Abstract
Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.
Highlights
Species distribution models have become a widely used tool in ecology and have tackled diverse scientific issues at different spatial and temporal scales in recent years [1,2,3]
The fundamental theory behind species distribution models (SDMs, hereafter) assumes that the presence of a species in a given location depends on the environment, which implies that ecologists are able to estimate past, current, or future species distributions based on the environmental characteristics of unsurveyed locations [3, 5, 7, 8]
Our analysis indicates that the use of citizen science (CS) data in the peer-reviewed SDM literature has increased in frequency over the past 10 years (Fig 2a)
Summary
Species distribution models have become a widely used tool in ecology and have tackled diverse scientific issues at different spatial and temporal scales in recent years [1,2,3]. Current global-scale issues such as climate and land-use changes have increased the need to be able to predict the distribution of migratory or invasive species across a landscape. SDMs link information about the presence of a species to the environmental variables of their known locations, and apply statistical models to predict the spatial distribution of species [4, 5, 9]. Species distribution models are widely used in both fundamental science and applied sciences in biogeography, evolution, dispersal, migration, species invasion, meta-population, conservation, and climate change [3]. SDMs have shown their value to assess species invasions [11], to predict spatial changes in response to climate change or land-use changes [12,13,14], or to assess the suitability of possible conservation areas [15,16,17]
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