To date, a majority of studies of microbial life in hyperalkaline settings focus on environments that are also highly saline (haloalkaline). Haloalkaline conditions offer microbes abundant workarounds to maintain pH homeostasis, as salt ions can be exchanged for protons by dedicated antiporter proteins. Yet hyperalkaline freshwater systems also occur both naturally and anthropogenically, such as the slag fill aquifers around former Lake Calumet (Chicago, IL, USA). In this study, 16S rRNA gene sequences and metagenomic sequence libraries were collected to assess the taxonomic composition and functional potential of microbes present in these slag-polluted waterways. Relative 16S rRNA gene abundances in Calumet sediment and water samples describe community compositions not significantly divergent from those in nearby circumneutral conditions. Major differences in composition are mainly driven by Proteobacteria, primarily one sequence cluster closely related to Hydrogenophaga, which comprises up to 85% of 16S rRNA gene abundance in hyperalkaline surface sediments. Sequence identity indicates this novel species belongs to the recently established genus Serpentinomonas, a bacterial lineage associated with natural freshwater hyperalkaline serpentinizing springs.

In this work, we use linkage disequilibrium-based methods to estimate recent population history from genotype data in Swedish cattle breeds, as well as international Holstein and Jersey cattle data for comparison. Our results suggest that these breeds have been effectively large up until recently, when they declined around the onset of systematic breeding. The inferred trajectories were qualitatively similar, with a large historical population and one decline. We used population genetic simulation to check the inferences. When comparing simulations from the inferred population histories to real data, the proportion low-frequency variants in real data was different than was implied by the inferred population histories, and there was somewhat higher genomic inbreeding in real data than implied by the inferred histories. The inferred population histories imply that much of the variation we see today is transient, and it will be lost as the populations settle into a new equilibrium, even if efforts to maintain effective population size at current levels are successful.

Large-effect recessive damaging variants found at relatively high frequencies are common enough in domestic animals that they cause problems in breeding programs. There are several potential reasons. Deleterious variants may rise in frequency because of genetic drift, be driven to intermediate frequency by balancing selection by heterozygote advantage, or be spread by exceptionally influential parents, usually sires, that sometimes occur in animal breeding programs. Further, animal populations are often connected by migration. In this paper, we use a series of population genetic models to address how large-effect damaging alleles establish themselves in small populations, such as domestic animal populations or wild populations of mammals. The results confirm that a lethal allele under directional selection is fairly unlikely to become common, unless there are influential sire events with very large contributions or the population size is very small. On the other hand, a lethal allele under balancing selection can easily become common, especially if there are influential parents. When there is balancing selection, migration between connected populations effectively spreads alleles. The results accord with the many examples of recessive damaging alleles in domestic animals.

Climate change is associated with an increasing in the frequency, intensity and durations of heat events, exposing even temperate regions to prolonged warm periods that can induce heat stress in dairy cattle, affecting milk production. This observational study investigated how warm weather affects milk yield in Nordic dairy cattle breeds kept under Swedish conditions. Deviations from the target lactation curve were analyzed to identify the onset of heat stress and predictors of sensitivity to heat stress using daily milk records from milking robots. The target curve was defined as the cow-specific expected lactation curve modeled using fourth-order polynomial quantile regression. Data included 907,403 daily milk yield (DMY) records for 1,056 Holstein, 566 Swedish Red, and 56 Jersey multiparous cows across 10 herds. Deviations from the target curve were quantified as residuals (RES), calculated as the difference between observed and expected milk yield, and were assumed to reflect responses to environmental disturbances, including heat stress. Heat stress was quantified by analyzing the effect of a 5-d average of maximum temperature (T_5d) on DMY and RES. T_5d was used as a smoother term in a generalized additive model (GAM) for each breed. Heat stress thresholds were determined based on the point where the fitted values of the smoother for T_5d began to decline, which occurred at ∼21°C for all breeds. After identifying this threshold, a heat load function was calculated and included as a linear covariate in a GAM model for all breeds. The results showed that Holstein cows were the most sensitive to heat stress, while Jersey cows were the least affected. This study demonstrates that RES is a reliable approach to estimate the effect of temperature on milk yield, allowing the identification of heat stress onset. High-producing breeds were more susceptible to heat stress, and cows in mid-lactation and their second and third lactation were more affected by high temperatures.