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47 results for “Lutra lutra”
FIG. 3 in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 3. — Évolution du nombre de loutres capturées en Belgique durant les mois de novembre entre 1889 et 1921.
FIG. 5 in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 5. — Répartition des fréquences d'apparition des individus au sein des captures en fonction de leur degré de maturité sexuelle. Abréviations: A, adulte; J, juvénile; NA, information non disponible.
FIG. 7 in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 7. — Variations inter-mensuelles des captures (une capture peut concerner plusieurs individus). NA, information non disponible.
FIG. 4 in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 4. — Répartition des fréquences d'apparition des individus au sein des captures en fonction de leur sexe. F, femelles; M, mâles; NA, information non disponible.
FIG. 6 in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 6. — Fréquence d'apparition des lieux de captures (une capture peut concerner plusieurs individus. NA, information non disponible).
FIG. 11. — Capture d in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 11. — Capture d'une loutre mâle de 6 kg prise au piège le 27 février 1942 par le garde Waldor Magerat à Lessines (province du Hainaut) (Anonyme 1942: 38).
FIG. 8 in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 8. — Fréquence d'apparition des techniques de captures (une capture peut concerner plusieurs individus).
Fig. 2 in Diet Composition Of Otters (Lutra Lutra L.) Living On Small Watercourses In Southwestern Hungary
Fig. 2. The diet pattern of otters living by streams and channels in the Dráva region. For locations (W1–5) see Fig. 1, n = number of spraint samples
Fig. 1 in Diet Composition Of Otters (Lutra Lutra L.) Living On Small Watercourses In Southwestern Hungary
Fig. 1. Locality of the small watercourses studied in the Drava region. 1 = Dombó-channel (W1, Gyékényes), 2 = Dombó-channel (W2, Berzence), 3 = Babócsai stream (W3, Babócsa), 4 = Barcs-Kom-
Fig. 3 in Diet Composition Of Otters (Lutra Lutra L.) Living On Small Watercourses In Southwestern Hungary
Fig. 3. Percentage biomass consumption (mean±SE) of fish prey in the diet of otters living by streams and channels, on the basis of fish weight (a) and guild (b). Fish guilds: RE – reophilic or flow preferring, EU – eurytopic or tolerant for rivers and stagnant waters and ST – stagnophilic or stagnant water
Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed through DNA metabarcoding and morphological analysis of prey remains
<p>Eurasian otters are apex predators of freshwater ecosystems and a recovering species across much of their European range; investigating the dietary variation of this predator over time and space therefore provides opportunities to identify changes in freshwater trophic interactions and factors influencing the conservation of otter populations. Here we sampled faeces from 300 dead otters across England and Wales between 2007 and 2016, conducting both morphological analysis of prey remains and dietary DNA metabarcoding. Comparison of these methods showed that greater taxonomic resolution and breadth could be achieved using DNA metabarcoding but combining data from both methodologies gave the most comprehensive dietary description. All otter demographics exploited a broad range of taxa and variation likely reflected changes in prey distributions and availability across the landscape. This study provides novel insights into the trophic generalism and adaptability of otters across Britain, which is likely to have aided their recent population recovery, and may increase their resilience to future environmental changes.</p>
figure 1 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 1 The area surveyed for collection of otter samples (40° 40' N, 39° 37' N). Red spots indicate the location of the collected samples. The blue lines highlight the main rivers (order 1) and their tributaries (order 2, 3 and 4 according to waterway hierarchy). The continuous red lines represent regional boundaries. In the inset, the current otter distribution (inferred from Balestrieri et al., 2016, modified) is reported in orange and the study area is defined by the black bold square.
figure 4 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 4 Principal Component Analysis (pca) performed on microsatellite genotypes (dots). Circles show the well-defined spatial groups. A) pca according to the belonging of genotypes to the six river basins: the Cilento basin (green dots); the Agri basin (pink dots); the Sinni basin (blue dots); the Lao basin (red dots); the Basento basin (orange dots); the Abatemarco basin (violet dots); black dots indicate the samples outside of the main river basins. Dashed line indicates geographically contiguous but genetically different genotypes. B) pca according to clusters inferred by STRUCTURE: genotypes assigned unambiguously to K2 (green dots), to K3 (yellow dots), to K5 (violet dots). Grey dots represent samples with mixed genotypes assignable to K1 and K4.
figure 3 in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 3 Genetic structure and distribution of the Italian otter genotypes in the study area. A) Estimated population structure based on the analysis of 11 microsatellite loci according to STRUCTURE (K = 5). Each bar represents a sample analysed. B) Geographic visualisation of genotypes in the study area performed using QGIS 3.4.1 software with base layers acquired from http://www.pnc.miniambiente. it/. Each circle represents a sample analysed. The colours indicate the percentage of assignment of an individual to each cluster: in blue, K1; in green, K2; in orange, K3; in red, K4; in violet, K5. The bold blue lines highlight the main rivers, while the tiny blue lines show all other waterways.
figure 6 Mantel test for A in Spatial genetic structure in the Eurasian otter (Lutra lutra) meta-population from its core range in Italy
figure 6 Mantel test for A) the correlation between geographic distance (GGDsq) and genetic distance (LinGD) (Rxy = 0.264, P = 0.0001) and for B) the correlation between resistance distance (a measure of ecological distance) (ECO500) and LinGD (Rxy = 0.217, P = 0.0001).
Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed through DNA metabarcoding and morphological analysis of prey remains
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FIG. 9 in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 9. — Répartition de la loutre en Belgique entre 1898 et 1950, suivant les données de capture.
FIG. 10 in La loutre (Lutra lutra Linnaeus, 1758) en Belgique: une espèce mal-aimée et malmenée (19 -début 20 siècles)
FIG. 10. — Piège à loutre (Anonyme 1937).
Spraints demonstrate small population size and reliance on fishponds for Eurasian otter (Lutra lutra) in Hong Kong
<p><span>Lack of data on population sizes and resource requirements are major impediments to the effective conservation of rare species globally. The conservation of the Eurasian otter (Lutra lutra) in Hong Kong reflects many of these key challenges for elusive and difficult-to-study mammals. It is a rare carnivore that has narrowly escaped extirpation, now surviving within a human-dominated environment. Using sign surveys and spraint analysis, we recorded only 40 fresh spraints from 246 otter signs locations, over four months of intensive sampling across two years. Records were restricted to the Mai Po wetlands, confirming this as the core area for Hong Kong's otter population. Molecular analysis and microsatellite genotyping identified a minimum of seven individuals, two pairs of which were likely related. The genetic and sign data together strongly indicate a small population. Fish dominated the otter diet, highlighting the importance of fishpond habitats as a premium foraging resource. Given the rapid changes surrounding the Mai Po area (especially the new Northern Metropolis Development Strategy), maintaining quality and connected habitats, in addition to sustaining commercial fishponds will be key to otter recovery and long-term population viability in Hong Kong.</span></p>
Country‐wide genetic monitoring over 21 years reveals lag in genetic recovery despite spatial connectivity in an expanding carnivore (Eurasian otter, Lutra lutra) population
<p>Numerous terrestrial mammal species have experienced extensive population declines during past centuries, due largely to anthropogenic pressures. For some species, including the Eurasian otter (<em>Lutra lutra</em>), environmental and legal protection has more recently led to population growth and recolonisation of parts of their historic ranges. While heralded as conservation successes, only a few such recoveries have been examined from a genetic perspective, i.e. whether genetic variability and connectivity have been restored. We here use large-scale and long-term genetic monitoring data from UK otters, whose population underwent a well-documented population decline between the 1950s to 1970s, to explore the dynamics of a population re-expansion over a 21-year period. We genotyped otters from across Wales and England at five time points between 1994 and 2014 using 15 microsatellite loci. We used this combination of long-term temporal and large-scale spatial sampling to evaluate 3 hypotheses relating to genetic recovery; that (i) gene flow between sub-populations would increase over time, (ii) genetic diversity of previously isolated populations would increase, and that (iii) genetic structuring would weaken over time. Although we found an increase in inter-regional gene flow and admixture levels among subpopulations, there was no significant temporal change in either heterozygosity or allelic richness. Genetic structuring among the main sub-populations hence remained strong and showed a clear historical continuity. These findings highlight an underappreciated aspect of population recovery of endangered species, that genetic recovery may often lag behind the processes of spatial and demographic recovery. In other words, the restoration of physical connectivity of populations does not necessarily lead to genetic connectivity. Our findings emphasise the need for genetic data as an integral part of conservation monitoring, to enable the potential vulnerability of populations to be evaluated.</p>
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