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Fig. 4 in Distributional Range Extension of the Shallow Water Scorpionfish Parascorpaena poseidon (Perciformes: Scorpaenidae), with a Revised Diagnosis of the Species

Fig. 4. Relationships between body width (A); head width (B); snout length (C); interorbital width at vertical midline of eye (D); upper-jaw length (E); maxilla depth (F); postorbital length (G); orbit diameter (H); and separation between opercular spine tips (I) (all as % of SL) and standard length (mm) in Parascorpaena poseidon, showing ontogenetic changes. Star indicates holotype [except for snout length, interorbital width at vertical midline of eye, and upper-jaw length—see text regarding measurements by Chou and Liao (2022)].

Fig. 2 in Distributional Range Extension of the Shallow Water Scorpionfish Parascorpaena poseidon (Perciformes: Scorpaenidae), with a Revised Diagnosis of the Species

Fig. 2. Variously-sized preserved specimens of Parascorpaena poseidon. A, FMNH 75818, 1 of 27 specimens, 35.3 mm SL, Galle, Sri Lanka; B, FMNH 75818, 1 of 27 specimens, 65.3 mm SL, Galle, Sri Lanka; C, NSMT-P 17865, 97.8 mm SL, Yaku-shima Island, Osumi Islands, Kagoshima, Japan; D, BPBM 27680, 1 of 2 specimens, 115.4 mm SL, Kovalam, Kerala India.

Fig. 1 in Distributional Range Extension of the Shallow Water Scorpionfish Parascorpaena poseidon (Perciformes: Scorpaenidae), with a Revised Diagnosis of the Species

Fig. 1. Fresh specimen of Parascorpaena poseidon from Kovalam, Kerala, India (BPBM 27680, 1 of 2 specimens, 115.4 mm SL). Photo by J. E. Randall (BPBM).

Fig. 5 in Distributional Range Extension of the Shallow Water Scorpionfish Parascorpaena poseidon (Perciformes: Scorpaenidae), with a Revised Diagnosis of the Species

Fig. 5. Distributional records of Parascorpaena poseidon, based on original description (triangles and star), literature record as P. mossambica (closed circle), and present study (open circles). Star indicates type locality.

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Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling

<p>A necessary component of understanding vector-borne disease risk is the accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data-rich species but may produce inaccurate results for sparsely-documented vectors. In light of global change, vectors that are currently not well-documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a<strong> </strong>sparsely-documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely-documented species. To demonstrate this approach, we evaluated the ability of twelve models—which differed in their pooling of data from multiple vector species and inclusion of disease data—to improve distribution estimates of sparsely-documented vectors. We assessed our models on two simulated data sets, which allowed us to generalize our results and examine their mechanisms. We found that when the focal species is sparsely documented, incorporating data on related vector species reduces uncertainty and improves accuracy by reducing overfitting. When data on vector species are already incorporated, disease data only marginally improve model performance. However, when data on other vectors are not available, disease data can improve model accuracy and reduce overfitting and uncertainty. We then assessed the approach on empirical data on ticks and tick-borne diseases in Florida and found that incorporating data on other vector species improved model performance. This study illustrates the value of exploiting correlated data via joint modeling to improve distribution models of data-limited species.</p>

FIGURE 5 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil

FIGURE 5 | The proportion of body depth at dorsal-fin origin (% SL) versus the standard length (mm) in females (red triangles) and males (blue dots) in Characidium onca. Symbols indicating the mature females are highlighted in the dashed area.

FIGURE 6 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil

FIGURE 6 | Maps of the tributaries of rio Paranaíba in Goiás State, Brazil in general view (left), with the rio São Bartolomeu basin detailed in the Distrito Federal (right). The type locality of Characidium onca is indicated by a star in the córrego Taquara. Abbreviations: BA, Bahia; DF, Distrito Federal; GO, Goiás; MG, Minas Gerais; MS, Mato Grosso do Sul; MT, Mato Grosso; and TO, Tocantins.

FIGURE 4 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil

FIGURE 4 | Pseudotympanum in Characidium onca (MZUSP 125801, paratype). Abbreviations: 5th pl, pleural rib of fifth vertebra; a, anterior window of pseudotympanum; lln, lateral line nerve; ls, lateralis superficialis; oi, obliquus inferioris; os, obliquus superioris; p, posterior window of pseudotympanum. Scale bar = 1 mm.

FIGURE 7 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil

FIGURE 7 | Satellite images of the Area de Preservação Ambiental das Bacias do Gama e Cabeça de Veado (highlighted in red), showing the land use and cover changes along the past 34 years, and predicted: in A. year of 1986, with urban areas concentrated north of the FAL–UNB, RECOR and EEJBB; B. year of 1996, illustrating the beginning of expansion of urban and rural areas just east of the FAL–UNB, RECOR and EEJBB; C. year of 2006, after the construction of the Juscelino Kubitschek bridge in 2002, illustrating a rapid expansion of urban areas in the eastern area; and D., predicted situation according to the Distrito Federal Territorial Planning Master Plan (2009, 2019). Green polygons indicate protected areas; yellow, urban areas; blue, rural areas; black, public land; and, red arrow, the Juscelino Kubitschek bridge. Abbreviations: FAL–UNB, Estação Experimental Fazenda Águas Limpas of the University of Brasília; RECOR, Reserva Ecológica do Instituo Brasileiro de Geografia e Estatística; and EEJBB, Estação Ecológica do Jardim Botânico de Brasília. Source of maps: Google Earth.

FIGURE 1 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil

FIGURE 1 | Characidium onca, holotype, MZUSP 125807, 40.1 mm SL, male. A. Specimen in lateral view soon after collection; B–D. Preserved specimen in lateral dorsal, ventral views.

FIGURE 3 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil

FIGURE 3 | Osteological characteristics in Characidium onca, paratypes: A. Right upper jaw in medial view (MZUSP 125803); B. Right dentary in medial view (MZUSP 125803); C. Left pectoral gridle in lateral view (MZUSP 125801); D. Skull in dorsal view (MZUSP 125803); E. Posterior portion of skull in dorsal view (MZUSP 125801). Abbreviations: ANT, antorbital; CLE, cleithrum; COR, coracoid; DEN, dentary; dentl, lateral row of dentary teeth; dentm; middle row of dentary teeth; dentr, replacement lateral row of dentary teeth; DPSO, dorsal process of the supraoccipital; ESC, extrascapular; FR, frontal; fbsc, frontal branch of the supraorbital canal; fo, fontanel; IO 1–6, infraorbitals 1 to 6; MCO, mesocoracoid; MEC, Meckel's cartilage; METH, mesethmoid; MX, maxilla; NA, nasal; PAR, parietal; pbsc, parietal branch of the supraorbital canal; PCP, posterior cleithral process; PCL 1–3, postcleithrum 1 to 3; PMX, premaxilla; pmxt, premaxilarry teeth; pmxtr, replacement premaxillary teeth; POST, posttemporal; PTE, pterotic; SC, scapula; SCL, supracleithum; SUO, supraorbital. Scale bar = 1 mm.

FIGURE 2 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil

FIGURE 2 | Paratypes of Characidium onca. A–B. MZUSP 125798, 23.8–27.0 mm SL respectively, córrego Taquara; C. MZUSP 125795, 28.6 mm SL, córrego Roncador; D. ZUEC 17242, 38.7 mm SL, córrego Roncador; E–F. MZUSP 125797, 39.0–44.1 mm SL, respectively, córrego Roncador. Scale bar = 5 mm.

Figure 8 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 8. Unrooted haplotype network for CQ11. Each circle represents a haplotype, and lines above each link indicate mutations.

Figure 7 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 7. Phylogenetic tree based on a 151-bp region within the CQ11 microsatellite region of Culex pipiens. The tree was constructed using the maximum likelihood method, and bootstrap values are shown as numbers on the tree.

Figure 5. Unrooted haplotype network. Each circle represents a in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 5. Unrooted haplotype network. Each circle represents a haplotype, and the lines above each link indicate one mutation. Small black dots indicate intermediate, missing, or unsampled haplotypes.

Figure 4 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 4. The phylogenetic tree is based on a 651-bp region of the Ace-2 gene from Culex pipiens. The tree was constructed using the maximum likelihood method, and bootstrap values are shown as numbers on the tree.

Fig.5 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia

Fig.5. Jamesoniella autumnalis distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.

Fig.3 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia

Fig.3. Neckera pennata distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzemes, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.

Fig.1 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia

Fig.1. Anomodon longifolius distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical reģions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumnkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J-upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N-upland Latgale, O-Austrumlatgale.

Fig.4 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia

Fig.4. Lejeunea cavifolia distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.