The availability of user‐friendly software and publicly available biodiversity databases has led to a rapid increase in the use of ecological niche modelling to predict species distributions. A potential source of error in publicly available data that may affect the accuracy of ecological niche models (ENMs), and one that is difficult to correct for, is incorrect (or incomplete) taxonomy. Here we remind researchers of the need for careful evaluation of database records prior to use in modelling, especially when the presence of cryptic species is suspected or many records are based on indirect evidence. To draw attention to this potential problem, we construct ENMs for the North American Sasquatch (i.e. Bigfoot). Specifically, we use a large database of georeferenced putative sightings and footprints for Sasquatch in western North America, demonstrating how convincing environmentally predicted distributions of a taxon’s potential range can be generated from questionable site‐occurrence data. We compare the distribution of Bigfoot with an ENM for the black bear, Ursus americanus, and suggest that many sightings of this cryptozoid may be cases of mistaken identity.

Three-dimensional visualization of the ecological marine units (EMUs) for the Banda Sea. EMUs are depicted as bands on cylinders, and pink colors indicate warmer EMUs, where blue colors represent colder EMUs. On land, the global ecological land units (ELUs) of Sayre et al. (2014) are shown. Global Open Ocean and Deep Seabed (GOODS) Biogeographic Characterization (UNESCO, 2009) Global, Benthic and Pelagic Expert recommended regions Deep-Sea Provinces (Watling et al., 2013) Global, Benthic Expert-derived revision of GOODS based on literature review Biogeochemical Provinces (Longhurst, 2007) Global Satellite ocean color Seafloor Map (GSFM) (Harris et al., 2014) Global Expert geomorphological feature extraction using 30 arcsecond bathymetry data Deep-Sea Seascapes Map (Harris and Whiteway, 2009) Global Multivariate analysis of seabed morphology and sediments

In 1964, E.H. Hammond proposed criteria for classifying and mapping physiographic regions of the United States. Hammond produced a map entitled "Classes of Land Surface Form in the Forty-Eight States,USA", which is regarded as a pioneering and rigorous treatment of regional physiography. Several researchers automated Hammond's model in GIS. However, these were local or regional in application, and resulted in inadequate characterization of tablelands. We used a global 250 m DEM to produce a new characterization of global Hammond landform regions. The improved algorithm we developed for the regional landform modeling: (1) incorporated a profile parameter for the delineation of tablelands; (2) accommodated negative elevation data values; (3) allowed neighborhood analysis window (NAW) size to vary between parameters; (4) more accurately bounded plains regions; and (5) mapped landform regions as opposed to discrete landform features. The new global Hammond landform regions product builds on an existing global Hammond landform featuresproduct developed by the U.S. Geological Survey, which, while globally comprehensive, did not include tablelands, used a fixed NAW size, and essentially classified pixels rather than regions. Our algorithm also permits the disaggregation of "mixed" Hammond types (e.g. plains with high mountains) into their component parts.

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Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units,