Pathogens,faecal indicators and human-specific microbial source-tracking markers in sewage

The objective of this review is to assess the current state of knowledge of pathogens, general faecal indicators and human-specific microbial source tracking markers in sewage. Most of the microbes present in sewage are from the microbiota of the human gut, including pathogens. Bacteria and viruses are the most abundant groups of microbes in the human gut microbiota. Most reports on this topic show that raw sewage microbiological profiles reflect the human gut microbiota. Human and animal faeces share many commensal microbes as well as pathogens. Faecal-orally transmitted pathogens constitute a serious public health problem that can be minimized through sanitation. Assessing both the sanitation processes and the contribution of sewage to the faecal contamination of water bodies requires knowledge of the content of pathogens in sewage, microbes indicating general faecal contamination and microbes that are only present in human faecal remains, which are known as the human-specific microbial source-tracking (MST) markers. Detection of pathogens would be the ideal option for managing sanitation and determining the microbiological quality of waters contaminated by sewage; but at present, this is neither practical nor feasible in routine testing. Traditionally, faecal indicator bacteria have been used as surrogate indicators of general faecal residues. However, in many water management circumstances, it becomes necessary to detect both the origin of faecal contamination, for which MST is paramount, and live micro-organisms, for which molecular methods are not suitable. The presence and concentrations of pathogens, general faecal indicators and human-specific MST markers most frequently reported in different areas of the world are summarized in this review.     

Leveraging plant hydraulics to yield predictive and dynamic plant leaf allocation in vegetation models with climate change

Plant functional traits provide a link in process‐based vegetation models between plant‐level physiology and ecosystem‐level responses. Recent advances in physiological understanding and computational efficiency have allowed for the incorporation of plant hydraulic processes in large‐scale vegetation models. However, a more mechanistic representation of water limitation that determines ecosystem responses to plant water stress necessitates a re‐evaluation of trait‐based constraints for plant carbon allocation, particularly allocation to leaf area. In this review, we examine model representations of plant allocation to leaves, which is often empirically set by plant functional type‐specific allometric relationships. We analyze the evolution of the representation of leaf allocation in models of different scales and complexities. We show the impacts of leaf allocation strategy on plant carbon uptake in the context of recent advancements in modeling hydraulic processes. Finally, we posit that deriving allometry from first principles using mechanistic hydraulic processes is possible and should become standard practice, rather than using prescribed allometries. The representation of allocation as an emergent property of scarce resource constraints is likely to be critical to representing how global change processes impact future ecosystem dynamics and carbon fluxes and may reduce the number of poorly constrained parameters in vegetation models.     

Dissecting the nonlinear response of maize yield to high temperature stress with model‐data integration

Evidence suggests that global maize yield declines with a warming climate, particularly with extreme heat events. However, the degree to which important maize processes such as biomass growth rate, growing season length (GSL) and grain formation are impacted by an increase in temperature is uncertain. Such knowledge is necessary to understand yield responses and develop crop adaptation strategies under warmer climate. Here crop models, satellite observations, survey, and field data were integrated to investigate how high temperature stress influences maize yield in the U.S. Midwest. We showed that both observational evidence and crop model ensemble mean (MEM) suggests the nonlinear sensitivity in yield was driven by the intensified sensitivity of harvest index (HI), but MEM underestimated the warming effects through HI and overstated the effects through GSL. Further analysis showed that the intensified sensitivity in HI mainly results from a greater sensitivity of yield to high temperature stress during the grain filling period, which explained more than half of the yield reduction. When warming effects were decomposed into direct heat stress and indirect water stress (WS), observational data suggest that yield is more reduced by direct heat stress (?4.6 ± 1.0%/°C) than by WS (?1.7 ± 0.65%/°C), whereas MEM gives opposite results. This discrepancy implies that yield reduction by heat stress is underestimated, whereas the yield benefit of increasing atmospheric CO₂ might be overestimated in crop models, because elevated CO₂ brings yield benefit through water conservation effect but produces limited benefit over heat stress. Our analysis through integrating data and crop models suggests that future adaptation strategies should be targeted at the heat stress during grain formation and changes in agricultural management need to be better accounted for to adequately estimate the effects of heat stress.