J OURNAL OF C RUSTACEAN B IOLOGY, 33(2), 293-300, 2013
INVASION OF RUSTY CRAYFISH, ORCONECTES RUSTICUS, IN THE UNITED STATES:
NICHE SHIFTS AND POTENTIAL FUTURE DISTRIBUTION
Reid L. Morehouse ∗ and Michael Tobler
Department of Zoology, Oklahoma State University, Stillwater, OK 74078, USA
ABSTRACT
Invasive species are among the foremost threats to freshwater ecosystems. Predicting their spread is important, especially if the species
is associated with undesirable effects on recipient ecosystems. Ecological niche modeling allows for the assessment of invasion potential
of non-native species. Here, we used native and invasive occurrence locations and the Maxent niche modeling algorithm to predict the
invasion potential of rusty crayfish, Orconectes rusticus Girard, 1852, in the United States. We built and compared three models based on:
1) native occurrences only, 2) invasive occurrences only, and 3) all occurrence points. We found that the model using native occurrences did
not accurately predict the current invasion of O. rusticus, as it omitted 58% of the known invaded locations. Furthermore, the model based
on invasive occurrences failed to accurately predict the native range of O. rusticus. Predicted suitable areas for O. rusticus closely matched
the known distribution when all occurrences were used to train the Maxent model. Differences in models based on native and invasive
occurrence points are likely due to a niche shift of O. rusticus during invasion. We recommend the use of multiple sources of information
to better understand the invasion potential of invasive species, as solely using native or invasive occurrence information ultimately may
provide inaccurate predictions about the spread of invasive species.
K EY W ORDS: conservation, crayfish, ecological niche modeling, invasive species, niche conservatism
DOI: 10.1163/1937240X-00002120
I NTRODUCTION
Invasive organisms threaten freshwater ecosystems worldwide, and crustaceans have been among the most successful
invaders, causing problems in a variety of freshwater environments (Hanfling et al., 2011). Invasive species negatively
affect freshwater ecosystems particularly due to their susceptibility to species invasions and extinctions as well as
high levels of endemism (Ricciardi and Rasmussen, 1999;
Dudgeon et al., 2006). Invasive species generate negative
impacts through a variety of mechanisms such as competitive interactions, predation, transmission of diseases, habitat alteration, and changes in ecosystem function. For example, the European green crab, Carcinus maenas (Linnaeus, 1758), has severely impacted native bivalves and crab
species on the East and West coasts of the United States
(Miron et al., 2005), reducing the abundance of both native
taxa by 90-95% within three years of invasion (Grosholz et
al., 2000). Similarly, in subtropical lakes that have higher average temperatures, invasive Daphnia lumholtzi Sars, 1885
now dominate zooplankton communities and potentially affect fish community structures (Havens et al., 2012).
A variety of crayfish species have also become problematic invaders, profoundly impacting ecosystems through
habitat alteration, changes in native community composition, and the transmission of non-native diseases (Rodriguez
et al., 2003; Geiger et al., 2005). In particular, European
crayfish have been decimated by intentional introductions
of North American and Australian species (Lodge et al.,
2000; Lodge et al., 2012). Competitive interactions with
non-indigenous species have altered realized niches of native species and caused declines in their abundance and distribution (Geiger et al., 2005; Gherardi, 2006). In addition,
declines in European crayfish populations are driven by the
introduction and spread of the fungal plague, Aphanomyces
astaci (Lodge et al., 2000), which commonly infects the resistant North American species but is highly lethal to native
crayfish (Geiger et al., 2005).
Given the adverse effects of invasive species, predicting
their spread is important for management and prevention
(Herborg et al., 2007a). Ecological niche modeling can be
used as a tool to predict potential habitat for invasive species
based on known occurrence data and associated environmental parameters (Peterson and Vieglais, 2001). Such modeling
approaches have been widely used to extrapolate native distributions to regions susceptible to invasion for a variety of
invasive crustaceans (Herborg et al., 2007b; Larson et al.,
2010; Capinha et al., 2011). A basic assumption of these
models is that a species’ niche is conserved through space
and time (niche conservatism; Pearman et al., 2008). However, invasive species can undergo pronounced niche shifts
as they colonize new habitats (Sax et al., 2007; Larson et
al., 2010; Schonrogge et al., 2012). Such niche shifts can be
caused by an expansion of the realized niche or rapid adaptation to new environmental conditions (Pearman et al., 2008).
Empirical studies on decapods support both niche conservatism and niche shifts. For example, Capinha et al. (2011)
∗ Corresponding
author: 501 Life Sciences West, Department of Zoology, Stillwater, OK 74078, USA, E-mail:
[email protected], Phone: 405-744-5555, Fax: 405-744-7824
© The Crustacean Society, 2013. Published by Brill NV, Leiden
DOI:10.1163/1937240X-00002120
294
JOURNAL OF CRUSTACEAN BIOLOGY, VOL. 33, NO. 2, 2013
used multiple calibration data sets to predict the global invasiveness of four decapod species and found that models
accurately predicted invaded ranges of three species, potentially indicating niche conservatism. In contrast, Larson et
al. (2010) uncovered strong evidence that the globally invasive signal crayfish, Pacifastacus leniusculus (Dana, 1852),
exhibits a pronounced climatic niche shift from the native to
the invaded regions. Additionally, Larson and Olden (2012)
use P. leniusculus as an ‘avatar’ species to generalize and
synthesize general patterns in species niche shifts, and apply
this method to uncover the potential distribution of an upcoming crayfish invader. Ultimately, the only way to test the
assumptions of niche conservatism is the analysis of multiple lines of evidence, including contrasting predictions from
models based on known native and the invaded locations.
Evaluating the accuracy of models solely based on native
ranges – which are based on the assumption of niche conservatism – is imperative, as low accuracy will ultimately lead
to poor management decisions due to the over- or underestimation of habitat suitability for any given invasive species
(Elith et al., 2006; Peterson, 2011).
In this study, we focused on rusty crayfish, Orconectes
rusticus (Girard, 1852), a species native to the Ohio River
and the lower Maumee River drainages that has invaded
aquatic habitats throughout the United States and Canada
(Taylor et al., 2007; Olden et al., 2009; Phillips et al.,
2009). The spread of this species in non-native habitats has
been facilitated beyond natural dispersal through intentional
release by lake managers for nuisance plant control, as well
as accidental introductions by anglers, biological supply and
pet industries (Lodge et al., 2000; Puth and Allen, 2005;
DiStefano et al., 2009). Orconectes rusticus negatively affect
populations of native macrophytes, benthic invertebrates,
fish, and other crayfish through predation, competition for
space and food, and physical disturbance of sediments by
burrowing behaviors (Lodge et al., 1994; McCarthy et al.,
2006; Rosenthal et al., 2006). The majority of information
regarding ecological impacts on congener displacement and
changes in food web structure caused by O. rusticus has
been collected in the upper Midwest region of the United
States (Capelli, 1982; Olden et al., 2006; Olden et al., 2011).
A recent study by Olden et al. (2011) assessed ecosystem
vulnerability to O. rusticus invasions in Wisconsin lakes and
streams, identifying management actions that result in the
most efficient ways to allocate resources for conservation.
Furthermore, they used ecological niche modeling to predict
locations that are susceptible to invasion by O. rusticus,
focusing prevention efforts on these regions. Nevertheless, it
still remains unknown how environmental conditions within
the native range of the species compare to the conditions in
known invaded habitats. Due to the wide expansion of O.
rusticus throughout the United States, testing for potential
niche shifts in this species is imperative to develop sound
management strategies.
Here, we investigated if and how the environmental conditions in the known invaded range relate to the conditions
within the native distribution. Specifically, we used native
and invasive occurrence points in conjunction with ecological niche modeling to address the following questions: 1) Do
environmental conditions in the native range accurately pre-
dict the known invaded range, and is there evidence for a
niche shift? 2) Do predicted ranges differ when either native
or invasive occurrences alone are used for ecological niche
modeling? If so, what are the key environmental differences
between native and invaded ranges? 3) What areas are potentially susceptible to future O. rusticus invasion?
M ATERIALS AND M ETHODS
To model the ecological niche of O. rusticus, we obtained 520 nationwide
occurrence records from the Illinois Natural History Survey and the
United States Geological Survey (USGS) Nonindigenous Aquatic Species
databases. Ninety-four records were located within the native range of the
species; the remaining 426 records represented invaded locality points. For
records lacking geographic coordinates, we geo-referenced locations using
GEOLocate v. 3.22 (Rios and Bart, 2010) based on collection information.
GEOLocate provides confidence levels (low, medium, high) that depend
on the detail of the collection information and the error associated with
geo-referencing. Only, records with high confidence were included in
subsequent analyses (native: N = 84; invasive: N = 391). All points
were mapped in ArcMap10 (ESRI, 2011) to ensure that the georeferenced
localities corresponded with the original descriptions. Our study area was
delimited using the known species’ distribution within the native range
(Ohio River Drainage) and the invaded range (United States political
border).
Environmental Variables
We initially considered 19 bioclimatic environmental variables to model the
potential distribution of O. rusticus. These variables are commonly used in
ecological niche modeling for crustaceans (Olden et al., 2006; Larson et
al., 2010; Capinha et al., 2011). Environmental variables were downloaded
from the WorldClim (Hijmans et al., 2005) database at ‘30 arc-seconds’
spatial resolution (http://www.worldclim.org/bioclim.htm), and then we
resampled at 1 km2 resolution. To reduce redundancy in the environmental
variables (some environmental variables can be highly correlated), we used
the Principal Components Analysis tool in the ArcGIS v.10 Spatial Analyst
extension to assemble a correlation matrix for all variables across our
spatial extent of analysis. We retained only a single variable for variables
that were correlated at r 2 > 0.9, preferentially choosing variables that
measured extremes over those measuring averages (Shepard and Burbrink,
2008). Environmental extremes are more likely to set range limits of
organisms due to physiological constraints (Kozak and Wiens, 2006). This
procedure reduced the initial dataset to 14 variables. Reducing the number
of variables to those considered ecologically relevant and non-redundant
simplifies hypothesis testing and interpretation of results (Elith et al., 2011).
Moreover, using fewer variables decreases the potential for model overfitting (Warren and Seifert, 2011). We then jackknifed the remaining 14
variables in Maxent (Phillips et al., 2006; see below) to further reduce
the number of variables used in the final models. Jackknifing determines
a variable’s contribution to a model’s overall accuracy gain, and we only
retained variables that contributed more than 5% to an initial model to
improve our predictions in the final models.
Modeling Procedure
To model the potential distribution of O. rusticus, we used the maximum
entropy ecological niche modeling method (Maxent v. 3.3.3e; http://www.
cs.princeton.edu/~schapire/maxent/) (Phillips et al., 2006), which has been
found to be the most conservative compared to other methods in regard
to model overfitting (Elith et al., 2006). Maxent estimates the probability
distribution for a species’ occurrence based on environmental constraints
derived from the environmental variables inputted into the model and
known occurrence points of the focus species (Phillips et al., 2006).
Maxent requires only species presence data and continuous or categorical
environmental variables layers for the given study area. Validation is
necessary to assess the predictive performance of the model, and we used
receiver operating characteristic (ROC) analysis (Peterson et al., 2008),
which plots sensitivity (y-axis, lack of omission error) against 1-specificity
(x-axis, commission error), to evaluate models. Omission error is defined
as known presences that are predicted absent, and commission error as
locations predicted suitable for which no presences are known. The area
under the ROC curve (AUC) was calculated to indicate prediction accuracy.
The AUC ranges from 0.5 (random assignment of presences and absences)
MOREHOUSE AND TOBLER: CRAYFISH INVASION OF THE UNITED STATES
to a maximum value of 1.0 (perfect discrimination of presences and
absences). The analysis was run for both the training dataset (80% of the
data points randomly chosen) and the testing dataset (remaining 20% of the
data points) to assess the average performance of the resulting models with
a fixed threshold of 0.10 (10% omission error), rejecting the lowest 10% of
possible predicted values.
We ran three separate models to address the questions raised in the
introduction. These models used either native occurrences only, invasive
occurrences only, or all occurrences for training. We trained our first
model with the native occurrence points and background and extrapolated
the results across the United States to test whether the native range can
accurately predict the known invaded range. We classified the Maxent
results to either predicted present or absent based on the minimum training
presence and determined the number of known invasive occurrences that
were not predicted.
We tested for potential niche shifts by investigating whether the
predicted invasive range differed, if only native or only invasive occurrences
were used to train the model. To do so, we ran a Maxent model using
the invaded occurrence points only for training and then used ENMtools
(Warren et al., 2010) to compare the two niche models (native only and
invasive only) for niche overlap and similarity. We used two tests introduced
by Warren et al. (2008) that quantify whether two ENMs are alike (niche
similarity tests) or no more similar than expected if localities are sampled
at random from the environmental background (background similarity test).
Both tests are based on the Schoener’s D value (Schoener, 1968). To test
for background similarity, we contrasted Schoener’s D values obtained by
comparing ENM projections to a distribution of values obtained by running
100 simulations comparing the ENMs generated using actual localities
from the models trained with native occurrences only to ENMs generated
from samples drawn randomly from the range occupied by the invasive
occurrences. Due to the individual models having different environmental
variables contributing to each model, we included all variables appearing in
one of the models to compare backgrounds. The additional variables from
each individual model had less than 5% contribution to the reciprocal model
and did not influence the background comparisons significantly.
We also tested for differences in environmental conditions between
native and invaded occurrences of O. rusticus by extracting environmental
data for each occurrence point and conducting a discriminant function
analysis (DFA). The DFA was used to determine the percentage of sites
that could be correctly assigned to the native or the invaded range solely
based on environmental conditions. This approach of ENM followed
by a multivariate analysis, such as DFA, provides a more rigorous test
of environmental differences among groups than either analysis alone
(McCormack et al., 2010). Ecological niche models provide a quantitative
estimate of the native and invaded environmental niche and identify
important variables shaping distributional patters in each group separately.
ENM-based tests for niche differentiation do not reveal the specific
environmental factors that differ among groups, because the contributions
of variables differ among species ENMs. Using DFA with the same set
of variables allows us to test, which variables best explain differences in
environmental conditions among native and invaded ranges. Discriminant
function analysis was performed using SPSS 19 (SPSS, Inc., 2007).
Finally, we combined all occurrence data to predict locations that are
vulnerable to further invasion by O. rusticus. By combining all occurrence
points, we are able to cover the array of environmental parameters that
O. rusticus currently experience. This should allow the model to more
accurately predict potential suitable habitats that could potentially be at high
risk of invasion.
R ESULTS
Does Native Range Accurately Predict Known Invaded
Range?
The Maxent model trained with native occurrences points
only had a high test AUC value of 0.852 and a test omission
rate of <0.001% at minimum training presence. When
extrapolated over the entire United States, the model failed
to accurately predict approximately 58% of the invasive
occurrence points (Fig. 1A). The model accurately predicted
the native range (Ohio River Basin) and invaded locations in
lower Wisconsin, areas in Colorado at the base of the Rocky
295
Mountains, and the eastern side of Appalachian Mountains
from Virginia northward to Maine. However, the model did
not accurately predict the known invasion occurrences in
northern Wisconsin and Minnesota, or the most western
occurrences in the Pacific Northwest. The most important
environmental variables contributing approximately 77%
to the model were mean temperature of warmest quarter,
isothermality, temperature annual range, and precipitation
seasonality (Table 1).
How Do Models Based on Native and Invaded Occurrence
Points Differ?
The Maxent model trained solely with invaded occurrence
points had a high test AUC value of 0.946 and a test omission
rate of 0.013% at minimum training presence. The model
failed to accurately predict the native range of O. rusticus, as
77% of the native occurrence points were predicted absent
(Fig. 1B). The environmental variables with high percent
of contribution to this model differed from the model using
native occurrence points as isothermality, precipitation of the
warmest quarter, and maximum temperature of the warmest
month had the highest loadings (∼71% combined, Table 1).
Schoener’s D (niche overlap) was 0.305 when the native occurrences model was compared to the invasive occurrences model, suggesting that the two models were dissimilar in their predicted distributions. Furthermore, background
tests (comparing Schoener’s D for background points) provided an approximate P -value of 0.402 ± 0.021 (standard
deviation) further implying that the niche overlap was not
significant and that the models’ predicted ranges differed
significantly. The DFA classified over 90% of the occurrence
points correctly to either the native or the invaded range of
O. rusticus (Fig. 2) highlighting the differences in environmental conditions between the native and the invaded range
(Table 2). Based on the DFA, the native range had higher average temperatures and more precipitation in the driest and
coldest quarters, while the invaded range had a larger range
of annual mean temperature and more variation in precipitation throughout the year (Fig. 2).
What Areas Are Potentially Susceptible to Future Invasion
by O. rusticus?
In an attempt to predict the future invasive potential of O.
rusticus, we ran a combined model including both native and
invasive occurrence points (Fig. 1C). This model had a test
AUC value of 0.926 and test omission rate of 0.000% suggesting the model accurately predicted the current distribution of O. rusticus throughout the contiguous United States
(Fig. 1C). Nearly half of the model contribution was provided by one variable: precipitation of warmest quarter (Table 1).
Since individual models differed in their predictions,
they provided complementary information about geographic
regions that may be susceptible to O. rusticus invasions in
the future. The model using native occurrences predicted
suitable habitat for O. rusticus along the eastern United
States from North Carolina, up the eastern coastline to
Maine, portions of Missouri and Nebraska and the upper
Ozark Mountains in Arkansas. The model using the invasive
occurrences predicted suitable habitat for O. rusticus along
the Appalachian Mountains, in the upper Midwestern states
296
JOURNAL OF CRUSTACEAN BIOLOGY, VOL. 33, NO. 2, 2013
Fig. 1. Potential distribution of Orconectes rusticus based on Maxent niche models. A, using native occurrences points only; B, invasive occurrences points
only; C, all occurrences points. Darker colors represent higher habitat suitability values based on bioclimatic variables. White circles indicate known invasive
range occurrences and white triangles indicate known native range occurrences.
of Wisconsin, Minnesota, Iowa, but not in areas where O.
rusticus has been found, e.g., Colorado, Pacific Northwest
(Fig. 3). Finally, the combined occurrence model predicted
similar areas as the other two models. The model built using
all available occurrences shows a clear connection between
the native and invaded ranges in the northern regions of
Illinois and Indiana. According to this model, areas that are
at risk of invasion are western Iowa and large portions of
Missouri and the eastern side of Nebraska.
D ISCUSSION
Ecological niche modeling offers insights into the potential distributions of invasive species (Peterson, 2003) and
has successfully predicted species invasions for a variety of
organisms (Peterson and Robins, 2003; Drake and Bossenbroek, 2004; Iguchi et al., 2004; Herborg et al., 2007a,
2007b). In this study, the Maxent model using the native
range occurrence data extrapolated to the invaded range
failed to accurately predict the known invaded range of O.
rusticus, and approximately 58% of the known invasive occurrences remained unpredicted. Our results coincide with
recent findings that also fail to accurately predict invaded
ranges solely based on native ranges (Fitzpatrick et al., 2007;
Beaumont et al., 2009).
When we modeled the potential range of O. rusticus
solely using invaded occurrences, the model failed to pre-
297
MOREHOUSE AND TOBLER: CRAYFISH INVASION OF THE UNITED STATES
Table 1. Environmental variables and their percent contribution in Maxent models for the predicted Orconectes rusticus distribution. Bold numbers indicate
the top three environmental variables that contributed to each model the most.
Environmental variable
Percent contribution
Native occurrences only
Mean temperature of warmest quarter
Isothermality
Temperature annual range
Precipitation seasonality
Precipitation of warmest quarter
Mean temperature of driest quarter
Precipitation of driest quarter
Precipitation of coldest quarter
Maximum temperature of warmest month
Precipitation of wettest quarter
27.7
20.7
17.3
11.1
10.0
7.9
4.5
0.6
0.3
dict the native range as suitable habitat and omitted 77%
of the known native occurrences. Consequently, low niche
overlap was found between native and invasive distribution
models indicating that the native and invaded ranges differ
in climatic conditions. The poor predictive ability of these
models is likely due to the different environmental conditions found in the native and invaded range. Climatic conditions are so distinct that we can assign a majority of the
known occurrences to the correct category (native vs. invasive) solely based on the environmental variables using
discriminant function analysis. Together, these results suggest that O. rusticus has undergone a significant climatic
Invasive occurrences only
All occurrences
32.2
24.7
27.4
8.5
6.5
7.5
11.2
7.1
44.5
23.2
7.6
niche shift in the process of invading new geographic regions.
Niche shifts occur whenever a species is exposed to
different environmental conditions in new regions or time
periods than the conditions found in the initial populations
(Capinha et al., 2011). Studies documenting niche shifts in
invasive crustaceans, especially decapods, have accumulated
over the past years. For example, Larson et al. (2010)
documented a climatic – but not a trophic – niche shift
between native and invasive populations of P. leniusculus.
In contrast, Capinha et al. (2011) found a possible niche
shift in only one of four crayfish species studied. Clearly,
Fig. 2. Frequency histogram based on discriminant function analysis (DFA) examining environmental differences between the native and the invasive range
of O. rusticus based on native standing. Black bars represent invasive range occurrences and gray bars represent native range occurrences.
298
JOURNAL OF CRUSTACEAN BIOLOGY, VOL. 33, NO. 2, 2013
Table 2. Results of the discriminant function analysis (DFA) assessing the
environmental differences between O. rusticus occurrence points from the
native and invaded range.
Variable
Precipitation of driest quarter
Precipitation of coldest quarter
Mean temperature of driest quarter
Temperature annual range
Precipitation seasonality
Max temperature of warmest month
Mean temperature of warmest quarter
Isothermality
Precipitation of wettest quarter
Precipitation of warmest quarter
Mean diurnal range
Canonical correlation
Eigenvalue
% of Variance
Chi-square
df
P
Function 1
0.800
0.797
0.709
−0.658
−0.638
0.554
0.542
0.498
0.334
0.147
0.047
0.736
1.184
100
392.508
11
<0.001
there is a need for broader taxonomic sampling to test for
generalities and identify organismal properties that facilitate
niche shifts in any given species. Our results, however,
highlight the importance of carefully choosing variables and
known species occurrences to build and calibrate models
when modeling potential distributions of invasive species, as
the fundamental assumption of niche conservatism may not
be scrutinized for all species.
While our niche models clearly indicate a niche shift between O. rusticus from the native and the introduced ranges,
the underlying mechanisms causing this niche shift remain
unclear. There are two non-mutually exclusive mechanisms
that could explain the pattern. 1) The modeled native niche
may not adequately represent the fundamental niche of a
species; thus, the niche shift observed in invasive populations would merely be an expansion of the realized niche.
Accordingly, O. rusticus may be restricted to the Ohio River
drainage not because environmental conditions outside the
drainage do not provide adequate habitat, but because biogeographic barriers historically restricted natural dispersal
to other suitable regions (Gallien et al., 2010). 2) The fundamental niche of invasive populations may shift due to rapid
contemporary evolution through adaptation to novel environmental conditions (Gallien et al., 2010). Based on current data, it is impossible to evaluate the relative role of
each mechanism in shaping niche characteristics in native
and invasive populations. Further experimental and comparative studies are required to quantify the fundamental niche
of O. rusticus in the native range and potential evolutionary
shifts in invasive populations.
The high invasion success of O. rusticus, along with our
results identifying suitable habitats throughout much of the
central and eastern United States, warrants a discussion in
regards to potential policy and management implications.
For example, our models indicate that large parts of Missouri
and Iowa provide suitable habitats for O. rusticus. The
bait industry could be a potential vector for non-indigenous
crayfish invasions, as bait shops in Missouri were found
to sell non-native crayfish species, including O. rusticus
(DiStefano et al., 2009). While there are no currently
confirmed records of O. rusticus in Missouri’s natural
habitats (Pflieger, 1996; DiStefano et al., 2009), the sale
of the species as fishing bait poses imminent risks for new
introductions that – based on our models – have a chance
of establishment. To mitigate the spread of invasive species,
many states have implemented regulations either banning
or restricting the sale of all or certain crayfish species
(DiStefano et al., 2009; Peters and Lodge, 2009; Lodge et
al., 2012). Still such crayfish trade remains regulated on
a state-by-state basis, and the possibility of states without
such legislation becoming facilitators of further invasion is
evident (see review by Peters and Lodge, 2009). We agree
with previous authors (DiStefano et al., 2009; Peters and
Lodge, 2009) that more strict regulations, enforcement of
current laws, and public education are required to limit
further crayfish invasions.
Overall, the results from our study are consistent with previous findings that suggest integrating information from both
Fig. 3. Differences between the models ran with native occurrences only and the model with invasive occurrences only. Black represents areas that the
native occurrence model predicted suitable that the invasive model did not, and gray represents areas that the invasive model predicted suitable that the native
model did not. White circles indicate known invasive range occurrences and white triangles indicate known native range occurrences.
MOREHOUSE AND TOBLER: CRAYFISH INVASION OF THE UNITED STATES
native and invaded regions is crucial for the exact estimation
of invasion potential (Broennimann and Guisan, 2008; Beaumont et al., 2009; Capinha et al., 2011), particularly because
invasive species may undergo niche shifts while spreading
into previously unoccupied regions. In future studies, it is
imperative to examine how dispersal abilities and biotic environmental factors contribute to or hinder the continuous
spread of O. rusticus. Environmental niche modeling as implemented here predominantly relies on abiotic environmental variables to project potential distributional patterns, and
integrating dispersal abilities and biotic factors with ENMs
in the form of hybrid models may further improve our ability
to predict potential future invasions (Gallien et al., 2010).
ACKNOWLEDGEMENTS
We thank M. Papeş, A. R. Dzialowski, the members of the Tobler lab, and
two anonymous reviewers for critical comments on previous versions of
this manuscript. We are indebted to Chris Taylor from the Illinois Natural
History Survey and Pam Fuller from USGS for access to their databases.
Funding for this study came from Oklahoma State University.
R EFERENCES
Beaumont, L. J., R. V. Gallagher, W. Thuiller, P. O. Downey, M. R.
Leishman, and L. Hughes. 2009. Different climatic envelopes among
invasive populations may lead to underestimations of current and future
biological invasions. Diversity and Distributions 15: 409-420.
Broennimann, O., and A. Guisan. 2008. Predicting current and future
biological invasions: both native and invaded ranges matter. Biology
Letters 4: 585-589.
Capelli, G. M. 1982. Displacement of northern Wisconson crayfish by
Orconectes rusticus (Girard). Limnology and Oceanography 27: 741745.
Capinha, C., B. Leung, and P. Anastacio. 2011. Predicting worldwide
invasiveness for four major problematic decapods: an evaluation of using
different calibration sets. Ecography 34: 448-459.
Dana, J. D. 1852. Crustacea, Part I. In United States exploring expedition,
during the years 1838, 1839, 1840, 1841, 1842, under the command of
Charles Wilkes, U.SM 13: viii + 685 pages.
DiStefano, R. J., M. E. Litvan, and P. T. Horner. 2009. The bait industry as a
potential vector for alien crayfish introductions: problem recognition by
fisheries agencies and a Missouri evaluation. Fisheries 34: 586-597.
Drake, J. M., and J. M. Bossenbroek. 2004. The potential distribution of
zebra mussels in the United States. Bioscience 54: 931-941.
Dudgeon, D., A. H. Arthington, M. O. Gessner, Z. I. Kawabata, D. J.
Knowler, C. Leveque, R. J. Naiman, A. H. Prieur-Richard, D. Soto,
M. L. J. Stiassny, and C. A. Sullivan. 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews
81: 163-182.
Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan,
R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li,
L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura,
Y. Nakazawa, J. M. Overton, A. T. Peterson, S. J. Phillips, K. Richardson,
R. Scachetti-Pereira, R. E. Schapire, J. Soberon, S. Williams, M. S. Wisz,
and N. E. Zimmermann. 2006. Novel methods improve prediction of
species’ distributions from occurrence data. Ecography 29: 129-151.
, S. J. Phillips, T. Hastie, M. Dudik, Y. E. Chee, and C. J. Yates.
2011. A statistical explanation of MaxEnt for ecologists. Diversity and
Distributions 17: 43-57.
ESRI. 2011. ArcGIS Desktop: Release 10. Environmental Systems Research Institute, Redlands, CA.
Fitzpatrick, M. C., J. F. Weltzin, N. J. Sanders, and R. R. Dunn. 2007. The
biogeography of prediction error: why does the introduced range of the
fire ant over-predict its native range? Global Ecology and Biogeography
16: 24-33.
Gallien, L., T. Munkemuller, C. H. Albert, I. Boulangeat, and W. Thuiller.
2010. Predicting potential distributions of invasive species: where to go
from here? Diversity and Distributions 16: 331-342.
299
Geiger, W., P. Alcorlo, A. Baltanas, and C. Montes. 2005. Impact of an
introduced crustacean on the trophic webs of Mediterranean wetlands.
Biological Invasions 7: 49-73.
Gherardi, F. 2006. Crayfish invading Europe: the case study of Procambarus clarkii. Marine and Freshwater Behaviour and Physiology 39: 175191.
Girard, C. 1852. A revision of the North American astaci, with observations on their habits and geographical distribution. Proceedings of the
Academy of Natural Sciences of Philadelphia 6: 87-91.
Grosholz, E. D., G. M. Ruiz, C. A. Dean, K. A. Shirley, J. L. Maron, and
P. G. Connors. 2000. The impacts of a nonindigenous marine predator in
a California bay. Ecology 81: 1206-1224.
Hanfling, B., F. Edwards, and F. Gherardi. 2011. Invasive alien Crustacea:
dispersal, establishment, impact and control. Biocontrol 56: 573-595.
Havens, K. E., J. R. Beaver, T. L. East, K. Work, E. J. Phlips, M. F. Cichra,
A. C. Croteau, A. J. Rodusky, R. S. Fulton, and T. C. Rosati. 2012.
The outcome of the invasion of Florida lakes by Daphnia lumholtzi.
Freshwater Biology 57: 552-562.
Herborg, L. M., N. E. Mandrak, B. C. Cudmore, and H. J. MacIsaac. 2007a.
Comparative distribution and invasion risk of snakehead (Channidae) and
asian carp (Cyprinidae) species in North America. Canadian Journal of
Fisheries and Aquatic Sciences 64: 1723-1735.
, D. A. Rudnick, Y. Siliang, D. M. Lodge, and H. J. MacIsaac.
2007b. Predicting the range of chinese mitten crabs in Europe. Conservation Biology 21: 1316-1323.
Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005.
Very high resolution interpolated climate surfaces for global land areas.
International Journal of Climatology 25: 1965-1978.
Iguchi, K., K. Matsuura, K. M. McNyset, A. T. Peterson, R. ScachettiPereira, K. A. Powers, D. A. Vieglais, E. O. Wiley, and T. Yodo.
2004. Predicting invasions of North American basses in Japan using
native range data and a genetic algorithm. Transactions of the American
Fisheries Society 133: 845-854.
Kozak, K. H., and J. J. Wiens. 2006. Does niche conservatism promote
speciation? A case study in North American salamanders. Evolution 60:
2604-2621.
Larson, E. R., and J. D. Olden. 2012. Using avatar species to model
the potential distribution of emerging invaders. Global Ecology and
Biogeography, in press, DOI:10.1111/j.1466-8238.2012.00758.x.
,
, and N. Usio. 2010. Decoupled conservatism of Grinnellian and Eltonian niches in an invasive arthropod. Ecosphere 1: art16.
Linnaeus, C. 1758. System Naturae per Regna Tria Naturae, Secundum
Classes, Ordines, Genera, Species, cum Characteribus, Differentiis,
Synomis, Locis. Edition 10: 1-824.
Lodge, D. M., A. Deines, F. Gherardi, D. C. J. Yeo, T. Arcella, A. K.
Baldridge, M. A. Barnes, W. L. Chadderton, J. L. Feder, C. A. Gantz,
G. W. Howard, C. L. Jerde, B. W. Peters, J. A. Peters, L. W. Sargent,
C. R. Turner, M. E. Wittmann, and Y. Zeng. 2012. Global introductions
of crayfishes: evaluating the impact of species invasions on ecosystem
services. Annual Review of Ecology, Evolution, and Systematics 43, in
press, DOI:10.1146/annurev-ecolsys-111511-103919.
, M. W. Kershner, J. E. Aloi, and A. P. Covich. 1994. Effects of an
omnivorous crayfish (Orconectes rusticus) on a fresh-water littoral foodweb. Ecology 75: 1265-1281.
, C. A. Taylor, D. M. Holdich, and J. Skurdal. 2000. Nonindigenous
crayfishes threaten North American freshwater biodiversity: lessons from
Europe. Fisheries 25: 7-20.
McCarthy, J. M., C. L. Hein, J. D. Olden, and M. J. Vander Zanden. 2006.
Coupling long-term studies with meta-analysis to investigate impacts of
non-native crayfish on zoobenthic communities. Freshwater Biology 51:
224-235.
McCormack, J. E., A. J. Zellmer, and L. L. Knowles. 2010. Does niche
divergence accompany allopatric divergence in Aphelocoma jays as
predicted under ecological speciation?: insights from test with niche
models. Evolution 64: 1231-1244.
Miron, G., D. Audet, T. Landry, and M. Moriyasu. 2005. Predation
potential of the invasive green crab (Carcinus maenas) and other common
predators on commercial bivalve species found on Prince Edward island.
Journal of Shellfish Research 24: 579-586.
Olden, J. D., J. W. Adams, and E. R. Larson. 2009. First record of
Orconectes rusticus (Girard, 1852) (Decapoda, Cambaridae) west of the
Great Continental Divide in North America. Crustaceana 82: 1347-1351.
, J. M. McCarthy, J. T. Maxted, W. W. Fetzer, and M. J. Vander
Zanden. 2006. The rapid spread of rusty crayfish (Orconectes rusticus)
300
JOURNAL OF CRUSTACEAN BIOLOGY, VOL. 33, NO. 2, 2013
with observations on native crayfish declines in Wisconsin (USA) over
the past 130 years. Biological Invasions 8: 1621-1628.
, M. J. Vander Zanden, and P. T. J. Johnson. 2011. Assessing
ecosystem vulnerability to invasive rusty crayfish (Orconectes rusticus).
Ecological Applications 21: 2587-2599.
Pearman, P. B., A. Guisan, O. Broennimann, and C. F. Randin. 2008. Niche
dynamics in space and time. Trends in Ecology & Evolution 23: 149158.
Peters, J. A., and D. M. Lodge. 2009. Invasive species policy at the regional
level: a multiple weak links problem. Fisheries 34: 373-381.
Peterson, A. T. 2003. Predicting the geography of species’ invasions via
ecological niche modeling. Quarterly Review of Biology 78: 419-433.
. 2011. Ecological niche conservatism: a time-structured review of
evidence. Journal of Biogeography 38: 817-827.
, M. Papes, and J. Soberon. 2008. Rethinking receiver operating
characteristic analysis applications in ecological niche modeling. Ecological Modelling 213: 63-72.
, and C. R. Robins. 2003. Using ecological-niche modeling to predict Barred owl invasions with implications for Spotted owl conservation.
Conservation Biology 17: 1161-1165.
, and D. A. Vieglais. 2001. Predicting species invasions using
ecological niche modeling: new approaches from bioinformatics attack
a pressing problem. Bioscience 51: 363-371.
Pflieger, W. L. 1996. The Crayfishes of Missouri. Missouri Department of
Conservation, Jefferson City, MO.
Phillips, I. D., R. D. Vinebrooke, and M. A. Turner. 2009. Ecosystem
consequences of potential range expansions of Orconectes virilis and
Orconectes rusticus crayfish in Canada – a review. Environmental
Reviews 17: 235-248.
Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy
modeling of species geographic distributions. Ecological Modelling 190:
231-259.
Puth, L. M., and T. F. H. Allen. 2005. Potential corridors for the rusty
crayfish, Orconectes rusticus, in northern Wisconsin (USA) lakes:
lessons for exotic invasions. Landscape Ecology 20: 567-577.
Ricciardi, A., and J. B. Rasmussen. 1999. Extinction rates of North
American freshwater fauna. Conservation Biology 13: 1220-1222.
Rios, N. E., and H. L. Bart. 2010. GEOLocate (Version 3.22) computer software. Tulane University Museum of Natural History, Belle Chasse, LA.
Available online at http://www.museum.tulane.edu/geolocate/ (accessed
18 October 2010).
Rodriguez, C. F., E. Becares, and M. Fernandez-Alaez. 2003. Shift from
clear to turbid phase in Lake Chozas (NW Spain) due to the introduction
of American red swamp crayfish (Procambarus clarkii). Hydrobiologia
506: 421-426.
Rosenthal, S. K., S. S. Stevens, and D. M. Lodge. 2006. Whole-lake effects
of invasive crayfish (Orconectes spp.) and the potential for restoration.
Canadian Journal of Fisheries and Aquatic Sciences 63: 1276-1285.
Sars, G. O. 1885. On some Australian cladocera raised from dried mud.
Christ. Vidensk.-Selsk. Forh. 8: 1-46.
Sax, D. F., J. J. Stachowicz, J. H. Brown, J. F. Bruno, M. N. Dawson, S. D.
Gaines, R. K. Grosberg, A. Hastings, R. D. Holt, M. M. Mayfield, M. I.
O’Connor, and W. R. Rice. 2007. Ecological and evolutionary insights
from species invasions. Trends in Ecology & Evolution 22: 465-471.
Schoener, T. W. 1968. Anolis lizards of Bimini – resource partitioning in a
complex fauna. Ecology 49: 704-726.
Schonrogge, K., T. Begg, R. Williams, G. Melika, Z. Randle, and G. N.
Stone. 2012. Range expansion and enemy recruitment by eight alien gall
wasp species in Britain. Insect Conservation and Diversity 5: 298-311.
Shepard, D. B., and F. T. Burbrink. 2008. Lineage diversification and
historical demography of a sky island salamander, Plethodon ouachitae,
from the Interior Highlands. Molecular Ecology 17: 5315-5335.
Taylor, C. A., G. A. Schuster, J. E. Cooper, R. J. DiStefano, A. G. Eversole,
P. Hamr, H. H. Hobbs, H. W. Robison, C. E. Skelton, and R. E. Thoma.
2007. Feature: endangered species – a reassessment of the conservation
status of crayfishes of the united states and Canada after 10+ years of
increased awareness. Fisheries 32: 372-389.
Warren, D. L., R. E. Glor, and M. Turelli. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution.
Evolution 62: 2868-2883.
,
, and
. 2010. ENMTools: a toolbox for comparative
studies of environmental niche models. Ecography 33: 607-611.
, and S. N. Seifert. 2011. Ecological niche modeling in Maxent: the
importance of model complexity and the performance of model selection
criteria. Ecological Applications 21: 335-342.
R ECEIVED: 3 July 2012.
ACCEPTED: 17 September 2012.
AVAILABLE ONLINE: 6 November 2012.