Botany picture #172: Acacia decurrens

I am reasonably optimistic that this is indeed Acacia decurrens (Fabaceae), one of many wattles that are currently in flower here in Canberra. This was a small tree in Mount Majura Nature Reserve. It is the first time that I notice an Acacia with the styles of the individual flowers sticking out of the heads like that.

Final update on using fastStructure and similar software

After my somewhat mixed experience trying to use fastStructure, I have recently found the time to throw my data at two other programs for inferring population structure.

To recap, I have thousands of SNPs for two groups of species, in one case from 91 individuals and in the other from 224 individuals and I want to know how best to group the individuals into separate 'populations', in the present case potential species. I originally used fastStructure because it was new and supposedly written specifically for large numbers of SNPs, but the results were ultimately odd. The clusters didn't make very much sense and the program found virtually no admixed individuals, that is hybrids, although there really should have been some.

Earlier this week I then tried the R package adegenet. On the plus side, it turned out to be very simple and user-friendly. Of course you need to know how to use R, but the manual of the package is well written, and adegenet has a straightforward "read" function for importing datasets. It easily imported my Structure file without any hiccups, and after that it was a simple manner of handing my data over to adegenet's "find.clusters" function.

However, I tried different settings and did not get reasonable populations with any of them. One problem in my dataset are missing data, and I found that setting allele frequencies to zero for those cases produced the most meaningful results, but still there were several populations with no samples in them and the populations that had samples didn't make a lot of sense.

Yesterday I finally tried my luck with good old Structure itself - somewhat hesitatingly because I feared it would be very slow with such a big dataset. Yes, even for my smaller dataset what I wanted to do ran overnight, but that is still faster than I feared, and the results are worth it. The populations make sense, and in marked contrast to fastStructure it finds evidence of admixture. My larger dataset will probably need several days to be analysed, but if that is necessary so be it.

There is probably a reason why that program is the most popular in the area...

A new low in science spam

Science spammers sometimes use scripts to mine journals or article databases for authors' contact details and then generate automatic spam. That saves them a lot of work, of course, but one has to wonder about the efficiency of that approach because the results are so bizarre and off-putting.

Behold:

Dear [my name]; [name of co-author]; [name of co-author]; [name of co-author]; [name of co-author]; [name of senior author],

Once you published a paper titled [name of our paper] in [name of the journal] . With such attractive theme [what follows are the keywords of our article] Cell size; chromosome; flow cytometry; genome size; guard cell size; ploidy, the article is so outstanding. It shows your professional and rigorous attitude. On behalf of the academic world, we appreciate your contribution to the research filed [sic!] very much. And we wonder if you have any new progress or you are doing any new study about your interested field.

Science Publishing Group [link], who [sic!] publishes Journals, Special Issues, Books and Conference Proceedings, now sincerely invites you to contribute your new articles to the website.
[another link]

Submit Your Latest Research

New progress of your latest research
New study in your research field
A view for the new research trends

Please submit your new papers via:  [another link]

Advantages to Publish with SciencePG

Peer Review: Effective and professional
High Visibility: Up to 40,000 visitors per day
Open Access: Open to the public free of charge
Low APC: Article Processing Charge ranges from 70 to 270USD
Abstracting and Indexing: WorldCat, CrossRef, JournalSeek, CASSI, etc.

If you are doing some new study, please kindly notify us. And we are looking forward to your participation.

It is even worse because all the fields that were filled in by the script are in italics in the original (like the keywords above), making it even easier to see what is going on. So sad.

Categorical Analysis of Neo- and Paleo-Endemism

So, after laying the groundwork by looking into biodiversity metrics such as (Species) Richness, Corrected Weighted Endemism (CWE), Phylogenetic Diversity (PD) and Phylogenetic Endemism (PE), we can come to the first of two recent papers that I wanted to discuss. A few weeks ago several friends and colleagues, some of them from my own institution, published a new, quantitative method for locating hotspots of endemism called Categorical Analysis of Neo- and Paleo-Endemism (CANAPE; Mishler et al., 2014).

Botany picture #171: Banksia coccinea

Banksia coccinea (Proteaceae), Western Australia, 2012. To my great surprise I found this species being sold as a cut-flower in our little local supermarket just yesterday. Because my wife likes the genus so much I bought one of them and it is now sitting on our dinner table.

Also, we are rather proud that our five year old daughter was able to immediately identify it as a Banksia although she had never seen this particular species before.

Diversity metrics

I want to blog about two recently published papers, one on keys and one on a method for spatial analyses of biodiversity, but for the latter some groundwork is necessary. This post will provide that groundwork so that I can then cunningly link back to it.

The last 25 years or so have seen the rise of spatial studies of patterns of biodiversity. They have been made possible by the increased availability of large databases with specimen occurrence records such as Australia's Virtual Herbarium, for example. Where a generation ago most information on the occurrence of species came from distribution maps drawn by specialists on the various groups of organisms, we can now enter a species name into a database search and are rewarded by a large list of geocoded specimens ready for use in our analyses.

Over the same time, several new diversity metrics have been developed to allow ever more sophisticated analyses. What is a diversity metric? It is a numerical value that tells us how diverse the organisms of our study group are in a particular part of our study area.

The study area as a whole is divided into cells; ideally these are equal area cells of for example 100 km x 100 km, alternatively they are biogeographical or political units. We can then look at our diversity metric and say, aha, in this cell there is particularly high diversity, and that might influence our decisions about what areas to prioritise for conservation. Okay, now what metrics are there?

Botany picture #170: Banksia integrifolia

Banksia integrifolia (Proteaceae), Jervis Bay, 2014. This may be the best known Banksia, for a variety of reasons. First, it occurs the coast near Sydney where a lot of foreign tourists can see the species. Second, that also means that it was one of the first species of the genus ever to be collected by a European scientist, Joseph Banks himself. Third, it is an easily cultivated plant found as an ornamental tree or even Bonsai in various parts of Australia. And fourth, it has even been introduced to other countries, and there is slight concern that it might turn into an invasive weed in New Zealand.

As can be seen in the above picture, the flower spikes as well as the fruiting cones are very attractive.