Flight-call as species-specific signal in South American parrots and the effect of species relatedness in call similarity

Vocal communication is fundamental to regulate the social interactions in most gregarious species especially after dispersal movements for foraging or predator escape. A species common acoustic signal may be beneficial to group members and is especially critical in species that disperse large distances like parrots. In this study, we investigated whether parrots flight-calls carry species-specific characteristics and tested its variability within and across species. We also assessed the hypothesis of relationship between similarity in species flight-calls and phylogeny. We studied the flight-calls of 10 parrot species all occurring in Cerrado habitat in central Brazil. Our results show that, spectrum wise, there is not a discrete spectral partition between species flight-calls. Flight-calls are conservative within most of the species. Both spectral and temporal dimensions contribute to the difference between species. The species specificity of the calls was confirmed by cross correlation approach. Nevertheless, we found a difference in the call variability with some species exhibiting stereotyped calls (e.g. Amazona aestiva) while others exhibited variable calls (Brotogeris chiriri), suggesting that the function of the flight-call may differ between species, from conveying species signatures to more specific information like group or individual signature. As expected, closely related species have more similar calls. These results show that parrots flight-calls have species-specific characteristics. In some species, these calls can potentially be used in the maintenance of the group or could code other type of information, suggesting that flight-calls may play different roles depending of the species life history.FCT - Fundação para a Ciência e Tecnologiainfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio