Occurrence of Apicomplexa protozoa in wild birds in the Northeast region of Brazil

Abstract Protozoa of the Apicomplexa phylum are worldwide distributed with capacity to infect endothermic animals. The study of these protozoa in wild birds in Brazil is scarce. This study aimed to evaluate the occurrence of apicomplexan protozoa in wild birds in the Northeast of Brazil. From October to December 2019, brain tissue samples were collected from 71 captive birds from the Wild Animal Screening Center of the Pernambuco State (CETRAS-Tangara) and 25 free-living birds from the Caatinga biome in Rio Grande do Norte, totaling 96 animals (41 species). Brain fragments were subjected to molecular diagnosis by nested PCR for the 18s rDNA gene of Apicomplexa parasites, followed by DNA sequencing. This gene was detected in 25% (24/96) of the samples, and it was possible to perform DNA sequencing of 14 samples, confirming three genera: Isospora, Sarcocystis and Toxoplasma from eight bird species (Amazona aestiva, Coereba flaveola, Egretta thula, Paroaria dominicana, Sporophila nigricollis, Cariama cristata, Columbina talpacoti, Crypturellus parvirostris). The occurrence these coccidia in wild birds provides important epidemiological information for the adoption of preventive measures for its conservation. Future studies are needed to better understand the consequence of Apicomplexa infection in birds in Caatinga and Atlantic Forest biomes

Mammalian Roadkill in a Semi-Arid Region of Brazil: Species, Landscape Patterns, Seasonality, and Hotspots

Roadkill is one of the principal causes of the loss of biodiversity around the world. The effects of roads on mammals are still poorly understood in regions with a semi-arid climate, where many knowledge gaps persist. The present study provides an inventory of the mammalian species affected on highways in northeastern Brazil, as well as identifying roadkill hotspots and contributing to the understanding of how seasonality and the landscape may influence the roadkill patterns of wild mammals. A total of 6192.52 km of road were sampled in 53 field surveys conducted between 2013 and 2017. Landsat 8 satellite images and data from the MapBiomas platform were used to classify land use and cover for analysis. Buffers of 1 km, 5 km, and 10 km were created around the study roads to identify the landscape variables associated with roadkill events. Ripley’s 2D K-Statistics and the 2D HotSpot test were used to identify roadkill aggregations and hotspots; GLMMs were generated for the landscape variables and evaluated using the Akaike Information Criterion. The Kruskal–Wallis test was applied to investigate the potential effects of seasonality. A total of 527 wild animal carcasses were recorded as a result of vehicular collision. The species with the highest roadkill records were Cerdocyon thous, Euphractus sexcinctus, and Procyon cancrivorus, while two species—Leopardus emiliae and Herpailurus yagouaroundi—are considered to be under threat of extinction. For mammals in general, the best GLMM indicated an increase in roadkills with increasing density of local vegetation areas, and a decrease as urban areas increased. The model also found that the mammals were less impacted in the vicinity of a protected area. In the specific case of C. thous, the roadkill rate was lower when urban infrastructure was more common than dense vegetation; the rate increased as areas of dense vegetation increased. In the case of P. cancrivorus and E. sexcinctus, the best models of roadkill patterns included an area of exposed soil and sparse vegetation, respectively. Roadkill rates were higher in the rainy season for all the mammals, with the exception of C. thous. These results reflect the ecological characteristics of the species with the highest roadkill rates. The findings of the present study raise concerns with regard to the impact of highways on the populations of C. thous, as well as the region’s most threatened species. They also indicate the potential functionality of the local protected area, as well as identifying roadkill hotspots, which will support the development of effective mitigation measures