Investigating the potential of social media and citizen science data to track changes in species' distributions

Exploring the potential for online data sources to enhance species threat mapping through the case study of global bat exploitation

Expanding digital data sources, including social media and online news, provide a low-cost way to examine human-nature interactions, such as wildlife exploitation. However, the extent to which using such data sources can expand or bias understanding of the distribution and intensity of threats has not been comprehensively assessed. To address this gap, we quantified the geographical and temporal distribution of online sources documenting the hunting and trapping, consumption, or trade of bats (Chiroptera) and compared these with the distribution of studies obtained from a systematic literature search and species listed as threatened by exploitation on the International Union for Conservation of Nature Red List. Online records were collected using automated searches of Facebook, Twitter, Google, and Bing and were filtered using machine classification. This yielded 953 relevant social media posts and web pages, encompassing 1099 unique records of bat exploitation from 84 countries. Although the number of records per country was significantly predicted by the number of academic studies per country, online records provided additional locations and more recent records of bat exploitation, including 22 countries not present in academic literature. This demonstrates the value of online resources in providing more complete geographical representation. However, confounding variables can bias the analysis of spatiotemporal trends. Online bat exploitation records showed peaks in 2020 and 2014, after accounting for increases in internet users through time. The second of these peaks could be attributed to the COVID-19 outbreak, and speculation about the role of bats in its epidemiology, rather than to true changes in exploitation. Overall, our results showed that data from online sources provide additional knowledge on the global extent of wildlife exploitation, which could be used to identify early warnings of emerging threats and pinpoint locations for further research.

Using social media records to inform conservation planning

Citizen science plays a crucial role in helping monitor biodiversity and inform conservation. With the widespread use of smartphones, many people share biodiversity information on social media, but this information is still not widely used in conservation. Focusing on Bangladesh, a tropical megadiverse and mega-populated country, we examined the importance of social media records in conservation decision-making. We collated species distribution records for birds and butterflies from Facebook and Global Biodiversity Information Facility (GBIF), grouped them into GBIF-only and combined GBIF and Facebook data, and investigated the differences in identifying critical conservation areas. Adding Facebook data to GBIF data improved the accuracy of systematic conservation planning assessments by identifying additional important conservation areas in the northwest, southeast, and central parts of Bangladesh, extending priority conservation areas by 4,000-10,000 km2 . Community efforts are needed to drive the implementation of the ambitious Kunming-Montreal Global Biodiversity Framework targets, especially in megadiverse tropical countries with a lack of reliable and up-to-date species distribution data. We highlight that conservation planning can be enhanced by including available data gathered from social media platforms.

Social media data for biodiversity conservation

Systematic conservation planning is considered best practice for identifying priority areas, but applications remain limited where biodiversity data are insufficient. In a recent article, Chowdhury et al. tap into citizen scientists via Facebook to address this gap in Bangladesh. Here, I discuss the importance of their demonstrated pipeline, from data acquisition to conservation prioritisation.

A comprehensive database of squirrel distribution and occurrence in South Asia

Background

The Squirrels of South Asia (SOSA) database compiles comprehensive distribution and occurrence information on all squirrel species that occur in this region (34 species). These 34 squirrel species, including tree, flying and ground squirrels, represent 14% of global sciurid diversity. The database collates curated data from various sources such as museums, literature, primary fieldwork, citizen science and social media platforms and covers the entire distributional ranges of the target species, including countries in Central Asia and Southeast Asia when required. The SOSA database enhances our understanding of squirrel distribution, population dynamics and their conservation needs in South Asia by consolidating information. It aims to be a valuable resource for researchers, conservationists and wildlife enthusiasts.

New information

As of March 2023, the database comprises over 40,000 records of 34 species in over 30 countries globally. Spending an average of 334 hours on each species, more than 20 data collectors put in over 10,000 hours to gather, curate and build this database. The database has resulted in novel records of species occurrence in regions and countries that are poorly represented in currently available global data repositories. The current version which has been made public via GBIF comprises of 1187 records of all 34 species across multiple sources. This is a subset of the SOSA database.