Avian blood analysis is a fundamental method for investigating a wide range of topics concerning individual birds and populations of birds. Determining precise blood cell counts helps researchers gain insights into the health condition of birds. For example, the ratio of heterophils to lymphocytes (H/L ratio) is a well-established index for comparing relative stress load. However, such measurements are currently often obtained manually by human experts. In this article, we present a novel approach to automatically quantify avian red and white blood cells in whole slide images. Our approach is based on two deep neural network models. The first model determines image regions that are suitable for counting blood cells, and the second model is an instance segmentation model that detects the cells in the determined image regions. The region selection model achieves up to 97.3% in terms of F1 score (i.e., the harmonic mean of precision and recall), and the instance segmentation model achieves up to 90.7% in terms of mean average precision. Our approach helps ornithologists acquire hematological data from avian blood smears more precisely and efficiently.

Introduction: Parasites play important roles in ecosystems. Through their interactions with host and vector species, they are capable of changing the behavior and population dynamics of their host species, and the shape of entire communities. Over the past years, many studies have acknowledged the role of parasitism for host populations and communities and discovered their important regulatory functions for many vertebrate populations. Although birds are a well-studied group of vertebrates, the infection patterns of endoparasites at the community level are not fully understood. Some bird species and families are known to have a higher susceptibility to certain endoparasites than others, which may be driven by their abundance in the community.Methods: Over the course of four consecutive breeding seasons (2019–2022), we monitored the patterns of endoparasite infections in a bird community of a temperate forest ecosystem. We sampled 483 birds belonging to 29 Palearctic species and investigated the prevalence of blood parasites (haemosporidian parasites) andTrichomonasspp. using molecular methods.Results: We found an overall prevalence of 48.1% of haemosporidians belonging to 53 genetic lineages of the three generaHaemoproteus,Leucocytozoon, andPlasmodiumspp. While the bird families Turdidae (94%) and Paridae (76%) showed a high prevalence of haemosporidians, Certhiidae, and Picidae were not infected (0%). Host–parasite network analysis detected high variability in interactions. Infections withTrichomonasspp. were not observed in the forest bird community.Discussion: We found that the prevalence and lineage diversity of haemosporidian parasites differed between avian families and that the parasite prevalence of a family could not serve as a predictor of lineage diversity. To further assess the consequences of these host–parasite interactions for bird communities, future research should aim to disentangle the infection pathways in different ecosystems while also considering the vector community and environmental factors.

Physiological parameters have the potential to serve as valuable early warning indicators for the conservation of animal populations. However, measuring physiological adaptations in wildlife is often challenging, due to intrinsic differences causing natural variations of physiological measures between individuals across species. This study is aimed at addressing this by investigating the influence of intrinsic factors, including sex, age, body condition, and the incubation of eggs on the H/L ratio of a forest bird community. As physiological measure, we used the heterophil to lymphocyte (H/L) ratio of individuals belonging to different species in the forest bird community, which was assessed using a novel deep learning approach based on convolutional neural networks (CNNs) applied to whole blood smear scans. Using phylogenetically controlled Bayesian analyses across the bird species, we found higher H/L ratios in adult birds than in juveniles and observed slightly higher H/L ratios in females than in males. While body condition had no effect on the H/L ratio, incubating birds tended to have higher H/L ratios than non‐reproductive birds, regardless of their sex. Furthermore, we found a robust phylogenetic signal of the H/L ratio in the studied bird community. Our results reveal significant general patterns of the effect of intrinsic factors on the H/L ratio across a bird community.

Physiological stress parameters have the potential to serve as valuable early warning indicators for the conservation of animal populations. However, measuring stress in wildlife is often challenging, due to the lack of knowledge about baseline levels, and intrinsic differences between individuals across species. This study is aimed at filling this gap by investigating the influence of intrinsic factors, including sex, age, body condition, and reproductive status on the physiological stress of a forest bird community. For measuring stress levels, we used the heterophil to lymphocyte (H/L) ratio of the bird community which was assessed using a novel deep learning approach based on Convolutional Neural Networks applied to whole blood smear scans. Using phylogenetically controlled analyses across the bird species, we found higher H/L ratios in adult birds than in juveniles and observed higher stress levels in females than in males. While body condition had no effect on the H/L ratio, reproductive birds tended to have higher H/L ratios than non-reproductive birds, regardless of their sex. Furthermore, we found a robust phylogenetic signal of the H/L ratio in the studied bird community. Our results emphasize the importance of considering intrinsic factors when using stress physiology for assessing the condition of bird populations and communities.