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Small herbaria contribute unique biogeographic records to
county, locality, and temporal scales
Travis D. Marsico1,15* , Erica R. Krimmel2,3,*, J. Richard Carter4, Emily L. Gillespie5,6, Phillip D. Lowe4, Ross McCauley7, Ashley B. Morris8,9,
Gil Nelson10,11, Michelle Smith10,12, Diana L. Soteropoulos1,13, and Anna K. Monls14
American Journal of Botany 107(11): 1577–1587, 2020; http://www.wileyonlinelibrary.com/journal/AJB © 2020 The Authors. American Journal of Botany
published by Wiley Periodicals LLC on behalf of Botanical Society of America. This is an open access article under the terms of the Creative Commons
Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is
non-commercial and no modications or adaptations are made.
Manuscript received 14 May 2020; revision accepted 8 July 2020.
1 Department of Biological Sciences,Arkansas State University,State
University, PO Box 599, AR 72467, USA
2 Sagehen Creek Field Station,University of California Berkeley, 11616
Sagehen Road, Truckee, CA 96160, USA
3 Present address: iDigBio,Florida State University, 142 Collegiate
Loop, Tallahassee, FL 32306, USA
4 Department of Biology,Valdosta State University, 1500 North
Patterson Street, Valdosta, GA 31698, USA
5 Department of Biological Sciences,Marshall University, One John
Marshall Drive, Huntington, WV 25755, USA
6 Present address: Department of Biological Sciences,Butler
University, 4600 Sunset Avenue, Indianapolis, IN 46208, USA
7 Department of Biology,Fort Lewis College, 1000 Rim Drive,
Durango, CO 81301, USA
8 Department of Biology,Middle Tennessee State University, Box 60,
Murfreesboro, TN 37132, USA
9 Present address: Department of Biology,Furman University, 3300
Poinsett Highway, Greenville, SC 29613, USA
10 Department of Biological Science,Florida State University, 142
Collegiate Loop, Tallahassee, FL 32306, USA
11 Present address: iDigBio,Florida Museum of Natural
History,University of Florida, 1659 Museum Road, Gainesville, FL
12 Present address: e Institute for Regional Conservation, 100 E.
Linton Blvd, Suite 302B, Delray Beach, FL 33483, USA
13 Arkansas Natural Heritage Commission, 1100 North Street, Little
Rock, AR 72201, USA
14 Department of Biology,Central Michigan University, 2401
Biosciences, Mount Pleasant, MI 48859, USA
15Author for correspondence (e-mail: firstname.lastname@example.org)
*ese authors contributed equally to this manuscript.
Citation: Marsico, T. D., E. R. Krimmel, J. R. Carter, E. L. Gillespie, P.
D. Lowe, R. McCauley, A. B., Morris, et al. 2020. Small herbaria
contribute unique biogeographic records to county, locality, and
temporal scales. American Journal of Botany. 107(11): 1577–1587.
PREMISE: With digitization and data sharing initiatives underway over the last 15 years, an
important need has been prioritizing specimens to digitize. Because duplicate specimens
are shared among herbaria in exchange and gift programs, we investigated the extent
to which unique biogeographic data are held in small herbaria vs. these data being
redundant with those held by larger institutions. We evaluated the unique specimen
contributions that small herbaria make to biogeographic understanding at county, locality,
and temporal scales.
METHODS: We sampled herbarium specimens of 40 plant taxa from each of eight states
of the United States of America in four broad status categories: extremely rare, very rare,
common native, and introduced. We gathered geographic information from specimens
held by large (≥100,000 specimens) and small (<100,000 specimens) herbaria. We built
generalized linear mixed models to assess which features of the collections may best
predict unique contributions of herbaria and used an Akaike information criterion-based
information-theoretic approach for our model selection to choose the best model for each
RESULTS: Small herbaria contributed unique specimens at all scales in proportion with
their contribution of specimens to our data set. The best models for all scales were the full
models that included the factors of species status and herbarium size when accounting for
state as a random variable.
CONCLUSIONS: We demonstrated that small herbaria contribute unique information for
research. It is clear that unique contributions cannot be predicted based on herbarium size
alone. We must prioritize digitization and data sharing from herbaria of all sizes.
KEY WORDS biodiversity collection; biogeography; herbarium; Index Herbariorum;
natural history collection; North American Network of Small Herbaria; rare plant; Small
Collections Network; specimen; voucher.
1578 • American Journal of Botany
Herbaria are critical components of biological research infra-
structure. e Index Herbariorum, a comprehensive, worldwide,
online inventory of herbaria and their holdings, reports 686 ac-
tive herbaria in the United States of America (USA; iers, 2020).
Collectively, these institutions serve as repositories for over 78
million specimens and represent the most extensive sampling of
vascular and nonvascular plant biodiversity in the USA, as well as
the only source of veriable data on botanical biodiversity over
time (Page et al., 2015; Heberling and Isaac, 2017; iers, 2020).
Traditional research uses of herbarium specimens include type
collections for species’ names and references for taxonomy, sys-
tematics, oristics, and biogeography. Over time the uses have
expanded to answer questions about invasive species, species
range shis, pollution trends, bioprospecting, etc. (Lavoie, 2013;
Heberling and Isaac, 2017; Heberling at al., 2019; McCartha et al.,
Herbarium specimens contribute to a diversity of research areas,
and researchers utilize an expanding set of techniques and analy-
ses that did not exist when the specimens were initially collected
(Heberling et al., 2019). For example, it is only since 2001 that her-
barium specimens have been used for molecular phylogenetic anal-
ysis (Ristaino et al., 2001; Lavoie, 2013). Biodiversity informatics
is another eld that brings new analytical methods to herbarium
specimen data, e.g., species distribution modeling (SDM) to map
biodiversity and predict response to climatic changes, in addition
to complementing studies that assess extinction risk and determine
conservation priorities (Guralnick and Hill, 2009; Bloom at al.,
2018; Lughadha et al., 2018). Herbarium specimens are also being
used to assess changes in phenology resulting from climate change
(Miller-Rushing et al., 2006; Calinger et al., 2013; Davis et al., 2015;
Park and Schwartz, 2015; Rawal et al., 2015; Pearse et al., 2017; Willis
et al., 2017; Brenskelle at al., 2019; Pearson, 2019). Digital imaging
has facilitated research at unprecedented scales via low-cost auto-
mated and semi-automated techniques for scoring morphological
characteristics or analyzing color (Gehan and Kellogg, 2017; Soltis,
Herbaria are essential partners in myriad large-scale, data-driven
research initiatives not only within the plant sciences, but also ex-
tending into ecology, human health, and economics (Gropp, 2003;
Winker, 2004; Pyke and Ehrlich, 2010; Heberling and Isaac, 2017).
Studies in disease ecology and public health cite publications that
use herbarium data from aggregated biodiversity occurrence data-
bases (Ball-Damerow et al., 2019), exemplifying the integral con-
nection between biodiversity and human health. ese diverse new
uses enhance, rather than replace, the traditional role of herbaria
as research infrastructure (Heberling and Isaac, 2017). In fact, over
the last century, citation of herbarium specimens has substantially
increased, underscoring the vital role that herbaria continue to play
in the future of cross-disciplinary, integrative science (Heberling
et al., 2019).
Many emergent research techniques benet from having more
specimen records accessible, and researchers are clamoring for data
to ll spatial, taxonomic, and temporal gaps (Ariño et al., 2013;
Lavoie, 2013). Ball-Damerow et al. (2019, p. 2) assert that “the big-
gest obstacle for biodiversity data users is obtaining records of suf-
cient quantity and quality for the region and taxonomic group of
interest.” In a literature review of works citing herbarium specimens
published between 1933 and 2012, Lavoie (2013) found that the me-
dian number of specimens referenced for biogeographic or conser-
vation-focused studies was >2800. Species distribution modeling is
a specic example of an approach greatly improved by a larger sam-
ple of specimen records, which might come from a combination of
continued collecting, more spatially distributed collecting, and bet-
ter access to existing specimen data (Feeley and Silman, 2011; Ball-
Damerow et al., 2019). e contribution of small herbaria to SDM
was addressed by Glon et al. (2017) in a case study of the Fuireneae
(Cyperaceae). Using a combination of digitized data from small and
large collections, the authors showed that species-specic mod-
els inclusive of data from small herbaria resulted in more rened
predictions of ecological niche and enhanced SDMs bridging geo-
Collection bias is another known challenge that can be addressed
on spatial, temporal, trait, phylogenetic, and collector planes by in-
cluding a large number of specimen records (Ward, 2012; Meyer
et al., 2016; Daru et al., 2017; Soltis, 2017). Bias can be minimized
by increasing not only the total number of specimens, but also
the number of collections providing specimens (Soberon, 1999;
Krishtalka and Humphrey, 2000). In a case study featuring a com-
mon insect taxon, Ferro and Flick (2015) found that they needed
specimens from a minimum of 15 collections to build a reasonable
Herbaria have a rich history both as regional collections and as
large institutions with national or global foci. Of the 686 herbaria in
the USA, only 13 hold in excess of 1 million specimens each, repre-
senting a collective 40 million specimens (iers, 2020). irty-ve
collections hold 450,000 specimens or more and represent a collec-
tive 54.7 million specimens (iers, 2020). is means that approx-
imately 30% (23 million) of the nation’s total herbarium specimens
are held across the 651 collections with fewer than 450,000 spec-
imens each, many of which have fewer than 100,000 specimens
(Barkworth and Murrell, 2012; iers, 2020). e sheer number and
vast geographic distribution of these herbaria contribute to their
collective value as research infrastructure and provide resources to
an active scientic community both within the USA and interna-
tionally (Barkworth and Murrell, 2012; Lavoie, 2013).
Small herbaria are oen regional in scope and contain fewer
specimens than larger herbaria with a national or global scope,
and regional herbaria are frequently dened by an ecological or
taxonomic specialty as well as a geographic focus (Monls et al.,
2020). ese collections may receive less research access than larger
herbaria, in part because of the logistical advantage of traveling
to a handful of larger herbaria over many smaller herbaria, a pat-
tern demonstrated by López and Sassone (2019) for herbaria in
Argentina. Similar visitation patterns based on collection size have
been reported for entomology collections (Cobb et al., 2019). In a
survey of herbaria globally, Lavoie (2013) found that the 63 indi-
vidual large herbaria with >1 million specimens each were accessed
three to six times more frequently than those with fewer specimens.
However, although individual small (<100,000 specimens; 407
collections) and medium (100,000–999,999 specimens; 263 col-
lections) herbaria were consulted less frequently, they collectively
received a roughly equal number of consultations per size class,
with small herbaria at 31% of consultations, medium herbaria at
39%, and large herbaria at 30%. Lavoie (2013) interpreted this as
evidence that, despite containing only a fraction of total specimens
worldwide, small herbaria contain specimens of local or national
importance. O’Connell et al. (2004) have a similar nding; in their
assessment of herbarium specimens collected on National Park
Service land, they found records from 78 institutions collected
between 1890 and 1980, with specimen detection rates inversely
November 2020, Volume 107 • Marsico et al.—Importance of small herbaria to biogeography knowledge • 1579
related to collection size and with relevant specimens most oen
held by collections geographically close to the region of interest.
e advent of specimen digitization means that the logistical
advantage of large vs. small herbaria is diminished because a re-
searcher can oen identify specimens of interest without visiting or
necessarily contacting each individual herbarium. Before 2004, the
use of digitized collections was practically nonexistent in the her-
barium literature, but in the intervening years, digital access has be-
come common and has facilitated the use of many more specimens
per study, from a median of 226 specimens in studies that did not
access digital records to a median of 15,295 specimens in studies
that did (Lavoie, 2013). As digitized specimen records become avail-
able online, they have an even broader reach. Ball-Damerow et al.
(2019) noted that online species occurrence databases are most
commonly used for studies on species distribution, species richness,
taxonomy, conservation, and invasive species—all research themes
that gained prominence long before digitization. ese online spe-
cies occurrence databases are democratizing access to herbarium
specimens from collections that have previously been dicult to
access due to location and/or stang. In fact, Lavoie (2013) attri-
butes the lag in publications using digitized specimen data, which
have been available in part since the 1970s, to the lack of online
For most of the last 200 years, access to specimen-based biodi-
versity records has depended primarily on researchers traveling to
collections or curators shipping loans upon request. To save time
and funds, researchers have oen limited their investigations to
large, well-known institutions and those with adequate resources
to support loan management, potentially ignoring important spec-
imens and data deposited in less accessible or discoverable institu-
tions, which are oen smaller (Casas-Marce et al., 2012). A baseline
understanding of the relative scientic contributions of specimen
data in variously sized herbaria is essential, especially in light of
the recent advances in collections digitization and data mobiliza-
tion catalyzed by the USA National Science Foundation’s (NSF)
Advancing Digitization of Biodiversity Collections (ADBC) pro-
gram, and given the continuing loss of support for biodiversity col-
lections of all types (Winker, 2004), including the potential loss of
the specimens themselves.
It is widely recognized that our knowledge of biodiversity is
far from complete, even on a coarse geographic scale (Sorrie and
Weakley, 2001; Meyer et al., 2016). Several authors have expressed
support for the importance of including small collections’ data for
understanding temporal and biogeographic diversity (Snow, 2005;
Barkworth and Murrell, 2012; Lavoie, 2013; Glon at al., 2017). One
recent publication, in particular, highlights the future importance of
discoverability and digitization of regional collections (Lendemer
et al., 2020). Here, we studied the extent to which the holdings of
small herbaria, oen regional in scope, contribute meaningfully to
our knowledge of plant biogeography at geographic and temporal
scales. is paper advances such understanding by quantifying the
unique contributions made by collections of all sizes.
MATERIALS AND METHODS
Herbarium specimen data were sampled in eight of the 50 USA
states (16%), based on locations of collaborating authors: Arkansas
(AR), California (CA), Colorado (CO), Florida (FL), Georgia (GA),
Michigan (MI), Tennessee (TN), and West Virginia (WV). Using a
state-based approach is justied because oras that contain distri-
bution and abundance data for species are oen written or com-
piled in state-specic oras and by state agencies, such as natural
heritage programs. e states included in this study span the nation
and represent a range of sizes (geographically and by population)
and endemism. Botanical history—including number of herbaria,
number of total specimens, and collection eort within the state—
also varies across these states.
For each state, plant species (or infraspecic taxa, “taxa” hereaf-
ter) were selected within each of four status categories: extremely
rare (S1, typically representing ≤5 population occurrences), very
rare (S2, typically representing 6–20 population occurrences), com-
mon native, and introduced. Due to dierences in phytogeography
and the historical emphasis on state-based plant projects, taxa were
selected for this project within each state rather than across states,
resulting in a compiled list of 320 taxa to sample (8 states × 4 status
categories × 10 taxa; except WV, which had 8 taxa in the common
native category and 12 taxa in the introduced category).
To identify sample taxa in the S1 and S2 status categories, we
acquired state-level lists for tracking rare/threatened/endangered
plants from state natural resource conservation agencies (see data
sources in Appendix S1). We chose to select taxa separately for the
S1 and S2 categories because we wanted to analyze the occurrence
of rare species records but were concerned that S1 taxa may be too
infrequently represented in the specimen data set. Taxa with dual
listings (i.e., S1/S2 or S2/S3) were excluded from our selections. Ten
S1 taxa within each state and 10 S2 taxa within each state were ran-
domly selected from the state-level lists using a random number
generator to identify a row in a spreadsheet (ltered by status, S1 or
S2) that correlated to a taxon. Or, in cases where the state-level list
was formatted for print, the random number generator identied
a page number on which the rst taxon matching the correct sta-
tus (S1 or S2) was selected. Despite this slight variation in selection
approach across state-level lists, each researcher ensured that taxa
were selected randomly to avoid bias.
To identify sample taxa in the introduced status category, we ac-
quired state-level lists for tracking introduced/invasive plant spe-
cies; if a state did not maintain its own introduced/invasive species
list, an analogous list from a neighboring state was used (see data
sources in Appendix S1). Introduced taxa within each state were
randomly selected from the state-level lists via the same methods as
above. In states that included data about the level of invasive threat
(CA, FL, GA, TN), the randomly selected introduced taxa were cho-
sen from a subset of those species representing the highest threat
level. In states lacking these data (AR, CO, MI, WV), the randomly
selected introduced taxa were compiled without accounting for per-
ceived threat level.
To identify sample taxa in the common native status category,
we acquired state-level lists of all taxa known to occur within the
state from checklists, atlases, oras, or databases (see data sources
in Appendix S1). Common native taxa within each state were ran-
domly selected from the state-level lists via the same methods as
above. We discarded any selected taxon listed as rare or introduced,
and a new random number was generated until the selected taxon
was absent from these other lists.
is design resulted in a random sample of taxa across states
and species statuses, which reduced overall bias. Based on the ran-
domly selected sample of 40 taxa per state, we attempted to acquire
specimen data from all herbaria located within each state during
the summer and fall of 2014 (see Appendix S2 for a list of herbaria
1580 • American Journal of Botany
contacted). We gathered specimen information from online data-
bases when available and by contacting curators or collections man-
agers when online data were not available. When data were not
available digitally, we digitized de novo from specimen images or
specimen loans and repatriated the transcribed data back to the col-
lection. Coauthors were responsible for acquiring and collating data
within their respective states.
Herbaria included in this study were categorized into two size
classes of small (<100,000 specimens) and large (≥100,000 speci-
mens). e 100,000-specimens cuto classies 85% of herbaria
in the USA as small (iers and Ramirez, 2020) and is reective
of recent publications in the USA herbarium community (Lavoie,
2013; Glon et al., 2017). Emerging research suggests that a more ap-
propriate cuto would be <175,000 specimens (classifying 90% of
herbaria in the USA as small; iers and Ramirez, 2020) based on
the Jenks natural breaks classication method (Monls et al., 2020).
To be conservative in our estimates and conclusions, we maintained
the more traditionally accepted 100,000-specimen cuto for small
vs. large herbaria in our primary analyses and discussion presented
here, although to be comprehensive we have also provided alterna-
tive analyses for the 175,000-specimen cuto.
For each specimen, at a minimum we recorded the catalog or
accession number, taxon identication, state, county, locality (as
transcribed from the specimen label), collector, and collection date.
e data were collated and nominally cleaned to accomplish the re-
search purposes of this project, e.g., date strings transformed into
formatted dates, taxon names synonymized with current taxonomy,
counties validated (see Appendix S3 for a data dictionary that briey
describes each eld and any transformations applied). e collated
data set consisted of 21,546 specimen records (see Data Availability
statement with this article) and included records lacking our mini-
mum data quality standards, which were agged and later excluded
during analyses. e original data had varying degrees of cleanli-
ness, but we did not x additional issues (e.g., incompletely parsed
locality information) that were beyond the scope of this research.
Specimen localities were georeferenced for spatial analysis. We
used geographic coordinate information when available either in
the original locality description (~7% of specimens, N = 1454)
or from the herbarium database (~11% of specimens, N = 2460).
Specimen localities without coordinates were georeferenced au-
tomatically using the GeoLocate API with OpenRene (~73% of
specimens, N = 15,807; Rios, 2019; OpenRene Core Team, 2018).
We georeferenced specimens for which GeoLocate could not auto-
matically determine coordinates using the online GeoLocate tool
in combination with research on Google Maps (~5% of specimens,
N = 1068). A small subset of specimens did not have enough in-
formation to georeference at a level of precision below county; we
reviewed and agged these as unable to be georeferenced (~4% of
specimens, N = 757). All coordinate data were evaluated in QGIS
(QGIS Development Team, 2019) to nd instances in which the
county recorded on the specimen label did not match the county
identity based on coordinates. Mismatches occurred for ~2000
specimen records, and we rened these georeferences using the on-
line GeoLocate tool in combination with research on Google Maps.
From the collated data set consisting of 21,546 specimen records,
we reviewed and excluded 1366 records with specic data quality
or scope issues, i.e., county information missing, multiple counties
listed, specimens suspected to be cultivated, multiple herbaria listed
(e.g., specimens of a small eld station herbarium managed physi-
cally on site at a large herbarium), and/or herbarium located out of
state. Among the 1366 specimens eliminated were records from two
out-of-state herbaria (RM in Wyoming and SJNM in New Mexico),
which had extensive holdings of Colorado material.
e data set was reviewed for duplicate specimens, and we as-
signed ags for categories of uniqueness using R (Bivand and Lewin-
Koh, 2019; Bivand and Rundel, 2019; Bivand et al., 2019; R Core
Team, 2019; Wickham et al., 2019; Zhu, 2019; see Data Availability
statement for code). For our purposes, we conservatively dened
duplicate specimens as those of the same taxon collected on the
same date in the same county by the same collector. We suspected
a priori that there may be a large number of duplicate specimens in
our data set due to the tradition of eld botanists and herbarium
curators developing extensive and long-lasting specimen exchanges
among institutions. Regardless of whether the duplicates were held
within a single herbarium or across multiple herbaria, we only re-
tained a single specimen from each set of duplicates shared within
an herbarium size class and discarded all specimens belonging to
duplicate sets that were shared between large and small herbaria.
We categorized uniqueness into three primary scales at which a
specimen may contribute novel spatiotemporal data to knowledge
of a taxon: (1) a county record (“unique county”), (2) a record of
a locality georeferenced as >1 km apart from any other locality
(“unique locality”), or (3) a record of a distinct historical time from
a previously sampled locality (“unique time”, determined as a year/
month/day previously unrepresented in the data). In our analyses,
we only included the largest scale for which a specimen contrib-
uted uniquely. In other words, although any specimen agged as
a unique county by default also represents a unique locality and a
unique time, we did not include unique county specimens in our
analyses of unique locality or time contributions.
To determine whether herbarium size class (small, large) and/or
species status (S1, S2, common native, introduced) were important
in predicting specimen uniqueness, we created three sets of general-
ized linear mixed-eects models with a binomial logistic regression,
one set for each of our three scales (county, locality, and temporal).
We then conducted model selection on each set with an informa-
tion-theoretic approach based on Akaike information criterion
(AIC; Anderson and Burnham, 2002). For each scale, our candidate
set consisted of a null model, individual xed eects models, and a
full model with each of the individual variables included as additive
xed eects. Uniqueness (1 for yes, 0 for no) at a given scale (county,
locality, or temporal) was our response variable, and herbarium size
and species status category were the xed eects. State was treated
as a random variable to account for our methods, which did not
sample states comprehensively, but rather based on locations of the
coauthors. We determined the best model in our candidate set by
identifying which had the lowest ΔAIC value that was also less than
2. Modelling was conducted in the R programming language using
the lme4 package (Bates et al., 2015; R Core Team, 2019; see Data
Availability statement for code). We conrmed t for each of our
full models (unique county, unique locality, and unique time) and
tested for collinearity by evaluating the variance ination factors
and Cramer’s V values in R (Lenth, 2020; Navarro, 2015; Fox and
Weisberg, 2019; see Data Availability statement for code).
One hundred thirty-eight herbaria contributed to our project, of
which 26 had ≥100,000 specimens, representing large herbaria
November 2020, Volume 107 • Marsico et al.—Importance of small herbaria to biogeography knowledge • 1581
(see Appendix S2). States ranged from having one large herbarium
within the state (AR, GA) to having 10 large herbaria (CA). One
hundred twelve herbaria represent small herbaria with <100,000
specimens, and states ranged from having 6 (WV) to 37 (CA)
small herbaria. According to estimates of total herbarium size,
specimens held by the large herbaria included in this study num-
ber 12,953,200 (87.5%), and specimens held by the small herbaria
number 1,858,833 (12.5%). is proportion is similar to that of all
United States herbaria recorded in Index Herbariorum, for which
large herbaria hold a collective 68 million (87.2%) and small her-
baria 10 million (12.8%) specimens (iers and Ramirez, 2020).
Within the original data set of 21,546 specimen records collated for
this project, large herbaria contributed 15,143 specimens (70.3%),
and small herbaria contributed 6403 specimens (29.7%). Aer ex-
cluding rows with data quality issues and accounting for duplicate
records (dened above in Materials and Methods), our data set was
condensed to 16,348 records, each representing a unique collecting
event. Most specimens (89% of those held by small herbaria and
83% of those held by large herbaria) were unduplicated, and dupli-
cates were more likely to be distributed only between large herbaria
than either only between small herbaria, or shared between large
and small herbaria (Table 1).
Our primary analysis was conducted on a further reduced
subset of these data (N = 15,792) by excluding an additional 137
records that were classied as a unique time by our agging but
that did not have a collecting day recorded. e relative contribu-
tion of specimens by herbarium size varied widely by state (Fig. 1),
but small herbaria across all states contributed a larger percentage
(30.7% of 15,792) of specimens to this study than expected based
on their holdings (12.5% of total specimens are held by the small
herbaria included in this study; Appendix S2). Patterns at each of
our uniqueness scales (county, locality, temporal) also varied widely
by state (Fig. 2; see Appendix S4 for the data used to generate this
gure). Small herbaria in some states exhibited similarities between
the proportion of records they contributed to the analysis data set
and the proportion of records they contributed to certain unique-
ness scales (compare Figs. 1 and 2). For example, small herbaria
contributed nearly one half of the specimens for Arkansas (Fig. 1),
and nearly half of the unique records at each uniqueness scale were
provided by small herbaria (Fig. 2). As expected, there were greater
unique contributions from the temporal scale than from locality
or county and from the common native and introduced taxa than
from the S1 and S2 taxa (Fig. 3A).
Modeling the eects of size class, species status, and state, and
then comparing these models using AIC (Table 2) allowed us to
parse high-level ndings from the complexity of our results. We
found that at all uniqueness scales (county, locality, temporal), the
full model was weighted 100%, meaning that it provided the best
balance between t and parsimony (Table 2). Our best model also
did well tting the observed data, which we used as a comparison to
assess the validity of our models in predicting the probability that a
specimen represents unique information at dierent biogeographic
scales (compare Fig. 3B with 3C). Because of the way we analyzed
our data, the probability of a specimen contributing uniquely at one
of the scales is 1 (Fig. 3B, C). In other words, with duplicated speci-
mens across herbarium size classes removed (2.6% of specimen re-
cords; Table 1), all specimens originating as unduplicated anywhere
or duplicated within size class represent unique contributions at the
county, locality, or temporal scale for a given herbarium size class.
e probabilities of uniqueness predicted by our models (Fig. 3C)
show that large herbaria are predicted to have nearly twice the prob-
ability of small herbaria to contribute unique county records, but
only slightly greater probability than small herbaria to contribute
unique locality records. Since the probabilities sum to 1 across the
uniqueness scales, small herbaria are predicted to have a greater
probability than large herbaria of providing unique records at the
temporal scale (Fig. 3C).
To account for emerging research (see Monls et al., 2020), we
produced the same models for our data using a cuto of 175,000
specimens to distinguish between small and large herbaria. We
found that the full model at each uniqueness scale was again
weighted 100% (results available in Appendix S5; also see Data
Availability statement). ese results indicate that the same factors
are at play for explaining unique contributions of small herbaria,
even if the cuto for what constitutes a small herbarium is raised.
Our results show that herbaria house primarily unduplicated spec-
imens within their states, and they represent unique knowledge at
all biogeographic scales (county, locality, temporal). Our ndings
demonstrate that research requiring a complete picture of existing
biogeographic knowledge at any scale must include specimens from
both small and large herbaria. Although previously it has not been
widely demonstrated that small herbaria curate unduplicated spec-
imens, we found that 97.4% of small herbarium specimens sampled
for this study are either totally unduplicated, or duplicated only by
another small herbarium (Table 1). ese unduplicated specimens
represent unique biogeographic knowledge in all species categories
(Fig. 3A, B), and our models predict how this uniqueness is distrib-
uted across biogeographic and temporal scales (Fig. 3C). We show
that within a given size class (small, large) and species status (S1, S2,
common native, introduced), a specimen has an increasing proba-
bility of representing uniqueness at the county vs. locality vs. tem-
poral scale. For example, our models predict that an unduplicated
specimen from a small herbarium of an S2 taxon has approximately
a 10% chance of representing a unique county, a 26% chance of
representing a unique locality (additive with unique county con-
tribution), and a 100% chance of representing a unique time in the
botanical collecting record for this taxon in this state (additive with
the previous two scales; Fig. 3). We observed (and our models pre-
dicted) that the additive unique county and locality probabilities
were always less than 0.5 for both herbarium size classes and all
four species statuses, indicating that a specimen has a probability of
providing a unique record at the temporal scale more than half of
the time. erefore, specimens in herbaria oen represent repeated
TABLE 1. Number of unique collecting events represented by unduplicated
and duplicated specimens held in large vs. small herbaria.
Duplicate type Large herbaria Small herbaria
Unduplicated specimens 9415 (83%) 4456 (89%)
Duplicated specimens held only
by large herbaria
1635 (14.4%) N/A
Duplicated specimens held only
by small herbaria
N/A 423 (8.4%)
Duplicated specimens held by
large and small herbaria
289 (2.6%) 130 (2.6%)
Total unique collecting events 11,339 (100%) 5009 (100%)
1582 • American Journal of Botany
collections from the same localities over time, possibly due to hab-
itat loss, proximity to the herbarium, other access-related factors
such as permits for collecting, or an emphasis on known botanical
areas of interest.
We suspect that small herbaria may be especially relevant to re-
search focused on regionally occurring taxa, as evidenced by the
17-percentage-point increase between the total number of speci-
mens held by small (vs. large) herbaria contacted for this project
(12.5%), and the number of relevant specimens that these small
herbaria contributed to the project data set (29.7%), which had
a focus on regional taxa. Small herbaria likely have sta and stu-
dents focused on collecting specimens from their own local vicinity.
Moreover, student collections may be repeated over time from the
same localities due to the nature of course assignments or access to
certain sites known by the curator of the herbarium. For a complete
understanding of species distributions, a thorough sampling of col-
lections of all sizes is warranted, and based on the idiosyncratic na-
ture of collections and curatorial research interest, it is dicult to
predict a priori which herbaria might be excluded without resulting
While a thorough sampling of many herbaria is challenging in
person, digitization oers an excellent compromise. We recommend
including herbaria of all sizes equally in digitization eorts and en-
couraging the mobilization of digitized data and media to biodi-
versity data aggregators such as iDigBio (www.idigb io.org) and the
Global Biodiversity Information Facility (www.gbif.org). Our data
collection was complicated by the uneven distribution of digitally
accessible data across herbaria. For collections that already had a
signicant amount of data digitized and available online, e.g., on the
Consortium of California Herbaria portal, we downloaded those
data directly, whereas for collections without an online presence of
specimen records, we requested data from each herbarium. If data
from portals were present, but not complete, then we missed some
existing data because we did not contact individual herbaria if data
for our target taxa were present in an on-
line format. Paradoxically, it is therefore
possible that we received more complete
data from herbaria without a digital pres-
ence at the time of data collection. Our
own experience highlights the impor-
tance of improving digital accessibility
for all herbarium specimens.
In the last decade, there has been
a genuine eort to include small
collections in digitization projects
funded through the National Science
Foundation’s Advancing Digitization of
Biodiversity Collections (ADBC) pro-
gram. e SouthEast Regional Network
of Expertise and Collections (SERNEC;
sernecportal.org) and the Southern
Rockies projects (Allen, 2018) are two
examples of how small herbaria have
successfully been integrated in digitiza-
tion projects beyond the scope of what
they might have had the individual ca-
pacity to do otherwise. We contend that
continuing to digitize herbaria of all
sizes will ameliorate the lack-of-data
situation to some degree, but we also
realize that continued regional collecting is necessary. Prather et al.
(2004) found that local collecting is on the decline in two-thirds
of the herbaria surveyed, regardless of herbarium size. Ferro and
Flick (2015) discovered that bias in entomology collections has a
serious eect on species distribution modelling and that the num-
ber of collections contributing specimens, rather than the number
of localities sampled or specimens themselves, is a better indicator
of exhaustiveness in avoiding bias. ey also argued that “main-
tenance and growth of numerous, regional natural history collec-
tions is important” (Ferro and Flick, 2015, p. 424), which applies to
herbaria as it does to entomology collections. Not only do sta at
small herbaria curate and make specimens accessible, but they also
foster regional expertise that may not be accurately captured in
specimen data alone. For instance, historic collecting localities can
be notoriously dicult to interpret for modern georeferencing, and
even more recently collected specimens may use local place names
to describe localities. Collections with a regional focus tend to be
associated with people who are more familiar with the surround-
ing geography and to whom local place names are meaningful.
is regional knowledge translates into georeferencing precision
and accuracy, which are the most highly desirable qualities sought
by users working with species occurrence data (Ariño et al., 2013).
Digitization, continued collecting, and maintaining and enhanc-
ing regional biogeographical knowledge require the recognition of
herbaria as critical research infrastructure and the understanding
that in the USA this infrastructure comprises 686 individual her-
baria, 85% of which are small collections with fewer than 100,000
specimens (iers and Ramirez, 2020). Our herbaria of all sizes
continue to need signicant nancial support, and to this extent,
it is key for university administrators to understand the value of
natural history collections. We provide a template letter of advo-
cacy from an herbarium curator to an institutional administrator
to assist in starting this discussion for readers in a position to do so
FIGURE 1. Number of specimen records included in this study’s primary analysis data set that were
contributed by large (≥100,000 specimens) and small herbaria (<100,000 specimens) in each partic-
CA MI TN FL CO GA WV AR
November 2020, Volume 107 • Marsico et al.—Importance of small herbaria to biogeography knowledge • 1583
Better access to digitized specimen data will allow future studies
to address the contributions of out-of-state herbaria to in-state bio-
geographical knowledge, which this study did not. We decided not
to include specimens held in out-of-state herbaria in our analyses
because of the complexity involved in data gathering, although we
think that doing so would aect our narrative in regard to duplicate
specimens and specimen uniqueness. Out-of-state holdings can
contain critical specimens for our understanding of certain areas.
For example, eld research for the Flora of the Four Corners Region
(Heil et al., 2013) resulted in a large number of collections from four
states since the ora followed an ecological rather than a political
boundary. Most of the specimens were deposited in the San Juan
College Herbarium (SJNM; Farmington, New Mexico, USA), since
the principal author curates the herbarium there. Another example
of important Colorado specimens being held out-of-state comes
from the large oristic inventory program of the Rocky Mountain
Herbarium (RM) at the University of Wyoming, Laramie, Wyoming.
is program was initiated in 1978 and resulted in more than 60
oristic studies across 13 states, contributing more than 640,000
specimens total and over 107,000 specimens from Colorado (Rocky
Mountain Herbarium, 2020). Moreover, we know that specimen
collecting and duplicate sharing can be inuenced by proximity and
social connections rather than the connes of a state’s boundaries.
For example, in Arkansas, multiple small herbaria shared dupli-
cates with the nearby but out-of-state herbarium at the University
of Louisiana at Monroe (NLU; Monroe, Louisiana, USA), a large
herbarium that makes a particularly interesting example because
it was orphaned by the university and subsequently transferred to
the Botanical Research Institute of Texas (BRIT) in 2017. A future
avenue for research aimed at understanding knowledge gaps in
biogeographic patterns from existing data should investigate speci-
men contributions held uniquely outside the state boundaries from
where the specimens were collected.
In sum, herbaria of all sizes are important resources for preserving
and expanding our knowledge of phytogeography. Small herbaria
are crucial components of this research infrastructure because they
contain records that ll gaps (this study), because more collections
ameliorate bias (Soberon, 1999; Ferro and Flick, 2015; Krishtalka
and Humphrey, 2000), and because most herbaria in the USA are
small (iers, 2020; iers and Ramirez, 2020). Digitization and
data sharing have removed the historical logistical barrier for a re-
searcher having to visit many separate collections to assess specimen
FIGURE 2. Number of specimen records included in this study’s primary analysis data set that were contributed by large (≥100,000 specimens) and
small herbaria (<100,000 specimens) in each participating state, faceted by scale of uniqueness (county, locality, temporal) and species status cate-
gory (S1, S2, common native, introduced).
1584 • American Journal of Botany
November 2020, Volume 107 • Marsico et al.—Importance of small herbaria to biogeography knowledge • 1585
holdings or acquire digital data, so digital data sharing is an essen-
tial strategy for democratizing access to all herbaria.
We dedicate our work to the late Dr. George Pryor Johnson (APCR)
who was a founding member of the North American Network of
Small Herbaria and who hoped this work would be published in
support of small collections. We thank all the herbaria (large and
small) that contributed data to our project. Details of herbaria
that contributed can be found in Appendix S2. We thank Hazel K.
Berríos for her assistance in working on early versions of data anal-
yses. A previous version of this manuscript was improved by analyt-
ical advice and edits from Virginie Rolland. We are grateful to two
anonymous reviewers who provided suggestions that strengthened
the manuscript. Financial support for this project came from NSF
grants EF-1410098, DUE-1564954, and DBI-1561743 to T.D.M. and
the Department of Biological Sciences and Environmental Science
Program at Arkansas State University, NSF grants DBI-1054366
and DBI-1458264 to J.R.C. at Valdosta State University, NSF grant
DBI-1410143 to E.L.G. at Marshall University, and NSF grant DBI-
1410087 to A.B.M. at Middle Tennessee State University.
T.D.M., E.R.K., J.R.C., E.L.G., P.D.L., R.M., A.B.M., G.N., M.S., and
A.K.M. conceived of the idea and gathered data from their state
herbaria. Countless hours were spent on conference calls to strat-
egize and implement a uniform approach to gathering and collat-
ing data. A.K.M. provided initial leadership and momentum. E.R.K.
and T.D.M. georeferenced any specimens for which it was necessary.
E.R.K. conducted the data compilation and preliminary analyses.
D.L.S. and E.R.K. conducted the modeling analyses. T.D.M. and
E.R.K. led the writing of the manuscript. All authors contributed to
and edited the manuscript.
Data collated for the purposes of this study and the analysis code
written in R are archived and available on Zenodo at https://doi.
org/10.5281/zenodo.3937865. A version of the analysis code ren-
dered for viewing in a web browser can be found at https://ekrim
mel.github.io/marsi co-et-al-2020/Marsi co-et-al-2020_v4.html.
Additional Supporting Information may be found online in the
supporting information tab for this article.
APPENDIX S1. Excel spreadsheet documenting all 320 taxa used
for this project (8 states × 40 taxa per state), including data sources
for each species category.
APPENDIX S2. Excel spreadsheet documenting all herbaria con-
tacted to provide data for this project, including information on
collection size and data contribution.
APPENDIX S3. Excel spreadsheet providing a data dictionary for
elds in our data and details about any transformations done to
them during compilation.
APPENDIX S4. Excel spreadsheet of results from analysis to de-
termine unique specimen records contributed to this study by large
(≥100,000 specimens) and small herbaria (<100,000 specimens) in
each participating state, faceted by scale of uniqueness (county, lo-
cality, temporal) and species category (S1, S2, common native, intro-
duced). Figure 2 is a visualization of these data.
APPENDIX S5. Analysis summary (equivalent to Appendix S4),
duplicate summary (equivalent to Table 1), and modelling results
(equivalent to Table 2) from an alternative analysis of data using
cuto of 175,000 specimens to dierentiate between large and small
APPENDIX S6. Example letter to university/institution adminis-
trators highlighting the work in this paper so that curators can help
justify the research contributions made by small herbaria.
Allen, J. R. 2018. Advancing digitization in the southern Rocky Mountain re-
gion. The Vasculum (newsletter of the Society of Herbarium Curators)
Anderson, D. R., and K. P. Burnham. 2002. Avoiding pitfalls when using informa-
tion-theoretic methods. Journal of Wildlife Management 66: 912–918.
FIGURE 3. Assessment of model validity in predicting the probability that a specimen represents unique information at dierent biogeographic
scales by comparing (A) observed specimen records and (B) probability in observed data to (C) probability predicted by model. Given that the herbar-
ium size class and species status of a specimen are inherent attributes of the specimen,this gure illustrates the scale of biogeographic uniqueness at
which a particular specimen might be expected to contribute.
TABLE 2. Model selection results of specimen uniqueness at the county,
locality, and temporal scales. Shown are the degrees of freedom, Akaike
information criterion (AIC) values, ΔAIC values, and AIC weights. In each model,
state is included as a random variable.
Response variable Model predictors df AIC ΔAIC AIC weight
Size class +
5 10958 0 1
Size class 2 11022 64.4 0
Species status 4 11076 118.3 0
No predictor 1 11123 165.5 0
Size class +
5 17332 0 1
Species status 4 17365 32.7 0
Size class 2 17422 90.0 0
No predictor 1 17459 126.9 0
Size class +
5 20408 0 1
Size class 2 20460 52.8 0
Species status 4 20566 158.5 0
No predictor 1 20616 208.8 0
1586 • American Journal of Botany
Ariño, A. H., V. Chavan, and D. P. Faith. 2013. Assessment of user needs of pri-
mary biodiversity data: Analysis, concerns, and challenges. Biodiversity
Informatics 8: 59–63.
Ball-Damerow, J. E., L. Brenskelle, N. Barve, P. S. Soltis, P. Sierwald, R. Bieler, R.
LaFrance, et al. 2019. Research applications of primary biodiversity databases
in the digital age. PLoS One 149: e0215794.
Barkworth, M., and Z. Murrell. 2012. e US Virtual Herbarium: working with
individual herbaria to build a national resource. ZooKeys 209: 55–73.
Bates, D., M. Maechler, B. Bolker, and S. Walker. 2015. Fitting linear mixed-eects
models using lme4. Journal of Statistical Software 67: 1–48.
Bivand, R., T. Keitt, and B. Rowlingson. 2019. rgdal: Bindings for the Geospatial
Data Abstraction Library (GDAL). R package version 1.4-7. Website: https://
CRAN.R-proje ct.org/packa ge=rgdal [accessed 01 March 2019].
Bivand, R., and N. Lewin-Koh. 2019. maptools: Tools for handling spatial ob-
jects. R package version 0.9-8. Website: https://CRAN.R-proje ct.org/packa
ge=maptools [accessed 01 March 2019].
Bivand, R., and C. Rundel. 2019. rgeos: Interface to Geometry Engine - Open
Source (GEOS). R package version 0.5-2. Website: https://CRAN.R-proje
ct.org/packa ge=rgeos [accessed 01 March 2019].
Bloom, T. D. S., A. Flower, and E. G. DeChaine. 2018. Why georeferencing mat-
ters: introducing a practical protocol to prepare species occurrence records
for spatial analysis. Ecology and Evolution 8: 765–777.
Brenskelle, L., B. J. Stucky, J. Deck, R. Walls, and R. P. Guralnick. 2019. Integrating
herbarium specimen observations into global phenology data systems.
Applications in Plant Sciences 7: e01231.
Calinger, K. M., S. Queenborough, and P. S. Curtis. 2013. Herbarium specimens
reveal the footprint of climate change on owering trends across north-cen-
tral North America. Ecology Letters 16: 1037–1044.
Casas-Marce, M., E. Revilla, M. Fernandes, A. Rodríguez, M. Delibes, and J. A.
Godoy. 2012. e value of hidden scientic resources: preserved animal
specimens from private collections and small museums. BioScience 62:
Cobb, N. S., L. F. Gall, J. M. Zaspel, N. J. Dowdy, L. M. McCabe, and A. Y.
Kawahara. 2019. Assessment of North American arthropod collections:
Prospects and challenges for addressing biodiversity research. PeerJ 7:
Daru, B. H., D. S. Park, R. B. Primack, C. G. Willis, D. S. Barrington, T. J. S. Whitfeld,
T. G. Seidler, et al. 2017. Widespread sampling biases in herbaria revealed
from large-scale digitization. New Phytologist 217: 939–955.
Davis, C. C., C. G. Willis, B. Connolly, C. Kelly, and A. M. Ellison. 2015. Herbarium
records are reliable sources of phenological change driven by climate and
provide novel insights into species’ phenological cueing mechanisms.
American Journal of Botany 102: 1599–1609.
Feeley, K. J., and M. R. Silman. 2011. Keep collecting: Accurate species distribu-
tion modelling requires more collections than previously thought. Diversity
and Distributions 1–9: 1132–1140.
Ferro, M. L., and A. J. Flick. 2015. Collection bias and the importance of nat-
ural history collections in species habitat modeling: a case study using
Thoracophorus costalis Erichson (Coleoptera: Staphylinidae: Osoriinae), a
critique of gbif.org. Coleopterists Bulletin 69: 415–425.
Fox, J., and S. Weisberg. 2019. car: companion to applied regression. R package
version 3.0-5. Website: https://cran.r-proje ct.org/web/packa ges/car/index.
html [accessed 01 June 2019].
Gehan, M. A., and E. A. Kellogg. 2017. High-throughput phenotyping. American
Journal of Botany 104: 505–508.
Glon, H. E., B. W. Heumann, J. R. Carter, J. M. Bartek, and A. K. Monls. 2017. e
contribution of small collections to species distribution modelling: A case
study from Fuireneae (Cyperaceae). Ecological Informatics 42: 67–78.
Gropp, R. E. 2003. Are university natural science collections going extinct?
BioScience 53: 550.
Guralnick, R., and A. Hill. 2009. Biodiversity informatics: automated approaches
for documenting global biodiversity patterns and processes. Bioinformatics
Heberling, J. M., and B. L. Isaac. 2017. Herbarium specimens as exaptations: new
uses for old collections. American Journal of Botany 104: 963–965.
Heberling, J. M., L. A. Prather, and S. J. Tonsor. 2019. e changing uses of her-
barium data in an era of global change: an overview using automated content
analysis. BioScience 69: 812–822.
Heil, K. D., S. L. O’Kane, L. M. Reeves, and A. Cliord. 2013. Flora of the Four
Corners Region: vascular plants of the San Juan River Drainage, Arizona,
Colorado, New Mexico, and Utah. Monographs in Systematic Botany from
the Missouri Botanical Garden, vol. 124. Missouri Botanical Garden Press,
St. Louis, MO, USA.
Krishtalka, L., and P. S. Humphrey. 2000. Can natural history museums capture
the future? BioScience 50: 611–617.
Lavoie, C. 2013. Biological collections in an ever changing world: Herbaria as
tools for biogeographical and environmental studies. Perspectives in Plant
Ecology, Evolution and Systematics 15: 68–76.
Lendemer, J., B. iers, A. K. Monls, J. Zaspel, E. R. Ellwood, A. Bentley, K.
Levan, et al. 2020. e Extended Specimen Network: a strategy to enhance
US biodiversity collections, promote research and education. BioScience
Lenth, R.2020. emmeans: estimated marginal means, aka least-squares means.
Website: https://CRAN.R-proje ct.org/packa ge=emmeans [accessed 06 May
López, A., and A. B. Sassone. 2019. e uses of herbaria in botanical research.
A review based on evidence from Argentina. Frontiers in Plant Science 10:
Lughadha, E. N., B. E. Walker, C. Canteiro, H. Chadburn, A. P. Davis, S. Hargreaves,
E. J. Lucas, et al. 2018. e use and misuse of herbarium specimens in evalu-
ating plant extinction risks. Philosophical Transactions of the Royal Society,
B, Biological Sciences 374: 20170402.
McCartha, G. L., C. M. Taylor, A. van der Ent, G. Eschevarria, D. M. Navarrete
Gutiérrez, and A. J. Pollard. 2019. Phylogenetic and geographic distribution
of nickel hyperaccumulation in neotropical Psychotria. American Journal of
Botany 106: 1377–1385.
Meyer, C., P. Weigelt, and H. Kre. 2016. Multidimensional biases, gaps and
uncertainties in global plant occurrence information. Ecology Letters 19:
Miller-Rushing, A. J., R. B. Primack, D. Primack, and S. Mukunda. 2006.
Photographs and herbarium specimens as tools to document phenological
changes in response to global warming. American Journal of Botany 93:
Monls, A. K., E. R. Krimmel, J. M. Bates, J. E. Bauer, M. W. Belitz, B. C. Cahill, A.
M. Caywood, et al. 2020. Regional collections are an essential component of
biodiversity research infrastructure. BioScience biaa102.
Navarro, D. J.2015. lsr: companion to “Learning statistics with R”. R package ver-
sion 0.5. Website: https://CRAN.R-proje ct.org/packa ge=lsr [accessed 01 June
O’Connell, A. F., A. T. Gilbert, and J. S. Hateld. 2004. Contribution of natu-
ral history collection data to biodiversity assessment in national parks.
Conservation Biology 18: 1254–1261.
OpenRene Core Team. 2018. OpenRene: a free, open source power tool for
working with messy data and improving it, version 2.8 for Mac. Website:
https://www.openr ene.org/ [accessed 01 January 2018].
Page, L. M., B. J. MacFadden, J. A. Fortes, P. S. Soltis, and G. Riccardi. 2015.
Digitization of biodiversity collections reveals biggest data on biodiversity.
BioScience 65: 841–842.
Park, I. W., and M. D. Schwartz. 2015. Long-term herbarium records reveal tem-
perature-dependent changes in owering phenology in the southeastern
USA. International Journal of Biometeorology 59: 347–355.
Pearse, W. D., C. C. Davis, D. W. Inouye, R. B. Primack, and T. J. Davies. 2017. A
statistical estimator for determining the limits of contemporary and historic
phenology. Nature Ecology & Evolution 1: 1876–1882.
Pearson, K. D. 2019. Spring- and fall-owering species show diverging pheno-
logical responses to climate in the southeast USA. International Journal of
Biometeorology 63: 481–492.
Prather, L. A., O. Alvarez-Fuentes, M. H. Mayeld, and C. J. Ferguson. 2004. e
decline of plant collecting in the United States: A threat to the infrastructure
of biodiversity studies. Systematic Botany 29: 15–28.
November 2020, Volume 107 • Marsico et al.—Importance of small herbaria to biogeography knowledge • 1587
Pyke, G. H., and P. R. Ehrlich. 2010. Biological collections and ecological/envi-
ronmental research: A review, some observations and a look to the future.
Biological Reviews 85: 247–266.
QGIS Development Team. 2019. QGIS Geographic Information System, version
3.4 for Mac. Website: http://qgis.osgeo.org [accessed 01 January 2019].
R Core Team. 2019. R: A language and environment for statistical computing,
version 3.6.1 for Mac. Website: https://www.R-proje ct.org/ [accessed 05 July
Rawal, D. S., S. Kasel, M. R. Keatley, and C. R. Nitschke. 2015. Herbarium records
identify sensitivity of owering phenology of eucalypts to climate: implica-
tions for species response to climate change. Austral Ecology 40: 117–125.
Rios, N.2019. GEOLocate soware for georeferencing natural history data.
Website: http://www.geo-locate.org [accessed 01 January 2019 through 30
Ristaino, J. B., C. T. Groves, and G. R. Parra. 2001. PCR amplication of the Irish
potato famine pathogen from historic specimens. Nature 411: 695–697.
Rocky Mountain Herbarium. 2020. Projects by graduate students in oristics/
sta/associates. University of Wyoming, Laramie, WY, USA. Website: https://
www.uwyo.edu/botan y/rocky -mount ain-herba rium/study -areas.pdf.
Snow, N. 2005. Successfully curating smaller herbaria and natural history collec-
tions in academic settings. BioScience 55: 771–779.
Soberon, J. 1999. Linking biodiversity information sources. Trends in Ecology &
Evolution 14: 291.
Soltis, P. S. 2017. Digitization of herbaria enables novel research. American
Journal of Botany 104: 1281–1284.
Sorrie, B., and A. Weakley. 2001. Coastal plain vascular plant endemics:
Phytogeographic patterns. Castanea 66(1/2): 50–82.
iers, B. M.2020. e world’s herbaria 2019: A summary report based on data
from Index Herbariorum. Website: http://sweet gum.nybg.org/scien ce/docs/
iers, B. M., and J. Ramirez. 2020.Index Herbariorum API, version 1.0. Website:
http://sweet gum.nybg.org/scien ce/api/v1/insti tutio ns/searc h?count
ry=u.s.a.&downl oad=yes [accessed 13 February 2020].
Ward, D. F. 2012. More than just records: Analysing natural history collections
for biodiversity planning. PLoS One 7: e50346.
Wickham, H., M. Averick, J. Bryan, W. Chang, L. D’Agostino McGowan, R.
François, G. Grolemund, et al. 2019. Welcome to the tidyverse. Journal of
Open Source Software 4: 1686.
Willis, C. G., E. R. Ellwood, R. B. Primack, C. C. Davis, K. D. Pearson, A. S. Gallinat,
J. M. Yost, et al. 2017. Old plants, new tricks: Phenological research using her-
barium specimens. Trends in Ecology & Evolution 32: 531–546.
Winker, K. 2004. Natural history museums in a postbiodiversity era. BioScience
Zhu, Hao. 2019. kableExtra: Construct Complex Table with ‘kable’ and Pipe
Syntax. R package version 1.1.0. Website: https://CRAN.R-proje ct.org/packa
ge=kable Extra [accessed 01 June 2019].