Estimating sampling effort for early detection of non-indigenous benthic species in the Toledo Harbor Region of Lake Erie

Abstract

Toledo Harbor (Maumee River and Maumee Bay) is a “port of concern” for introduction of non-indigenous species into the Great Lakes due to the large amounts of ballast water from outside the Great Lakes discharged at the port, the amenable habitat for many potential invasives, and the large amount of ballast water transported from Toledo to other Great Lakes ports, making Toledo a potential source of invasives throughout the entire region. To estimate sampling intensity needed to detect rare or new non-indigenous species, 27 benthic grab samples from 13 locations near Toledo Harbor were collected during autumn, 2010. Benthic organisms were identified, and sampling intensity needed to detect rare or new non-indigenous species was evaluated via a Chao asymptotic richness estimator. Morphological taxonomic criteria and cytochrome oxidase I (COI) sequence barcodes identified 29 different taxa (20 to species level) in the samples, including six non-indigenous taxa (Branchiura sowerbyi, Bithynia tentaculata, Corbicula fluminea, Dreissena polymorpha, Dreissena bugensis, Lipiniella sp.). While all the non-indigenous species had previously been reported in Lake Erie or nearby Ohio waters, several North American species are not previously listed in Ohio. Richness estimates indicate that >75% of the benthic species in the area were encountered and that 90% of the species could be detected with less than a doubling of collecting effort. Since sampling for this study occurred only in the autumn and detectable life stages of benthic organisms may vary seasonally, additional species may be observed with more extensive sampling over a broader seasonal range.

Full text

Management of Biological Invasions (2014) Volume 5, Issue 3: 209–216
doi: http://dx.doi.org/10.3391/mbi.2014.5.3.03
© 2014 The Author(s). Journal compilation © 2014 REABIC
Open Access
Proceedings of the 18th International Conference on Aquatic Invasive Species (April 21–25, 2013, Niagara Falls, Canada)
209
Research Article
Estimating sampling effort for early detection of non-indigenous benthic species
in the Toledo Harbor Region of Lake Erie
Jeffrey L. Ram1*, Fady Banno1, Richard R. Gala1, Jason P. Gizicki1 and Donna R. Kashian2
1Department of Physiology, Wayne State University, Detroit, MI 48201, USA
2Department of Biological Sciences, Wayne State University, Detroit, MI 48202, USA
*Corresponding author
E-mail: jeffram@wayne.edu
Received: 30 October 2013 / Accepted: 29 April 2014 / Published online: 21 July 2014
Handling editor: Calum MacNeil
Abstract
Toledo Harbor (Maumee River and Maumee Bay) is a “port of concern” for introduction of non-indigenous species into the Great Lakes due
to the large amounts of ballast water from outside the Great Lakes discharged at the port, the amenable habitat for many potential invasives,
and the large amount of ballast water transported from Toledo to other Great Lakes ports, making Toledo a potential source of invasives
throughout the entire region. To estimate sampling intensity needed to detect rare or new non-indigenous species, 27 benthic grab samples
from 13 locations near Toledo Harbor were collected during autumn, 2010. Benthic organisms were identified, and sampling intensity
needed to detect rare or new non-indigenous species was evaluated via a Chao asymptotic richness estimator. Morphological taxonomic
criteria and cytochrome oxidase I (COI) sequence barcodes identified 29 different taxa (20 to species level) in the samples, including six
non-indigenous taxa (Branchiura sowerbyi, Bithynia tentaculata, Corbicula fluminea, Dreissena polymorpha, Dreissena bugensis, Lipiniella
sp.). While all the non-indigenous species had previously been reported in Lake Erie or nearby Ohio waters, several North American species
are not previously listed in Ohio. Richness estimates indicate that >75% of the benthic species in the area were encountered and that 90% of
the species could be detected with less than a doubling of collecting effort. Since sampling for this study occurred only in the autumn and
detectable life stages of benthic organisms may vary seasonally, additional species may be observed with more extensive sampling over a
broader seasonal range.
Key words: benthic organisms, Chao richness estimator, DNA barcode, early detection, invasive species, sampling efficiency
Introduction
Invasions of non-indigenous species (NIS) are
among the most important problems facing the
Great Lakes. Beginning in the 1800’s, the
introduction of NIS into North America has had
overwhelmingly negative impacts on human health,
ecosystems, and economic activities including
social, cultural, recreational and industrial use of
Great Lakes waters, tributaries, harbors, and coastal
regions. The St. Lawrence Seaway accelerated
these introductions by providing direct migration
routes from the oceans and mediating the entry
of foreign ships that discharge large of amounts
of ballast water into ports in the Great Lakes. As
a result, NIS are among the most significant threats
to Great Lakes ecosystems.
Non-indigenous species have entered the
Great Lakes through ballast water (over half of
all damaging introductions), aquaculture-associated
introductions (e.g., Asian carp which have arrived
at the “doorstep” of the Great Lakes near Chicago
and the headwaters of the Wabash River), and
trade in live organisms. Prior to applying stricter
permitting and regulations to ballast water manage-
ment in 2006, a new NIS was discovered in the
Great Lakes on average every 28 weeks (Ricciardi
2006). Estimated annual costs in the United States
associated with aquatic NIS are >$5 billion due to
fish, $1 billion for dreissenid mussels and Asiatic
clams, $100 million for aquatic plants, and $40
million for green crabs (Pimentel 2005). The costs
of allowing such trends to continue are potentially
enormous, so it is wise to invest in preventing

J.L. Ram et al.
210
Figure 1. Sites A – M along the Maumee River and Maumee Bay
at which benthic samples were collected during September and
October 2010. The site collection area is located in the box in the
inset.
NIS introduction and limiting their spread with
the help of early detection programs that are
crucial to managers and early response programs.
Economic analysis of prevention, detection,
and control costs have indicated that detecting
NIS early in an invasion may decrease the ultimate
cost of subsequent control measures (Mehta et al.
2007). For example, early estimates of costs
associated with zebra mussels as high as $4 billion
per year (Morton 1997) have been reduced
substantially (Ram and Palazzolo 2008). Some of
the reductions in costs have been due to widespread
but locally managed use of dreissenid detection
strategies, which enable managers to anticipate
the arrival of mussels and to avoid unnecessary
treatment when mussels are not present (Connelly
et al. 2007). Understanding the benefits of early
detection of NIS has led to studying strategies to
detect NIS in the Great Lakes.
Toledo Harbor (Maumee River and Maumee
Bay) has been characterized by United States
Environmental Protection Agency (EPA) as “the
port of greatest concern…” for new ballast water
mediated introductions throughout the Great
Lakes due to the large amount of ballast water
discharged there from outside the Great Lakes
and its highly suitable habitat for many potential
NIS (US Environmental Protection Agency 2008).
Although this conclusion by the EPA was based
on the assumption that data analyzed for 2006–
2007 was representative of relative ballast
discharge patterns over several years, a recent
doubling of the size of the Toledo Harbor seaport
(Toledo Lucas County Port Authority 2014)
probably means that, if anything, the risk of
introductions may have grown further. Another
consideration is that the low flood plain between
the Wabash River (Mississippi River watershed)
and the Maumee River, in which the port of
Toledo is located, makes the Maumee River a
potential entry point for NIS from the
Mississippi watershed during high water events
(Hebert 2010). Also, the nearby large population
centers of Toledo, Cleveland, and Detroit
increase the risk of introductions from the trade
in live organisms, including bait.
Previous studies by EPA’s Office of Research
and Development (EPA-ORD) in Duluth-Superior
Harbor (DSH) have shown that intensive survey
methods and careful taxonomic analysis are
effective for discovering previously undetected
NIS (Trebitz et al. 2009; Trebitz et al. 2010). DNA
analysis methods were used when morphological
characters proved to be inadequate (Grigorovich
et al. 2008). These EPA-ORD studies identified
19 species of non-indigenous benthic invertebrates,
including 8 that had not previously been detected
in DSH (Trebitz et al. 2010). The present study
applies similar methods to those used by EPA-
ORD in DSH, complemented by a more intensive
application of molecular identification methods,
to predict the sampling intensity that may be
required for efficient detection of new NIS in
Toledo Harbor.
Methods and materials
Sampling sites ranged from riverine (i.e., in the
Maumee River itself) to open bay (beyond the
mouth of the Maumee River). Figure 1 shows the
location of the 13 collecting sites at which benthic
samples were collected on one to three of the
following dates (as detailed in the results) during
early autumn, 2010: September 24, October 4,
and/or October 5, 2010. Sediments were collected
with a bottom dredge (Ben Meadows, 25 lb,
bottom dredge; cat. # 125006)) with an effective
sampling area of 213 cm2, which is about 10%
smaller than the petite ponar grab sampler (area
236 cm2) used by Trebitz et al. (2009)). Depths
(range from 0.6 m – 3.6 m), GPS coordinates, and
vegetative cover were recorded for each collected
sample.
Sediments were sieved in the field with a 500
μm screen (Cole-Palmer, brass, #35, cat. No.YO-
59990-09) and preserved in 90 percent ethanol
on ice for subsequent laboratory analysis. After
resieving on a 500 μm sieve in the lab, samples

Benthic invertebrates in Toledo Harbor
211
were stored in 90% ethanol at 4 oC until sorting
or other processing. Samples were searched visually
and under the dissecting microscope for organisms,
using a quick scan approach in which benthic
samples are processed by visually scanning for as-
yet unseen species rather than enumerating all,
taking care that the smallest organisms possible,
down to 500 m, were not missed. Representative
unique organisms, including those that were
represented by as few as a single specimen and
potentially identifiable molluscan shells, were
selected from each sample. Voucher samples have
been retained in 90% ethanol at 4 oC.
Specimens used as positive controls. Previously
collected or laboratory grown organisms that were
preserved in ethanol and for which the taxonomic
identification is unambiguous were used as positive
controls for methods development and quality
assurance tests. Such specimens included adult
zebra mussels and quagga mussels, Daphnia spp.
obtained from Dr. Donna Kashian and Dr.
Christopher Steiner (Dept. of Biological Sciences,
Wayne State University), and specimens for
which taxonomic identification is assured by
biological supply companies (e.g., Lumbriculus
variegates from Carolina Biological Supply).
Taxonomic analysis. Gross-level identification
and tabulation of easily recognized taxa (e.g.,
Dreissenidae, Amphipoda, Oligochaeta, Diptera,
other) were performed during a quick visual
scan, sorting, and selection step. The selected
representative organisms from each sample and
several positive control organisms (blinded; i.e.,
not identified as already known) were individually
photographed and shipped one to a vial in 90%
ethanol to EcoAnalysts, Inc., a professional
taxonomic services company, for identification
according to classical morphological criteria.
Organisms identified by EcoAnalysts were returned
to the Ram laboratory either in ethanol in their
original vial, or, in the case of oligochaetes,
permanently slide-mounted.
DNA barcoding by the Canadian Centre for
DNA Barcoding (CCDB). Small tissue samples
(about 2 mm in diameter, each) from organisms
were submitted in 90% ethanol in 96-well plates,
according to Standard Operating Procedures
required by CCDB. These organisms included
tissue from specimens identified by EcoAnalysts,
additional oligochaete specimens (since the slide-
mounted oligochaetes could not be used), and
various positive controls and other specimens, as
detailed further in the results. All organisms
from which the tissue samples were taken were
photographed. Upon receipt of the preserved tissues,
CCDB extracts DNA and analyzes sequences for
the mitochondrial cytochrome oxidase I (COI)
“barcode” region of each sample. Control
experiments tested that CCDB obtained identical
barcode sequences for DNA extracted by the
Ram laboratory (DNA extracted and purified
using the DNeasy Blood and Tissue Kit, Qiagen
cat. no. 69506, Valencia, CA) and submitted in
addition to blinded tissue samples from the same
organisms. Sequences for selected specimens are
given in the supplement (Appendix S1).
Taxa accumulation analysis. Taxa accumulation
curves (i.e., a curve showing how many additional
species types are identified with increasing numbers
of samples assessed) were plotted to provide a
means of assessing the likelihood that all possible
species in the sampled habitats had been
encountered. If an accumulation of taxa plotted
against the number of samples yields an ascending
curve without reaching an asymptote, then it is
highly probable that additional taxa remain to be
found. The species incidence data (i.e., the number
of sediment samples containing particular species
or other taxonomic classification) were then
analyzed by the Chao asymptotic richness estimator
(Chao et al. 2009; Colwell 2009) to estimate the
total number of species likely to be present in the
sampled habitat.
Results
Taxonomic analysis
The 27 benthic samples varied in numbers of
organismal types identified by the quick scan
method from as few as one unique organism per
sample to as many as seven. EcoAnalysts identified
25 different taxa from the 142 animals or shells
sent to them (Table 1). Photographs (one view only)
of each of the 25 different types of organisms
identified by EcoAnalysts are shown in the
supplement in Figure S1. Of the 25 different
organism types, 19 were identified to species, and
the others were identified to the genus or family.
Sixteen of the organism types are molluscs; five
are annelids; and four are arthropods. Among the
annelids, 17 oligochaetes were identified as
Limnodrillus hoffmeisteri, two as Limnodrillus
udekemianus, one as Branchiura sowerbyi, and
five as unidentifiable fragments. Other annelids
were leeches, identified as Helobdella elongata
(three specimens), and Helobdella stagnalis (two
specimens). In the 27 samples collected, seven of
the 25 species were encountered in only one

J.L. Ram et al.
212
Table 1. Sites at which 25 different organism types or their shells were collected in Toledo Harbor (Maumee River and Maumee Bay).
n Species name Sample location-date1
1 Bithynia tentaculata (Linnaeus, 1758) C2, E1, E2, E3, L3, K3
2 Branchiura sowerbyi (Beddard, 1892) B2
3 Chironomus sp. (Meigen, 1803) H2, B1, A2, A3, C2, D1, D2, J2, J3, E1, E2, E3, L3, M3
4 Coelotanypus sp.( Kieffer, 1913) G3, B1, A3, C1, C2, D1, J3, F1
5 Corbicula fluminea (Lindholm, 1927) D1, J2, J3, E1, E3, L3, M3
6 Dreissena bugensis (Andrusov, 1897) D3, E3, F2 (also known as Dreissena rostriformis Deshayes, 1838)
7 Dreissena polymorpha (Pallas, 1771) A1, C1, D1, J2, E2, F1, F3, L3, M3
8 Ferrissia sp. (Walker, 1903) H3
9 Gyraulus sp. (Charpentier, 1837) I2, B1, B2
10 Helobdella elongata (Castle, 1900) A3, J3, L3 (also known as Gloiobdella elongata Castle, 1900)
11 Helobdella stagnalis (Linnaeus, 1758) E1, E3
12 Hexagenia limbata (Serville, 1829) F3
13 Hydrobiidae (Stimpson, 1865) A1, D3
14 Limnodrilus hoffmeisteri (Claparede, 1862) G3, I3, B1, A1, A2, A3, C1, C2, D1, D2, F1, F2, F3
15 Limnodrilus udekemianus (Claparede, 1862) D2, D3
16 Lipiniella sp. (Shilova, 1961) H2
17 Musculium securis (Prime, 1852) E1
18 Musculium transversum (Say, 1829) H3, I2,A1, C1, C2, D2, D3, J2, J3, E3, F3, L3, M3
19 Physella gyrina (Say, 1821) D3
20 Pisidium compressum (Prime, 1852) B1, D1, D2, D3 (also Pisidium sp. in I3)
21 Pleurocera acuta (Rafinesque, 1818) G2,C1,C2(also Pleurocedidae in H3, G2, I2, I3, B2, C2)
22 Probythinella lacustris (F. C. Baker, 1928) L3 (also known as Probythinella emarginata Kuster, 1852)
23 Somatogyrus subglobosus (Say ) J2, M3
24 Sphaerium simile (Say, 1817) G2, B2, C1, D2 (also Sphaeridae in B1, A3, D1, I2)
25 Valvata sincera (Say, 1824) C2, D2 (also Valvata sp. in E3)
1Location-date format: The letter refers to sites on the map in Figure 1. The number refers to one of three collection dates in 2010: 1,
September 26; 2, October 4; or 3, October 5. Identifications are according to EcoAnaysts, Inc. Authority and year of each taxon are from
http://zipcodezoo.com and cross-checked on http://www.marinespecies.org/. Synonyms and different opinions about the valid name are
indicated as “also known as”.
sample while four species were encountered in
only two samples.
Several Daphnia pulex/pulicaria sent to
EcoAnalysts as blind positive controls were
correctly identified but one sample was said to
be Daphnia catawba.
Barcode molecular analysis by CCDB
Out of 105 samples sent to CCDB (seven positive
controls and 98 “unknowns”), CCDB obtained
quality COI sequences from all seven positive
controls and from 81 of the unknowns.
For the positive controls, CCDB obtained 100%
matches to the correct organism for purified
DNA submitted as blind samples from Dreissena
polymorpha and Dreissena bugensis. Purified
DNA from a portion of two different chironomids
and the rest of each organism submitted as
separate blind samples gave identical DNA
sequences with respect to which organism the
DNA was from. DNA extracted from a leech was
correctly identified as being from the genus
Helobdella despite a >15% divergence of the
sequence from previously known leech sequences.
Among the 98 unknowns, 37 were from
specimens that had also been analyzed by
EcoAnalysts. Of these, six had matches (identical
in >97% of the sequence) in the Genbank or
CCDB reference DNA databases at the genus or
species level: Lipiniella sp., (99.7%), two
specimens of Bithynia tentaculata (both 99.7%),
two specimens of Dreissena (99.7% and 100%
match to D. polymorpha), and Hexagenia limbata
(99.4%). Newly identified barcodes (i.e., organisms
identified to species by EcoAnalysts for which
no previous COI barcode had been identified; see
supplement Appendix S1 sequences 1 and 2) include
Pisidium compressum (five specimens) and
Musculium transversum (one specimen). Among
specimens that had not been analyzed by Eco-
Analysts, species sequence matches in the reference
databases were obtained for Branchiura sowerbyi
(two specimens, 100% match), Chironomus cf.
decorus (99.7%), Helobdela elongata (97.9%),
and Corbicula fluminea (100%).

Benthic invertebrates in Toledo Harbor
213
Figure 2. Accumulation of taxa incidence as a function of number
of samples analyzed. These data are based on EcoAnalyst’s
identifications of unique organisms in 27 sediment samples.
Seven organisms identified by EcoAnalysts as
Coelotanypus sp. had identical barcode sequences
(Appendix S1 sequence 3) and have 100% matches
to reference sequences from chironomids.
Unfortunately, none of these sequences have
been identified by CCDB at a level of genus or
species. The nearest species matches in the
reference databases differ from these sequences
at more than 10% of their bases. The same sequence
was also obtained for 3 other chironomids that
were submitted to CCDB without prior
classification by EcoAnalysts.
Similarly, 14 specimens identified by Eco-
Analysts as Chironomus sp. had nearly identical
sequences to each other (no differences within
the group of more than 1%), matched 100% to
sequences in the reference databases that were
identified to family level as Chironomidae, and
differed from all previously identified genus or
species barcodes by greater than 10%. An additional
three chironomid specimens that had not been
classified by EcoAnalysts also had sequences
identical to this group. A representative sequence
for these Chironomus sp. specimens is given in
Appendix S1 (sequence 4).
EcoAnalysts identified three species of oligo-
chaetes: Limnodrillus hoffmeisteri, Limnodrillus
udekemianus, and Branchiura sowerbyi. Due to
the difficulty of extracting DNA from the mounted
specimens, similar but unclassified oligochaetes
were submitted to CCDB for bar-coding. The
morphology of Branchiura sowerbyi is distinct,
and two such specimens were correctly predicted
to have that barcode (100% match). Among the
other oligochaetes submitted to CCDB, all of the
barcodes differed by more than 10% from previously
identified genera or species in Genbank or the
CCDB database. These sequences fell into four
barcode groups (see Appendix S1, sequences 5a -
d), one containing 18 specimens, another of three
specimens, another of two specimens, and one
with one specimen.
One leech that was identified by EcoAnalysts
as Helobdella stagnalis differed from previously
barcoded H. stagnalis sequences by more than
15% (see supplement, sequence 6). In fact, the
sequence seen in this single leech specimen was
identical to the sequence obtained from the
single leech specimen submitted to CCDB as an
annelid positive control (see above).
Taxa accumulation
Figure 2 illustrates the accumulation of the 25
taxa identified by EcoAnalysts. The curve is still
rising, indicating that more intensive sampling
by the same methods would likely yield more
species. Analyzing the taxa incidence data in Table
1 with the Chao asymptotic richness estimator,
the number of taxa present is estimated to be
approximately 31, suggesting that approximately
80% of the taxa present in this environment have
been detected by this sampling regime. Additional
calculations estimate that to encounter 100% of
the taxa present would require approximately 100
more similar samples to be collected and analyzed.
However, calculation estimates indicate that by
collecting only 15 more sediment samples than
those analyzed in this study 90% of all taxa
present may be captured, and so on. A caveat is
that not all of the organisms were identified to
species level. If that were taken into account,
this could change these numbers significantly.
These calculations can also be performed
taking into account the greater species richness
indicated by the CCDB molecular barcoding
data. The main effect of the molecular data is to
enable the differentiation of several additional
identifiable taxonomic units among groups that
could not be distinguished by EcoAnalysts.
Thus, among the oligochaetes, instead of just
three species, the molecular analysis indicates at
least five oligochaete species are likely present.
Several additional chironomid species may also
be differentiated. One specimen that had a barcode
of Chironomus cf. decorus was clearly different
from sequences of other chironomid specimens
and brought the total number of taxa identified to

J.L. Ram et al.
214
species to 20. Taking the molecular data into
account indicates that the number of species
sampled was at least 29, while the number of
unique (seen in only one sample) and duplicate
(observed in just two samples) taxa were nine
and six, respectively. With these values, the total
number of species in the sampled environment is
estimated to be 35, indicating that approximately
82% of them have been encountered. To encounter
100% of the species would require 44 additional
samples; 90% should be encountered with five
more samples. The taxa accumulation curve (not
shown) is similar to Figure 2 and had R2 = 0.952.
Discussion
This study used a combination of classical taxo-
nomic analysis and molecular taxonomic methods
based on the mitochondrial COI barcode region
in a search for rare, novel, or non-indigenous
benthic organisms in Toledo Harbor. The detected
taxa were compared to the Nature Serve and
Integrated Taxonomic Information System (ITIS)
databases of known species in North America
(http://www.natureserve.org/index.jsp and http://www.
itis.gov) and to various lists of NIS, including those
published by the United States Geological Survey
(http://nas.er.usgs.gov/), National Exotic Marine
and Estuarine Species Information System
(NEMESIS; http://invasions.si.edu/nemesis/browse
DB/searchTaxa.jsp?taxon=branchiura; see (Fofonoff
et al. 2003); Great Lakes and Mississippi River
Interbasin Study (GLMRIS) (http://glmris.anl.gov/
documents/ans/index.cfm; see also (Veraldi et al.
2011), the Global Invasive Species Database
(http://www.issg.org/database/welcome), and the
EPA (US Environmental Protection Agency 2008).
Pisidium compressum and Musculium transversum,
are North American species that had not previously
been reported in Ohio waters, according to the
Nature Serve and ITIS databases. Non-indigenous
species in the EcoAnalysts dataset (Table 1) include
Branchiura sowerbyi, Bithynia tentaculata (Kipp
and Benson 2011), Corbicula fluminea, Dreissena
polymorpha, and Dreissena bugensis. Lipiniella
sp., usually described as a European species but
also reported elsewhere in North America, was
also found. These were all confirmed by CCDB
DNA barcodes.
All of the NIS had previously been reported in
Lake Erie or nearby Ohio waters; including
several that are comparatively rare (Branchiura
sowerbyi and Lipiniella sp. accounted for fewer
than 1% of the identified specimens). Part of the
difficulty in identifying new NIS is the lack of
information about the species already present.
The sequences for many of the annelids had no
matches in reference COI databases, likely due
to the lack of prior investment in getting those
organisms sequenced. For example, a leech identi-
fied as Helobdella stagnalis and another leech
with an identical sequence both differed by >15%
from previously sequenced H. stagnalis, and all
previous leech sequences. Generally, organisms
in the same species differ in barcode sequences
by less than 3%. However, leech barcodes that vary
by as much as 7% between different populations
of H. stagnalis are nevertheless still considered
to be from the same species (Oceguera-Figueroa
et al. 2010). We encountered several of these
specimens, so they may be fairly common. Whether
they represent a new introduction or a new, but
cryptic species not previously named remains for
future work, possibly including sequencing of
nuclear genes to confirm these divergences.
Similarly, little is known about COI barcode
sequences for oligochaetes and chironomids. Adding
molecular analysis to classical taxonomic
identification increased the numbers of species
detected and may also reveal cryptic previously
unrecognized indigenous and non-indigenous taxa.
For taxonomic identifications, this project
used commercial taxonomy services, such as
EcoAnalysts and CCDB, in part, to determine if
such services were sufficiently accurate for
future early detection surveys of non-indigenous
organisms. We assessed the quality of their
results with various blinded positive controls. In
general, these vendors did well, although
EcoAnalysts identified one Daphnia pulex as
Daphnia catawba. Transcription errors can also
occur: By examining internal consistency of data
entries (e.g., does the phylum agree with the
indicated genus?) and comparing various entries
in vendor datasheets with photographs and voucher
specimens, we identified several such errors. These
companies do not guarantee 100% accuracy (e.g.,
EcoAnalysts QA documents indicate that >90%
agreement between independent taxonomists meets
their quality standard). Such errors have been
corrected when detected. The use of two methods
(barcodes and morphology) for species identification
provides a further double-check on identifications.
Such issues reinforce the need for photographic
documentation and retention of archival specimens
whenever possible.
The present survey was similar in methods to
the study by Trebitz et al. (2009) in Duluth-Superior

Benthic invertebrates in Toledo Harbor
215
Harbor. Although Trebitz et al. (2009) had a
greater collecting intensity (77 benthic samples)
and identified a larger number of benthic taxa
(158 taxa), their accumulation curves, like ours,
were still rising. Their estimate is that they had
detected only 80% (158 out of 197 taxa) of the
taxa predicted to be in the system by the Chao
asymptotic richness estimator. Altogether, approxi-
mately 8% (13 out of 158 taxa) of the benthic
taxa identified by Trebitz et al. (2009) were NIS.
In comparison, approximately 20% of the taxa
detected in the Toledo Harbor area in the present
study were NIS. The Chao estimator similarly
estimated for the present study that about 80% of
the taxa in the sampled environment had been
detected despite the much lower number of
samples (27) collected in Toledo Harbor than in
Duluth-Superior Harbor. However, as has recently
been pointed out (Lopez et al. 2012), richness
estimators (Chao and others) consistently
underestimate the total abundance of taxa when
sample sizes are small. Applying their suggested
correction formula (Sest,corrected = Sest(1+P2),
where P is the proportion of singleton or unique
taxa in the samples) to adjust the richness estimator
produces an adjusted number of benthic taxa
predicted to be present in Toledo Harbor upward
by about 10%. This lowers the estimate of the
proportion of total taxa detected by the 27
samples to about 75% and increases estimates of
the sampling effort that would be required to
achieve 90% detection.
A further consideration is that the samples for
this study were collected on three days in late
September/early October, a time when rooted
vegetation, known to be present earlier in the
season, had already disappeared from collecting
sites. Trebitz et al. (2009) had also collected during
a short time period, but it was in late summer
when vegetation was still present in about a third
of their collecting sites. Since Trebitz et al.
(2009) detected significantly more rare and non-
indigenous species in shallow vegetated areas,
the lack of this identifying factor and habitat
could also have decreased the number and types
of non-indigenous and rare species detected in
the present study. The limited collecting periods
may also have resulted in missing various benthic
organisms whose numbers may vary seasonally.
The results thus apply to a limited range of
substrates and may be seasonally specific as well.
Potentially, a more extensive sampling regimen
that includes more types of habitat substrates and
a broader seasonal distribution than in this study
will reveal additional species and substrate types
that favor detection of rare or non-native species.
To develop management programs for specific
ports, studies like this can provide a guide for
future collecting effort and therefore likely costs
to provide effective early detection of rare or
non-indigenous species in the area, which may
differ from port to port. The taxonomic complexity
and predicted number of samples needed for an
effective survey of Toledo Harbor appears to be
lower than observed in Duluth-Superior Harbor
by Trebitz et al. (2009). The EPA, in its 2010 Great
Lakes Restoration Initiative call for proposals,
suggested that an appropriate oversampling strategy
for early detection of NIS should be to capture
and identify roughly 90% or more of all taxa
present in the biological component of the system
being sampled. To achieve >90% detection of all
species present and an increased likelihood of
detecting NIS will require a substantial increase
in the number of samples collected and should
include a broader seasonal range and habitats
such as vegetated sites. Nevertheless, the experience
gained from navigating the area and sampling
these sites near the Port of Toledo should enable
resource managers to conduct future surveys
with greater efficiency and appropriately increased
sampling effort.
Acknowledgements
We thank Healing Our Waters and the Western Lake Erie
Waterkeeper Association for their financial support for this study.
Alisha Dahlstrom provided helpful comments. Work on this
project, including preparation of this manuscript, is continuing
with support from the Environmental Protection Agency Great
Lakes Restoration Initiative (grant GL00E00808-0 to JLR and
DRK). We thank reviewers of this paper for helpful suggestions.
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Supplementary material
The following supplementary material is available for this article:
Figure S1. Examples of representative specimens of the 25 different taxa collected from Toledo Harbor that were classified by
EcoAnalysts.
Appendix S1. Representative sequences of Maumee Bay and Maumee River organisms with no previously identified barcode match.
This material is available as part of online article from:
http://www.reabic.net/journals/mbi/2014/Supplements/MBI_2014_Ra m_e tal _Supplement.pdf