Blog written by Emilie Roy-Dufresne. Read the full paper here.
Where are they and where could they go? Those are the main two questions asked by conservation scientists when it comes to invasive species. Originally from other regions and introduced accidentally or intentionally for recreation and commercial purposes, introduced species often migrate into new areas where environmental conditions differ from their native range. When these new environmental conditions are favourable, introduced species can thrive in their new habitats and become invasive. In most cases, invasive species upset the ecological balance of their new host ecosystems, resulting in the extinction of native species and a myriad of socio-economic impacts. Understanding how new environmental conditions can affect an invasive species’ population dynamics and distribution are critical so that resources can be deployed to the right time and place to support effective management and environmental protection.
Many scientific approaches are used to characterise invasive species’ distributions and their interaction with their new host environments, with monitoring and field experiments the most commonly understood and applied. While field studies are invaluable resources of detailed ecological information regard invasive species, their application at large scale can be difficult as they are very time consuming and costly, leading to delays in efficient and effective management actions. An alternative, pro-active approach is to use statistical models known as correlative-SDMs (short for Species Distribution Models), to predict where in a new habitat an invasive species may establish and persist. These sites can then be targeted by landscape managers for control or eradication programs.
Correlative-SDMs are powerful and flexible methods, as they only require known presence and absence locations of the species, and information on the environmental conditions recorded at these sites (e.g. maximum and minimum temperature, rainfall, index of green vegetation, etc.). This information is statistically analysed to generate maps of ‘environmental niche preference’ for the species – that is, areas where the species is likely to establish and persist. A regional comparison can then be used to identify regions with the highest potential risk of being invaded.
Correlative-SDMs are heavily reliant on good data to make good predictions. The best models are built from data covering the entire range of environmental conditions suitable for any given species. While collecting these data is not as complex as directly monitoring a species abundance, it remains challenging because invasive species can be widely distributed in their non-native habitat, and so data may be missing from regions which have not been surveyed due to logistical or financial constraints. One way to overcome this data limitation is to supplement the data collected by experts with data collected by volunteer citizen scientists (e.g. through phone apps device).
In our study, we used the case of the European rabbit (Oryctolagus cuniculus) in Australia to explore the advantages and disadvantages associated with the use of citizen science data within correlative-SDMs. The European rabbit (Oryctolagus cuniculus) was introduced into Australia in 1788. Rabbits are now considered a significant pest of agricultural and environmental ecosystems, being listed as a Key Threatening Process to Australian ecosystems and biodiversity in 1999. They compete with native fauna and local livestock by overgrazing both native and introduced plants, which can lead to soil erosion. In the past 50 years, the presence and abundance of rabbits have been monitored through extended management programs led by expert scientists across Australia (Roy-Dufresne et al., 2019). In 2009, a citizen science app was developed as a vigilance program for the rabbit (Feral Scan Data, 2016). This allowed us the opportunity to investigate the pros and cons associated with the use of citizen science data within correlative-SDMs, by comparing the models’ performance when expert and citizen science data were used separately or together.
We found that there were massive advantages to using both expert and citizen science datasets when formulating correlative-SDMs, with great improvements in model performance. Addition of the citizen science dataset doubled the spatial coverage of expert-only derived occurrence data used to build our models – adding an additional total area equivalent to a third of the total landmass of Australia. On top of this boost in data coverage, citizen science data also provided new and critical information on the environmental conditions associated with the ecological niche of rabbits in Australia. Together, these insights drastically improved model performance and reliability, and highlighted the invaluable value-add of using citizen science when studying and managing invasive species.
Citizen science can therefore be a crucial value-adding component to the development and implementation of more effective monitoring programs for invasive species on a national scale. Our work shows that collaborations between experts and citizen scientists can be a valuable tool, when addressing data deficits associated with low levels of monitoring in areas which are difficult and costly to access. Although it remains important that fieldwork led by experts persists, especially when evaluating the accuracy of data collected by citizen scientists and the level to which the invasive species impact their host habitats, directing the activities of citizen scientists towards areas with missing information can provide an effective solution to collect data quickly and cheaply so that management decisions can occur on a more productive and effective timescale. Frameworks and networks such as the one we used can easily be extended to further other collaborative actions. For example, the collection of dead rabbit carcasses by citizen scientists can help scientists to survey more widely in order to understand diseases dynamics in rabbit populations across Australia, data which can then be used to understand and improve the effectiveness of biological control programs across the country.
In an attempt to improve the data accessibility in science, we published our complete datasets in an open online publication (Roy-Dufresne et al., 2019).
Feral Scan Data. (2016). https://www.feralscan.org.au/
Roy-Dufresne, E., Lurgi, M., Brown, S. C., Wells, K., Cooke, B., Mutze, G., … Fordham, D. A. (2019). The Australian National Rabbit Database: 50 yr of population monitoring of an invasive species. Ecology, 100, e02750.