Estimation of the seed dispersal kernel from exact identification of source plants

The exact identification of individual seed sources through genetic analysis of seed tissue of maternal origin has recently brought the full analytical potential of parentage analysis to the study of seed dispersal. No specific statistical methodology has been described so far, however, for estimation of the dispersal kernel function from categorical maternity assignment. In this study, we introduce a maximum-likelihood procedure to estimate the seed dispersal kernel from exact identification of seed sources. Using numerical simulations, we show that the proposed method, unlike other approaches, is independent of seed fecundity variation, yielding accurate estimates of the shape and range of the seed dispersal kernel under varied sampling and dispersal conditions. We also demonstrate how an obvious estimator of the dispersal kernel, the maximum-likelihood fit of the observed distribution of dispersal distances to seed traps, can be strongly biased due to the spatial arrangement of seed traps relative to source plants. Finally, we illustrate the use of the proposed method with a previously published empirical example for the animal-dispersed tree species Prunus mahaleb.     

Diversification and adaptive evolution of putative sweet taste receptors in threespine stickleback

The threespine stickleback Gasterosteus aculeatus is known to include several morphologically and ecologically divergent forms. Its phenotypic traits related to feeding vary among forms, and are considered to be a result of adaptations to various environments to find foods effectively. To examine whether the diversification of feeding modes in the stickleback involves genetic changes of the sense of taste, taste receptor family 1 (T1R) genes in stickleback were analyzed and compared with those in other model fishes. Ten T1R genes and 2 pseudogenes were identified from the stickleback genomic sequences. In particular, putative sweet taste receptors (T1R2s) highly increased in number in stickleback (8 genes and 2 pseudogenes) compared to other fishes (2-3 genes). Maximum likelihood estimations of nonsynonymous-synonymous nucleotide substitution rate have indicated that stickleback T1R2 are under positive selection. Expression analysis by RT-PCR revealed that most stickleback T1R genes were expressed in the taste organs; however, at least two T1R2 genes were not expressed in the taste organs, suggesting that the expression levels of these T1R2 genes may be fluctuated through the life history. In addition, sequencing analysis showed that several T1R2 genes in an anadromous form stickleback individual collected from the western Pacific (Japan) were substantially different from those in genomic data derived from a freshwater form individual collected in North America. This suggested that intra-specific variations of stickleback T1R2 genes were considerably large. Our results imply that, in stickleback, T1R2s have diversified through adaptation to various environments, probably to perceive substances important for its survival and reproduction.     

Likelihood and approximate likelihood analyses of genetic structure in a linear habitat: performance and robustness to model mis-specification

We evaluate the performance of maximum likelihood (ML) analysis of allele frequency data in a linear array of populations. The parameters are a mutation rate and either the dispersal rate in a stepping stone model or a dispersal rate and a scale parameter in a geometric dispersal model. An approximate procedure known as maximum product of approximate conditional (PAC) likelihood is found to perform as well as ML. Mis-specification biases may occur because the importance sampling algorithm is formally defined in term of mutation and migration rates scaled by the total size of the population, and this size may differ widely in the statistical model and in reality. As could be expected, ML generally performs well when the statistical model is correctly specified. Otherwise, mutation rate estimates are much closer to mutation probability scaled by number of demes in the statistical model than scaled by number of demes in reality when mutation probability is high and dispersal is most limited. This mis-specification bias actually has practical benefits. However, opposite results are found in opposite conditions. Migration rate estimates show roughly similar trends, but they may not always be easily interpreted as low-bias estimates of dispersal rate under any scaling. Estimation of the dispersal scale parameter is also affected by mis-specification of the number of demes, and the different biases compensate each other in such a way that good estimation of the so-called neighborhood size (or more precisely the product of population density and mean-squared parent-offspring dispersal distance) is achieved. Results congruent with these findings are found in an application to a damselfly data set.     

Eating yourself sick: transmission of disease as a function of foraging ecology

Species interactions may profoundly influence disease outbreaks. However, disease ecology has only begun to integrate interactions between hosts and their food resources (foraging ecology) despite that hosts often encounter their parasites while feeding. A zooplankton–fungal system illustrated this central connection between foraging and transmission. Using experiments that varied food density for Daphnia hosts, density of fungal spores and body size of Daphnia , we produced mechanistic yet general models for disease transmission rate based on broadly applicable components of feeding biology. Best performing models could explain why prevalence of infection declined at high food density and rose sharply as host size increased (a pattern echoed in nature). In comparison, the classic mass-action model for transmission performed quite poorly. These foraging-based models should broadly apply to systems in which hosts encounter parasites while eating, and they will catalyse future integration of the roles of Daphnia as grazer and host.     

A Primer to Molecular Phylogenetic Analysis in Plants

Reconstructing a tree of life by inferring evolutionary history is an important focus of evolutionary biology. Phylogenetic reconstructions also provide useful information for a range of scientific disciplines such as botany, zoology, phylogeography, archaeology and biological anthropology. Until the development of protein and DNA sequencing techniques in the 1960s and 1970s, phylogenetic reconstructions were based on fossil records and comparative morphological/physiological analyses. Since then, progress in molecular phylogenetics has compensated for some of the shortcomings of phenotype-based comparisons. Comparisons at the molecular level increase the accuracy of phylogenetic inference because there is no environmental influence on DNA/peptide sequences and evaluation of sequence similarity is not subjective. While the number of morphological/physiological characters that are sufficiently conserved for phylogenetic inference is limited, molecular data provide a large number of datapoints and enable comparisons from diverse taxa. Over the last 20 years, developments in molecular phylogenetics have greatly contributed to our understanding of plant evolutionary relationships. Regions in the plant nuclear and organellar genomes that are optimal for phylogenetic inference have been determined and recent advances in DNA sequencing techniques have enabled comparisons at the whole genome level. Sequences from the nuclear and organellar genomes of thousands of plant species are readily available in public databases, enabling researchers without access to molecular biology tools to investigate phylogenetic relationships by sequence comparisons using the appropriate nucleotide substitution models and tree building algorithms. In the present review, the statistical models and algorithms used to reconstruct phylogenetic trees are introduced and advances in the exploration and utilization of plant genomes for molecular phylogenetic analyses are discussed.     

Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood

We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance–covariance matrices ( G ). Large‐sample theory shows that maximum‐likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G . This suggests that sampling estimates of G from this distribution can be used to assess the variability of estimates of G , and of functions of G . We refer to this as the REML‐MVN method. This has been implemented in the mixed‐model program WOMBAT. Estimates of sampling variances from REML‐MVN were compared to those from the parametric bootstrap and from a Bayesian Markov chain Monte Carlo (MCMC) approach (implemented in the R package MCMCglmm). We apply each approach to evolvability statistics previously estimated for a large, 20‐dimensional data set for Drosophila wings. REML‐MVN and MCMC sampling variances are close to those estimated with the parametric bootstrap. Both slightly underestimate the error in the best‐estimated aspects of the G matrix. REML analysis supports the previous conclusion that the G matrix for this population is full rank. REML‐MVN is computationally very efficient, making it an attractive alternative to both data resampling and MCMC approaches to assessing confidence in parameters of evolutionary interest.     

Benzene Exposure at Gasoline Stations: Health Risk Assessment

This study aimed to assess the health risk of benzene exposure among gasoline stations workers. There were 98 workers from each zone of gasoline station in Muang Khon Kaen,Thailand. The benzene concentrations were monitored via personal air sampling and analyzed with a gas chromatography flame-ionization detector (GC-FID). Information about adverse symptoms were obtained by an interview questionnaire. Levels of health risk were calculated from the likelihood level of exposure and the adverse symptoms level, which were summarized in a risk matrix. Most workers had experienced adverse symptoms related to benzene toxicity (68.4%). The health risk of benzene exposure was higher than an acceptable level in 15.3% of workers and in a similarly proportion of fueling workers and cashiers. Moreover, the proportion of risk group was found highest in urban zone (18%) and the risk scores had a significant difference across all zones. This study showed that over 15% of workers were under health risk conditions on benzene exposure and over 60% of workers had experienced adverse symptoms related to benzene toxicity. Therefore, findings suggest the need of the health surveillance program and safety training for workers to increase awareness of benzene exposure.     

Disentangling the relative importance of climate,size and competition on tree growth in Iberian forests: implications for forest management under global change

Most large‐scale multispecies studies of tree growth have been conducted in tropical and cool temperate forests, whereas Mediterranean water‐limited ecosystems have received much less attention. This limits our understanding of how growth of coexisting tree species varies along environmental gradients in these forests, and the implications for species interactions and community assembly under current and future climatic conditions. Here, we quantify the absolute effect and relative importance of climate, tree size and competition as determinants of tree growth patterns in Iberian forests, and explore interspecific differences in the two components of competitive ability (competitive response and effect) along climatic and size gradients. Spatially explicit neighborhood models were developed to predict tree growth for the 15 most abundant Iberian tree species using permanent‐plot data from the Spanish Second and Third National Forest Inventory (IFN). Our neighborhood analyses showed a climatic and size effect on tree growth, but also revealed that competition from neighbors has a comparatively much larger impact on growth in Iberian forests. Moreover, the sensitivity to competition (i.e. competitive response) of target trees varied markedly along climatic gradients causing significant rank reversals in species performance, particularly under xeric conditions. We also found compelling evidence for strong species‐specific competitive effects in these forests. Altogether, these results constitute critical new information which not only furthers our understanding of important theoretical questions about the assembly of Mediterranean forests, but will also be of help in developing new guidelines for adapting forests in this climatic boundary to global change. If we consider the climatic gradients of this study as a surrogate for future climatic conditions, then we should expect absolute growth rates to decrease and sensitivity to competition to increase in most forests of the Iberian Peninsula (in all but the northern Atlantic forests), making these management considerations even more important in the future.