Like many species, the model plant Arabidopsis thaliana exhibits multiple different life histories in natural environments. We grew mutants impaired in different signaling pathways in field experiments across the species' native European range in order to dissect the mechanisms underlying this variation. Unexpectedly, mutational loss at loci implicated in the cold requirement for flowering had little effect on life history except in late-summer cohorts. A genetically informed photothermal model of progression toward flowering explained most of the observed variation and predicted an abrupt transition from autumn flowering to spring flowering in late-summer germinants. Environmental signals control the timing of this transition, creating a critical window of acute sensitivity to genetic and climatic change that may be common for seasonally regulated life history traits.

stricting their application in retrospective or validation studies (Hutchinson, 1991). Crop growth models require solar irradiance as input data, yetThe need for solar irradiance data for crop models there are few places where such data are routinely measured. For has led researchers to develop a number of methods for locations where measured values are not available, solar irradiance simulating such data. For example, some crop modelers can be estimated using empirical models such as the Bristow-(e.g., Rosenthal et al., 1989) have incorporated stochas-Campbell (B-C) model. This study was conducted to assess the spatial and seasonal accuracy of the B-C model for midcontinental locations tic weather generators into their simulations. These in Kansas. A 30-year data set from Manhattan, KS, was used to weather generators simulate irradiance and other metecalibrate and evaluate unmodified and modified forms of the B-C orological and climatological inputs based on probabilismodel. The effect of seasonality was investigated by subdividing the tic criteria. This approach eliminates the need for meayearly data into two subsets, a high noontime solar elevation angle sured solar irradiance; however, it seems reasonable period, ranging from DOY 121 to 273, and a low noontime elevation that estimated, rather than randomly generated, solar angle period comprising the remainder of the year. The B-C model irradiance values would also result in improved yield eswas also evaluated without seasonal division of the year. The calitimates. brated models were then tested against measured solar irradiance A number of techniques are available for estimating values for 10 sites distributed across the state of Kansas. Results solar irradiance. These vary in sophistication from simindicate that, for the calibration site at Manhattan, irradiance was ple empirical formulations based on common weather more accurately estimated using a modified form of the B-C model. For the yearly data, root mean square error (RMSE) was 3.9 MJ m Ϫ2 or climate data to complex radiative transfer schemes d Ϫ1 (25% error), compared with 5.2 MJ m Ϫ2 d Ϫ1 (24% error) for the that explicitly model the absorption and scattering of high solar elevation angle period and 3.6 MJ m Ϫ2 d Ϫ1 (32% error) the solar beam as it passes through the atmosphere. for the low solar elevation angle period. The RMSE for the 10 test Hall, Kansas State University, Manhattan, KS 66506-0801; R.L. Vanwhere A, B, and C are empirical coefficients. Although derlip,

Identifying suitable genetic stock for restoration often employs a ‘best guess’ approach. Without adaptive variation studies, restoration may be misguided. We test the extent to which climate in central US grasslands exerts selection pressure on a foundation grass big bluestem (Andropogon gerardii), widely used in restorations, and resulting in local adaptation. We seeded three regional ecotypes of A. gerardii in reciprocal transplant garden communities across 1150 km precipitation gradient. We measured ecological responses over several timescales (instantaneous gas exchange, medium-term chlorophyll absorbance, and long-term responses of establishment and cover) in response to climate and biotic factors and tested if ecotypes could expand range. The ecotype from the driest region exhibited greatest cover under low rainfall, suggesting local adaptation under abiotic stress. Unexpectedly, no evidence for cover differences between ecotypes exists at mesic sites where establishment and cover of all ecotypes were low, perhaps due to strong biotic pressures. Expression of adaptive differences is strongly environment specific. Given observed adaptive variation, the most conservative restoration strategy would be to plant the local ecotype, especially in drier locations. With superior performance of the most xeric ecotype under dry conditions and predicted drought, this ecotype may migrate eastward, naturally or with assistance in restorations.

There is limited information on agronomic practices affecting wheat (Triticum aestivum L.) yield in intensively managed dryland systems despite the opportunity to narrow the existing yield gap (YG). We used a unique database of 100 intensively managed field‐years entered in the Kansas Wheat Yield Contest during the 2010 to 2017 harvest seasons to (i) quantify the YG, (ii) describe wheat management, and (iii) identify management opportunities and weather patterns associated with yield. We simulated wheat water‐limited yield (Yw) using Simple Simulation Modeling–Wheat (SSM‐Wheat) model for each field‐year to estimate YG as the difference between Yw and actual yield (Ya) and used 11 statistical approaches to test the association of management practices and weather variables with Ya. Wheat Ya averaged 5.5 Mg ha−1, and simulated Yw averaged 6.4 Mg ha−1, resulting in a YG of 0.9 Mg ha−1 (15% of Yw). High‐yielding fields had lower maximum and minimum temperatures and greater cumulative solar radiation and precipitation during grain fill. Varieties susceptible to fungal diseases responded to foliar fungicide (0.8–1.4 Mg ha−1), whereas resistant varieties did not. Seeding rate was negatively associated with Ya, as yield quantile 0.99 was 7.5 Mg ha−1 and decreased by 2.7 Mg ha−1 for every 100‐seed m−2 increase in seeding rate above 305 seeds m−2. In‐furrow P fertilizer, previous crop, tillage practice, and N timing were also associated with Ya. We conclude that fields entered in yield contests have closed the exploitable YG, and there are opportunities to improve Ya through improved management in regions with stagnant wheat yield.

Many prior studies have uncovered evidence for local adaptation using reciprocal transplant experiments. However, these studies are rarely conducted for a long enough time to observe succession and competitive dynamics in a community context, limiting inferences for long‐lived species. Furthermore, the genetic basis of local adaptation and genetic associations with climate has rarely been identified. Here, we report on a long‐term (6‐year) experiment conducted under natural conditions focused on Andropogon gerardii, the dominant grass of the North American Great Plains tallgrass ecosystem. We focus on this foundation grass that comprises 80% of tallgrass prairie biomass and is widely used in 20,000 km2 of restoration. Specifically, we asked the following questions: (a) Whether ecotypes are locally adapted to regional climate in realistic ecological communities. (b) Does adaptive genetic variation underpin divergent phenotypes across the climate gradient? (c) Is there evidence of local adaptation if the plants are exposed to competition among ecotypes in mixed ecotype plots? Finally, (d) are local adaptation and genetic divergence related to climate? Reciprocal gardens were planted with 3 regional ecotypes (originating from dry, mesic, wet climate sources) of Andropogon gerardii across a precipitation gradient (500–1,200 mm/year) in the US Great Plains. We demonstrate local adaptation and differentiation of ecotypes in wet and dry environments. Surprisingly, the apparent generalist mesic ecotype performed comparably under all rainfall conditions. Ecotype performance was underpinned by differences in neutral diversity and candidate genes corroborating strong differences among ecotypes. Ecotype differentiation was related to climate, primarily rainfall. Without long‐term studies, wrong conclusions would have been reached based on the first two years. Further, restoring prairies with climate‐matched ecotypes is critical to future ecology, conservation, and sustainability under climate change.

Big bluestem (Andropogon gerardii) is an ecologically dominant grass with wide distribution across the environmental gradient of U.S. Midwest grasslands. This system offers an ideal natural laboratory to study population divergence and adaptation in spatially varying climates. Objectives were to: (i) characterize neutral genetic diversity and structure within and among three regional ecotypes derived from 11 prairies across the U.S. Midwest environmental gradient, (ii) distinguish between the relative roles of isolation by distance (IBD) vs. isolation by environment (IBE) on ecotype divergence, (iii) identify outlier loci under selection and (iv) assess the association between outlier loci and climate. Using two primer sets, we genotyped 378 plants at 384 polymorphic AFLP loci across regional ecotypes from central and eastern Kansas and Illinois. Neighbour-joining tree and PCoA revealed strong genetic differentiation between Kansas and Illinois ecotypes, which was better explained by IBE than IBD. We found high genetic variability within prairies (80%) and even fragmented Illinois prairies, surprisingly, contained high within-prairie genetic diversity (92%). Using Bayenv2, 14 top-ranked outlier loci among ecotypes were associated with temperature and precipitation variables. Six of seven BayeScanFST outliers were in common with Bayenv2 outliers. High genetic diversity may enable big bluestem populations to better withstand changing climates; however, population divergence supports the use of local ecotypes in grassland restoration. Knowledge of genetic variation in this ecological dominant and other grassland species will be critical to understanding grassland response and restoration challenges in the face of a changing climate.

Circadian clocks have evolved independently in all three domains of life, suggesting that internal mechanisms of time-keeping are adaptive in contemporary populations. However, the performance consequences of either discrete or quantitative clock variation have rarely been tested in field settings. Clock sensitivity of diverse segregating lines to the environment remains uncharacterized as do the statistical genetic parameters that determine evolutionary potential. In field studies with Arabidopsis thaliana, we found that major perturbations to circadian cycle length (referred to as clock period) via mutation reduce both survival and fecundity. Subtler adjustments via genomic introgression of naturally occurring alleles indicated that clock periods slightly >24 hr were adaptive, consistent with prior models describing how well the timing of biological processes is adjusted within a diurnal cycle (referred to as phase). In segregating recombinant inbred lines (RILs), circadian phase varied up to 2 hr across months of the growing season, and both period and phase expressed significant genetic variances. Performance metrics including developmental rate, size and fruit set were described by principal components (PC) analyses and circadian parameters correlated with the first PC, such that period lengths slightly >24 hr were associated with improved performance in multiple RIL sets. These experiments translate functional analyses of clock behaviour performed in controlled settings to natural ones, demonstrating that quantitative variation in circadian phase is highly responsive to seasonally variable abiotic factors. The results expand upon prior studies in controlled settings, showing that discrete and quantitative variation in clock phenotypes correlates with performance in nature.

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