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Biological Invasions

, Volume 17, Issue 5, pp 1485–1496 | Cite as

Distribution, spread and habitat preferences of nutria (Myocastor coypus) invading the lower Nakdong River, South Korea

  • Sungwon Hong
  • Yuno Do
  • Ji Yoon Kim
  • Dong-Kyun Kim
  • Gea-Jae Joo
Original Paper

Abstract

Nutria (Myocastor coypus) were introduced into South Korea in 1985 for fur farming and meat production. However, failures of nutria farm management in the late 1990s resulted in the accidental and/or intentional release of nutria into the wild, and they have spread and become serious pests. The successful management of invasive species like nutria somewhat depends on the comprehensive knowledge of distribution patterns. Therefore, this study aimed to identify the distribution, spread, and habitat preferences of nutria invading the lower Nakdong River in South Korea to facilitate current control and eradication endeavours. Nutria populations were recorded in 45 of 236 sites investigated. The distribution patterns revealed that the spread of nutria from farming sites has mainly proceeded along rivers via tributaries. Important factors associated with the establishment of new populations were food availability, levees with suitable burrow materials, and slow water flow. The winter climate was also important, and the total number of days below −4 °C significantly affected nutria occurrence. To date, efforts to control or locally eradicate nutria populations have had little success due to insufficient budgets and the lack of coordinated management activities between local governments. To improve the efficiency of nutria eradication programmes, local governments must establish an integrated and coordinated strategy that is overseen by a single, national agency. To ensure the success of the programmes, this agency should make the best use of ecological information about nutria distribution, and utilize optimal control techniques and timing.

Keywords

Distribution Eradication Nutria Habitat preference Myocastor coypus 

Introduction

Nutria, or coypu (Myocastor coypus), are semi-aquatic rodents native to South America that have been imported into Europe, Asia, Africa, and North America for fur farming and meat production. In many countries, they subsequently escaped and/or were released into natural habitats where they successfully established populations along riverbanks and in wetlands, particularly near nutria farms (Bertolino and Genovesi 2007). Their feeding behaviour has caused severe economic damage to crops, forested areas, and drainage systems (Linscombe et al. 1981; Carter et al. 1999; Meyer 2006; Bertolino and Viterbi 2010). Additionally, nutria populations disturb natural habitats by feeding on aquatic vegetation and destroying the nests and eggs of aquatic birds (Bertolino et al. 2012).

Many studies have investigated nutria habitat preference and behavioural patterns, including breeding, feeding, foraging, and interactions with other species. In their native South America, studies of nutria have focused on food habits, habitat preference, and social structure (Guichón et al. 2003; Corriale et al. 2006). Studies of their non-native range have investigated how the physical and biological characteristics of different habitats have influenced habitat selection, spread, and the distribution of the species (Buckley 2008). Moreover, other analyses have focused on nutria adaptation and spread (Wilson et al. 1966; Willner et al. 1979; Abbas 1991; Meyer 2006). This ecological information and specific management-related research are important factors for nutria control and eradication (Dixon et al. 1979; Baker and Clarke 1988; Panzacchi et al. 2007).

It is generally known that nutria were introduced to South Korea across the four major river basins in 1985 to establish fur farms. A decrease in the price of fur and anti-fur campaigns resulted in the failure of nutria farming. Since the late 1990s, nutria have subsequently been accidentally and/or intentionally released into the wild (Fig. 1). Within the last two decades, they have spread over a wide area of South Korea, and the extent of the spread is probably influenced by the dominance of mountainous areas (occupying approx. 70 % of the national land area), the large fraction of urban land coverage (>90 % urbanization), and the high density of small streams (Chung et al. 2004; Lee et al. 2013). Nutria have had an impact on native plant biodiversity and natural ecosystems by feeding on endangered plants and damaging nationally protected wetland areas, and these behaviours have also resulted in economic damage to crops. The Korean government designated nutria as an alien species in 2009, and a programme involving several local governments and private sector organizations was implemented to control and eradicate nutria. However, the programme was hampered by the lack of information, such as that on nutria distribution and habitat preference, thus resulting in ineffective management.
Fig. 1

Nutria farming sites in South Korea and surveyed areas in the Nakdong River basin

Nutria populations quickly stabilized along the Nakdong River, which contains an abundance of natural wetlands throughout the river basin. The populations readily dispersed along the river and wetlands, with potentially serious impacts on natural ecosystems. To sustain biological diversity, nutria trapping has been undertaken in the Upo Wetlands, which encompass the largest floodplain in the Nakdong River basin. Since 2005, several local governments have implemented management policies, but most are based on temporary solutions (e.g. trapping) to reduce crop damage. More information is needed to improve current efforts to control and eradicate nutria; therefore, this study aimed to identify the distribution, spread, and habitat preferences of nutria invading the lower Nakdong River in South Korea to facilitate effective and efficient control and eradication.

Materials and methods

Study area

The Nakdong River flows for approximately 520 km, and its catchment covers approximately 23,800 km2 (a quarter of South Korea’s land area). The river has several multi-purpose dams, an estuary weir, and numerous small reservoirs and wetlands. The winter temperatures vary significantly across the river basin (−3 to 2.2 °C), while summer temperatures are more uniform (25–26 °C). Annual precipitation is approximately 1,200 mm, with intense summer monsoon rainfall and occasional typhoons. Nutria farms were originally located at three sites near tributaries in the middle reaches of the Nakdong River (Fig. 1). The areas around farms B and C contain numerous natural wetlands, comprising >70 % of wetlands in the Nakdong River basin (Do et al. 2012), which are highly suitable habitats for nutria populations.

Information review and field survey of the nutria distribution

We collected nutria occurrence data between 1984 and 2013 from newspaper articles via the web news library (http://newslibrary.naver.com), two project reports published by the Korean Ministry of the Environment (Kil et al. 2010; Lee 2010), and two scientific articles (Kil et al. 2012; Lee et al. 2012). We also conducted field surveys at 236 sites (54 on the main channel and 182 on the tributaries) in the Nakdong River basin (including streams, rivers, and wetlands) from January 2012 to June 2013 (Fig. 1). At each site, we investigated the presence or absence of nutria by searching 1-km transects that were assessed as suitable nutria habitat. Nutria presence was determined based on footprints, dens, faeces, and direct observation. We also interviewed local residents about the presence or absence of nutria near the sites. Based on published information, the reports from local residents, and our surveys, we analysed the temporal trend of nutria-observed sites using simple linear regression. The number of nutria-observed sites between the main channel and the tributaries was compared using a paired t test. We calculated the proportion of the total length of the river system and its tributaries colonized by nutria as well as the rates of spread (i.e. dispersal distance per year) throughout the Nakdong River basin by measuring the maximum distance of nutria dispersal between sites in 1 year and the following year. The spatial relationships between nutria populations (i.e. randomness of nutria distribution) were assessed using the average distances between populations via a nearest neighbor analysis after 2007 (Clark and Evans 1954); and this timeframe was chosen because of insufficient data points before 2007. The nearest-neighbor analysis was implemented to determine the clustering pattern of nutria-observed sites by using a spatial analysis tool within ArcGIS. The determination for clustering or randomness of nutria populations was based on the nearest neighbor ratios (NNR), which indicate clustering at a lower value and scattering at a higher value.

Habitat suitability and climate factors in relation to nutria distribution

Habitat suitability at each site was measured by scoring the following characteristics (0 or 1, Table 1): (1) location in the river system (Guichón et al. 2003), (2) food availability (Carter et al. 1999), (3) riparian buffer zone (Gosling et al. 1980), (4) levee type (Corriale et al. 2006), (5) water flow rate (Atwood 1950; Ehrlich 1962), (6) sand dune occurrence (Corriale et al. 2006; Meyer 2006), and (7) sheltering area at the edge of the water (Willner et al. 1979; Taylor et al. 1997; Guichón et al. 2003; Corriale et al. 2006). The relationship between these habitat characteristics and nutria occurrence (i.e. presence or absence) in 2013 was assessed using the Kendall rank correlation, which measures the association of two variables based on the number of concordant and discordant pairs.
Table 1

Measures used to assess the suitability of the nutria habitat using the Kendall rank correlation for nutria detection

Measures

Number of sites

Kendall’s Tau

1

0

Location

Location in river (main river = 1, tributary = 0)

207

29

0.124

Food

Aquatic vegetation (presence = 1, absence = 0)

57

179

0.657**

Habitat

Riparian buffer zone (presence = 1, absence = 0)

192

44

0.134*

Levee type (natural = 1, artificial = 0)

74

162

0.544**

Water flow rate (slow = 1, fast = 0)

27

209

0.646**

Sand dune occurrence (yes = 1, no = 0)

119

117

0.302**

Sheltering area in the edge of water (yes = 1, no = 0)

96

140

0.439**

*, ** p < 0.05 and p < 0.01 respectively

We identified 15 climate factors that affect nutria distribution based on the literature (Baroch and Hafner 2002). The 15 factors include: (1) average daily temperature, (2) maximum number of consecutive days with an average temperature <−4 °C (Gosling et al. 1980), (3) maximum number of consecutive days with an average temperature <0 °C (Gosling 1981a), (4) total number of days with an average temperature <−4 °C (Sierra de Soriano 1969), (5) total number of days with an average temperature <0 °C (Segal 1978), (6) total number of days with a maximum temperature >34 °C (Warkentin 1968), (7) maximum number of consecutive days with a maximum temperature >34 °C, (8) total number of days with an average temperature <−4 °C during winter, (9) average daily temperature during winter (Gosling 1981b), (10) total number of days with an average temperature <0 °C during winter, (11) average of the daily minimum temperature during winter, (12) maximum of the daily minimum temperature during winter, (13) minimum of the daily minimum temperature during winter; (14) daily temperature range (Ensminger and Nichols 1957; Chabreck and Palmisano 1973), and (15) total annual rainfall (Doncaster and Micol 1990; Table 2). We obtained climate data monitored at 42 stations that were operated by the Korea Meteorological Administration from March 2012 to February 2013. We extracted the values of the 15 climate factors at each study site based on the inverse distance weighted interpolation of a GIS tool (Arc Map 9.1, ESRI, USA).
Table 2

Evaluation of 15 climatic factors in relation to nutria occurrence using binary logistic regression analyses

Abbreviation

Contents

Slope coefficient Logit1

Slope coefficient Logit2

Prediction accuracy based on Logit2

AveTemp

Average daily temperature

2.340

37.519

90.3 % (213/236: Total)

75.6 % (34/45: Nutria)

ConDay−4

Maximum number of consecutive days with average temperature below −4 °C

−5.278

13.192

Con0

Maximum number of consecutive days with average temperature below 0 °C

−1.314

5.301

D −4

Total number of days with average temperature below −4 °C

−3.565

−17,807.875

D 0

Total number of days with average temperature below 0 °C

−1.630

−45.954

D 34

Total number of days with maximum temperature above 34 °C

0.352

0.848

ConDay34

Maximum number of consecutive days with maximum temperature above 34 °C

0.181

−2.929

Win−4

Total number of days with average temperature below −4 °C during winter

−3.544

17,679.248

WinAveTemp

Average daily temperature during winter

1.820

−9.380

Win0

Total number of days with average temperature below 0 °C during winter

−1.458

32.473

WinMinAve

Average of daily minimum temperature during winter

1.762

−48.877

WinMinMax

Maximum of daily minimum temperature during winter

1.632

2.144

WinMinTemp

Minimum of daily minimum temperature during winter

2.216

7.057

DTR

Dairy temperature range

−0.699

−10.652

Rainfall

Total annual rainfall

1.085

2.288

Logit1 was based on 15 individual regression models, each of which includes one climatic factor. Logit2 was based on one regression model that includes all 15 climatic factors (see Methods)

We then developed two models (Logit1 and Logit2) to predict the nutria occurrence (presence/absence) in 2013 and the ambient climate factors using binary logistic regression (SPSS 18, IBM Inc., Chicago, IL, USA). In addition to the prediction of occurrence, we also examined the relationship between the models using the slope coefficients of the regression models. The Logit1 model was based on 15 individual regression models, each of which included only one climatic factor. The Logit2 model was based on one regression model that included all 15 climatic factors. The former predicts the nutria occurrence based on independent relations with each climatic factor, while the latter considers dependency (i.e. correlation and covariance) among these factors. Hence, we postulated that the factors that consistently account for the same relationships (+: positive or −: negative) in both models would be more influential or important to the presence or absence of nutria.

Impact of local government efforts to manage nutria

Using data from 69 local governments, the location records and numbers of nutria trapped from 2005 to 2013 during local government control or eradication as well as results from control efforts in response to civil petitions regarding crop damage were collected. Some local governments offered a bounty on captured nutria as part of public campaigns to address the nutria problem. Other local governments employed full- or part-time trappers who also responded to local residents’ complaints of nutria damage. We obtained information on the nutria control and eradication budgets from local governments by interviewing government officials. To assess the effects of nutria control on distribution and spread, we analysed the number of nutria trapped each year and the number of sites with nutria occurrence using multiple regression analyses. Changes in the number of sites where nutria were recorded were also compared with the number of individuals trapped and the control/eradication budgets.

Results

Nutria distribution and spread

Although it is known that nutria farming commenced in 1985, the first record of nutria observation (in Upo wetlands) was reported in 1999. The number of sites where nutria occurred dramatically increased to 45 from 1999 to 2013. From 1999 to 2001, the nutria dispersal distance was approximately 49.7 km year−1. In 2003, nutria were recorded at three sites near the original farms, specifically in the lower Nakdong River. During the next 5 years, however, the mean annual dispersal distance was significantly lower (11 km year−1); and in 2006 and 2007, the nutria-observed sites were identical (dispersal distance equal to zero). In 2008, the dispersal distance rebounded to 42.6 km year−1. Therefore, since 2009, the dispersal distances have fluctuated (Fig. 2).
Fig. 2

Annual dispersal distance (km year−1) of nutria populations for 15 years (from 1999 to 2013). The horizontal line indicates average distance

The results of the simple linear regression (r 2 = 0.97, p < 0.01) indicate that the number of nutria-occupied sites showed a stronger positive trend with time (i.e. year; slope coefficient: β = 1.02, p < 0.01) in the main channel than in the tributaries (β = 0.99, p < 0.01, Fig. 3). In 2010 and 2011, local residents reported nutria inhabitation at two sites above the dam that is located upstream of the main tributary (refer to dashed circles in Fig. 3). However, we did not observe any nutria in that area during the 2012–2013 field surveys. A total of 318.3 km (20.6 %) of the Nakdong River and its tributaries were occupied by nutria between 1999 and 2013. In 2013, the proportion of nutria coverage was nearly 29.0 % (152.4/525.15 km) in the main channel and 16.3 % (165.9/1016.6 km) in the major tributaries. The average NNR (=observed mean distance between the populations/expected mean distance between random distributions) decreased significantly with time (β ± S.E. = −0.91 ± 0.08, p < 0.05). In 2008 and 2009, nutria populations were significantly dispersed (NNR = 1.70, p < 0.01 in 2008; NNR = 1.61, p < 0.01 in 2009), but the populations became denser from 2010 to 2013 (NNR = 0.76, p < 0.05 in 2010; NNR = 0.74, p < 0.01 in 2011; NNR = 0.66, p < 0.01 in 2012; NNR = 0.69, p < 0.01 in 2013; Fig. 2).
Fig. 3

Changes in the distribution of nutria in the Nakdong River basin from 1999 to 2013. Circled sites are locations where there were public reports of nutria, but no nutria were detected in the 2012–2013 survey. Rectangles indicate sites where public reports were made (1984–2013), and circles represent nutria presence observed by field survey (2012–2013). Empty points show nutria-occupied sites that were not targeted by the eradication system (n = number of nutria-occupied sites; NNR = nearest neighbor ratio; d = dispersal distance)

Habitat suitability and climate factors in relation to nutria occurrence

Food availability (τ = 0.657, p < 0.01), water flow rate (τ = 0.646, p < 0.01), and levee type (τ = 0.544, p < 0.01) were strongly correlated with nutria occurrence. Sheltering area at the edge of the water (τ = 0.439, p < 0.01), sand dune occurrence (τ = 0.302, p < 0.01), and riparian buffer zone (τ = 0.134, p < 0.05) were also correlated; however, the location in the river system (τ = 0.124, p > 0.05) was not correlated (Table 1). The prediction accuracy of nutria occurrence (presence and absence) based on Logit2 was 90.3 % (213/236 = number of correct predictions out of total number of sites), and the accuracy of nutria presence was 75.6 % (34/45 = number of correct predictions out of total number of nutria-present sites). The total number of days with average temperatures <−4 °C was the strongest factor influencing nutria occurrence based on the slope coefficient of the models (please see both magnitude and signal of the coefficients in Logit1 and Logit2 in Table 2). The total number of days with average temperatures <0 °C, the daily average temperature, and the daily temperature range were also detected as substantially important factors that affect nutria occurrence; however, the total annual rainfall and the total number of days with maximum temperatures >34 °C were less influential.

Evaluation of local government management of nutria

During 2007–2013, 4,722 nutria were captured by government-employed trappers (approx. 32 %) and the public (approx. 68 %; Table 3). Local governments spent US $136,900 on bounties and trappers between 2007 and 2013, and the expenditure slightly increased (85.6 %) over that period (p < 0.01). The number of trapped nutria was more highly correlated with the annual budget (r 2 = 0.992, p < 0.01) than with the year (β = −0.422, p > 0.05) and the number of nutria-occupied sites (β = 0.574, p > 0.05). However, the number of nutria-occupied sites was not significantly correlated with the number of trapped nutria (β = 2.476, p > 0.05) and the budget expenditures (β = −1.56, p > 0.05).
Table 3

Number of nutria trapped by citizens to collect the bounty from public trapping campaigns and employed trappers in various local government areas from 2007 to 2013

Local governments

2007

2008

2009

2010

2011

2012

2013

Total

Busan

 

 Activity

  

Trapper

Campaign

 

 Number trapped

  

14

134

163

358

1,252

1,921

 Budget

  

$2,000

$2,000

$5,000

$35,000

$44,000

Changnyeong

 

 Activity

Trapper

Trapper & Campaign

 

 Number trapped

100

186

91

120

180

171

105

953

 Budget

$5,400

$1,500

$3,150

$10,050

Changwon

 

 Activity

     

Trapper

Trapper

 

 Number trapped

   

 

72

2

74

 Budget

   

 

$2,900

$2,900

Daegu

 

 Activity

    

Trapper

   

 Number trapped

    

5

  

5

 Budget

    

  

Haman

 

 Activity

    

Campaign

 

 Number trapped

    

163

250

369

782

 Budget

    

$3,200

$750

$11,000

$14,950

Hapcheon

 

 Activity

     

Trapper

 

 Number trapped

     

4

18

22

 Budget

     

$1,600

$1,000

$2,600

Jinju

 

 Activity

     

Trapper

 

 Number trapped

    

17

80

25

122

 Budget

    

$4,400

$10,000

$6,000

$20,400

Miryang

 

 Activity

   

Trapper

Campaign

 

 Number trapped

   

150

177

94

92

513

 Budget

   

$6,000

$6,000

$6,000

$6,000

$24,000

Yangsan

 

 Activity

Trapper

  

 Number trapped

20

90

10

60

76

74

 

330

 Budget

$3,000

$3,000

$3,000

$3,000

$3,000

$3,000

 

$18,000

Total

 

 Number trapped

120

276

115

464

781

1,103

1,863

4,122

 Budget

$3,000

$3,000

$3,000

$11,000

$24,000

$30,750

$62,150

$136,900

The budget is based on the US dollar

Discussion

In the early survey period, the annual dispersal distance was the largest, but it dramatically decreased; however, the dispersal distance subsequently remained steady for the next 6 years (Fig. 2). From 1999 to 2001, the dispersal rates were highest, and we hypothesize that nutria were actively seeking suitable habitats during the stabilization period. Previous studies on the daily home range of established nutria reported that they traveled distances of up to 50 m in terrestrial areas and 300 m in aquatic areas (Kim 1980; Linscombe et al. 1981; Nolfo-Clements 2009). Consequently, very low dispersal distances (approx. 9.17 km year−1) might be related to the habitat stabilization of nutria populations. In 2008, the dispersal distance increased again (42.6 km), and we suggest that, as the population expanded, this rebound of dispersal distance was closely associated with competition for both food availability and mates (Doncaster and Micol 1989). At low densities, nutria migration in established populations differs from their migratory patterns in saturated habitats, and nutria generally travel 5–6 km overnight (Linscombe et al. 1981). Aliev (1968) reported a nutria dispersal expansion of 120 km over a two-year period in Eastern Europe. After 2008, however, we could not find a significant pattern of dispersal distance. Given that the dispersal distances oscillated, presumably, nutria could repeatedly move and settle down in shorter timeframes.

The results of the Kendall rank correlation analysis suggest that nutria occupancy was correlated with aquatic vegetation (for food and nesting) and food sources and shelter, which is consistent with previous research (Laurie 1946; Baroch and Hafner 2002). Regarding these studies, nutria occupancy was inversely correlated with water flow rate, and was high in lentic systems and stagnant water areas of the river and tributary channels. This may relate to the metabolic mechanism of energy consumption, which implies that more energy must be used when swimming in fast-flowing water channels than in slow-flowing water channels (Atwood 1950; Stephens 1986). Nutria distribution was also strongly correlated with a levee type that is associated with burrowing. Such burrows not only offer protection from predators, but also provide effective thermal protection. Sierra de Soriano (1969) reported that internal burrow temperatures ranged from 8 to 10 °C daily, whereas outside temperatures ranged from −4 to 24 °C. Artificial levee materials, such as impermeable riprap walls and concrete structures, are likely to prevent burrowing behaviour.

In addition to habitat characteristics, we examined how different climatic factors influenced nutria occurrence using logistic regression models. The results indicate that temperature is the most important factor that determines nutria occurrence. A lower probability of nutria occupancy was observed in areas where the winter temperatures were lower, which suggests that temperature may act as a limiting factor of nutria dispersal. Freezing temperatures play an important role in nutria behaviour and population dynamics (Aliev 1973; Gosling et al. 1980; Gosling 1981b). In Europe, the nutria population was significantly reduced by consecutive below-average cold winters (Wilson et al. 1966; Aliev 1973; Carter and Leonard 2002). Reggiani et al. (1995) reported the nutria population decreased 44–64 % in Italy. However, Doncaster and Micol (1990), demonstrated that severe cold weather conditions can accelerate the reproductive rate of the population; thereby, enabling nutria to maximize population recovery and re-colonization of vacant habitats. The results of our study indicate that the D −4 climatic factor was the most sensitive to nutria occurrence. Given this relationship, we estimated the maximum number of D −4 days that would allow observable nutria populations, and calculated approximately 17 days of temperatures <−4 °C using the spatial analysis (see Fig. 4).
Fig. 4

Maps of nutria-occupied sites in 2013 overlaid with influential climate factors (AveTemp: average daily temperature; D −4: total number of days with average temperature below −4 °C; D 0 : total number of days with average temperature below 0 °C; D 34 : total number of days with maximum temperature above 34 °C; DTR: daily temperature range; rainfall: total annual rainfall)

To recap, slow stream flow, aquatic plant dominance, favourable sheltering conditions, and warmer temperatures seem to provide optimal conditions for nutria to spread and stabilize. Therefore, the lower part of Nakdong River basin provides favourable conditions for nutria inhabitation. Opdam (1991) suggests that ideal, prosperous habitats are capable of creating new contiguous habitats by facilitating the establishment of subpopulations, and this is true even if the habitat is not suited for the new population. In this regard, the lower part of the Nakdong River may play a significant role in facilitating the establishment of new nutria populations. Albeit, we also believe that the spread of nutria populations will be suppressed by lower temperatures (Fig. 4). In this study, nutria occurrence was rarely reported and observed in the upper part of Nakdong River basin. Considering that the average dispersal distance of a nutria population is 31.2 km year−1 (Fig. 2) and that the total length of the river is approximately 520 km, the past 20 years is a sufficient timeframe for nutria to inhabit the entire Nakdong River basin. Therefore, many animal experts and scientists give more weight to stabilization than to the dissemination of the nutria population in South Korea, and there is other evidence to support our assumptions. The original nutria farming sites were distributed across the four major river basins throughout the country, and feral nutria were observed everywhere during the early period (Fig. 1); however, nutria are currently only observed in the Nakdong River basin (Fig. 2). Although habitat characteristics seem to influence nutria habitation in the Youngsan River, we would also like to determine why nutria stabilization is subject to temperature conditions. The temperature is significantly lower in the Han and Geum Rivers, and therefore nutria might not survive in these areas. In the Nakdong River basin in 2008, nutria were observed in the middle part of the river, but the dispersal no longer expanded to the north. Instead, populations began to move towards the tributaries. The longitudinal shape (north to south) of the Nakdong River differs from that of other rivers, and the nutria occurrence pattern is clearly separated into northern and southern parts of the river basin. However, given the increase in temperatures during the last 15 years, this pattern may vary slightly in the future (Fig. 5). As temperatures increase, the nutria-occupied sites may also expand, and the southern part of the river would provide more suitable nutria habitat characteristics. In particular, the connectivity between rivers and wetlands may have facilitated nutria range expansion because the species prefers wetlands with emergent vegetation and areas with succulent vegetation (LeBlanc 1983; Corriale et al. 2006; Do et al. 2012).
Fig. 5

Temporal trend of average temperature from 1999 to 2013. The temperature values were collected at 32 stations within the Nakdong River basin

Despite its national designation as an invasive species, nutria eradication programmes have not been implemented at a national level. Rather, some local governments have independently implemented and supported nutria trapping since 2005. However, this trapping has had little impact on the nutria population or on the control of population dispersal because no specific targets or goals were established at the outset. Trapping has not been conducted over the entire nutria distribution, and trappers have mostly only operated during winter when nutria are more easily trapped. Moreover, much of the trapping has been reactive responses to calls from local residents reporting the presence of nutria or damage caused by the animals. Some local governments have paid a bounty for nutria capture to try to ensure that wide areas were covered and to attract participation of the public in the eradication campaign. However, most participants were unskilled trappers who concentrated trapping in areas with high nutria density to maximize their return on effort; thus, leaving low-density populations uncontrolled. Nonetheless, since the financial incentive showed a strong positive correlation with the number of trapped nutria, such an approach might be useful for initial population reduction, provided it does not trigger additional nutria dispersion. During the period of local government nutria control, nutria distribution increased, and the rate of spread did not diminish; therefore, a more appropriate eradication system needs to be implemented (Bertolino and Viterbi 2010). This could involve the creation of a separate agency to oversee the eradication programme (Buckley 2008), which could include the combined efforts of trappers and researchers. Under direction, trappers would be responsible for nutria control and survey, while the scientists research aspects of nutria ecology that are relevant to the improvement of control methods (e.g. food habit, social behaviour, etc.; Gosling 1989). Moreover, researchers could advise on the application of successful methods from previous nutria eradication campaigns such as the use of baited rafts (Gosling 1981b; Baker and Clarke 1988). Despite the limited success of local governments in controlling nutria, their progress and a better understanding of the factors affecting nutria distribution and spread have recently convinced the Korean Ministry of Environment to implement the eradication of nutria at the national level. In this study, the north delegation for nutria was established, and the habitat characteristics that were identified highlighted areas where control and eradication efforts should be focused. For 15 years, the invasion of nutria has not exceeded that of other countries. The results of this study clarified nutria habitat preference; therefore, by concentrating efforts, nutria numbers will be reduced.

Notes

Acknowledgments

This study was financially supported by the Ministry of Environment and the National Institute of Environmental Research (Korea), and the results of this study form part of the "Survey and valuation of Aquatic Ecosystem Health in Korea, 2013". We are very grateful to anonymous reviewers for the helpful comments used to improve the quality of the manuscript.

References

  1. Abbas A (1991) Feeding strategy of coypus (Myocastor coypus) in central western France. J Zool 224(3):385–401CrossRefGoogle Scholar
  2. Aliev F (1968) Contribution to the study of nutria migrations (Myocastor coypus). Säugetierkd Mitt 16:301–303Google Scholar
  3. Aliev F (1973) Cases of mass mortality of nutria in the wetlands of Azerbaidzhan in winter 1971–1972. Mammalia 36:539–540Google Scholar
  4. Atwood EL (1950) Life history studies of nutria, or coypu, in coastal Louisiana. J Wildl Manage 14(3):249–265CrossRefGoogle Scholar
  5. Baker S, Clarke C (1988) Cage trapping coypus (Myocastor coypus) on baited farts. J Appl Ecol 25(1):41–48CrossRefGoogle Scholar
  6. Baroch J, Hafner M (2002) Biology and natural history of the nutria, with special reference to Nutria in Louisiana. Nutria (Myocastor coypus) in Louisiana. Genesis Laboratories Inc, WellingtonGoogle Scholar
  7. Bertolino S, Genovesi P (2007) Semiaquatic mammals introduced into Italy: case studies in biological invasion. In: Gherardi F (ed) Biological invaders in inland waters: Profiles, distribution, and threats. Springer, pp 175–191Google Scholar
  8. Bertolino S, Viterbi R (2010) Long-term cost-effectiveness of coypu (Myocastor coypus) control in Piedmont (Italy). Biol Invasions 12(8):2549–2558CrossRefGoogle Scholar
  9. Bertolino S, Angelici C, Monaco E, Monaco A, Capizzi D (2012) Interactions between Coypu (Myocastor coypus) and bird nests in three mediterranean wetlands of central Italy. Hystrix 22(2):333–339Google Scholar
  10. Buckley YM (2008) The role of research for integrated management of invasive species, invaded landscapes and communities. J Appl Ecol 45(2):397–402CrossRefGoogle Scholar
  11. Carter J, Foote AL, Johnson-Randall A (1999) Modeling the effects of nutria (Myocastor coypus) on wetland loss. Wetlands 19(1):209–219CrossRefGoogle Scholar
  12. Carter J, Leonard BP (2002) A review of the literature on the worldwide distribution, spread of, and efforts to eradicate the coypu (Myocastor coypus). Wildl Soc B 30(1):162–175Google Scholar
  13. Chabreck RH, Palmisano A (1973) The effects of Hurricane Camille on the marshes of the Mississippi River delta. Ecology 54:1118–1123CrossRefGoogle Scholar
  14. Chung U, Choi J, Yun JI (2004) Urbanization effect on the observed change in mean monthly temperatures between 1951–1980 and 1971–2000 in Korea. Clim Change 66(1–2):127–136CrossRefGoogle Scholar
  15. Clark PJ, Evans FC (1954) Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35(4):445–453CrossRefGoogle Scholar
  16. Corriale MJ, Arias SM, Bó RF, Porini G (2006) Habitat-use patterns of the coypu (Myocastor coypus) in an urban wetland of its original distribution. Acta Theriol 51(3):295–302CrossRefGoogle Scholar
  17. Dixon KR, Willner GR, Chapman JA, Lane WC, Pursley D (1979) Effects of trapping and weather on body weights of feral nutria in Maryland. J Appl Ecol 16(1):69–76CrossRefGoogle Scholar
  18. Do Y, Kim JY, Im R-Y, Kim S-B, Choi J-Y, Joo G-J (2012) Spatial distribution and social characteristics for wetlands in Gyeongsangnam-do province. Korean J Limnol 45(2):252–260Google Scholar
  19. Doncaster CP, Micol T (1989) Annual cycle of a coypu (Myocastor coyus) population: male and female strategies. J Zool 217(2):227–240CrossRefGoogle Scholar
  20. Doncaster CP, Micol T (1990) Response by coypus to catastrophic events of cold and flooding. Ecography 13(2):98–104CrossRefGoogle Scholar
  21. Ehrlich S (1962) Two experiments in inducing nutria aggregation in unfenced artificial ponds. Hydrobiologia 19:316–333CrossRefGoogle Scholar
  22. Ensminger AB, Nichols LG (1957) Hurricane damage to Rockefeller refuge. In: Proceedings of the Annual Conference of the Southeastern Association of Game and Fish Commission, pp 52–56Google Scholar
  23. Gosling LM (1981a) Climatic determinants of spring littering by feral coypus, Myocastor Coypus. J Zool 195(3):281–288CrossRefGoogle Scholar
  24. Gosling LM (1981b) The effect of cold weather on success in trapping feral coypus (Myocastor coypus). J Appl Ecol 18:467–470Google Scholar
  25. Gosling LM (1989) Extinction to order. New Sci 121(1654):44–49Google Scholar
  26. Gosling L, Guyon G, Wright KMH (1980) Diurnal activity of feral coypus (Myocastor coypus) during the cold winter of 1978–9. J Zool 192(2):143–146CrossRefGoogle Scholar
  27. Guichón M, Benitez V, Abba A, Borgnia M, Cassini M (2003) Foraging behaviour of coypus Myocastor coypus: why do coypus consume aquatic plants? Acta Oecol 24(5):241–246CrossRefGoogle Scholar
  28. Kil J, Kim Y, Lee D, Lee C, Hwang S, Kim D, Kim H, Kim J, Kim M (2010) Monitoring of invasive alien species designated by the wildlife protection act (IV). National Institute of Environmental Research, IncheonGoogle Scholar
  29. Kil J, Kim J, Kim Y, Kim H, Lee D, Kim D, Hwang S, Lee J, Shin H (2012) Invasive Alien Species. National Institute of Environmental Research, IncheonGoogle Scholar
  30. Kim P (1980) The coypu (Myocastor coypus) in the Netherlands: reproduction, home range and manner of seeking food. Lutra 23:55–64Google Scholar
  31. Laurie E (1946) The coypu (Myocastor coypus) in Great Britain. J Anim Ecol 15(1):22–34CrossRefGoogle Scholar
  32. LeBlanc DJ (1983) Nutria prevention and control of wildlife damage. Great Plains Agricultural Council, Wildlife Resources Committee and Nebraska Cooperative Extension Service, Institute of Agriculture and Natural Resources, University of Nebraska–Lincoln (Lincoln)Google Scholar
  33. Lee S (2010) Investigation of habitat use of Nutria (Myocastor coypu) for efficient eradication. Nakdong River Basin Environmental Office, ChangwonGoogle Scholar
  34. Lee D-H, Kil J, Yang G (2012) Ecological characteristics for the sustainable management of Nutria (Myocastor coypus) in Korea. National Institute of Environmental Research, IncheonGoogle Scholar
  35. Lee C-B, Chun J-H, Song H-K, Cho H-J (2013) Altitudinal patterns of plant species richness on the Baekdudaegan Mountains, South Korea: mid-domain effect, area, climate, and Rapoport’s rule. Ecol Res 28(1):67–79CrossRefGoogle Scholar
  36. Linscombe G, Kinler N, Wright V (1981) Nutria population density and vegetative changes in brackish marsh in coastal Louisiana. In: Chapman JA, Pursley D (eds) Worldwide Furbearer Conference proceedings, Frostburg, pp 129–141Google Scholar
  37. Meyer A (2006) The impacts of nutria on vegetation and erosion in Oregon. University of Colorado, BoulderGoogle Scholar
  38. Nolfo-Clements LE (2009) Nutria survivorship, movement patterns, and home ranges. Southeast Nat 8(3):399–410CrossRefGoogle Scholar
  39. Opdam P (1991) Metapopulation theory and habitat fragmentation: a review of holarctic breeding bird studies. Landsc Ecol 5(2):93–106CrossRefGoogle Scholar
  40. Panzacchi M, Cocchi R, Genovesi P, Bertolino S (2007) Population control of coypu Myocastor coypus in Italy compared to eradication in UK: a cost-benefit analysis. Wildl Biol 13(2):159–171CrossRefGoogle Scholar
  41. Reggiani G, Boitani L, Stefano R (1995) Population dynamics and regulation in the coypu Myocastor coypus in central Italy. Ecography 18(2):138–146CrossRefGoogle Scholar
  42. Segal A (1978) Thermoregulation in coypu (Myocastor coypus) in summer. Zool zh 57:1878–1883Google Scholar
  43. Sierra de Soriano B (1969) La habitación de Myocastor coypus bonariensis Geoffroy “nutria”. prim Congreso Sudamericano de Zoología :145–152Google Scholar
  44. Stephens DW (1986) Foraging theory. Princeton University Press, PrincetonGoogle Scholar
  45. Taylor K, Grace J, Marx B (1997) The effects of herbivory on neighbor interactions along a coastal marsh gradient. Am J Bot 84(5):709–715CrossRefPubMedGoogle Scholar
  46. Warkentin M (1968) Observations on the behavior and ecology of the nutria in Louisiana. Tulane Stud Zool Bot 15(1):10–17Google Scholar
  47. Willner GR, Chapman JA, Pursley D (1979) Reproduction, physiological responses, food habits, and abundance of nutria on Maryland marshes. Wildl monogr 65:3–43Google Scholar
  48. Wilson E, Newson R, Aliev F (1966) Enemies and competitors of the nutria in USSR. J Mammal 47(2):353–355CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sungwon Hong
    • 1
  • Yuno Do
    • 1
  • Ji Yoon Kim
    • 1
  • Dong-Kyun Kim
    • 2
  • Gea-Jae Joo
    • 1
  1. 1.Department of Biological SciencesPusan National UniversityBusanRepublic of Korea
  2. 2.Department of Physical & Environmental SciencesUniversity of TorontoTorontoCanada

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