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Figure 2 in Gyrodactylus magadiensis n. sp. (Monogenea, Gyrodactylidae) parasitising the gills of Alcolapia grahami (Perciformes, Cichlidae), a fish inhabiting the extreme environment of Lake Magadi, Kenya

Figure 2. Line drawings of the haptoral sclerites and MCO of Gyrodactylus magadiensis n. sp. from Alcolapia grahami in Lake Magadi, Kenya. (A) Haptoral sclerites with hamulus (ha), dorsal bar (db), and ventral bar (vb); (B) marginal hook; (C) male copulatory organ (MCO). Scale bars – (A) 20 µm; (B and C) 5 µm.

Figure 4 in Gyrodactylus magadiensis n. sp. (Monogenea, Gyrodactylidae) parasitising the gills of Alcolapia grahami (Perciformes, Cichlidae), a fish inhabiting the extreme environment of Lake Magadi, Kenya

Figure 4. Evolutionary history of Gyrodactylus magadiensis n. sp. based on Bayesian Inference approaches using ITS sequences for selected gyrodactylids. Statistical support for Bayesian inference (BI) and maximum likelihood (ML) methods indicated at branch nodes with posterior probabilities and bootstrap support indicated, respectively (ML/BI).

Figure 1 in Gyrodactylus magadiensis n. sp. (Monogenea, Gyrodactylidae) parasitising the gills of Alcolapia grahami (Perciformes, Cichlidae), a fish inhabiting the extreme environment of Lake Magadi, Kenya

Figure 1. Collection sites from which Alcolapia grahami were collected. (A) Silhouette of Africa showing area of study; (B) map of study area indicating countries, water bodies, and relation of Lake Magadi to Nairobi; (C) fish spring lagoon of Lake Magadi from which the fish specimens were collected.

Figure 3 in Gyrodactylus magadiensis n. sp. (Monogenea, Gyrodactylidae) parasitising the gills of Alcolapia grahami (Perciformes, Cichlidae), a fish inhabiting the extreme environment of Lake Magadi, Kenya

Figure 3. Light (LM) and scanning electron (SEM) micrographs of the haptoral sclerites and male copulatory organ (MCO) of Gyrodactylus magadiensis n. sp. (A) Haptoral sclerites with hamulus (ha), dorsal bar (db), and marginal hooks (mh), GAP (LM); (B) hamulus (ha) and dorsal bar (db) after soft tissue digestion (SEM); (C) isolated marginal hook (SEM); (D) dorsal bar (LM); (E) dorsal view of dorsal bar (SEM); (F) ventral view of dorsal bar (SEM); (G) male copulatory organ (MCO) with large central spine and six spinelets, two large and four small (LM). Scale bars – (A and B) 20 µm; (C and G) 5 µm; (D–F) 10 µm.

opencc-by-4.0Aug 2014View details →

Fig. 2 in A walk on the tundra: Host-parasite interactions in an extreme environment

Fig. 2. Representation of historical drivers for host and parasite distributions across North America during the Last Glacial Maximum and the post-Pleistocene. The map depicts the current geography of the continent showing an overlay of the maximum extent of past glaciations, pathways for expansion and episodic range shifts by ungulates and parasitic nematodes, and the contemporary distributions of caribou of the migratory Dolphin and Union herd, and of the sedentary Kangerlussuaq-Sisimiut and AkiaManiitsoq herds of West Greenland.

opencc-by-4.0Aug 2014View details →

Fig. 1 in A walk on the tundra: Host-parasite interactions in an extreme environment

Fig. 1. The parasite fauna of Arctic ungulates has been shaped by historical and contemporary processes. Today, the Arctic today is characterized by extremes in temperature, high seasonality, and low host species diversity and abundance. Rapid climate warming is now a dominant feature that is altering host–parasite interactions in several ways. Temperatures directly affect parasite development and survival in the environment and in ectotherm hosts, and although warming temperatures may initially accelerate transmission, they may quickly exceed the upper thermal tolerance limits for some arctic parasites. Using the Metabolic Theory of Ecology, temperature dependencies can be modeled and generalized to provide broader insights across genera and ecological regions. Climate changes may also alter both host and parasite life-history strategies and phenology, including migration patterns, leading to non-linear changes and tipping points in transmission ecology. Climate warming and associated changes in the cryosphere also alters ecological barriers and corridors, leading to range shifts and new contact zones.

opencc-by-4.0Jan 2021View details →

Figure 1 in Life in the extreme environment: Structure and species richness of bird assemblages on Yuzhny Island of Novaya Zemlya, Russia

Figure 1. Study region on Yuzhny Island of Novaya Zemlya. (A) Map of Novaya Zemlya. The yellow circle indicates the study region of Yuzhny Island. (B) Detailed map of the study region with the location of counting routes. The shaded area represents the study area (1). The dashed red lines indicate the counting routes (2). The dashed black line indicates the helicopter route (3).

opencc-by-4.0Jan 2021View details →

Figure 4 in Life in the extreme environment: Structure and species richness of bird assemblages on Yuzhny Island of Novaya Zemlya, Russia

Figure 4. Relative bird species abundance (log10 scale) over habitat patches on Yuzhny Island of Novaya Zemlya. Numbers of (01) – (10) are the codes of the habitat types. Differences between assemblages were all significant (Kruskal-Wallis test: p = 0.003). Images show habitat types; numbers indicate their codes (see Table 2 for detail). (Photos: V. M. Spitsyn).

opencc-by-4.0Jan 2021View details →

Figure 3 in Life in the extreme environment: Structure and species richness of bird assemblages on Yuzhny Island of Novaya Zemlya, Russia

Figure 3. Species diversity of bird assemblages on Yuzhny Island of Novaya Zemlya. (A) Bi-plot of detrended correspondence analysis (DCA) with supplementary variables, showing the ordination of species and environmental variables. Circles indicate bird species abundance (categorical estimations by using a logarithmic scale, see Table 3), abundances decrease with increasing distance from each point in a unimodal fashion (ter Braak and Smilauer, 2002). Data represent independent samples from various habitats (n = 10). Total variation is 2.44, supplementary variables account for 66.1% (adjusted explained variation is 23.8%). Eigenvalues (lambda) are 0.675, 0.162, 0.069, and 0.025 for first (horizontal), second (vertical), third and fourth axes, respectively. The first two axes explain 34.4% of the variation. The pseudo-canonical correlations of bird abundance and environmental variables for axes 1 and 2 are 0.77 and 0.91, respectively. For an explanation of environmental variables, see Table 4. For abbreviations of species names see Fig. 4. (B) Bi-plot of the same analysis revealing the ordination of species richness over a range of habitats and environmental variables. Circles indicate bird assemblages in primary types of habitats (size of each circle corresponds to the number of bird species). The red numbers near the circles indicate species richness. The black numbers near the circles (01–10) indicate the codes of habitat types (see Fig. 4 and Table 2 for detail).

FIGURE 5 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 5 Historical demographic trajectories of Parochlus steinenii within its distribution in the Magellanic Subantarctic Ecoregion (red), South Georgia (yellow) and South Shetland Islands (violet). Left panels: Past demographic changes constructed using Bayesian Skyline Plot approach based on cox1 haplotypes. The y-axis is the product of the effective population sizes (Ne) and generation length in a log scale. The x-axis is the time before present (Myr). The median estimate (solid black line) and 95% highest probability density (HPD) limits (colored area) are shown. The thick dashed line represents the time of the most recent common ancestor (TMRCA). Right panels: Distribution of pairwise differences of cox1 for each demographic unit. Values of Tau are shown.

FIGURE 3 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 3 Haplotype network for Parochlus steinenii based on 151 mtDNA cox1 sequences spanning the species' distribution. Neighbor-joining network illustrating the distribution of haplotypes across lakes in the Magellanic Subantarctic Ecoregion, South Georgia and the South Shetland Islands. Circles sizes are proportional to haplotype frequency.

FIGURE 4 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 4 Spatial genetic structure of Parochlus steinenii from the spatial model in Geneland across the three biogeographic regions sampled. Higher posterior probabilities of population membership are indicated in yellow for each sampling site (A) MSE, (B) SG, (C) MA. Black circles indicate the relative position of the sampling localities. Posterior probabilities of membership were plotted with the shapefiles of Scotia Arc coastline available in the repository in the Antarctic digital database from the British Antarctic survey (BAS). https://add.data.bas.ac.uk.

FIGURE 6 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 6 Phylogenetic reconstruction based on haplotypes cox1 data showing divergence times of Parochlus steinenii across its distribution in sub- and maritime Antarctica. Nodes ages are the median values from both Bayesian Molecular Clock analyses and TMRCA of each clade estimated with Bayesian Skyline Plot. In each clade of interest (MSE, SG and MA), nodes bar indicated the 95% HPD. The colored tip represents the colors of the sampling region.

FIGURE 2 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 2 Phylogenetic reconstruction of Podonominae based on concatenated data. Maximum Likelihood reconstruction, including members of the subfamily Podonominae with emphasis on Parochlus spp. Information in brackets represent the sequence code used for the analysis (within P. steinenii), and sampling site for each sequence. Values for the nodes support are indicated for posterior probability/bootstrap, respectively.

FIGURE 1 in Ancient diversification in extreme environments: exploring the historical biogeography of the Antarctic winged midge Parochlus steinenii (Diptera: Chironomidae)

FIGURE 1 Historical biogeography reconstruction based on 151 cox1 sequences of Parochlus steinenii across the sampling areas. (A) Map of the sampling region in the Magellanic Subantarctic regions (MSE, red); sub-Antarctic Island of South Georgia (SG, orange), and Maritime Antarctic (MA, violet); (B) Bayesian Inference reconstruction using P. steinenii individuals. The values for node support are indicated for posterior probability/bootstrap from BI and ML analyses, respectively.

opencc-by-4.0Sep 2024View details →

Data from: A snow-dwelling tropical butterfly? An unprecedented discovery of a new genus of the Pedaliodes clade in an extreme, high-altitude Andean environment (Lepidoptera: Nymphalidae, Satyrinae)

A new genus of satyrine butterflies, Nivaliodes gen. nov., is described for three species, all new – N. negrobueno sp. nov.,N. virococha sp. nov. and N. puriq sp. nov. (Lepidoptera, Nymphalidae) – with a support of molecular data and adult morphology. Target enrichment-based phylogeny indicates Nivaliodes gen. nov. is sister to the genus Pherepedaliodeswithin an extremely diverse Pedaliodes clade of the predominantly Andean subtribe Pronophilina. Whereas an overwhelming majority of species of this group occur in tropical montane forests, N. negrobueno sp. nov. was discovered in a central Peruvian desert puna at some 4600-4800 m asl., the highest elevation reported for any species of the Pronophilina. Individuals were observed overflying rocky slopes and resting directly on snow-covered surfaces, which is an exceptionally unusual behaviour among butterflies. The other two species of the new genus were found at lower elevations, some 3300-4200 m asl. at the timberline and in puna grassland.

Figure 10 in Dragonflies and damselflies (Odonata) from Flores Island, Lesser Sunda Archipelago: New occurrences in extreme environments and an island-level checklist of this group

Figure 10. Rhyothemis phyllis ixias from coastal marsh site near Labuan Bajo, Flores Island, Lesser Sunda Archipelago, Indonesia: (A, D) females (RMBH: dried specimens; scale bar = 5 mm); (B, E) tip of abdomen of the females; and (C, F) head of the females (frontal view).

Figure 8 in Dragonflies and damselflies (Odonata) from Flores Island, Lesser Sunda Archipelago: New occurrences in extreme environments and an island-level checklist of this group

Figure 8. Neurothemis terminata from coastal marsh site near Labuan Bajo, Flores Island, Lesser Sunda Archipelago, Indonesia: (A) male, and (C) female (RMBH: dried specimens; scale bar = 5 mm); (B) anal appendages of the male (lateral view); and (D) tip of the female abdomen (lateral view).

Figure 7 in Dragonflies and damselflies (Odonata) from Flores Island, Lesser Sunda Archipelago: New occurrences in extreme environments and an island-level checklist of this group

Figure 7. Neurothemis intermedia excelsa from coastal marsh site near Labuan Bajo, Flores Island, Lesser Sunda Archipelago, Indonesia: (A) female (RMBH: dried specimen; scale bar = 5 mm); and (B) tip of abdomen of the female.

Figure 5 in Dragonflies and damselflies (Odonata) from Flores Island, Lesser Sunda Archipelago: New occurrences in extreme environments and an island-level checklist of this group

Figure 5. Orthetrum sabina from Sano Ngoang Lake, Flores Island, Lesser Sunda Archipelago, Indonesia: (A) male, and (C) female (RMBH: ethanol-preserved specimens; scale bar = 5 mm); (B) anal appendages of the male (lateral view); and (D) tip of the female abdomen (lateral view).