Citizen science participant motivations and behaviour: Implications for biodiversity data coverage

Thompson, Maureen M.; Moon, Katie; Woods, Adam; Rowley, Jodi J. L.; Poore, Alistair G. B.; Kingsford, Richard T.; Callaghan, Corey T.

doi:10.1016/j.biocon.2023.110079

Cited by 10 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The engagement of citizen science recorders in adaptive sampling schemes is likely to be an important determinant of its effectiveness (Callaghan et al, 2023) and will impact spatial coverage and representativeness (Pocock et al, 2023). Fortunately, studies have found that recorder motivations often align with the goal of sampling schemes (Thompson et al, 2023), suggesting that citizen scientist-based adaptive sampling could be a very powerful tool (Callaghan et al, 2023;Thompson et al, 2023). In our simulations, SDMs using citizen science datasets would benefit even if only 1% of data were sampled adaptively.…”

Section: F I G U R Ementioning

confidence: 87%

“…To do this, it is key that future simulation work engages with recorders, to implement and assess real-world adaptive recording activities. Such projects could be very successful given the desire of recorders to help with conservation focused wildlife recording (Callaghan et al, 2023;Thompson et al, 2023). Adaptive sampling clearly has a large amount of potential for improving citizen science datasets (Callaghan et al, 2023;Kays et al, 2021) and more work is needed to determine whether and how it can be exploited to address existing biases Isaac & Pocock, 2015).…”

Section: Con Clus Ionsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

Mondain‐Monval,

Pocock,

Rolph

et al. 2024

Methods Ecol Evol

View full text Add to dashboard Cite

Volunteer recorders generate large amounts of biodiversity data through citizen science which is used in conservation planning and policy decision‐making. Unstructured sampling, where the volunteer can record what they want, where they want, leads to spatial unevenness in these data. While there are many statistical techniques to account for the resulting biases, it may be possible to improve datasets by directing a subset of recorders to sample in the most informative locations, known as adaptive sampling. We investigated the potential for adaptive sampling to improve the performance of species distribution models built on citizen science data using simulated ecological communities. We simulated ecological assemblages across Great Britain based on current butterfly data and modelled the distributions of each species. We then simulated the sampling of new data based on five adaptive sampling methods (one empirical method based simply on gap‐filling, and four model‐based methods using various measures from the model outputs) and one non‐adaptive method (a method in which recording continued in the current pattern), and re‐ran the species distribution models. In these, we also varied the rate of recording effort that was distributed according to adaptive sampling. The model predictions using the original and adaptively sampled data were compared to true species distributions to evaluate the performance of each method. We found that all adaptive sampling approaches improved model performance, with greatest improvement for model‐based approaches compared to the empirical sampling method (i.e. simple gap‐filling). All four model‐based adaptive sampling approaches provided similar benefits for model outputs. Improvements in model performance were greatest when the amount of adaptive sampling changed from no uptake to 1% uptake, indicating that only a small amount of change in recorder behaviour is needed to improve model performance. Directing volunteer recorders to places where records are most needed, based on information from model outputs, can improve species distribution models built on citizen science data, even with minimal uptake of suggested locations. Our results therefore suggest that adaptive sampling by recorders could be beneficial for real‐world citizen science datasets.

show abstract

Section: F I G U R Ementioning

confidence: 87%

Section: Con Clus Ionsmentioning

confidence: 99%

Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

Mondain‐Monval,

Pocock,

Rolph

et al. 2024

Methods Ecol Evol

View full text Add to dashboard Cite

show abstract

“…2019) and communicating spatial and temporal priorities to participants could be used to further optimise data collection (Callaghan et al . 2023; Thompson et al . 2023), including for invasive species monitoring.…”

Section: Discussionmentioning

confidence: 99%

Tracking the spread of the eastern dwarf tree frog (Litoria fallax) in Australia using citizen science

Rowley

Callaghan

2023

Aust. J. Zool.

Self Cite

View full text Add to dashboard Cite

An increasing number of species are establishing populations outside of their native ranges, often with negative ecological and economic impacts. The detection and surveillance of invasive species presents a huge logistical challenge, given the large spatial regions in which new populations can appear. However, data collected through citizen science projects are increasingly recognised as a valuable source for detection and monitoring of invasive species. We use data from a national citizen science project, FrogID, to quantify the spread of the eastern dwarf tree frog (Litoria fallax) outside its historical native range in Australia. Of 48 012 records of L. fallax in the FrogID database, 485 were located far outside the historical native range of the species. L. fallax has established geographically large populations hundreds of kilometres away from its native range, and these appear to be spreading in extent over time. These populations have resulted in novel species co-occurrences, with L. fallax now co-occurring with at least two frog species not present in their native range. Although the impacts of the invasive populations of L. fallax remain unknown, our work highlights the value in leveraging citizen science projects to detect and monitor native species that can become invasive far outside their historical range.

show abstract

“…The order of these filters is not mutually exclusive, but the end result is a less diverse, publicly reported pool of species that is skewed due to social-ecological biases that inhibit the collection of all species. circumstances-for example, when recreating in green spaces or alongside a trail/road versus commuting through grey spaces (Lopez et al, 2020), or choosing where to sample based on proximity to home (Thompson et al, 2023). This sampling preference for greenspaces creates additional spatial biases globally, with wealthier areas typically having more greenspaces (Chen et al, 2022;Rigolon et al, 2018).…”

Section: Box 1 Terms and Definitionsmentioning

confidence: 99%

A framework for contextualizing social‐ecological biases in contributory science data

Carlen,

Estien,

Caspi

et al. 2024

People and Nature

View full text Add to dashboard Cite

Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts. The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases. We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation, detectability, sampling and preference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data. Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

show abstract

Citizen science participant motivations and behaviour: Implications for biodiversity data coverage

Cited by 10 publications

References 41 publications

Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

Adaptive sampling by citizen scientists improves species distribution model performance: A simulation study

Tracking the spread of the eastern dwarf tree frog (Litoria fallax) in Australia using citizen science

A framework for contextualizing social‐ecological biases in contributory science data

Contact Info

Product

Resources

About