An evaluation of stringent filtering to improve species distribution models from citizen science data

Abstract

Aim Citizen science data are increasingly used for modelling species distributions because they offer broad spatiotemporal coverage of local observations. However, such data are often collected without experimental design or set survey methods, raising the risk that bias and noise will compromise modelled predictions. We tested the ability of species distribution models (SDMs) built from these low‐structure citizen science data to match the quality of SDMs from systematically collected data and tested whether stringent data filtering improved predictions. Location Northeastern USA. Methods We evaluated models built from a rapidly growing dataset of avian occurrences reported by birders—eBird—against models built from four independent, systematically collected datasets. We developed SDMs for 96 species using both data sources and compared their predictive abilities. We also tested whether culling eBird data by applying stringent data filters on survey effort or observer expertise improved predictions. Results We found that SDMs built from low‐structure citizen science data matched or exceeded performance of SDMs from systematically collected datasets for 12%–31% of species (truex¯ = 22%), depending on the dataset. At least one culling option produced equivalent or better performance for 40%–70% of species (truex¯ = 49%). Data culling by restricting survey effort improved predictions more than restricting by observer expertise. The optimal effort restriction differed by dataset, and for three of the datasets was further informed by species traits. Main conclusions Species distribution models developed using low‐structure citizen science data sometimes performed as well as those from systematic data. Culling generally improved models, but results were heterogeneous, prohibiting clear recommendations for how to cull. Our results indicate that the growing availability of citizen science data holds potential for creating high‐quality spatial predictions, but that time should be invested in determining how best to cull datasets and that one‐size‐fits‐all solutions beyond basic outlier filtering may be hard to find.

Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias

“…The metrics and axes we have developed can be used in data analyses to account for biases in data collection created by variation in recorder behaviour. Biases can be addressed by filtering data to a less-biased subset prior to analysis 26 , and our metrics and axes provide a range of options for this approach. Studies that rely on the assumption that non-detection indicates absence, such as occupancy models, could filter users based on the recording potential axis, thereby removing participants who tend to record short lists of common species, and who have recorded a small proportion of taxa overall.…”

“…time of the observations that include measures of expertise of the observer [26][27][28][29] , measures of effort 26,28,[30][31][32][33] (e.g. duration of the survey) and survey completeness 26,28,30,[32][33][34] (i.e. did the participant recorded every species they saw?).…”

“…In some studies these variables are used to filter the data in an effort to remove biases that will hide signals of interest in the data. Survey completeness is commonly used to only include data where absences can be inferred because all species observed-within the taxonomic group of interest-were recorded 26,30,32 . It has also been shown that filtering data by a measure of effort can improve species distribution models 26 .…”

“…Survey completeness is commonly used to only include data where absences can be inferred because all species observed-within the taxonomic group of interest-were recorded 26,30,32 . It has also been shown that filtering data by a measure of effort can improve species distribution models 26 . As an addition or alternative to filtering, these metadata can be included in models to account for biases.…”

The role of passive surveillance and citizen science in plant health

“…Depending on the pest in question, validation may be as simple as using photographs provided by the observer, but often requires field visits and/or processing of laboratory samples. Validation ensures information flowing to plant health officials is accurate and is an important first step towards reliable analysis (Brown et al 2017a ; Steen et al 2019 ). Volunteered data can be most readily used when host species are readily identifiable, and symptoms are distinct (Crall et al 2011 ).…”