sampbias, a method for quantifying geographic sampling biases in species distribution data

Abstract

Geo-referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and data sets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence data sets. sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and data sets. We demonstrate the use of sampbias on a data set of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. sampbias is implemented as a well-documented, open-access and user-friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.

Impediments to Understanding Seagrasses’ Response to Global Change

“…Improvement of analytical and computational tools is an important priority for handling the analyses for large-scale comparative analyses of seagrass species. For instance, the US National Science Foundation-funded software BiotaPhy facilitates integration, data collection and analysis by connecting to existing data repositories such as the Open Tree of Life, iDigBio, and Lifemapper (BiotaPhy, 2020), whereas the open-source package sampbias allows quantification of geographic sampling biases in species distribution data (Zizka et al, 2020). The R software package phyloregion -designed for biogeographic regionalization and macroecology -can overcome some computational challenges (Daru et al, 2020b).…”

An Orchid in Retrograde: Climate-Driven Range Shift Patterns of Ophrys helenae in Greece

“…The environmental filtering procedure can improve model performance [82] and was based on the representative and uncorrelated environmental variables occurring in the study area (see environmental data below) following [82]. Finally, we evaluated whether any geographical sampling bias existed in our species occurrence data by comparing the statistical distance distribution observed in our dataset to a simulated distribution expected under random sampling via the 'sampbias' 1.0.4 [83] R package.…”

Estimation of Current and Future Suitable Areas for Tapirus pinchaque in Ecuador

“…This consisted of the following steps: (1) removing duplicate records, (2) verifying records with geographic inconsistencies, and (3) reducing areas with a high density of records, linked to oversampling close to accessible areas (settlements, roads, rivers, etc.) [44,45], in order to mitigate spatial autocorrelation and overfitting in the models [46,47]. This process was performed using the spThin package in R [48], managing a minimum distance of 1 km between each record.…”