Dataset Information

Methods for broad-scale plant phenology assessments using citizen scientists' photographs.

ABSTRACT: Premise:Citizen science platforms for sharing photographed digital vouchers, such as iNaturalist, are a promising source of phenology data, but methods and best practices for use have not been developed. Here we introduce methods using Yucca flowering phenology as a case study, because drivers of Yucca phenology are not well understood despite the need to synchronize flowering with obligate pollinators. There is also evidence of recent anomalous winter flowering events, but with unknown spatiotemporal extents. Methods:We collaboratively developed a rigorous, consensus-based approach for annotating and sharing whole plant and flower presence data from iNaturalist and applied it to Yucca records. We compared spatiotemporal flowering coverage from our annotations with other broad-scale monitoring networks (e.g., the National Phenology Network) in order to determine the unique value of photograph-based citizen science resources. Results:Annotations from iNaturalist were uniquely able to delineate extents of unusual flowering events in Yucca. These events, which occurred in two different regions of the Desert Southwest, did not appear to disrupt the typical-period flowering. Discussion:Our work demonstrates that best practice approaches to scoring iNaturalist records provide fine-scale delimitation of phenological events. This approach can be applied to other plant groups to better understand how phenology responds to changing climate.

SUBMITTER: Barve VV

PROVIDER: S-EPMC6976896 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Methods for broad-scale plant phenology assessments using citizen scientists' photographs.

Barve Vijay V VV Brenskelle Laura L Li Daijiang D Stucky Brian J BJ Barve Narayani V NV Hantak Maggie M MM McLean Bryan S BS Paluh Daniel J DJ Oswald Jessica A JA Belitz Michael W MW Folk Ryan A RA Guralnick Robert P RP

Applications in plant sciences 20200108 1

<h4>Premise</h4>Citizen science platforms for sharing photographed digital vouchers, such as iNaturalist, are a promising source of phenology data, but methods and best practices for use have not been developed. Here we introduce methods using <i>Yucca</i> flowering phenology as a case study, because drivers of <i>Yucca</i> phenology are not well understood despite the need to synchronize flowering with obligate pollinators. There is also evidence of recent anomalous winter flowering events, but ...[more]

PMID: 31993257

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Project description:Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and interannual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science USA National Phenology Network (USA-NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built statistical and process based phenology models for species common to each data set. From these models, we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both USA-NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two data sets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for USA-NPN data had the lowest error when using models built from the USA-NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. This difference in the cross-scale model comparison is likely due to variation in phenological requirements within species. Models using the USA-NPN data set can integrate parameters over a large spatial scale while those using an LTER data set can only estimate parameters for a single location. Accordingly, the choice of data set depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if USA-NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale USA-NPN data, which has high spatial representation and a large regional species pool. LTER data sets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models that integrate the strengths of both data sets.

Dataset Information

Methods for broad-scale plant phenology assessments using citizen scientists' photographs.

Publications

Methods for broad-scale plant phenology assessments using citizen scientists' photographs.

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