The uncertain role of rising atmospheric CO2 on global plant transpiration

As CO2 concentration in the atmosphere rises, there is a need for improved physical understanding of its impact on global plant transpiration. This knowledge gap poses a major hurdle in robustly projecting changes in the global hydrologic cycle. For this reason, here we review the different processes by which atmospheric CO2 concentration affects plant transpiration, the several uncertainties related to the complex physiological and radiative processes involved, and the knowledge gaps which need to be filled in order to improve predictions of plant transpiration. Although there is a high degree of certainty that rising CO2 will impact plant transpiration, the exact nature of this impact remains unclear due to complex interactions between CO2 and climate, and key aspects of plant morphology and physiology. The interplay between these factors has substantial consequences not only for future climate and global vegetation, but also for water availability needed for sustaining the productivity of terrestrial ecosystems. Future changes in global plant transpiration in response to enhanced CO2 are expected to be driven by water availability, atmospheric evaporative demand, plant physiological processes, emergent plant disturbances related to increasing temperatures, and the modification of plant physiology and coverage. Considering the universal sensitivity of natural and agricultural systems to terrestrial water availability we argue that reliable future projections of transpiration is an issue of the highest priority, which can only be achieved by integrating monitoring and modeling efforts to improve the representation of CO2 effects on plant transpiration in the next generation of earth system models. © 2022 The Author

Seasonal and habitat effects on the nutritional properties of savanna vegetation: Potential implications for early hominin dietary ecology

The African savannas that many early hominins occupied likely experienced stark seasonality and contained mosaic habitats (i.e., combinations of woodlands, wetlands, grasslands, etc.). Most would agree that the bulk of dietary calories obtained by taxa such as Australopithecus and Paranthropus came from the consumption of vegetation growing across these landscapes. It is also likely that many early hominins were selective feeders that consumed particular plants/plant parts (e.g., leaves, fruit, storage organs) depending on the habitat and season within which they were foraging. Thus, improving our understanding of how the nutritional properties of potential hominin plant foods growing in modern African savanna ecosystems respond to season and vary by habitat will improve our ability to model early hominin dietary behavior. Here, we present nutritional analyses (crude protein and acid detergent fiber) of plants growing in eastern and southern African savanna habitats across both wet and dry seasons. We find that many assumptions about savanna vegetation are warranted. For instance, plants growing in our woodland habitats have higher average protein/fiber ratios than those growing in our wetland and grassland transects. However, we find that the effects of season and habitat are complex, an example being the unexpectedly higher protein levels we observe in the grasses and sedges growing in our Amboseli wetlands during the dry season. Also, we find significant differences between the vegetation growing in our eastern and southern African field sites, particularly among plants using the C4 photosynthetic pathway. This may have implications for the differences we see between the stable carbon isotope compositions and dental microwear patterns of eastern and southern African Paranthropus species, despite their shared, highly derived craniodental anatomy.Horizon 2020(H2020)ERC-Stg 677576Bioarchaeolog

Grass leaves as potential hominin dietary resources

Discussions about early hominin diets have generally excluded grass leaves as a staple food resource, despite their ubiquity in most early hominin habitats. In particular, stable carbon isotope studies have shown a prevalent C4 component in the diets of most taxa, and grass leaves are the single most abundant C4 resource in African savannas. Grass leaves are typically portrayed as having little nutritional value (e.g., low in protein and high in fiber) for hominins lacking specialized digestive systems. It has also been argued that they present mechanical challenges (i.e., high toughness) for hominins with bunodont dentition. Here, we compare the nutritional and mechanical properties of grass leaves with the plants growing alongside them in African savanna habitats. We also compare grass leaves to the leaves consumed by other hominoids and demonstrate that many, though by no means all, compare favorably with the nutritional and mechanical properties of known primate foods. Our data reveal that grass leaves exhibit tremendous variation and suggest that future reconstructions of hominin dietary ecology take a more nuanced approach when considering grass leaves as a potential hominin dietary resource.Horizon 2020(H2020)ERC-STG 677576Bioarchaeolog

Seasonal prediction of Horn of Africa long rains using machine learning: the pitfalls of preselecting correlated predictors

The Horn of Africa is highly vulnerable to droughts and floods, and reliable long-term forecasting is a key part of building resilience. However, the prediction of the “long rains” season (March–May) is particularly challenging for dynamical climate prediction models. Meanwhile, the potential for machine learning to improve seasonal precipitation forecasts in the region has yet to be uncovered. Here, we implement and evaluate four data-driven models for prediction of long rains rainfall: ridge and lasso linear regressions, random forests and a single-layer neural network. Predictors are based on SSTs, zonal winds, land state, and climate indices, and the target variables are precipitation totals for each separate month (March, April, and May) in the Horn of Africa drylands, with separate predictions made for lead-times of 1–3 months. Results reveal a tendency for overfitting when predictors are preselected based on correlations to the target variable over the entire historical period, a frequent practice in machine learning-based seasonal forecasting. Using this conventional approach, the data-driven methods—and particularly the lasso and ridge regressions—often outperform dynamical seasonal hindcasts. However, when the selection of predictors is done independently of both the train and test data, by performing this predictor selection within the cross-validation loop, the performance of all four data-driven models is poorer than that of the dynamical hindcasts. These findings should not discourage future applications of machine learning for rainfall forecasting in the region. Yet, they should be seen as a note of caution to prevent optimistically biased results that are not indicative of the true power in operational forecast systems

A lagrangian analysis of the sources of rainfall over the Horn of Africa drylands

The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons—long and short rains—exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modeling and observation‐based studies, here we analyze the HAD rainfall from an observationally‐constrained Lagrangian perspective. We quantify and map the region's major oceanic and terrestrial sources of moisture. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low‐level jet) contribute ∼80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ∼20% from the second month onwards, that is, when the oceanic‐origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, and have important implications for (sub‐)seasonal forecasts and long‐term projections of the HAD rainfall

Assessment of vehicle safety problems for special driving populations. Final report.

National Highway Traffic Safety Administration, Washington, D.C.Mode of access: Internet.Author corporate affiliation: National Public Services Research Institute, Alexandria, Va.Author corporate affiliation: Central Missouri State University, Warrensburg, Mo.Contract amount - $39,434. Released Sept 1979Subject code: DGEPDSubject code: EHHSubject code: PEGSubject code: SBE