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Incomplete sampling of species' geographic distributions has challenged biogeographers for many years to precisely quantify global-scale biodiversity patterns. After correcting for the spatial inequality of sample completeness, we generated a global species diversity map for woody angiosperms (82,974 species, 13,959,780 occurrence records). The standardized diversity estimated more pronounced latitudinal and longitudinal diversity gradients than the raw data and improved the spatial prediction of diversity based on environmental factors. We identified areas with potentially high species richness and rarity that are poorly explored, unprotected, and threatened by increasing human pressure: They are distributed mostly at low latitudes across central South America, Central Africa, subtropical China, and Indomalayan islands. These priority areas for botanical exploration can help to efficiently fill spatial knowledge gaps for better describing the status of biodiversity and improve the effectiveness of the protected area network for global woody plant conservation.

Motivation: Historical changes in sea level caused shifting coastlines that affected the distribution and evolution of marine and terrestrial biota. At the onset of the Last Glacial Maximum (LGM) 26 ka, sea levels were >130 m lower than at present, resulting in seaward-shifted coastlines and shallow shelf seas, with emerging land bridges leading to the isolation of marine biota and the connection of land-bridge islands to the continents. At the end of the last ice age, sea levels started to rise at unprecedented rates, leading to coastal retreat, drowning of land bridges and contraction of island areas. Although a growing number of studies take historical coastline dynamics into consideration, they are mostly based on past global sea-level stands and present-day water depths and neglect the influence of global geophysical changes on historical coastline positions. Here, we present a novel geophysically corrected global historical coastline position raster for the period from 26 ka to the present. This coastline raster allows, for the first time, calculation of global and regional coastline retreat rates and land loss rates. Additionally, we produced, per time step, 53 shelf sea rasters to present shelf sea positions and to calculate the shelf sea expansion rates. These metrics are essential to assess the role of isolation and connectivity in shaping marine and insular biodiversity patterns and evolutionary signatures within species and species assemblages., Main Types of Variables Contained: The coastline age raster contains cells with ages in thousands of years before present (bp), representing the time since the coastline was positioned in the raster cells, for the period between 26 ka and the present. A total of 53 shelf sea rasters (sea levels <140 m) are presented, showing the extent of land (1), shelf sea (0) and deep sea (NULL) per time step of 0.5 kyr from 26 ka to the present., Spatial Location and Grain: The coastline age raster and shelf sea rasters have a global representation. The spatial resolution is scaled to 120 arcsec (0.333° × 0.333°), implying cells of c . 3,704 m around the equator, 3,207 m around the tropics (±30°) and 1,853 m in the temperate zone (±60°)., Time Period and Temporal Resolution: The coastline age raster shows the age of coastline positions since the onset of the LGM 26 ka, with time steps of 0.5 kyr. The 53 shelf sea rasters show, for each time step of 0.5 kyr, the position of the shelf seas (seas shallower than 140 m) and the extent of land., Level of Measurement: Both the coastline age raster and the 53 shelf sea rasters are provided as TIFF files with spatial reference system WGS84 (SRID 4326). The values of the coastline age raster per grid cell correspond to the most recent coastline position (in steps of 0.5 kyr). Values range from 0 (0 ka, i.e., present day) to 260 (26 ka) in bins of 5 (0.5 kyr). A value of "no data" is ascribed to pixels that have remained below sea level since 26 ka., Software Format: All data processing was done using the R programming language., (© 2022 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd.)

Following its initial appearance in December 2019, coronavirus disease 2019 (COVID-19) quickly spread around the globe. Here, we evaluated the role of climate (temperature and precipitation), region-specific COVID-19 susceptibility (BCG vaccination factors, malaria incidence, and percentage of the population aged over 65 years), and human mobility (relative amounts of international visitors) in shaping the geographical patterns of COVID-19 case numbers across 1,020 countries/regions, and examined the sequential shift that occurred from December 2019 to June 30, 2020 in multiple drivers of the cumulative number of COVID-19 cases. Our regression model adequately explains the cumulative COVID-19 case numbers (per 1 million population). As the COVID-19 spread progressed, the explanatory power (R2) of the model increased, reaching > 70% in April 2020. Climate, host mobility, and host susceptibility to COVID-19 largely explained the variance among COVID-19 case numbers across locations; the relative importance of host mobility and that of host susceptibility to COVID-19 were both greater than that of climate. Notably, the relative importance of these factors changed over time; the number of days from outbreak onset drove COVID-19 spread in the early stage, then human mobility accelerated the pandemic, and lastly climate (temperature) propelled the phase following disease expansion. Our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific COVID-19 susceptibility. The identification of these multiple drivers of the COVID-19 outbreak trajectory, based on mapping the spread of COVID-19, will contribute to a better understanding of the COVID-19 disease transmission risk and inform long-term preventative measures against this disease., Competing Interests: The authors have declared that no competing interests exist.

A major research question concerning global pelagic biodiversity remains unanswered: when did the apparent tropical biodiversity depression (i.e., bimodality of latitudinal diversity gradient [LDG]) begin? The bimodal LDG may be a consequence of recent ocean warming or of deep-time evolutionary speciation and extinction processes. Using rich fossil datasets of planktonic foraminifers, we show here that a unimodal (or only weakly bimodal) diversity gradient, with a plateau in the tropics, occurred during the last ice age and has since then developed into a bimodal gradient through species distribution shifts driven by postglacial ocean warming. The bimodal LDG likely emerged before the Anthropocene and industrialization, and perhaps ∼15,000 y ago, indicating a strong environmental control of tropical diversity even before the start of anthropogenic warming. However, our model projections suggest that future anthropogenic warming further diminishes tropical pelagic diversity to a level not seen in millions of years., Competing Interests: The authors declare no competing interest.

The pattern of species abundance, represented by the number of individuals per species within an ecological community, is one of the fundamental characteristics of biodiversity. However, despite their obvious significance in ecology and biogeography, there is still no clear understanding of these patterns at large spatial scales. Here, we develop a hierarchical modelling approach to estimate macroscale patterns of species abundance. Using this approach, estimates of absolute abundance of 1248 woody plant species at a 10-km-grid-square resolution over East Asian islands across subtropical to temperate biomes are obtained. We provide two examples of the basic and applied use of the estimated species abundance for (1) inference of macroevolutionary processes underpinning regional biodiversity patterns and (2) quantitative community-wide assessment of a national red list. These results highlight the potential of the elucidation of macroscale species abundance that has thus far been an inaccessible but critical property of biodiversity.