Past week I had to present in the Ecology, Evolution & Environment (EEE) seminar series at the University of Sheffield. I was invited by a good friend Donatella Zona with whom I’m collaborating on some arctic research involving changes in snow melt dynamics and their influence on ecosystem productivity.

I had the pleasure to present an overview of past and current research, mostly dealing with vegetation phenology. It was a varied crowd so I glossed over some of the more intricate details. Comments afterwards were positive, suggesting that everyone understood my talk. The latter is key in talking to a broad audience, as you don’t want to lose half of the people before you are halfway through.

I also had a interesting chat with Gareth Phoenix on his arctic research and how camera based work might help him. We also discussed some of his research on the coupling of above- and below-ground turnover rates and carbon stocks to leaf area index (LAI) in arctic ecosystems ((http://onlinelibrary.wiley.com/doi/10.1111/gcb.12322/abstract)). This research, by Sloan et al., suggests that: “the coupling of leaf and root carbon stocks and turnover rates to LAI across plant communities allow estimates of fine root and leaf carbon pool size and cycling rates across heter-ogeneous Arctic landscapes, using just one readily remotely sensed parameter – LAI”. These results have important implications towards PhenoCam use in arctic ecosystems, given the strong relationship between LAI and and the greenness chromatic coordinate (Gcc).

(header image: fall colours in the peak district West of Sheffield)

The phenology of deciduous forests is similar year to year. In it’s simplest form, modeling the influence of climate on phenology can therefore be modeled as discrete events, such as leave emergence in the spring. However, the appearance of leaves for some vegetation types is less well-defined seasonally, because changes are gradual: continuous leaf growth in grasslands or slow changes in the leaf physiology in evergreen forests (more on evergreens in a later post). Modeling this ongoing phenology is more challenging, as it requires an understanding of the biological and physical processes that govern the yearly trajectory rather than just a discrete event, such as leaf emergence.

Given my experience in with data loggers and input/output (I/O) on various platforms such as Arduino’s and Raspberry Pi’s I was asked to put together a little workshop on data logging.

I just finished some of the first analysis of the Jungle Rhythms beta trials. Below I show a few of the classification results. The red polygons outline the yearly sections, this outlines the area over which I evaluate the data. The red polygons will serve as a reference, given that the sheets of paper from which the data were digitized were sometimes warped, Black lines with blue end points are the life cycle events as marked by citizen scientists. Although this is a visual representation, the combination of the coordinates of the lines relative to the red polygon(s) provide me with enough information to calculate the week(s) in which certain life cycle events occur.

I’ve been quietly working on a citizen science project called Jungle Rhythms. The Jungle Rhythms project is started to ensure the preservation and transcriptions of historical hand-drawn observations of the life cycle events of over 500 different tropical tree species. These observations together with meteorological data will tell us the response of a tree’s life cycle events to changing environmental conditions, ultimately allowing us to predict the state of the forest in a changing climate.

Below you see an example of a sheet with hand-observations, where on the horizontal grid lines fine pencil marks delineate the timing of life cycle events.

In order to test the workflow of the annotation and gather feedback on the content of the project I released a public beta. In under a day the 90 images provided as a test case were viewed by 15 individual users. A big thank you goes out to the mostly anonymous zooniverse user community for annotating this dataset and providing me with the necessary feedback to ensure a high quality project.

The feedback was in general positive, recurring comments were made on the clarity of the project description or intended use of the data. Stepping back a bit I can see these shortcomings and I will address these in the next and probably final release of the project. The annotation data gathered will also provide me with a sense of accuracy and consistency of the classification between users. Once these annotation data are processed I will report her on my blog as well as on the Jungle Rhythms project page.

Once more my thanks go out to all the zooniverse users who tested my project!