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Article

A Noninvasive Genetic Insight into the Spatial and Social Organization of an Endangered Population of the Eurasian Otter (Lutra lutra, Mustelidae, Carnivora)

by
Laura Lerone
1,2,
Chiara Mengoni
3,
Mirko Di Febbraro
1,*,
Hannah Krupa
1,2 and
Anna Loy
1,2
1
Dipartimento di Bioscienze e Territorio, Università del Molise, Contrada Fonte Lappone, 86090 Pesche, Italy
2
IUCN Species Survival Commission, Otter Specialist Group, Rue Mauverney 28, 1196 Gland, Switzerland
3
Unit for Conservation Genetics (BIO-CGE), Italian Institute for Environmental Protection and Research (ISPRA), Ozzano dell’Emilia, 40064 Bologna, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(4), 1943; https://doi.org/10.3390/su14041943
Submission received: 29 December 2021 / Revised: 3 February 2022 / Accepted: 4 February 2022 / Published: 9 February 2022
(This article belongs to the Special Issue Mammal Status: Diversity, Abundance and Dynamics)
Browse Figures
Versions Notes

Abstract

:
The Eurasian otter is endangered in Italy, only surviving in southern river basins. The spatial and social structure of a population living at the border of the current range was explored through a noninvasive genetic study along 174 km of the Sangro river. Sampling was conducted in 2011 and 2012, collecting spraints and anal jellies at 62 marking sites. Samples were successfully genotyped at 13 nuclear microsatellites and the ZFX/ZFY locus for molecular sexing, resulting in 14 distinct genotypes (4 females, 2 possible females, 8 males), from 35 marking sites. Mean captures/recaptures rate was 3.8 captures/individual, with males being recaptured more frequently than females. Spatial overlap among individuals was analyzed through a linear regression model fitted against sibship categories and sex pairing. Nine out of the fourteen genotyped individuals belonged to three full-sib clusters, while five individuals had no full-sibs in the population. Full-sibs overlapped more than half-sibs, while male–male pairs showed significantly higher spatial overlap than both male–female and female–female pairs. Estimated mean density was 0.152 otters/km and 2.4 individuals/10 × 10 km grid cell. Accordingly, the 3440 grid cells of otter occurrence in Italy could likely host about 8000 otters, suggesting the current population has become larger than the minimum viable population size.

1. Introduction

Successful conservation and management of threatened species require accurate information on population abundance, structure, and genetic variability. Nevertheless, such data are often difficult to obtain for wild populations, especially for elusive and rare species. During the last century alone, Eurasian otter (Lutra lutra) populations have declined throughout Europe and have experienced a hard habitat reduction and fragmentation all over their distribution range [1,2,3]. In Italy, the Eurasian otter population survived only in the southern part of the peninsula [4,5,6,7]. Following strict protection and the ban of polychlorinated biphenyl (PCB) compounds in the EU since 1987, the species is now recovering in most European countries [2,3]. Since 2000, the small Italian population started to slowly recover and repopulate areas in south-central rivers of the peninsula that had been part of their historic distribution range [6,7,8]. Otter individuals also reappeared in 2011 in the north-eastern Alps following range expansion into Austria and Slovenia [9,10,11,12]. However, the Italian core population is geographically and genetically isolated from other European populations [13], and the species is still one of the most threatened mammals in Italy, classified as Endangered in the national red list [6,11,14]. Diet composition, habitat preference, distribution pattern, and genetic variation of the Italian otters have been explored by various authors [4,5,6,7,8,9,10,11,12,13,15,16,17,18,19,20,21,22,23,24,25,26]. However, in spite of its isolation and genetic uniqueness, the spatial organization and structure of the Italian population are still largely unknown, the only available information being from one male and one female otter radiotracked in southern Italy [27].
Traditional techniques used to gather information on population structure, density, and social and spatial behavior of wild mammals involve their capture and handling to fit tracking devices [28,29]. As for otters, these invasive techniques are particularly risky and controversial, since they employ the use of invasive capture devices and surgery to implant intra-peritoneal transmitters [29]. Technical advances in molecular ecology offer an alternative way to monitor these rare and elusive species in a noninvasive way. Specifically, the analysis of variable microsatellite loci allows obtaining a genetic fingerprint that can be used to estimate population abundance, genetic structure, and degree of relatedness in many species, including otters [4,30,31,32,33,34,35,36,37], as well as to gather information on territorial and social behavior [38,39,40,41,42,43,44,45,46,47,48,49]. Most noninvasive genetic sampling on otters is based on DNA extraction from spraints (i.e., feces) and anal jelly produced by their anal glands and used for scent marking [49,50,51]. Although this technique has been applied to gather information on the population density and genetic structure of otters in various European countries [4,34,35,36,37,38,39,40,41,42,43,44,45], its use in exploring spatial and social organization has yet to be examined except by a few studies [38,39,40,42,43,45]. Specifically, noninvasively collected sample material has been used to explore the marking behavior of male and female otters in Germany [49] and individual distribution and dispersal in Spain, Germany, Portugal, France, and the Netherlands [38,39,43,44,45].
Information on spatial and social behavior is especially valuable for rare and elusive species, such as the Italian population of the Eurasian otter, as they help in devising accurate conservation strategies that might fail if based on data derived from different geographic and environmental contexts [52]. This study was aimed at gathering first-hand insight into the population density and spatial dynamics of an otter metapopulation living at the northernmost limit of its range in southern Italy. Specifically, the sampling design was constructed to answer the following questions: (1) Is the otter density at the range boundary similar to the density found in the range core in southern Italy [4]? (2) Is the home-range extent similar to that observed in other Mediterranean areas [27,53,54]? (3) Is there any spatial overlap among individuals, and between or within sexes, that could account for a social organization other than the solitary and polygynous mating system known for this species?

2. Materials and Methods

2.1. Study Area

The study was conducted in southern Italy along 174 km of the Sangro river and its main tributaries (Figure 1). The river represents the northernmost limit of the otter range in Italy (not considering the recent return in the Alps). The river basin was recolonized by the Eurasian otter in 2007 after its extinction during the 1980s [11,55], and it is included among the priority areas of the otter national action plan [6]. The main course of the river flows to the Adriatic Sea from 1441 m a.s.l. for about 122 km. The whole basin covers an area of 1545 km2.

2.2. Sample Collection and Storage

Field sampling was conducted from April to September 2011 and from June to September 2012. Spraints and anal jelly samples were regularly collected at 62 known marking sites previously identified during a four-month pilot study [51] (Figure 1). Geographic coordinates were recorded at each sampling site to implement spatial analyses. Although high temperatures could represent a critical issue for genotyping success [51,56], samples were also collected during summer to reach marking sites that were inaccessible in other seasons (Figure 1). To guarantee the collection of fresh samples (within 24 h from deposition), marking sites were checked daily after clearing older spraints [51]. At sites that were less often marked by otters, we also collected samples of medium freshness and unknown deposition time. Spraints and anal jelly were stored in 96% ethanol in 1.5 mL tubes preserved at −20 °C until DNA extraction.

2.3. DNA Extraction and Analyses

DNA was extracted from otter spraints and anal jelly samples using DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer protocol and then amplified according to [51]. Genotypes were initially obtained by GeneMapper v. 4.0 (Applied Biosystems, ThermoFisher Scientific, Waltham, MA, USA). Matches were checked using GIMLET software [57], where genotypes matched at all but one or two alleles. We reviewed electropherograms and, when required, performed an additional four PCR repeats for uncertain loci. To carry out individual identification, DNA samples were analyzed through a panel of 13 nuclear microsatellite loci—Lut453, Lut604, Lut701, Lut832, Lut833, Lut902 [58] and OT04, OT05, OT07, OT14, OT17, OT19, OT22 [51,59]. Genotyped samples were also analyzed at ZFX/ZFY locus for molecular sexing [60]. In all analyses, contamination risks were minimized using a laboratory dedicated to the pre-polymerase chain reaction (PCR) handling of noninvasively collected samples [61]. Working with noninvasive samples, we decided to perform a multiple tube approach with a minimum number of four replicates per sample in order to assess the rate of allelic dropout (ADO) and false alleles (FA) [61]. Using RELIOTYPE [62], we determined the reliability value for each sample and checked if further replicates were needed.
We used GENALEX v. 6.1 [63] to estimate the allele frequencies by locus and population, mean number of alleles per locus (Na), observed (Ho) and expected unbiased (UHe) heterozygosity, and the related chi-square test (χ2) for deviations from Hardy–Weinberg equilibrium (computation was adjusted using the Bonferroni correction) [64]. False allele (FA) and allelic dropout (ADO) rates were estimated using GIMLET software v.1.3.3. Observed (HO) and unbiased expected (HE) [65] heterozygosity values were computed for combinations of all loci; Hardy–Weinberg and linkage disequilibrium were then tested in ARLEQUIN 3.5.1.2 [66] through the Markov chain exact test with a chain length of 100,000 and 3000 dememorization steps, AMOVA, and F-statistics (testing the null hypothesis of no differentiation by permuting genotypes between populations with 10,000 replicates at p < 0.001). Population size estimate was based on genotypes identified during 2011 to allow capture–mark–recapture estimates for a closed population [67]. We grouped identical multilocus genotypes to produce individual capture–recapture histories over the sampling season. Population size was estimated using the CAPWIRE R package [68], a platform specifically designed for DNA-based capture–recapture data and well-performing with small populations (n < 100). Within CAPWIRE, population size and confidence intervals were computed using the TIRM model for heterogeneous capturability rates. To infer the degree of relatedness among genotyped individuals, we performed the sibship analysis [69,70] on the whole sample of genotyped individuals in the two years, using the software COLONY (Ver. 2.0.5.8) [71].
For individuals that were only detected once, we considered a minimum range extent of 7 km (centered on the detection site), corresponding to 97% of probability of otter presence from a marking site [34]. This was also applied to individuals captured twice within 1 km. Marking sites of those individuals that were recaptured at least twice at a distance >1 km were interpolated following the main river course, between neighboring capture/recapture sites. Spatial overlap among individual pairs was analyzed through linear regressions where the length of spatial overlap was used as the response variable, while sibship category (i.e., full-sibs, half-sibs, unrelated) and sex pairing (i.e., male–male, male–female, female–female) were included in turn as the explanatory variable. Regression goodness-of-fit was assessed by calculating the coefficient of determination R2.

3. Results

A total of 191 fresh spraints (67 in 2011 and 44 in 2012) were collected at 62 marking sites along 174 km of the Sangro river. A total of 74 spraints from 35 sampling sites (covering 64 km) were successfully genotyped with a mean genotyping success of 31.5% (Figure 2). The success rate was higher for pure anal jelly (79.3%), followed by mixed spraint/jelly (28.0%) and spraints (27.0%). The mean frequencies of ADO and FA among samples were 0.144 and 0.337, respectively (for details on allele frequencies and microsatellite loci performances, see [51]). All loci but Lut453 were polymorphic (range 2–4 alleles). After the Bonferroni correction, all loci except OT17 were at Hardy–Weinberg equilibrium, and only 9 out of 78 comparisons among loci indicated a linkage disequilibrium (see [51] for details).
Overall, 14 individuals were identified from the whole sample. Among these, we identified 8 males and 4 females. For two specimens, PCR results were inconclusive. Based on the prevalence of males in most of the results (8 out of 12), the two unidentified individuals were more likely to be females (F5 and F6). For two otters sharing the same genotype (M2 and F2), we completed four extra PCR replicates and analyzed four extra loci (Lut715, Lut733, Lut782, Lut818). Since the latter were monomorphic, the distinction between the two individuals was inferred by combining molecular sexing, spatial location, and dates of sample collection. The probability of identity critical value was 0.001 (microsatellite loci) for unrelated individuals (PID) and 0.003 (13 loci) for related individuals (PIDsibs).
Individual capture/recaptures rates varied from 1 (n = 2) to 14 (n = 1; Table 1), with a mean of 3.8 captures per individual. Males were recaptured slightly more often than females (mean males = 5.4, mean females = 3.4) but the difference was not significant (t = 1.010, p-value = 0.356). Distances between recaptures are reported in Table 1, whereas potential full-sib families are listed in Table 2. For individuals captured once or recaptured at distances less than 1 km, we added 7 km of river stretch, considering that within this length there is an 89.67% probability of the individual’s occurrence [34].

3.1. Spatial Organisation and Kinship

The longest distances covered by a single individual were 40.5 km for a male (M3; Table 1) that was recaptured 14 times and 11.85 km for a female recaptured 7 times (F2; Table 1). The range of M3 was not the result of a temporal shift, as the same otter was recaptured in 2012 within the same range detected in 2011 (Figure 3). Recaptures of M3 were therefore consistent with the activity of an adult resident male. The longest distance covered during a single night was 14.65 km, recorded for male M7 in August 2012 (Figure 2). For individuals with a longer capture history, we could infer not only a broad temporal overlap with other otters but, in some cases, we also recorded the presence of more individuals during the same night at the same sampling site or a few meters apart (Figure 2, Table S1). This is the case of M1–F2 (full siblings, 3 May 2011 and 5 August 2011), F2–M3 (unrelated, 3 June 2011 and 2 August 2011), M1–M3 (unrelated, 5 August 2011), F3–F4 (unrelated, 28 April 2011), and M3–M4 (full siblings, 23 September 11).
As expected by a polygynous species, several overlaps were observed between male and female ranges (M3–all females, M1–F2, M2–F2, M4–F2) (Figure 2). However, our results also detected both related and unrelated male–male (M1–M2–M3–M4, M8–M2–M4) and female–female (F3–F4–F5–F6) overlaps (Figure 2).
For three individuals (F2, M2, M3), we obtained comparable capture histories distributed over several months, allowing us to infer their activity ranges. F2 continuously marked at least 11.85 km, while M2 and M3 marked at least 29.3 and 40.5 km, respectively. Linear regression fitted between spatial overlap and sibship category indicated full-sibs overlapped significantly more than half-sibs (coefficient = 6262, p = 0.034), while this difference is only close to significance considering unrelated individuals (coefficient = 4833, p = 0.058). In addition, there is no significant difference in spatial overlap between half-sibs and unrelated individuals (coefficient = −1430, p = 0.743). As for sex pairing, male–male pair exhibited a significantly higher overlap than both male–female (coefficient = 8845, p < 0.0001) and female–female (coefficient = 8612, p < 0.0001) pairs, while the latter two did not show any significant difference in their spatial overlap (coefficient = −233, p = 0.987). In particular, the variation in spatial overlap resulted more explained by sex pairing, where the R2 is equal to 0.34, than by sibship category (R2 = 0.08).
To investigate possible mechanisms underlying spatial interactions within and between sexes, all genotypes were tested for sibship using COLONY [70] to identify potential full-sibs families. COLONY takes into account allele frequencies and error rates, using a group approach (i.e., the simultaneous analysis and comparison of the entire sample) to infer full-sibs families. For sibship analysis, we considered the whole sample set. No evidence of inbreeding was found, and 9 out of the 14 genotyped individuals belonged to three full-sib clusters, while five female individuals (F1, F3, F4, F5, and F6) had no full-sibs in the population (Figure 3).
Full-sibs individuals also showed to be half-sibs to other individuals presented in Figure 3. An important aspect to underline is that parent–offspring and full-sibs dyads share the same relatedness coefficient, measuring the dyad overall identity by descendant (IBD). The inability to directly observe otters and to obtain age estimates from the scat samples prevent the ability to infer age-related relationships among the individuals.

3.2. Population Size, Density, and Structure

To estimate population density, we considered 64.75 km of river stretches where we obtained positive genotyping results and added a 7 km buffer at both ends, corresponding to 97% probability of otter’s presence from a sampling site [56], obtaining a total length of 78.75 km. Population size analyses provided an estimate of 12 otters (CI95% = 11–14), corresponding to a mean otter density of 0.152 otters/km of watercourse (CI95% 0.139–0.177). Considering that the hydrographic river network of the area covers five 10 × 10 km grid cells, these values correspond to a density of 2.4 individuals/cell.

4. Discussion

During the two sampling years, we successfully genotyped 14 individual otters along 64 km of the river Sangro, corresponding to 0.152 individuals/km. This value falls within the estimates provided for the European range, spanning from 0.012 otters/km in England to 1.14 otters/km in Germany [49,72,73,74,75]. Specifically, the values from our study were close to those reported by [4] in southern Italy (0.18–0.20 otters/km) and by [38] in Germany (0.21 individuals/km), but lower than those reported by [50] along pond shores in Germany (0.34 to 0.48) or by [76] for the Bohemian forest (0.032 individuals/km). Furthermore, the density of 2.4 individuals/10 × 10 km grid cell can be used for population size and trend estimation according to the European regulation 92/43/EC (Habitat Directive), since reporting cycles are based on number and distribution of 10 × 10 km grid cells occupied by the species. Accordingly, the 3440 grid cells of otter occurrence in Italy reported in the last reporting cycle (i.e., 2013–2018) likely host around 8250 otters, which suggests that the current Italian population is now larger than the minimum viable population size [77].
Additionally, the high number of recaptures and the degree of relatedness of the recaptured individuals provided new relevant information on the density, structure, and social organization of the Eurasian otter metapopulation living in the Sangro river basin. As expected for a polygynous species, we observed ranges of related individuals to partly or totally overlap, and ranges of unrelated males to overlap with those of several females. These results are also congruent with previous studies that reported solitary male otters having extremely large ranges overlapping with several females, while related females share group territories with individual exclusive core areas [49,50,54,72]. However, we also observed large spatial overlaps of both unrelated males and females, and a higher influence of sex-pairing on explaining spatial overlap compared to sibship. This phenomenon was especially marked in males, where male–male overlaps were significantly higher compared to both male–female and female–female ones. These high overlaps among unrelated males have never been reported in previous studies, suggesting an even more flexible social system than previously expected for a solitary and territorial polygynous species.
In fact, we recorded the presence of more individuals during the same night at the same sampling site, or a few meters apart. This was not only the case of both full sibling and unrelated male–female dyads that could indicate the temporary bond of a mother and son (full sibling) or reproductive pair (unrelated male–female), but also the case of unrelated male–male and female–female pairs. We are aware that spatial overlap does not necessarily equal true dyad encounters and that in populations where dispersal is limited, wandering males continue to overlap each other [78]. As interactions form the basis for relationships between individuals and are the foundation for analyzing social structure [77,78,79,80,81], this evidence needs further confirmation, likely by other approaches such as camera trapping or radiotracking [82]. We are also aware that, unless coupled with hormone detection [38,39], noninvasive genetic analyses are unable to provide information about age and reproductive status of individuals. Thus, we cannot exclude that these overlapping individuals were either juveniles or subadults detected during one-way dispersal from their natal area.
Moreover, related females were only detected downstream, where fish abundance is likely higher, although the lack of females upstream could be due to a failure in genotyping spraints in this portion of the study area. As a matter of fact, although in a completely different scenario (Shetlands) [83], male otters were observed to mark/spraint more frequently than females, especially during summer. If seasonality was a cause, a male bias in the sampling could have been caused by the low female marking/sprainting rates. In addition, higher marking frequencies might be related to the higher dispersal movements of males compared to females [43,54]. Another explanation could be inferred from the classical mustelid sociobiology which predicts resource-based female home-ranges [84]. The absence of females in the upper watercourse of the Sangro river could be related to a shortage of food resources in this portion of the study area. Although the lower course of the Sangro river is known to host higher fish diversity compared to upstream (see for example [85]), this latter hypothesis deserves further investigation to assess any differential feeding resource abundance along the Sangro river basin.
Finally, for one female and two male Eurasian otters in the study, we obtained comparable capture histories distributed over several months, which allowed us to infer their range use and size. Specifically, one female was detected leaving spraints along at least 11.85 km of river stretches, whereas two males scent-marked along 29.3 and 40.5 km of river stretches, respectively. These values are coherent with home-range estimates recorded in previous studies, i.e., 12–30 km for female otters and 21–67 km for male otters [27,86]. Moreover, larger distances covered by the males are in accordance with the general pattern observed in polygynous carnivores [87,88,89]. However, these values were larger than those reported for the seasonal home-range extent of the only two individual otters tracked in Italy, which were 20 and 30 km for the female and the male, respectively [27]. In accordance with the resource dispersion hypothesis [83], these larger distances might be either related to the lower availability of feeding resources or to lower densities that characterize the periphery of the range [25].
Since extensive variations in social organization, habits, and spatial distribution have been recorded for several mustelids among different study areas, seasons, and years [27,53,87,90,91,92,93], we are aware that further studies are still needed from different parts of the range to ultimately describe the behavioral ecology of this small and isolated population of Eurasian otters. In addition, the extension of the same approach to the neighboring rivers will likely contribute to evaluating gene flow and degree of isolation among river basins [26,27], as well as the dispersal patterns at the boundary of the Italian range, which were out of the scope of this work. Nevertheless, our combined approach gives the first relevant contribution to the comprehension of range dynamics and social structure of the isolated and endangered population of the Eurasian otter occurring in Italy. We also highlight the noninvasiveness and success of the method that could be suitable for other taxa that are also lacking such data.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14041943/s1, Table S1: Details on dyads that were detected at the same site at least once (F = full-sibs, H = half-sibs, U = unrelated; 1 = male–female couple, 2 = male–male couple, 3 = female–female couple). Full-sibs are defined as progenies having the same male and female parents, while half-sibs are progenies having only one parent in common.

Author Contributions

Conceptualization, A.L. and L.L.; methodology, A.L., L.L. and C.M.; software, L.L., C.M. and M.D.F.; validation, L.L., C.M. and A.L.; formal analysis, L.L.; investigation, L.L.; resources, A.L.; data curation, L.L.; writing—original draft preparation, L.L. and A.L.; writing—review and editing, H.K.; visualization, L.L.; supervision, A.L.; project administration, H.K.; funding acquisition, A.L. and L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Park of Abruzzo Lazio and Molise, project CUP C26C18000900005, to A.L., and by PhD scholarship to L.L.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing research activities.

Acknowledgments

We are grateful to the three anonymous reviewers for their very accurate and detailed comments that greatly contributed to improving the clarity and accuracy of this essay. We are also grateful to Cinzia Sulli, head of the research office of NP Abruzzo, Lazio and Molise, for providing constant support to the fieldwork, and Arturo Leone for his help in implementing new versions of the figures.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Roos, A.; Loy, A.; de Silva, P.; Hajkova, P.; Zemanová, B. Lutra lutra The IUCN Red List of Threatened Species. Version 2015.2. 2015. Available online: www.iucnredlist.org (accessed on 11 July 2015).
  2. Conroy, J.W.H.; Chanin, P.R.F. The Status of the Eurasian Otter (Lutra lutra) in Europe. A Review. In Proceedings of the First Otter Toxicology Conference, Isle of Skye, UK, September 2000; Conroy, J.W.H., Yoxon, P., Gutleb, A.C., Eds.; Journal of the International Otter Survival Fund: Broadford, UK, 2000; p. 28. [Google Scholar]
  3. Cianfrani, C.; Lay, G.L.; Maiorano, L.; Satizábal, H.F.; Loy, A.; Guisan, A. Adapting Global Conservation Strategies to Climate Change at the European Scale: The Otter as a Flagship Species. Biol. Conserv. 2011, 144, 2068–2080. [Google Scholar] [CrossRef]
  4. Prigioni, C.; Remonti, L.; Balestrieri, A.; Sgrosso, S.; Priore, G.; Mucci, N.; Randi, E. Estimation of European Otter (Lutra lutra) Population Size by Fecal DNA Typing in Southern Italy. J. Mammal. 2006, 87, 855–858. [Google Scholar] [CrossRef]
  5. Prigioni, C.; Balestrieri, A.; Remonti, L. Decline and Recovery in Otter Lutra lutra Populations in Italy. Mammal Rev. 2007, 37, 71–79. [Google Scholar] [CrossRef]
  6. Loy, A.; Boitani, L.; Bonesi, L.; Canu, A.; Di Croce, A.; Fiorentino, P.L.; Genovesi, P.; Mattei, L.; Panzacchi, M.; Prigioni, C.; et al. The Italian Action Plan for the Endangered Eurasian Otter Lutra Lutra. Hystrix Ital. J. Mammal. 2010, 21, 19–33. [Google Scholar]
  7. Loy, A.; Bucci, L.; Carranza, M.L.; De Castro, G.; Marzio, D.; Reggiani, P. Survey and Habitat Evaluation for a Peripheral Population of the Eurasian Otter in Italy. IUCN Otter Spec. Group 2004, 21, 1–9. [Google Scholar]
  8. Giovacchini, S.; Marrese, M.; Loy, A. Good News from the South: Filling the Gap between Two Otter Populations in Italy. IUCN Otter Spec. Group 2018, 35, 212–221. [Google Scholar]
  9. Lapini, L.; Bonesi, L. Evidence of the Natural Recovery of the Eurasian Otter in NE Italy. In Proceedings of the 29th European Mustelid Colloquium, Southampton, UK, 3–4 December 2011; pp. 3–4. [Google Scholar]
  10. Righetti, D. Return of the Otter in South Tyrol (NE Italy). In Proceedings of the 11th International Otter Colloquium, Pavia, Italy, 30 August–4 September 2011. [Google Scholar]
  11. Loy, A.; Balestrieri, A.; Bartolomei, R.; Bonesi, L.; Caldarella, M.; De Castro, G.; Salda, D.; Fulco, L.; Fusillo, E.; Gariano, R.; et al. The Eurasian Otter (Lutra lutra) in Italy: Distribution, Trend and Threats. In Proceedings of the European Otter Workshop, Stockholm, Sweden, 8–11 June 2015; IUCN Otter Specialist Group Bulletin. Volume 32, pp. 9–10. [Google Scholar]
  12. Pavanello, M.; Lapini, L.; Kranz, A.; Iordan, F. Rediscovering the Eurasian Otter (Lutra lutra L.) in Friuli Venezia Giulia and Notes on Its Possible Expansion in Northern Italy. IUCN/SCC Otter Spec. Group Bull. 2015, 32, 12–20. [Google Scholar]
  13. Randi, E.; Davoli, F.; Pierpaoli, M.; Pertoldi, C.; Madsen, A.B.; Loeschcke, V. Genetic Structure in Otter (Lutra lutra) Populations in Europe: Implications for Conservation. Anim. Conserv. 2003, 6, 93–100. [Google Scholar] [CrossRef]
  14. Rondinini, C.; Battistoni, A.; Peronace, V.; Teofili, C. Lista Rossa IUCN dei Vertebrati Italiani; Comitato Italiano IUCN e Ministero dell’Ambiente e della Tutela del Territorio e del Mare: Rome, Italy, 2013. [Google Scholar]
  15. Prigioni, C.; Balestrieri, A.; Remonti, L.; Gargaro, A.; Priore, G. Diet of the Eurasian Otter (Lutra lutra) in Relation to Freshwater Habitats and Alien Fish Species in Southern Italy. Ethol. Ecol. Evol. 2006, 18, 307–320. [Google Scholar] [CrossRef]
  16. Remonti, L.; Prigioni, C.; Balestrieri, A.; Sgrosso, S.; Priore, G. Trophic Flexibility of the Otter (Lutra lutra) in Southern Italy. Mamm. Biol. 2008, 73, 293–302. [Google Scholar] [CrossRef]
  17. Smiroldo, G.; Balestrieri, A.; Remonti, L.; Prigioni, C. Seasonal and Habitat-Related Variation of Otter Lutra lutra Diet in a Mediterranean River Catchment (Italy). Folia Zool. 2009, 58, 87–97. [Google Scholar]
  18. Prigioni, C.; Smiroldo, G.; Remonti, L.; Balestrieri, A. Distribution and Diet of Reintroduced Otters (Lutra lutra) on the River Ticino (Northern Italy). Hystrix Ital. J. Mammal. 2009, 20, 4432. [Google Scholar] [CrossRef]
  19. Marcolin, F.; Iordan, F.; Pizzul, E.; Pallavicini, A.; Torboli, V.; Manfrin, C.; Quaglietta, L. Otter Diet and Prey Selection in a Recently Recolonized Area Assessed Using Microscope Analysis and DNA barcoding. Hystrix Ital. J. Mammal. 2020, 31, 64–72. [Google Scholar]
  20. Scorpio, V.; Loy, A.; Di Febbraro, M.; Rizzo, A.; Aucelli, P. Hydromorphology Meets Mammal Ecology: River Morphological Quality, Recent Channel Adjustments and Otter Resilience. River Res. Appl. 2014, 32, 267–279. [Google Scholar] [CrossRef]
  21. Loy, A.; Carranza, M.L.; Cianfrani, C.; D’Alessandro, E.; Bonesi, L.; Di Marzio, P.; Minotti, M.; Reggiani, G. Otter Lutra lutra population expansion: Assessing habitat suitability and connectivity in south-central Italy. Folia Zool. 2009, 58, 309–326. [Google Scholar]
  22. Cianfrani, C.; Le Lay, G.; Hirzel, A.H.; Loy, A. Do habitat Suitability Models Reliably Predict the Recovery Areas of Threatened Species? J. Appl. Ecol. 2010, 47, 421–430. [Google Scholar] [CrossRef]
  23. Carranza, M.L.; D’Alessandro, E.; Saura, S.; Loy, A. Connectivity Providers for Semi-Aquatic Vertebrates: The Case of the Endangered Otter in Italy. Landsc. Ecol. 2012, 27, 281–290. [Google Scholar] [CrossRef]
  24. Carone, M.T.; Guisan, A.; Cianfrani, C.; Simoniello, T.; Loy, A.; Carranza, M.L. A Multi-Temporal Approach to Model Endangered Species Distribution in Europe: The Case of the Eurasian Otter in Italy. Ecol. Model. 2014, 274, 21–28. [Google Scholar]
  25. Marcelli, M.; Fusillo, R. Assessing Range Re-Expansion and Recolonization of Human-Impacted Landscapes by Threatened Species: A Case Study of the Otter (Lutra lutra) in Italy. Biodivers. Conserv. 2009, 18, 2941–2959. [Google Scholar] [CrossRef]
  26. Buglione, M.; Petrelli, S.; Troiano, C.; Notomista, T.; Petrella, A.; De Riso, L.; Poerio, L.; Cascini, V.; Bartolomei, R.; Fulgione, D. Spatial Genetic Structure in the Eurasian Otter (Lutra lutra) Meta-Population from its Core Range in Italy. Contrib. Zool. 2020, 90, 70–92. [Google Scholar] [CrossRef]
  27. Quaglietta, L.; Fusillo, R.; Marcelli, M.; Loy, A.; Boitani, L. First Telemetry Data on Wild Individuals from the Threatened, Isolated Italian Otter (Lutra lutra) Population. Mammalia 2019, 83, 447–452. [Google Scholar] [CrossRef]
  28. Johnson, D.H.; Boitani, L.; Fuller, T.K. Research Techniques in Animal Ecology: Controversies and Consequences. J. Wildl. Manag. 2001, 65, 599. [Google Scholar] [CrossRef]
  29. Fernández-Morán, J.; Saavedra, D.; Manteca-Vilanova, X. Reintroduction of the Eurasian Otter (Lutra lutra) in Northeastern Spain: Trapping, Handling, and Medical Management. J. Zoo Wildl. Med. 2002, 33, 222–227. [Google Scholar] [CrossRef] [PubMed]
  30. Arrendal, J.; Walker, C.W.; Sundqvist, A.K.; Hellborg, L.; Vilà, C. Genetic Evaluation of an Otter Translocation Program. Conserv. Genet. 2004, 5, 79–88. [Google Scholar] [CrossRef]
  31. Tison, J.-L.; Blennow, V.; Palkopoulou, E.; Gustafsson, P.; Roos, A.; Dalén, L. Population Structure and Recent Temporal Changes in Genetic Variation in Eurasian Otters from Sweden. Conserv. Genet. 2015, 16, 371–384. [Google Scholar] [CrossRef]
  32. Hájková, P.; Zemanová, B.; Roche, K.; Hájek, B. An Evaluation of Field and Noninvasive Genetic Methods for Estimating Eurasian Otter Population Size. Conserv. Genet. 2009, 10, 1667–1681. [Google Scholar] [CrossRef]
  33. Stanton, D.; Hobbs, G.I.; Chadwick, E.; Slater, F.M.; Bruford, M.W. Mitochondrial Genetic Diversity and Structure of the European Otter (Lutra lutra) in Britain. Conserv. Genet. 2009, 10, 733–737. [Google Scholar] [CrossRef]
  34. Janssens, X.; Fontaine, M.C.; Michaux, J.R.; Libois, R.; De Kermabon, J.; Defourny, P.; Baret, P.V. Genetic Pattern of the Recent Recovery of European Otters in Southern France. Ecography 2008, 31, 176–186. [Google Scholar] [CrossRef]
  35. Aristizábal Duque, S.L.; Orozco-Jiménez, L.Y.; Zapata-Escobar, C.; Palacio-Baena, J.A. Conservation genetics of otters: Review about the use of non-invasive samples. Therya 2018, 9, 85–93. [Google Scholar] [CrossRef] [Green Version]
  36. Lanszki, J.; Hidas, A.; Szentes, K.; Révay, T.; Lehoczky, I.; Jeney, Z.; Weiss, S. Genetic Structure of Otter (Lutra lutra) Populations from Two Fishpond Systems in Hungary. Mamm. Biol. 2010, 75, 447–450. [Google Scholar] [CrossRef]
  37. Hájková, P.; Pertoldi, C.; Zemanová, B.; Roche, K.; Hájek, B.; Bryja, J.; Zima, J. Genetic structure and evidence for recent population decline in Eurasian otter populations in the Czech and Slovak Republics: Implications for conservation. J. Zool. 2007, 272, 1–9. [Google Scholar] [CrossRef]
  38. Kalz, B.; Jewgenow, K.; Fickel, J. Structure of an Otter (Lutra lutra) Population in Germany—Results of DNA and Hormone Analyses from Faecal Samples. Mamm. Biol. 2006, 71, 321–335. [Google Scholar] [CrossRef]
  39. Janssen, M.H.M.; Hayden, S.; Gilman, C.; Cox, C.L.; Dalton, R.; Niemuller, C.; Spindler, R. Fecal Steroid Analysis to Assess Reproduction and Adrenal Activity in the Spotted-Necked Otter (Lutra maculicollis). Biol. Reprod. 2006, 167. [Google Scholar]
  40. Koelewijn, H.P.; Pérez-Haro, M.; Jansman, H.A.H.; Boerwinkel, M.C.; Bovenschen, J.; Lammertsma, D.R.; Niewold, F.J.J.; Kuiters, A.T. The Reintroduction of the Eurasian Otter (Lutra lutra) into the Netherlands: Hidden Life Revealed by Noninvasive Genetic Monitoring. Conserv. Genet. 2010, 11, 601–614. [Google Scholar] [CrossRef] [Green Version]
  41. Arrendal, J.; Vilà, C.; Björklund, M. Reliability of Noninvasive Genetic Census of Otters Compared to Field Censuses. Conserv. Genet. 2007, 8, 1097–1107. [Google Scholar] [CrossRef]
  42. Ferrando, A.; Lecis, R.; Domingo-Roura, X.; Ponsà, M. Genetic Diversity and Individual Identification of Reintroduced Otters (Lutra lutra) in North-Eastern Spain by DNA Genotyping of Spraints. Conserv. Genet. 2008, 9, 129–139. [Google Scholar] [CrossRef]
  43. Quaglietta, L.; Fonseca, V.C.; Hájková, P.; Mira, A.; Boitani, L. Fine-Scale Population Genetic Structure and Short-Range Sex-Biased Dispersal in a Solitary Carnivore, Lutra lutra. J. Mammal. 2013, 94, 561–571. [Google Scholar] [CrossRef] [Green Version]
  44. Quaglietta, L.; Hájková, P.; Mira, A.; Boitani, L. Eurasian Otter (Lutra lutra) Density Estimate Based on Radio Tracking and Other Data Sources. Mamm. Res. 2015, 60, 127–137. [Google Scholar] [CrossRef] [Green Version]
  45. Vergara, M.; Ruiz-González, A.; de Luzuriaga, J.L.; Gómez-Moliner, B.J. Individual Identification and Distribution Assessment of Otters (Lutra lutra) through Non-Invasive Genetic Sampling: Recovery of an endangered species in the Basque Country (Northern Spain). Mamm. Biol. 2014, 79, 259–267. [Google Scholar] [CrossRef]
  46. Trinca, C.S.; Jaeger, C.F.; Eizirik, E. Molecular Ecology of the Neotropical Otter (Lontra longicaudis): Non-Invasive Sampling Yields Insights into Local Population Dynamics. Biol. J. Linn. Soc. 2013, 109, 932–948. [Google Scholar] [CrossRef] [Green Version]
  47. Ribas, C.; Cunha, H.A.; Damasceno, G.; Magnusson, W.E.; Sole-Cava, A.; Mourão, G. More than meets the Eye: Kinship and Social Organization in Giant Otters (Pteronura brasiliensis). Behav. Ecol. Sociobiol. 2016, 70, 61–72. [Google Scholar] [CrossRef]
  48. Brzeski, K.E.; Gunther, M.S.; Black, J.M. Evaluating River Otter Demography Using Noninvasive Genetic Methods. J. Wildl. Manag. 2013, 77, 1523–1531. [Google Scholar] [CrossRef]
  49. Lampa, S.; Mihoub, J.B.; Gruber, B.; Klenke, R.; Henle, K. Non-Invasive Genetic Mark-Recapture as a Means to Study Population Sizes and Marking Behaviour of the Elusive Eurasian Otter (Lutra lutra). PLoS ONE 2015, 10, e0125684. [Google Scholar] [CrossRef] [PubMed]
  50. Kruuk, H. Otters: Ecology, Behaviour and Conservation; Oxford Oxford University Press: Oxford, UK, 2006. [Google Scholar]
  51. Lerone, L.; Mengoni, C.; Carpaneto, G.M.; Randi, E.; Loy, A. Procedures to Genotype Problematic Non-Invasive Otter (Lutra lutra) Samples. Acta Theriol. 2014, 59, 511–520. [Google Scholar] [CrossRef]
  52. Sutherland, W.J.; Pullin, A.S.; Dolman, P.M.; Knight, T.M. The need for evidence-based conservation. Trends Ecol. Evol. 2004, 19, 305–308. [Google Scholar] [CrossRef]
  53. Ruiz-Olmo, J.; Jiménez, J.; López-Martín, J.M. Radio-Tracking of Otters Lutra lutra in North-Eastern Spain. Lutra 1995, 38, 11–21. [Google Scholar]
  54. Quaglietta, L.; Fonseca, V.C.; Mira, A.; Boitani, L. Sociospatial Organization of a Solitary Carnivore, the Eurasian Otter (Lutra lutra). J. Mammal. 2014, 95, 140–150. [Google Scholar] [CrossRef]
  55. De Castro, G.; Loy, A. Un Nuovo Censimento della Lontra (Lutra Lutra Carnivora Mammalia) nel Fiume Sangro (Abruzzo): Inizia la Ricolonizzazione dell’Italia Centrale? In Proceedings of the 68° Convegno Unione Zoologica Italiana, Lecce, Italy, 24–27 September 2007; p. 105. [Google Scholar]
  56. Hajkova, P.; Zemanova, B.; Bryja, J.; Hajek, B.; Roche, K.; Tkadlec, E.; Zima, J. Factors Affecting Success of PCR Amplification of Microsatellite Loci from Otter Faeces. Mol. Ecol. Notes 2006, 6, 559–562. [Google Scholar] [CrossRef]
  57. Valière, N. GIMLET: A Computer Program for Analysing Genetic Individual Identification Data. Mol. Ecol. Notes 2002, 2, 377–379. [Google Scholar] [CrossRef]
  58. Dallas, J.F.; Conroy, J.W.H.; Green, R.; Jefferies, D.J.; Marshall, F.; Bacon, P.J.; Carss, D.N.; Kruuk, H.; Piertney, S.B.; Racey, P.A. Genetic Diversity in the Eurasian Otter, Lutra lutra, in Scotland. Evidence from Microsatellite Polymorphism. Biol. J. Linn. Soc. 1999, 68, 73–86. [Google Scholar] [CrossRef]
  59. Huang, C.C.; Hsu, Y.C.; Lee, L.L.; Li, S.H. Isolation and Characterization of Tetramicrosatellite DNA Markers in the Eurasian Otter (Lutra lutra). Mol. Ecol. Notes 2005, 5, 314–316. [Google Scholar] [CrossRef]
  60. Mucci, N.; Randi, E. Sex Identification of Eurasian Otter (Lutra lutra) Non-Invasive DNA Samples Using ZFX/ZFY Sequences. Conserv. Genet. 2007, 8, 1479–1482. [Google Scholar] [CrossRef]
  61. Taberlet, P.; Waits, L.P.; Luikart, G. Noninvasive Genetic Sampling: Look Before you Leap. Trends Ecol. Evol. 1999, 14, 323–327. [Google Scholar] [CrossRef]
  62. Miller, C.R.; Joyce, P.; Waits, L.P. Assessing Allelic Dropout and Genotype Reliability Using Maximum Likelihood. Genetics 2002, 160, 357–366. [Google Scholar] [CrossRef]
  63. Peakall, R.O.D.; Smouse, P.E. GENALEX 6: Genetic analysis in Excel. Population Genetic Software for Teaching And Research. Mol. Ecol. Notes 2006, 6, 288–295. [Google Scholar] [CrossRef]
  64. Rohlf, F.J.; Sokal, R.R. Statistical Tables; Macmillan Publishers: New York, NY, USA, 1995. [Google Scholar]
  65. Nei, M. Estimation of Average Heterozygosity and Genetic Distance from a Small Number of Individuals. Genetics 1978, 89, 583–590. [Google Scholar] [CrossRef]
  66. Excoffier, L.; Lischer, H.E. Arlequin suite ver 3.5: A New Series of Programs to Perform Population Genetics Analyses under Linux and Windows. Mol. Ecol. Resour. 2010, 10, 567. [Google Scholar] [CrossRef]
  67. Miller, C.R.; Joyce, P.; Waits, L.P.A. New Method for Estimating the Size of Small Populations from Genetic Mark-Recapture Data: Genetic Mark-Recapture Estimation. Mol. Ecol. 2005, 14, 1991–2005. [Google Scholar] [CrossRef]
  68. Pennell, M.W.; Stansbury, C.R.; Waits, L.P.; Miller, C.R. CAPWIRE: A R Package for Estimating Population Census Size from Non-Invasive Genetic Sampling. Mol. Ecol. Resour. 2012, 13, 154–157. [Google Scholar] [CrossRef]
  69. Blouin, M.S. DNA-Based Methods for Pedigree Reconstruction and Kinship Analysis in Natural Populations. Trends Ecol. Evol. 2003, 18, 503–511. [Google Scholar] [CrossRef]
  70. Wang, J. A New Method for Estimating Effective Population Sizes from a Single Sample of Multilocus Genotypes. Mol. Ecol. 2009, 18, 2148–2164. [Google Scholar] [CrossRef] [PubMed]
  71. Jones, O.R.; Wang, J. Colony: A Program for Parentage and Sibship Inference from Multilocus Genotype Data. Mol. Ecol. Resour. 2010, 10, 551–555. [Google Scholar] [CrossRef] [PubMed]
  72. Kruuk, H.; Moorhouse, A. The Spatial Organization of Otters (Lutra lutra) in Shetland. J. Zool. 1991, 224, 41–57. [Google Scholar] [CrossRef]
  73. Kruuk, H.; Moorhouse, A.; Conroy, J.W.H.; Durbin, L.; Frears, S. An Estimate of Numbers and Habitat Preferences of Otters Lutra lutra in Shetland, UK. Biol. Conserv. 1989, 49, 241–254. [Google Scholar] [CrossRef]
  74. Ansorge, H. Zur Situation Des Eurasischen Fischotters Lutra lutra Linné, 1758 Im Raum Oberlausitz-Sachsen. Säugetierkd. Inf. 1994, 3, 617–622. [Google Scholar]
  75. Hauer, S.; Ansorge, H.; Zinke, O. Mortality Patterns of Otters (Lutra lutra) from Eastern Germany. J. Zool. 2002, 256, 361–368. [Google Scholar] [CrossRef]
  76. Martin, E.A.; Heurich, M.; Müller, J.; Bufka, L.; Bubliy, O.; Fickel, J. Genetic Variability and Size Estimates of the Eurasian Otter (Lutra lutra) Population in the Bohemian Forest Ecosystem. Mamm. Biol. 2017, 86, 42–47. [Google Scholar] [CrossRef]
  77. Loy, A.; Diamente, S.; Di Febbraro, M. Population Viability Analysis of the Italian Otter Population. In Book of Abstracts, Proceedings of the 8th European Congress of Mammalogy, Warsaw, Poland, 23–27 September 2019; Borowski, Z., Ed.; Convention+ Spółka z Ograniczoną Odpowiedzialnością: Warsaw, Poland, 2019; Volume 175. [Google Scholar]
  78. Vander Wal, E.; Laforge, M.P.; McLoughlin, P.D. Density Dependence in Social Behaviour: Home Range Overlap and Density Interacts to Affect Conspecific Encounter Rates in a Gregarious Ungulate. Behav. Ecol. Sociobiol. 2014, 68, 383–390. [Google Scholar] [CrossRef]
  79. Hinde, R.A. Interactions, Relationships and Social Structure. Man 1976, 11, 1–17. [Google Scholar] [CrossRef]
  80. Whitehead, H. Analysing animal social structure. Anim. Behav. 1997, 53, 1053–1067. [Google Scholar] [CrossRef] [Green Version]
  81. Wronski, T.; Apio, A. Home-Range Overlap, Social Vicinity and Agonistic Interactions Denoting Matrilineal Organisation in Bushbuck, Tragelaphus scriptus. Behav. Ecol. Sociobiol. 2006, 59, 819–828. [Google Scholar] [CrossRef]
  82. Green, J.; Green, R. A Radio-Tracking Survey of Otters Lutra lutra on a Perthshire River System. Lutra 1984, 27, 85–145. [Google Scholar]
  83. Kruuk, H. Hunter and Hunted: Relationships between Carnivores and People; Cambridge University Press: New York, NY, USA, 2002. [Google Scholar]
  84. Johnson, D.D.P.; Kays, R.; Blackwell, P.G.; Macdonald, D.W. Does the Resource Dispersion Hypothesis Explain Group Living? Trends Ecol. Evol. 2002, 17, 563–570. [Google Scholar] [CrossRef]
  85. Askeyev, A.; Askeyev, O.; Yanybaev, N.; Askeyev, I.; Monakhov, S.; Marić, S.; Hulsman, K. River Fish Assemblages along an Elevation Gradient in the Eastern Extremity of Europe. Environ. Biol. Fishes 2017, 100, 585–596. [Google Scholar] [CrossRef]
  86. Durbin, L.S. Some Changes in the Habitat Use of a Free-ranging Female Otter Lutra lutra during Breeding. J. Zool. 1996, 240, 761–764. [Google Scholar] [CrossRef]
  87. Clutton-Brock, T.H. Review Lecture: Mammalian mating systems. Proc. R. Soc. Lond. B Boil. Sci. 1989, 236, 339–372. [Google Scholar]
  88. Macdonald, D.W. The Ecology of Carnivore Social Behaviour. Nature 1983, 301, 379–384. [Google Scholar] [CrossRef]
  89. Sandell, M. The Mating Tactics and Spacing Patterns of Solitary Carnivores. In Carnivore Behavior, Ecology and Evolution; Gittleman, J.L., Ed.; Springer: Boston, MA, USA, 1989; pp. 164–182. [Google Scholar]
  90. Saavedra, D. Reintroduction of the Eurasian Otter (Lutra Lutra) in Muga and Fluvià Basins (North-Eastern Spain): Viability, Development, Monitoring and Trends of the New Population. Ph.D. Thesis, University of Girona, Girona, Spain, 2002. [Google Scholar]
  91. Carter, S.K.; Rosas, F.C.W. Biology and Conservation of the Giant Otter Pteronura brasiliensis. Mammal Rev. 1997, 27, 1–26. [Google Scholar] [CrossRef]
  92. Ornocker, M.G.; Messick, J.P.; Melquist, W.E. Spatial Strategies in Three Species of Mustelidae. Acta Zool. Fenn. 1983, 174, 185–188. [Google Scholar]
  93. Reid, D.G.; Code, T.E.; Reid, A.C.H.; Herrero, S.M. Spacing, Movements, and Habitat Selection of the River Otter in Boreal Alberta. Can. J. Zool. 1994, 72, 1314–1324. [Google Scholar] [CrossRef]
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Figure 1. Location of the study area within the otter range in Italy (grey) and distribution of the 62 sampling sites (green circles) surveyed along 174 km of the river Sangro in 2011 and 2012.
Figure 1. Location of the study area within the otter range in Italy (grey) and distribution of the 62 sampling sites (green circles) surveyed along 174 km of the river Sangro in 2011 and 2012.
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Figure 2. Pattern of spatial overlap among individuals genotyped in 2011 and 2012. Dotted lines indicate family groups (see text).
Figure 2. Pattern of spatial overlap among individuals genotyped in 2011 and 2012. Dotted lines indicate family groups (see text).
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Figure 3. Graphic representation of relationships obtained from COLONY: full-sibs are shown above the diagonal (orange diamonds), half-sibs below the diagonal (green triangles). As an example, M1, M2, and F2 are full-sibs, whereas they are half-sib with M5, M7, and F4.
Figure 3. Graphic representation of relationships obtained from COLONY: full-sibs are shown above the diagonal (orange diamonds), half-sibs below the diagonal (green triangles). As an example, M1, M2, and F2 are full-sibs, whereas they are half-sib with M5, M7, and F4.
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Table
Table 1. Details on captures/recaptures (N) of otters sampled in the study area during the sampling season 2011. For individuals recaptured at distances less than 1 km we added 7 km of river stretch where the probability of occurrence of the same individual is about 90% [34].
Table 1. Details on captures/recaptures (N) of otters sampled in the study area during the sampling season 2011. For individuals recaptured at distances less than 1 km we added 7 km of river stretch where the probability of occurrence of the same individual is about 90% [34].
CodeFirst CaptureLast RecaptureN. Captures/RecapturesRiver Length Covered (M)
M13 June 20115 August 20112650 (7650 corrected)
M23 June 201116 September 2011729,300
M35 April 201123 September 20111440,500
M415 September 201123 September 2011229,250
M818 September 201121 September 2011225,950
F12 May 20116 May 2011225 (7025 corrected)
F23 June 201121 September 2011711,850
F328 April 201115 July 201144500
F428 April 201121 August 201125550
F5 likely female22 July 2011-17000 (corrected)
F6 likely female24 September 2011-17000 (corrected)
Table
Table 2. Each row represents a potential full-sib family. The inclusive probability, Prob (Inc.), is the probability that all individuals listed on the family row are full-sibs (i.e., share both parents), while the exclusive probability, Prob (Exc.), is the probability that all individuals of the full-sib family and no other individuals are full-sibs with this family.
Table 2. Each row represents a potential full-sib family. The inclusive probability, Prob (Inc.), is the probability that all individuals listed on the family row are full-sibs (i.e., share both parents), while the exclusive probability, Prob (Exc.), is the probability that all individuals of the full-sib family and no other individuals are full-sibs with this family.
FullSibIndexProb (Inc.)Prob (Exc.)Member 1Member 2Member 3Member 4
20.98460.983M1F2M2
40.99860.9986M3M4M6M8
50.9950.995M5M7
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Lerone, L.; Mengoni, C.; Di Febbraro, M.; Krupa, H.; Loy, A. A Noninvasive Genetic Insight into the Spatial and Social Organization of an Endangered Population of the Eurasian Otter (Lutra lutra, Mustelidae, Carnivora). Sustainability 2022, 14, 1943. https://doi.org/10.3390/su14041943

AMA Style

Lerone L, Mengoni C, Di Febbraro M, Krupa H, Loy A. A Noninvasive Genetic Insight into the Spatial and Social Organization of an Endangered Population of the Eurasian Otter (Lutra lutra, Mustelidae, Carnivora). Sustainability. 2022; 14(4):1943. https://doi.org/10.3390/su14041943

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Lerone, Laura, Chiara Mengoni, Mirko Di Febbraro, Hannah Krupa, and Anna Loy. 2022. "A Noninvasive Genetic Insight into the Spatial and Social Organization of an Endangered Population of the Eurasian Otter (Lutra lutra, Mustelidae, Carnivora)" Sustainability 14, no. 4: 1943. https://doi.org/10.3390/su14041943

APA Style

Lerone, L., Mengoni, C., Di Febbraro, M., Krupa, H., & Loy, A. (2022). A Noninvasive Genetic Insight into the Spatial and Social Organization of an Endangered Population of the Eurasian Otter (Lutra lutra, Mustelidae, Carnivora). Sustainability, 14(4), 1943. https://doi.org/10.3390/su14041943

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