Large-scale biodiversity databases have become crucial information sources in many analyses in biogeography, macroecology, and conservation biology, often involving development of empirical models of species’ ecological niches and predictions of their geographic distributions. These analyses, however, can be impaired by the presence of errors, particularly as regards taxonomic identifications and accurate geographic coordinates. Here, we present a detailed data-cleaning exercise based on two contrasting datasets; we link these example data with a step-by-step guide to overcoming these problems and improving data quality for analyses based on these data.

The Asian giant hornet (AGH,Vespa mandarinia) is the world’s largest hornet, occurring naturally in the Indomalayan region, where it is a voracious predator of pollinating insects including honey bees. In September 2019, a nest of Asian giant hornets was detected outside of Vancouver, British Columbia; multiple individuals were detected in British Columbia and Washington state in 2020; and another nest was found and eradicated in Washington state in November 2020, indicating that the AGH may have successfully wintered in North America. Because hornets tend to spread rapidly and become pests, reliable estimates of the potential invasive range ofV. mandariniain North America are needed to assess likely human and economic impacts, and to guide future eradication attempts. Here, we assess climatic suitability for AGH in North America, and suggest that, without control, this species could establish populations across the Pacific Northwest and much of eastern North America. Predicted suitable areas for AGH in North America overlap broadly with areas where honey production is highest, as well as with species-rich areas for native bumble bees and stingless bees of the genusMeliponain Mexico, highlighting the economic and environmental necessity of controlling this nascent invasion.

Ecological niche modeling (ENM) and species distribution modeling (SDM) are sets of tools that allow the estimation of distributional areas on the basis of establishing relationships among known occurrences and environmental variables. These tools have a wide range of applications, particularly in biogeography, macroecology, and conservation biology, granting prediction of species potential distributional patterns in the present and dynamics of these areas in different periods or scenarios. Due to their relevance and practical applications, the usage of these methodologies has significantly increased throughout the years. Here, we provide a manual with the basic routines used in this field and a practical example of its implementation to promote good practices and guidance for new users.