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191 results for “Otter”
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).
Data and R code used for the GLMM and NBDA analyses in 'Captive Asian short-clawed otters (Aonyx cinereus) learn to exploit unfamiliar natural prey'
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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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Outputs of current speed and sea otter abundance models in Glacier Bay, Alaska
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Data from: Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis)
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Archaeological mitogenomes illuminate the historical ecology of sea otters (Enhydra lutris) and the viability of reintroduction
<p class="CxSpFirst">Genetic analyses are an important contribution to wildlife reintroductions, particularly in the modern context of extirpations and ecological destruction. To address the complex historical ecology of the sea otter (<i>Enhydra lutris</i>) and its failed 1970s reintroduction to coastal Oregon, we compared mitochondrial genomes of pre-extirpation Oregon sea otters to extant and historical populations across the range. We sequenced the first complete ancient mitogenomes from archaeological Oregon sea otter dentine and historical sea otter dental calculus. Archaeological Oregon sea otters (N=20) represent ten haplotypes, which cluster with haplotypes from Alaska, Washington, and British Columbia, and exhibit a clear division from California haplotypes. Our results suggest that extant northern populations are appropriate for future reintroduction efforts. This project demonstrates the feasibility of mitogenome capture and sequencing from non-human dental calculus and the diverse applications of ancient DNA analyses to pressing ecological and conservation topics and the management of at-risk/extirpated species.</p>
Life underground by Tom Otterness
This bronze sculpture is part of a series called [Life underground](https://en.wikipedia.org/wiki/Life_Underground) by Tom Otterness, which you can find in the NYC subway. I scanned another one a while ago: [https://skfb.ly/FW6B](https://skfb.ly/FW6B) Created with Polycam's photo mode. Source: Objaverse 1.0 / Sketchfab
Fig. 2 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 2. General diel activity of otters in the study area.
Fig. 1 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 1. The study area.
Fig. 4 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 4. Otter recordings, correlated with local time and day-night graph.
Metabarcoding to decipher the fish diet of Eurasian otters and seals
<p><span>Long-lived top predators shape biodiversity structure in their ecosystems and predator-prey interactions are critical in decoding how communities function. </span><span>Studies on the foraging ecology of seals and Eurasian otters in Western Europe are outdated and most studies solely performed traditional hard part analysis. </span><span>Molecular metabarcoding can be used as an innovative non-invasive diet analysis tool, which has proven efficient and complementary to hard part analysis, however lacking application in the </span><span>wider North Sea area. In this study, DNA from digesta, collected between 2014-2020, were used to identify fish species in the diet of 47 Eurasian otters, 54 harbour seals and 21 grey seals by applying a next-generation metabarcoding approach. A newly designed 16S rRNA primer, providing the best coverage of >130 local marine and freshwater fish species, was used to amplify prey DNA from seal scats and otter gut content sampled from the North Sea and regional freshwater bodies. Frequent fish species included</span><span> </span><span>tench</span><span>, ninespine stickleback and white bream in otters; hooknose and common roach in grey seals and Pleuronectidae and sand gobies in harbour seals. Bipartite network analysis showed a strong overlap of harbour and grey seal diets. Otter diet intersected with both seal species in terms of freshwater species. </span><span>This study provides new knowledge about dietary composition and community assemblage of fish prey in otters and seals in the North Sea and regional freshwaters, and a new molecular tool to elucidate predator-prey interactions and interspecies competition in complex and changing ecosystems under pressure from anthropogenic activities.</span></p>
Figure 1 in Characterization of shelters of the giant otter (Pteronura brasiliensis, Mammalia, Carnivora, Mustelidae) in the pantanal wetlands, state of Mato Grosso, Brazil
Figure 1. Demarcation of shelters (dens and campsites) and latrines of giant otter in Espírito Santo Creek, Natural Heritage Private Reserve, SESC Pantanal, municipality of Barão de Melgaço, northern portion of Pantanal, state of Mato Grosso, Brazil.
Fig. 2 in Biological Data From Post Mortem Analysis Of Otters In Hungary
Fig. 2. Distribution of otter stomach contents according to weight categories in Hungary
Fig. 1 in Biological Data From Post Mortem Analysis Of Otters In Hungary
Fig. 1. Location of otter carcasses collected in different regions of Hungary
Fig. 1 in Testing microsatellite loci and preliminary genetic study for Eurasian otter in South Korea
Fig. 1. Spraints collection sites along Ungokcheon Stream, Bonghwa-gun, Gyeongsangbuk-do.
Otter qPCR Data at SAFE
<b>Description: </b><p>All data collected during my project at the SAFE project, March-April 2017. This document contains metadata that should summarise all data collected.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/166"><b>Using environmental DNA (eDNA) as a tool for monitoring the biodiversity of tropical otter species</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=13">here</a></p><p><b>Data worksheets: </b>There are 2 data worksheets in this dataset:</p><ol><li><p><b>eDNA</b> (Worksheet eDNA)</p><p>Dimensions: 66 rows by 33 columns</p><p>Description: eDNA' contains all data related to the qPCR elements of this project</p><p>Fields: </p><ul><li><b>Date</b>: Date Collected (Field type: Date)</li><li><b>Session</b>: Morning or Evening (Field type: Categorical)</li><li><b>Site</b>: Catchment Site; indicates metres upriver from SAFE Project hydrology datalogger (Field type: ID)</li><li><b>Code</b>: Unique Code given to each samplea and subsample (1a, 1b, 1c…) (Field type: ID)</li><li><b>Location</b>: Riparian transect sample collected from (Field type: Location)</li><li><b>Time_Start</b>: Time arrived at site (Field type: Time)</li><li><b>Time_Finish</b>: Time left site (Field type: Time)</li><li><b>eDNA_Start</b>: Sample collection starting time (Field type: Time)</li><li><b>eDNA_Stop</b>: Sample collection ending time (Field type: Time)</li><li><b>T_w</b>: Water Temperature at time of sample (Field type: Numeric)</li><li><b>T_a</b>: Air Temperature at time of sample (Field type: Numeric)</li><li><b>pH</b>: pH of surface water (Field type: Numeric)</li><li><b>R_H</b>: Relative humidty at site (Field type: Numeric)</li><li><b>Lux</b>: Lux score at time of sample (Field type: Numeric)</li><li><b>Precip</b>: Precipitation Present (Yes or No) (Field type: Categorical)</li><li><b>Shade</b>: Presence of shade during collection (Field type: Categorical)</li><li><b>Leaf_Litter</b>: Presence of leaf litter during collection (Field type: Categorical)</li><li><b>Substrate</b>: Type of substrate (Field type: Categorical)</li><li><b>Time1</b>: Time taken for "bottle" to travel River_dist; used to calculate flow rate (Field type: Numeric)</li><li><b>Time2</b>: Time taken for "bottle" to travel River_dist; used to calculate flow rate (Field type: Numeric)</li><li><b>Time3</b>: Time taken for "bottle" to travel River_dist; used to calculate flow rate (Field type: Numeric)</li><li><b>TimeAv</b>: Average time taken for "bottle" to travel River_dist; used to calculate flow rate (Field type: Numeric)</li><li><b>Depth1</b>: Depth of sample (Field type: Numeric)</li><li><b>Depth2</b>: Depth of sample (Field type: Numeric)</li><li><b>Depth3</b>: Depth of sample (Field type: Numeric)</li><li><b>DepthAv</b>: Average_depth of sample (Field type: Numeric)</li><li><b>River_Dist</b>: Distance "bottle" travelled at sample site ; used to calculate flow rate (Field type: Numeric)</li><li><b>Flow</b>: Calculated flow (speed = distance/ time) (Field type: Numeric)</li><li><b>Presence</b>: Presence of otters detected (to a level deemed above background noise on qPCR) (Field type: Categorical)</li><li><b>N. Wells Pos</b>: Presence of otters detected (to a level deemed above background noise on qPCR) (Field type: Abundance)</li><li><b>Positive NTC</b>: Number of positive negative controls out of 12 (Field type: Numeric)</li><li><b>Notes</b>: Any important notes during sampling (Field type: Comments)</li></ul><br></li><li><p><b>Traditional</b> (Worksheet Traditional)</p><p>Dimensions: 122 rows by 12 columns</p><p>Description: Traditional' contains data collected using traditional surveys</p><p>Fields: </p><ul><li><b>Location</b>: Riparian transect sample collected from (Field type: Location)</li><li><b>Session</b>: Morning or Evening (Field type: Categorical)</li><li><b>Date</b>: Date of sampling (Field type: Date)</li><li><b>Time</b>: Time of sampling (Field type: Time)</li><li><b>Site</b>: Catchment Site; indicates metres upriver from SAFE Project hydrology datalogger (Field type: ID)</li><li><b>Otter</b>: Otter observed at site? 0=no, 1=yes (Field type: Abundance)</li><li><b>Spraint</b>: Otter faeces at site? 0=no, 1=yes (Field type: Abundance)</li><li><b>Den</b>: Den present at site? 0=no, 1=yes (Field type: Abundance)</li><li><b>Footprints</b>: Footprints at site? 0=no, 1=yes (Field type: Abundance)</li><li><b>Remains</b>: Remains of prey present? 0=no, 1=yes (Field type: Abundance)</li><li><b>Notes </b>: Any additional notes deemed important (Field type: Comments)</li></ul><br></li></ol><p><b>Date range: </b>2017-03-14 to 2017-04-11</p><p><b>Latitudinal extent: </b>4.6508 to 4.7255</p><p><b>Longitudinal extent: </b>117.5765 to 117.5980</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Carnivora<br> -  -  -  - Mustelidae<br></div><p></p>
Data from: Aquatic adaptation and depleted diversity: a deep dive into the genomes of the sea otter and giant otter
Despite its recent invasion into the marine realm, the sea otter (Enhydra lutris) has evolved a suite of adaptations for life in cold coastal waters, including limb modifications and dense insulating fur. This uniquely dense coat led to the near-extinction of sea otters during the 18th-20th century fur trade and an extreme population bottleneck. We used the de novo genome of the southern sea otter (E. l. nereis) to reconstruct its evolutionary history, identify genes influencing aquatic adaptation, and detect signals of population bottlenecks. We compared the genome of the southern sea otter to the tropical freshwater-living giant otter (Pteronura brasiliensis) to assess common and divergent genomic trends between otter species, and to the closely related northern sea otter (E. l. kenyoni) to uncover population-level trends. We found signals of positive selection in genes related to aquatic adaptations, particularly limb development and polygenic selection on genes related to hair follicle development. We found extensive pseudogenization of olfactory receptor genes in both the sea otter and giant otter lineages, consistent with patterns of sensory gene loss in other aquatic mammals. At the population level, the southern sea otter and the northern sea otter showed extremely low genomic diversity, signals of recent inbreeding, and demographic histories marked by population declines. These declines pre-date the fur trade and appear to have resulted in an increase in putatively deleterious variants that could impact the future recovery of the sea otter.
Fig. 1 in Diet of the smooth-coated otter Lutrogale perspicillata (Geoffroy, 1826) at natural and modified sites in Singapore
Fig. 1. Location of the four study sites along the northern shore of Singapore.
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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