DOI: 10.5061/dryad.zw3r2287z
title: Sampling biases shape our view of the natural world

description: Spatial patterns of biodiversity are inextricably linked to their
 collection methods, yet no synthesis of bias patterns or their
 consequences exists. As such, views of organismal distribution and the
 ecosystems they make up may be incorrect, undermining countless ecological
 and evolutionary studies. Using 742 million records of 374,900 species, we
 explore the global patterns and impacts of biases related to taxonomy,
 accessibility, ecotype, and data type across terrestrial and marine
 systems. Pervasive sampling and observation biases exist across animals,
 with only 6.74% of the globe sampled, and disproportionately poor tropical
 sampling. High -elevations and deep -seas are particularly unknown. Over
 50% of records in most groups account for under 2% of species, and
 citizen-science only exacerbates biases. Additional data will be needed to
 overcome many of these biases, but we must increasingly value data
 publication to bridge this gap and better represent species’ distributions
 from more distant and inaccessible areas, and provide the necessary basis
 for conservation and management.
description: Full methods are given in the paper
 <b>Supplemental methods</b> A
 representative sample of vertebrates and invertebrates was selected for
 terrestrial and ocean systems. All four major terrestrial vertebrate
 groups were selected (Aves, Amphibia, Mammalia and Reptilia) along with a
 selection of groups found in both realms: Actinopterygii, Annelida,
 Arachnida, Cnidaria, Elasmobranchii, Gastropoda and Malacostraca (insects
 were not selected because they are virtually absent in marine systems and
 studies show that even the best-studied groups, such as bees, are
 exceedingly biased in coverage, 46). Whilst some of these groups are more
 diverse in either oceanic or terrestrial systems, most have
 representatives in both and show an array of different life-forms (between
 and within groups, and were selected at certain taxonomic levels to try to
 control for diversity of life-form) to enable extrapolation to other
 groups showing similar habits. In total, 742,161,633 records were
 analyzed, including 38,313,609 of 57,252,510 potential OBIS records for
 marine systems (80% of animal records, 67% of all records) and 703,848,024
 of 1.23 billion potential GBIF records for terrestrial systems (71% of
 animal records, 57% of all records; details in <b>Data S1,
 </b>DOIs are listed in <b>Data S2</b>). Number of
 species to reach 50% of the data was also quantified from GBIF
 directly. <b>Terrestrial</b>
 All GBIF records for amphibians, birds, mammals and reptiles
 within GBIF were downloaded on the 19th of April 2019 and other taxa were
 downloaded on July 3<sup>rd</sup>. The relationship between
 species distributions and various environmental parameters (coasts,
 rivers, cities etc-see below) was examined. Analysis was done for global
 and national scales and results at a national level are available in
 supplements, as relationships varied across taxa. For each country, we
 aggregated the number of records collected via different methods, with
 specimens and human observations dominant. GBIF data
 were cleaned and filtered using taxon-specific lists for terrestrial
 vertebrates (http://www.birds.cornell.edu/clementschecklist/download/;
 https://mammaldiversity.org;
 http://www.reptile-database.org/db-info/news.html;
 http://research.amnh.org/vz/herpetology/amphibia/index.php), numbers of
 species per genus were calculated based on this data, and the proportion
 of extinct species (http://fossilworks.org/) added as this may influence
 the relationship to roads. Roads were downloaded from
 the GRIP dataset (https://www.globio.info/download-grip-dataset), and
 distance from roads calculated in ArcGIS in a Cylindrical Equal-areas
 projection. The same projection was also used to measure distance from the
 coast (https://gadm.org/download_country_v3.html), distance from rivers
 (https://hydrosheds.org/page/hydrobasins) and distance to cities (based on
 reclassifying the lights at night Blackmarble 2016 data
 (https://eoimages.gsfc.nasa.gov) to show only concentrated aggregations of
 light, based on testing what threshold of light brightness could be
 reliably used to define urban masses compared to Nature Vue imagery, based
 on global comparison of cities and relative brightness). The relationships
 between each road and coastline and sampling localities were then examined
 by looking at the number and percentage of records at 0-1km, 1-2.5km,
 2.5-5km and over 5km from each. The change in
 relationships within protected areas (https://www.protectedplanet.net/)
 was examined by calculating the number of records within 2.5km of a road
 inside and outside protected areas, the same analysis was conducted for
 KBAs (https://www.birdlife.org/key-biodiversity-areas). The latest versions of ecoregions
 were used (https://ecoregions2017.appspot.com/) to examine the level of
 sampling relative to the size and richness of each ecoregion, and ocean
 realms were downloaded from
 https://auckland.figshare.com/search?q=realms&searchMode=1;
 percentage coverage and degree of sampling was calculated in each system.
 Elevation was downloaded from (https://eatlas.org.au)
 and average altitude at the four buffer distances was calculated using the
 zonal statistics function in Arc. To examine
 representativeness of sampling for high-altitude biomes, we used two
 definitions. The first used a simple, standardized metric, using the top
 quartile of all elevations globally and classing these as high-altitude.
 The second approach was more representative of high biomes from an
 ecophysiological perspective, but required a more complex method. This
 required the integration of several different approaches which functioned
 better in different parts of the world with the aim of differentiating
 ecosystems above and below the treeline, and extracting developed and crop
 ecosystems. Tree density was reclassified to show just forest cover
 (https://figshare.com/articles/Global_map_of_tree_density/3179986) and
 non-tree areas above 1000m extracted. Cities and croplands
 (https://www.croplands.org/) were also removed from analysis. This was
 found to capture the zone above the treeline exceedingly well (based on
 testing with high-resolution imagery data) across the Northern Hemisphere
 above 30 degrees latitude, but over-predicted nival (above the treeline)
 habitats below this. For the Southern Hemisphere, using a mean temperature
 of 5.5 <sup>o</sup>C as a boundary demarcated nival areas
 well, whereas in the US between 15-50<sup>o</sup> North a mean
 of 2.5<sup> o</sup>C performed better. These three zones were
 combined to give a global map of zones above treelines and tested using
 high-resolution imagery. The elevation of the nival zone was then
 extracted using a polygon of nival areas, then the lowest elevation of
 each nival zone calculated using zonal statistics. The region 500m below
 the treeline was created by subtracting 500 from the lowest treeline
 altitude for each area. A 10km buffer was marked around each treeline, and
 used to mask the elevation. The mask was then given the value of the 500m
 below treeline using zonal statistics and the value of the buffer
 elevation was mosaicked with the minus 500m elevation mask to retain the
 highest value. Areas above the 500m limit were then extracted by
 subtracting the value of the buffer, and the non-zero areas to create a
 mask. This area was then checked globally to examine this top tier of tree
 ecosystems. For the elevation zones and ecoregions, the
 number of records and degree of coverage was then calculated. For coverage
 statistics, we gridded the world into 5 and 10km tiles, then assessed what
 percentage of all tiles had records. This was conducted for each taxa for
 all analysis where different zones or countries were examined and to
 understand how well-sampled terrestrial systems were for different
 taxa. <b>Oceanic</b> For
 Ocean records, OBIS (https://obis.org) was used to assess sampling and
 coverage (downloaded on 3<sup>rd</sup> July, See Supplemental
 Data 2). Overall sampling was calculated on the 5km and 10km grids
 overall, for each country’s Exclusive Economic Zone (from the Convention
 for the Conservation of Antarctic Marine Living Resources:
 https://data.ccamlr.org/sites/default/files/eez-shapefile-EPSG102020_0.zip), and for each marine realm (Costello et al. 2017: https://objectext.auckland.ac.nz/figshare/9737926/MarineRealmsShapeFile.zip). The level of GDP per capita for each territory was calculated and compared to the percentage coverage of the Exclusive Economic Zone to the GDP. Data on exclusive economic zones was downloaded from Worldbank (https://data.worldbank.org/indicator/NY.GDP.MKTP.CD), and other sources were used when no published data were available on a country or territory. GDP was compared to coverage in each country in addition to oceanic exclusive economic zone, and to the percentage of results from different sources. We also compared the percentage of records via human observations or specimens direct to GDP using the same approach. To collate tourist numbers, we used https://www.indexmundi.com/facts/indicators/ST.INT.ARVL/rankings ; https://www.tourismdashboard.org/wp-content/uploads/2016/11/GlobalTourismDashboard2016-Infographic-1024x726.png) for the majority of countries, then filled in missing countries using national tourism statistics numbers or other reliable sources to the nearest available year (2013-2017) (Supplemental Data S2). To understand the level of coverage for the deep-sea, the same SRTM was used as for terrestrial ecosystems and classified into depths of 1000m to 1800m (Webb 2010) and below -1800m, more indepth analysis of how marine diversity varies with depth is available from Costello & Chaudhary, 2017. For the groups under study, the records where depths were recorded were then filtered to provide a dataset with only species records which included depths. Records corresponding to the two depth cutoffs were maintained and the number of records and coverage levels calculated for the two zones. For Marine Protected Areas (MPA) we calculated the percentage coverage for each group within the areas using the shapefiles from Protected Planet and the tabulate area tool in ArcMap 10.3.
description: Uploaded files include species and samples per cell for each
 group for the land and ocean. See readme for specifics. Five excel files
 of supplemental data are also included.