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• W2894773080 abstract "Abstract Spatiotemporal forecasts of ecological phenomena are highly useful and significant in scientific and socio-economic applications. Nevertheless, developing the correlative models to make these forecasts is often stalled by the inadequate availability of the ecological time-series data. On the contrary, considerable amounts of temporally discrete biological records are being stored in public databases, and often include the sites and dates of the observation. While these data are reasonably suitable for the development of spatiotemporal forecast models, this possibility remains mostly untested. In this paper, we test an approach to develop spatiotemporal forecasts based on the dates and locations found in species occurrence records. This approach is based on ‘time-series classification’, a field of machine learning, and involves the application of a machine-learning algorithm to classify between time-series representing the environmental conditions that precede the occurrence records and time-series representing other environmental conditions, such as those that generally occur in the sites of the records. We employed this framework to predict the timing of emergence of fruiting bodies of two mushroom species ( Boletus edulis and Macrolepiota procera ) in countries of Europe, from 2009 to 2015. We compared the predictions from this approach with those from a ‘null’ model, based on the calendar dates of the records. Forecasts made from the environmental-based approach were consistently superior to those drawn from the date-based approach, averaging an area under the receiver operating characteristic curve (AUC) of 0.9 for B. edulis and 0.88 for M. procera , compared to an average AUC of 0.83 achieved by the null models for both species. Prediction errors were distributed across the study area and along the years, lending support to the spatiotemporal representativeness of the values of accuracy measured. Our approach, based on species occurrence records, was able to provide useful forecasts of the timing of emergence of two mushroom species across Europe. Given the increased availability and information contained in this type of records, particularly those supplemented with photographs, the range of events that could be possible to forecast is vast." @default.
• W2894773080 created "2018-10-12" @default.
• W2894773080 creator A5043058259 @default.
• W2894773080 date "2018-10-05" @default.
• W2894773080 modified "2023-09-23" @default.
• W2894773080 title "A machine learning approach for the spatiotemporal forecasting of ecological phenomena using dates of species occurrence records" @default.
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• W2894773080 doi "https://doi.org/10.1101/435289" @default.
• W2894773080 hasPublicationYear "2018" @default.
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