“The reports of my death are greatly exaggerated.” – Palynology
A colleague of mine (also a paleoecologist) recently recounted a story where, when learning about his research, a senior scientist remarked, “I thought pollen was dead?” This is never something someone wants to hear about one’s primary methodology, particularly at the start of one’s career. But rather than get defensive, or point to the many active scientists and the exciting research we’re all doing, I want to unpack that statement a bit more.
*Paleoecology has been criticized for being too qualitative — certainly, in the early days of pollen analysis (palynology), the emphasis was on characterizing past environmental change, and less on explicit hypothesis testing. With new analytical tools, improved radiocarbon dating methods, and improved spatial and temporal sampling resolutions, it’s less common to see studies characterizing basic vegetation change through time (unless they’re from previously un-sampled parts of the world). In North America, large-scale databases like Neotoma and research initiatives like PaLEON show just how powerful these large aggregated collections of data can be. Rather than simply characterizing the environmental history at a single lake, we now have powerful tools and data to understand how and why vegetation responds to global change through time and across space.
Certainly, from the perspective of global change science, pollen data are in high demand, to help validate global climate models or to provide baselines for conservation. So, how can pollen be both in high demand, and perceived to be dead? I think the root lies in the interface between data generation and data utilization. Certainly, there are many active pollen-based researchers in the US, but there seem to be few resources for teaching the next generation skills in pollen analysis (though my fellow paleo-blogger **Simon Goring and I are working on a short course for late this fall at UMaine). I was told I wouldn’t be marketable as “just” a pollen analyst– that it’s basically the equivalent of being a tech. There currently aren’t pollen-based faculty at Minnesota or Brown, for decades two main “schools” of North American paleoecology.
Pollen analysis is expensive, time-consuming, and even hazardous (hello, hydrofluoric acid!). I often joke that a week in the field translates to a year in the lab; It can take as much as a year to produce a single pollen diagram from one sediment core. Add to that the time and costs associated with radiocarbon dating (anywhere from $250 to $600 a pop!) and any other analyses to fill out the environmental picture. In the time it takes someone to publish one paper based on pollen data they collected, someone analyzing pollen data can generate several papers. There’s arguably less reward (from a publications perspective) for that single site than there is from a multi-site synthesis. With these in mind, it’s easy to see how it can be more attractive to work with pollen data than to generate it.
Here’s the thing: We need to be generating pollen records. There are major gaps in spatial and temporal coverage even in North America, let alone the rest of the world– South America, Africa, Asia, and Australia have some excellent records but nowhere near the spatial or temporal coverage of Europe and North America. Add complementary proxies like Sporormiella (a dung fungus proxy for megaherbivores that I’ve used in my own work), charcoal, fossil and archaeological records, and emerging techniques in geochemical analyses and ancient DNA, and we are in a better position than ever to test hypotheses about biotic interactions, paleo-ecosystem ecology, the consequences of extinction or invasion, community assembly, and the effects of human activity or disturbance.
Generating pollen diagrams may not be as sexy as the new Big Data initiatives, but we can’t have Big Data without Little Data. We need to be generating new records, too. We need to be re-evaluating our assumptions about methods and doing proof-of-concept studies. As folks call for students to learn programming and statistics, I’d like us to also remind people to get out into the field and in the lab, too. I don’t want to see paleoecology move from a discipline that generates data to one that primarily analyzes or models it. In my ideal lab model, undergrads work with samples, masters students generate data to test a hypothesis, PhD students balance data collection with syntheses or modeling, postdocs analyze and model. Everyone, from undergraduates to postdocs, has something to contribute and to learn from one another, in all phases of the data life cycle.
What do you think? Do data users have an obligation to be data contributors, too? Is pollen analysis dying? Do we reward Big Data more than Little Data? Would you be interested in a palynology course? I’d love to hear your thoughts.
*In this post, I’m primarily discussing pollen data, though I think it applies to other kinds of paleoecological data and even neo-ecology. I don’t mean to give the snails, tree rings, diatoms, or packrats short shrift!
** Simon, who works on the PaLEON project, also has a post that touches on Big Data’s obligations to Little Data here.