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opencc-by-4.0Mar 2015View details →

Fig. 3 in Population density and habitat of an endangered cave fish Eigenmannia vicentespelaea Triques, 1996 (Ostariophysi: Gymnotiformes) from a karst area in central Brazil

Fig. 3. Monthly rainfall recorded in the years of 1999, 2000 and 2001. Source: INMET, Posse municipality, Goiás State, central Brazil.

opencc-by-4.0Mar 2015View details →

Fig. 5 in Population density and habitat of an endangered cave fish Eigenmannia vicentespelaea Triques, 1996 (Ostariophysi: Gymnotiformes) from a karst area in central Brazil

Fig. 5. Biplot resulting from Principal Component Analysis with seven variables. Dark circles represent sampling units.

Figure 2 in Impact of poaching on the population structure and insect associates of the Endangered Encephalartos eugene-maraisii from South Africa

Figure 2. Insects associated with Encephalartos eugene-maraisii in Entabeni: A, B, Amorphocerus cf. setosus; C, D, Apinotropis verdoornae; E, F, Zerenopsis lepida. Photographs: P.D. Janse van Rensburg.

Figure 4 in Impact of poaching on the population structure and insect associates of the Endangered Encephalartos eugene-maraisii from South Africa

Figure 4. Large Encephalartos eugene-maraisii stem that was pushed over by poachers. Photographer: P.D. Janse van Rensburg.

Figure 1 in Impact of poaching on the population structure and insect associates of the Endangered Encephalartos eugene-maraisii from South Africa

Figure 1. Typical architecture of an Encephalartos eugene-maraisii plant. Photographer: P.D. Janse van Rensburg.

Figure 3 in Impact of poaching on the population structure and insect associates of the Endangered Encephalartos eugene-maraisii from South Africa

Figure 3. Distribution of Encephalartos eugene-maraisii in the Entabeni Safari Conservancy between 2021 and 2022, compared to 2008. A, heatmap of E. eugene-maraisii plants re-recorded between 2021 and 2022; B, heatmap of E. eugene-maraisii plants that were not relocated. Lines indicate the fence line. The map is given without a geographical reference because E. eugene-maraisii is vulnerable to poaching.

Figure 3 in Endangered White-spotted Ketsi Blue butterfly, Lepidochrysops ketsi leucomacula, in KwaZulu-Natal

Figure 3. White-spotted Ketsi Blue butterflies, Lepidochrysops ketsi leucomacula, nectaring at various plant species with pink flowers. A, Alepidea sp.; B, Ophrestia oblongifolia (E.Mey.) H.M.L.Forbes; C, Tephrosia cf. grandiflora (Aiton) Pers. (Photos by the authors.)

Figure 5 in Endangered White-spotted Ketsi Blue butterfly, Lepidochrysops ketsi leucomacula, in KwaZulu-Natal

Figure 5. Tractor-mowed portion of the provincially critically endangered Pondoland-Ugu Sandstone Coastal Sourveld at the Endangered Lepidochrysops ketsi leucomacula monitoring area in Umtamvuna Nature Reserve in May 2022.

Figure 4. A in Endangered White-spotted Ketsi Blue butterfly, Lepidochrysops ketsi leucomacula, in KwaZulu-Natal

Figure 4. A, Southern Gaudy Commodore, Precis octavia sesamus Trimen, 1883, nectaring at Lasiosiphon anthylloides (L.f.) Meisn.; and B, Painted Lady, Vanessa cardui (Linnaeus, 1758), nectaring at Pentanisia sp. on 8 February 2022 in the habitat of Lepidochrysops ketsi leucomacula at Umtamvuna Nature Reserve. (Photos by the authors.)

Figure 2. A–D in Endangered White-spotted Ketsi Blue butterfly, Lepidochrysops ketsi leucomacula, in KwaZulu-Natal

Figure 2. A–D, female White-spotted Ketsi Blue butterflies, Lepidochrysops ketsi leucomacula, ovipositing on the hostplant Selago tarachodes Hilliard. (Photos by the authors.)

Figure 3 in Seasonal analysis of food items and feeding habits of endangered riverine catfish Rita rita (Hamilton, 1822)

Figure 3. Seasonal variation in frequency of food items assessed by non-metric multidimensional scaling (nMDS) analysis in R. rita sampled from Padma River.

Figure 2 in Seasonal analysis of food items and feeding habits of endangered riverine catfish Rita rita (Hamilton, 1822)

Figure 2. Fullness index of fish stomach in different seasons (a) and size groups (b) of R. rita sampled from Padma River.

Figure 6 in Seasonal analysis of food items and feeding habits of endangered riverine catfish Rita rita (Hamilton, 1822)

Figure 6. Canonical correspondence analysis of food items and morphometric measures of R. rita sampled from Padma river (TL = Total length; BW = Body weight; HG = horizontal mouth gape; VG = vertical mouth gape; MA = mouth area)

Figure 4 in Seasonal analysis of food items and feeding habits of endangered riverine catfish Rita rita (Hamilton, 1822)

Figure 4. Principle component analysis (PCA) on fish size groups and food items in R. rita sampled from Padma River (Roman numbers indicated the different size group of fish, such as I = 9-14 cm, II =>14-19 cm, III =>19-24 cm, IV =>24-29 cm and V =>29-34 cm).

opencc-by-4.0Dec 2020View details →

Fig. 2 in A report of 12 unrecorded prokaryotic species isolated from gastrointestinal tracts and feces of various endangered animals in Korea

Fig. 2. Phylogenetic tree based on 16S rRNA gene sequence comparisons, showing the relationship between the isolated strains in this study and the notable species from phylum Firmicutes (a) order Lactobacillales (In particular Enterococcus, Lactobacillus and Vagococcus), phylum Actinobacteria (c) and phylum Proteobacteria (d) and. The trees were mainly reconstructed using the neighbor-joining algorithm (NJ), Maximum parsimony (MP) and maximum likelihood (ML) algorithms were applied for additional comparison. Filled diamonds indicate branches present in the phylogenetic trees generated using the three different methods. Numbers on the nodes (>70%) represent bootstrap values as percentages of 1000 replicates (NJ/MP/ML). Clostridium butyricum DSM 10702T (AQQF01000149), Bifidobacterium bifidum ATCC 29521T (KE993182) and Spirochaeta aurantia subsp. aurantia DSM 1902T (FR749896) were used as outgroups, respectively. Bar, 0.02 (a, c, d) and 0.01 (b) accumulated changes per nucleotide.

opencc-by-4.0Dec 2020View details →

Fig. 1 in A report of 12 unrecorded prokaryotic species isolated from gastrointestinal tracts and feces of various endangered animals in Korea

Fig. 1. Transmission electron micrographs of the isolated strains. Strain-1, AE4-1; strain-2, B3; strain-3, M3; strain-4, VM3408; strain-5, VT2418; strain-6, VM2501; strain-7, VT2414; strain-8, VT2504.

Fig. 1 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila

Fig. 1. Methodology used to predict morning emergence time of Gambelia sila. Emergence was predicted as the time of day immediately preceding a distinct upward slope in the lizard's T b (triangles and dotted line) based on the assumption that it would take several minutes for the radio transmitter to heat in the sun. The rising Tb was also typically associated with the departure from the burrow physical model temperatures (diamonds and long-dashed line) and the approach of the open (sun) physical model temperatures (squares and short-dashed line). In each case, the predicted time was then compared to the observed emergence time when the lizard's head first appeared outside its burrow. The average difference between observed and predicted emergence times was 11 minutes and 37 seconds.

Fig. 4 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila

Fig. 4. Proportions of correctly predicted observations of microhabitat use of Gambelia sila using temperature-based activity estimation based on physical model temperatures. Lizard microhabitat use was predicted correctly most often when they were in the open, but overall microhabitat use was not accurately predicted with TBAE in this heliothermic lizard.

Fig. 3 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila

Fig. 3. Temperature-based activity estimation resulted in accurate prediction of above-ground activity by Gambelia sila more often than accurate prediction of below-ground (burrow) occupation. Using air temperature (T air) was superior to using physical model temperatures when predicting below-ground occupation. For both methods, ~93% of observations predicted to be above ground were correct, whereas 62% (using T air) and 51% (using physical models) were correct for below-ground predictions.

Fig. 2 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila

Fig. 2. Proportions of correct predictions using air temperature to predict surface activity versus below-ground refuge use of Gambelia sila. This method resulted in accurate predictions 64–76% of the time among the various temperature differentials shown on the x-axis. Predictions were maximized (76% correct) using the criterion that lizards are above ground when their body temperatures (T b) are at least 6 °C above the air temperature (T ).