrot, b13.31186.7
    Zipf-Mandelbrot, c1.41.2
    Log series, c0.90.9
    Pareto fitted exponent, β−1.4−1.3
    Weibull scale18.140.6
    Weibull shape0.40.5
    Poisson lognormal, m4.07 × 10−81.7
    Poisson lognormal, s2.92.6
    Pareto with finite sample
    exponential adjustment (28)
    fitted Pareto exponent, β
    −1.3−1.1
    Pareto with finite sample
    exponential adjustment:
    Exponential parameter, Ω
    0.10.1

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/11/eaaz0414/DC1

    Supplementary Document

    Table S1. As in Table 1 but for specimen data found within 1° proximity to each plot.

    Table S2. As with Table 2 but for specimens near plots.

    Table S3. Summary statics of OLS linear regression models and SAR models for predicting the Menhinick rarity index.

    Table S4. Summary statics of OLS linear regression models for predicting the Menhinick rarity index.

    Table S5. Summary statics of SAR models for predicting the Menhinick rarity index.

    Table S6. Summary statics of OLS linear regression models and SAR models for predicting the Margalef rarity index.

    Table S7. Summary statics of OLS linear regression models for predicting the Margalef rarity index.

    Table S8. Summary statics of SAR models for predicting the Margalef rarity index.

    Fig. S1. Sampling density for different data types in BIEN.

    Fig. S2. Scatter plots showing the relationships between bivariate relationship between Menhinick rarity index and environmental variables.

    Fig. S3. Scatter plots showing the relationships between bivariate relationship between Margalef rarity index and environmental variables.

    Fig. S4. Predicted changes of Margalef rarity index using either the OLS or the SAR models.

    Fig. S5. Historical and future global temperature velocities.

    References (59108)

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Document
    • Table S1. As in Table 1 but for specimen data found within 1° proximity to each plot.
    • Table S2. As with Table 2 but for specimens near plots.
    • Table S3. Summary statics of OLS linear regression models and SAR models for predicting the Menhinick rarity index.
    • Table S4. Summary statics of OLS linear regression models for predicting the Menhinick rarity index.
    • Table S5. Summary statics of SAR models for predicting the Menhinick rarity index.
    • Table S6. Summary statics of OLS linear regression models and SAR models for predicting the Margalef rarity index.
    • Table S7. Summary statics of OLS linear regression models for predicting the Margalef rarity index.
    • Table S8. Summary statics of SAR models for predicting the Margalef rarity index.
    • Fig. S1. Sampling density for different data types in BIEN.
    • Fig. S2. Scatter plots showing the relationships between bivariate relationship between Menhinick rarity index and environmental variables.
    • Fig. S3. Scatter plots showing the relationships between bivariate relationship between Margalef rarity index and environmental variables.
    • Fig. S4. Predicted changes of Margalef rarity index using either the OLS or the SAR models.
    • Fig. S5. Historical and future global temperature velocities.
    • References (59108)

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