New species of Hemiodus (ostariophysi, characiformes, hemiodontidae) from the Rio Tocantins, Brazil, with comments on color patterns and tooth shapes within the species and genus

A new hemiodontid species, Hemiodus tocantinensis, is described from the rio Tocantins, Amazon basin, Brazil. It is most closely related to H. ternezi and H. thayeria based on the presence of a dark longitudinal stripe extending from behind the eye or the opercle to the tip of lower caudal fin lobe but is distinguished by the possession of 51 to 58 perforated lateral line scales and an oblique dark blotch on the dorsal fin extending from its anterior distal portion through the middle basal portion of the fin. The evolution of color patterns and tooth shapes present in the Hemiodus species is commented.

Color pattern variation in Trichomycterus iheringi (Eigenmann, 1917) (Siluriformes: Trichomycteridae) from rio Itatinga and rio Claro, São Paulo, Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Color polymorphism in Leptodactylus fuscus (Anura, Leptodactylidae): A defensive strategy against predators?

Color patterns are strongly related to defensive strategies in anurans. Some anurans present more than one morphotype. Leptodactylus fuscus, for example, present two morphotypes (with and without vertebral white line). The proportion of each pattern in nature is different, whereby there are always more individuals without stripes. Therefore, we speculated if this difference in the observed color pattern is due to unequal predation pressures (i.e. stronger over the striped morphotype), and/or if there is a genetic component related to autossomic heritage. To test the selective predation over the morphotypes, we prepared plasticine models of L. fuscus with both phenotypes and placed them in the field. We did not find evidence of predation selection and as we found significant relationships between the proportions of the phenotypes and Mendelian proportions, we suggest that the phenotypes observed in this species are genetically determined (involving dominant and recessive alleles) and may not have a defensive function.

Skin glands, poison and mimicry in dendrobatid and leptodactylid amphibians

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Geographic Distribution and Morphological Variation of Striped and Nonstriped Populations of the Brazilian Atlantic Forest Treefrog Hypsiboas bischoffi (Anura: Hylidae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Developmental biology, polymorphism and ecological aspects of Stiretrus decemguttatus (Hemiptera, Pentatomidae), an important predator of cassidine beetles

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Three new species of Neoplecostomus (Teleostei: Siluriformes: Loricariidae) from the Upper Rio Parana basin of southeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Biogeografia evolutiva: a seleção sexual e o índice de predação como fatores evolutivos do Lebistes (Poecilia reticulata) em comunidades íctias

Pós-graduação em Geografia - IGCE

Variabilidade genética de três colônias de Triatoma rubrovaria (Blanchard, 1843), (Hemiptera, Reduviidae), oriundas do estado do Rio Grande do Sul, avaliadas por meio do seqüenciamento de genes do DNA mitocondrial e ribossomal

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Revisão taxonômica e sistemática de Oxyrhopus clathratus Duméril, Bibron & Duméril, 1854 (Serpentes, Dispsadidade; Pseudoboini)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The Taxonomic Status of Dendropsophus allenorum and Dendropsophus timbeba (Anura: Hylidae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Pimelodus maculatus e P. platicirris (Siluriformes: pimelodidae) no alto rio Paraná: caracterização morfológica, dieta e reprodução

Pós-graduação em Aquicultura - FCAV

Eye color as an indicator of social rank in the fish Nile tilapia

We investigated the association of eye color with the dominant-subordinate relationship in the fish Nile tilapia, Oreochromis niloticus. Eye color pattern was also examined in relation to the intensity of attacks. We paired 20 size-matched fish (intruder: 73.69 ± 11.49 g; resident: 75.42 ± 8.83 g) and evaluated eye color and fights. These fish were isolated in individual aquaria for 10 days and then their eye color was measured 5 min before pairing (basal values). Twenty minutes after pairing, eye color and fights were quantified for 10 min. Clear establishment of social hierarchy was observed in 7 of 10 pairs of fish. Number of attacks ranged from 1 to 168 among pairs. The quartile was calculated for these data and the pairs were then divided into two classes: low-attack (1 to 111 attacks - 2 lower quartiles) or high-attack (112 to 168 attacks - 2 higher quartiles). Dominance decreased the eye-darkening patterns of the fish after pairing, while subordinance increased darkening compared to dominance. Subordinate fish in low-attack confrontations presented a darker eye compared to dominant fish and to the basal condition. We also observed a paler eye pattern in dominants that shared low-attack interactions after pairing compared to the subordinates and within the group. However, we found no differences in the darkening pattern between dominants and subordinates from the high-attack groups. We conclude that eye color is associated with social rank in this species. Moreover, the association between eye color and social rank in the low-attack pairs may function to reduce aggression.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.