Innovative tools and technologies for improving biodiversity surveys using citizen science

Abstract

This thesis advances knowledge of biodiversity monitoring using citizen science and demonstrates the potential of innovative tools and technology to improve the data generated by citizen science to inform species conservation and ecosystem management. Biodiversity around the world is in crisis and there are many challenges. Australian biodiversity is declining rapidly, with the worst mammal extinction rate in the world. Climate change, pollution, land clearing and other anthropogenic pressures are increasing and exacerbating pressures on ecosystems and wildlife. Biodiversity monitoring is crucial to inform us as to the current state and trends of ecosystems. Resources are limited for traditional scientific monitoring, thus other efficient and effective methods are being sought to augment biodiversity conservation research and management. Effective management solutions require stakeholder engagement, so community participation is one key part of solving this crisis. Citizen science is seen as part of the solution by engaging citizens in local actions that contribute to local and global improved outcomes. However, data contributed by citizen scientists are often seen as biased in space and time, and lacking in essential metadata, such as accurate effort data. The aim of this thesis was to investigate and develop methods to enhance data collected by citizen scientists to improve wildlife monitoring. The objectives were to: 1. assess the potential of automatic collection of key monitoring metadata, such as species location and observer search paths, to enable more accurate assessments of observer effort and species absence; 2. increase knowledge on population distribution and abundance of an iconic Australian mammal species using citizen science and compare spatial coverage of this monitoring to traditional observations, using protected areas and geographic remoteness indicators; 3. assess how CS monitoring performed compared to other forms of monitoring when faced with major disruptions to community activities and movements caused by a global pandemic. These objectives were addressed through three component studies. Firstly, a mobile app was developed which automatically recorded accurate metadata for each observation. Extra information about participants' search effort, including time taken and search path followed, was also automatically recorded. This app was used for a citizen science event to gather information about koala (Phascolarctos cinereus) populations and their habitats in South Australia. Results showed that recording of observations, search effort and search path data was accurate and useful for both species population assessment and management of citizen science monitoring. For objective two, a mobile app was developed to enable citizen scientists across Australia to record observational data and improve knowledge on the iconic short-beaked echidna (Tachyglossus aculeatus). Widespread participation over three years more than doubled observation counts across the continent compared to contemporary scientific observations from national and state repositories, while geographic coverage was similar, except for in some highly protected areas and very remote areas. Finally, citizen science observational data for short-beaked echidna were compared to data from three biodiversity data repositories and demonstrated that citizen science monitoring was resilient to the effects of restrictions on community activities while other forms of monitoring were significantly reduced under harsh restrictions and more concentrated in highly protected areas than usual. This thesis contributes towards efforts to understand and improve citizen science data for monitoring wildlife and biodiversity by enhancing data collection methods. The automatic collection of citizen scientist search paths and effort provides key information about where monitoring has occurred, even without observations being recorded. This is vital information for both modelling species populations and distribution and also for improved management of citizen science monitoring. Baseline echidna population distribution and abundance information has been improved across Australia and will help determine future population trends. This also contributes to our understanding of spatial biases of citizen science and scientific monitoring. Demonstrating the robustness of citizen science monitoring to disruptions caused by restrictions to community activity provides further important knowledge for assessing effective monitoring methods, particularly in light of the current pandemic and ongoing climate change effects. This knowledge will inform management of both CS and scientific biodiversity monitoring and further improve methods for biodiversity conservation in Australia and around the world.