Outlier detection methods to improve the quality of citizen science data.

Citizen science involves public participation in research, usually through volunteer observation and reporting. Data collected by citizen scientists are a valuable resource in many fields of research that require long-term observations at large geographic scales. However, such data may be perceived as less accurate than those collected by trained professionals. Here, we analyze the quality of data from a plant phenology network, which tracks biological response to climate change. We apply five algorithms designed to detect outlier observations or inconsistent observers. These methods rely on different quantitative approaches, including residuals of linear models, correlations among observers, deviations from multivariate clusters, and percentile-based outlier removal. We evaluated these methods by comparing the resulting cleaned datasets in terms of time series means, spatial data coverage, and spatial autocorrelations after outlier removal. Spatial autocorrelations were used to determine the efficacy of outlier removal, as they are expected to increase if outliers and inconsistent observations are successfully removed. All data cleaning methods resulted in better Moran's I autocorrelation statistics, with percentile-based outlier removal and the clustering method showing the greatest improvement. Methods based on residual analysis of linear models had the strongest impact on the final bloom time mean estimates, but were among the weakest based on autocorrelation analysis. Removing entire sets of observations from potentially unreliable observers proved least effective. In conclusion, percentile-based outlier removal emerges as a simple and effective method to improve reliability of citizen science phenology observations.

Plant phenology networks of citizen scientists: recommendations from two decades of experience in Canada.

Plant phenology networks of citizen scientists have a long history and have recently contributed to our understanding of climate change effects on ecosystems. This paper describes the development of the Alberta and Canada PlantWatch programs, which coordinate networks of citizen scientists who track spring development timing for common plants. Tracking spring phenology is highly suited to volunteers and, with effective volunteer management, observers will stay loyal to a phenology program for many years. Over two decades beginning in 1987, Alberta PlantWatch volunteers reported 47,000 records, the majority contributed by observers who participated for more than 9 years. We present a quantitative analysis of factors that determine the quality of this phenological data and explore sources of variation. Our goal is to help those who wish to initiate new observer networks with an analysis of the effectiveness of program protocols including selected plant species and bloom stages.

Plant phenology in western Canada: trends and links to the view from space.

One feature of climate change is the trends to earlier spring onset in many north temperate areas of the world. The timing of spring flowering and leafing of perennial plants is largely controlled by temperature accumulation; both temperature and phenological records illustrate changes in recent decades. Phenology studies date back over a century, with extensive databases existing for western Canada. Earlier spring flowering has been noted for many woody plants, with larger trends seen for species that develop at spring's start. Implications for ecosystems of trends to earlier spring arrival include changes in plant species composition, changes in timing and distribution of pests and disease, and potentially disrupted ecological interactions. While Alberta has extensive phenology databases (for species, years, and geographic coverage) for recent decades, these data cannot provide continuous ground coverage. There is great potential for phenological data to provide ground validation for satellite imagery interpretation, especially as new remote sensors are becoming available. Phenological networks are experiencing a resurgence of interest in Canada (www.plantwatch.ca) and globally, and linking these ground-based observations with the view from space will greatly enhance our capacity to track the biotic response to climate changes.