Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media

Benjamin M Marshall,Colin T Strine

doi:10.7717/peerj.8059

Abstract

A species’ distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated (1) occurrence data of a cryptic group of animals: non-marine snakes, in a biodiversity database (Global Biodiversity Information Facility (GBIF)) and determined (2) whether incidental occurrence records extracted from geo-tagged social media images (Flickr) could improve distribution models for 18 tropical snake species. We provide R code to search for and extract data from images using Flickr’s API. We show the biodiversity database’s 302,386 records disproportionately originate from North America, Europe and Oceania (250,063, 82.7%), with substantial gaps in tropical areas that host the highest snake diversity. North America, Europe and Oceania averaged several hundred records per species; whereas Asia, Africa and South America averaged less than 35 per species. Occurrence density showed similar patterns; Asia, Africa and South America have roughly ten-fold fewer records per 100 km2than other regions. Social media provided 44,687 potential records. However, including them in distribution models only marginally impacted niche estimations; niche overlap indices were consistently over 0.9. Similarly, we show negligible differences in Maxent model performance between models trained using GBIF-only and Flickr-supplemented datasets. Model performance appeared dependent on species, rather than number of occurrences or training dataset. We suggest that for tropical snakes, accessible social media currently fails to deliver appreciable benefits for estimating species distributions; but due to the variation between species and the rapid growth in social media data, may still be worth considering in future contexts.

Highlights

Species distribution models can yield insight into a species’ niche and habitat (Santos et al, 2006)
We describe the current state of snake occurrence records in the Global Biodiversity Information Facility (GBIF) database, highlighting gaps in surveying
As an additional measure of the Flickr data’s contribution to models, we examined the niche overlap between models trained on only GBIF records and those trained on datasets supplemented with Flickr occurrences

Summary

Introduction

Species distribution models can yield insight into a species’ niche and habitat (Santos et al, 2006). Information on a species’ niche provides some ability to predict species’. Predictions from species distribution models can inform protected area allocation (Tulloch et al, 2016), support conservation status assessments (Solano & Feria, 2007; Fourcade et al, 2013), invasion potential (Pearson, 2015; Mutascio et al, 2018; with complications Phillips, Chipperfield & Kearney, 2008) and identify potential human-wildlife conflicts (Yañez-Arenas et al, 2014). The utility of a species distribution model is dependent on the underlying species occurrence data used in constructing the model. Gaps and incomplete data can lead to misidentifying the target species’ niche (Monsarrat et al, 2019). Ways to mitigate data gaps need thorough investigation

Methods

Results

Discussion

Conclusion