Submitted 22 November 2017
Accepted 16 March 2018
Published 3 April 2018
Casey A. Jones, email@example.com
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2018 Jones and Daehler
Creative Commons CC-BY 4.0
Herbarium specimens can reveal impacts
of climate change on plant phenology; a
review of methods and applications
Casey A. Jones and Curtis C. Daehler
Department of Botany, University of Hawaii at Manoa, Honolulu, HI, United States of America
Studies in plant phenology have provided some of the best evidence for large-scale
responses to recent climate change. Over the last decade, more than thirty studies
have used herbarium specimens to analyze changes in flowering phenology over time,
although studies from tropical environments are thus far generally lacking. In this
review, we summarize the approaches and applications used to date. Reproductive
plant phenology has primarily been analyzed using two summary statistics, the mean
flowering day of year and first-flowering day of year, but mean flowering day has
proven to be a more robust statistic. Two types of regression models have been
applied to test for associations between flowering, temperature and time: flowering day
regressed on year and flowering day regressed on temperature. Most studies analyzed
the effect of temperature by averaging temperatures from three months prior to the
date of flowering. On average, published studies have used 55 herbarium specimens
per species to characterize changes in phenology over time, but in many cases fewer
specimens were used. Geospatial grid data are increasingly being used for determining
average temperatures at herbarium specimen collection locations, allowing testing
for finer scale correspondence between phenology and climate. Multiple studies have
shown that inferences from herbarium specimen data are comparable to findings from
systematically collected field observations. Understanding phenological responses to
climate change is a crucial step towards recognizing implications for higher trophic
levels and large-scale ecosystem processes. As herbaria are increasingly being digitized
worldwide, more data are becoming available for future studies. As temperatures
continue to rise globally, herbarium specimens are expected to become an increasingly
important resource for analyzing plant responses to climate change.
Subjects Plant Science, Climate Change Biology
Keywords Climate change, Phenology, Herbarium specimens, Methods
Carl Linnaeus pioneered the study of phenology when he outlined methods for investigating
associations between flowering and climate in the 1700s (Linnaeus, 1751;Von Linné, 2003;
Puppi, 2007). Around 1850, Charles Morren introduced the term ‘‘phenology’’ to describe
his observational studies of yearly flowering (Morren, 1853;Demarée & Rutishauser, 2009).
Early field studies of plant phenology have been thoroughly reviewed by Van Schaik,
Terborgh & Wright (1993),Fenner (1998) and Forrest & Miller-rushing (2010). Long-term
observations in field studies have provided a valuable resource for analyzing phenological
How to cite this article Jones and Daehler (2018), Herbarium specimens can reveal impacts of climate change on plant phenology; a re-
view of methods and applications. PeerJ 6:e4576; DOI 10.7717/peerj.4576
responses to recent climate change (Walther et al., 2002;Parmesan & Yohe, 2003). A
growing need for historical data that allows for the exploration of ecological implications
of climate change prompted researchers to look to herbarium specimens. A few phenology
studies such as Borchert (1996) and Rivera & Borchert (2001) used herbarium specimens
to study flowering periodicity, but not in the context of climate change. The first study
to use herbarium specimens to understand phenological responses to climate change was
published in 2004 by Primack et al. (2004).Primack et al. (2004) analyzed 372 specimen
records (1885–2002) and found flowering had advanced approximately eight days over the
last century. Between 2004 and 2017, more than 30 studies were published using herbarium
specimens to examine changes in phenology in response to climate change.
The most common approach found in studies using herbarium specimens follows the
procedure set by Primack et al. (2004). This can be summarized as collecting Julian dates
from herbarium specimens, collecting long-term temperature data from an independent
source, and then using regression analyses to analyze correlations between Julian dates,
temperatures and time (Primack et al., 2004;Miller-Rushing et al., 2006;Gallagher, Hughes
& Leishman, 2009;Robbirt et al., 2011;Gaira, Dhar & Belwal, 2011;Molnár et al., 2012;
Panchen et al., 2012;Park, 2012;Primack & Miller-rushing, 2012;Li et al., 2013;Calinger,
Queenborough & Curtis, 2013;Hart et al., 2014;Rawal et al., 2015;Park & Schwartz, 2015).
Primack et al. (2004) recorded the date of collection from each herbarium specimen and
then extracted Julian dates from the collection dates. A Julian date is a value between 1 and
365 corresponding to the day of year when the specimen was collected. Linear regression
models are also the most widely used statistical models in field studies investigating
flowering phenology (Zhao et al., 2013).
An early criticism of using herbarium specimens was that plant parts preserved as
herbarium specimens might not have been collected during their peak flowering season,
potentially biasing interpretations (Lamoureux, 1973). Daru, Van der Bank & Davies (2017)
also found spatial, temporal, trait, phylogenetic, and collector biases among herbarium
specimen samples. Daru, Van der Bank & Davies (2017) concluded that while some of
these biases can be accounted for using statistical approaches, future herbarium collections
should focus on filling large gaps in the data. Other studies have found that large sample
sizes afforded by herbarium specimens, and the use of mean flowering times (mean of
Julian dates), could yield valid inferences, even if specimens were not collected at the time
of peak flowering (Primack et al., 2004;Bertin, 2015). Collector bias and plant size choice
have also been overcome by statistical analyses when mean flowering times were used as
the variable of interest, rather than the date of first-flowering (Robbirt et al., 2011;Davis et
Most of the studies we reviewed used two types of linear regression models to show
evidence of associations between phenology and climate change (Table 1). These studies
regressed flowering day on temperature (82%) and flowering day on year (64%) (Table 1).
These studies have primarily been conducted with specimens from herbaria in temperate
latitudes such as the Eastern Himalayas (Gaira, Dhar & Belwal, 2011;Li et al., 2013;Gaira
et al., 2014;Hart et al., 2014), Southern Australia (Gallagher, Hughes & Leishman, 2009;
Rawal et al., 2015), Northern Europe (Robbirt et al., 2011;Diskin et al., 2012;Molnár et al.,
Jones and Daehler (2018), PeerJ, DOI 10.7717/peerj.4576 2/16
Table 1 Methods of studies. The column ‘‘Flw Day ∼Temp’’ represents studies that conducted a type of regression analysis with flowering day
(Flw Day) as the dependent variable and temperature average (temp) or year as the independent variable. The ‘‘1¯x’’ symbol represents studies that
analyzed a difference in the mean flowering day between historic and current time period groups rather than using a type of regression analysis.
Species Specimens Specimen
Authors Year Geographic region (flw ∼temp) (flw ∼year)
1 117 117 Gaira et al. 2011 Eastern Himalayas x
1 N/A N/A Gaira et al. 2014 Eastern Himalayas x x
1 192 192 Robbirt et al. 2011 Northern Europe x
5 158 32 Rawal et al. 2015 Southern Australia x x
5 540 108 Diskin et al. 2012 Northern Europe x x
20 371 19 Gallagher et al. 2009 Southern Australia x x
20 1,108 55 Davis et al. 2015 North America x x
28 1,587 57 Panchen et al. 2012 North America x x
36 460 13 Hart et al. 2014 Eastern Himalayas x
>37 372 10 Primack et al. 2004 North America x x
39 216 6 Lavoie & Lachange 2006 North America x
39 5,424 139 Molnár et al. 2012 Northern Europe x
41 909 22 Li et al. 2013 Eastern Himalayas x x
42 142 3 Miller-Rushing et al. 2006 North America x x
43 N/A N/A Primack & Miller-Rushing 2012 North America x
87 N/A N/A Neil et al. 2010 North America x
141 5,053 36 Calinger et al. 2013 North America x
186 30,000 161 Bertin 2015 North America 1¯x
370 1,125 3 Searcy 2012 North America 1¯x
1,185 5,949 5 Park 2012 North America x
>1,700 19,328 11 Park 2014 North America x
24,105 823,033 34 Park & Schwartz 2015 North America x x
2012), and North America (Primack et al., 2004;Lavoie & Lachance, 2006;Miller-Rushing et
al., 2006;Primack & Miller-Rushing, 2009;Neil, Landrum & Wu, 2010;Panchen et al., 2012;
Park, 2012;Primack & Miller-rushing, 2012;Searcy, 2012;Calinger, Queenborough & Curtis,
2013;Park, 2014;Park & Schwartz, 2015;Bertin, 2015;Davis et al., 2015). Although studies
by Borchert (1996) and Zalamea et al. (2016) analyzed flowering periodicity in tropical
plants using herbarium specimens, we found no study to date that has used herbarium
specimens to analyze effects of recent climate change in a tropical region. In this review,
we examined how studies chose sample sizes, flowering specimens, temperature averages
and geographical scale in their analyses. We also examined how these studies validated the
use of herbarium specimens and we provide suggestions for methods to be used in future
Between 2015 and 2017, we compiled and reviewed studies that used herbarium specimens
to assess climate change and flowering phenology. We searched Web of Science (1900—
present), JSTOR (1665—present) and Google Scholar for studies containing the terms
herbarium, specimen, phenology, and climate change. The methods of each study
Jones and Daehler (2018), PeerJ, DOI 10.7717/peerj.4576 3/16
were reviewed for; sample size, determining flowering status of specimens, approach
to determining temperatures, geographic variation, and any validations of the use of
herbarium specimens (e.g., comparisons to field observations). Studies and methods were
then categorized and a synthesis of each category is discussed; sample sizes and regression
methods were also summarized (Table 1).
Specimen sample sizes
Sample size, or the number of specimens used per species, varied across studies (Table 1).
The minimum number of specimens used per species was occasionally as low as two or three
records (Searcy, 2012). Some studies using herbarium data have set a minimum number of
herbarium specimens per species or a minimum time range for collections in order to more
accurately estimate phenologies and change over time. Calinger, Queenborough & Curtis
(2013) and Gallagher, Hughes & Leishman (2009) set a minimum of 10 specimens in order
to meet statistical assumptions of different models. Molnár et al. (2012) eliminated a species
from analyses because collections only yielded dates across an eight year time span. Park
& Schwartz (2015) eliminated species with records that spanned less than three years. Neil,
Landrum & Wu (2010) organized species into functional groups (spring ephemerals, spring
shrubs, fall ephemerals, winter-spring ephemerals, and winter-spring shrubs) in order to
overcome the problem of low sample sizes for each species but found that responses of
individual species varied greatly within functional groups.
Several studies found sample size had a greater influence on first-flowering estimates
than on mean flowering estimates. Miller-Rushing & Primack (2008) used field data and
found that small sample sizes led to biased estimations of first-flowering dates, but mean
flowering day was not biased by sample sizes. Moussus, Julliard & Jiguet (2010) investigated
sample sizes by simulating 10 known phenological estimators, such as mean flowering day
and first-flowering date. After comparing known phenological shifts from simulated sample
data with shift estimations from models using the same data, Moussus, Julliard & Jiguet
(2010) concluded that first-flowering dates were inaccurate because they showed much
greater differences in comparisons than mean flowering day. Low sample sizes prompted
Bertin (2015) to provide a detailed analysis of how sample size affected mean, median,
range, early flowering and late flowering summary statistics. In random simulations
comparing sample sizes, mean flowering day values deviated less than five days for species
with as few as four samples (Bertin, 2015). Bertin (2015) concluded that the mean was
a more robust measure of phenology than other estimators of early flowering. Bertin
(2015) also showed that by increasing the sample size to 20, mean flowering times deviated
only one to two days. A recent study by Pearse et al. (2017) used a Weibull distribution
to estimate the start of the process of flowering rather than using only first-flowering
observations. Pearse et al. (2017) showed that by controlling for differences in sampling,
first-flowering, peak-flowering (median) and cessation of flowering show similar changes
over time in response to climate change. The model used by Pearse et al. (2017) was also
shown to be consistent with changes in mean-flowering from a separate sample using an
early time period.
Jones and Daehler (2018), PeerJ, DOI 10.7717/peerj.4576 4/16
Larger sample sizes may be required if phenology varies across a species’ geographic
range. In order to analyze species distributions using herbarium specimens, Van Proosdij
et al. (2016) found that the minimum number of herbarium specimens sampled should be
between 14 and 25 depending on the geographical range of the species. The Van Proosdij et
al. (2016) study used simulated species to assess the minimum herbarium samples required
for acceptable model performance in both virtual and real study areas. Some species with
narrow geographical ranges could be modeled with as few as 14 herbarium records while
wide ranging species could be satisfactorily modeled with a minimum of 25 records (Van
Proosdij et al., 2016). Based on these studies, we recommend caution when interpreting
results from samples sizes with fewer than 30 records (Miller-Rushing & Primack, 2008;
Moussus, Julliard & Jiguet, 2010;Bertin, 2015). The average sample size across studies in
this review was about 55 records per species (Table 1). We also recommend using the mean
flowering day of year rather than averages of first flowering dates (Calinger, Queenborough
& Curtis, 2013;Gallagher, Hughes & Leishman, 2009;Pearse et al., 2017).
Determining ﬂowering status of specimens
Some studies have simply recorded the presence or absence of flowers from herbarium
specimens as an indicator of flowering, but other studies have used more detailed criteria
to assess flowering status on specimens. Haggerty, Hove & Mazer (2012) provided a primer
to assist researchers with collecting data from herbarium specimens. Haggerty, Hove &
Mazer (2012) suggested researchers assign a phenophase for each specimen, such as pre-
flowering, first-flowering or peak flowering. Haggerty, Hove & Mazer (2012) also noted
that researchers must assume the stem on the herbarium sheet represents the flowering
phenophase for the entire plant. Past studies, such as Diskin et al. (2012), have used a
scoring system from 1 to 5 to categorize phenophase stages raging from ‘‘no flowers’’
to ‘‘end of fruiting’’ on each specimen. Diskin et al. (2012) categorized flowering as 50%
of buds open on the specimen. Calinger, Queenborough & Curtis (2013) also categorized
flowering as 50% of flower buds in anthesis to ensure that the samples were in peak
flowering. For a species with an inflorescence, Davis et al. (2015) only counted specimens
as flowering if greater than 75% of flowers were open. Standardization of phenological
terms remains a core challenge of mining phenological data (Willis et al., 2017). Initiatives
such as the Plant Phenology Ontology (PPO) working group are currently structuring
phenological terms for more uniform application across studies (Willis et al., 2017).
Studies in temperate regions have used varying methods to determine flowering status
for species with long flowering durations. For example, Molnár et al. (2012) and Bertin
(2015) excluded species that flowered outside of the peak flowering season of the region,
defined as the period from late-spring to early-summer. Molnár et al. (2012) removed one
species because its peak flowering date was in September and focused on 40 other taxa that
had flowering peaks from in spring and early-summer. The excluded species was a strong
outlier and it was suggested that autumn climate events may affect species differently than
spring climate events (Molnár et al., 2012). Park (2012) also removed outlier records when
flowering records fell outside the peak regional flowering season. Flowering records before
Julian day 45 and after Julian day 310 were removed from analyses to reduce biases caused
Jones and Daehler (2018), PeerJ, DOI 10.7717/peerj.4576 5/16
by winter flowering species. Additionally, Park (2012) removed records that were 150 days
after the median flowering date for each species to reduce errors caused by any second
flowerings that can happen in autumn months. Several other studies removed taxa with
long flowering durations to reduce variance among species. Bertin (2015) excluded native
weedy species with flowering durations from spring to fall. Gallagher, Hughes & Leishman
(2009) only used species with a flowering duration of less than three months. Panchen et
al. (2012) chose to use only species with clear beginning and ending points to investigate
long and short flowering duration. Panchen et al. (2012) found that plants with shorter
flowering durations required smaller sample sizes to produce significant results when
regressing flowering day on year.
Other studies such as Calinger, Queenborough & Curtis (2013) and Lavoie & Lachance
(2006) disregarded the effect of flowering duration and noted the results of Primack
et al. (2004), which reported no bias associated with long or short flowering durations
when mean estimations are analyzed. Plants in tropical regions often have long flowering
durations (Van Schaik, Terborgh & Wright, 1993;Fenner, 1998), but as long as flowering
is not continuous throughout the year, methods applied to temperate regions should
also yield valuable insight into effects of climate change on phenology in the tropics.
While studies using herbarium specimens to analyze long-term changes have been limited
to temperate regions, future studies could use circular statistics to analyze long-term
phenological changes in tropical regions (Fisher, 1993;Morellato, Alberti & Hudson, 2010).
Circular statistics have been used to analyze flowering phenology in several tropical field
studies, but these studies lacked long-term climate change analyses (Novotny & Basset,
1998;Morellato et al., 2000;Cruz, Mello & Van Sluys, 2006;Rogerio & Araujo, 2010;Tesfaye
et al., 2011;Nadia, Morellato & Machado, 2012;Nazareno & Dos Reis, 2012;Staggemeier,
Diniz-Filho & Morellato, 2010;Carvalho & Sartori, 2015;Kebede & Isotalo, 2016).
The foundational study by Primack et al. (2004) examined temperature averages from three
calendar months prior to the specimen flowering date, with the assumption that flowering
date is a function of temperatures experienced in past months. Field investigations such
as Fitter et al. (1995) have shown temperature averages from different sets of months
preceding flowering affected flowering phenology in different ways. More recently, Calinger,
Queenborough & Curtis (2013) chose to regress the month of flowering with temperature
averages from each of the eleven months prior to flowering. They found that temperature
averages from three months prior to the date of flowering showed the strongest correlations
with flowering (Calinger, Queenborough & Curtis, 2013). Robbirt et al. (2011) investigated
three sets of temperature averages over three month intervals and also found that three
months prior to flowering had the most predictive power. Similarly, Rawal et al. (2015)
regressed flowering on temperature averages for each species from 1, 3, 6, 9, and 12 months
prior to flowering, because responses can vary by species. Rawal et al. (2015) also found
that mean temperatures three months prior had the greatest influence on flowering time
for all species.
Jones and Daehler (2018), PeerJ, DOI 10.7717/peerj.4576 6/16
Other studies have used average temperatures from spring months because spring
temperatures generally have the most predictive power for flowering date (Miller-
Rushing & Primack, 2008;Primack & Miller-Rushing, 2009;Robbirt et al., 2011;Calinger,
Queenborough & Curtis, 2013;Park, 2014;Park & Schwartz, 2015). Bertin (2015) found an
interesting trend that supported the effect of spring temperatures: the earlier a species’ mean
flowering time occurred in the spring, the more the species’ mean dates had shifted toward
an earlier day of year over time. Robbirt et al. (2011) also found the highest correlations of
flowering day with spring temperature averages across March, April and May. Calinger,
Queenborough & Curtis (2013) found significant changes in flowering in response to average
spring temperatures (February–May) but not in response to summer temperatures (June–
September). Gaira, Dhar & Belwal (2011) found the highest correlations between flowering
and temperatures in earlier months from December–February in a Himalayan perennial.
As an alternative to using mean monthly temperatures, Diskin et al. (2012) investigated
the averages of temperature anomalies, or deviations from the overall long-term mean,
for 2, 3, and 6 month periods from January to June and found averages from six months
prior to flowering had the strongest correlations. Park (2014) used temperature averages
across three month periods from early spring to late summer and found a similar trend.
Temperature averages were organized into early, mid, and late seasonal classes within
the months of February–October. Park (2014) found warming temperatures had affected
species in the early spring class more than other classes. Park & Schwartz (2015) also used
early, mid and late seasonal classes for spring and summer and found that mid-season
phenology events should be modeled differently than early or late season events. Hart et al.
(2014) used annual temperatures and temperatures from each season (spring, summer, fall,
and winter) and found significant correlations for annual and fall temperature averages,
but with opposite effects. Hart et al. (2014) discussed that warmer fall temperatures may
delay the chilling requirement for Rhododendron species, resulting in a delay in flowering
while warmer annual temperatures will lead to advances in flowering overall. Other studies
found annual temperature means were as useful as spring temperatures. Davis et al. (2015)
found similar results between spring and annual temperature averages and used annual
averages in analyses. Gallagher, Hughes & Leishman (2009) also used annual temperature
means for analyses and explained that seasonal means were correlated with annual means.
We recommend investigating the effect of temperature by analyzing averages from
multiple sets of months prior to flowering for each species rather than using only
one fixed spring interval or only annual temperatures (Diskin et al., 2012;Robbirt et al.,
2011;Calinger, Queenborough & Curtis, 2013). Caution should be taken when analyzing
temperature averages from the same months prior to flowering for all species when
flowering month varies by species. For example, when analyzing the effect of temperature
averages from three months prior for all species, Calinger, Queenborough & Curtis (2013)
found that for many species, flowering was correlated with temperatures three months
earlier, yet for species with an earlier mean flowering day in April, January temperatures
(three months prior to flowering) did not predict flowering date; instead, temperature
averages from the months of February, March and April were better predictors for those
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Among species that have broad geographic ranges, differences in climate in different parts
of the species’ range can complicate attempts to correlate a species’ flowering day with
temperature. Several methods have been used to account for climate variability across
a species’ range. An early study by Lavoie & Lachance (2006) investigated the effects of
climate variation on the phenology of Coltsfoot (Tussilago farfara L.) across a range of
about 10,000 km2in Quebec, Canada. Temperature data from 88 meteorological stations
were averaged together across this range. To account for early snow cover melt in the
southern part of this range, flowering dates from individuals in southern locations were
normalized with individuals in northern locations by subtracting extra periods of snow
cover from individuals in the north. The adjusted dates indicated flowering occurred 33
days earlier over the last century while original (unadjusted) dates indicated flowering
occurred 19 days earlier over the last century.
While the study by Lavoie & Lachance (2006) adjusted actual dates for analyses, more
recent studies mostly account for climate variation using georeferenced climate data at
various scales. Calinger, Queenborough & Curtis (2013) accounted for climate variation
across Ohio by using temperature averages from ten US Climate Divisions across the
state, each about 8,000 km2. A total of 344 Climatic Divisions were established across the
contiguous United States in 1895 in order to monitor climate records more accurately.
These divisions have now accumulated about 100 years of climate records (Guttman &
Quayle, 1995). A later study by Park (2014) used average temperatures across the US county
where each specimen was collected.
Other studies accounted for climate variation across longitude, latitude, or elevation.
Robbirt et al. (2011) analyzed the geographical effect of longitude and found that flowering
occurred 4.86 days earlier per degree of longitude in a westward direction across the
southern coastal counties of England. A later study by Bertin (2015) used Hopkins’
bioclimatic law to normalize dates on specimens. Hopkins (1918) generally stated that for
every increase in a degree of latitude, or increase of 121.92 m elevation, the life history events
of plants and animals were delayed by four days. Bertin (2015) found consistencies with
Hopkins’ bioclimatic law using latitude and elevation and chose to normalize flowering
dates by adding expected phenological deviations from both latitude and elevation. Gaira,
Dhar & Belwal (2011) also analyzed climate variation using elevation when temperature
data were not available, assuming a 6.5 ◦C change in temperature per 1,000 m change in
elevation in the Himalayan region.
Other studies used temperature averages across large regions. Li et al. (2013) used
temperature data that was averaged from 36 meteorological stations across the
Tibet Autonomous Region. Molnár et al. (2012) used temperature averages from 10
meteorological stations across Hungary and stated that the data were statistically
indistinguishable across stations (∼93,030 km2). Park & Schwartz (2015) averaged
temperatures from 13 stations across South Carolina, USA (∼82, 931 km2). A later
study by Robbirt et al. (2014) used temperature averages from an area between Bristol,
Preston, and London, across the United Kingdom (∼17,000 km2). Robbirt et al. (2014)
used geographical divisions called Watsonian vice-counties specifically delineated for the
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purposes of collecting scientific data, much like the US Climate Divisions. Robbirt et al.
(2014) found temperature averages were sufficient because climate variation across the
Watsonian vice-counties used in their study did not significantly differ.
In order to more accurately estimate temperature averages across a region, recent studies
have used Geographical Information Systems (GIS) to project finer-scale climate layers
across a region and extract temperature data from specific Global Positioning System (GPS)
points. Gallagher, Hughes & Leishman (2009) referenced GPS locations for each specimen
and extracted the temperature averages at specimen GPS points from a gridded map of
temperature averages across Australia (∼5 km2resolution). Hereford, Schmitt & Ackerly
(2017) also extracted climate data from 176 collection locations in order to analyze species
distributions and phenology. Rawal et al. (2015) used the nearest data point from gridded
climate averages across Victoria, Australia. Edwards & Still (2008) analyzed the climate
envelopes of grasses by assigning GPS points to herbarium specimen locations in order
to extract temperature averages from gridded climate maps (250 m2resolution). Kosanic
et al. (2018) manually geo-referenced locations using herbarium specimen localities and
provided a methodology for assigning GPS coordinates when analyzing species distributions
and phenology. Standardizing methods for geo-referencing localities of herbarium records
without GPS coordinates could allow for more specimen data and larger sample sizes.
Bloom, Flower & DeChaine (2018) developed a comprehensive protocol for standardizing
spatial accuracy of geo-referenced specimen localities for species distributions.
Future studies of phenology could benefit from such geo-referencing methods because
several phenology studies only included data from specimens with GPS coordinates. Studies
using GPS data are able to account for climate variation with higher resolution, although
accuracy still depends on the underlying empirical data and modeling approach used to
generate GIS climate layers.
We recommend using the most spatially precise temperature data available, such as
climate divisions (Calinger, Queenborough & Curtis, 2013;Robbirt et al., 2014) rather than
state or region averages (Li et al., 2013;Park & Schwartz, 2015). Using GPS specimen
data to identify local climate conditions from GIS climate layers (Gallagher, Hughes &
Leishman, 2009;Edwards & Still, 2008) is also now generally more precise and convenient
in comparison to making generic and coarse-scale corrections for latitude, longitude or
elevation (Gaira, Dhar & Belwal, 2011;Robbirt et al., 2011;Bertin, 2015). If temperature
averages from larger areas are used, we recommend testing for climate variability across
smaller divisions before using averages across the larger area (Lavoie & Lachance, 2006;
Molnár et al., 2012;Robbirt et al., 2014).
Validation: herbarium specimens versus ﬁeld observations
Field data are often combined with herbarium specimen data in analyses, allowing for
comparison and sometimes allowing for validation of conclusions based on herbarium
data (Primack et al., 2004;Miller-Rushing et al., 2006;Bertin, 2015). Primack et al. (2004)
used herbarium specimens for historic data and field observations for current data and
combined the two in analyses. Studies by Miller-Rushing et al. (2006) and Bertin (2015)
also compared herbarium specimen data with field observations. Miller-Rushing et al.
Jones and Daehler (2018), PeerJ, DOI 10.7717/peerj.4576 9/16
(2006) found that phenology inferences from herbarium specimens alone differed from
the combined data by only about one day.
An early study by Borchert (1996) found that herbarium specimen data produced slightly
longer flowering durations than field data, but noted that durations were mostly similar
overall. Borchert (1996) and Rivera & Borchert (2001) found phenology data from field sites
largely overlapped that of herbarium specimens with only slight differences. The negligible
differences between herbarium specimen data and field data in these studies helped
justify the use of herbarium specimen data to analyze phenology in more recent studies.
Nevertheless, several more recent studies specifically compared phenology estimates from
field data to those made from herbarium specimens.
Bolmgren & Lonnberg (2005) compared herbarium specimen data directly to field data
and found the two data sets were overall highly correlated with only minor differences.
For example, herbarium specimens showed a slightly earlier mean flowering for spring-
flowering plants than field data, but the difference was not significant (Bolmgren &
Lonnberg, 2005). Later studies by Robbirt et al. (2011) and Davis et al. (2015) also primarily
focused on testing the validity of using herbarium specimen data. Robbirt et al. (2011) used
a principal axis regression analysis to compare herbarium derived peak-flowering dates
with field derived peak-flowering dates and found a high degree of correlation. Robbirt
et al. (2011) discussed how the high degree of correlation between herbarium and field
data also supports the notion that geographically different records will not significantly
alter the robustness of either data set. A study by Davis et al. (2015) used a paired t-test to
compare mean first-flowering day between herbarium specimens and field data and found
no statistical difference. Davis et al. (2015) concluded that both specimen and field data
could be combined and used as a whole.
In order to increase sample sizes, Molnár et al. (2012) added about 2,000 field
observations to about 5,000 herbarium records, resulting in 70% herbarium records
for the study. Similarly, Panchen et al. (2012) added about 2,000 field records to about
1,500 herbarium records, for a total of 43% herbarium records for the study. Searcy (2012)
combined herbarium specimen and field data and then split the combined data into
two time periods (1863–1935 and 1994–2008). Herbarium specimen data may provide
some advantages over field data. Bolmgren & Lonnberg (2005) and Primack et al. (2004)
noted that using herbarium specimens conserves time and resources, especially when
species are located in difficult to access geographical areas such as mountain peaks or
islands. Herbarium specimens are also collected over a greater period of time from a
larger geographical area while field data are often from specific localities over a shorter
time period (Primack et al., 2004;Bolmgren & Lonnberg, 2005;Bertin, 2015;Davis et al.,
2015). Herbarium specimens also provide long-term records that are widely accessible for
multiple studies. Despite criticisms, herbarium specimen data have been shown to produce
similar enough results to field data that herbarium specimen data are now widely accepted
in phenological studies.
Jones and Daehler (2018), PeerJ, DOI 10.7717/peerj.4576 10/16
The use of herbarium specimens for the investigation of flowering phenology has grown
considerably during the past decade. As efforts to produce digital copies of specimens and
label information have amassed large datasets, new approaches for analyzing responses to
climate change are rapidly becoming available. Although small sample sizes have often been
used in early studies of phenology, various factors, such as wide geographic range, may
require larger sample sizes. Based on recent validations, estimations of mean-flowering
should be used rather than first-flowering because estimates of first-flowering are more
sensitive to sampling. Statistically modeling the start of the flowering process appears
to be another promising approach to investigating how climate change has affected the
beginning of a flowering cycle (Pearse et al., 2017). The use of GPS data appears to be the
way forward for the advancement of methods in the study of phenology. GPS point data
allow for correspondence with higher resolution temperature data in climatically diverse
geographical regions. Studies using herbarium specimen data will continue to help us
understand the impact of recent climate change on plant reproductive phenology. Other
aspects of plant phenology that can be analyzed using herbarium specimens, such as fruit
ripening and spring leaf emergence, have important implications for higher trophic levels,
which may include rare animals dependent on plant resources (Everill et al., 2014;Zohner
& Renner, 2014;Mendoza, Peres & Morellato, 2016). Studies using herbarium specimens
have become an asset for long-term climate change vulnerability assessment. These
studies have begun to analyze the effects of climate change on community composition
(Miller-Rushing & Primack, 2008;Park, 2014), species distribution (Hereford, Schmitt &
Ackerly, 2017;Kosanic et al., 2018), coevolved plant pollinator relationships (Molnár et al.,
2012;Robbirt et al., 2014), functional groups (Miller-Rushing & Primack, 2008;Panchen et
al., 2012;Calinger, Queenborough & Curtis, 2013;Munson & Long, 2017), and phylogenetic
relationships (Bolmgren & Lonnberg, 2005;Molnár et al., 2012;Primack & Miller-rushing,
2012). Future studies investigating phylogenetic signals and plasticity are needed to further
improve our understanding of adaptation and resilience to climate change. As temperatures
continue to rise globally, herbarium specimens will continue to be crucial resources for
analyzing phenological responses to climate change.
ADDITIONAL INFORMATION AND DECLARATIONS
The authors received no funding for this work.
Curtis C. Daehler is an Academic Editor for PeerJ.
•Casey A. Jones conceived and designed the experiments, performed the experiments,
analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or
tables, authored or reviewed drafts of the paper, approved the final draft.
Jones and Daehler (2018), PeerJ, DOI 10.7717/peerj.4576 11/16
•Curtis C. Daehler prepared figures and/or tables, authored or reviewed drafts of the
paper, approved the final draft.
The following information was supplied regarding data availability:
The research in this article did not generate any data or code (literature review).
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