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

John T Rotenberry,Priya Balasubramaniam

doi:10.1111/ecog.04871

John T Rotenberry, Priya Balasubramaniam

Open Access

https://doi.org/10.1111/ecog.04871

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Journal: Ecography	Publication Date: Mar 23, 2020
Citations: 19	License type: CC BY 3.0

Affiliation: University of Minnesota, University of Nevada Reno

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 non‐random 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.

Highlights

Animal life histories are shaped by the environmental attributes associated with a species’ geographical distribution and by any phylogenetic constraints, the latter associated with the species’ evolutionary history (Roff 1992)
Whereas most analyses treat elevation and latitude as proxies for variation in a suite of environmental variables (Jetz et al 2008 is an exception in this regard), better understanding of the role of extrinsic drivers of life history evolution will come from replacing meters and degrees with actual values of environmental variables associated with species’ distributions into our analyses
Median geographical location of a species’ distribution varied little between latitude and longitude determined from range maps versus reported point locations (Table 1, Fig. 1); over all species the correlation between the two data sources was 0.99 for longitude and 0.98 for latitude, with regression coefficients of 0.99 and 1.00 respectively

Summary

Introduction

Animal life histories are shaped by the environmental attributes associated with a species’ geographical distribution and by any phylogenetic constraints, the latter associated with the species’ evolutionary history (Roff 1992). Whereas most analyses treat elevation (measured in meters) and latitude (measured in degrees north or south of the equator) as proxies for variation in a suite of environmental variables (Jetz et al 2008 is an exception in this regard), better understanding of the role of extrinsic drivers of life history evolution will come from replacing meters and degrees with actual values of environmental variables (e.g. annual precipitation or net primary productivity, annual temperature and its seasonality, hours of daylight during the breeding season, etc.) associated with species’ distributions into our analyses This will allow us to test alternative theories of the drivers of life history evolution along such gradients more directly. Frequent practical applications of these relationships include species and habitat conservation planning, reserve design, habitat management and predicting species’ responses to environmental change (Gusian et al 2013, Franklin et al 2014)

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