Honey bee stressor networks are complex and dependent on crop and region.

Honey bees play a major role in crop pollination but have experienced declining health throughout most of the globe. Despite decades of research on key honey bee stressors (e.g., parasitic Varroa destructor mites and viruses), researchers cannot fully explain or predict colony mortality, potentially because it is caused by exposure to multiple interacting stressors in the field. Understanding which honey bee stressors co-occur and have the potential to interact is therefore of profound importance. Here, we used the emerging field of systems theory to characterize the stressor networks found in honey bee colonies after they were placed in fields containing economically valuable crops across Canada. Honey bee stressor networks were often highly complex, with hundreds of potential interactions between stressors. Their placement in crops for the pollination season generally exposed colonies to more complex stressor networks, with an average of 23 stressors and 307 interactions. We discovered that the most influential stressors in a network-those that substantively impacted network architecture-are not currently addressed by beekeepers. Finally, the stressor networks showed substantial divergence among crop systems from different regions, which is consistent with the knowledge that some crops (e.g., highbush blueberry) are traditionally riskier to honey bees than others. Our approach sheds light on the stressor networks that honey bees encounter in the field and underscores the importance of considering interactions among stressors. Clearly, addressing and managing these issues will require solutions that are tailored to specific crops and regions and their associated stressor networks.

Heritability Estimates of Antler and Body Traits in White-Tailed Deer (Odocoileus virginianus) From Genomic-Relatedness Matrices.

Estimating heritability (h2) is required to predict the response to selection and is useful in species that are managed or farmed using trait information. Estimating h2 in free-ranging populations is challenging due to the need for pedigrees; genomic-relatedness matrices (GRMs) circumvent this need and can be implemented in nearly any system where phenotypic and genome-wide single-nucleotide polymorphism (SNP) data are available. We estimated the heritability of 5 body and 3 antler traits in a free-ranging population of white-tailed deer (Odocoileus virginianus) on Anticosti Island, Quebec, Canada. We generated classic and robust GRMs from >10,000 SNPs: hind foot length, dressed body mass, and peroneus muscle mass had high h2 values of 0.62, 0.44, and 0.55, respectively. Heritability in male-only antler features ranged from 0.07 to 0.33. We explored the influence of filtering by minor allele frequency and data completion on h2: GRMs derived from fewer SNPs had reduced h2 estimates and the relatedness coefficients significantly deviated from those generated with more SNPs. As a corollary, we discussed limitations to the application of GRMs in the wild, notably how skewed GRMs, specifically many unrelated individuals, can increase variance around h2 estimates. This is the first study to estimate h2 on a free-ranging population of white-tailed deer and should be informative for breeding designs and management as these traits could respond to selection.