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Genetic structure of otter (Lutra lutra) populations from two fishpond systems in Hungary
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SHORT COMMUNICATION
Genetic structure of otter (Lutra lutra) populations from two fishpond systems in Hungary J. Lanszkia,, A. Hidasb, K. Szentesb, T. Re´vayb, I. Lehoczkyc, Zs. Jeneyc, S. Weissd a
Department of Nature Conservation, University of Kaposva´r, P.O. Box 16, 7401 Kaposva´r, Hungary + Hungary Genetic Laboratory, Research Institute for Animal Breeding and Nutrition, Me´he´szet 1, 2100 Go¨do¨llo, c Department of Fish Biology, Research Institute for Fisheries, Aquaculture and Irrigation, P.O. Box 47, 5541 Szarvas, Hungary d Institute of Zoology, Karl-Franzens University Graz, Universita¨tsplatz 2, 8010 Graz, Austria b
Received 16 April 2009; accepted 23 September 2009
Keywords: Density; Microsatellite loci; Population; Sex; Spraint
The Eurasian otter Lutra lutra was once a widely distributed top predator in freshwater habitats, but their numbers declined drastically due to environmental contaminants, increasing road traffic and poaching (Kruuk 1995). Most recently populations have begun to rebound throughout many parts of its range (Conroy and Chanin 2002). Data exist on otter densities in natural lakes (Erlinge 1968; Kalz et al. 2006), but the otter’s primary habitat in Hungary is fish ponds (Lanszki et al. 2001) and we have little knowledge on the density or demography of such pond populations. This knowledge deficit hampers habitat management and conservation plans. Non-invasive sampling of faecal material and subsequent molecular genetic analysis although prone to genotyping errors due to e.g. degradation (Taberlet et al. 1996; Creel et al. 2003; McKelvey and Schwartz 2004), can result in minimum estimation of population size (Hung et al. 2004; Kalz et al. 2006), genetic structure (Dallas et al. 2002; Randi et al. 2003), and sex ratio (Dallas and Piertney 1998; Dallas et al. 2003; Hung et al. 2004; Kalz et al. 2006). The objective of this study was to determine the genetic structure and size of two otter populations living on fish ponds.
Corresponding author. Tel: +36 82 31 41 55; fax: +36 82 32 01 75.
E-mail address: [email protected] (J. Lanszki)
The study was carried out on the Fono´ fishpond (area 1: A1), a 30 ha wetland surrounded by farmland and the Boronka area (area 2: A2), containing 83 ha of fishponds surrounded by lightly managed forest (Lanszki et al. 2001). These areas are located in two different catchments and are ca. 38 km apart from each other. Fresh spraint and anal jelly samples were collected once per month between June 2002 and September 2004 (24 months in total) along standard routes (A1: 1.2 km, A2: 4.2 km shoreline) during early morning hours. Fresh samples (1 ml) were stored in 96% ethanol at 20 1C. DNA was extracted using a modified hexadecyltrimethyl-ammonium bromide (CTAB) protocol (Hung et al. 2004), including the substitution of diatomaceous earth binding for column filtration steps. To increase efficiency, spraint sample lysate was first examined on a 2% agarose gel (stained with 0,5 mg/ml ethidium bromide) to ensure DNA presence. Samples not containing DNA were excluded from further procedures. This step cannot exclude DNA from other species, so successful PCR is still not guarenteed. Extracted DNA was stored in water (MilliQ) at 20 1C. Nine microsatellite loci, described in Dallas and Piertney (1998) were analyzed (Table 1). Additionally, the locus Lut-SRY was applied for sex identification (Dallas et al. 2000). This locus was multi-plexed with Lut-701. If amplification failed (no target product detected) the PCR reaction was repeated three more
1616-5047/$ - see front matter & 2009 Deutsche Gesellschaft fu¨r Sa¨ugetierkunde. Published by Elsevier Gmbh. All rights reserved. doi:10.1016/j.mambio.2009.09.006 Mamm. biol. 75 (2010) 447–450
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J. Lanszki et al. / Mamm. biol. 75 (2010) 447–450
Table 1. Size, frequency and probability of identity of microsatellite alleles across the nine loci screened in otter populations from two fishpond systems in Hungary. Locus
Area
N
A
He/Ho
Allele size (bp) and frequency
PHWE
PID/locus
11 15
2 4
0.52/0.73 0.67/0.84
170 0.545 0.500
174 0.455 0.211
178
A1 A2
0.079
3.77 10 1.56 10
1
0.211
0.255 0.113
A1 A2
9 15
4 4
0.73/0.78 0.66/0.83
182 0.111 0.083
186 0.444 0.444
190 0.278 0.389
194 0.167 0.083
0.536 0.092
1.52 10 2.03 10
1
18 15
2 3
0.51/0.29 0.64/0.67
204 0.559 0.500
208 0.441 0.250
212
A1 A2
3.38 10 2.04 10
1
0.250
0.141 0.009
A1 A2
18 15
5 3
0.63/0.00 0.53/0.00
253 0.059 0.056
255 0.118 0.333
257 0.588 0.611
259 0.118
0.001 0.001
1.69 10 4.47 10
1
13 15
4 5
0.62/0.62 0.72/0.83
152 0.577 0.361
156
A1 A2
164 0.192 0.306
168 0.038 0.028
0.461 0.023
2.20 10 1.51 10
1
0.278
160 0.192 0.028
3 5
0.48/0.25 0.72/0.67
126 0.656 0.472
132
17 15
124 0.312 0.194
130
A1 A2
138 0.031
0.056
2.93 10 1.25 10
1
0.111
0.024 0.005
A1 A2
6 15
4 5
0.73/0.67 0.74/0.42
128 0.167 0.026
130 0.167 0.237
132 0.500 0.395
134 0.167 0.237
0.099 0.001
1.57 10 1.20 10
1
A1 A2
15 14
8 5
0.73/0.33 0.70/0.35
187 0.067 0.176
191 0.067
195 0.500 0.235
199 0.067 0.029
203 0.033
0.001 0.001
1.07 10 1 1.54 10 1
190 A1 A2
12 15
4 4
0.69/0.64 0.62/0.72
194 0.227
198 0.091 0.583
202 0.500
206 0.182 0.139
0.137 0.339
1.61 10 2.21 10
L733
L832
L715
L615
L833
L435
L604
L717
L701
0.111
182
261 0.118
1
1
1
1
1
144 0.167
1
136 0.105 207 0.067
211 0.133
215 0.067 0.500
1
219 0.059
210 0.167
1 1
A1 = Fono´ area and A2 = Boronka area. N = number of individuals typed for each loci, A = number of different alleles observed, He = expected heterozygosity, Ho = observed heterozygosity, PHWE = P value for deviation from HWE, PID/locus = probability of identity for individual locus corrected for small sample size.
times. PCR products were genotyped (Pertoldi et al. 2001) on an ALF Express II DNA sequencer (Amersham-Biosciences). Internal and external molecular mass standards (100, 200, 300 bp) were used for sizing microsatellite allels and allelic ladders were run in every fifth lane. While our spraint-based study can not directly control for genotype errors based on DNA degradation or somatic mutations, we did repeat each run to control for in situ typing errors, and all loci were first tested on a sample (n=25) of otter tissue collected from post-mortem analysis. Probability of identity (PID/locus) was calculated using the software GIMLET (ver. 1.3.3; Valie`re 2002). The mean number of alleles, allele frequencies, FST, observed (He) and expected (Ho) heterozygosities were calculated for each locus using GENEPOP (ver 3.3; Raymond and Rousset 1995). Allelic richness was
calculated using FSTAT (ver 2.9.3.2; Goudet 1995). Deviation from Hardy-Weinberg equilibrium (HWE) and potential presence of heterozygote deficiency were tested using the Markov chain procedure implemented in GENEPOP. Nei’s minimum genetic distance (Takezaki and Nei 1996) among individuals was calculated using the software POPULATIONS (ver. 1.2.28; Langella 1999) and a Neighbour-Joining tree of individual genotypes was visualised with TREEVIEW (Page 1996). Otter density based in terms of the minimum numbers of otters alive (MNA) in each area was based on the number of unique genotypes (Dg). Minimum population densities were calculated based on monthly identified genotypes for both surface (ha) of wetland and kilometres of shoreline. Individuals detected in different months were assumed to be present continuously on the basis of the Manly and Parr (1968)
J. Lanszki et al. / Mamm. biol. 75 (2010) 447–450
capture history method. Means and standard errors are shown for a variety of sample measures. Where appropriate, measures were compared between the two sample locations with Student’s t-tests using the SPSS 10 for Windows (1999) statistical package. From a total of 316 collected fresh spraints (A1=123; A2=193), 46 (14.6%) DNA extractions were successfully genotyped, a somewhat lower percentage than reported from Dallas et al. (2003)(19.7%) or Kalz et al. (2006)(24.1%). From these 46 extractions, 33 unique genetic profiles were detected relating to 18 individuals in A1 and 15 in A2. Otter densities (MNA) were significantly higher in A1 (mean7SE, 4.670.52/100 ha; 1.270.13/km) compared to A2 (1.870.18/100 ha; 0.3570.035/km)(Po 0.001). Variability in otter densities among habitat types is largely influenced by food availability and habitat quality (Kruuk 1995). Mean fish density in A2 (80-90 kg/ha) was lower than in A1 (250- 300 kg/ha) during the study. Our otter densities are as high or higher than those reported from Taiwanese streams (0.8-1.8 otter/km; Hung et al. 2004), river and lake habitats in Germany (0.22/km; Kalz et al. 2006), riverine habitats in Southern Italy (0.20/km; Prigioni et al. 2006) and riverine and backwater habitats in Hungary (0.17/km; Lanszki et al. 2008). Thus, our otter densities surely correspond to the concentrated supply of prey in our fish ponds. All microsatellite loci were polymorphic with the number of alleles per locus ranging from three to nine and an average of 4.0 (A1) to 4.2 (A2, Table 1). These values are among the lowest reported, similar to the 3.89 reported from Pertoldi et al. (2001) from Denmark and 4.5 reported by Ha´jkova´ et al. (2007) from Czech Republic. The mean observed heterozygosity across loci was similar (A1: 0.6470.030, A2: 0.6670.035, P=0.718), as was allelic richness (A1: 3.5670.352, A2: 3.5570.240, P=0.992). No heterozygotes were found at locus 615, despite the presence of
449
5 alleles globally (Table 1). This locus also showed very low observed heterozygosity in Lanszki et al. (2008), and thus we assumed that null alleles may be present, and therefore removed this locus from further analysis. The mean expected heterozygosity did not differ significantly between sites (A1: 0.6370.038, A2: 0.6870.013, P=0.292) and were similar to values shown in the summary review of Lanszki et al. (2008). Two loci showed significant deviations from the HWE in A1, and five in A2 (Table 1). The mean genetic distance between individuals (A1: 0.4770.016, range: 0.03–1.00, Fig. 1a, A2: 0.3670.013, range: 0.03–0.58, Fig. 1b) differed significantly between areas (Po0.001), however the FST value between the two areas was moderate (0.069) but highly significant. From A1, all individuals were recorded once, while from A2, seven individuals were recorded multiple times, four in the same month and three in different months. In A1 there were five months when two different individuals were recorded and one month (March 2003) when four different individuals were detected. In A2 there were three months in which three different individuals were recorded and five months in which two different individuals were recorded. The sex ratio (male/female 3.6) was approximately the same in the two samples with few females (Fig. 1a and b), but from the damaged DNA of faecal samples amplification was not always successful. Although males disperse farther than females (Kruuk 1995), a balanced sex ratio has been found in most other studies (Dallas et al. 2003; Kalz et al. 2006). These results indicate that in A1 (Fig. 1a) many of the otters are probably transients or migrants (Hung et al. 2004; Kalz et al. 2006), while in A2 there are probable two otter families (Fig. 1b), as well as several transient individuals. These results demonstrate that fishponds are considerable core areas for otters and act as ecological corridors, which should be borne in mind
Fig. 1. Neighbour Joining clustering of individual genotypes (Nei’s minimum distance, Dm) on the Fono´ area (a) and on the Boronka area (b). Date (month/year) or interval of individual identifications, and sex (M: male, F: female) is marked if available. Scale 0.1 means Dm.
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J. Lanszki et al. / Mamm. biol. 75 (2010) 447–450
when dealing with conservation and management of these aquatic areas. In conclusion, this initial study of fish pond otters in Hungary revealed high genetic diversity, a scarcity of females, a medium to high overall density and the presence of several migrants.
Acknowledgements We thank Dr. Jim Conroy and two anonymous referees for advice. This work was supported by the OTKA (K62216).
References Conroy, J.W.H., Chanin, P.R.F., 2002. The status of the Eurasian otter (Lutra lutra). IUCN OSG Bull. 19A, 24–48. Creel, S., Spong, G., Sands, J.L., Rotella, J., Zeigle, J., Joe, L., Murphy, K.M., Smith, D., 2003. Population size estimation in Yellowstone wolves with error-prone non-invasive microsatellite genotypes. Mol. Ecol. 12, 2003–2009. Dallas, J.F., Piertney, S.B., 1998. Microsatellite primers for the Eurasian otter. Mol. Ecol. 7, 1248–1251. Dallas, J.F., Carss, D.N., Marshall, F., Koepfli, K.P., Kruuk, H., Bacon, P.J., Piertney, S.B., 2000. Sex identification of the Eurasian otter Lutra lutra by PCR typing of spraints. Conserv. Genet. 1, 181–183. Dallas, J.F., Marshall, F., Piertney, S.B., Bacon, P.J., Racey, P.A., 2002. Spatially restricted gene flow and reduced microsatellite polymorphism in the Eurasian otter Lutra lutra in Britain. Conserv. Genet. 3, 15–29. Dallas, J.F., Coxon, K.E., Sykes, T., Chanin, P.R.F., Marshall, F., Carss, D.N., Bacon, P.J., Piertney, S.B., Racey, P.A., 2003. Similar estimates of population genetic composition and sex ratio derived from carcasses and faeces of Eurasian otter Lutra lutra. Mol. Ecol. 12, 275–282. Erlinge, S., 1968. Territoriality of the otter Lutra lutra L. Oikos 19, 81–98. Goudet, J., 1995. FSTAT (ver. 1.2): a computer program to calculate F-statistics. J. Hered. 86, 485–486. Ha´jkova´, P., Pertoldi, C., Zemanova´, B., Roche, K., Ha´jek, B., Bryja, J., Zima, J., 2007. Genetic structure and evidence for recent population decline in Eurasian otter populations in the Czech and Slovak Republics: implications for conservation. J. Zool. (London) 272, 1–9. Hung, C.M., Li, S.H., Lee, L.L., 2004. Faecal DNA typing to determine the abundance and spatial organization of otters (Lutra lutra) along two streams in Kinmen. Anim. Conserv. 7, 301–311.
Kalz, B., Jewgenow, K., Fickel, J., 2006. Structure of an otter (Lutra lutra) population in Germany – results of DNA and hormone analyses from faecal samples. Mamm. Biol. 71, 321–335. Kruuk, H., 1995. Wild Otters. Oxford University Press, Oxford. Langella, O., 1999. Populations 1.2.28 Software /http://www. prs-gif.frS. Lanszki, J., Ko¨rmendi, S., Hancz, C., Martin, T.G., 2001. Examination of some factors affecting selection of fish prey by otters (Lutra lutra) living by eutrophic fishponds. J. Zool. (London) 255, 97–103. Lanszki, J., Hidas, A., Szentes, K., Re´vay, I., Lehoczky, I., Weiss, S., 2008. Relative spraint density and genetic structure of otter (Lutra lutra) along the Drava River in Hungary. Mamm. Biol. 73, 40–47. McKelvey, K.S., Schwartz, M.K., 2004. Genetic errors associated with population estimation using non-invasive molecular tagging: problems and new solutions. J. Wildl. Manage. 68, 439–448. Manly, B.F.J., Parr, M.J., 1968. A new method of estimating population size, survivorship, and birth rate from capturerecapture data, Trans. Soc. Br. Entomol. 18, 81–89. Page, R.D.M., 1996. TREEVIEW: an application to display phylogenetic trees on personal computers. Comput. Appl. Biosci. 12, 357–358. Pertoldi, C., Hansen, M.H., Loeschcke, V., Madsen, A.B., Jacobsen, L., Baagoe, H., 2001. Genetic consequences of population decline in the European otter (Lutra lutra): an assessment of microsatellite DNA variation in Danish otters from 1883 to 1993. Proc. R. Soc. London Ser. B. 268, 1775–1781. Prigioni, C., Remonti, L., Balestrieri, A., Sgrosso, S., Priore, G., Mucci, N., Randi, E., 2006. Estimation of European otter (Lutra lutra) population size by fecal DNA typing in southern Italy. J. Mamm. 87, 855–858. Randi, E., Davoli, F., Pierpaoli, M., Pertoldi, C., Madsen, A.B., Loeschcke, V., 2003. Genetic structure in otter (Lutra lutra) populations in Europe: implications for conservation. Anim. Conserv. 6, 93–100. Raymond, M., Rousset, F., 1995. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249. SPSS 10 for Windows, 1999. SPSS Inc., Chicago. Taberlet, P., Griffin, S., Goossens, B., Questiau, S., Manceau, V., Escaravage, N., Waits, L.P., Bouvet, J., 1996. Reliable genotyping of samples with a very low DNA quantities using PCR. Nucleid Acids Res. 24, 3189–3194. Takezaki, N., Nei, M., 1996. Genetic distances and reconstruction of Phylogenetic trees from Microsatellite DNA. Genetics 144, 189–399. Valie`re, N., 2002. Gimlet: a computer program for analysing genetic individual identification data. Mol. Ecol. Notes 2, 377–379.
SHORT COMMUNICATION
Genetic structure of otter (Lutra lutra) populations from two fishpond systems in Hungary J. Lanszkia,, A. Hidasb, K. Szentesb, T. Re´vayb, I. Lehoczkyc, Zs. Jeneyc, S. Weissd a
Department of Nature Conservation, University of Kaposva´r, P.O. Box 16, 7401 Kaposva´r, Hungary + Hungary Genetic Laboratory, Research Institute for Animal Breeding and Nutrition, Me´he´szet 1, 2100 Go¨do¨llo, c Department of Fish Biology, Research Institute for Fisheries, Aquaculture and Irrigation, P.O. Box 47, 5541 Szarvas, Hungary d Institute of Zoology, Karl-Franzens University Graz, Universita¨tsplatz 2, 8010 Graz, Austria b
Received 16 April 2009; accepted 23 September 2009
Keywords: Density; Microsatellite loci; Population; Sex; Spraint
The Eurasian otter Lutra lutra was once a widely distributed top predator in freshwater habitats, but their numbers declined drastically due to environmental contaminants, increasing road traffic and poaching (Kruuk 1995). Most recently populations have begun to rebound throughout many parts of its range (Conroy and Chanin 2002). Data exist on otter densities in natural lakes (Erlinge 1968; Kalz et al. 2006), but the otter’s primary habitat in Hungary is fish ponds (Lanszki et al. 2001) and we have little knowledge on the density or demography of such pond populations. This knowledge deficit hampers habitat management and conservation plans. Non-invasive sampling of faecal material and subsequent molecular genetic analysis although prone to genotyping errors due to e.g. degradation (Taberlet et al. 1996; Creel et al. 2003; McKelvey and Schwartz 2004), can result in minimum estimation of population size (Hung et al. 2004; Kalz et al. 2006), genetic structure (Dallas et al. 2002; Randi et al. 2003), and sex ratio (Dallas and Piertney 1998; Dallas et al. 2003; Hung et al. 2004; Kalz et al. 2006). The objective of this study was to determine the genetic structure and size of two otter populations living on fish ponds.
Corresponding author. Tel: +36 82 31 41 55; fax: +36 82 32 01 75.
E-mail address: [email protected] (J. Lanszki)
The study was carried out on the Fono´ fishpond (area 1: A1), a 30 ha wetland surrounded by farmland and the Boronka area (area 2: A2), containing 83 ha of fishponds surrounded by lightly managed forest (Lanszki et al. 2001). These areas are located in two different catchments and are ca. 38 km apart from each other. Fresh spraint and anal jelly samples were collected once per month between June 2002 and September 2004 (24 months in total) along standard routes (A1: 1.2 km, A2: 4.2 km shoreline) during early morning hours. Fresh samples (1 ml) were stored in 96% ethanol at 20 1C. DNA was extracted using a modified hexadecyltrimethyl-ammonium bromide (CTAB) protocol (Hung et al. 2004), including the substitution of diatomaceous earth binding for column filtration steps. To increase efficiency, spraint sample lysate was first examined on a 2% agarose gel (stained with 0,5 mg/ml ethidium bromide) to ensure DNA presence. Samples not containing DNA were excluded from further procedures. This step cannot exclude DNA from other species, so successful PCR is still not guarenteed. Extracted DNA was stored in water (MilliQ) at 20 1C. Nine microsatellite loci, described in Dallas and Piertney (1998) were analyzed (Table 1). Additionally, the locus Lut-SRY was applied for sex identification (Dallas et al. 2000). This locus was multi-plexed with Lut-701. If amplification failed (no target product detected) the PCR reaction was repeated three more
1616-5047/$ - see front matter & 2009 Deutsche Gesellschaft fu¨r Sa¨ugetierkunde. Published by Elsevier Gmbh. All rights reserved. doi:10.1016/j.mambio.2009.09.006 Mamm. biol. 75 (2010) 447–450
448
J. Lanszki et al. / Mamm. biol. 75 (2010) 447–450
Table 1. Size, frequency and probability of identity of microsatellite alleles across the nine loci screened in otter populations from two fishpond systems in Hungary. Locus
Area
N
A
He/Ho
Allele size (bp) and frequency
PHWE
PID/locus
11 15
2 4
0.52/0.73 0.67/0.84
170 0.545 0.500
174 0.455 0.211
178
A1 A2
0.079
3.77 10 1.56 10
1
0.211
0.255 0.113
A1 A2
9 15
4 4
0.73/0.78 0.66/0.83
182 0.111 0.083
186 0.444 0.444
190 0.278 0.389
194 0.167 0.083
0.536 0.092
1.52 10 2.03 10
1
18 15
2 3
0.51/0.29 0.64/0.67
204 0.559 0.500
208 0.441 0.250
212
A1 A2
3.38 10 2.04 10
1
0.250
0.141 0.009
A1 A2
18 15
5 3
0.63/0.00 0.53/0.00
253 0.059 0.056
255 0.118 0.333
257 0.588 0.611
259 0.118
0.001 0.001
1.69 10 4.47 10
1
13 15
4 5
0.62/0.62 0.72/0.83
152 0.577 0.361
156
A1 A2
164 0.192 0.306
168 0.038 0.028
0.461 0.023
2.20 10 1.51 10
1
0.278
160 0.192 0.028
3 5
0.48/0.25 0.72/0.67
126 0.656 0.472
132
17 15
124 0.312 0.194
130
A1 A2
138 0.031
0.056
2.93 10 1.25 10
1
0.111
0.024 0.005
A1 A2
6 15
4 5
0.73/0.67 0.74/0.42
128 0.167 0.026
130 0.167 0.237
132 0.500 0.395
134 0.167 0.237
0.099 0.001
1.57 10 1.20 10
1
A1 A2
15 14
8 5
0.73/0.33 0.70/0.35
187 0.067 0.176
191 0.067
195 0.500 0.235
199 0.067 0.029
203 0.033
0.001 0.001
1.07 10 1 1.54 10 1
190 A1 A2
12 15
4 4
0.69/0.64 0.62/0.72
194 0.227
198 0.091 0.583
202 0.500
206 0.182 0.139
0.137 0.339
1.61 10 2.21 10
L733
L832
L715
L615
L833
L435
L604
L717
L701
0.111
182
261 0.118
1
1
1
1
1
144 0.167
1
136 0.105 207 0.067
211 0.133
215 0.067 0.500
1
219 0.059
210 0.167
1 1
A1 = Fono´ area and A2 = Boronka area. N = number of individuals typed for each loci, A = number of different alleles observed, He = expected heterozygosity, Ho = observed heterozygosity, PHWE = P value for deviation from HWE, PID/locus = probability of identity for individual locus corrected for small sample size.
times. PCR products were genotyped (Pertoldi et al. 2001) on an ALF Express II DNA sequencer (Amersham-Biosciences). Internal and external molecular mass standards (100, 200, 300 bp) were used for sizing microsatellite allels and allelic ladders were run in every fifth lane. While our spraint-based study can not directly control for genotype errors based on DNA degradation or somatic mutations, we did repeat each run to control for in situ typing errors, and all loci were first tested on a sample (n=25) of otter tissue collected from post-mortem analysis. Probability of identity (PID/locus) was calculated using the software GIMLET (ver. 1.3.3; Valie`re 2002). The mean number of alleles, allele frequencies, FST, observed (He) and expected (Ho) heterozygosities were calculated for each locus using GENEPOP (ver 3.3; Raymond and Rousset 1995). Allelic richness was
calculated using FSTAT (ver 2.9.3.2; Goudet 1995). Deviation from Hardy-Weinberg equilibrium (HWE) and potential presence of heterozygote deficiency were tested using the Markov chain procedure implemented in GENEPOP. Nei’s minimum genetic distance (Takezaki and Nei 1996) among individuals was calculated using the software POPULATIONS (ver. 1.2.28; Langella 1999) and a Neighbour-Joining tree of individual genotypes was visualised with TREEVIEW (Page 1996). Otter density based in terms of the minimum numbers of otters alive (MNA) in each area was based on the number of unique genotypes (Dg). Minimum population densities were calculated based on monthly identified genotypes for both surface (ha) of wetland and kilometres of shoreline. Individuals detected in different months were assumed to be present continuously on the basis of the Manly and Parr (1968)
J. Lanszki et al. / Mamm. biol. 75 (2010) 447–450
capture history method. Means and standard errors are shown for a variety of sample measures. Where appropriate, measures were compared between the two sample locations with Student’s t-tests using the SPSS 10 for Windows (1999) statistical package. From a total of 316 collected fresh spraints (A1=123; A2=193), 46 (14.6%) DNA extractions were successfully genotyped, a somewhat lower percentage than reported from Dallas et al. (2003)(19.7%) or Kalz et al. (2006)(24.1%). From these 46 extractions, 33 unique genetic profiles were detected relating to 18 individuals in A1 and 15 in A2. Otter densities (MNA) were significantly higher in A1 (mean7SE, 4.670.52/100 ha; 1.270.13/km) compared to A2 (1.870.18/100 ha; 0.3570.035/km)(Po 0.001). Variability in otter densities among habitat types is largely influenced by food availability and habitat quality (Kruuk 1995). Mean fish density in A2 (80-90 kg/ha) was lower than in A1 (250- 300 kg/ha) during the study. Our otter densities are as high or higher than those reported from Taiwanese streams (0.8-1.8 otter/km; Hung et al. 2004), river and lake habitats in Germany (0.22/km; Kalz et al. 2006), riverine habitats in Southern Italy (0.20/km; Prigioni et al. 2006) and riverine and backwater habitats in Hungary (0.17/km; Lanszki et al. 2008). Thus, our otter densities surely correspond to the concentrated supply of prey in our fish ponds. All microsatellite loci were polymorphic with the number of alleles per locus ranging from three to nine and an average of 4.0 (A1) to 4.2 (A2, Table 1). These values are among the lowest reported, similar to the 3.89 reported from Pertoldi et al. (2001) from Denmark and 4.5 reported by Ha´jkova´ et al. (2007) from Czech Republic. The mean observed heterozygosity across loci was similar (A1: 0.6470.030, A2: 0.6670.035, P=0.718), as was allelic richness (A1: 3.5670.352, A2: 3.5570.240, P=0.992). No heterozygotes were found at locus 615, despite the presence of
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5 alleles globally (Table 1). This locus also showed very low observed heterozygosity in Lanszki et al. (2008), and thus we assumed that null alleles may be present, and therefore removed this locus from further analysis. The mean expected heterozygosity did not differ significantly between sites (A1: 0.6370.038, A2: 0.6870.013, P=0.292) and were similar to values shown in the summary review of Lanszki et al. (2008). Two loci showed significant deviations from the HWE in A1, and five in A2 (Table 1). The mean genetic distance between individuals (A1: 0.4770.016, range: 0.03–1.00, Fig. 1a, A2: 0.3670.013, range: 0.03–0.58, Fig. 1b) differed significantly between areas (Po0.001), however the FST value between the two areas was moderate (0.069) but highly significant. From A1, all individuals were recorded once, while from A2, seven individuals were recorded multiple times, four in the same month and three in different months. In A1 there were five months when two different individuals were recorded and one month (March 2003) when four different individuals were detected. In A2 there were three months in which three different individuals were recorded and five months in which two different individuals were recorded. The sex ratio (male/female 3.6) was approximately the same in the two samples with few females (Fig. 1a and b), but from the damaged DNA of faecal samples amplification was not always successful. Although males disperse farther than females (Kruuk 1995), a balanced sex ratio has been found in most other studies (Dallas et al. 2003; Kalz et al. 2006). These results indicate that in A1 (Fig. 1a) many of the otters are probably transients or migrants (Hung et al. 2004; Kalz et al. 2006), while in A2 there are probable two otter families (Fig. 1b), as well as several transient individuals. These results demonstrate that fishponds are considerable core areas for otters and act as ecological corridors, which should be borne in mind
Fig. 1. Neighbour Joining clustering of individual genotypes (Nei’s minimum distance, Dm) on the Fono´ area (a) and on the Boronka area (b). Date (month/year) or interval of individual identifications, and sex (M: male, F: female) is marked if available. Scale 0.1 means Dm.
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when dealing with conservation and management of these aquatic areas. In conclusion, this initial study of fish pond otters in Hungary revealed high genetic diversity, a scarcity of females, a medium to high overall density and the presence of several migrants.
Acknowledgements We thank Dr. Jim Conroy and two anonymous referees for advice. This work was supported by the OTKA (K62216).
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