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

Abstract

15Geo-referenced species occurrences from public databases have become essential to biodiversity 16 research and conservation. However, geographical biases are widely recognized as a factor 17 limiting the usefulness of such data for understanding species diversity and distribution. In 18 particular, differences in sampling intensity across a landscape due to differences in human 19 accessibility are ubiquitous but may differ in strength among taxonomic groups and datasets. 20Although several factors have been described to influence human access (such as presence of 21 roads, rivers, airports and cities), quantifying their specific and combined effects on recorded 22 occurrence data remains challenging. Here we present sampbias, an algorithm and software 23 for quantifying the effect of accessibility biases in species occurrence datasets. Sampbias uses 24 a Bayesian approach to estimate how sampling rates vary as a function of proximity to one 25 or multiple bias factors. The results are comparable among bias factors and datasets. We 26 demonstrate the use of sampbias on a dataset of mammal occurrences from the island of 27 Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. 28 Sampbias is implemented as a well-documented, open-access and user-friendly R package 29 that we hope will become a standard tool for anyone working with species occurrences in 30 ecology, evolution, conservation and related fields.31

Plant Diversity Patterns and Conservation Implications under Climate-Change Scenarios in the Mediterranean: The Case of Crete (Aegean, Greece)

“…This data cleaning and organising procedure followed the protocols as set out in [48], and we used the "biogeo" 1.0 [47] and "spThin" 0.1.0 [46] R packages. We also 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" 0.1.1 [49] R package.…”

Plant and fungal collections: Current status, future perspectives

“…There are taxonomic, spatial and temporal biases, uncertainties and errors related to the data already mobilized (Daru et al., 2018; Meineke et al., 2018; Meyer et al., 2016; Nekola, Hutchins, Schofield, Najev, & Perez, 2019; Zizka, Antonelli, & Silvestro, 2020). In order to explore the taxonomic biases, we considered coverage of vascular plants, bryophytes, and fungi at different taxonomic ranks (Figure 5a).…”

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.…”