Behavior Identification in Two-Stage Games for Incentivizing Citizen Science Exploration

Xue, Yexiang; Davies, Ian; Fink, Daniel; Wood, Christopher; Gomes, Carla P.

doi:10.1007/978-3-319-44953-1_44

Cited by 9 publications

(26 citation statements)

References 17 publications

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“…The TNC point counts acted as targeted surveys in undersurveyed habitat, which previous work has shown can improve the accuracy of distribution models using eBird checklists (e.g. Xue et al 2016). The complementary nature of the datasets is also shown when examining the important predictors.…”

Section: Discussionmentioning

confidence: 99%

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Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Robinson

Ruiz‐Gutiérrez

Reynolds³

et al. 2019

Preprint

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AbstractInformation on species’ habitat associations and distributions, across a wide range of spatial and temporal scales, are a fundamental source of ecological knowledge. However, collecting biological information at relevant scales if often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen-science data has been signaled as an increasingly important source of biological information needed to fill in data gaps needed to make more comprehensive and robust inferences on species distributions. However, there are perceived trade-offs of combining highly structured, scientific survey data with largely unstructured, citizen-science data. As a result, the focus of most methodological advances to combine these sources of information has been on treating these sources as independent. The degree to which each source of information is allowed to directly inform a common underlying process (e.g. species distribution) depends on the perceived quality of the data. In this paper, we explore these trade-offs by applying a simplified approach of filtering citizen-science data to resemble structured survey data, and analyze both sources of data under a common framework. To accomplish this, we explored ways of integrating high-resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality. The integration of survey data with the filtered citizen-science data in eBird resulted in improved inference and predictive ability, and increased the extent and accuracy of inferences on shorebirds for the Central Valley. The structured surveys were found to improve the overall accuracy of ecological inference based only on citizen-science data, by increasing the representation of data collected from high quality habitats for shorebirds (e.g. rice fields). The practical approach we have shown for data integration can be also be used to improve the efficiency of designing biological surveys in the context of larger, citizen-science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple processing and filtering method we present can be used to integrate other types of data (e.g. camera traps) with more localized efforts (e.g. research projects), ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.

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Section: Discussionmentioning

confidence: 99%

“…However, even citizenscience datasets that do collect effort information are often lacking information on sampling locations, although incentivizing participants to collect data in these data-poor locations has been shown to improve the accuracy of distribution models (e.g. avicaching; Xue et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Robinson

Ruiz‐Gutiérrez

Reynolds³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Avicaching; Xue, Davies, Fink, Wood, & Gomes, 2016). One way to decrease this temporal bias in the data would be to incentivize eBird users to collect more checklists during the winter (e.g.…”

Section: Brier Scorementioning

confidence: 99%

“…One way to decrease this temporal bias in the data would be to incentivize eBird users to collect more checklists during the winter (e.g. Avicaching; Xue, Davies, Fink, Wood, & Gomes, 2016). Xue et al (2016) showed that participants in citizen science could be influenced to collect data in under-sampled areas and that the data collected produced more accurate distribution maps than those based on data that were not part of the incentivized study.…”

Section: Kappamentioning

confidence: 99%

Correcting for bias in distribution modelling for rare species using citizen science data

Robinson

Ruiz‐Gutiérrez

Fink

2017

Diversity and Distributions

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123

142

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Aim:To improve the accuracy of inferences on habitat associations and distribution patterns of rare species by combining machine-learning, spatial filtering and resampling to address class imbalance and spatial bias of large volumes of citizen science data. Innovation:Modelling rare species' distributions is a pressing challenge for conservation and applied research. Often, a large number of surveys are required before enough detections occur to model distributions of rare species accurately, resulting in a data set with a high proportion of non-detections (i.e. class imbalance). Citizen science data can provide a cost-effective source of surveys but likely suffer from class imbalance.Citizen science data also suffer from spatial bias, likely from preferential sampling. To correct for class imbalance and spatial bias, we used spatial filtering to under-sample the majority class (non-detection) while maintaining all of the limited information from the minority class (detection). We investigated the use of spatial under-sampling with randomForest models and compared it to common approaches used for imbalanced data, the synthetic minority oversampling technique (SMOTE), weighted random forest and balanced random forest models. Model accuracy was assessed using kappa, Brier score and AUC. We demonstrate the method by evaluating habitat associations and seasonal distribution patterns using citizen science data for a rare species, the tricoloured blackbird (Agelaius tricolor).Main Conclusions: Spatial under-sampling increased the accuracy of each model and outperformed the approach typically used to direct under-sampling in the SMOTE algorithm. Our approach is the first to characterize winter distribution and movement of tricoloured blackbirds. Our results show that tricoloured blackbirds are positively associated with grassland, pasture and wetland habitats, and negatively associated with high elevations or evergreen forests during both winter and breeding seasons. The seasonal differences in distribution indicate that individuals move to the coast during the winter, as suggested by historical accounts. K E Y W O R D Scitizen science, class imbalance, random forest, spatial bias, species distribution model, tricoloured blackbird | 461 ROBINSON et al.

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“…By motivating citizens with rewards 1 to perform the most crucial tasks, eBird organizers witnessed a promising reduction in spatial clustering: ≈ 20% of birdwatching effort shifted from popular to under-sampled locations during a 3-month pilot study in upstate New York, USA [31]. For allocating rewards in Avicaching, Xue et al folded the agents' reasoning process (termed "identification problem") as linear constraints into the reward allocation scheme ("pricing problem"), which they solved using an off-the-shelf Mixed-Integer Programming (MIP) solver [32]. However, this MIP formulation scales poorly as the number of locations in the game increases, hampering the technique's use in large citizen science programs.…”

Section: Introductionmentioning

confidence: 99%

GPU-accelerated principal-agent game for scalable citizen science

Kabra

Xue

Gomes

2019

Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies

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Citizen science programs have been instrumental in boosting sustainability projects, large-scale scientific discovery, and crowdsourced experimentation. Nevertheless, these programs witness challenges in submissions' quality, such as sampling bias resulting from citizens' preferences to complete some tasks over others. The sampling bias frequently manifests itself in the program's dataset as spatially clustered submissions, which reduce the efficacy of the dataset for subsequent scientific studies. To address the spatial clustering problem, programs use reward schemes obtained from game-theoretical models to incentivize citizens to perform tasks that are more meaningful from a scientific point of view. Herein we propose a GPU-accelerated approach for the Avicaching game, which was recently introduced by the eBird citizen science program to incentivize birdwatchers to collect bird data from under-sampled locations. Avicaching is a Principal-Agent game, in which the principal corresponds to the citizen science program (eBird) and the agents to the birdwatchers or citizen scientists. Previous approaches for solving the Avicaching game used approximations based on mixed-integer programming and knapsack algorithms combined with learning algorithms, using standard CPU hardware. Following the recent advances in scalable deep learning and parallel computation on Graphical Processing Units (GPUs), we propose a novel approach to solve the Avicaching game, which takes advantage of neural networks and parallelism for large-scale games. We demonstrate that our approach better captures agents' behavior, which allows better learning and more effective incentive distribution in a real-world bird observation dataset. Our approach also allows for massive speedups using GPUs. As Avicaching is representative of games that are aimed at reducing spatial clustering in citizen science programs, our scalable reformulation for Avicaching enables citizen science programs to tackle sampling bias and improve submission quality on a large scale. CCS CONCEPTS • Computing methodologies → Neural networks; • Applied computing → Decision analysis.

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Behavior Identification in Two-Stage Games for Incentivizing Citizen Science Exploration

Cited by 9 publications

References 17 publications

Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Correcting for bias in distribution modelling for rare species using citizen science data

GPU-accelerated principal-agent game for scalable citizen science

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