Dataset Information

Estimating flowering transition dates from status-based phenological observations: a test of methods.

ABSTRACT: The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly introduced methods using concepts from other fields. Comparing the proficiency of different estimators is difficult since true transition dates are rarely known. Here I use a recently released dataset of in-situ flowering observations of the perennial forb Echinacea angustifolia. In this dataset, due to high sampling frequency and unique physiology, the transition dates of onset, peak, and end of flowering are known to within 3 days. I used a Monte Carlo analysis to test eight different estimators across two scales using a range of sample sizes and proportion of flowering presence observations. I evaluated the estimators accuracy in predicting the onset, peak, and end of flowering at the population level, and predicting onset and end of flowering for individual plants. Overall, a method using a Weibull distribution performed the best for population level onset and end estimates, but other estimators may be more appropriate when there is a large amount of absence observations relative to presence observations. For individual estimates a method using the midway point between the first flower presence and most prior flower absence, within 7 days, is the best option as long as the restriction does not limit the final sample size. Otherwise, the Weibull method is adequate for individual estimates as well. These methods allow practitioners to effectively utilize the large amount of status-based phenological observations currently available.

SUBMITTER: Taylor SD

PROVIDER: S-EPMC6764363 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Estimating flowering transition dates from status-based phenological observations: a test of methods.

Taylor Shawn D SD

PeerJ 20190924

The scale of phenological research has expanded due to the digitization of herbarium specimens and volunteer based contributions. These data are status-based, representing the presence or absence of a specific phenophase. Modelling the progress of plant dormancy to growth and reproduction and back to dormancy requires estimating the transition dates from these status-based observations. There are several methods available for this ranging from statistical moments using the day of year to newly i ...[more]

PMID: 31579602

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Project description:PurposeIn 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. The aim of this study was to explore the potential that exists in the broad and rich volunteer-collected dataset maintained by the USA-NPN for constructing models predicting the timing of phenological transition across species' ranges within the continental 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 (2009-present). Whether data exhibiting such limitations can be used to develop predictive models appropriate for use across large geographic regions has not yet been explored.MethodsWe constructed predictive models for 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.ResultsSufficient data were available to construct 107 individual species × phenophase models. Remarkably, given the limited temporal depth of this dataset and the simple modeling approach used, fifteen of these models (14%) met our criteria for model fit and error. The majority of these models represented the "breaking leaf buds" and "leaves" phenophases and represented shrub or tree growth forms. Accumulated growing degree day (GDD) thresholds that emerged ranged from 454 GDDs (Amelanchier canadensis-breaking leaf buds) to 1,300 GDDs (Prunus serotina-open flowers). Such candidate thermal time thresholds can be used to produce real-time and short-term forecast maps of the timing of these phenophase transition. In addition, many of the candidate models that emerged were suitable for use across the majority of the species' geographic ranges. Real-time and forecast maps of phenophase transitions could support a wide range of natural resource management applications, including invasive plant management, issuing asthma and allergy alerts, and anticipating frost damage for crops in vulnerable states.ImplicationsOur finding that several viable thermal time threshold models that work across the majority of the species ranges could be constructed from the USA-NPN database provides clear evidence that great potential exists this dataset to develop more enhanced predictive models for additional species and phenophases. Further, the candidate models that emerged have immediate utility for supporting a wide range of management applications.

Dataset Information

Estimating flowering transition dates from status-based phenological observations: a test of methods.

Publications

Estimating flowering transition dates from status-based phenological observations: a test of methods.

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