Abstract
Disparate ecological datasets are often organized into databases post hoc and then analyzed and interpreted in ways that may diverge from the purposes of the original data collections. Few studies, however, have attempted to quantify how biases inherent in these data (for example, species richness, replication, climate) affect their suitability for addressing broad scientific questions, especially in under-represented systems (for example, deserts, tropical forests) and wild communities. Here, we quantitatively compare the sensitivity of species first flowering and leafing dates to spring warmth in two phenological databases from the Northern Hemisphere. One—PEP725—has high replication within and across sites, but has low species diversity and spans a limited climate gradient. The other—NECTAR—includes many more species and a wider range of climates, but has fewer sites and low replication of species across sites. PEP725, despite low species diversity and relatively low seasonality, accurately captures the magnitude and seasonality of warming responses at climatically similar NECTAR sites, with most species showing earlier phenological events in response to warming. In NECTAR, the prevalence of temperature responders significantly declines with increasing mean annual temperature, a pattern that cannot be detected across the limited climate gradient spanned by the PEP725 flowering and leafing data. Our results showcase broad areas of agreement between the two databases, despite significant differences in species richness and geographic coverage, while also noting areas where including data across broader climate gradients may provide added value. Such comparisons help to identify gaps in our observations and knowledge base that can be addressed by ongoing monitoring and research efforts. Resolving these issues will be critical for improving predictions in understudied and under-sampled systems outside of the temperature seasonal mid-latitudes.
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Acknowledgments
Primary data collections were made possible through the support of many granting agencies; please see ESM for complete information and grant numbers. This work was conducted as a part of the “Forecasting Phenology” Working Group supported by the National Center for Ecological Analysis and Synthesis, a center funded by the National Science Foundation (NSF) (Grant #EF- 0553768), the University of California, Santa Barbara, and the State of California. Special thanks to the many data holders and data managers who assisted us throughout the process including K. Vanderbilt and K. Wetherill (SEV), Chris Nytch and Jess Zimmerman (LUQ), George Aldridge and David Inouye (GTH), John O’Keefe (HVD), and Paul Huth, Shanan Smiley, and John Thompson from the Mohonk Preserve (MHK). Some data used in this publication were obtained by scientists of the Hubbard Brook Ecosystem Study; this publication has not been reviewed by those scientists. The Hubbard Brook Experimental Forest is operated and maintained by the Northeastern Research Station, U.S. Department of Agriculture, Newtown Square, Pennsylvania. Additional support was also provided by the USA National Phenology Research Coordination Network, supported by NSF grant #IOS-0639794. NJBK was supported by the NSERC CREATE Training Program in Biodiversity Research. PEP725 data were provided by the members of the PEP725 project. Special thanks to E. Koch and W. Lipa for providing the PEP725 data and the accompanying climate data. Support for EMW came from the NSF Postdoctoral Fellow program (Grant #DBI-0905806). Thanks to the editors, two anonymous reviewers, Jonathan Hanes, and David Inouye for providing valuable comments that significantly improved the quality of this manuscript. LDEO contribution number #7580.
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All authors contributed to the study design and offered comments on this manuscript as part of the “Forecasting Phenology” working group funded by the National Center for Ecological Analysis and Synthesis. Author Cook conceived of and designed the study, analyzed the data, and wrote the paper. Wolkovich and Davies contributed significantly to the refinement of the ideas and analyses and assisted with the writing. Ault contributed significantly to the analyses, including processing of the GHCN climate data. Betancourt contributed significantly to the writing and study design. The first five authors are listed in order of their contributions; all other authors are listed alphabetically.
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Cook, B.I., Wolkovich, E.M., Davies, T.J. et al. Sensitivity of Spring Phenology to Warming Across Temporal and Spatial Climate Gradients in Two Independent Databases. Ecosystems 15, 1283–1294 (2012). https://doi.org/10.1007/s10021-012-9584-5
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DOI: https://doi.org/10.1007/s10021-012-9584-5