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April 16, 2014

18:00
Background: Mangroves are key components of coastal ecosystems in tropical and subtropical regions worldwide. However, the patterns and mechanisms of modern distribution of mangroves are still not well understood. Historical vicariance and dispersal are two hypothetic biogeographic processes in shaping the patterns of present-day species distributions. Here we investigate evolutionary biogeography of mangroves in the Indo-West Pacific (IWP) and western Atlantic-East Pacific (AEP) regions using a large sample of populations of Rhizophora (the most representative mangrove genus) and a combination of chloroplast and nuclear DNA sequences and genome-wide ISSR markers. Results: Our comparative analyses of biogeographic patterns amongst Rhizophora taxa worldwide support the hypothesis that ancient dispersals along the Tethys Seaway and subsequent vicariant events that divided the IWP and AEP lineages resulted in the major disjunctions. We dated the deep split between the Old and New World lineages to early Eocene based on fossil calibration and geological and tectonic changes. Our data also provide evidence for other vicariant processes within the Indo-West Pacific region in separating conspecific lineages of SE Asia and Australia-Pacific at the Oligocene-Miocene boundary. Close genetic affinities exist between extant Fijian and American lineages; East African and Australian lineages; and Australian and Pacific lineages; indicating relatively more recent oceanic long-distance dispersal events. Conclusions: Our study demonstrates that neither vicariance nor dispersal alone could explain the observed global occurrences of Rhizophora, but a combination of vicariant events and oceanic long-distance dispersals can account for historical diversification and present-day biogeographic patterns of mangroves.
18:00
Background: Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of less than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics. Methods: We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere. Results: We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores. Conclusions: These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets.
10:15

The following text was written a few years ago, but much of it never got published. So, I thought that this might be a good opportunity to make it available, since what it says is still true today.

Since a phylogenetic tree is interpreted in terms of the monophyletic groups that it hypothesizes, it is important to quantitatively assess the robustness of all of these groups (i.e. the degree of support for each branch in the tree) — is the support for a particular group any better than would be expected from a random data set? This issue of clade robustness is the same as assessing branch support on the tree, since each branch represents a clade. Many different techniques have been developed, including:
  1. analytical procedures, such as interior-branch tests (Nei et al. 1985; Sneath 1986), likelihood-ratio tests (Felsenstein 1988; Huelsenbeck et al. 1996b), and clade significance (Lee 2000);
  2. resampling procedures, such as the bootstrap (Felsenstein 1985), the jackknife (Lanyon 1985), topology-dependent permutation (Faith 1991), and clade credibility or posterior probability (Larget and Simon 1999); and
  3. non-statistical procedures, such as the decay index (Bremer 1988), clade stability (Davis 1993), and spectral signals (Hendy and Penny 1993).
Of these, far and away the most popular and widely used method has been the bootstrap technique (Holmes 2003; Soltis and Soltis 2003).


The bootstrap

This method was first introduced by Efron (1979) as an alternative method to jackknifing for producing standard errors on estimates of central location other than the mean (e.g. the median), but it has since been expanded to cover probabilistic confidence intervals as well (Efron and Tibshirani 1993; Davison and Hinkley 1997). It was introduced into phylogenetic studies by Penny et al. (1982) and then formalized by Felsenstein (1985), who suggested that it could be implemented by holding the taxa constant and resampling the characters randomly with replacement, the tree-building analysis then being applied to each of the bootstrap resamples.

Bootstrapping is a monte carlo procedure that it generates "pseudo" data sets from the original data, and uses these new data sets for its inferences. That is, it tries to derive the population inferences (i.e the "true" answer) from repeated generation of new samples, each sample being constrained by the characteristics of the original data sample. It thus relies on an explicit analogy between the sample and the appropriate population: that sampling from the sample is the same as sampling from the population. Clearly, the strongest requirement for bootstrapping to work is that the sample be a reasonable representation of the population.

Bootstrap confidence intervals are only ever approximate, especially for complex data structures, as they are a fundamentally more ambitious measure of accuracy than is a simple standard error (SE). For example, the usual formula for calculating a confidence interval (CI) when the population frequency distribution is assumed to be normal is: CI = t * SE, where t is the Student t-value associated with the particular sample size and confidence percentage required. However, the main use of bootstrapping is in situations where the population frequency distribution is either indeterminate or is difficult to obtain empirically, and so this simple formula cannot be applied. Getting from the standard error to a confidence interval is then not straightforward. As a result, there are actually several quite distinct procedures for performing bootstrapping (Carpenter and Bithell 2000), with varying degrees of expected success.

Types of bootstrap

The original technique is called the percentile bootstrap. It is based on the principle of using the minimum number of ad hoc assumptions, and so it merely counts the percentage of bootstrap resamples that meet the specified criteria. F§or example, to estimate the standard error of a median, the median can be calculated for each bootstrap resample and then the standard deviation of the resulting frequency distribution will be the estimated standard error of the original median. The method is thus rather simplistic, and is often referred to as the naïve bootstrap, because it assumes no knowledge of how to calculate population estimates. It is a widespread method, as it can be applied even when the other methods cannot. However, it is known to have certain problems associated with the estimates produced, particularly for confidence intervals, such as bias and skewness (especially when the parent frequency distribution is not symmetrical). These were pointed out right from the start (Efron 1979), and efforts have subsequently been made to deal with them. Nevertheless, this is the form of bootstrap introduced by Felsenstein (1985), and it is the one used by most phylogeny computer programs. It is therefore the one that will be discussed in more detail below.

These known problems with the naïve bootstrap can be overcome by using bias-corrected (BC) bootstrap estimates — that is, the bias is estimated and removed from the calculation of the confidence interval. Possible dependence of the standard error on the parameter being estimated, which creates skewness, can be dealt with by using bias-corrected and accelerated (BCa) bootstrap estimates, so that the bias and skewness are both estimated and removed from the calculation of the confidence interval. The BCa method is the one usually recommended for use (Carpenter and Bithell 2000), because it corrects for both bias and skewness. This method is much slower to calculate than the simple percentile bootstrap, because it requires an extra parameter to be estimated for each of the bias and skewness corrections, and the latter correction is actually estimated by performing a separate jackknife analysis on each bootstrap resample (which means that the analysis can take 100 times as long as a naïve analysis). There have been several attempts to apply this form of correction methodology to bootstrapping in a phylogenetic context (Rodrigo 1993; Zharkikh and Li 1995; Efron et al. 1996; Shimodaira 2002), but while these can be successful at correcting bias and skewness (Sanderson and Wojciechowski 2000) these have not caught on, possibly because of the time factor involved.

Alternatively, we can decide not to be naïve when calculating confidence intervals, and to therefore calculate them in the traditional manner, using the standard error and the t-distribution. However, we then need to overcome any non-normal distribution problems of these two estimates by estimating both of them using bootstrapping. That is, bootstrapped-t confidence intervals are derived by calculating both the standard error and the t-value using bootstrapping, and then calculating the confidence interval as ±t * SE. To many people, this is the most natural way to calculate confidence intervals, since it matches the usual parametric procedure, and thus it is frequently recommended (Carpenter and Bithell 2000). Once again, this method is much slower to calculate than the percentile bootstrap, because the t-value is actually estimated by performing a separate bootstrap analysis on each bootstrap resample (which means that the analysis can take 100 times as long as a naïve analysis). This methodology seems not to have yet been suggested in a phylogenetic context, and in any case the time factor may be restrictive.

It is also possible to calculate test-inversion confidence intervals. This idea is based on the reciprocal relationship of statistical tests and confidence intervals, where (for example) non-overlapping 95% confidence intervals indicate statistically significant patterns at p75% tend to be underestimates of the amount of support while they are overestimates below this level. The graph is based upon 1000 bootstrap resamples of 100 simulated characters for a clade of three taxa plus outgroup (based on data presented by Zharkikh and Li 1992a). The true probability represents the amount of character support for the clade in the simulated data, while the bootstrap probability is the proportion of resamples that included the clade.
These studies have demonstrated that the probability of bootstrap resampling supporting the true tree may be either under- or overestimated, depending on the particular situation. For example, bootstrap values >75% tend to be underestimates of the amount of support, while they may be overestimates below this level, as shown in the first graph (above). That is, when the branch support is strong (i.e. the clade is part of the true tree) there will be an underestimation and when the support is weak (i.e. the clade is not part of the true tree) there will be an overestimation. This situation has been reported time and time again, with various theoretical explanations (e.g. Felsenstein and Kishino 1993; Efron et al. 1996; Newton 1996), although there are dissenting voices (e.g. Taylor and Piel 2004) as would be expected for a complex situation. Unfortunately, practitioners seem to ignore this fact, and to assume incorrectly that bootstrap values are always underestimates.

Just as importantly, the theoretical studies show that the pattern of over- and underestimation depends on (i) the shape of the tree and the branch lengths, (ii) the number of taxa, (iii) the number of characters, (iv) the evolutionary model used, and (v) the number of bootstrap resamples. This was first reported by Zharkikh and Li (1992a), and has been reconfirmed since then. For example, with few characters the bootstrap index tends to overestimate the support for a clade and to underestimate it for more characters. This is particularly true if the number of phylogenetically informative characters is increased or the number of non-independent characters is increased; and the index becomes progressively more conservative (i.e. lower values) as the number of taxa is increased.

Moreover, these patterns of under- and overestimation are increased with an increasing number of bootstrap replications, as shown in the next graph — this called "being wrong, with confidence".

An example of the relationship between the true clade probability and the observed non-parametric bootstrap proportion for two simulated data sets with different numbers of characters (as shown). The lines are based on data presented by Zharkikh & Li, (1995) for 1000 bootstrap resamples of a clade of three taxa plus outgroup.
The following graph pair of graphs show the effect of varying the evolutionary model used to generate the data, where under-specification of the analysis model leads to a general over-estimate of the true probability (cross-over at p=0.8, as shown in the first graph of the pair), while matching the generating and analysis models leads to a general under-estimation (cross-over at p=0.3, as shown in the second graph of the pair).

An example of the relationship between the true tree probability and the difference between the observed percentile bootstrap proportion and the true probability for two simulated data sets. The label in the bottom corner shows the substitution model used to simulate the data, then the model assumed in the bootstrap analysis (the sequence length is 100 nucleotides); JC69 = Jukes-Cantor, GTRG = general time- reversible + gamma-distributed among-site rate variation. The points are based on data presented by Huelsenbeck & Rannala (2004).
These are serious issues, which seem to be often ignored by practitioners. We can't just assume that the "true" support value is larger than our observed bootstrap value. In particular, this means that bootstrap values are not directly comparable between trees, even for the same taxa, and thus there can be no "agreed" level of bootstrap support that can be considered to be "statistically significant". A bootstrap value of 90% on a branch on one tree may actually represent less support than a bootstrap value of 85% on another tree, depending on the characteristics of the dataset concerned and the bootstrapping procedure used (although within a single tree the values should be comparable).

This complex situation means that we have to consider carefully how best to interpret bootstrap values in a phylogenetic context (Sanderson 1995). The bootstrap proportion (i.e. the proportion of resampled trees containing the branch/clade of interest) has variously been interpreted as (Berry and Gascuel 1996):
  1. a measure of reliability, telling us what would be expected to happen if we repeated our experiment;
  2. a measure of accuracy, telling us about the probability of our experimental result being true; and
  3. a measure of confidence, interpreted as a conditional probability similar to those in standard statistical hypothesis tests (i.e. measuring Type I errors or false positives).
The bootstrap was originally designed for purpose (1), and all of the problems identified above relate to trying to use it for purposes (2) and (3). The values derived from the naïve bootstrap need correcting for purposes (2) and (3), and the degree of correction depends on the particular data set being examined (Efron et al. 1996; Goloboff et al. 2003).

The issue of support values depending on the number of bootstrap replicates is also of interest. It is usually recommended that at least 1,000–2,000 bootstrap resamples are taken for estimating confidence intervals, and this generality has been applied to phylogenetic trees (Hedges 1992). However, it is important to recognize that these suggestions relate to the precision of the confidence estimates not to their accuracy. Accuracy refers to how close the estimates are to the true value (i.e. correctness) while precision refers to how variable are the estimates (i.e. repeatability). Accuracy depends on a complex set of characteristics many of which have nothing to do with bootstrap replication. Precision, on the other hand, is entirely to do with the number of bootstrap replicates and the expected accuracy of the estimates. As shown in the next graph, 100 replicates at a conventional level of accuracy produces estimates that are expected to be within ±4% of the "true" values while 2,000 replicates produces estimates ±1%. This needs to be borne in mind when deciding whether to call a particular value "significant support" or not.

The number of bootstrap replicates needed to achieve a specified amount of precision, given statistical testing at two different levels of probability. For example (as shown by the dotted line), 100 bootstrap replicates means that, if the bootstrap value is accurate at the 95% confidence level, then the estimated bootstrap percentage will be precise to ±4.3%. In order to get ±1% precision then nearly 2,000 bootstrap replicates are needed.
There have also been attempts to overcome some of the practical limitations of bootstrapping for large data sets by adopting heuristic procedures, including resampling estimated likelihoods for maximum-likelihood analyses (Waddell et al. 2002) and reduced tree-search effort for the bootstrap replicates. However, approaches using reduced tree-search effort produce even more conservative estimates of branch support, and the magnitude of the effect increases with decreasing bootstrap values (DeBry and Olmstead 2000; Mort et al. 2000; Sanderson and Wojciechowski 2000).

References

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Alfaro M.E., Zoller S., Lutzoni F. 2003. Bayes or bootstrap? A simulation study comparing the performance of bayesian markov chain monte carlo sampling and bootstrapping in assessing phylogenetic confidence. Mol. Biol. Evol. 20, 255-266.

Berry V., Gascuel O. 1996. On the interpretation of bootstrap trees: appropriate threshold of clade selection and induced gain. Mol. Biol. Evol. 13, 999-1011.

Bremer K. 1988. The limits of amino acid sequence data in angiosperm phylogenetic reconstruction. Evolution 42, 795-803.

Buckley T.R., Cunningham C.W. 2002. The effects of nucleotide substitution model assumptions on estimates of nonparametric bootstrap support. Mol. Biol. Evol. 19, 394-405.

Buckley T.R., Simon C., Chambers G.K. 2001. Exploring among-site rate variation models in a maximum likelihood framework using empirical data: effects of model assumptions on estimates of topology, branch lengths and bootstrap support. Syst. Biol. 50, 67-86.

Carpenter J., Bithell J. 2000. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat. Med. 19, 1141-1164.

Davis J.I. 1993. Character removal as a means for assessing the stability of clades. Cladistics 9, 201-210.

Davison A.C., Hinkley D.V. 1997. Bootstrap Methods and Their Applications. Cambridge Uni. Press, Cambridge.

DeBry R.W., Olmstead R.G. 2000. A simulation study of reduced tree-search effort in bootstrap resampling analysis. Syst. Biol. 49, 171-179.

Efron B. 1979. Bootstrapping methods: another look at the jackknife. Ann. Stat. 7, 1-26.

Efron B., Halloran E., Holmes S. 1996. Bootstrap confidence levels for phylogenetic trees. Proc. Nat. Acad. Sci. U.S.A. 93, 7085-7090.

Efron B., Tibshirani R.J. 1993. An Introduction to the Bootstrap. Chapman & Hall, London.

Erixon P., Svennblad B., Britton T., Oxelman B. 2003. Reliability of bayesian probabilities and bootstrap frequencies in phylogenetics. Syst. Biol. 52, 665-673.

Faith D.P. 1991. Cladistic permutation tests for monophyly and nonmonophyly. Syst. Zool. 40, 366-375.

Felsenstein J. 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783-791.

Felsenstein J. 1988. Phylogenies from molecular sequences: inference and reliability. Annu. Rev. Genet. 22, 521-565.

Felsenstein J., Kishino H. 1993. Is there something wrong with the bootstrap on phylogenies? A reply to Hillis and Bull. Syst. Biol. 42, 193-200.

Galtier N. 2004. Sampling properties of the bootstrap support in molecular phylogeny: influence of nonindependence among sites. Syst. Biol. 53, 38-46.

Goldman N. 1993. Statistical tests of models of DNA substitution. J. Mol. Evol. 36, 182-198.

Goloboff P.A., Farris J.S., Källersjö M., Oxelman B., Ramırez M.J., Szumik C.A. 2003. Improvements to resampling measures of group support. Cladistics 19, 324-332.

Hedges S.B. 1992. The number of replications needed for accurate estimation of the bootstrap P value in phylogenetic studies. Mol. Biol. Evol. 9, 366-369.

Hendy M.D., Penny D. 1993. Spectral analysis of phylogenetic data. J. Classific. 10, 5-24.

Hillis D.M., Bull J.J. 1993. An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis. Syst. Biol. 42, 182-192.

Holmes S. 2003. Bootstrapping phylogenetic trees: theory and methods. Statist. Sci. 18, 241-255.

Huelsenbeck J.P., Hillis D.M., Jones R. 1996a. Parametric bootstrapping in molecular phylogenetics: applications and performance. In: Ferraris, J.D., Palumbi, S.R. (Eds), Molecular

Huelsenbeck J.P., Hillis D.M., Nielsen R. 1996b. A likelihood ratio test of monophyly. Syst. Biol. 45, 546-558.

Huelsenbeck J.P., Rannala B. 2004. Frequentist properties of bayesian posterior probabilities of phylogenetic trees under simple and complex substitution models. Syst. Biol. 53, 904-913.

Lanyon S.M. 1985. Detecting internal inconsistencies in distance data. Syst. Zool. 34, 397-403.

Larget B., Simon D.L. 1999. Markov chain monte carlo algorithms for the bayesian analysis of phylogenetic trees. Mol. Biol. Evol. 16, 750-759.

Lee M.S.Y. 2000. Tree robustness and clade significance. Syst. Biol. 49, 829-836.

Li W.-H., Zharkikh A. 1994. What is the bootstrap technique? Syst. Biol. 43, 424-430.

Mort M.E., Soltis P.S., Soltis D.E., Mabry M.L. 2000. Comparison of three methods for estimating internal support on phylogenetic trees. Syst. Biol. 49, 160-171.

Nei M., Stevens J.C., Saitou M. 1985. Methods for computing the standard errors of branching points in an evolutionary tree and their application to molecular data from humans and apes. Mol. Biol. Evol. 2, 66-85.

Newton M.A. 1996. Bootstrapping phylogenies: large deviations and dispersion effects. Biometrika 83, 315-328.

Penny D., Foulds L.R., Hendy M.D. 1982. Testing the theory of evolution by comparing phylogenetic trees constructed from five different protein sequences. Nature 297, 197-200.

Rodrigo A.G. 1993. Calibrating the bootstrap test of monophyly. Int. J. Parasitol. 23, 507-514.

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Sanderson M.J., Wojciechowski M.F. 2000. Improved bootstrap confidence limits in large-scale phylogenies, with an example from Neo-Astragalus (Leguminosae). Syst. Biol. 49, 671-685.

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Soltis P.S., Soltis D.E. 2003. Applying the bootstrap in phylogeny reconstruction. Statist. Sci. 18, 256-267.

Suzuki Y., Glazko G.V., Nei M. 2002. Overcredibility of molecular phylogenies obtained by bayesian phylogenetics. Proc. Nat. Acad. Sci. U.S.A. 99, 16138-16143.

Taylor D.J., Piel W.H. 2004. An assessment of accuracy, error, and conflict with support values from genome-scale phylogenetic data. Mol. Biol. Evol. 21, 1534-1537.

Waddell P.J., Kishino H. and Ota, R. 2002). Very fast algorithms for evaluating the stability of ML and Bayesian phylogenetic trees from se- quence data. Genome Informatics 13, 82-92.

Wilcox T.P., Zwickl D., Heath T.A., Hillis D.M. 2002. Phylogenetic relationships of the dwarf boas and a comparison of bayesian and bootstrap measures of phylogenetic support. Mol. Phylogenet. Evol. 25, 361-371.

Zharkikh A., Li W.-H. 1992a. Statistical properties of bootstrap estimation of phylogenetic variability from nucleotide sequences. I. Four taxa with a molecular clock. Mol. Biol. Evol. 9, 1119-1147.

Zharkikh A., Li W.-H. 1992b. Statistical properties of bootstrap estimation of phylogenetic variability from nucleotide sequences. II. Four taxa without a molecular clock. J. Mol. Evol. 35, 356-366.

Zharkikh A., Li W.-H. 1995. Estimation of confidence in phylogeny: the complete-and-partial bootstrap technique. Mol. Phylogenet. Evol. 4, 44-63.

02:20

—_000_34B6042C690E442DBCA61E5160F441F1wustledu_ Content-Type: text/plain; charset=”Windows-1252” Content-Transfer-Encoding: quoted-printable Microbial sociality postdocs in the Queller-Strassmann lab at Washington University in St. Louis. One or more postdoctoral positions for 2-3 years are available for work on either of two projects involving evolutionary aspects of microbial sociality. 1. Experimental evolution of cooperation in microbes, especially in population structures relevant to “higher” organisms (Queller et al. 2013 Biology Letters 9:20130636; Kuzdzal-Fick et al. 2011. Science 334: 1548-1551) 2. The farming and defensive symbioses of Dictyostelium discoideum amoebas and bacteria (Brock et al. 2011 Nature 469:393-396; Brock et al 2013 Nature Communications 4:2385; Stallforth et al. 2013PNAS 110:14528-14533) David Queller and Joan Strassmann lead a friendly and interactive team of highly motivated, creative, and smart investigators. We are seeking energetic postdocs with strong backgrounds in areas such as evolutionary biology, social behavior, mutualism, microbial evolution, genomics, and molecular biology. Check out our website, (http://bit.ly/14aHGcx) for more information on our lab, or Strassmann’s blog (http://bit.ly/17dDCTx). To apply, please email Patrick Clark (pclark@wustl.edu), specifying either “experimental evolution postdoc” or “farming postdoc” in the subject line. Please attach a single file including CV, statement of research interests, and the names, phone numbers, and email addresses of three references. Women and underrepresented minorities are particularly encouraged to apply. Funding is from the John Templeton Foundation. We will begin reviewing applications by 15 May 2014 and will continue to accept them until the positions are filled. Postdocs may start immediately but date is negotiable. —_000_34B6042C690E442DBCA61E5160F441F1wustledu_ Content-Type: text/html; charset=”Windows-1252” Content-ID: Content-Transfer-Encoding: quoted-printable

Microbial sociality postdocs in the Queller-Strassmann lab at Washington University in St. Louis. 

One or more postdoctoral positions for 2-3 years are available for work on either of two projects involving evolutionary aspects of microbial sociality.

1.     Experimental evolution of cooperation in microbes, especially in population structures relevant to “higher” organisms (Queller et al. 2013 Biology Letters 9:20130636; Kuzdzal-Fick et al.  2011. Science 334: 1548-1551)

2.     The farming and defensive symbioses of Dictyostelium discoideum amoebas and bacteria (Brock et al. 2011 Nature 469:393-396; Brock et al 2013 Nature Communications 4:2385; Stallforth et al. 2013PNAS 110:14528-14533)

David Queller and Joan Strassmann lead a friendly and interactive team of highly motivated, creative, and smart investigators. We are seeking energetic postdocs with strong backgrounds in areas such as evolutionary biology, social behavior, mutualism, microbial evolution, genomics, and molecular biology.   Check out our website, (http://bit.ly/14aHGcx) for more information on our lab, or Strassmann’s blog (http://bit.ly/17dDCTx). To apply, please email Patrick Clark (pclark@wustl.edu), specifying either “experimental evolution postdoc” or “farming postdoc” in the subject line.  Please attach a single file including CV, statement of research interests, and the names, phone numbers, and email addresses of three references. Women and underrepresented minorities are particularly encouraged to apply.  Funding is from the John Templeton Foundation.  We will begin reviewing applications by 15 May 2014 and will continue to accept them until the positions are filled.  Postdocs may start immediately but date is negotiable.

—_000_34B6042C690E442DBCA61E5160F441F1wustledu via Gmail
Source: EVOLDIR
02:02
The Biodiversity Heritage Library (BHL) has recently introduced a feature that I strongly dislike. The post describing this feature (Inspiring discovery through free access to biodiversity knowledge... states:

Now BHL is expanding the data model for its portal to be able to accommodate references to content in other well-known repositories. This is highly beneficial to end users as it allows them to search for articles, alongside books and journals, within a single search interface instead of having to search each of these siloes separately.
What this means is that, whereas in the past a search in BHL would only turn up content actually in BHL, now that search may return results from other sources. What's not to like? Well, for me this breaks the fundamental BHL experience that I've come to rely on, namely:

If I find something in BHL I can read it there and then
With the new feature, the search results may include links to other sources. Sometimes these are useful, but sometimes they are anything but. Once you start including external links in your search results, you have limited control over what those links point to. For example, if I search BHL for the journal Revista Chilena de Historia Natural I get two hits. Cool! So I click on one hit and I can read a fairly limited set of scanned volumes in BHL, if I click on the other hit I'm taken to a page at the Digital Library of the Real Jardín Botánico of Madrid. This is a great resource, but the experience is a little jarring. Worse, for this journal the Real Jardín Botánico doesn't actually have any content, instead the "View Book" link takes me to SciElo in Chile, where I can see a list of recent volumes of this journal.

In this case, BHL is basically a link farm that doesn't give me direct access to content, but instead sends me on a series of hops around the Internet until I find something (and I could have gotten there more quickly via Google).

What is wrong with this?
There are two reasons I dislike what BHL have done. The first is that it breaks the experience of search then read within a consistent user interface. Now I am presented with different reading experiences, or, indeed, no reading at all, just links to where I might find something to read.

More subtlety, it undermines a nice feature of BHL, namely searching by taxonomic names. The content BHL has scanned has also been indexed by taxonomic name, so often I find what I'm looking for not by using bibliographic details (journal name, volume, etc.), which are often a bit messy, but by searching on a name. External content has not been indexed by name, so it can't be found in this way. Whereas before, if I search by name I would be reasonably confident that if BHL had something on that name I could find it (barring OCR errors), now BHL may well have what I looking for (in an external source) but can't show me that because it hasn't been indexed.

From my perpsective, the things I've come to rely on have been broken by this new feature (and I haven't even begun to talk about how this breaks things I rely on to harvest BHL for article metadata, which I then put into BioStor, which in turn gets fed back into BHL).

What should BHL have done?
To be clear, I'm not arguing against BHL being "able to accommodate references to content in other well-known repositories". Indeed, I'd wish they'd go further and incorporate content from BHL-Europe, whose portal is, frankly, a mess. Rather, my argument is that they should not have done this within the existing BHL portal. Doing so dilutes the fundamental experience of that portal ("if I find it I can read it").

Here's what I would do instead:
  1. Keep the current BHL portal as it was, with only content actually scanned and indexed by BHL.
  2. Create a new site that indexes all relevant content (e.g., BHL, BHL-Europe, and other repositories.
  3. Model this new portal on something like CrossRef's wonderful metadata search. That is, throw all the metadata into a NoSQL database, add a decent search engine, and provide users with a simple, fast tool.
  4. The portal should clearly distinguish hits that are to BHL content (e.g. by showing thumbnails) and hits that are to external links (and please filter links to links!).
  5. Add taxonomic names to the index (you have these for BHL content, adding them for external content is pretty easy).
  6. Even more useful, start indexing full text content, maybe starting at articles ("parts"). At the moment Google is doing a better job of indexing BHL content (indirectly via indexing Internet Archive) than BHL does.

Creating a new tool would also give BHL the freedom to explore some new approaches without annoying users like me who have come to rely on the currently portal working in a certain way. Otherwise BHL risks "feature creep", however well motivated.
Source: iPhylo
01:33

—_000_400FE53178244FDCBCA413666A3A0913wustledu_ Content-Type: text/plain; charset=”Windows-1252” Content-Transfer-Encoding: quoted-printable Evolution of genomic imprinting postdoctoral fellowships at Washington University in St. Louis. Social insects provide an outstanding opportunity to test novel predictions of the kinship theory of genomic imprinting in social insects (Queller 2003 BMC Evolutionary Biology 3:15). This project involve testing for such imprinting using genomic techniques. Experience with RNA-seq methods and analysis is desirable. David Queller and Joan Strassmann lead a friendly and interactive team of highly motivated, creative, and smart investigators. We are seeking energetic postdocs with strong backgrounds in areas such as evolutionary biology, social behavior, mutualism, microbial evolution, genomics, and molecular biology. Check out our website, (http://bit.ly/14aHGcx) for more information on our lab, or Strassmann’s blog (http://bit.ly/17dDCTx). To apply, please email Patrick Clark (pclark@wustl.edu), specifying “imprinting postdoc” in the subject line. Please attach a single file including CV, statement of research interests, and the names, phone numbers, and email addresses of three references. Women and underrepresented minorities are particularly encouraged to apply. Funding is from the John Templeton Foundation. We will begin reviewing applications by 15 May 2014 and will continue to accept them until the positions are fill ed. Postdocs may start immediately but date is negotiable. —_000_400FE53178244FDCBCA413666A3A0913wustledu_ Content-Type: text/html; charset=”Windows-1252” Content-ID: Content-Transfer-Encoding: quoted-printable

Evolution of genomic imprinting postdoctoral fellowships at Washington University in St. Louis.   Social insects provide an outstanding opportunity to test novel predictions of the kinship theory of genomic imprinting in social insects (Queller 2003 BMC Evolutionary Biology 3:15).  This project involve testing for such imprinting using genomic techniques.  Experience with RNA-seq methods and analysis is desirable.

David Queller and Joan Strassmann lead a friendly and interactive team of highly motivated, creative, and smart investigators. We are seeking energetic postdocs with strong backgrounds in areas such as evolutionary biology, social behavior, mutualism, microbial evolution, genomics, and molecular biology.   Check out our website, (http://bit.ly/14aHGcx) for more information on our lab, or Strassmann’s blog (http://bit.ly/17dDCTx). To apply, please email Patrick Clark (pclark@wustl.edu), specifying “imprinting postdoc” in the subject line.  Please attach a single file including CV, statement of research interests, and the names, phone numbers, and email addresses of three references. Women and underrepresented minorities are particularly encouraged to apply.  Funding is from the John Templeton Foundation.  We will begin reviewing applications by 15 May 2014 and will continue to accept them until the positions are filled.  Postdocs may start immediately but date is negotiable.

—_000_400FE53178244FDCBCA413666A3A0913wustledu via Gmail
Source: EVOLDIR
01:19

This is a multi-part message in MIME format. via Gmail

Source: EVOLDIR
00:17
The early registration and abstract submission deadline for the 2014 Informatics for Evolutionary Biology conference is Wednesday, April 16. Registration and abstract submission is being done jointly with Evolution: http://bit.ly/1jHfAtr iEvoBio is a forum bringing together biologists working in evolution, systematics, and biodiversity, with software developers, and mathematicians. The goal of iEvoBio is both to catalyse the development of new tools, and to increase awareness of the possibilities offered by existing technologies. April 16 is the deadline for lightning talks and for software bazaar submissions. Submitted talks and software demos should be in the area of informatics aimed at advancing research in phylogenetics, evolution, and biodiversity, including new tools, cyberinfrastructure development, large-scale data analysis, and visualization. If a submission concerns a specific software system, that software must be licensed with a recognized Open Source License. For more information, including details of the open source requirement, see http://ievobio.org. Karen Cranston, PhD Training Coordinator and Informatics Project Manager nescent.org @kcranstn http://bit.ly/1iXV30Q Karen Cranston via Gmail
Source: EVOLDIR
00:02

****NOTE that the early application deadline is next week: April 22nd. *Workshop / Summer school “Quantitative Evolutionary Biology — understanding evolution with models and genomes.” *Time and Place September 14-21, 2014, Mathematics Village, Sirince, near Izmir (western Turkey). *Keynote lecturers Aida Andres (MPI for Evolutionary Anthropology, Leipzig) Nick Barton (IST-Austria, Klosterneuburg) Thomas Lenormand (CEFE/CNRS, Montpellier) Pleuni Pennings (Stanford University, California) *Description The workshop is mainly targeted towards advanced graduate students and early post-docs studying evolutionary theory, evolutionary genetics or evolutionary genomics. However, we also accept a limited number of more experienced researchers in these fields that can share their knowledge with the students and that are open to collaboration with the scientists in the workshop. The main body of the workshop will consist of keynote lectures (see above), and group projects developed by students under the supervision of young scientists. There will also be various short lectures and seminars during the workshop. *For more information, visit http://bit.ly/1ktrg3p *Application deadlines Early: April 22, 2014 (recommended as the number of attendees and fellowships are limited) Late: June 15, 2014 *Other Participating Scientists Melis Akman (UC, Davis), Tugce Bilgin (UZH, Zurich), Emily Jane McTavish (University of Kansas), Tiago Paixao (IST Austria), Lilia Perfeito (Gulbenkian Inst, Oeiras) *Organizers Mehmet Somel (METU, Ankara), Hannes Svardal (GMI, Vienna), Murat Tugrul (IST Austria) *Co-sponsor: ESEB Global Training Initiative & NESCent Murat Turul PhD Student @ Barton Group Evolutionary Genetics, IST-Austria via Gmail

Source: EVOLDIR
00:02
[Imperial College London] Research Technician Imperial College London -Department of Life Sciences Faculty of Natural Sciences Salary scale: £25,370 - £28,040 per annum (maximum starting salary £25,370) A Research Technician position is available in the research group of Dr Richard Gill to support research looking into the effects of environmental stressors on insect pollinators. The post holder will be based in the Department of Life Sciences at the Silwood Park Campus (near Ascot in Berkshire) of Imperial College London and will become an active member of the Grand Challenges in Ecosystems and Environment initiative (http://bit.ly/1nQwXKf). A substantial part of the Research Technician’s role will be to support a recently funded NERC project looking at the behavioural and molecular responses to pesticide exposure in bees. You will also be expected to assist in the running of the Gill Research Laboratory. The postholder will take part in leading edge research which will include novel, interesting and important axes of research. The main duties of the role will include the collection, preparation, processing and storage of bees, plants and chemicals. You will also be expected to assist with the running of manipulation experiments and data collection and be involved with bee and plant husbandry. You will provide assistance in the laboratory to the research group and assist with presentation of results to the group, collaborators, and to the research community in general. You may also be expected to prepare molecular samples such as DNA and RNA extractions and associated optimisation methods. You must have 2 A-levels in relevant subjects, or equivalent vocational qualifications, plus work experience, preferably in a relevant technical/scientific role. Proven experience in assisting to carry out scientific research in a laboratory and/or field setting, aided with observations of animal behaviour, and experience in providing support for analyses of large data are essential. You must also have experience in collection, preparation, processing and/or storage of animal and plant specimens and a background in a similar work environment. You should have a methodical approach to your work, good interpersonal and organisational skills, and be able to communicate well within a research group. You will be expected to organise and prioritise your work in response to deadlines, while paying close attention to detail. This is a fixed-term appointment available for up to 24 months on a full-time basis or up to 36 months on a part-time basis. Informal enquiries should be directed to Dr Richard Gill at r.gill@imperial.ac.uk. The preferred method of application is online via our website http://bit.ly/1c1zTPb (please select “Job Search” then enter the job title or vacancy reference number including spaces - NS 2014 049 JT - into “Keywords”). Please complete and upload an application form as directed. Alternatively, if you are unable to apply online, please contact Christine Short by email c.j.short@imperial.ac.uk, to request an application form. Closing date: 30 April 2014 (midnight BST) “Gill, Richard J” via Gmail
Source: EVOLDIR

April 15, 2014

22:00
Wednesday, 12:00 PM at NESCent, Ninth Street and Main Street, Erwin Mill Building, 2024 W. Main Street, Suite A200. For more information, call 919-668-4551
Source: NESCent
18:00
Background: Polyandry is a common mating strategy in animals, increasing female fitness through direct (material) and indirect (genetic) benefits. Most theories about the benefits of polyandry come from studies of terrestrial animals, which have relatively complex mating systems and behaviors; less is known about the potential benefits of polyandry in sessile marine animals, for which potential mates may be scarce and females have less control over pre-copulatory mate choice. Here, we used microsatellite markers to examine multiple paternity in natural aggregations of the Pacific gooseneck barnacle Pollicipes elegans, testing the effect of density on paternity and mate relatedness on male reproductive success. Results: We found that multiple paternity was very common (79% of broods), with up to five fathers contributing to a brood, though power was relatively low to detect more than four fathers. Density had a significant and positive linear effect on the number of fathers siring a brood, though this relationship leveled off at high numbers of fathers, which may reflect a lack of power and/or an upper limit to polyandry in this species. Significant skew in male reproductive contribution in multiply-sired broods was observed and we found a positive and significant relationship between the proportion of offspring sired and the genetic similarity between mates, suggesting that genetic compatibility may influence reproductive success in this species. Conclusions: To our knowledge, this is the first study to show high levels of multiple paternity in a barnacle, and overall, patterns of paternity in P. elegans appear to be driven primarily by mate availability. Evidence of paternity bias for males with higher relatedness suggests some form of post-copulatory sexual selection is taking place, but more work is needed to determine whether it operates during or post-fertilization. Overall, our results suggest that while polyandry in P. elegans is driven by mate availability, it may also provide a mechanism for females to ensure fertilization by compatible gametes and increase reproductive success in this sessile species.
00:37

WORKSHOP SOCIAL EVOLUTION: MERITS AND LIMITATIONS OF INCLUSIVE FITNESS THEORY WHEN: 13-15 July 2014 WHERE: Arolla (Swiss Alps) DESCRIPTION: Inclusive fitness theory is generally assumed to sufficiently explain the evolution of social behaviour. However, empirical evidence is accumulating that other evolutionary concepts need to be involved to explain cooperation and social structure in a wide range of taxa. This includes the archetypical examples of altruism, like the reproductive division of labour in eusocial hymenoptera and the cooperative breeding groups observed in many vertebrates. Recent evidence from insects and vertebrates reveals that high levels of relatedness can even reduce rather than further cooperation and altruism. The aim of this workshop is to combine pertinent evolutionary theoreticians and empiricists to discuss complementary evolutionary mechanisms to inclusive fitness theory. This is a topic of great interest to students and biologists in general, because there is a growing awareness that explanations based on inclusive fitness, which have dominated the theoretical and empirical literature for the past half century, cannot account for many examples of apparently altruistic behaviour observed in nature. INFO & REGISTARTION: http://bit.ly/1gzvdmO SPEAKERS: Prof. Rufus Johnstone, University of Cambridge (UK) Prof. Laurent Lehmann, University of Lausanne (CH) Dr Christina Riehl, Harvard University (US) Prof. Hanna Kokko, Australian National University (AU) Dr Erol Akcay, University of Pennsylvania (US) Dr Elli Leadbeater, University of London (UK) via Gmail

Source: EVOLDIR
00:05

Hi, Next semester, I will be teaching an undergraduate course on Vertebrate form and function, which will have a large component of functional morphology. The course will include a weekly 2h 30 min lab, which should complement the lecture part. I would like to make the lab more interesting and practical than just focusing it on a comparative study of bones/skeletons and anatomies. I was wondering if anyone has suggestions on how I could make the lab more interesting and possibly include some research - based experience for the students. Thanks in advance, Ylenia via Gmail

Source: EVOLDIR

April 14, 2014

23:51
Submit your best evolution-themed video for screening at this year’s Evolution meeting Deadline: May 31 Scientists and science communicators of all stripes are invited to enter the fourth annual NESCent Evolution Video Competition. To enter, please submit a video that explains a fun fact, key concept, compelling question, or exciting area of evolution research in three minutes or less. Entries may be related or unrelated to your own research, and should be suitable for use in a classroom (K-12, undergraduate, graduate…your choice). Animations, music videos, and mini documentaries are all fair game. The finalists will be screened at the Evolution 2014 conference in Raleigh, NC . You don’t need to attend the conference to enter. The first- and second-place winners will receive travel awards to attend the scientific meeting of their choice. All videos submitted by May 31 are eligible to win. For more information visit filmfestival.nescent.org/ Robin Ann Smith, Ph.D. Science Writing and Communications National Evolutionary Synthesis Center 2024 W. Main Street, Suite A200 Durham, NC 27705 Tel: 919-668-4544 rsmith@nescent.org http://bit.ly/1gzn6qv http://bit.ly/1gzn41V www.nescent.org/ ras10@duke.edu via Gmail
Source: EVOLDIR
23:35

Dear colleagues, A permanent position as Bioinformatics specialist/Scientific programmer is available in Thijs Ettema’s lab, Cell and Molecular Biology, Uppsala University, Sweden. Applications here: http://bit.ly/1iQYlmM We are looking for: A Bioinformatics specialist or Scientific programmer that will work at the lab of Thijs Ettema at the Department for Cell- and Molecular biology, Uppsala University. The position is permanent with a 6-month probationary period beginning June 1st or by appointment. The Ettema-lab: cutting edge research in microbial and evolutionary genomics Prof. Ettema’s lab is applying emerging genomics technologies to analyze microorganisms that defy cultivation under laboratory conditions. These organisms are predicted to comprise up to 99% of all microbes on our planet. By combining such cultivation-independent methods, such as single cell genomics and metagenomics, with next-generation sequencing-based analyses, the Ettema lab aims to study uncultured microorganisms (‘microbial dark matter’) at the genomic level. Such studies will paint a detailed picture of the overall diversity and origin of life on our planet, and will eventually also reveal important clues about how complex life emerged. For more information, please see: http://bit.ly/1eCN4ec The Ettema-lab is located at the Biomedical Center (BMC) and will enter brand-new office spaces and a fully equipped genomics lab during the autumn of 2014. Moreover, the lab is well connected to several technology platforms of the SciLifeLab, including platform-based services for next-generation-sequencing, computational support and bioinformatics. We are also actively engaged in a new SciLifeLab platform for single cell genomics (http://bit.ly/1eCN4ed). The research activities in the Ettema-lab are supported by a number of prestigious grants, such as the European Research Council, the Foundation for Strategic Research and the Swedish Research Council. Job assignment: We are now looking for an experienced bioinformatician that will provide computational support to ongoing research projects within the Ettema lab. The successful candidate will have the following tasks: - In collaboration with the researchers of the lab, design and perform bioinformatics analysis in molecular evolution, phylogenomics, sequence analysis and metagenomics, among others. - Set up and maintain pipelines for the assembly, annotation and analysis of large-scale genome and metagenome sequencing datasets; set up and maintain databases for comparative and evolutionary studies of uncultivated microorganisms. - System administration of the existing local computational infrastructure (server, workstations) and accounts (about 5% of the time). Potentially, the successful candidate might be asked to develop novel bioinformatics applications. Qualifications: Requirements: - A PhD or similar research experience in bioinformatics and genomics (within academia or industry). - Extensive experience with the analysis of next-generation sequencing datasets. - Demonstrated experience working in a Linux environment and fluency in at least one programming or scripting language (bash, Perl, python, R, etc.). - Fluent oral and written communication in English. Personal qualities: - Strong sense of organization - Ability to work in cooperation with academic researchers - Excellent communication skills - Pedagogic skills to teach researchers with limited bioinformatics experience - Ability to work independently Merits: - Postdoctoral studies in a related field. - Experience of development and applications of bioinformatics and/or biostatistics methodologies or pipelines for design and analysis of large-scale sequencing datasets (e.g. Galaxy) - Experience of system administration The positions are permanent with a 6-months trial period. The positions will be placed in the Ettema lab at the Department for Cell and Molecular Biology, Uppsala University. For further details about the position, please contact Thijs Ettema: thijs.ettema@icm.uu.se, +46 18 471 45 21. For more information about the activities in the Ettema-lab, please see http://bit.ly/1eCN4ec Application: Please send your application marked with ref.nr UFV-PA 2014/1062 as soon as possible, but no later than 2014-04-30. Lionel Guy Molecular Evolution, Uppsala University, Uppsala, Sweden postal address: Box 596, SE-751 24 Uppsala; visiting address: BMC B7:213e, Husargatan 3, SE-752 37 Uppsala phone: +46 18 471 6129, mobile +46 73 976 0618 lionel.guy@icm.uu.se guy.lionel@gmail.com via Gmail

Source: EVOLDIR