Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities

Macroeconomic conditions and banking performance in Hong Kong SAR: A panel data study.

Abstract Using confidential supervisory bank-level data, this paper examines the determinants of banking performance in Hong Kong, with a focus on the impact of macroeconomic developments on the net interest margin and asset quality. The empirical analysis suggests that banking performance is affected by macroeconomic developments with smaller banks being more exposed to changes in economic conditions. The bursting of the property "bubble" also put banks under stress, but property-related loans remained relatively safe assets compared with other types of bank lending

Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review

peer reviewedIn pursuit of precision livestock farming, the real-time measurement for heat strain-related data has been more and more valued. Efforts have been made recently to use more sensitive physiological indicators with the hope to better inform decision-making in heat abatement in dairy farms. To get an insight into the early detection of heat strain in dairy cows, the present review focuses on the recent efforts developing early detection methods of heat strain in dairy cows based on body temperatures and respiratory dynamics. For every candidate animal-based indicator, state-of-the-art measurement methods and existing thresholds were summarized. Body surface temperature and respiration rate were concluded to be the best early indicators of heat strain due to their high feasibility of measurement and sensitivity to heat stress. Future studies should customize heat strain thresholds according to different internal and external factors that have an impact on the sensitivity to heat stress. Wearable devices are most promising to achieve real-time measurement in practical dairy farms. Combined with internet of things technologies, a comprehensive strategy based on both animal- and environment-based indicators is expected to increase the precision of early detection of heat strain in dairy cows

Distinct Biogeography of Different Fungal Guilds and Their Associations With Plant Species Richness in Forest Ecosystems

Plant pathogens are increasingly considered as important agents in promoting plant coexistence, while plant symbionts like ectomycorrhizal fungi (EMF) can facilitate plant dominance by helping conspecific individuals to defend against plant pathogens. However, we know little about their relationships with plants at large scales. Here, using soil fungal data collected from 28 forest reserves across China, we explored the latitudinal diversity gradients of overall fungi and different fungal functional guilds, including putative plant pathogens, EMF, and saprotrophic fungi. We further linked the spatial patterns of alpha diversities of putative plant pathogens and EMF to the variation of plant species richness. We found that the relationships between latitude and alpha diversities of putative plant pathogens and EMF were region-dependent with sharp diversity shifts around the mid-latitude (similar to 35 degrees N), which differed from the unimodal diversity distributions of the overall and saprotrophic fungi. The variations in the diversities of putative plant pathogens and EMF were largely explained by the spatial regions (south vs. north/subtropical zone vs. temperate zone). Additionally, the alpha diversities of these two fungal guilds exhibited opposing trends across latitude. EMF could alter the relationship between diversities of putative plant pathogens and plants in the south/subtropical region, but not vice versa. We also found that the ratio of their alpha diversities (EMF to putative plant pathogens) was negatively related to plant species richness across the spatial regions (north to south), and explained similar to 10% of the variation of plant species richness. Overall, our findings suggest that plant-microbe interactions not only shape the local plant diversity but also may have non-negligible contributions to the large-scale patterns of plant diversity in forest ecosystems.National Natural Science Foundation of China [31600403, 31800422, 41673111, U1501232, 41622106]; Natural Science Foundation of Guangdong Province, China [2016A030312003]; U.S. National Science Foundation MacroSystems Biology program [NSF EF-1065844]; Candidates at Sun Yat-Sen UniversityOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Predicting physiological responses of dairy cows using comprehensive variables

peer reviewedHeat stress is increasingly affecting the production, health, and reproduction of dairy cows. Previous studies used limited variables as predictors of physiological responses, and the developed models poorly predict animal responses in evaporatively cooled environments. The aim of this study was to build machine learning models using comprehensive variables to predict physiological responses of dairy cows raised on an actual dairy farm equipped with sprinklers. Four algorithms including random forests, gradient boosting machines, artificial neural networks (ANN), and regularized linear regression were used to predict respiration rate (RR), vaginal temperature (VT), and eye temperature (ET) with 13 predictor variables from three dimensions: production, cow-related, and environmental factors. The classification performance of the predicted values in recognizing individual heat stress states was compared with commonly used thermal indices. The performance on the testing sets shows that the ANN models yielded the lowest root mean squared error for predicting RR (13.24 breaths/min), VT (0.30 °C), and ET (0.29 °C). The results interpreted with partial dependence plots and Local Interpretable Model-agnostic Explanations show that P.M. measurements and winter calving contributed most to high RR and VT predictions, whereas lying posture, high ambient temperature, and low wind speed contributed most to high ET predictions. When determining the ground-truth heat stress state by the actual RR, the best classification performance was yielded by the predicted RR with an accuracy of 77.7%; when determining the ground-truth heat stress state by the actual VT, the best classification performance was yielded by the predicted VT with an accuracy of 75.3%. This study demonstrates the ability of ANN in predicting physiological responses of dairy cows raised on actual farms with access to sprinklers. Adding more predictors other than meteorological parameters into training could increase predictive performance. Recognizing the heat stress state of individual animals, especially those at the highest risk, based on the predicted physiological responses and their interpretations can inform better heat abatement decisions

Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods.

peer reviewedThe behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification

Evaluation of environmental and physiological indicators in lactating dairy cows exposed to heat stress.

peer reviewedThis study aimed to better understand environmental heat stress and physiological heat strain indicators in lactating dairy cows. Sixteen heat stress indicators were derived using microenvironmental parameters that were measured at the surrounding of cows and at usual fixed locations in the barn by using handheld and fixed subarea sensors, respectively. Twenty high-producing Holstein-Friesian dairy cows (> 30.0 kg/day) from an intensive dairy farm were chosen to measure respiration rate (RR), vaginal temperature (VT), and body surface temperature of forehead (FT), eye (ET), and muzzle (MT). Our results show that microenvironments measured by the handheld sensor were slightly warmer and drier than those measured by the fixed subarea sensor; however, their derived heat stress indicators correlated equally well with physiological indicators. Interestingly, ambient temperature (Ta) had the highest correlations with physiological indicators and the best classification performance in recognizing actual heat strain state. Using segmented mixed models, the determined Ta thresholds for maximum FT, mean FT, RR, maximum ET, mean ET, VT, mean MT, and maximum MT were 24.1 °C, 24.2 °C, 24.4 °C, 24.6 °C, 24.6 °C, 25.3 °C, 25.4 °C, and 25.4 °C, respectively. Thus, we concluded that the fixed subarea sensor is a reliable tool for measuring cows' microenvironments; Ta is an appropriate heat stress indicator; FT, RR, and ET are good early heat strain indicators. The results of this study could be helpful for dairy practitioners in a similar intensive setting to detect and respond to heat strain with more appropriate indicators

Explaining ecosystem multifunction with evolutionary models

Ecosystem function is the outcome of species interactions, traits, and niche overlap – all of which are influenced by evolution. However, it is not well understood how the tempo and mode of niche evolution can influence ecosystem function. In evolutionary models where either species differences accumulate through random drift in a single trait or species differences accumulate through divergent selection among close relatives, we should expect that ecosystem function is strongly related to diversity. However, when strong selection causes species to converge on specific niches or when novel traits that directly affect function evolve in some clades but not others, the relationship between diversity and ecosystem function might not be very strong. We test these ideas using a field experiment that established plant mixtures with differing phylogenetic diversities and we measured ten different community functions. We show that some functions were strongly predicted by species richness and mean pairwise phylogenetic distance (MPD, a measure of phylogenetic diversity), including biomass production and the reduction of herbivore and pathogen damage in polyculture, while other functions had weaker (litter production and structural complexity) or nonsignificant relationships (e.g., flower production and arthropod abundance) with MPD and richness. However, these divergent results can be explained by different models of niche evolution. These results show that diversity‐ecosystem function relationships are the product of evolution, but that the nature of how evolution influences ecosystem function is complex