Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence

Abstract

Connecting the geographical occurrence of a species with underlying environmental variables is fundamental for many analyses of life history evolution and for modeling species distributions for both basic and practical ends. However, raw distributional information comes principally in two forms: points of occurrence (specific geographical coordinates where a species has been observed), and expert-prepared range maps. Each form has potential short-comings: range maps tend to overestimate the true occurrence of a species, whereas occurrence points (because of their frequent nonrandom spatial distribution) tend to underestimate it. Whereas previous comparisons of the two forms have focused on how they may differ when estimating species richness, less attention has been paid to the extent to which the two forms actually differ in their representation of a species' environmental associations. We assess such differences using the globally distributed avian order Galliformes (294 species). For each species we overlaid range maps obtained from IUCN and point-of-occurrence data obtained from GBIF on global maps of four climate variables and elevation. Over all species, the median difference in distribution centroids was 234 km, and median values of all five environmental variables were highly correlated, although there were a few species outliers for each variable. We also acquired species' elevational distribution mid-points (mid-point between minimum and maximum elevational extent) from the literature; median elevations from point occurrences and ranges were consistently lower (median −420 m) than mid-points. We concluded that in most cases occurrence points were likely to produce better estimates of underlying environmental variables than range maps, although differences were often slight. We also concluded that elevational range mid-points were biased high, and that elevation distributions based on either points or range maps provided better estimates.

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

“…In these cases, expert maps may be the only estimates of a species' range. However, these are often not reproducible, are difficult to update as new data become available, and have a precision that is too coarse to be meaningful for many conservation activities (Ocampo-Peñuela et al 2016, Hurlbert and Jetz 2007, Rotenberry and Balasubramaniam 2020, Di Marco et al 2017). Nevertheless, expert-drawn maps are still valuable, as they often reflect a range of processes, including habitat selection, dispersal limitation, and biotic interactions that must be considered to estimate a species' realized distribution.…”

Shifts in bird ranges and conservation priorities in China under climate change

“…On a world scale, studies have presented maps of changes in species distribution ranges (birds, amphibians, and mammals) under future climate change scenarios [ 14 , 15 ], and noted that biodiversity hotspots may move to high-latitudes. However, these world-scale studies have low spatial resolution and use polygon range data based on expert experience, that may lead to worse estimates of underlying environmental variables [ 16 , 17 ]. Therefore, these results are difficult to use to guide the planning and management of protected areas in response to climate change at the country scale.…”

A data‐driven geospatial workflow to map species distributions for conservation assessments

“…The geospatial workflow emphasized confirmed records and excluded areas of known absence which increased the spatial accuracy of predictions (Elith et al., 2006; Mainali et al., 2020). Besides the more reliable estimates from using primary biodiversity data (Peterson et al., 2018; Rotenberry & Balasubramaniam, 2020), our approach has the advantage that it can be adjusted based on user needs, and experts can review the produced range maps for quality checks to further improve their accuracy (Graham & Hijmans, 2006; Merow et al., 2017; Velásquez‐Tibatá et al., 2019). This can facilitate the integration of mapping species ranges with extinction risk assessments for the IUCN Red List, especially since our protocol starts with defining species extent of occurrence (EOO), which serves both as a metric under criterion B of the Red List and as the limits of the mapping area for a given species.…”

“…Clip the EOO to the elevational limits of a species by extracting the elevation of each occurrence point using a Digital Elevation Model (DEM). Elevational limits from point data provide better estimates than gathering from the literature (Rotenberry & Balasubramaniam, 2020) but it is advisable to filter for elevational outliers beforehand (see case study workflow). The choice of DEM resolution depends on the spatial extent of the data and the intended analysis; 90‐m and 250‐m resolutions are commonly employed (Amatulli et al., 2018) Overlay presence and absence points to the clipped EOO.…”