Abstract:The identity of Azara's No. 239 "Cola aguda cola sanguina" has never been convincingly elucidated and the only previous proposed identity as Plain-crowned Spinetail Synallaxis gujanensis is demonstrably incorrect. Azara provides a brief and imperfect, but diagnostic description of a bird which is clearly in the genus Synallaxis, of which eight species occur in Paraguay. The description of the crown being concolorous with the upperparts, the wing and tail pattern, and the measurements provided are sufficient to… Show more
“…White-lored Spinetail was selected as the target species because it is a common bird in our study area (Brazilian Pantanal, see next section), and there is very limited knowledge about its ecology (Rubio and Pinho 2008). The Spinetail shows a distribution range restricted to the Pantanal and the borders of Humid Chaco of Bolivia and Paraguay (Smith 2020). We therefore consider it interesting to improve our knowledge of the vocal behavior of such a restricted range species.…”
Machine learning tools are widely used in support of bioacoustics studies, and there are numerous publications on the applicability of convolutional neural networks (CNNs) to the automated presence-absence detection of species.However, the relation between the merit of acoustic background modeling and the recognition performance needs to be better understood. In this study, we investigated the influence of acoustic background substance on the performance of the acoustic detector of the White-lored Spinetail (Synallaxis albilora). Two detector designs were evaluated: the 152-layer ResNet with transfer learning and a purposely created CNN. We experimented with acoustic background representations trained with season-specific (dry, wet, and all-season) data and without explicit modeling to evaluate its influence on the detection performance.The detector permits monitoring of the diel behavior and breeding time of Whitelored Spinetail solely based on the changes in the vocal activity patterns. We report an advantageous performance when background modeling is used, precisely when trained with all-season data. The highest classification accuracy (84.5%) was observed for the purposely created CNN model. Our findings contribute to an improved understanding of the importance of acoustic background modeling, which is essential for increasing the performance of CNNbased species detectors.
“…White-lored Spinetail was selected as the target species because it is a common bird in our study area (Brazilian Pantanal, see next section), and there is very limited knowledge about its ecology (Rubio and Pinho 2008). The Spinetail shows a distribution range restricted to the Pantanal and the borders of Humid Chaco of Bolivia and Paraguay (Smith 2020). We therefore consider it interesting to improve our knowledge of the vocal behavior of such a restricted range species.…”
Machine learning tools are widely used in support of bioacoustics studies, and there are numerous publications on the applicability of convolutional neural networks (CNNs) to the automated presence-absence detection of species.However, the relation between the merit of acoustic background modeling and the recognition performance needs to be better understood. In this study, we investigated the influence of acoustic background substance on the performance of the acoustic detector of the White-lored Spinetail (Synallaxis albilora). Two detector designs were evaluated: the 152-layer ResNet with transfer learning and a purposely created CNN. We experimented with acoustic background representations trained with season-specific (dry, wet, and all-season) data and without explicit modeling to evaluate its influence on the detection performance.The detector permits monitoring of the diel behavior and breeding time of Whitelored Spinetail solely based on the changes in the vocal activity patterns. We report an advantageous performance when background modeling is used, precisely when trained with all-season data. The highest classification accuracy (84.5%) was observed for the purposely created CNN model. Our findings contribute to an improved understanding of the importance of acoustic background modeling, which is essential for increasing the performance of CNNbased species detectors.
The identity of the bird described from Paraguay by Félix de Azara as No. 372 Ypacahá del Pardo is confirmed as the immature plumage of the Spotted Rail Pardirallus maculatus maculatus (Boddaert, 1783). This description is the basis for the name Rallus rytirhynchos Vieillot, 1819 which is thus a junior subjective synonym and an available name. Rallus rytirhynchos Vieillot, 1819 is the type species of the genus Ortygonax Heine, 1890. Ortygonax Heine, 1890 is a junior subjective synonym of Pardirallus Bonaparte, 1856 and is also available for application.
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