In today’s BBC Nature news:
After a cold spell, British scientists are concerned about the late arrival of rare butterfly species.
Although my current research is focused primarily on marine plankton phenology, dramatic examples of year to year changes in terrestrial biology are interesting to mark. The recorded observations of flowering events, leaf-out, ice-out and annual migratory patterns comprise phenology across many different ecosystems. Shifting phenological timing due to climatic conditions is difficult to track unless long-term records of both climate and species occurrences are marked.
In contrast with previous year’s observations, the timing of this year’s insects was up to a month later. What role do rare species play in this complex ecosystem interplay of phenological timing and response to environmental conditions?
Photographer:Peter ParksRights:© Image Quest 3-D
Chaetognaths are an ecologically important and abundant group of predatory marine zooplankton. These taxa have been routinely found in our zooplankton sampling since 1984. In the Gulf of Naples, Chaetognaths reach their peak abundance in October after the peak of summer copepods occurs.
Using mechanoreceptors, chaetognaths are able to sense their prey and utilize ambush feeding behaviors! These sensitive receptors also determine the size of their prey catch based on the size of their mouth. Pretty amazing stuff!
I’ll try and post images of the taxa I study so we can put some names with all the numbers.
This plot is based on a figure in the 2004 Edwards and Richardson Nature paper in which the timing of central tendency was used to estimate a change in timing by calculating the difference between the start and end timing values in a time series. In that case, the data considered plankton data from the continuous plankton recorder (CPR) time series. In this case, we are considering a different time series spanning from 1984-2010 with a 4 year gap.
A negative difference between the start of the time series (1984) and 2010 indicates that the phenological timing of a taxa is anticipated or their season is starting earlier. A positive difference is interpreted that the species season is delayed or later.
The taxa considered here have been evaluated at species level, but for the sake of this plot are depicted into plankton classes. In total there are 30 taxa (in this study) with a distinct unimodal season that include phytoplankton (n=14 species) and zooplankton (n=16).
Edwards, M., and A. J. Richardson. 2004. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430: 881–884.
When we think of science writing or research in science, “we” (meaning myself of course but perhaps a general collective) think of publishing results, data, hard and fast truth. What many people don’t realize is that science also involves some highly creative, original writing. In fact, what I didn’t realize when I started my doctoral research was just how much writing I would be doing!
This is normal they assured me, it will be helpful for your thesis, it will produce manuscripts!
I didn’t realize that the writing would become such an integral part of the process throughout the rest of my research efforts.
This week I realized how some of my work will contribute to the “long/term” efforts of timeseries (herein referred to as ts) research; at least at this laboratory. If you consider the multitude of graduate degree projects and theses, post-doctoral projects and other contract positions, these are often highly productive time periods (despite the slow start and learning curve time-lags), but relatively short-lived. Once a student or post-doc completes this project, the grant funds have dwindled, and it’s time to move onto another project.
There may be drawbacks to completely setting aside the previous work. For example, in ts research, the project continues; data collection is often a long term committeemen or research priority for an institution and there are often long range goals that direct the research objectives within the laboratory. However, I see an area within this type of research where the day to day work of a specialized project (either a graduate student or post-doc) can benefit the entire project.
I’m going to use the work I’m currently doing as an example. I’m learning several new computational and statistical tools which I can apply to analyze this ts dataset. Once I complete this research, the skills I’ve obtained from learning how to use these “tools” will reside with me. Effectively, when someone finishes a job, there is always a period of time where knowledge-transfer (I like to call it the “brain dump”) occurs. I’ve been on both sides of this equation, acting in the role of the person receiving the information from a previous employee or student, as well as the information-provider.
So, you may be asking where does zen come into this?
I cringe at the thought of calling myself an “armchair oceanographer” because in my mind it equates to less and less time spent in the field and more and more time spent in the confines of an office. At some point in our careers, there comes a time when we face the music. From a fellow colleague and well seasoned oceanographer in the field, I’ve been told there isn’t anything wrong with doing armchair science. What this all means to those of you not familiar with this terminology is that rather than being a field-based ecologist with manipulative experiments or extensive survey plots to count, I spend my days in front of large data sets and experiment with a vast array of working hypotheses to test associations and relationships within my data.
This branch of computational science or biology is now being termed “data science”. A recent NYT article discussed the future job prospects of this field and from what I’ve seen it seems data scientists are going to be a hot commodity in the marketplace. I should be happy right? The immediate answer to this question is both yes and no.
An interesting “infographic” from Wikibon.org presents the application of data science to various fields from social networking to time series. While I agree that data science is the new black, I would urge caution about how it is applied. The mechanics of how data science is carried out are fascinating and involve everything from cloud computing to hacking, programming and high level statistics. This part is the “science” in my opinion. The other side of this equation is the why, or experimental approach if you will. Why do data science? What does it tell us? Do you really want to data mine every facebook profile and related tweet to find out what the next generation is thinking, buying and saying? The philosophical side of me thinks this new branch of science should be grounded in a guiding approach to manage not only the accumulation of data but whether it’s truly worth our data-mining efforts.
Regardless of the ethics, data science is the new sexy. Now, let’s test the significance of this statement… off to the reality of science!