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Possible future increases in atmospheric temperature may threaten wheat (Triticum aestivum L.) production and food security. The purpose of this research is to determine the response of wheat growth to supplemental heating and to seasonal air temperature from an unusually wide range of planting dates. A field study was conducted at Maricopa, AZ, where wheat was planted from September to May over a 2-yr period for a total of 12 planting dates. Supplemental heating was provided for 6 of the 12 planting dates using infrared heaters placed above the crop which increased canopy temperature by 1.3°C during the day and 2.7°C during the night. Grain yield declined 42 g m−2 (6.9%) per 1°C increase in seasonal temperature above 16.3°C. Supplemental heating had no effect on grain yield for plantings in winter (Dec./Jan.) since temperatures were near optimum (14.9°C). However, in spring (Mar.) plantings where temperature (22.2°C) was above optimum, supplemental heating decreased grain yield from 510 to 368 g m−2. Supplemental heating had the greatest effect in the early fall plantings (Sept./Oct.) when temperature was slightly below optimum (13.8°C) and mid-season frost limited the yield of unheated plots to only 3 g m−2 whereas yield of heated plots was 435 g m−2. Thus, possible future increases in temperature may decrease wheat yield for late plantings and shift optimum planting windows to earlier dates in areas of the world similar to the desert southwest of the United States.

Design and decision-making for marine protected areas (MPAs) on coral reefs require prediction of MPA effects with population models. Modeling of MPAs has shown how the persistence of metapopulations in systems of MPAs depends on the size and spacing of MPAs, and levels of fishing outside the MPAs. However, the pattern of demographic connectivity produced by larval dispersal is a key uncertainty in those modeling studies. The information required to assess population persistence is a dispersal matrix containing the fraction of larvae traveling to each location from each location, not just the current number of larvae exchanged among locations. Recent metapopulation modeling research with hypothetical dispersal matrices has shown how the spatial scale of dispersal, degree of advection versus diffusion, total larval output, and temporal and spatial variability in dispersal influence population persistence. Recent empirical studies using population genetics, parentage analysis, and geochemical and artificial marks in calcified structures have improved the understanding of dispersal. However, many such studies report current self-recruitment (locally produced settlement/settlement from elsewhere), which is not as directly useful as local retention (locally produced settlement/total locally released), which is a component of the dispersal matrix. Modeling of biophysical circulation with larval particle tracking can provide the required elements of dispersal matrices and assess their sensitivity to flows and larval behavior, but it requires more assumptions than direct empirical methods. To make rapid progress in understanding the scales and patterns of connectivity, greater communication between empiricists and population modelers will be needed. Empiricists need to focus more on identifying the characteristics of the dispersal matrix, while population modelers need to track and assimilate evolving empirical results.

Climate warming may raise wheat (Triticum aestivum L.) yields in cooler climates and lower them in warmer climates. To understand these contrasting effects, infrared heating lamps were used to warm irrigated spring wheat by 1.5°C (day) and 3.0°C (night) above unheated controls during different times of the year at Maricopa, AZ. Changes in wheat growth with warming were used to test hypotheses for temperature effects on crop growth in the process model ecosys. Infrared heating substantially raised phytomass growth and grain yield under lower air temperature (Ta) following plantings from September through December. The same heating, however, lowered growth and yield under higher Ta following plantings from January through March. Gains in wheat yield of as much as 200 g C m−2 with heating under lower Ta were attributed in the model to more rapid CO2 fixation and to reduced chilling effects on seed set. These gains were only partially offset by losses from shortened wheat growth periods. Losses in wheat yield of as much as 100 g C m−2 with heating under higher Ta were attributed in the model to adverse effects of heating on crop water status and on CO2 fixation vs. respiration, to greater heat stress effects on seed set, and to shortened crop growth periods. Model hypotheses thus explained contrasting effects of heating on wheat yields under different Ta found in the field experiment as well as in many earlier studies. Well-constrained tests of these hypotheses are vital for models used to project climate change impacts on agricultural ecosystems.

In coral reef fishes, density-dependent population regulation is commonly mediated via predation on juveniles that have recently settled from the plankton. All else being equal, strong density-dependent mortality should select against the formation of high-density aggregations, yet the juveniles of many reef fishes aggregate. In light of this apparent contradiction, we hypothesized that the form and intensity of density dependence vary with the spatial scale of measurement. Individual groups might enjoy safety in numbers, but predators could still produce density-dependent mortality at larger spatial scales. We investigated this possibility using recently settled juvenile bluehead wrasse, Thalassoma bifasciatum, a small, aggregating reef fish. An initial caging experiment demonstrated that juvenile bluehead wrasse settlers suffer high predation, and spatial settlement patterns indicated that bluehead wrasse juveniles preferentially settle in groups, although they are also found singly. We then monitored the mortality of recently settled juveniles at two spatial scales: microsites, occupied by individual fish or groups of fish and separated by centimeters, and sites, consisting of ∼2400-m2 areas of reef and separated by kilometers. At the microsite scale, we measured group size and effective population density independently and found that per capita mortality decreased with group size but was not related to density. At the larger spatial scale, however, per capita mortality increased with settler density. This shift in the form of density dependence with spatial scale could reconcile the existence of small-scale aggregative behavior typical of many reef fishes with the population-scale density dependence that is essential to population stability and persistence.

Cereal production is strongly influenced by flowering date. Wheat (Triticum aestivum L.) models simulate days to flower by assuming that development is modified by vernalization and photoperiodism. Cultivar differences are parameterized by vernalization requirement, photoperiod sensitivity, and earliness per se. The parameters are usually estimated by comparing simulations with field observations but appear estimable from genetic information. For wheat, the Vrn and Ppd loci, which affect vernalization and photoperiodism, were logical candidates for estimating parameters in the model CSM-Cropsim-CERES. Two parameters were estimated conventionally and then re-estimated with linear effects of Vrn and Ppd. Flowering data were obtained for 29 cultivars from international nurseries and divided into calibration (14 locations) and evaluation (34 locations) sets. Simulations with a generic cultivar explained 95% of variation in flowering for calibration data (10 d RMSE) and 89% for evaluation data (10 d RMSE), indicating the large effect of environment. Nonetheless, for the calibration data, the gene-based model explained 29% of remaining variation, and the conventional model, 54%. For the evaluation data, the gene-based model explained 17% of remaining variation, and the conventional model, 27%. Gene-based prediction of wheat phenology appears feasible, but more extensive genetic characterization of cultivars is needed.

While there is great interest in the degree to which local interactions \"scale-up\" to predict regional patterns of abundance, few studies in marine systems have simultaneously examined patterns of abundance at both the large scale (tens of kilometers) typical of larval movement and the small scale (meters) typical of post-settlement interactions. We addressed this gap by monitoring larval supply, adult survivorship, and giant kelp (Macrocystis pyrifera, a primary habitat-forming species) abundance for 13 populations of kelp bass (Paralabrax clathratus) spread over ∼200 km in the Santa Barbara Channel, California, USA. At the small, within-site scale, both recruitment and adult survivorship of kelp bass were density-dependent and positively related to kelp abundance. At the larger, among-site scale, the spatial pattern of adult kelp bass abundance was predicted well by the pattern of kelp bass larval supply, but there was a consistent negative spatial relationship between kelp abundance and kelp bass larval supply despite the positive effects of kelp on kelp bass at the smaller spatial scale. This large-scale negative relationship was likely a product of a channel-wide spatial mismatch between oceanographic conditions that favor kelp survival and those that concentrate and distribute fish larvae. These results generally support the recruit—adult hypothesis: kelp bass populations are limited by recruitment at low recruit densities but by density-dependent competition for food resources and/or predator refuges at high recruit densities. At the same time, spatial variation in kelp abundance produced substantial spatiotemporal heterogeneity in kelp bass demographics, which argues for a multispecies, metacommunity approach to predicting kelp bass dynamics.

To determine the likely effects of global warming on field-grown wheat (Triticum aestivum L.), a “Hot Serial Cereal” experiment was conducted—so-called “Cereal” because wheat was the crop, “Serial” because the wheat was planted about every 6 wk for 2 yr, and “Hot” because infrared heaters were deployed on six of the planting dates in a temperature free-air controlled enhancement (T-FACE) system, which warmed the canopies of the Heated plots. During the experiment, measurements of canopy reflectance were made two to five times per week from which values of normalized difference vegetation index (NDVI) were calculated. As expected, curves of NDVI from the Heated plots vs. time and vs. growing degree days (GDD) computed from air temperatures generally were ahead of those from Reference plots. However, when plotted against GDD computed from canopy temperatures the curves coalesced, which gives confidence that the infrared-heater treatment simulates natural warming and will produce plant responses not unlike those expected with future global warming. Biomass and grain yields were correlated with the areas under the NDVI vs. GDD curves for the air-temperature-based GDDs, but high variability prevented such a correlation to be detected using canopy-temperature-based GDD. Large differences existed between the total amounts of air or canopy temperature-based GDDs required for wheat to mature in our irrigated fields in an arid region. This implies that GDD based on air temperatures should be regarded only as a local guide to plant development rates, whereas those based on canopy temperatures would be more universal.

CIMMYT (International Maize and Wheat Improvement Centre) and other research groups within the Consultative Group for International Agricultural Research (CGIAR) have made major contributions to agricultural development, e.g. underpinning the 'green revolution', but it is unlikely they will continue making such far-reaching contributions without the ability to collect, analyse and assimilate large amounts of spatially orientated agronomic and climatic data. Increasingly, application of modern tools and technologies are crucial elements in order to support and enhance the effectiveness of international agricultural research. Bread and durum wheats (Triticum aestivum and Triticum durum) occupy an estimated 200 million ha globally, are grown from sea level to over 3500 m asl, and from the equator to latitudes above 60 ° N in Canada, Europe, and Asia. For organizations like CIMMYT, which seek to improve wheat production in the developing world, understanding the geographic context of wheat production is crucial for priority setting, promoting collaboration, and targeting germplasm or management practices to specific environments. Increasingly important is forecasting how the environments, and their associated biotic and abiotic stress patterns, shift with changing climate patterns. There is also a growing need to classify production environments by combining biophysical criteria with socio-economic factors. Geospatial technologies, especially geographic information systems (GIS), are playing a role in each of these areas, and spatial analysis provides unique insights. Use of GIS to characterize wheat production environments is described, drawing from examples at CIMMYT. Since the 1980s, the CIMMYT wheat programme has classified production regions into mega-environments (MEs) based on climatic, edaphic, and biotic constraints. Advances in spatially disaggregated datasets and GIS tools allow MEs to be characterized and mapped in a much more quantitative manner. Parallel advances are improving characterizations of the actual (v. potential) distribution of major crops, including wheat. The combination of improved crop distribution data and key biophysical data at high spatial resolutions also permits exploring scenarios for disease epidemics, as illustrated for the stem rust race Ug99. Availability of spatial data describing future climate conditions may provide insights into potential changes in wheat production environments in the coming decades. There is a pressing need to advance beyond static definitions of environments and incorporate temporal aspects to define locations or regions in terms of probability or frequency of occurrence of different environment types. Increased availability of near real-time daily weather data derived from remote sensing should further improve characterization of environments, as well as permit regional-scale modelling of dynamic processes such as disease progression or crop water status.

Study Design: Retrospective radiographic study. Objectives: T1 slope is an important parameter of sagittal spinal balance. However, the T1 superior endplate can be difficult to visualize on radiographs due to overlying anatomical structures. C7 slope has been proposed as a potential substitute for T1 slope when the T1 superior endplate is not well visualized. The objective of this study was 2-fold: (1) to assess the correlation between C7 and T1 slopes on upright cervical spine radiographs and (2) to evaluate the interrater reliability of C7 slope. Methods: Cervical spine radiographs taken between December 2017 and June 2018 at a single institution were reviewed. Two observers measured upper C7 slope, lower C7 slope, and T1 slope. The correlations between upper and lower C7 slope and T1 slope were evaluated, and linear regression analyses were performed. Interrater reliability of C7 slope was also assessed. Results: In this cohort of 152 patients, there was a strong correlation between upper C7 slope and T1 slope (r = 0.91, P < .001), as well as between lower C7 slope and T1 slope (r = 0.90, P < .001). T1 slope could be estimated from the linear regression equation, T1 slope = 0.87 × C7 slope + 7, with an overall model fit of R 2 = 0.8. There was strong interrater reliability for upper (intraclass correlation coefficient [ICC] = 0.95, P < .001) and lower C7 slope (ICC = 0.96, P < .001). Conclusions: Both the upper and lower C7 slope are strongly correlated with T1 slope and can be used as a substitute to estimate T1 slope when the superior endplate of T1 is not well visualized.

Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.