Data For: Herbarium specimens provide reliable estimates of phenological responsiveness to climate at unparalleled taxonomic and spatiotemporal scales

Understanding the effects of climate change on the phenological structure
of plant communities will require measuring variation in sensitivity among
thousands of co-occurring species across regions. Herbarium collections
provide vast resources with which to do this, but may also exhibit biases
as sources of phenological data. Despite general recognition of these
caveats, validation of herbarium-based estimates of phenological
sensitivity against estimates obtained using field observations remain
rare and limited in scope. Here, we leveraged extensive datasets of
herbarium specimens and of field observations from the USA National
Phenology Network for 21 species in the United States and, for each
species, compared herbarium- and field-based standardized estimates of
peak flowering dates and of sensitivity of peak flowering time to
geographic and interannual variation in mean spring minimum temperatures
(TMIN). We found strong agreement between herbarium- and field-based
estimates for standardized peak flowering time (r=0.91, p<0.001)
and for the direction and magnitude of sensitivity to both geographic TMIN
variation (r=0.88, p <0.001) and interannual TMIN variation
(r=0.82, p<0.001). This agreement was robust to substantial
differences between datasets in 1) the long-term TMIN conditions observed
among collection and phenological monitoring sites and 2) the interannual
TMIN conditions observed in the time periods encompassed by both datasets
for most species. Our results show that herbarium-based sensitivity
estimates are reliable among species spanning a wide diversity of life
histories and biomes, demonstrating their utility in a broad range of
ecological contexts, and underscoring the potential of herbarium
collections to enable phenoclimatic analysis at taxonomic and
spatiotemporal scales not yet captured by observational data.  

Phenological data The dataset of field observations consisted of all
records of flowering onset and termination available in the USA National
Phenology Network database (NPNdb), representing an initial 1,105,764
phenological observations. To ensure the quality of the observational
data, we retained only observations for which we could determine that the
dates of onset and termination of flowering had an arbitrary maximum error
of 14 days. To do this, we filtered the data to include only records for
which the date on which the first open flower on an individual was
observed was preceded by an observation of the same individual without
flowers no more than 14 days prior, and for which the date on which the
last flower was recorded was followed by an observation of the same
individual without flowers no more than 14 days later. After filtering,
field observations in our data had an average maximum error of 6.4 days
for the onset of flowering, and of 6.6 days for the termination of
flowering. The herbarium dataset was constructed using an initial 894,392
digital herbarium specimen records archived by 72 herbaria across North
America. We excluded from analysis all specimens not explicitly recorded
as being in flower, or for which GPS coordinates or dates of collection
were not available. We further filtered both datasets by only retaining
species that were found in both datasets and that were represented by
observations at a minimum of 15 unique sites in the NPN dataset. For each
species, and to more closely match the geographic ranges covered by each
dataset, we filtered the herbarium dataset to include only specimens
within the range of latitudes and longitudes represented by the field
observations in the NPN data. Finally, we retained only species
represented by 70 or more herbarium specimens to ensure sufficient sample
sizes for phenoclimatic modeling. This procedure identified a final set of
21 native species represented in 3,243 field observations across 1,406
unique site-year combinations, and a final sample of 5,405 herbarium
specimens across 4,906 unique site-year combinations. For the herbarium
dataset, sample sizes ranged from 69 unique sites and 74 specimens for
Prosopis velutina, to 1,323 unique sites containing 1,368 specimens for
Achillea millefolium. Sample sizes in the NPN dataset ranged from 15
unique sites with 74 observations for Impatiens capensis, 108 unique sites
with 321 observations for Cornus florida. These 21 species represented 15
families and 17 genera, spanning a diverse range of life-history
strategies and growth forms, including evergreen and deciduous shrubs and
trees (e.g., Quercus agrifolia and Tilia americana, respectively), as well
as herbaceous perennials (e.g., Achillea millefolium) and annuals (e.g.,
Impatiens capensis). Our focal species covered a wide variety of biomes
and regions including Western deserts (e.g., Fouquieria splendens),
Mediterranean shrublands and oak woodlands (e.g., Baccharis pilularis,
Quercus agrifolia), and Eastern deciduous forests (e.g., Quercus rubra,
Tilia Americana). To estimate flowering dates in the herbarium dataset, we
employed the day of year of collection (henceforth ‘DOY’) of each specimen
collected while in flower as a proxy. Herbarium specimens in flower could
have been collected at any point between the onset and termination of
their flowering period and botanists may preferentially collect
individuals in their flowering peak for many species. Therefore, herbarium
specimen collection dates are more likely to reflect peak flowering dates
than flowering onset dates. To maximize the phenological equivalence of
the field and herbarium datasets, we used the median date between onset
and termination of flowering for each individual in each year in the NPN
data as a proxy for peak flowering time. Due to the maximum error of 14
days for flowering onset and termination dates in the NPN dataset, median
flowering dates also had a maximum error of 14 days, with an average
maximum error among observations of 6.5 days. To account for the
artificial DOY discontinuity between December 31st (DOY = 365 or 366 in a
leap year) to January 1st (DOY = 1), we converted DOY in both datasets
into a circular variable using an Azimuthal correction. Climate data Daily
minimum temperatures mediate key developmental processes including the
break of dormancy, floral induction, and anthesis. Therefore, we used
minimum surface temperatures averaged over the three months leading up to
(and including) the mean flowering month for each species (hereafter
‘TMIN’) as the climatic correlate of flowering time in this study;
consequently, the specific months over which temperatures were averaged
varied among species. Using TMIN calculated over different time periods
instead (e.g., during spring for all species) did not qualitatively affect
our results. Then, we partitioned variation among sites into spatial and
temporal components, characterizing TMIN for each observation by the
long-term mean TMIN at its site of collection (henceforth ‘TMIN normals’),
and by the deviation between its TMIN in the year of collection (for the
three-month window of interest) and its long-term mean TMIN (henceforth
‘TMIN anomalies’). For each site, we obtained a monthly time series of
TMIN from January, 1901, and December, 2016, using ClimateNA v6.30, a
software package that interpolates 4km2 resolution climate data from PRISM
Climate Group from Oregon State University, (http://prism.oregonstate.edu)
to generate elevation-adjusted climate estimates. To calculate TMIN
normals, we averaged observed TMIN for the three months leading up to the
mean flowering date of each species across all years between 1901 and 2016
for each site. TMIN anomalies relative to long-term conditions were
calculated by subtracting TMIN normals from observed TMIN conditions in
the year of collection. Therefore, positive and negative values of the
anomalies respectively reflect warmer-than-average and colder-than-average
conditions in a given year. Analysis We also provide R code to reproduce
all results presented in the main text and the supplemental materials of
our study. This code includes 1) all steps necessary to merge herbarium
and field data into a single dataset ready for analysis, 2) the
formulation and specification of the varying-intercepts and varying-slopes
Bayesian model used to generate herbarium- vs. field-based estimates of
phenology and its sensitivity to TMINsp, 3) the steps required to process
the output of the Bayesian model and to obtain all metrics required for
the analyses in the paper, and 4) the code used to generate each figure.
Contributing Herbaria Data used in this study was contributed by the Yale
Peabody Museum of Natural History, the George Safford Torrey Herbarium at
the University of Connecticut, the Acadia University Herbarium, the
Chrysler Herbarium at Rutgers University, the University of Montreal
Herbarium, the Harvard University Herbarium, the Albion Hodgdon Herbarium
at the University of New Hampshire, the Academy of Natural Sciences of
Drexel University, the Jepson Herbarium at the University of
California-Berkeley, the University of California-Berkeley Sagehen Creek
Field Station Herbarium, the California Polytechnic State University
Herbarium, the University of Santa Cruz Herbarium, the Black Hills State
University Herbarium, the Luther College Herbarium, the Minot State
University Herbarium, the Tarleton State University Herbarium, the South
Dakota State University Herbarium, the Pittsburg State University
Herbarium, the Montana State University-Billings Herbarium, the Sul Ross
University Herbarium, the Fort Hays State University Herbarium, the Utah
State University Herbarium, the Brigham Young University Herbarium, the
Eastern Nevada Landscape Coalition Herbarium, the University of Nevada
Herbarium, the Natural History Museum of Utah, the Western Illinois
University Herbarium, the Eastern Illinois University Herbarium, the
Northern Illinois University Herbarium, the Morton Arboretum Herbarium,
the Chicago Botanic Garden Herbarium, the Field Museum of Natural History,
the University of Wisconsin-Madison Herbarium, the University of Michigan
Herbarium, the Indiana University Herbarium, the Universidad de Sonora
Herbarium, the Centro de Investigaciones Biológicas del Noroeste, S. C.,
the Instituto Politécnico Nacional, CIIDIR Unidad Durango, the University
of California-Riverside Herbarium, the San Diego State University
Herbarium, the Granite Mountains Desert Research Center, the University of
South Carolina Herbarium, the Auburn University Museum of Natural History,
the Clemson University Herbarium, the Eastern Kentucky University
Herbarium, the College of William and Mary Herbarium, the Appalachian
State University Herbarium, the University of North Carolina Herbarium,
the University of Memphis Herbarium, the Mississippi State University
Herbarium, the University of Mississippi Herbarium, the University of
Southern Mississippi Herbarium, the Mississippi Museum of Natural Science,
the Marshall University Herbarium, the Longwood University Herbarium, the
Herbarium of Western Carolina University, the Northern Kentucky University
Herbarium, the Salem College Herbarium, the Troy University Herbarium, the
Arizona State University Herbarium, the University of Arizona Herbarium,
the Desert Botanical Garden, the Deaver Herbarium, the Navajo Nation
Department of Fish and Wildlife, the Grand Canyon National Park Herbarium,
the University of New Mexico Herbarium, the Western New Mexico
University  Herbarium, the Museum of Northern Arizona, the Gil
National Forest Herbarium, the Arizona Western College Herbarium, and the
Natural History Institute.

The README_Ramirez-Parada_Park_Mazer.txt contains relevant usage notes.