Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (ly-. ing bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine -learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sen-sitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential

Colonisation of urban environments is associated with reduced migratory behaviour, facilitating divergence from ancestral populations

How individuals colonising novel environments overcome the diverse suite of new selection pressures is a fundamental question in ecology and evolution. Urban environments differ markedly from the rural ones that they replace and successful colonisation of urban areas may therefore require local adaptation and phenotypic/genetic divergence from ancestral populations. Such a process would be facilitated by limited dispersal to and from the novel habitat. Here we assess divergence in migratory behaviour between seven pairs of urban and rural European blackbird Turdus merula populations along a 2800 km transect across Europe. This former forest specialist is now amongst the most abundant urban birds across most of its range. We use a stable isotope approach due to the lack of sufficient ringing data from multiple urban populations, and compare hydrogen isotopic ratios of tissues grown in the breeding (feathers) and wintering areas (claws) to derive an index of long distance migratory behaviour. We find a tendency for urban blackbirds to be more sedentary than rural ones at all sites and this divergence is particularly strong at the north-eastern limit of our transect, i.e. in Estonia and Latvia. These urban populations are those that have been established most recently (from the late 1930s to 1950s) implying that urbanisation can promote rapid ecological divergence. The increased sedentary behaviour of urban birds could promote further ecological divergence between rural and urban populations, such as the earlier breeding of urban blackbirds, and in some cases may contribute to their previously documented genetic divergence

Chemotaxis increases vertical migration and apparent transverse dispersion of bacteria in a bench-scale microcosm.

The success of in situ bioremediation is often limited by the inability to bring bacteria in contact with the pollutant, which they will degrade. A bench-scale model aquifer was used to evaluate the impact of chemotaxis on the migration of bacteria toward the source of a chemical pollutant. The model was packed with sand and aqueous media was pumped across horizontally, simulating groundwater flow in a homogenous aquifer. A vertical gradient in chemoattractant was created by either a continuous injection of sodium benzoate or a pulse injection of sodium acetate. A pulse of chemotactic Pseudomonas putida F1 or a non-chemotactic mutant of the same species was injected below the attractant. The eluent was sampled at the microcosm outlet to generate vertical concentration profiles of the bacteria and chemoattractant. Moment analysis was used to determine the center and variance of the bacterial profiles. The center of the chemotactic bacterial population was located at an average of 0.74 ± 0.07 cm closer to the level at which the chemoattractant was injected than its non-chemotactic mutant in benzoate experiments (P < 0.015) and 0.4 ± 0.2 cm closer in acetate experiments (P < 0.05). The transverse dispersivity of the chemotactic bacteria was 4 ± 1 × 10(-3) cm higher in benzoate experiments than the transverse dispersivity of the non-chemotactic mutant and 1 ± 2 × 10(-3) cm higher in acetate experiments. These results underscore the contribution of chemotaxis to improve transport of bacteria to contaminant sources, potentially enhancing the effectiveness of in situ bioremediation