Weekend bias in Citizen Science data reporting: implications for phenology studies

Courter, Jason R.; Johnson, Ron J.; Stuyck, Claire M.; Lang, Brian A.; Kaiser, Evan W.

doi:10.1007/s00484-012-0598-7

Cited by 78 publications

(60 citation statements)

References 21 publications

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“…Many of the systematic biases in citizen‐science data are the same biases that occur in professionally collected data: spatially and temporally non‐random observations (biased by things such as time of day or week, weather, and human population density; eg Courter et al . ), non‐standardized capture or search effort, under‐detection of organisms (Elkinton et al . ; Crall et al .…”

Section: Techniques For Producing High‐quality Ecological Citizen‐scimentioning

confidence: 99%

Assessing data quality in citizen science

Kosmala

Wiggins

Swanson

et al. 2016

Frontiers in Ecol & Environ

666

561

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Ecological and environmental citizen‐science projects have enormous potential to advance scientific knowledge, influence policy, and guide resource management by producing datasets that would otherwise be infeasible to generate. However, this potential can only be realized if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen‐science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development, volunteer training and testing, expert validation, replication across volunteers, and statistical modeling of systematic error. Each citizen‐science dataset should therefore be judged individually, according to project design and application, and not assumed to be substandard simply because volunteers generated it.

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Section: Techniques For Producing High‐quality Ecological Citizen‐scimentioning

confidence: 99%

Assessing data quality in citizen science

Kosmala

Wiggins

Swanson

et al. 2016

Frontiers in Ecol & Environ

666

561

View full text Add to dashboard Cite

show abstract

“…roadsides; Kadmon, Farber, & Danin, 2004), or in accessible areas where biodiversity is known to be high (Prendergast, Wood, Lawton, & Eversham, 1993). weekend bias; Courter, Johnson, Stuyck, Lang, & Kaiser, 2012). Citizen science data may also suffer from temporal bias, providing more information from specific times of the year when observers are more active, when certain species are present or when they are available to conduct surveys (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Robinson

Ruiz‐Gutiérrez

Fink

2017

Diversity and Distributions

112

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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“…; Courter et al. ). The use of the photographic application on mobile phones enabled us to confirm the counted animal as a koala.…”

Section: Discussionmentioning

confidence: 97%

Distribution models for koalas in South Australia using citizen science‐collected data

Sequeira

Roetman

Daniels

et al. 2014

Ecology and Evolution

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The koala (Phascolarctos cinereus) occurs in the eucalypt forests of eastern and southern Australia and is currently threatened by habitat fragmentation, climate change, sexually transmitted diseases, and low genetic variability throughout most of its range. Using data collected during the Great Koala Count (a 1-day citizen science project in the state of South Australia), we developed generalized linear mixed-effects models to predict habitat suitability across South Australia accounting for potential errors associated with the dataset. We derived spatial environmental predictors for vegetation (based on dominant species of Eucalyptus or other vegetation), topographic water features, rain, elevation, and temperature range. We also included predictors accounting for human disturbance based on transport infrastructure (sealed and unsealed roads). We generated random pseudo-absences to account for the high prevalence bias typical of citizen-collected data. We accounted for biased sampling effort along sealed and unsealed roads by including an offset for distance to transport infrastructures. The model with the highest statistical support (wAICc ∼ 1) included all variables except rain, which was highly correlated with elevation. The same model also explained the highest deviance (61.6%), resulted in high R2(m) (76.4) and R2(c) (81.0), and had a good performance according to Cohen's κ (0.46). Cross-validation error was low (∼ 0.1). Temperature range, elevation, and rain were the best predictors of koala occurrence. Our models predict high habitat suitability in Kangaroo Island, along the Mount Lofty Ranges, and at the tips of the Eyre, Yorke and Fleurieu Peninsulas. In the highest-density region (5576 km2) of the Adelaide–Mount Lofty Ranges, a density–suitability relationship predicts a population of 113,704 (95% confidence interval: 27,685–199,723; average density = 5.0–35.8 km−2). We demonstrate the power of citizen science data for predicting species' distributions provided that the statistical approaches applied account for some uncertainties and potential biases. A future improvement to citizen science surveys to provide better data on search effort is that smartphone apps could be activated at the start of the search. The results of our models provide preliminary ranges of habitat suitability and population size for a species for which previous data have been difficult or impossible to gather otherwise.

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Weekend bias in Citizen Science data reporting: implications for phenology studies

Cited by 78 publications

References 21 publications

Assessing data quality in citizen science

Assessing data quality in citizen science

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

Distribution models for koalas in South Australia using citizen science‐collected data

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