Multivariate Analysis of Stream Substrates in Subpopulations
of Harperella (Ptilimnium nodosum (Rose) Mathias: Apiaceae)
at Sideling Hill Creek, Maryland, USA
Author(s) :Christopher T. Frye and Samantha M. Tessel
Source: Castanea, 77(1):2-10. 2012.
Published By: Southern Appalachian Botanical Society
DOI: http://dx.doi.org/10.2179/11-023
URL: http://www.bioone.org/doi/full/10.2179/11-023
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CASTANEA 77(1): 2–10. MARCH 2012
Copyright 2012 Southern Appalachian Botanical Society
Multivariate Analysis of Stream Substrates
in Subpopulations of Harperella
(Ptilimnium nodosum (Rose) Mathias: Apiaceae)
at Sideling Hill Creek, Maryland, USA
Christopher T. Frye1* and Samantha M. Tessel2
Maryland Department of Natural Resources, Wildlife and Heritage Service, Natural Heritage
Program, Wye Mills Field Office, PO Box 68, Wye Mills, Maryland 21679
2
Curriculum for the Environment and Ecology, 207 Coates Building, CB#3275, UNC-Chapel
Hill, Chapel Hill, North Carolina 27599
1
ABSTRACT Harperella (Ptilimnium nodosum) is a federally endangered plant species with a
large population along Sideling Hill Creek, Allegany and Washington Counties, Maryland.
Monitoring of this species is difficult owing to the unpredictable flood events that change the
distribution and composition of stream substrates of its rocky shoal habitat. We characterized
substrate types in 80 quadrats using two methods of nonmetric multidimensional scaling (NMS
and MDS). We employed multiple-response permutation procedure (MRPP) to examine
differences in substrate composition between habitats occupied (N 5 52) and unoccupied (N 5
28) by Harperella. The NMS and MDS ordinations show that Harperella cover and amount of
fine sediments are positively associated. Harperella occupies specific microhabitat with high
cover of fine sediments, often held in crevices of exposed bedrock. The MRPP results
demonstrated that substrate composition in occupied versus unoccupied habitats differ
significantly. This difference is chiefly attributable to cover of fine sediments in occupied
habitats and cobble, gravel, and sand in unoccupied habitats. We conclude that the local
distribution and abundance of Harperella patches in Sideling Hill Creek is constrained by the
abundance of appropriate substrate microhabitat in any given year and recommend that
annual census be modified to focus on large persistent patches.
Key words: Harperella, multiple-response permutation procedure, nonmetric multidimensional scaling, Sideling Hill Creek, stream substrates.
INTRODUCTION Harperella (Ptilimnium
nodosum (Rose) Mathias; Apiaceae) is a
federally endangered plant species native to
rocky streams in Maryland, West Virginia,
Virginia, North Carolina, Alabama, and
Arkansas, and nonriparian habitats in South
Carolina and Georgia. Twenty-four populations are extant: nine in Arkansas, and one to
three in each of the other states (USFWS
2008). Rose (1906, 1911) originally described
three taxa in the genus Harperella (transferred
to Ptilimnium by Mathias [1936]) based upon
ecological discontinuities: H. nodosum, a plant
of pools and ponds; H. fluviatilis, a plant of
rocky streams; and H. viviparum, also a plant
of rocky streams but differing in the production of late-season vegetative ramets. Kress
et al. (1994) provided some genetic basis
(isozymes) for these assignments and more
recently Feist and Downie (2008) found
evidence of some geographical structuring in
nuclear rDNA internal transcribed spacer
(ITS) sequences. However, until the taxonomic complexities of the genus are resolved, we
will follow Kral’s (1981) treatment as P.
nodosum s.l. and hereafter use the common
name Harperella in this manuscript.
A large population of Harperella is extant
along Sideling Hill Creek, in Washington and
*email address: [email protected]
Received June 27, 2011; Accepted September 2, 2011.
DOI: 10.2179/11-023
2
2012
FRYE, TESSEL: STREAM SUBSTRATES IN HARPERELLA HABITATS
Allegany Counties, Maryland. Riverine Harperella habitat is dynamic and plants appear,
disappear, and reappear according to naturally occurring changes in stream flow and
in response to physical reworking of stream
substrates during periodic flood events. Water
flow peaks in Sideling Hill during winter and
spring months but lies at less than full bank
during much of the late summer and fall
when rocky shoals and bedrock are exposed.
Harperella occurs along Sideling Hill in
ephemeral and persistent stands (based on
20 yr of monitoring data). Plants flower in
July and August, releasing seed in September
and October. Germination can occur immediately (Maddox and Bartgis 1992), and seed
germination rates have been reported as high
as 83% (Wells et al. 2004). In the fall, asexual
reproduction occurs as a production of vegetative offshoots from the base and nodal buds
along culms. The parent rosette, vegetative
shoots, and seedlings are all capable of
overwintering. The ramet/genet ratio in
patches is unknown. Typical of annuals,
Harperella population sizes may fluctuate
dramatically from year to year.
During the last 20 yr of demographic and
life-history studies of Harperella at Sideling
Hill Creek, one enigmatic feature of Harperella ecology has repeatedly emerged—unoccupied but apparently suitable habitat is
abundant in Sideling Hill Creek and in closely
adjacent watersheds, yet the species’ local
distribution remains patchy and newly founded patches are rarely persistent. Microhabitat
features are suspected to play an important
role in colonization and persistence of Harperella (Maddox and Bartgis 1989, Wells et al.
2004, Marcinko 2007). Frequently noted are
sites that (a) allow full sun during most of the
day, (b) simultaneously offer protection from
severe erosion while receiving annual scouring during natural high flow events in the
spring, and (c) exhibit a narrow range of
water depths during flowering and fruiting
periods (USFWS 1990, Maddox and Bartgis
1992, Wells and Ingram 2003, Wells et al.
2004). Apparently suitable but unoccupied
habitat (e.g., cobble and gravel bars) occurs
over large areas along Sideling Hill Creek
(Maddox and Bartgis 1989). This pattern has
also been observed in Fifteenmile Creek in
Maryland (where a single patch is known) and
3
along streams in West Virginia and Arkansas
(P.J. Harmon, botanist, West Virginia Natural
Heritage Program, pers. comm.; T. Witsell,
botanist, Arkansas Natural Heritage Program,
pers. comm.). Our approach to examining
Harperella habitat focuses on the stream
substrates in which the plants are rooted. We
use substrate type defined by particle size class
as a variable characterizing Harperella habitat. Thus, we use multivariate techniques to
investigate the influence of substrate compositions on Harperella abundance. We utilize
techniques common to community ecologists,
namely quadrat-based sampling using cover
estimates followed by analyses using nonparametric ordination procedures to describe and
visualize the breadth of variation in substrate
composition (Peet et al. 1986, McCune and
Grace 2002). Our sampling effort focused on
sections of Sideling Hill Creek where Harperella is known to occur with regularity (based
on 20 yr of census data). Specifically we ask the
following questions:
1. Is there evidence that the current distribution and abundance of Harperella
along Sideling Hill Creek represents
occupation of a narrow range of substrate composition?
2. Are there differences in substrate composition between occupied (Harperella
present) and unoccupied (Harperella
absent) habitats?
The influence of stream substrate types on
conservation of Harperella has received little
attention (but see Maddox and Bartgis 1989).
This seems to be an oversight. The range of
sites that may be potential Harperella habitat
has been examined using transplanted material from the Sideling Hill population.
Transplant experiments into unoccupied but
apparently suitable habitat have had dismal
success rates along the Potomac mainstem
(Maddox and Bartgis 1992, Wells et al. 2004)
and have been equivocal within Sideling Hill.
Maddox and Bartgis (1989) found that survivorship of plants planted at ‘‘home’’ (occupied) versus ‘‘away’’ (unoccupied but apparently suitable) was higher; however, four
‘‘away’’ sites performed nearly as well but
survivorship was not tracked past 30 d. A
second transplant experiment (Maddox and
4
CASTANEA
Table 1. List of standard stream substrate classes
(abbreviated) and their qualitative scales and
diameters in millimeters
Stream Substrate
Class (diameter)
Qualitative Scale and Size
Bedrock
Cob 180+
Cob 128
Cob 90
Cob 64
Cob 45
Cob 32
Cob 22.6
Grav 16
Grav 11
Grav 8
Sand
Fine
Immovable, determined in the field
Large cobble $180 mm
Large cobble 128 mm
Medium cobble 90 mm
Medium cobble 64 mm
Medium cobble 45 mm
Small cobble 32 mm
Small cobble 22.6 mm
Large gravel 16 mm
Medium gravel 11 mm
Small gravel 8 mm
Particles 2.8–5.6 mm
Fine sediments #2 mm
Bartgis 1992) resulted in high mortality
among all transplants after 320 d but a small
but significant portion of the ‘‘home’’ transplants survived. If there are particular substrate types where Harperella abundance is
maximized, then long-term monitoring of the
dynamics and persistence of these habitats
should be a conservation priority. Alternatively, the absence of differences in substrate
composition in occupied versus unoccupied
habitat would suggest that other factors
(light, water depth, etc.) and not stream
substrate are responsible for the observed
patterns of Harperella colonization and persistence.
Field Site Description
Sideling Hill Creek is a 40-km-long stream in
the Ridge and Valley Physiographic Province
that originates in Bedford and Fulton Counties, Pennsylvania, and flows south to the
Potomac River in Maryland about 130 km
northwest of Washington, DC. The topography is rolling to steep over much of the
watershed, and Sideling Hill Creek has cut
deep into Devonian shales and sandstones of
the Chemung and Hampshire formations.
Elevation drops from 204 m at the Pennsylvania line to 125 m at the Potomac River.
METHODS In an annual Harperella census
(2007) along Sideling Hill Creek, we established 45 Global Positioning System waypoints
(buffered to 30 m) over 9.2 km of streamside
habitat where Harperella occurred. Estimates
of Harperella abundance among these way-
VOL. 77
points varied from fewer than 10 stems to
.1,000 stems over each 30 m of stream bank.
We used these waypoints as locations to
sample: (a) the relative cover of 13 stream
substrate sizes (identified in Table 1), and
(b) the relative cover of Harperella in individual 0.25-m2 quadrats. The choice of sampling
grain (0.25 m2) is sufficient to capture most
substrate sizes and was small enough to allow
efficient cover estimation. We used a handheld
device called a Gravelometer (also known as a
gravel template and pebble meter) to measure
gravel- and cobble-sized bed sediments in the
field. Constructed of 3.2-mm aluminum, it has
14 square holes of common sieve sizes (1/2-phi
unit classes) ranging in diameter from 2 mm
to 180 mm (available from Wildco, Wildlife
Supply Company, Buffalo, New York). For ease
of estimation in the field, we aggregated the
2.8- to 5.6-mm-diameter particle sizes into one
class we called Sand. We considered Bedrock
any large continuous band of consolidated
rock that was immovable. The Cob 180+ class
is the group of large, movable rock $180 mm
diameter. In practice, the Cob 180+ class was
rare in our sample and most members of this
group were ,400 mm. We estimated relative
cover using a nine-point scale commonly
employed in vegetation surveys (1 5 trace,
2 5 ,1%, 3 5 1–2%, 4 5 2–5%, 5 5 5–10%, 6 5
10–25%, 7 5 25–50%, 8 5 50–75%, 9 5 75–
100%), modified from Peet et al. (1986). Cover
estimates of substrate sizes represents a ‘‘bird’seye view’’ of the substrates visible at the
surface. Care was taken to carefully excavate
and replace cobble. Small gravel-sized substrates were removed with forceps.
A single observer made all cover measurements over a 48-hr period where no weather
events impacted stream levels. We randomized the 45 waypoints using a random draw
and at each waypoint we established a series
of five potential quadrat locations using a
random integer generator, marking each
location with a wire flag. Harperella habitat
is neither continuous nor uniform so there is
some subjectivity in the placement of quadrats. For example, a quadrat that would have
been necessarily placed in a deep pool or
vertically at a cliff wall was skipped and we
used the next quadrat location in the series.
To limit observer bias in the orientation of the
quadrat, a polyvinyl chloride quadrat frame
2012
FRYE, TESSEL: STREAM SUBSTRATES IN HARPERELLA HABITATS
was tossed at the flags from a distance of a
few meters. We obtained 52 quadrats where
Harperella was present and 28 quadrats that
were unoccupied but within the waypoint
buffer, for example, within the habitat area
known to hold Harperella. We are aware of
the potential for autocorrelation to affect our
analysis, especially in a linear system where
everything is potentially spatially correlated.
To address this potential source of error, we
examined a multivariate Mantel correlogram
of the substrate data and saw no evidence
of strong spatial autocorrelation (data not
shown).
For our analyses we use four data matrices:
(a) a quadrat by substrate matrix (52 3 13)
to explore patterns of substrate composition
among the occupied quadrats, (b) a quadrat
by substrate matrix (80 3 13) to explore
patterns of substrate preference in all quadrats
(occupied and unoccupied), (c) a quadrat by
Harperella cover (52 3 1) matrix for use as a
second matrix to examine the correlations
between Harperella cover in occupied quadrats
and ordination axes in substrate space, and (d)
a quadrat by group (80 3 1) matrix identifying
the quadrat as occupied or unoccupied.
We used nonmetric multidimensional scaling (NMS) as employed in PC-ORD (v. 3.04,
MjM Software, Gleneden Beach, Oregon) to
explore patterns of substrate composition in
our quadrats. NMS is commonly used by
community ecologists to examine patterns of
species composition among communities and
has rapidly become the method of choice for
ecological data (see Clarke 1993, McCune and
Grace 2002). NMS is an indirect ordination
technique that works without assuming that a
species responds to environmental gradients in
a linear or unimodal fashion and is robust to
large numbers of zero values. The strategy of
NMS is to locate samples in a reduceddimension ordination space such that the
difference (stress) between the distances in
high-dimensional space and distances in the
two-dimensional plot of the ordination have
the same rank order. NMS does not provide a
single solution or configuration of sample
units. The strength of NMS with ecological
data is that any solution should show the same
underlying patterns. We used the raw cover
values and set Euclidean distance as the
measure for constructing the distance matrix.
5
The NMS procedure in PC-ORD also calculates
parametric Pearson (r) and nonparametric
Kendall (tau) correlation coefficients to examine correlations between ordination space and
input variables (e.g., cover of a particular
substrate) and between ordination space and
a second matrix (e.g., Harperella occurrence or
abundance). The familiar Pearson (r) correlation coefficients are reported here generally for
descriptive purposes, as the statistical distributions of ordinal cover data rarely meet the
assumptions of parametric statistics. Additionally, outliers in correlation analyses, especially
in data sets dominated by zeroes that are
typical of species/community data, have a
strong effect on the resulting coefficients
whether parametric or nonparametric. We
performed multiple trial runs with NMS using
50 runs of real data and 100 runs of randomized data to determine the optimal number of
axes. For each run, we used a maximum of 400
iterations until we obtained minimum stress
(good fit) and a stable solution.
Additionally, we use multidimensional scaling using the Kruskal algorithm (called MDS)
employed in SYSTAT 12 (SYSTAT Software, San
Jose, California, www.systat.com) to illustrate
relationships of occupied and unoccupied
quadrats to substrates in low-dimensional
space. For this analysis we used the nonmetric
unfolding model applied to a rank-ordered
matrix. The MDS plots quadrats in a lowdimensional space in a manner similar to
NMS in PC-ORD but differs in that the resulting
graph is a plot of all quadrats and their factors
(substrates) wherein each quadrat occurs closest to the coordinate of its preferred substrate.
The graph may be interpreted similarly to a
factor analysis.
We used multiple-response permutation
procedure (MRPP as employed in PC-ORD),
a multivariate analog of analysis of variance
to test the null hypothesis of no significant
differences in substrate composition between
occupied and unoccupied habitat. Details of
the method may be found in Mielke and Berry
(2001). The strategy of MRPP is to compare
the observed intragroup average distances
with the average distances that would have
resulted from all the other possible combinations of the data under the null hypothesis.
The test statistic, usually symbolized with a
lowercase delta, d, is the average of the
6
CASTANEA
VOL. 77
Figure 1. Nonmetric multidimensional scaling (NMS) ordination of occupied quadrats (N 5 52). The central
vector shows direction of increasing Harperella cover; substrate vectors are interpretations of the ordination axes.
observed intragroup distances weighted by
relative group size. The observed delta (dobs) is
compared to the possible deltas (d) resulting
from every permutation of the data. The MRPP
reports a test statistic (T) describing the
separation between groups, a measure of effect
size (A) describing within-group agreement,
and a p-value representing the likelihood of
finding an equal or smaller delta than the
observed based on all possible partitions of the
data set using the Pearson Type III distribution
of deltas. We used Euclidean distance and a
ranked distance matrix following the protocols
in McCune and Grace (2002). A small constant
(0.001) was added to elements of the matrix to
replace zeroes.
RESULTS
Nonmetric Scaling Ordinations
The NMS substrate ordination of the 52
occupied quadrats stabilized after 200 iterations with a final stress of 8.00180, a final
instability of 0.00005, and optimal dimensionality of three axes. Harperella cover was
used as a second matrix in the ordination
to examine correlations between Harperella
cover and gradients along ordination axes.
Coefficients of determination (r2) for the three
axes cumulatively accounted for 95.1% of the
variation in the data. Axes 1 (NMS1, r2 5
32.9%) and 2 (NMS2, r2 5 37.2%) were
chosen for interpretation as they cumulatively accounted for the greatest variation in the
data. The NMS scatterplot (Figure 1) represents the occupied quadrats as points in
ordination (substrate size) space. The central
vector line indicates the magnitude and
direction of the correlation of Harperella
cover along NMS1 (r 5 0.490, tau 5 0.360).
The spatial arrangement of quadrats with
high Harperella cover corresponded well to
quadrats with high cover of Fine sediments.
Correlation coefficients between raw Harperella cover and cover of Fine sediments in
occupied quadrats are significant (r 5 0.500,
Spearman’s rho 5 0.534 [p , 0.001]). Pearson
and Kendall correlations of individual substrate sizes with substrate ordination axes
FRYE, TESSEL: STREAM SUBSTRATES IN HARPERELLA HABITATS
2012
7
Table 2. Nonmetric multidimensional scaling (NMS) correlations (r and tau) between substrates and
ordination axes. r . 0.5 shown in bold. See Table 1 for list of stream substrate classes
NMS 1
NMS 2
NMS 3
Substrate
r
r2
tau
r
r2
tau
r
r2
tau
Bedrock
Cob180+
Cob 128
Cob 90
Cob 64
Cob 45
Cob 32
Cob 22.6
Grav 16
Grav 11
Grav 8
Sand
Fine
20.802
0.067
20.142
0.017
0.133
0.079
20.051
20.161
20.225
20.170
20.398
20.390
0.805
0.643
0.004
0.020
0.000
0.018
0.006
0.003
0.026
0.051
0.029
0.158
0.152
0.648
20.653
20.030
20.014
0.028
0.118
0.092
20.012
20.108
20.196
20.188
20.274
20.220
0.644
0.814
20.293
20.555
20.558
20.540
20.481
20.502
20.577
20.569
20.543
20.360
20.415
20.146
0.663
0.086
0.308
0.312
0.292
0.232
0.252
0.333
0.324
0.295
0.130
0.172
0.021
0.664
20.240
20.476
20.398
20.443
20.434
20.434
20.491
20.478
20.463
20.361
20.396
20.031
20.322
0.948
0.052
20.268
20.163
20.260
20.079
20.029
0.121
0.373
0.304
0.051
0.147
0.103
0.898
0.003
0.072
0.027
0.068
0.006
0.001
0.015
0.139
0.092
0.003
0.022
20.219
0.683
0.101
20.260
20.149
20.268
20.118
20.064
0.092
0.152
0.150
0.030
0.193
show some strong relationships (Table 2).
Quadrats with positive scores along axis 1
reflect a strong association (r 5 0.805, tau 5
0.644) with Fine sediments (,2.0 mm in
diameter) whereas quadrats with negative
scores reflect a strong association (r 5
20.802, tau 5 20.653) with Bedrock. Along
axis 2, quadrats with positive scores reflect a
singular association with Bedrock (r 5 0.814,
tau 5 0.664) whereas quadrats with negative
scores reflect an association with all Cobble,
Gravel, and Sand (see Table 2). Bedrock and
Fine sediments are the major components
driving the NMS ordination; the other substrate sizes contribute little to the overall
configuration of points.
The MDS ordination of all 80 quadrats
resulted in two dimensions with a final stress
of 0.070 (ranges between 0 and 1 in SYSTAT).
Coefficients of determination for the two axes
account for 70.1% of the variation in the data.
Figure 2 shows occupied versus unoccupied
quadrats and their proximities/affinities for a
given substrate. Similar to the NMS ordination, we interpret three factors that are responsible for the configuration. The first factor is
the set of Cobble, Gravel, and Sand; the second
factor is Bedrock; and the third factor is Fine
sediments. In Figure 2 we see that the majority
of unoccupied plots occur close to the coordinates of Cobble (all sizes), Gravel (all sizes),
and Sand, whereas the occupied plots occur
throughout the graph but group to the positive
side of dimension 1 close to the coordinates of
Bedrock and Fine sediments.
Multiple-Response Permutation Procedure
Substrate composition in habitats occupied (n
5 52) and unoccupied (n 5 28) by Harperella
differed significantly (T 5 29.111, A 5 0.075,
p , 0.0001). Average within-group distance
(0.390) of the occupied quadrats is smaller
than the average within-group distance of the
unoccupied quadrats (0.596), indicating a
narrower range of substrate compositions
that provide Harperella habitat. We compared the magnitude of the T-statistic within
each substrate class for occupied and unoccupied habitats (Table 3). Significant p-values
(p , 0.05) were obtained for Fine sediments,
and medium (45) and large Cobble (180, 128,
90). We interpret Table 3 as showing the
cover of Fine sediments (high cover in occupied plots) to be largely responsible for overall
separation between groups and alternately,
the cover of medium and large Cobble (high
cover in unoccupied plots) is responsible for
the overall, between-group values for T.
DISCUSSION We found a positive association between Harperella cover and cover of
Fine sediments. We hypothesize that Fine
sediments settle between and around Cobble
and Gravel but are more apparent when they
accumulate in larger bedrock crevices and
depressions. This may account for the strong
ordination of some occupied quadrats with
Bedrock and the MDS configuration where
Bedrock and Fine sediments are the important
factors. A large cover value for Bedrock may
frequently co-occur with a smaller cover of
8
CASTANEA
VOL. 77
Figure 2. Multidimensional scaling using the Kruskal algorithm (MDS) ordination of all quadrats (N 5 80).
Individual quadrats (open or filled circles) occur closest to their preferred substrate (stars). In the upper left
quadrant the factor ‘‘Grav & Sand’’ is a composite of Grav 11, 16, and Sand as the coordinates overlapped.
Fine sediment given that a sediment-filled
crevice may be only a few centimeters in width.
These crevices, although small, often hold
many Harperella plants. Other habitats with
high cover values of Fine sediments are depressions running parallel with exposed bands of
Bedrock. These are often linear and may cover
many square meters. In this circumstance the
cover value of Fine sediments may be large and
cover values for other substrates relatively
small. In these microhabitats Harperella plants
are very dense and may constitute values
greater than 90 percent cover. We observed
that Harperella roots readily in this habitat and
the resulting vegetative propagation results in
high densities of Harperella.
We found an overall difference in substrate
composition between occupied and unoccupied
Table 3. Results of multiple-response permutation procedure test by substrate showing average withingroup distance for occupied and unoccupied quadrats: T—the statistic measuring group separation, A—the
statistic measuring within-group agreement, and p—the probability of finding a higher value of T in all
permutations of the data. Significant p-values (a # 0.05) shown in bold. See Table 1 for list of stream
substrate classes
Substrate
Size (mm)
Bedrock
Cobble 180
Cobble 128
Cobble 90
Cobble 64
Cobble 45
Cobble 32
Cobble 22.6
Gravel 16
Gravel 11
Gravel 8
Sand
Fine
Avg. Within-Group Distance
Occupied
Unoccupied
T
A
p
0.431
0.231
0.292
0.284
0.324
0.312
0.322
0.307
0.268
0.293
0.330
0.320
0.360
0.465
0.379
0.412
0.400
0.439
0.422
0.396
0.415
0.392
0.348
0.371
0.392
0.414
0.109
23.847
23.129
23.503
20.655
24.097
0.270
0.251
20.688
20.381
0.342
20.226
213.708
20.001
0.047
0.037
0.042
0.007
0.050
20.003
20.003
0.008
0.005
20.004
0.002
0.140
0.384
0.010
0.018
0.013
0.188
0.007
0.472
0.474
0.175
0.230
0.502
0.290
,0.001
2012
FRYE, TESSEL: STREAM SUBSTRATES IN HARPERELLA HABITATS
quadrats. We compared the magnitude of the
T-statistic for each substrate size and found that
the difference was largely attributable to the
abundance of Fine sediments in occupied plots
and the abundance of Cobble in the unoccupied plots. Harperella was more frequently
found in Fine sediment deposits or among
crevices in Bedrock whereas medium and large
Cobbles were more frequently found in unoccupied habitats along with small Cobble- and
Gravel-sized substrates. This is in agreement
with the NMS ordination of the occupied plots
where Cobble, Gravel, and Sand are minor
gradients of the ordination. Smaller diameter
substrates (small Cobble, Gravel, and Sand)
may not provide a configuration amenable to
retention of Fine sediments. These smaller
diameter substrates would be more easily
transported during flood events, and we hypothesize that while they may provide habitat
during periods of low stream flow, they may
never act as habitat for persistent stands. For
example, Harperella plants rooted in small
Cobble, Gravel, and Sand substrates that are
easily moved about during flood events may be
dislodged or buried. Long-term demographic
data (Maryland Natural Heritage Program
1989–2010) indicate that large patches are
persistent and colonization of other putatively
similar habitats occurs rarely. For example,
turnover rates for stands numbering .500
stems were much lower than those with fewer
than 500 stems and large patches (.1,000)
remained stable from 1990 to 1993 (Maddox
and Bartgis 1994). Life-history components
may reinforce this trend. Wells et al. (2004)
observed that Harperella flowers and fruits
profusely during dry years whereas vegetative
propagation is associated with submergence.
Although briefly mentioned by Kral (1981),
Maddox and Bartgis (1989, 1992) were perhaps
the first to note the ecological significance of
vegetative rosettes arising from basal and stem
nodes. They theorized that these might be both
additional propagules capable of colonizing
new sites when detached and also a mechanism
for (genet) persistence and maintenance of
population size. When detached from the
maternal plant, there is a much higher probability that the free-floating plantlets and dislodged seed will settle on the sand and gravel
bars that constitute much of the stream habitat.
These newly founded populations experience
9
large temporal variation in habitat quality
depending on the prevailing stream flow
patterns. If stream flow remains low and flood
events are few, then these patches may expand;
however, during moderate to large flood events
these patches over gravel and cobble are more
likely to be extirpated. What is important, it
appears, is that both forms of reproduction
contribute to overall population (census) size
each year but that each contribution may be
greatly determined by annual patterns of
stream flow. Riparian plants in particular
may have strong ties between life-history
characteristics and the timing of environmental
events such as periods of low versus high stream
flow (Smith et al. 2005, Thomson and Schwartz
2006). If Harperella is narrowly adapted to a
pattern of historical average flow and flood
events, then it may be particularly susceptible
to alterations in those patterns. Eshelman
(2007) modeled the putative impacts of a
proposal to release 0.3–0.9 million gallons per
day of treated wastewater into a tributary of
Fifteenmile Creek and concluded that it would
dramatically alter the flow regime by eliminating the low-flow periods that are characteristic
of streams in the Allegany Forests region. Lowflow periods appear to be a critical life-history
phase for Harperella when plants flower and
fruit profusely. Harperella is an example of a
plant species that requires management at a
watershed level. Certainly efforts to secure
buffers immediately adjacent to occupied habitat improve the overall conservation probability; however, the timing, frequency, and duration of flood events fall outside of local control.
Marcinko (2007) also concluded that environmental conditions were limiting factors to the
species’ distribution and abundance. We recommend modifying the current annual census
program to focus on substrate composition,
hydrology, and population dynamics in large
patches. Future work will include an analysis of
population genetic diversity and structuring at
Sideling Hill Creek.
ACKNOWLEDGMENTS This research was
supported by United States Fish and Wildlife
Service, Fund for Endangered Species, under
Section 6 of the Endangered Species Act and
the Maryland Department of Natural Resources, Wildlife and Heritage Service. The
authors wish to express their appreciation to
Elizabeth Wells and two unknown reviewers
10
CASTANEA
for critical and editorial comments that
greatly improved the manuscript and to
Michael Baranski for editing an early draft
of the manuscript.
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