DOI: 10.5061/dryad.0cfxpnw8d
title: Intrinsic factors influence a physiological measure across a forest bird community

description: 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.
description: Birds were captured during the breeding seasons between mid-March
 and August at ten sites in the forest interior. No lures were used to
 attract the birds to the nets to avoid artificially influencing their
 natural movement or stress levels. The mist nets were checked every 15 to
 20 minutes. Upon capture, each bird was marked with a ring for future
 identification (e.g., re-captures), with the necessary permissions
 obtained from the Heligoland Bird Observatory (Institute of Avian Research
 "Vogelwarte Helgoland" (IAR), Germany). We measured
 morphological characteristics of each bird and assessed sex, age, and
 breeding status. Further, a blood sample was taken for identifying the sex
 if morphological identification was not possible, and a blood smear for
 leukocyte counts was taken. The blood smears were fixed in methanol and
 treated with Giemsa stain. To determine the H/L ratio, we digitized the
 blood smears by scanning them with a Leica Aperio AT2 Scanner and a 40X
 microscope objective lens and applied a novel Convolutional Neural Network
 for determining and counting the cells.
description: # Intrinsic factors influence physiological stress in a forest bird
 community
 [https://doi.org/10.5061/dryad.0cfxpnw8d](https://doi.org/10.5061/dryad.0cfxpnw8d) ## Description of the data and file structure This dataset contains empty cells and NA values, which may be present due to undetermined measurements in birds, missing samples, or low-quality data that prevented further analysis. Please take this into account when working with the dataset. The provided R script in this repository already accounts for these NA values. Tabellen ID: Individual Identifier for each data row Year, Month, Day, Date: The sampling day Net, latitude, longitude: Location capture-event: p = planned and scheduled capture event, ap = outside of the regular schedule Success: Whether or not a bird was captured during the capture event Recapture: Whether or not the bird was a first- or a recapture Ring_ID: Ring number of the bird which serves as individual identifier Species_lat, Species_engl: Latin and English species names sex: The sex of the bird, either morphologically determined or genetically (P2/P8 Primers) age: age of the bird: dj = hatched this year, ndj = not hatched this year, vj = hatched in the previous year, nvj = older than hatched in the previous year ad_juv: ad = adult bird (ndj, vj, nvj), juv = juvenile bird (dj) brood_patch = Whether or not the bird had a brood patch for incubating eggs family = avian family that the bird belongs to brutzeit = breeding season, indicates whether or not the capture date was within the breeding season of the bird species brutgeschlecht = breeding sex, indicating which sex or sexes are incubating the eggs in the species, 1 = only females, 2= males and females pot_brut = whether or not the bird was potentially breeding based on the species, sex, age, and date migration = if and how the bird species migrates: SV (not migrating), ZV (migrating), TZ (partially migrating or short distance migrating) Ticks = whether or not the bird was infested by ticks wing_mm = wing length in mm P8_mm = Length of the third outermost primary (P8) in mm weight_g = weight in g fat = fat score of the bird (0-8) muscle = muscle score of the bird (0-3) KI50_Er = Erythrocyte counts (Automatically obtained via deep learning; Vogelbacher et al. 2024) KI50_H = Heterophil counts (Automatically obtained via deep learning; Vogelbacher et al. 2024) KI50_L = Lymphocyte counts (Automatically obtained via deep learning; Vogelbacher et al. 2024) KI50_E = Eosinophil counts (Automatically obtained via deep learning; Vogelbacher et al. 2024) KI50_B = Basophil counts (not reliable) (Automatically obtained via deep learning; Vogelbacher et al. 2024) KI50_M = Monocyte counts (not reliable) (Automatically obtained via deep learning; Vogelbacher et al. 2024) KI50_P = Bloodparasite counts (not reliable) (Automatically obtained via deep learning; Vogelbacher et al. 2024) infected = whether or not the bird was infected by blood parasites (Haemosporida) category = Which blood parasites were found in this bird (genera) Vogelbacher M, Strehmann F, Bellafkir H, Mühling M, Korfhage N, Schneider D, Rösner S, Schabo DG, Farwig N, Freisleben B. Identifying and Counting Avian Blood Cells in Whole Slide Images via Deep Learning. *Birds*. 2024; 5(1):48-66. [https://doi.org/10.3390/birds5010004](https://doi.org/10.3390/birds5010004)