Potential microbial bioindicators of phosphorus mining in a temperate deciduous forest.

AIMS: The soil microbial community plays a critical role in increasing phosphorus (P) availability in low-P, weathered soils by "mining" recalcitrant organic P through the production of phosphatase enzymes. However, there is a lack of data on the fungal and bacterial taxa which are directly involved in P mining, which could also serve as potential microbial bioindicators of low P availability. METHODS AND RESULTS: Leveraging a 5-year P enrichment experiment on low-P forest soils, high-throughput sequencing was used to profile the microbial community to determine which taxa associate closely with P availability. We hypothesized that there would be a specialized group of soil micro-organisms that could access recalcitrant P and whose presence could serve as a bioindicator of P mining. Community profiling revealed several candidate bioindicators of P mining (Russulales, Acidobacteria Subgroup 2, Acidobacteriales, Obscuribacterales and Solibacterales), whose relative abundance declined with elevated P and had a significant, positive association with phosphatase production. In addition, we identified candidate bioindicators of high P availability (Mytilinidales, Sebacinales, Chitinophagales, Cytophagales, Saccharimonadales, Opitulales and Gemmatales). CONCLUSIONS: This research provides evidence that mitigating P limitation in this ecosystem may be a specialized trait and is mediated by a few microbial taxa. SIGNIFICANCE AND IMPACT OF THE STUDY: Here, we characterize Orders of soil microbes associated with manipulated phosphorus availability in forest soils to determine bioindicator candidates for phosphorus. Likewise, we provide evidence that the microbial trait to utilize recalcitrant organic forms of P (e.g. P mining) is likely a specialized trait and not common to all members of the soil microbial community. This work further elucidates the role that a complex microbial community plays in the cycling of P in low-P soils, and provides evidence for future studies on microbial linkages to human-induced ecosystem changes.

Postpartum anoestrus in five seasonally-calving dairy farms in Victoria, Australia.

OBJECTIVE: A cross-sectional study of seasonally-calving dairy cows in south-western Victoria to quantify the prevalence of anoestrus and to assess the effect of body condition score (BCS), days in milk (DIM) at mating start date (MSD) and age on the likelihood of being diagnosed as anoestrus. METHODS: Age, DIM and BCS details were collected from 1795 cycling and 1399 cows diagnosed as anoestrus in five seasonally-calving dairy herds in Victoria. Multivariable logistic regression was used to quantify the association between BCS, DIM and age and the probability of being diagnosed as anoestrus. RESULTS: The apparent prevalence of anoestrus in cows that took part in the study was 44% (95% confidence interval (CI) 42-46%). The risk of anoestrus increased with decreases in DIM and BCS. There was a significant interaction between BCS and DIM. For cows >90 DIM at MSD (the reference category) the odds of anoestrus in cows with BCS <4.00 was 8.05-fold (95% CI 3.2-20.2) that of cows of BCS ≥4.50. The risk of anoestrus decreased with increases in cow age. Cows aged 2 and 3-5 years had 2.4-fold (95% CI 1.79-3.22) and 1.4 (95% CI 1.11-1.77) odds of anoestrus, respectively, compared with cows aged ≥6 years. CONCLUSION: Reproductive management to maximise the number of days calved at MSD and nutritional management to ensure cows are in BCS >4.5 at mating will reduce the prevalence of anoestrus in Victorian dairy herds.

Phytobeneficial Properties of Bacteria Isolated from the Rhizosphere of Maize in Southwestern Nigerian Soils.

Biocontrol agents isolated outside Africa have performed inconsistently under field conditions in Africa. The development of indigenous phytobeneficial microbial strains that suit local environments may help enhance competitiveness with in situ microorganisms and effectiveness at suppressing local pathogen strains. We isolated bacteria from the rhizosphere of maize growing in southwestern Nigeria and assessed them for growth-promoting characteristics. The best isolates were characterized using 16S rRNA genes and were further evaluated in the greenhouse on maize seedlings. Four isolates (EBS8, IGBR11, EPR2, and ADS14) were outstanding in in vitro assays of antagonistic activity against a local strain of Fusarium verticillioides, phosphate solubilization efficiency, chitinase enzyme activity, and indole-3-acetic acid production. Inoculation of maize seeds with these isolates resulted in ≥95% maize seed germination and significantly enhanced radicle and plumule length. In the greenhouse, maize seedling height, stem girth, number of leaves, leaf area, shoot mass (dry matter), and nutrient contents were significantly enhanced. The bioprotectant and phytobeneficial effects were strongest and most consistent for isolate EBS8, which was identified as a Bacillus strain by 16S rRNA gene analysis. As a bacterial strain that exhibits multiple growth-promoting characteristics and is adapted to local conditions, EBS8 should be considered for the development of indigenous biological fertilizer treatments.

An automated walk-over weighing system as a tool for measuring liveweight change in lactating dairy cows.

Automated walk-over weighing systems can be used to monitor liveweights of cattle. Minimal literature exists to describe agreement between automated and static scales, and no known studies describe repeatability when used for daily measurements of dairy cows. This study establishes the repeatability of an automated walk-over cattle-weighing system, and agreement with static electronic scales, when used in a commercial dairy herd to weigh lactating cows. Forty-six lactating dairy cows from a seasonal calving, pasture-based dairy herd in southwest Victoria, Australia, were weighed once using a set of static scales and repeatedly using an automated walk-over weighing system at the exit of a rotary dairy. Substantial agreement was observed between the automated and static scales when assessed using Lin's concordance correlation coefficient. Weights measured by the automated walkover scales were within 5% of those measured by the static scales in 96% of weighings. Bland and Altman's 95% limits of agreement were -23.3 to 43.6 kg, a range of 66.9 kg. The 95% repeatability coefficient for automated weighings was 46.3 kg. Removal of a single outlier from the data set increased Lin's concordance coefficient, narrowed Bland and Altman's 95% limits of agreement to a range of 32.5 kg, and reduced the 95% repeatability coefficient to 18.7 kg. Cow misbehavior during walk-over weighing accounted for many of the larger weight discrepancies. The automated walk-over weighing system showed substantial agreement with the static scales when assessed using Lin's concordance correlation coefficient. This contrasted with limited agreement when assessed using Bland and Altman's method, largely due to poor repeatability. This suggests the automated weighing system is inadequate for detecting small liveweight differences in individual cows based on comparisons of single weights. Misbehaviors and other factors can result in the recording of spurious values on walk-over scales. Excluding outlier weights and comparing means of 7 consecutive daily weights may improve agreement sufficiently to allow meaningful assessment of small short-term changes in automated weights in individuals and groups of cows.

Analysis of T-RFLP data using analysis of variance and ordination methods: a comparative study.

The analysis of T-RFLP data has developed considerably over the last decade, but there remains a lack of consensus about which statistical analyses offer the best means for finding trends in these data. In this study, we empirically tested and theoretically compared ten diverse T-RFLP datasets derived from soil microbial communities using the more common ordination methods in the literature: principal component analysis (PCA), nonmetric multidimensional scaling (NMS) with Sørensen, Jaccard and Euclidean distance measures, correspondence analysis (CA), detrended correspondence analysis (DCA) and a technique new to T-RFLP data analysis, the Additive Main Effects and Multiplicative Interaction (AMMI) model. Our objectives were i) to determine the distribution of variation in T-RFLP datasets using analysis of variance (ANOVA), ii) to determine the more robust and informative multivariate ordination methods for analyzing T-RFLP data, and iii) to compare the methods based on theoretical considerations. For the 10 datasets examined in this study, ANOVA revealed that the variation from Environment main effects was always small, variation from T-RFs main effects was large, and variation from T-RFxEnvironment (TxE) interactions was intermediate. Larger variation due to TxE indicated larger differences in microbial communities between environments/treatments and thus demonstrated the utility of ANOVA to provide an objective assessment of community dissimilarity. The comparison of statistical methods typically yielded similar empirical results. AMMI, T-RF-centered PCA, and DCA were the most robust methods in terms of producing ordinations that consistently reached a consensus with other methods. In datasets with high sample heterogeneity, NMS analyses with Sørensen and Jaccard distance were the most sensitive for recovery of complex gradients. The theoretical comparison showed that some methods hold distinct advantages for T-RFLP analysis, such as estimations of variation captured, realistic or minimal assumptions about the data, reduced weight placed on rare T-RFs, and uniqueness of solutions. Our results lead us to recommend that method selection be guided by T-RFLP dataset complexity and the outlined theoretical criteria. Finally, we recommend using binary or relativized peak height data with soil-based T-RFLP data for ordination-based exploratory microbial analyses.