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USA National Phenology Network’s volunteer-contributed observations yield predictive models of phenological transitions

PLoS ONE

By:
, , , , and
https://doi.org/10.1371/journal.pone.0182919

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Abstract

In support of science and society, the USA National Phenology Network (USA-NPN) maintains a rapidly growing, continental-scale, species-rich dataset of plant and animal phenology observations that with over 10 million records is the largest such database in the United States. Contributed voluntarily by professional and citizen scientists, these opportunistically collected observations are characterized by spatial clustering, inconsistent spatial and temporal sampling, and short temporal depth. We explore the potential for developing models of phenophase transitions suitable for use at the continental scale, which could be applied to a wide range of resource management contexts. We constructed predictive models of the onset of breaking leaf buds, leaves, open flowers, and ripe fruits – phenophases that are the most abundant in the database and also relevant to management applications – for all species with available data, regardless of plant growth habit, location, geographic extent, or temporal depth of the observations. We implemented a very basic model formulation - thermal time models with a fixed start date. Sufficient data were available to construct 107 individual species × phenophase models. Of these, fifteen models (14%) met our criteria for model fit and error and were suitable for use across the majority of the species’ geographic ranges. These findings indicate that the USA-NPN dataset holds promise for further and more refined modeling efforts. Further, the candidate models that emerged could be used to produce real-time and short-term forecast maps of the timing of such transitions to directly support natural resource management.

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Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
USA National Phenology Network’s volunteer-contributed observations yield predictive models of phenological transitions
Series title:
PLoS ONE
DOI:
10.1371/journal.pone.0182919
Volume:
12
Issue:
8
Year Published:
2017
Language:
English
Publisher:
PLOS ONE
Contributing office(s):
National Phenology Network
Description:
e0182919; 17 p.
First page:
1
Last page:
17
Country:
United States