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2012 SOUTHEASTERN NATURALIST 11(2):183–204
Habitat Selection by Nutria in a Freshwater Louisiana
Lauren E. Nolfo-Clements*
Abstract - Eleven Myocastor coypus (Nutria) were implanted with radiotransmitters
and monitored on a freshwater floating marsh. I evaluated the habitat selection of these
individuals at three scales. I examined patterns of second- and third-order selection
(macrohabitat) using a vegetation-cover map and Nutria movement data. There was
no difference between habitat proportions found in the study area overall versus those
of individual Nutria home ranges (second-order of selection) nor between proportions
within individual home ranges versus habitat coded Nutria location points (third-order
selection). To evaluate fourth-order habitat selection (microhabitat), I compared plant
species relative abundances by biomass and diversity indices between random samples
taken throughout the available study area and plant samples taken at each Nutria location.
During the colder winter months, Nutria appeared to be selecting areas with plant
species that offered structure and protection from the elements. In contrast, during the
spring and summer months, Nutria selected areas characterized by thin-mat and floating
aquatic species that facilitated access to open water.
Myocastor coypus Molina (Nutria) is a medium- to large-sized, semi-aquatic
hystricomorph rodent native to the Patagonian subregion of South America
(Woods et al. 1992). The Nutria has been introduced to 22 states in the US and to
various locations throughout the world as a prized furbearer and efficient weed
eater (Carter and Leonard 2002). Many of these introduced populations have
become established, resulting in the institution of population control measures
(Bounds and Carowan 2000, Kuhn and Peloquin 1974). Gaining an understanding
of the habitat-selection patterns of Nutria on a seasonal basis has the potential
to greatly enhance the focus and efficiency of control efforts.
Numerous studies have aimed at evaluating the ranging behavior and habitat
use of the Nutria both in its native and introduced range (Corriale et al. 2006,
D’Adamo et al. 2000, Doncaster and Micol 1989, Guichon and Cassini 1999,
Reggiani et al. 1993, Ryszkowski 1966). Nolfo-Clements (2009) examined the
movement patterns and home ranges of Nutria at this study sight. However,
only Coreil et al. (1988) performed any analysis on habitat preference. These
researchers evaluated the second-order selection of female Nutria by comparing
habitat-type proportions within the entire study area versus those proportions
found in each individual’s home range.
*Department of Ecology and Evolutionary Biology, Tulane University, 310 Dinwiddie
Hall, New Orleans, LA 70118. Current address - Department of Biology, Suffolk University,
41 Temple Street, Boston, MA 02114; email@example.com.
184 Southeastern Naturalist Vol. 11, No. 2
In addition to the broad-scale habitat analysis at the second-order of selection,
this study also aimed to evaluate habitat use at the finer scale of third- and
fourth-order selection. Following Johnson (1980), first-order selection is the
overall geographic range in which a species is found, second-order selection is
the use of specific areas within a study sight (overall home-range habitat proportions),
third-order selection is the use of different areas within the individual’s
home range (location points), and fourth-order selection is the use of vegetative
or terrain characteristics within these areas of use.
Although the vast majority of animal habitat selection studies focus on second-
and third-order selection (e.g., Franco et al. 2004, Gosselink et al. 2003,
Lyons et al. 2003, McCorquodale 2003, Moore et al. 2002, Nikula et al. 2004),
when the home range of an animal is temporally variable or habitat use is dependent
on small-scale phenomena such as nesting or feeding sites, these levels of
resolution may be insufficient to uncover patterns of habitat selection (Mysterud
and Ims 1998, Orians and Wittenberger 1991). One such system where the habitat
is both seasonally and spatially variable is the freshwater marshes of Louisiana
where this study took place.
This study was conducted in the Barataria Unit of Jean Lafitte National
Historical Park and Preserve (JLNHPP) located about 24 km south of New
Orleans in Jefferson Parish, LA (Visitor’s Center location: 29°47'43.153"N,
90°6'18.364"W). The wetland habitats of the Park comprise about 4900 ha of
the total Park area of ca. 7500 ha (D.P. Muth, JLNHPP, New Orleans, LA, pers.
comm.). The climate is subtropical with annual rainfall exceeding 1600 mm
and mean annual temperature of 21 °C (summer average of 28.5 °C, winter
average of 12.2 °C) (White et al. 1983). The growing season typically exceeds
The wetland habitats of the Park include floating marsh, spoil banks, cypress
swamps, and open-water habitats. The floating marsh habitat roughly coincides
with types 1–5 thick- and thin-mat fresh floating marsh, as characterized by
Sasser et al. (1994). The floating marsh is nearly devoid of woody vegetation
except for patches of Morella cerifera L. (Small) (Wax Myrtle) distributed across
Canals that were dug primarily for access for oil and gas exploration provide
access to the marsh. These canals are lined by elevated spoil banks that were
constructed from the sediments excavated in the digging of the canals. Woody
vegetation dominates this habitat type, in contrast to the herbaceous species that
dominate the surrounding canal and marsh communities.
Smaller bodies of water that traverse open expanses of marsh are referred to
as trenasses. Trenasses average 2–3 m wide and are usually completely filled with
floating aquatic vegetation during the growing season. For a complete description
of the wetland habitats of the Park, in addition to a complete species list, see
2012 L.E. Nolfo-Clements 185
On 24 January 2004 and 19 January 2005, 11 adult Nutria (2004: 6 animals; 5
females, 1 male; 2005: 5 animals; 2 females, 3 males) were captured by airboat
and implanted with 9.75- x 3.4-cm VHS transmitters (Telonics Inc., Mesa, AZ)
that weighed 85 to 90 grams (≤2.6% of the animal’s body weight). Frequencies
ranged from 164.00 to 165.00 MHz, and each was equipped with an internal antenna
and an 8-hour mortality sensor. Units had an expected battery life of about
2 years. Capture and implantation procedures followed Nolfo and Hammond
(2006). These animals were released within 24 hrs. post-surgery at a location
within 100 m of their capture site. Animals were located at least twice per week
using a 3-element Yagi antenna attached to a LA12-Q receiver (AVM Instrument
Company, Colfax, CA). Animals were tracked from the time of release until their
time of death or until June 2005 when the study ended. For a full description of
the ranging behavior of the Nutria in this study, see Nolfo-Clements (2009). This
project was covered under Tulane University Institutional Animal Care and Use
Committee protocol # 0230-3-16-082, and all procedures meet the guidelines
recommended by the American Society of Mammalogists (Animal Care and Use
Radio-marked Nutria were located from boat or on foot in the marsh. Contact
with an animal was visually confirmed. Locations were taken using a hand-held
GPS (Garmin Etrex Venture GPS, Garmin International, Inc., Olathe, KS). A
plant sample was taken at all locations in floating marsh, Wax Myrtle thickets,
and any plant-filled trenasse habitat types (see below). For locations on spoil
banks or canals, a GPS point was taken and the habitat type noted.
In order to evaluate the second- and third-order selection of the Nutria, I
constructed a habitat-type map. First, I divided the available habitat (minimum
convex polygon [MCP] area that encompassed all of the Nutria location
points) into 3 basic habitat types: marsh, wood, and water. The formation of
these categories was partially based upon the USGS wetlands classification
system guidelines (Cowardin et al. 1979). Areas classified as palustrine, emergent,
persistent wetlands (i.e., wetlands dominated by emergent herbaceous
vegetation that is present in some form year round) were labeled as “marsh”.
Cypress swamp, Wax Myrtle thicket, and spoil-bank habitats are characterized
by an overstory of woody species and were thus labeled as “wood”. Canals and
trenasses are classified as riverine, lower perennial, unconsolidated bottom,
permanently flooded systems and were labeled as “water”. I was able to delineate
and find the areas of these 3 habitat types through ground truthing and by
using 1998 and 2004 digital orthophoto quarter quadrangles (DOQQ) of the
study area downloaded from the Louisiana statewide GIS site (www.atlas.lsu.
edu) and projected into ArcMap 9.1 2005 (Environmental Systems Research
Institute, Inc. [ESRI], Redlands, CA).
186 Southeastern Naturalist Vol. 11, No. 2
Plant samples were taken at all Nutria locations in floating marsh, Wax Myrtle
thickets, and in vegetation-filled trenasses within the study area. For locations on
spoil banks or canals, a GPS point was taken and the habitat type noted.
Nutria plant samples were taken within 2 m of the confirmed location of the
animal each time an animal moved >100 m from its last location or >60 days
had elapsed since the last plant sample. I selected 60 days as a sampling interval
for sedentary individuals because plant species composition may change at a set
location over that length of time (L.E. Nolfo-Clements, unpubl. data).
One hundred random plant samples (25 each season) were also taken from
a pool of 150 randomly generated GPS coordinates (in UTMs) using research
randomizer (http://www.randomizer.org). These coordinates were then superimposed
on a DOQQ map of the Park using ArcView GIS Version 3.2. 1999
(ESRI). A combination of map referencing and GPS tracking was used to locate
all random points on the marsh. If a data point was not accessible on foot due
to unforeseen habitat features such as vegetation-filled trenasses that appear as
marsh on satellite images or extremely thin mat conditions that do not allow for
walking, that plot was omitted or a sample was taken as close as possible to the
Most of the random plots were taken prior to the tracking of the Nutria in this
study; hence, some of them were located in the general vicinity of areas later
frequented by study animals while others were completely outside of their area of
use (Fig. 1). The purpose of the random plots was to get a sample of the habitats
and plant communities available in the study area regardless of whether or not
they would later be utilized by study animals.
Plant sampling at Nutria locations and random available habitat localities
followed an identical protocol. The plot was identified as one of the 3 habitat
types described above. All aboveground biomass within an area of 0.5 m2 was
clipped from the plot to within 1 cm of the substrate. Only living plant matter
was included in aboveground samples. A core was also taken from the approximate
center of each plot using a 5.7-cm-diameter garden bulb planter. All core
samples were taken to a depth of 10 cm. Each core was rinsed to remove most of
the substrate attached to plant roots.
All above- and belowground biomass samples were dried at 70 °C to constant
mass. Dried samples were weighed to the nearest 0.1 gm. Aboveground samples
were separated by taxon, and the mass of each was recorded for relative abundance
and diversity analyses.
Flowering plants were identified following Correll and Johnston (1979),
Godfrey and Wooten (1979, 1981), Allen (1992), and Stutzenbaker (1999). Ferns
were identified following Thieret (1980). All scientific nomenclature follows the
US Department of Agriculture’s online Integrated Taxonomic Information System
(ITIS) database (www.itis.gov). All specimens that could not be identified to
species are listed by generic name only.
2012 L.E. Nolfo-Clements 187
The MCP for the total available habitat and each Nutria MCP were plotted and
calculated using the Animal Movements extension for ArcView version 1.1 (Hooge
and Eichenlaub 1997). An F-test and t-test were performed on the areas of the MCP
of males versus females to test for sex-based home-range differences.
Compositional analyses (CA) were performed for both second- and thirdorder
selection following Aebischer et al. (1993). This entailed two analyses,
the first comparing available habitat MCP proportions to individual MCP proportions
for each animal and then comparing the individual MCP to individual
animal location points. A value of 0.7 was substituted for any zero values (Bingham
et al. 2007). Data for 5 of the animals with small numbers of locations
(n < 20) were omitted from the CA analysis. All CA analyses were performed
using the Resource Selection for Windows (RSW) software version 1.0 beta 8.4
To evaluate fourth-order selection, plant samples taken from random plots
were compared with those taken at Nutria locations. Due to an insufficient number
of fall Nutria plant samples (8 samples from 1 individual), both Nutria and
random plot data for this season were omitted from the analysis.
I assessed fourth-order habitat selection at the plant species level by first
calculating species richness for all samples. I then calculated the Shannon H'
diversity index for all Nutria and random plant samples.
To test for seasonal differences in aboveground biomass, belowground biomass,
species diversity, and species richness within and between Nutria versus
random samples, I performed a 2-way MANOVA and generated a residual correlation
matrix to check for associations between dependent variables. I considered
correlations > 0.3 to be noteworthy following Tabachnick and Fidell (2001:367).
Based upon the results of the 2-way MANOVA, I performed a series of 2-way
ANOVAs and Tukey’s pairwise comparison post-hoc tests on each of the dependent
variables separated by plot type and season (Nutria summer plots, random
summer plots, Nutria spring plots, etc.).
To uncover which plant taxa were actually preferred by the Nutria, I calculated
the relative abundances of all species based upon aboveground biomass
across seasons for both the Nutria and the random plant samples. I then ran three
MANOVAs one for each season (winter, spring, and summer), comparing the
arcsine transformed relative abundances for all plant species found in both Nutria
and random plot samples in each season with Nutria plot data divided between
males and females for the winter and spring (there was insufficient data to run
this analysis for the summer).
The resulting canonical variates analysis (CVA) loadings resulting from the
MANOVA were then consulted to interpret which plant taxa were contributing to
the variation between the random and Nutria samples. Taxa with loadings > 0.3
were interpreted as contributing to the variability detected between Nutria and
random samples and between the sexes, as suggested by Tabachnick and Fidell
(2001:199). The average relative abundances of all species for Nutria and random
samples were also ranked for each season, and these rankings were compared
188 Southeastern Naturalist Vol. 11, No. 2
with the results of the CVA to uncover the plant taxa that were most significantly
contributing to trends in Nutria habitat selection.
All t-tests, ANOVAs, MANOVAs, CVAs, and species richness and diversity
analyses were performed in the Paleontological Statistics Software Package for
Education and Data Analysis (PAST; Hammer et al. 2001) and IBM SPSS Statistics,
version 19. Descriptive statistics were calculated using Microsoft Excel
2007. α = 0.05.
The purpose of this study was to evaluate the habitat selection patterns of Nutria,
not necessarily to assess or quantify the impacts of Nutria grazing on these habitats.
As such, I did not measure Nutria abundance or grazing in the random plots and thus
cannot be certain that they were different from the Nutria telemetry plots. However,
for this analysis, I assume Nutria grazing pressure was lower in the random plots.
The following analysis abides by that assumption.
I collected a total of 249 locations for all 11 Nutria (Table 1, Fig. 1). One
hundred fifty-three plant samples were taken at Nutria locations following the
guidelines outlined above. Since data from the fall were omitted, I included 232
locations and 144 plant samples in my analyses. Animals survived 5–486 days
(x̅ = 105 days; Table 1). Only 3 animals survived until the end of the study in June
2005. For a full discussion of the high levels of mortality and movement patterns
of animals in this study see Nolfo-Clements (2009). A MCP was constructed for six
of the animals that had sufficient numbers of locations (x̅ = 40, n ≥ 15, 3 males and
3 females) based upon additional fixed-kernel home-range analyses comparisons
(Nolfo-Clements 2009, White and Garrott 1990). The average home range MCP
was 28.8 ha, with the females averaging 36.8 ha (stand. dev. = 24.31) and the males
averaging 20.9 ha (stand. dev. = 9.66). There was no statistical difference between
MCP variances for males and females (F-test, P = 0.136). The difference in homerange
area between males and females was not statistically significant (t = 1.05,
Table 1. Capture date, sex, identification #, survivorship, and number of locations recorded for
Nutria monitored at JLNHPP, Jefferson Parish, LA in 2004–2005.
Capture date Sex (#) Survivorship (days) # of locations
24 January 2004 Female (n1) 10 3
24 January 2004 Male (n2) 486 97
24 January 2004 Female (n3) 10 2
24 January 2004 Female (n5) 80 17
24 January 2004 Female (n6) 36 8
24 January 2004 Female (n7) 212 39
19 January 2005 Male (n10) 126 31
19 January 2005 Female (n13) 5 2
19 January 2005 Female (n14) 126 33
19 January 2005 Male (n15) 5 2
19 January 2005 Male (n16) 61 15
2012 L.E. Nolfo-Clements 189
df = 4, P = 0.353). However, due to the small sample size, a type II error in this
analysis is possible. I used the overall MCP (551.0 ha) for all of the tracked Nutria
as the available habitat area in subsequent analyses (Fig. 2).
Second- and third-order selection
The second-order habitat selection CA of the available habitat category proportions
versus the individual Nutria MCP habitat category proportions was not
significant (λ = 1.436, P = 0.488). The third-order CA of the individual Nutria
MCP habitat category proportions versus the animal location point proportions
also did not show significant differences (λ = 4.504, P = 0.1052; Table 2).
Figure 1. Nutria locations and random plot locations at JLNHPP, Jefferson Parish,
LA, 2004–2005. Nutria points are indicated by circles and random points are indicated
Table 2. Nutria locations by habitat type in relation to area proportions for Nutria monitored at
JLNHPP, Jefferson Parish, LA in 2004–2005.
Habitat type Total # Nutria locations % of area of use
Marsh 151 63
Wood 61 27
Water 37 10
190 Southeastern Naturalist Vol. 11, No. 2
Fourth-order selection: Overall biomass, species richness, and diversity
The 2-way MANOVA to evaluate aboveground biomass, belowground biomass,
species richness, and species diversity between Nutria and random plots for
each season showed significant differences for all interactions except between plot
type (Nutria and random) and plant species diversity (df = 1, F = 0.000, P = 0.997).
The residual correlation matrix revealed a positive association between species
richness and diversity (0.611) and a negative correlation between aboveground
biomass and diversity (-0.477).
The total aboveground biomass ANOVA showed significant differences
between nutria and random plots (df = 1, F = 9.89, P = 0.002), between
seasons (df = 2, F = 30.96, P < 0.001) and in the interaction between plot type
and season (df = 2, F = 22.71, P < 0.001). The Tukey’s pairwise comparison revealed
that random plots showed significant differences in aboveground biomass
across winter, spring, and summer, with peak biomass occurring in the summer.
In contrast, Nutria samples showed no significant differences between seasons.
The Nutria and random samples had the same means in the winter and spring but
showed a difference in the summer, with Nutria summer plant sample biomass
values being much lower than the random plot biomasses (Table 3).
The total belowground biomass ANOVA also revealed significant differences
between nutria and random plots (df = 1, F = 40.77, P < 0.001), between seasons
Figure 2. Available habitat polygon and individual MCP home ranges for monitored
Nutria at JLNHPP, Jefferson Parish, LA, 2004–2005 (n = 6). Bodies of water (canals and
trenasses) are black, marsh areas are lighter grey, and wooded areas (Wax Myrtle thickets,
cypress swamps, and spoil banks) are dark grey. White dots on spoil banks along the
canals indicate camps.
2012 L.E. Nolfo-Clements 191
(df = 2, F = 5.16, P = 0.006), and in the interaction between plot type and season
(ANOVA: df = 2, F = 10.15, P < 0.001). The pairwise comparisons revealed that
belowground biomass remained constant across seasons in the random samples,
whereas it differed significantly between the winter and the summer in the Nutria
samples. The Nutria versus random samples were not different in the winter but
showed marked differences in both the spring and summer, with the Nutria plots
having lower mean belowground biomasses in both seasons (Table 4).
The species richness ANOVA showed no significant differences between
Nutria and random plots (df = 1, F = 0.002, P = 0.962) but did reveal differences
between seasons (df = 2, F = 29.44, P < 0.001) and in the interaction between
plot type and season (df = 2, F = 9.49, P < 0.001). The Tukey’s pairwise comparisons
revealed that species richness remained constant across seasons in
the random plots, with a mean of 5.43 species per plot. In contrast, species
richness differed between seasons in the Nutria plots (P < 0.001). The summer
showed the highest species richness (x̅ = 10.07), followed by spring (x̅ = 6.51),
Table 3. Q values and P values from Tukey’s pairwise comparisons of seasonal Nutria and random
aboveground biomass samples taken at JLNHPP, Jefferson Parish, LA in 2004–2005. An “n” before
a season indicates Nutria data and an “r” indicates random data. Significant values are in bold.
Seasons nwinter nspring nsummer rwinter rspring rsummer
nspring Q = 3.2, -
P = 0.2
nsummer Q = 2.1, Q = 1.1, -
P = 0.7 P = 0.97
rwinter Q = 1.8, Q = 5.0, Q = 3.9, -
P = 0.005 P = 0.005 P = 0.07
rspring Q= 2.9, Q = 0.4, Q = 1.1, Q = 4.6, -
P = 0.3 P = 0.99 P = 0.99 P = 0.01
rsummer Q = 13.1, Q = 9.9, Q = 11.0, Q = 14.9 Q = 10.3, -
P < 0.001 P < 0.001 P < 0.001 P < 0.001 P < 0.001
Table 4. Q values and P values from Tukey’s pairwise comparisons of seasonal Nutria and random
belowground biomass samples taken at JLNHPP, Jefferson Parish, Louisiana in 2004–2005. An
“n” before a season indicates Nutria data and an “r” indicates random data. Significant values are
Seasons nwinter nspring nsummer rwinter rspring rsummer
nspring Q = 3.5, -
P = 0.1
nsummer Q = 7.3, Q = 3.88, -
P < 0.001 P = 0.07
rwinter Q = 0.4, Q = 3.9, Q = 7.8, -
P = 0.99 P = 0.06 P < 0.001
rspring Q= 2.0, Q = 5.5, Q = 9.3, Q = 1.6, -
P = 0.7 P = 0.002 P < 0.001 P = 0.9
rsummer Q = 2.1, Q = 5.5, Q = 9.4, Q = 1.6, Q = 0.05, -
P = 0.7 P = 0.001 P < 0.001 P = 0.9 P = 1
192 Southeastern Naturalist Vol. 11, No. 2
and winter (x̅ = 4.06). Richness did not differ in Nutria versus random plots in
the winter and spring, but was markedly higher in Nutria plots in summer (P <
0.001; Table 5).
The Shannon H' diversity index ANOVA showed no significant difference
between Nutria and random plots (df = 1, F = 1.005, P = 0.317), but did show
differences between seasons (df = 2, F = 34.77, P < 0.001), and in the interaction
between plot type and season (df = 2, F = 12.45, P < 0.001). The Tukey’s pairwise
comparisons showed some seasonal differences in diversity both between
and among Nutria and random plots. Random plots had similar plant species
diversity in the winter and summer (P = 0.07), while the diversity found in the
spring differed from diversity found in the winter (P = 0.02) but not the summer
diversity (P = 0.99).Nutria plot plant species diversity varied significantly across
all seasons, with peak diversity occurring in the summer and the lowest diversity
occurring the winter (Table 5). Between Nutria and random plots, plant diversities
were similar in the winter and the spring, but were significantly higher in the
summer in Nutria plots (P < 0.001; Table 5).
Fourth order selection: relative abundance comparisons
I identified a total of 56 plant taxa: 49 were identified in the random plots
and 34 in the Nutria plots. Of these species, 22 were only found in random plots,
whereas 7 species were only found in Nutria plots (see Appendix 1 for a complete
list of the species including authorities and common names).
In winter and spring, 8 taxa accounted for over 80% of the total relative abundance
by biomass for both the Nutria and the random samples, whereas in the
summer, this was true of 10 taxa. The taxa contained in these lists differ between
Nutria and random samples and by season (Table 6).
The MANOVA on the arcsine transformed relative abundances for all species
for winter samples revealed differences between the random and the Nutria
plots (P < 0.001), but no difference in male and female plots. The species that
contributed most to this variation, according to the CVA loading generated in this
analysis, were Sagittaria lancifolia (-1.51), Alternanthera philoxeroides (-1.59),
Eleocharis spp. (0.79), Leersia hexandra (1.11), Hydrocotyle spp. (0.589)
Table 5. Means, standard error, and sample sizes for aboveground biomass, belowground biomass,
species richness, and Shannon H' diversity for Nutria and random plot plant samples taken at
JLNHPP, Jefferson Parish, Louisiana in 2004-2005. Standard errors are given in parentheses ().
Aboveground Belowground Species Diversity
Season biomass biomass richness (Shannon H')
Nutria (n = 34) 37.51 (10.91) 18.99 (0.45) 4.06 (0.19) 0.65 (0.07)
Random(n = 25) 22.98 (5.62) 19.54 (1.34) 4.60 (0.24) 0.88 (0.08)
Nutria (n = 84) 64.08 (5.00) 14.79 (0.92) 6.51 (0.26) 1.00 (0.05)
Random (n = 25) 60.84 (4.96) 21.44 (0.81) 5.80 (0.32) 1.21 (0.08)
Nutria (n = 26) 54.68 (5.97) 10.06 (1.73) 10.08 (0.49) 1.61 (0.09)
Random(n = 25) 144.76 (10.28) 21.51 (1.02) 5.88 (0.37) 1.17 (0.05)
2012 L.E. Nolfo-Clements 193
Table 6. Ranked lists of plant taxa from higher to lower biomass that account for over 80% of the mean relative abundance for Nutria versus random plant
samples in the winter, spring, and summer at JLNHPP, LA, 2004–2005.
Winter Spring Summer
Rank Nutria plots Random plots Nutria plots Random plots Nutria plots Random plots
1 Schoenoplectus americanus Eleocharis spp. Sagittaria lancifolia Sagittaria lancifolia Sagittaria lancifolia Sagittaria lancifolia
2 Eleocharis spp. Panicum spp. Eleocharis spp. Eleocharis spp. Sagittaria latifolia Eleocharis spp.
3 Sagittaria lancifolia A. philoxeroides Typha spp. Typha spp. Eleocharis spp. Typha spp.
4 Schoenoplectus californicus Leersia hexandra A. philoxeroides A. philoxeroides Ludwigia repens Leersia hexandra
5 Spartina patens Sagittaria lancifolia Eichhornia crassipes Panicum spp. A. philoxeroides Panicum spp.
6 Panicum spp. Hydrocotyle spp. Schoenoplectus Leersia hexandra Polygonum puctatum Polygonum
7 Alternanthera philoxeroides Polygonum punctatum Zizaniopsis miliacea Hydrocotyle spp. Eichhornia crassipes A. philoxeroides
8 Typha spp. Typha spp. Hydrocotyle spp. Polygonum punctatum Hydrocotyle spp. Hydrocotyle spp.
9 Ceratophyllum demersum Phyla lanceolata
10 Salvinia minima Sacciolepis striata
194 Southeastern Naturalist Vol. 11, No. 2
Panicum dichotomiflorum (1.90), Schoenoplectus americanus (-3.38), Schoenoplectus
californicus (-1.22), Cyperus spp. (0.60), Echhornia crassipes (0.53),
and Spartina patens (-1.10).
The MANOVA for the spring samples revealed differences (P < 0.001)
between random versus Nutria plots and between the sexes (Fig. 3). The differences
detected between the random and the male Nutria plots were primarily
due to the taxa Sagittaria lancifolia (-0.71), Alternanthera philoxeroides (-0.39),
Eleocharis spp. (0.73), Typha spp. (-0.37), Leersia hexandra (0.34), Hydrocotyle
spp. (0.58), Thelypteris palustris (0.53), Panicum dichotomiflorum (0.51), Zizaniopsis
miliacea (-0.37), Eichhornia crassipes (-0.36), and an unknown grass
(0.40). For female Nutria versus random plots, the taxa of greatest importance
were Alternanthera philoxeroides (-0.48), Typha spp. (-1.08), Leersia hexandra
(-1.04), Thelypteris palustris (-0.36), Schoenoplectus americanus (1.24), Salvinia
minima (0.43), Sphagnum spp. (-0.45), Bacopa monnieri (0.32), Echhornia
crassipes (1.09), Ludwigia repens (0.40), and an unknown grass (-0.53). The
differences between male and female plots were not due to the effects of any
The MANOVA for summer samples showed a difference (P < 0.001) between
Nutria and random plots. However, there was insufficient sample size to test for
differences due to sex. The CVA loadings did not reveal any individual species
Figure 3. Canonical Variates Analysis (CVA) loadings resulting from a MANOVA of
male Nutria, female Nutria, and random spring plant sample relative abundance data collected
at JLNHPP, LA in 2004–2005. Only the results of the first 2 axes, which account
for >99% of the variation in the data sets, are shown.
2012 L.E. Nolfo-Clements 195
that had a large effect on the variability detected between random and Nutria
points for this season.
There were significant differences between the Nutria and random plots,
and those differences correspond to what you would expect from differential
grazing. However, I did not measure Nutria abundance or grazing pressure in
the random plots. Therefore, I cannot say to what extent the differences were
indeed due to Nutria grazing pressure or to some other cause(s). So while
alternative hypotheses are not excluded, they are not considered here, and the
following discussion is based on the assumption that Nutria grazing pressure
was lower in the random plots.
MCP home ranges
Overall, it may be noted that male Nutria have consistently been recorded
to have larger home ranges then females, even in studies with low sample sizes
(Denena et al. 2003, Lohmeier 1981). In numerous studies from both the United
States and Europe, the size of Nutria home ranges vary widely by habitat ranging
from 1.6–46.3 ha for females and 3.6–93.9 ha for males (Doncaster and Micol
1989, Gosling and Baker 1989, Ras 1999, Reggiani et al. 1993). Coreil et al.
(1988) radio-tracked female Nutria in an intermediate marsh habitat in Louisiana
and found that animals had the largest home ranges in the winter (138 ha) and the
smallest in the summer (7.2 ha).
The results presented here differ from those of other, similar studies in that
both sexes were found to have similar home-range sizes. The relatively large size
of these animal’s home ranges compared with those of other Nutria studies may
be due to patchy resource distribution and/or low population density as suggested
by Gosling and Baker (1989), or the pooling of seasonal movement data (Coreil
et al. 1988), or it might be an artifact of the low sample size.
Second- and third-order selection
The CAs showed no significant difference between second- or third-order
habitat-type selection and availability. The possible reasons for this lack of difference
are 1) no difference exists, 2) insufficient sample size, or 3) insufficient
resolution due to the pooling of data into habitat types. Aebischer et al. (1993)
recommends a minimum of six animals for this analysis because this is the smallest
sample size needed to show a significant difference from zero at P < 0.05 by
randomization. Additionally, the transformation of proportions into ranks for this
analysis may have further increased the probability of a type II error by reducing
the sensitivity of the analysis to large differences in simultaneously ranked
proportions (Johnson 1980).
Plant above- and belowground biomass, species richness, and diversity
Nutria are considered a noxious invasive throughout their introduced range
primarily due to their effects on wetland vegetation and the banks of waterways
196 Southeastern Naturalist Vol. 11, No. 2
(Carter and Leonard 2002). Therefore, much of the discussion on Nutria focuses
on their negative effects on plant communities and wetland landscapes (Carter et
al. 1999, Llewellyn and Shaffer 1993, Reggiani et al. 1995).
Nutria have been shown to be social foragers (Ehrlich 1958, Guichon et al.
2003, Warkentin 1968) and territorial (Gosling and Wright 1994). Therefore, if
the individual Nutria tracked in this study were members or outcasts of a social
group, their patterns of habitat selection may have been influenced by the
presence or absence of other, unmarked Nutria in the vicinity. However, the assessment
of such influences is outside the scope of this study.
Although no study to date has assessed plant community structure in relation
to Nutria telemetry locations, there are many studies that have examined
the effects of Nutria on marsh plant community characteristics. These studies
usually involve the construction of exclosures that exclude herbivores and allow
for the comparison of grazed and ungrazed areas. The majority of these studies
reveal negative effects of Nutria herbivory not only on above- and belowground
biomass (Evers et al. 1998, Johnson and Foote 1997, Randall and Foote 2005,
Taylor and Grace 1995) but also on species density, diversity, and/or evenness
(Ford and Grace 1998, Nyman et al. 1993, Shaffer et al. 1992). Only two studies
have concluded that Nutria have no effect on species richness (Gough and Grace
1998, Taylor and Grace 1995), and all studies have consistently reported Nutria
to have a negative effect on plant biomass. My finding that Nutria occupied areas
of lower aboveground biomass in summer is consistent with these reported
studies. However, my results may be due to the comparatively lower levels of
aboveground biomass in trenasses and thin-mat marshes that Nutria commonly
occupied during the summer rather than any herbivory impacts.
When considering the results of the belowground biomass portion of this
study, the effects of plant samples taken in trenasses must be considered. These
narrow waterways that traverse the marsh are often filled with vegetation during
the growing season and were thus sampled as Nutria localities in both the spring
and summer. However, since the vegetation in trenasses floats independent from
any root mat, the resulting belowground biomass of those plots was 0. Because
these areas were covered in vegetation without the muddy mat substrate, they
were considered equivalent to marsh plant samples.
Additionally, the ecological forces that maintain trenasses and interior marsh
ponds are uncertain, and it is possible that Nutria herbivory contributes to their
presence (J. Muth, JLNHPP New Orleans, LA, pers. comm., and L.E. Nolfo-Clements,
unpubl. data). Evers et al. (1998) found that Nutria significantly reduced
belowground biomass to a depth of 10 cm, and those findings concur with the
selection patterns reported in this study.
In contrast, the patterns of species richness found in this study are surprising.
Nutria herbivory is usually implicated in the reduction of species richness in the
growing season, but in this case, Nutria were found in areas of greater species
richness during the peak of the growing season. Although such effects may be
unusual in studies of Nutria herbivory, low- to moderate-intensity grazing has
been shown to increase species richness in other systems (Kleyer et al. 2003,
2012 L.E. Nolfo-Clements 197
Zeevalking and Fresco 1977). Alternately, Nutria may simply be selecting for
areas with higher plant species richness.
The results of the plant species diversity calculations were also noteworthy.
While random plots showed peak plant diversity in the spring, Nutria plot
diversity peaked in the summer. Additionally, although Nutria selected areas
with similar diversities to random in the winter and spring, they were found in
areas of significantly higher diversity than random in the summer. This finding
may be due to intermediate levels of grazing as discussed above, Nutria’s
selection of areas with higher plant diversity during the peak of the growing
season, or it may be an artifact of the Nutria’s increased time spent in trenasses,
whose plant species diversity and biomass peak during the summer (L.E.
Nolfo-Clements, unpubl. data ).
Relative abundance comparisons
Nutria are not seasonal breeders, and females will often produce an average of
2–3 litters a year (Atwood 1950, Newson 1966, Willner et al. 1979). Therefore, it
was assumed that any habitat selection differences by females during one season
versus another were not due to patterns of reproduction.
The MANOVA on winter plant samples revealed habitat selection by the Nutria
with no sex-based differences. The species that had the greatest contribution
to the variation between random and Nutria plots, according to the CVA loadings,
were Sagittaria lancifolia, Alternanthera philoxeroides, Eleocharis spp.,
Leersia hexandra, Hydrocotyle spp., Panicum dichotomiflorum, Schoenoplectus
americanus, Schoenoplectus californicus, Cyperus spp., Echhornia crassipes,
and Spartina patens.
Sagittaria lancifolia and Schoenoplectus americanus ranked higher on the list
of species that contributed most to the total relative abundance for Nutria plots
versus that for the random plots in the winter, while Schoenoplectus californicus
and Spartina patens only appeared on the list for Nutria plots (Table 6). All 4
of these plants maintain some level of live biomass and a large amount of dead
biomass during the winter months. This cover serves as protection for Nutria
from the elements and allows for the construction of dry resting platforms above
the marsh surface for grooming and feeding (Atwood 1950). Additionally, Nutria
often consume roots and rhizomes, especially during the winter, and all four of
the structurally desirable species listed above have substantial rhizomes.
The spring MANOVA showed significant differences between not only the
random and Nutria plots but also between male and female Nutria (Fig. 3). The
overall relative abundance ranks for Nutria versus random plots did not show
many differences for this season (Table 6).
Alternanthera philoxeroides, Typha spp., Leersia hexandra, Thelypteris
palustris, and Echhornia crassippes were preferred by both males and females in
the spring. It is not clear if these plants were preferred as food sources, for cover,
or if their presence in the habitat was indicative of a specific mat thickness. Although
it may be noted that the floating aquatic Eichhornia crassippes not only
had high CVA loadings for both male and female Nutria, but also ranked much
198 Southeastern Naturalist Vol. 11, No. 2
higher for Nutria then random in the overall relative abundance ranks (Table 6),
this finding was most likely a result of Nutria spending more time in trenasses as
the weather got warmer.
The CVA loadings for the summer MANOVA revealed that no specific plant
species had a large effect on the variability detected between random and Nutria
points for this season. However this overall difference may be due to the great
amount of time spent on thin-mat marshes and in trenasses by Nutria during the
The variability in floating marsh mat thickness has been noted by Sasser et
al. (1994, 1996). The different mat thicknesses are associated with whole suites
of characteristic species (Nolfo-Clements 2006). The species Sagittaria latifolia
and Ludwigia repens that are common in thin-mat floating marshes and the three
obligate aquatic species Eichhornia crassippes, Ceratophyllum demersum, and
Salvinia minima were present in higher relative abundances in the Nutria plots
versus the random plots during the summer (Nolfo-Clements 2006; Sasser et al.
1994, 1996; Table 6). The Nutria appear to prefer thin-mat marsh in the warmer
months due to the ease of burrowing through the mat, allowing for entry into the
viscous substrate below. I have even witnessed Nutria burrowing through the
mat, swimming beneath it, and reappearing in a nearby trenasse or canal.
I was unable to detect differences in proportions between available versus
selected habitat types at the second and third order of selection using CA. This
finding may have resulted from an insufficient sample size, increased type II error
caused by the pooling of the data into discrete habitat types, or the possibility that
no differences existed. Because I did not measure Nutria grazing or abundance
in the random plots, there is no evidence that the differences detected between
random and Nutria plots were due to differences in grazing pressure; however,
that is my working hypothesis.
Although the seasonal and overall trends in above- and belowground biomass
in Nutria versus random plots were in agreement with the results of Nutria herbivory
studies, the trends in species richness and diversity were not (Evers et al.
1998, Ford and Grace 1998). The high plant species richness and diversity in
Nutria plots during the summer may have been due to low to moderate grazing
levels during the peak of the growing season, which has been shown to increase
species richness in other systems. Previous studies of the Nutria’s effects on plant
composition may have been conducted in areas with much higher Nutria population
densities, hence resulting in reduced plant species richness and diversity due
The seasonal differences in species composition in Nutria versus random plots
may be explained in light of the Nutria’s differing needs during the course of
the year. The Nutria are very sensitive to cold and often experience high levels
of mortality during periods of prolonged winter weather (Doncaster and Micol
1990, Gosling et al. 1983, Reggiani et al. 1995). Therefore, it is not surprising
that Nutria selected for plant species that offered protection from the cold and a
2012 L.E. Nolfo-Clements 199
means of elevating and possibly insulating themselves from the marsh surface in
the winter. In the summer, Nutria occupied thin-mat and trenasse habitats possibly
to avoid the heat of the interior marsh.
Overall, habitat selection by Nutria appears to be dependent on small-scale
habitat characteristics that vary seasonally. For some organisms, trends in
habitat selection may be uncovered using traditional methods such as CA, but
for others, this may not be the case. When sample sizes are low and the habitat
is highly variable at a fine scale, a consideration of fourth-order selection may
be more appropriate.
I would like to thank the staff of Jean Lafitte National Historical Park and Preserve for
their cooperation in this study. Special thanks go to N. Walters, whose assistance with
GIS and Nutria capture was critical to the project and to L. Zahm and W. Adams for boat
use, maintenance, and field support. Thanks are also extended to the Maryland Cooperative
Fish and Wildlife Research Unit for the loan of radio-transmitters and to the staff
of the Tulane Museum of Natural History and the Tulane Herbarium for assistance with
processing plant samples. C.S. Hood provided valuable editorial comments on an early
draft of the manuscript. Sincere thanks to J. Carter, whose tireless and meticulous editing
greatly improved the quality of this manuscript. Thanks also to M. Haramis, D. Birch, and
3 anonymous reviewers whose comments and suggestions served to focus and enhance
the manuscript. This research was funded by grants from the National Park Service and
the Coypu Foundation.
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2012 L.E. Nolfo-Clements 203
Appendix 1. Complete species list for random (n = 100) and Nutria (n = 153) plant
sample plots taken at JLNHPP, Louisiana in 2004–2005.
Species Common name
Taxa found in both random plots and Nutria plots
Alternanthera philoxeroides (Mart.) Griseb. Alligatorweed
Bacopa monnieri (L.) Pennel Coastal Waterhyssop
Ceratophyllum demersum (L.) Coon’s Tail
Cirsium spp. Thistle
Cyperus spp. Flatsedge
Eichhornia crassipes (Mart.) Solms Water Hyacinth
Eleocharis spp. Spikerush
Hydrocotyle spp. Pennywort
Leersia hexandra Schwartz Southern Cutgrass
Morella cerifera (L.) Small Wax Myrtle
Panicum spp. Panicgrass
Phyla lanceolata (Michx.) Greene Frog Fruit
Polygonum punctatum Ell. Dotted Smartweed
Ptilimnium capillaceum (Michx.) Raf. Herbwilliam
Sacciolepis striata (L.) Nash American Cupscale
Sagittaria lancifolia L. Bulltongue
Sagittaria latifolia Willd. Broadleaf Arrowhead
Salvinia minima Baker Water Fern
Schoenoplectus americanus Pers. American Bulrush
Schoenoplectus californicus (C. Meyer) Palla California Bulrush
Solidago spp. Goldenrod
Symphyotrichum subulatum (Michx.) Nesom Eastern Annual Saltmarsh Aster
Thelypteris palustris Schott. Eastern marsh Fern
Typha spp. Cattail
Zizaniopsis miliacea (Michx.) Doell. & Asch. Giant Cutgrass
Taxa found in random plots only
Acer rubrum L. Red Maple
Andropogon spp. Bluestem
Bidens laevis (L.) BSP Bur Marigold
Cardamine parviflora L. Sand Bittercress
Carex alata Torrey Broadwing Sedge
Carex comosa Boott. Longhair Sedge
Decodon verticillatus (L.) Ell. Swamp Loosestrife
Eupatorium capillifolium (Lam.) Small Dogfennel
Fuirena pumila (Torr.) Spreng Dwarf Umbrella-Sedge
Galium tinctorium L. Dye Bedstraw
Juncus spp. Rush
Ludwigia spp. Primrose-Willow
Micranthemum umbrosum (J.F. Gmel.) Blake Shade Mudflower
Oxycaryum cubense (Poppig. & Kunth) Lye Cuban Bulrush
Pluchea spp. Camphorweed
Pontederia cordata L. Pickerelweed
Rhynchospora microcephala Britt ex Small Smallhead Beaksedge
204 Southeastern Naturalist Vol. 11, No. 2
Species Common name
Saururus cernuus L. Lizard’s Tail
Sphagnum spp. Sphagnum
Triadenum virginicum (L.) Raf. Marsh St. John’s Wort
Woodwardia areolata (L.) T. Moore Chainfern
Xyris laxifolia Mart. var. iridifolia Chapman Yelloweyegrass
Taxa found in Nutria plots only:
Alopecurus carolinianus Carolina Foxtail
Kosteletzkya virginica (L.) Gray Virginia Saltmarsh Mallow
Limnobium spongia (Bosc.) L.C. Rich ex Stued. Frogbite
Ludwigia repens Forst. Creeping Primrose-Willow
Phalaris caroliniana Walt. Carolina Canarygrass
Spartina patens (Ait.) Muhl. Marshhay Cordgrass
Vigna luteola (Jacq.) Benth Deer Pea