Common Sunflower Seedling Emergence across the U.S. Midwest

Predictions of weed emergence can be used by practitioners to schedule POST weed management operations. Common sunflower seed from Kansas was used at six Midwestern U.S. sites to examine the variability that 16 climates had on common sunflower emergence. Nonlinear mixed effects models, using a flexible sigmoidal Weibull function that included thermal time, hydrothermal time, and a modified hydrothermal time (with accumulation starting from January 1 of each year), were developed to describe the emergence data. An iterative method was used to select an optimal base temperature (Tb) and base and ceiling soil matric potentials (ψb and ψc) that resulted in a best-fit regional model. The most parsimonious model, based on Akaike\u27s information criterion (AIC), resulted when Tb = 4.4 C, and ψb = −20000 kPa. Deviations among model fits for individual site years indicated a negative relationship (r = −0.75; P \u3c 0.001) between the duration of seedling emergence and growing degree days (Tb = 10 C) from October (fall planting) to March. Thus, seeds exposed to warmer conditions from fall burial to spring emergence had longer emergence periods

An Affinity–Effect Relationship for Microbial Communities in Plant–Soil Feedback Loops

Feedback loops involving soil microorganisms can regulate plant populations. Here, we hypothesize that microorganisms are most likely to play a role in plant–soil feedback loops when they possess an affinity for a particular plant and the capacity to consistently affect the growth of that plant for good or ill. We characterized microbial communities using whole-community DNA fingerprinting from multiple "home-and-away" experiments involving giant ragweed (Ambrosia trifida L.) and common sunflower (Helianthus annuus L.), and we looked for affinity–effect relationships in these microbial communities. Using canonical ordination and partial least squares regression, we developed indices expressing each microorganism's affinity for ragweed or sunflower and its putative effect on plant biomass, and we used linear regression to analyze the relationship between microbial affinity and effect. Significant linear affinity–effect relationships were found in 75% of cases. Affinity–effect relationships were stronger for ragweed than for sunflower, and ragweed affinity–effect relationships showed consistent potential for negative feedback loops. The ragweed feedback relationships indicated the potential involvement of multiple microbial taxa, resulting in strong, consistent affinity–effect relationships in spite of large-scale microbial variability between trials. In contrast, sunflower plant–soil feedback may involve just a few key players, making it more sensitive to underlying microbial variation. We propose that affinity–effect relationship can be used to determine key microbial players in plant–soil feedback against a low "signal-to-noise" background of complex microbial datasets

Local Conditions, Not Regional Gradients, Drive Demographic Variation of Giant Ragweed (\u3ci\u3eAmbrosia trifida\u3c/i\u3e) and Common Sunflower (\u3ci\u3eHelianthus annuus\u3c/i\u3e) Across Northern U.S. Maize Belt

Knowledge of environmental factors influencing demography of weed species will improve understanding of current and future weed invasions. The objective of this study was to quantify regional-scale variation in vital rates of giant ragweed and common sunflower . To accomplish this objective, a common field experiment was conducted across seven sites between 2006 and 2008 throughout the north central U.S. maize belt. Demographic parameters of both weed species were measured in intra- and interspecific competitive environments, and environmental data were collected within site-years. Site was the strongest predictor of below ground vital rates (summer and winter seed survival and seedling recruitment), indicating sensitivity to local abiotic conditions. However, biotic factors influenced above ground vital rates (seedling survival and fecundity). Partial least squares regression (PLSR) indicated that demography of both species was most strongly influenced by thermal time and precipitation. The first PLSR components, both characterized by thermal time, explained 63.2% and 77.0% of variation in the demography of giant ragweed and common sunflower, respectively; the second PLSR components, both characterized by precipitation, explained 18.3% and 8.5% of variation, respectively. The influence of temperature and precipitation is important in understanding the population dynamics and potential distribution of these species in response to climate change

FelixJoelCropSoilScienceAffinity-EffectRelationship.pdf

Feedback loops involving soil microorganisms can regulate plant populations. Here, we hypothesize that microorganisms are most likely to play a role in plant–soil feedback loops when they possess an affinity for a particular plant and the capacity to consistently affect the growth of that plant for good or ill. We characterized microbial communities using whole-community DNA fingerprinting from multiple "home-and-away" experiments involving giant ragweed (Ambrosia trifida L.) and common sunflower (Helianthus annuus L.), and we looked for affinity–effect relationships in these microbial communities. Using canonical ordination and partial least squares regression, we developed indices expressing each microorganism's affinity for ragweed or sunflower and its putative effect on plant biomass, and we used linear regression to analyze the relationship between microbial affinity and effect. Significant linear affinity–effect relationships were found in 75% of cases. Affinity–effect relationships were stronger for ragweed than for sunflower, and ragweed affinity–effect relationships showed consistent potential for negative feedback loops. The ragweed feedback relationships indicated the potential involvement of multiple microbial taxa, resulting in strong, consistent affinity–effect relationships in spite of large-scale microbial variability between trials. In contrast, sunflower plant–soil feedback may involve just a few key players, making it more sensitive to underlying microbial variation. We propose that affinity–effect relationship can be used to determine key microbial players in plant–soil feedback against a low "signal-to-noise" background of complex microbial datasets

An Affinity–Effect Relationship for Microbial Communities in Plant–Soil Feedback Loops

Feedback loops involving soil microorganisms can regulate plant populations. Here, we hypothesize that microorganisms are most likely to play a role in plant–soil feedback loops when they possess an affinity for a particular plant and the capacity to consistently affect the growth of that plant for good or ill. We characterized microbial communities using whole-community DNA fingerprinting from multiple home-and-away experiments involving giant ragweed (Ambrosia trifida L.) and common sunflower (Helianthus annuus L.), and we looked for affinity–effect relationships in these microbial communities. Using canonical ordination and partial least squares regression, we developed indices expressing each microorganism\u27s affinity for ragweed or sunflower and its putative effect on plant biomass, and we used linear regression to analyze the relationship between microbial affinity and effect. Significant linear affinity–effect relationships were found in 75 % of cases. Affinity–effect relationships were stronger for ragweed than for sunflower, and ragweed affinity–effect relationships showed consistent potential for negative feedback loops. The ragweed feedback relationships indicated the potential involvement of multiple microbial taxa, resulting in strong, consistent affinity–effect relationships in spite of large-scale microbial variability between trials. In contrast, sunflower plant–soil feedback may involve just a few key players, making it more sensitive to underlying microbial variation. We propose that affinity–effect relationship can be used to determine key microbial players in plant–soil feedback against a low signal-to-noise background of complex microbial datasets

FelixJoelCropSoilScienceAffinity-EffectRelationship_SupplementaryMaterial.zip

Feedback loops involving soil microorganisms can regulate plant populations. Here, we hypothesize that microorganisms are most likely to play a role in plant–soil feedback loops when they possess an affinity for a particular plant and the capacity to consistently affect the growth of that plant for good or ill. We characterized microbial communities using whole-community DNA fingerprinting from multiple "home-and-away" experiments involving giant ragweed (Ambrosia trifida L.) and common sunflower (Helianthus annuus L.), and we looked for affinity–effect relationships in these microbial communities. Using canonical ordination and partial least squares regression, we developed indices expressing each microorganism's affinity for ragweed or sunflower and its putative effect on plant biomass, and we used linear regression to analyze the relationship between microbial affinity and effect. Significant linear affinity–effect relationships were found in 75% of cases. Affinity–effect relationships were stronger for ragweed than for sunflower, and ragweed affinity–effect relationships showed consistent potential for negative feedback loops. The ragweed feedback relationships indicated the potential involvement of multiple microbial taxa, resulting in strong, consistent affinity–effect relationships in spite of large-scale microbial variability between trials. In contrast, sunflower plant–soil feedback may involve just a few key players, making it more sensitive to underlying microbial variation. We propose that affinity–effect relationship can be used to determine key microbial players in plant–soil feedback against a low "signal-to-noise" background of complex microbial datasets