RESUMEN
Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires automatic identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27 hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources have been made available at https://soundclim.github.io/anuraweb/ .
Asunto(s)
Anuros , Vocalización Animal , Animales , Acústica , EcosistemaRESUMEN
Odonata species live through their immature development in aquatic habitats, where the eggs are adapted to survive the initial stages. The survival of the eggs is linked to the layers that make up the eggshell. These layers are common to all odonate species, but show variations among them, depending on the oviposition method. For example, endophytic eggs (laid in plant tissues) and exophytic eggs (laid on the water surface) have different morphologies, which may reveal egg adaptations to environmental conditions and stresses. The differences in the egg morphologies could be shown in the length of the egg layers, their histochemical composition, and in the ultrastructure. The histochemical composition can reveal several features that could lead us to a better understanding of its function, for example, how glycoproteins and total proteins could regulate the humidity and water retention in the eggs according to the environments in which the eggs are placed.
