Ecosystem Consequences of Plant Genetic Divergence with Colonization of New Habitat

When plants colonize new habitats altered by natural or anthropogenic disturbances, those individuals may encounter biotic and abiotic conditions novel to the species, which can cause plant functional trait divergence. Over time, site-driven adaptation can give rise to population-level genetic variation, with consequences for plant community dynamics and ecosystem processes. We used a series of 3000-yr-old, lava-created forest fragments on the Island of Hawai`i to examine whether disturbance and subsequent colonization can lead to genetically differentiated populations, and where differentiation occurs, if there are ecosystem consequences of trait-driven changes. These fragments are dominated by a single tree species, Metrosideros polymorpha (Myrtaceae) or ʻohiʻa, which have been actively colonizing the surrounding lava flow created in 1858. To test our ideas about differentiation of genetically determined traits, we (1) created rooted cuttings of ʻohiʻa individuals sampled from fragment interiors and open lava sites, raised these individuals in a greenhouse, and then used these cuttings to create a common garden where plant growth was monitored for three years; and (2) assessed genetic variation and made QST/FST comparisons using microsatellite repeat markers. Results from the greenhouse showed quantitative trait divergence in plant height and pubescence across plants sampled from fragment interior and matrix sites. Results from the subsequent common garden study confirmed that the matrix environment can select for individuals with 9.1% less shoot production and 17.3% higher leaf pubescence. We found no difference in molecular genetic variation indicating gene flow among the populations. The strongest QST level was greater than the FST estimate, indicating sympatric genetic divergence in growth traits. Tree height was correlated with ecosystem properties such as soil carbon and nitrogen storage, soil carbon turnover rates, and soil phosphatase activity, indicating that selection for growth traits will influence structure, function, and dynamics of developing ecosystems. These data show that divergence can occur on centennial timescales of early colonization

Do multiple outcome measures require p-value adjustment?

BACKGROUND: Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used. DISCUSSION: The classicists believe that the chance of finding at least one test statistically significant due to chance and incorrectly declaring a difference increases as the number of comparisons increases. The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II errors or needing to increase the sample size. SUMMARY: Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value

Discovering joint associations between disease and gene pairs with a novel similarity test

Genes in a functional pathway can have complex interactions. A gene might activate or suppress another gene, so it is of interest to test joint associations of gene pairs. To simultaneously detect the joint association between disease and two genes (or two chromosomal regions), we propose a new test with the use of genomic similarities. Our test is designed to detect epistasis in the absence of main effects, main effects in the absence of epistasis, or the presence of both main effects and epistasis. Results: The simulation results show that our similarity test with the matching measure is more powerful than the Pearson's chi(2) test when the disease mutants were introduced at common haplotypes, but is less powerful when the disease mutants were introduced at rare haplotypes. Our similarity tests with the counting measures are more sensitive to marker informativity and linkage disequilibrium patterns, and thus are often inferior to the similarity test with the matching measure and the Pearson 's chi(2) test. Conclusions: In detecting joint associations between disease and gene pairs, our similarity test is a complementary method to the Pearson's chi(2) test

Competition, Innovation, and Competition Law: Dissecting the Interplay

The digital revolution has reinvigorated the discussion about the problem how to consider innovation in the application of competition law. This raises difficult questions about the relationship between competition and innovation as well as what kind of assessment concepts competition authorities should use for investigating innovation effects, e.g., in merger cases. This paper, on one hand, reviews briefly our economic knowledge about competition and innovation, and claims that it is necessary to go beyond the limited insights that can be gained from industrial economics research about innovation (Schumpeter vs. Arrow discussion), and take into account much more insights from innovation research, evolutionary innovation economics, and business and management studies. On the other hand, it is also necessary to develop much more innovation-specific assessment concepts in competition law (beyond the traditional product market concept). Using the example of assessing innovation competition in merger cases, this article suggests to analyze much more systematically the resources (specialized assets) that are necessary for innovation. This concept is directly linked to the new discussion about the Dow/DuPont case in the EU and about data as necessary resource for (data-driven) innovation