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opencc-by-4.0Apr 2021View details →

Fig. 1. Phylogenetic relationships between 74 in Prevalence and molecular characterization of novel species of the Diplomonad genus Octomitus (Diplomonadida: Giardiinae) from wildlife in a New York watershed

Fig. 1. Phylogenetic relationships between 74 sequences of Octomitus representing 14 genotypes estimated by maximum likelihood analysis. The GTR + I + G model (gamma shape = 0.338, prop. invariable sites = 0.619) was chosen by jModelTest2 to be the best-fitting evolutionary model. Branches with less than 70% bootstrap support were not considered statistically robust and were collapsed during manual editing of the visualization. Inset: ML phylogeny computed from Octomitus genotypes aligned with the homologous region of available Diplomonad 18S rDNA sequences from Giardia, Spironucleus, Hexamita, Trimitus, and Enteromonas.

opencc-by-4.0Aug 2021View details →

Fig. 6 in Whale lice (Isocyamus deltobranchium & Isocyamus delphinii; Cyamidae) prevalence in odontocetes off the German and Dutch coasts - morphological and molecular characterization and health implications

Fig. 6. Intralesional whale lice (arrow) in lesion with mild hyperplasia of the adjacent epidermis and granulation tissue in the superficial dermis (scale bar 2 mm).

opencc-by-4.0Aug 2024View details →

Fig. 2 in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding

Fig. 2. Nematode component community in winter with a) the number of nematode taxa detected at each study area and b) the number of nematode taxa shared among study areas.

opencc-by-4.0Aug 2024View details →

Fig. 3 in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding

Fig. 3. Prevalence in each study area of the six most common nematodes detected. Whiskers indicate 95% confidence intervals.

opencc-by-4.0Aug 2024View details →

Fig. 1. A in Elucidating nematode diversity and prevalence in moose across a wide latitudinal gradient using DNA metabarcoding

Fig. 1. A map showing the distribution of the five study areas across Norway ranging from 59.6◦N to 70.5◦N.

Fig. 4 in Prevalence and diversity of parasitic bird lice (Insecta: Psocodea) in northeast Arkansas

Fig. 4. Phylogeny of lice in the genera Myrsidea (a) and Brueelia (b) based on a concatenated alignment of cox1 and EF1-α sequences. Bootstrap values are located above the associated branches. Only values>50% are shown. Novel samples are labeled with the host species name followed by a 7-digit extraction code. All other ingroup samples were obtained from NCBI GenBank and are labeled with host species names. Outgroups are labeled with genus of louse followed by host species.

Fig. 3 in Prevalence and diversity of parasitic bird lice (Insecta: Psocodea) in northeast Arkansas

Fig. 3. Prevalence of lice, prevalence of mites, and co-occurrence of lice and mites recovered from different families of birds. Parentheses next to family names indicate sample sizes and lines on the bar plots indicate standard error. Significant p-values for chi-square and Fisher's exact tests are indicated by the asterisk to the left of family names.

Fig. 2 in Prevalence and diversity of parasitic bird lice (Insecta: Psocodea) in northeast Arkansas

Fig. 2. Prevalence, mean intensity, and mean abundance among lice from hosts in the family Turdidae (a) and Parulidae (b). Lines on the bar plots indicate 95% confidence intervals. Parentheses next to species names indicate sample sizes. Phylogenies are cladograms generated from distributions of trees from birdtree.org.

Fig. 1 in Prevalence and diversity of parasitic bird lice (Insecta: Psocodea) in northeast Arkansas

Fig. 1. The diversity of louse genera collected from 28 families of birds. Colors associated with each louse genus are indicated in the right-side legend. The numerical values indicate percentages. The sample size is indicated by the parenthesize to the right of genus names. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 9. Size differences between infected and non-infected cormorants. The black line represents the median length (a and b) and median weight (c). The grey box represents the middle 50% of the data (n = 65).

Fig. 7 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 7. Size differences of cormorants between Kustavi and Airisto in terms of body length and body weight (n = 65). The black line represents the median length and weight. The grey box represents the middle 50% of the data (n = 65).

Fig. 6 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 6. Left: Body length of herring in the Archipelago Sea (n = 1167) and the Bothnian Sea (n = 1528) in 2018 and in the infected and non-infected herring (n = 7002). The black line represents the median length, and the grey box is the middle 50% of the data.

Fig. 2 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 2. Mean annual salinity (PSU) and temperature (T, ◦C) of the winter months (January–April) in the Bothnian Sea at 0–50 m depth during 1980–2021. Data from ICES Oceanographic dataset, 2021. ICES, Copenhagen.

Fig. 8 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 8. Size differences of cormorants between sexes (a, b, and c) and adults and juveniles (d, e, and f). The black line represents the median length (a, b, d, and e) and median weight (c and f). The grey box represents the middle 50% of the data (n = 65).

Fig. 1 in Trichinella T9 in wild bears in Japan: Prevalence, species/genotype identification, and public health implications

Fig. 1. Map of Japan (A) illustrating the bear capture sites in this study (B). The red shapes indicate the capture sites for brown bears (Ursus arctos) and blue for Japanese black bears (Ursus thibetanus japonicus). The filled shapes indicate the locations of Trichinella-positive animals: A, Numata, Sorachi Subprefecture; B, Shimamaki, Shiribeshi Subprefecture; and C, Kaminokuni, Hiyama Subprefecture; D, Shiwa, Iwate Prefecture. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 3. Acanthocephala parasites in the body cavity of a herring (left; the red circle indicates the position of worms) and on the inner surface of the intestine of a cormorant (right). The upper photo indicates the size of parasites compared to a match (photos: J. Sahlst´en). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 1. Map showing the sampling sites in the northern Baltic Sea: A = Archipelago Sea, B = Uusikaupunki, C = Merikarvia. The sampling locations in region A: 1 = Taivassalo, 2 = Velkua trawl area, 3 = western Rym¨attyl¨a, 4 = northern Airisto Inlet, 5 = Peimari.

Fig. 5 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 5. Prevalence (%) of the corynosoma infection in the Baltic herring in the Bothnian Sea (n = 1528) and the Archipelago Sea in 2018 (n = 1167). The black solid line expresses a linear trend: y = 7.55x + 6.33, r = 1.00.

opencc-by-4.0Dec 2022View details →

Fig. 2. A in Prevalence and geographic distribution of Babesia conradae and detection of Babesia vogeli in free-ranging California coyotes (Canis latrans)

Fig. 2. A) PCR positivity (indicated by color) of coyotes (Canis latrans) carcasses recovered (▴) in each county between 2015 and 2019. B) Map of southern California including Los Angeles, Orange, Ventura, San Bernardino, Riverside, and San Diego counties showing B. conradae PCR positivity (indicated by color) in each city where coyote carcasses were recovered. The number of coyotes sampled at each location is indicated by the size of the circle.