academic
- These data are the average seed set estimates for dichogamous and adichogamous Chamerion angustifolium at different inflorescence sizes.
- maternalID: Identification code for the maternal plant (i.e., grandmother of the counted seeds).
- individualID: Identification code of the plant.
- array#: The array identification number.
- dichogamyType: Indicates if the plant was dichogamous.
- flowerPosition: Flowers were sampled from either the bottom or top of the inflorescence.
- inflorescenceSize: The number of open flowers on each plant in the array.
- seedCount: Number of full seeds
- notSeedCount: Number of aborted seeds
- Routley, M.B. & B.C. Husband. 2003. The effect of protandry on siring success in Chamerion angustifolium (Onagraceae) with different inflorescence sizes. Evolution, 57: 240-248 PubMed PDF
- These data are the average siring-success estimates for dichogamous and adichogamous Chamerion angustifolium. Siring success is estimated from the proportion of heterozygous progeny produced at the PGI locus. Dichogamy classes were homozygous for alternate PGI alleles, so that heterozygous progeny represent interclass pollen transfer.
- Array: The array identification number.
- DichogamyType: The dichogamy status of the plants in the array.
- FlowerSize: The number of open flowers on each plant in the array.
- ProportionHeterozygousProgeny: The ratio of heterozygous to homozygous progeny at the PGI locus.
- Routley, M.B. & B.C. Husband. 2003. The effect of protandry on siring success in Chamerion angustifolium (Onagraceae) with different inflorescence sizes. Evolution, 57: 240-248 PubMed ProtandryDiscounting.pdf
- These data are pollen counts from anthers before and after single bee visits in populations of Chamerion angustifolium from Montana. Pollen was quantified with a Beckman-Coulter Multisizer 3 particle counter.
- Population: The population sampled, either tetraploid or diploid.
- Sample: An identification code representing the plant and flower sampled.
- StigmaPresence: Some flowers had their stigma and style removed with forceps. Others were left intact.
- Visitation: Whether the anther was sampled before or after a single bee visit.
- PollenCount: The estimated amount of pollen present in the flower.
JSTOR import script
I’ve written a script that imports a JSTOR citation page into BibDesk. To use the script, I suggest adding it to your script menu. Then, with the JSTOR citation page as the active web page in Safari, run the script and the citation will be added to the active BibDesk file. I use the first author’s last name and last two digits of the year as a cite key (e.g. Darwin59), you may want to change this to suit your style.
The script is written in perl and bracketed by two Applescript commands: one to extract the html source from the JSTOR page and the other to add the citation to BibDesk. Unfortunately, JSTOR citation pages contain almost no semantic markup, so I am not convinced that the approach is entirely robust. However, so far it has worked well for me and might be useful to you. Any feedback is welcome.
Download the script. JSTORImport.pl.txt
Ecology Retreat, University of Calgary
Routley, M.B. Measuring the male gain curve. Ecology Retreat, University of Calgary
Pollinator networks
Plants are sessile and, consequently, many species rely on pollinators for mating opportunities. However, pollinators do not necessarily visit every individual in a population with equal frequency. Plant attributes, such as floral display and reward provisioning, can influence the frequency of pollinator visitation. Furthermore, aspects of population density and structure may also influence visitation patterns. One effect of this unequal distribution of pollinator activity is that pollinators create networks of connections between plants in which a few plant receive many visits and many plants receive few visits. Such networks are termed ‘scale-free’ and can be contrasted with random networks. Random networks follow a Poisson distribution of connection frequency and have the familiar bell shape characteristic of many biological patterns. Scale-free networks have power-law distributions with no peak, just a steady decline in the frequency of nodes with increasing number of connections. Technically, in a scale-free network the probability that any node is connected to k other nodes is proportional to 1/k^n^, where n is usually around 2.
Measuring networks
The hallmark of a scale-free network is a hub or node with a high number of connections. A relatively simple test for hubs is to plot a histogram of the number of connections between plants. This is illustrated in the following figures. On the left, many plants have a low frequency of connections, while a few plants have many connections. Those plants with many connections could be hubs. Contrast this with the right. The distribution of connections follows a bell shape with no plants having an excessive number of connections. There are no hub plants in this population.
Importance of scale-free networks
The analysis of scale-free networks has provided insights into fields as diverse as scientific-citation patterns, disease epidemics, world-wide-web structure, and cellular metabolism. Such power suggests that applying network thinking to pollination biology may be useful. These networks are produced through a process of growth and unequal creation of connections. Clearly plant populations experience changes in population size and pollinator behaviour often leads to 'traplining’ or enhanced visitation to particular phenotypes. Consequently, plant populations have some of the prerequisites for scale-free networks. Scale-free networks are very resilient to the random loss of nodes, because the vast majority of network function is provided by the hubs. In a plant population context, if a population can be characterised by a power-law distribution, population growth rates and persistence are likely driven by a small subset of the plants. Such an asymmetry would have important implications for evolutionary ecology and conservation questions.
An important first step is to determine if pollinator-visitation patterns follow a power-law distribution. Hub plants could then be identified and the mechanisms producing the pattern investigated. Are hubs spatially clustered? Do they have larger-than-average floral displays or brighter floral pigments? What are the demographic consequences of removing hubs from a plant population? All we need is data on pollinator visits to plant populations. The data I have access to are inconclusive, mostly because they have insufficient visit frequencies. Any other data sets would be appreciated.
For more information on networks visit www.nd.edu/~networks.
Society for the Study of Evolution meetings 2004
Routley, M.B., L.D. Harder, & S.A. Richards. Ovule fates. Colorado State University
Download SSE2004.pdf
Dynamic State Variable Models in Ecology
There’s a powerful approach to modelling called dynamic state variable programming, covered in Dynamic State Variable Models In Ecology by Clark & Mangel. I’ll post more about the approach sometime, but for now I wanted to make an example from the book available. The first chapter of the book includes a guide through the creation of a patch foraging model. A fully implemented version is available in True BASIC, but I’ve decided to use R for all of my modelling and analyses. Consequently, I’ve implemented the patch selection model from Clark & Mangel in R. It is available from http://public.me.com/mroutley/patchSelection.txt for anyone interested.
Journal abbreviations
Until recently, I was able to use journal abbreviations in all of my manuscripts. Consequently, my .bib file contains only abbreviations in the journal field. Now I need to produce some bibliographies with full journal names. With a .bib file you can use macros to handle changing abbreviated names to full names. However, BibDesk cannot use macros. Instead I wrote a perl script that searches through a .bib file and creates a new file with journal abbreviations changed to full names.
This script has been quite useful to me, so I decided to make it generally available here as longJournals. Feel free to modify it to suit your database. The important sections are near the top where the input and output files are declared and the substitution rules are listed. There are two types of substitution rules to handle some abbreviation conflicts (for example, Ecol. could be Ecology or Ecological). The first set of rules takes precedence over the second.
Rather than maintain two .bib files, I decided to use the abbreviated file as the main file and then periodically replace the long-journals file. The script could be improved to make it more flexible, but I only maintain one .bib file so it made sense to hardcode the file names and replacement arrays in the script. The copy linked to above has most of the substitution rules removed, since I assume they are not generally useful.
Suggestions are appreciated. Be sure to test out the script with a backup database first. Changes are not undoable.
Seed size
I have been investigating issues of ovule and seed development recently. One question that has come up is: How much variation is there in seed size? I had analysed some seed set data for some earlier work with some image analysis software. Consequently I have a large data set of seed area and perimeter for Chamerion angustifolium. A rough look at the data is:
The data set is available as seedSizeData.txt.zip. Bewarned, the file contains over 100,000 lines – be sure to use an efficient text editor or stats package. All dimensions are in cm. The seed? column contains a 1 for seeds and 2 for objects smaller than seeds. See this for details.
Confronting models with data
The Ecological Detective by Ray Hilborn and Marc Mangel is an excellent source for learning how to analyse ecological data with sophistication. Traditionally, ecological data is analysed from the binary perspective of hypothesis testing. The goal of such testing is to either accept or reject a null hypothesis. Although it is well entrenched in ecological training and publication, this hypothesis testing has repeatedly been attacked by statisticians and many ecologists.
Without entering into this debate now, Hilborn and Mangel present an alternative of constructing models of the biological system of interest and then testing the model with collected data. This approach offers a much more nuanced and powerful way of understanding ecological processes. No longer is the ecology simply used to accept or reject a null hypothesis. Rather, a deeper understanding is required that leads to greater insights into the process.
As part of reading through the Ecological Detective, I worked through the pseudo-code examples provided and implemented them in Mathematica. I’’ve decided to make these files available: EcologicalDetective.zip. My hope is that someone else will find them useful. I would be particularly interested in discussing these files and the book with anyone interested. I should point out that this was my first use of Mathematica, so I would appreciate any feedback on how to use it more effectively.
Character assignments in phylogenetic analyses
In some recent research (http://public.me.com/mroutley/SIandDichogamy.pdf) I had to make inferences about families based on character states of the species within the family. One approach is to use a simple majority rule. For example, if more than half of the species possess character state x rather than y, then the family can be described as x. However, this approach seemed rather liberal, which led to a 2/3 majority criterion: if more than 2/3 of the species are x, the family is x; If less than 1/3 is x the family is y; otherwise the family is ambiguous.
A significant drawback to arbitrarily creating such criteria is that I had no idea what the consequences were for making Type I and Type II errors. Presumably, as the criterion becomes more stringent, Type I errors are less likely, but such uncertainty is not comforting. I decided a better approach would be to attempt a simulation study using different criteria coupled with a more sophisticated character state reconstruction algorithm.
The general approach was to create a family of twenty species and randomly assign a fixed proportion of each species one of two possible character states. The fixed probability chosen represented the decision criteria to be evaluated. For example, a 51% proportion is equivalent to the simple majority criterion. I then randomly generated 5,000 phylogenetic tree topologies for the family. To evaluate any given decision criterion, I compared the family characterization from the decision criterion to the ancestral-state reconstruction from Schluter et al’s maximum-likelihood analysis. The idea is that the maximum-likelihood reconstruction should be reasonably accurate, since it incorporates tree topology into its calculations. If the decision criterion and maximum-likelihood approaches yield similar answers, the decision criterion may be a good choice for describing families in the absence of species-level phylogenetic resolution.
I tested three decision criteria. Their results are: an 80% criterion was 98.1% accurate, 65% was 91.6%, and 55% was 60.8%. Clearly more stringent criteria are most similar to the more sophisticated maximum-likelihood analyses. However, stringency does exclude more data from the analysis as more families become ambiguously coded. A proper trade-off between stringency and sample size is required to make the best use of data.
I conducted these simulations with Mesquite and the data file is available(http://public.me.com/mroutley/simulations.nex).
Reference management
I have been working through my references and papers trying to regain some control over the literature. Being reintroduced to the tedium of reference management, it seems like there must be a better way to catalogue and organize this important component of research. Ideally, with the Internet and some good citation support from publishers, I would never have to type a citation – just automagically download whatever I need. Obviously this is not currently available.
I use BibDesk for my reference management and the author has some interesting ideas about sharing reference databases easily among colleagues. In this spirit of sharing, I’’ve decided to make my reference database available here. It is in BibTeX format, which most useful reference management software should recognize. As BibDesk matures I hope to make this database accessible in a more useful format (i.e., automatic synchronization). Until then I will update the publicly available database as often as possible. Ideally the database will become a group effort, maintained and expanded by whoever uses it. If you are interested in participating, let me know. There are some fields in the database that may not be useful. In particular, there is a link to the PDF location on my harddrive. I considered transfering these links and associated files to the Internet as well. However, there are copyright concerns with such a setup that need to be considered.
Seed set of dichogamous plants
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Ecology Division Seminar Series, University of Calgary
Routley, M.B. The evolutionary significance of being one gender at a time. Ecology Division Seminar Series, University of Calgary
Download https://matt.routleynet.org/uploads/2020/97fb2da280.pdf
Siring success of dichogamous plants
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Pollen removal after single bee visits in the field
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Floral Integration
Unrelated to my “official” thesis work, I have been thinking about floral form and its influence on plant fitness. As an excuse to start a discussion with anyone interested, I’ve posted this overview of what I hope to work on next.
Plant mating systems control the transmission of genes between generations and, therefore, are a fundamental characteristic of populations. Since flowers are the reproductive organs of plants, floral form fundamentally influences plant mating systems. However, research into floral evolution has traditionally “atomized” flowers into conspicuous traits that are then investigated independently. Despite the undeniable success of this reductionist approach, an alternate research strategy called phenotypic integration, found at the intersection of morphometrics, quantitative genetics, reproductive ecology, and plant evolution, offers a unique perspective. Floral integration, in particular, asserts that the variance-covariance structure of entire flowers, rather than mean values of individual traits, may be an important target for selection. This is especially relevant for animal-pollinated, hermaphroditic flowers (i.e., most angiosperms) in which the male and female sexual organs must be positioned precisely within the path of pollen movement. Consequently, I expect high integration for anther and stigma placement relative to, for example, vegetative characters. After a long period of neglect, floral integration is beginning to receive more attention. To date, most of this research has focussed on quantifying the magnitude of integration, whereas the evolutionary significance of variation in floral integration remains an open question.
Society for the Study of Evolution meetings 2003
Routley, M.B. & B.C. Husband. Responses to selection on protandry in Chamerion angustifolium (Onagraceae). Chico, California