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The influence of data type and functional traits on native bee phenology metrics: Opportunistic versus inventory records

View ORCID ProfileJoan M. Meiners, View ORCID ProfileMichael C. Orr, View ORCID ProfileRiemer Kristina, View ORCID ProfileGriswold Terry, View ORCID ProfileJuniper L. Simonis
doi: https://doi.org/10.1101/2020.04.16.044750
Joan M. Meiners
1School of Natural Resources and Environment, University of Florida, Gainesville, Florida 32603
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  • For correspondence: joan.meiners@gmail.com
Michael C. Orr
2Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P.R. China
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Riemer Kristina
3University of Arizona, Tucson, Arizona 85721 & Wildlife Ecology and Conservation Department, University of Florida, Gainesville, Florida, 32611
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Griswold Terry
4USDA-ARS Pollinating Insects Research Unit, Utah State University, Logan, Utah 84322-5310
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Juniper L. Simonis
5DAPPER Stats, Portland, OR 97212
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ABSTRACT

Efforts to understand activity patterns of bees, our most important pollinators, often rely on opportunistically collected museum records to model temporal shifts or declines. This type of data, however, may not be suitable for this purpose given high spatiotemporal variability of native bee activity. By comparing phenological metrics calculated from intensive systematic inventory data with those from opportunistic museum records for bee species spanning a range of functional traits, we explored biases and limitations of data types to determine best practices for bee monitoring and assessment. We compiled half a million records of wild bee occurrence from opportunistic museum collections and six systematic inventory efforts, focusing analyses on 45 well-represented species that spanned five functional traits: sociality, nesting habits, floral specialization, voltinism, and body size. We then used permutation tests to evaluate differences between data types in estimating three phenology metrics: flight duration, number of annual abundance peaks, and date of the highest peak. We used GLMs to test for patterns of data type significance across traits. All 45 species differed significantly in the value of at least one phenology metric depending on the data type used. The date of the highest abundance peak differed for 40 species, flight duration for 34 species, and the number of peaks for 15 species. The number of peaks was more likely to differ between data types for larger bees, and flight duration was more likely to differ for larger bees and specialist bees. Our results reveal a strong influence of data type on phenology metrics that necessitates consideration of data source when evaluating changes in phenological activity, possibly applicable to many taxa. Accurately assessing phenological change may require expanding wild bee monitoring and data sharing.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 18, 2020.
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The influence of data type and functional traits on native bee phenology metrics: Opportunistic versus inventory records
Joan M. Meiners, Michael C. Orr, Riemer Kristina, Griswold Terry, Juniper L. Simonis
bioRxiv 2020.04.16.044750; doi: https://doi.org/10.1101/2020.04.16.044750
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The influence of data type and functional traits on native bee phenology metrics: Opportunistic versus inventory records
Joan M. Meiners, Michael C. Orr, Riemer Kristina, Griswold Terry, Juniper L. Simonis
bioRxiv 2020.04.16.044750; doi: https://doi.org/10.1101/2020.04.16.044750

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