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Habitat Selection by Nutria in a Freshwater Louisiana Marsh
Lauren E. Nolfo-Clements

Southeastern Naturalist, Volume 11, Issue 2 (2012): 183–204

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2012 SOUTHEASTERN NATURALIST 11(2):183–204 Habitat Selection by Nutria in a Freshwater Louisiana Marsh 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. Introduction 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; 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. Field-Site Description 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 260 days. 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 the marsh. 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 Nolfo-Clements (2006). 2012 L.E. Nolfo-Clements 185 Methods Radiotelemetry 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 Committee 1998). 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. Vegetative-cover mapping 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 sampling 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 ( 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 plotted point. 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 ( All specimens that could not be identified to species are listed by generic name only. 2012 L.E. Nolfo-Clements 187 Statistical analyses 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 (Leban 1999). 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. Results 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. Radiotelemetry 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 by crosses. 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 nwinter - 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 in bold. Seasons nwinter nspring nsummer rwinter rspring rsummer nwinter - 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') Winter 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) Spring 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) Summer 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 americanus punctatum 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 particular taxa. 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. Discussion 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 summer months. 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. Conclusions 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 to overgrazing. 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. Acknowledgments 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. 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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