My dissertation has finally been posted online for all the world to see. Click on the title below if you with to download it:
Applications of quantitative methods and chaos theory in ichnology for analysis of invertebrate behavior and evolution
Since it finally has been published I wanted to share some highlights of it.
Individual published chapters:
Chapter 2: Fractal analysis of graphoglyptid trace fossils
Chapter 3: Pitfalls, traps, and webs in ichnology: Traces and trace fossils of an understudied behavioral strategy
Chapter 4: Analytical tools for quantifying the morphology of invertebrate trace fossils
Trace fossils are the result of animal behaviors, such as burrowing and feeding, recorded in the rock record. Previous research has been mainly on the systematic description of trace fossils and their paleoenvironmental implications, not how animal behaviors have evolved. This study analyzes behavioral evolution using the quantification of a group of trace fossils, termed graphoglyptids. Graphoglyptids are deep marine trace fossils, typically found preserved as casts on the bottom of turbidite beds. The analytical techniques performed on the graphoglyptids include calculating fractal dimension, branching angles, and tortuosity, among other analyses, for each individual trace fossil and were performed on over 400 trace fossils, ranging from the Cambrian to the modern.
These techniques were used to determine various behavioral activities of the trace makers, including feeding and behavioral evolution. Graphoglyptids have been previously identified as representing mining, grazing, farming, and/or trapping. By comparing graphoglyptids to known mining burrows and grazing trails, using fractal analysis, it was possible to rule out mining and grazing behaviors for graphoglyptids. To determine between farming and trapping, a review of all known trapping burrows was required. The hypothesis that graphoglyptids were trappers was based entirely on the hypothesized feeding behaviors of the worm Paraonis. Close examination of Paraonis burrows indicated that the burrows are not traps. This means that, since Paraonis does not trap prey, graphoglyptids should not be considered traps either. Therefore, graphoglyptids likely represent farming behavior. This study also shows that previous interpretations of graphoglyptid behavioral evolution was far too simple. The results of the morphological analyses indicate that major changes to the behavioral evolution occurred during the Late Cretaceous and the Early Eocene. Previous hypotheses about Late Cretaceous evolutionary influences were validated. However there were additional influences like the Paleocene-Eocene Thermal Maximum that were not overly emphasized before. Finally, of the many theories about the driving force of evolution, chaos theory has often been overlooked. Chaos theory is a powerful tool, such that, by knowing the similarities between chaos theory and evolutionary theory, it may be possible to map out how environmental changes could shift the evolution of a species.
I tried to see how old a reference I could get in there. 1844 was the best I could do. I have a friend who managed to cite the Bible. I'm a bit jealous.
Emmons, E. 1844. The Taconic System: Based on Observations in New-York, Massachusetts, Maine, Vermont, and Rhode-Island. Carroll and Cook, Albany, NY.
This entry was published about 2 weeks before my dissertation went final final. I was able to squeeze it in during formatting edits.
Ekdale, A. A., and J. M. de Gibert. 2014. Late Miocene deep-sea trace fossil associations in the Vera Basin, Almería, Southeastern Spain. Spanish Journal of Paleontology 29(1):95-104.
In addition to the references I also make mentions of:
Return of the Jedi
The Lost World by Michael Crichton
Mr. Potato Head
Stats and Numbers
There are 433 numbered pages with a total of 446 pages.
6 Primary chapters.
- 3 currently published chapters.
- 2 publishable chapters currently in review.
My entire PhD took 1,806 days to complete
Right as I was starting to do my analyses, I had saved a backup of my data around once or twice a week. I figured I could actually track the size of my data as it was growing through the analyses. I used a lot of GIS files, and anyone who knows anything about GIS files knows that for every file you create, you are actually creating 7 or 8 files. So the number of files escalated really fast. A lot of the jumps in file size were actually due to me starting a new analysis. In the end, I ended up worth over 34,000 files and 35 GB of data.
Not sure how useful this is, but I found it interesting to watch it grow.