The scale insect that took three years to grow

If you want to see the adult of this scale insect species, you’ve better come to Sapporo, Hokkaido in Japan the right year. And if my calculations are accurate, it should be in the summer 2015!

Most famous scale insects are usually dangerous plant pests, attacking ornamental plants in your garden, or making fruits unaesthetic to be sold (although still edible in general…). Those species have at least one full generation per year, sometimes two and in greenhouses can make several generations a year.

But it is completely different for Xylococcus japonicus Oguma. In 1919, Dr Oguma from the Hokkaido Imperial University (now Hokkaido University) published a detailed study on the biology of X. japonicus. He received a branch of alder (Alnus japonica) in the spring of 1913, where there was, imbedded in plant tissues, a mysterious scale insect. Its presence was only betrayed by some white projections coming out of the branches.

Dr Oguma was wondering why the scale insect seemed to only attack the tree’s old tissue and did not seem to affect the youngest parts of the branches. He then followed the development of the insect for three entire years before understanding why.

This is how it all happened between 1913 and 1916:

Notebook 4-5

Summer 1913: Dr Oguma was not in Hokkaido and when he came back, he only found dead adults. Argh, too bad, maybe next year?

Spring 1914: the eggs laid by the adult females at the end of the summer 1913 finally hatched. Right after, they sought shelter within the cracks of alder’s bark, inserted they sucking mouthparts to feed and started to secrete wax protection.

Fall 1914: after a whole summer of gorging themselves with alder juice, the first-instars finally molted to second-instar nymphs.

Winter 1915: the second-instar hibernated.

Spring 1915: in May, the insects became active again, producing more wax filaments and becoming larger

Summer 1915: they were still in second-instar stage throughout the summer. They had become so large compared to the cracks they have been hiding in that they could not move an inch anymore.

Fall 1915: stuck in the cracks, still not molting..

Winter 1916: hibernation happened, and still stuck in cracks.

Spring 1916: the second molting event happened. Yay! third-instar nymphs!

Summer 1916: by August, females and males (though still third instar nymphs) were distinguishable. At the end of August, the females molted to their young adult form but the males entered their quiescent pupal stage (different from an actual insect pupa but still called pupa, confusing…). The pupal stage lasted only one week.

Reproduction: the female being completely stuck in bark cracks waits for a male (he moves and is completely different from the female – see post on sexual dimorphism soon!) with her posterior popped out from the crack. Copulation only happens in the morning. Although the male, after a minute of copulation goes away looking for more females, the freshly copulated females hides herself back in the crack and only pops her butt out again the next morning. Because adult males only live one day, the next morning always is a new male.

Fall 1916: the female started to lay eggs and by the end of September, she was found dead, her body acting as a cover to protect the eggs.

And this was the generation of Xylococcus japonicus between 1913 and 1916!

The time of development explained why only older tree branches were affected by the scale insect as one generation only feeds on the same tissue for 3 years. The affected branches are severely damaged and often fall off. Maybe, if this species had produced yearly generations, the host plant would have been easily been decimated. Because this scale insect seems to be very picky on which tree juice to drink (only feeding on alder), could it have developed this 3-year-long generation to give enough time for the host plant to recover?

I have made my calculations and if right, the next adults should emerge in August 2015, 33 generations and almost 100 years after the adults observed by Dr Oguma in 1916.

Well, I am not sure if I will still be in Japan then, but it is definitely worth looking for the scales this summer up in Hokkaido and bring some photos of second instar nymphs to show you!

To be continued…

Reference: Oguma, K. 1919. A new scale-insect, Xylococcus alni, on alder, with special reference to its metamorphosis and anatomy. Journal of the College of Agriculture, Hokkaido Imperial University 8: 77-109. 


Would you have survived on the Titanic? (Udacity Intro to Data Science)

Last week, I started an online course at Udacity to introduce myself to data science and try to understand what it takes to analyse big datasets.

About Udacity: I have successfully (and unsuccessfully, while I was writing my Ph.D. dissertation) taken courses at Coursera and stumbled upon Udacity from an iPad app I downloaded to view my Coursera lectures. Coursera offers courses designed and taught by university professors, as opposed to Udacity where a lot of courses on programming in general and data science are taught by people working in the tech industry. So I was curious to see how differently they teach a class. I chose to pay the course (although you can also take it for free), which provides a coach (i.e., a real person that Google Hangouts with you!), follow up on your progress and feedback on the final project, and a verified certificate for my resume.

Starting this course, I had just taken an introduction to data analysis using R at Coursera.

About the course: Intro to Data Science is aimed at people who have some basics in statistics and programming. The programming language is Python and we also also Pandas, a data analysis library which looks like a combination of R and Python. The course is divided into 5 main lessons, each of them are accompanied by a project. In the final assignment, we will communicate as a blog post the results of the project developed during the course using datasets of the NYC subway and NYC weather.

Project #1: Intro to the Titanic survival dataset
After a few lecture videos introducing what data science is, I started to work on the first project.  At first, we don’t start on the main project but us a dataset and problem from a Kaggle project. Although most of Kaggle competitions are really intimidating, this project was created for people starting in data science. Using a dataset of surviving passengers from the Titanic tragedy, we had to write a small program that predicts the survival of half of the passenger list. The survival outcome of the first half is provided, with information about each passenger (age, gender, social class, how much they paid etc…). With this information (and also intuition), it is possible to estimate which type of passengers were more likely to survive and then write a script that will predict the survival outcome of the second half of the list. Kaggle provides tutorials to solve this problem with Excel (I learned some neat stuff there), Python, Random Forest and R. In Udacity however, you have to use Python and the power of Pandas. Project #1 includes three exercices that walks you through making a simple prediction script, based on one variable, to a more complex and customized way you want to think about the problem. So, do you think that a woman with 2 children in 3rd class of the Titanic had a chance to survive?

What I liked: Watching the videos of lesson 1, I really liked that some of them introduced real data scientists, what their definition of data science was and why they decided to follow such career path. In project #1, using a Kaggle competition dataset and project was really nice as it is not just limited to the scope of the course. I learned ways to work with Excel and the competition motivated me to try to find the highest prediction possible.

What I liked less: For the same reason, the downside was after doing the tutorials in Kaggle, I tried to obtain the best prediction right in the first exercice in Udacity, which made the next two exercices useless.