Ecological and genetic correlates of long-term population trends in the Park Grass Experiment

The Park Grass Experiment (PGE) is the longest observed set of experimental plant communities in existence. Although the gross composition of the vegetation was at equilibrium over the 60-yr period from 1920 to 1979, annual records show that individual species exhibited a range of dynamics. We tested two hypotheses to explain why some species initially increased nd why subsequently some of these (the outbreak species) decreased gain. The study was designed around eight phylogenetically ndependent contrasts (PICs), each containing related species with ifferent dynamics. Our first hypothesis was that persistent increasers and utbreakers have higher intrinsic rates of natural increase than ontrol species (species without trends), allowing them to spread hen interspecific competition is reduced by drought. This was tested by measuring establishment and seed production of species in ield experiments, with and without interspecific competition. Seed production in outbreak species responded more strongly to release from interspecific competition than it did in either of the ther groups of species. Our second hypothesis was that outbreak species eventually declined because they lacked the genetic variation ecessary to adapt to the novel habitats to which they had initially spread. We tested this by measuring mating systems and genetic diversity in persistent and outbreak species in the PGE. In seven out of seven PICs tested, the outbreak species was more selfing than its persistent relative. There was a significant positive correlation between outcrossing rate and gene diversity. These results support roles for both ecological and genetic traits in long-term dynamics

Citizen Science Reveals Unexpected Continental-Scale Evolutionary Change in a Model Organism

Organisms provide some of the most sensitive indicators of climate change and evolutionary responses are becoming apparent in species with short generation times. Large datasets on genetic polymorphism that can provide an historical benchmark against which to test for recent evolutionary responses are very rare, but an exception is found in the brown-lipped banded snail (Cepaea nemoralis). This species is sensitive to its thermal environment and exhibits several polymorphisms of shell colour and banding pattern affecting shell albedo in the majority of populations within its native range in Europe. We tested for evolutionary changes in shell albedo that might have been driven by the warming of the climate in Europe over the last half century by compiling an historical dataset for 6,515 native populations of C. nemoralis and comparing this with new data on nearly 3,000 populations. The new data were sampled mainly in 2009 through the Evolution MegaLab, a citizen science project that engaged thousands of volunteers in 15 countries throughout Europe in the biggest such exercise ever undertaken. A known geographic cline in the frequency of the colour phenotype with the highest albedo (yellow) was shown to have persisted and a difference in colour frequency between woodland and more open habitats was confirmed, but there was no general increase in the frequency of yellow shells. This may have been because snails adapted to a warming climate through behavioural thermoregulation. By contrast, we detected an unexpected decrease in the frequency of Unbanded shells and an increase in the Mid-banded morph. Neither of these evolutionary changes appears to be a direct response to climate change, indicating that the influence of other selective agents, possibly related to changing predation pressure and habitat change with effects on micro-climate

Justify your alpha

Benjamin et al. proposed changing the conventional “statistical significance” threshold (i.e.,the alpha level) from p ≤ .05 to p ≤ .005 for all novel claims with relatively low prior odds. They provided two arguments for why lowering the significance threshold would “immediately improve the reproducibility of scientific research.” First, a p-value near .05provides weak evidence for the alternative hypothesis. Second, under certain assumptions, an alpha of .05 leads to high false positive report probabilities (FPRP2 ; the probability that a significant finding is a false positive

Justify your alpha

In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level

Nonstochastic variation of species-level diversification rates within angiosperms

Variations in the origination and extinction rates of species over geological time often are linked with a range of factors, including the evolution of key innovations, changes in ecosystem structure, and environmental factors such as shifts in climate and physical geography. Before hypothesizing causality of a single factor, it is critical to demonstrate that the observed variation in diversification is significantly greater than one would expect due to natural stochasticity in the evolutionary branching process. Here, we use a likelihood-ratio test to compare taxonomic rate heterogeneity to a neutral birth-death model, using data on well-supported sister pairs of taxa and their species richness. We test the likelihood that the distribution of extant species among angiosperm genera and families could be the result of constant diversification rates. Results strongly support the conclusion that there is significantly more heterogeneity in diversity at the species level within angiosperms than would be expected due to stochastic processes. This result is consistent in datasets of genus pairs and family pairs and is not affected significantly by degrading pairs to simulate inaccuracy in the assumption of simultaneous origin of sister taxa. When we parse taxon pairs among higher groups of angiosperms, results indicate that a constant rates model is not rejected by rosid and basal eudicot pairs but is rejected by asterid and eumagnoliid pairs. These results provide strong support for the hypothesis that species-level rates of origination and/or extinction have varied nonrandomly within angiosperms and that the magnitude of heterogeneity varies among major groups within angiosperms

A likelihood-based method for testing for nonstochastic variation of diversification rates in phylogenies

Observed variations in rates of taxonomic diversification have been attributed to a range of factors including biological innovations, ecosystem restructuring, and environmental changes. Before inferring causality of any particular factor, however, it is critical to demonstrate that the observed variation in diversity is significantly greater than that expected from natural stochastic processes. Relative tests that assess whether observed asymmetry in species richness between sister taxa in monophyletic pairs is greater than would be expected under a symmetric model have been used widely in studies of rate heterogeneity and are particularly useful for groups in which paleontological data are problematic. Although one such test introduced by Slowinski and Guyer a decade ago has been applied to a wide range of clades and evolutionary questions, the statistical behavior of the test has not been examined extensively, particularly when used with Fisher's procedure for combining probabilities to analyze data from multiple independent taxon pairs. Here, certain pragmatic difficulties with the Slowinski-Guyer test are described, further details of the development of a recently introduced likelihood-based relative rates test are presented, and standard simulation procedures are used to assess the behavior of the two tests in a range of situations to determine: (1) the accuracy of the tests' nominal Type I error rate; (2) the statistical power of the tests; (3) the sensitivity of the tests to inclusion of taxon pairs with few species; (4) the behavior of the tests with datasets comprised of few taxon pairs; and (5) the sensitivity of the tests to certain violations of the null model assumptions. Our results indicate that in most biologically plausible scenarios, the likelihood-based test has superior statistical properties in terms of both Type I error rate and power, and we found no scenario in which the Slowinski-Guyer test was distinctly superior, although the degree of the discrepancy varies among the different scenarios. The Slowinski-Guyer test tends to be much more conservative (i.e., very disinclined to reject the null hypothesis) in datasets with many small pairs. In most situations, the performance of both the likelihood-based test and particularly the Slowinski-Guyer test improve when pairs with few species are excluded from the computation, although this is balanced against a decline in the tests' power and accuracy as fewer pairs are included in the dataset. The performance of both tests is quite poor when they are applied to datasets in which the taxon sizes do not conform to the distribution implied by the usual null model. Thus, results of analyses of taxonomic rate heterogeneity using the Slowinski-Guyer test can be misleading because the test's ability to reject the null hypothesis (equal rates) when true is often inaccurate and its ability to reject the null hypothesis when the alternative (unequal rates) is true is poor, particularly when small taxon pairs are included. Although not always perfect, the likelihood-based test provides a more accurate and powerful alternative as a relative rates test

Regression models for censored serological data

This paper aims to assess the impact of censored serological measurements on regression equations fitted to data from panels of sera tested by different laboratories, for the purpose of standardising serosurvey results to common units. Several methods that adjust for censoring were compared, such as deletion, simple substitution, multiple imputation and censored regression. Simulations were generated from different scenarios for varying proportions of data censored. The scenarios were based on serological panel comparisons tested by different national laboratories and assays as part of the European Sero-Epidemiology Network (ESEN2) project. The results showed that the simple substitution and deletion methods worked reasonably well for low proportions of data censored (<20%). However, in general, the censored regression method gave estimates closer to the truth than the other methods examined under different scenarios, such as types of equations used and violation of regression assumptions. Interval censored regression produced the least biased estimates for assay data resulting from dilution series. Censored regression produced the least biased estimates in comparison to the other methods examined. In addition, a case has been made for applying in the future interval censored regression for assay data resulting from dilution series