Unbiased inference of plant flowering phenology from biological recording data

Predicting the timing of ecological phenomena using dates of species occurrence records: a methodological approach and test case with mushrooms

Spatiotemporal predictions of ecological phenomena are highly useful and significant in scientific and socio-economic applications. However, the inadequate availability of ecological time-series data often impedes the development of statistical predictions. On the other hand, considerable amounts of temporally discrete biological records (commonly known as 'species occurrence records') are being stored in public databases, and often include the location and date of the observation. In this paper, we describe an approach to develop spatiotemporal predictions based on the dates and locations found in species occurrence records. The approach is based on 'time-series classification', a field of machine learning, and consists of applying a machine-learning algorithm to classify between time series representing the environmental variation that precedes the occurrence records and time series representing the full range of environmental variation that is available in the location of the records. We exemplify the application of the approach for predicting the timing of emergence of fruiting bodies of two mushroom species (Boletus edulis and Macrolepiota procera) in Europe, from 2009 to 2015. Predictions made from this approach were superior to those provided by a 'null' model representing the average seasonality of the species. Given the increased availability and information contained in species occurrence records, particularly those supplemented with photographs, the range of environmental events that could be possible to predict using this approach is vast.

How training citizen scientists affects the accuracy and precision of phenological data

Monitoring plant and animal phenology is a critical step to anticipating and predicting changes in species interactions and biodiversity. Because phenology necessarily involves frequent and repeated observations over time, citizen scientists have become a vital part of collecting phenological data. However, there is still concern over the accuracy and precision of citizen science data. It is possible that training citizen scientists can improve data quality though there are few comparisons of trained and untrained citizen scientists in the ability of each to accurately and precisely measure phenology. We assessed how three types of observers-experts, trained citizen scientists that make repeated observations, and untrained citizen scientists making once-per-year observations-differ in quantifying temporal change in flower and fruit abundance of American mountain ash trees (Sorbus americana Marsh.) and arthropods in Acadia National Park, Maine, USA. We found that trained more so than untrained citizen science observers over- or under-estimated abundances leading to precise but inaccurate characterizations of phenological patterns. Our results suggest a new type of bias induced by repeated observations: A type of learning takes place that reduces the independence of observations taken on different trees or different dates. Thus, in this and many other cases, having individuals make one-off observations of marked plants may produce data as good if not better than individuals making repeated observations. For citizen science programs related to phenology, our results underscore the importance of (a) attracting the most number of observers possible even if they only make one observation, (b) producing easy-to-use and informative data sheets, and