Mapping the landscape of automated species identification apps

Numerous automated species identification apps can be found online, free or for a fee. This proprietary landscape is both rich and dynamic, with apps coming and going, and thus its nature changes over time. To map the landscape of automated species identification apps, we used a method that mirrors a typical user's path to find an app that can help identify a specimen via a smartphone. Initial queries were made on the two predominant platforms for app acquisition, Google Playstore and Apple's App Store, using broad search terms (e.g. 'species identification AI', 'species ID AI', and 'species identifier'). These were then iteratively refined as well as expanded on with more targeted queries (e.g. 'plants', 'insects', 'birds', 'fish' and 'mushrooms') in response to the landscape that slowly became visible and to ensure a thorough cataloguing. During this process, we also came across adjacent topics such as 'rocks', 'plant diseases and care', as well as ‘pets’. To ensure that no important natural history apps were missed, we included these topics in our searches, and as they were part of our findings, we included them in the final dataset.