Operationalizing expert knowledge in species' range estimates using diverse data types

Estimates of species’ ranges can inform many aspects biodiversity research and conservation-management decisions. Many practical applications need high-precision range estimates that are sufficiently reliable to use as input data in downstream applications. One solution has involved expert-generated maps reflect on-the-ground field information implicitly capture various processes may limit a geographic distribution. However, expert often subjective rarely reproducible. In contrast, species distribution models (SDMs) typically have finer resolution reproducible because explicit links data. Yet, SDMs higher uncertainty when sparse, which is an issue for most species. Also, only subset the factors determine distributions (e.g., climate) hence require significant post-processing better estimate current realized distributions. Here, we demonstrate how knowledge, diverse types, be used together transparent modeling workflow. Specifically, show knowledge regarding habitat use, elevation, biotic interactions, environmental tolerances make refine using sources, including high-resolution remotely sensed products. This range-refinement approach primed with continuously improving spatial or temporal resolution. To facilitate such analyses, compile comprehensive suite tools new R package, maskRangeR, provide worked examples. These wide variety basic applied requires ranges, quantifications its change over time.