“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.
Categories: Commentary Research
It is difficult to truly understand and then quantify the limits and uncertainties of paleo-data of any kind without field and lab experience. I am reticent to let any student at any level work in my lab without some field experience. It’s expensive and
challenging but can lead to wonderful insights and brilliant mistakes.
Palynology was very important in the 2013 paper in Science, lead author Julie Brigham Grette, re the Siberian meteor-impact lake that yielded superb sediment core, virtually undisturbed thru Pliocene. Lots of media attention owing to their finding of what Arctic vegetation is like at 400 ppm in Pliocene, and hence suspected for our own future, once everything equilibrates.
I am a science writer (now deeply into climate change science videos; as books have died), who was inspired by two palynologists (Hazel Delcourt, author of “Forests in Peril”; I met her just before she retired), and a generation before her was Paul S. Martin (who published his first palynology paper in 1958) and who died 3 years ago. Paul was my great mentor and collaborator in the first assisted migration project (Torreya taxifolia). Check out all the cool links from the “Tribute to Paul S. Martin, Pleistocene Ecologist” page I made to help keep his work in circulation online.
Also, akin to pollen, think about how important fungal spores were to the work of Guy Robinson — nailing the timing of the loss of megaherbivores (loss of the fungal spores generated from large-mammal dung) to precisely when presumably human-generated fire charcoal showed up in bogs. I met Guy at Paul Martin’s memorial service. He’s at Fordham U now.
So pollen rocks!
One aside: J, I will send you an email about this, but I see in your bio that you are also “interested in women in science.” I am thrilled that right now women in science are big contributors to making a difference in climate change understandings. Not just Julie Brigham-Grette, but also hugely Jennifer Francis, whose most important 2013 conference talk connecting Arctic sea ice loss with jet stream disruption I excerpted into a riveting 42-minute video superb for streaming in classes (she is grateful for that). Just go on youtube and search for “Climate Change and Extreme Weather: Jennifer Francis” and check it out. While there, check out another mashed video I made that highlights the work of 4 women in climate science. Search for “Hot Climate Women Scientists in Cool Places.”
In public talks I’ve given I’ve discovered that not only are audience members grateful to learn about Jennifer Francis’s science, but also overall about how plate tectonics has shifted climate regimes in the past: notably, Isthmus of Panama connect some 3 million years ago and opening up of full Arctic Circumpolar Current (not 20 million years ago, as you recently posted, but new work — by another woman scientist studying bottom forms off Antarctic Peninsula — puts it smack at the start of Oligocene cooling. I would love to see more (and accessible) videos on this — as a point that I always make (and that James Balog recently did in a vid) is that humans have become a geological force. That is a powerful realization. Anyway, I’d be grateful if you can get (ideally women) scientists to present a series of talks on this topic to undergraduate students or a public audience. The audience is crucial. Julie Brigham-Grette is amazing when speaking to undergrads (that is the video I excerpted) but way to sober and technical when speaking to peers.
Reblogged this on Mark Solock Blog.
Palynology is alive and well in the USA – have two undergraduates working with a graduate student on fungal paleoecology in wetland environments just this summer! One of the deposits we’re examining is a re-examination as the original study I completed on it many years ago did not process the samples appropriately or count them in such a way to answer the new questions we have. New data *ahhhh* is a good thing: helps you correct errors or omissions in your models.
I’ve found that my lab works best where we are all processing samples, generating data, and analyzing the data/producing models. Very synergistic this way, and you produce better-qualified UG’s, instead of lab monkeys.
My lab has been 100% HF-free since summer of 2007, and we consistently get very good results while not risking the safety of beginners. For modern samples, or those geologically young samples that still have a lot of kitt or cellulosic material associated with them, I use enzymatic processing – about the same cost as acetolysis, but with NO safety hazards at all (well, the Liquinox detergent does taste bad if you choose to drink your sample).
I work mostly in the upper Cretaceous – Miocene, and apply modern robust data analyses where possible, but do have to rely on less robust metrics where we are limited by taphonomic processes or our ability to sample rock/sediment units at a fine-enough scale.
Sadly, I’m unable to offer a course in just palynology, but do incorporate palynology into guest lectures in our paleontology course and into the work of my undergraduate research fellows.
Want to see how palynology is being used by scientists worldwide, both the data generators and the big data users? Come to the AASP-The Palynological Society meeting in San Francisco this fall! It is a joint meeting with CAP, NAMS, Dino10, and CIMP – so there will be palynomorphs from Cambrian – Modern and other microfossil groups as well!
I absolutely agree that palynology is alive and well in the US (I was hired as a pollen faculty last year!)– I definitely wasn’t trying to imply that, but rather to use a senior scientist’s comments as a jumping point into a discussion of some emerging trends (though it a bit disappointing that UMN and Brown haven’t kept up their pollen traditions). Most of the work I’ve done thus far involves late glacial sediments that are very clay-rich, and HF is the most effective (and quickest) way I’ve found of dealing with those.
Thanks for the plug for the AASP meeting. I’ve never gone (I tend to go to INQUA, AMQUA, IBS, and ESA). My colleague, Simon, who I mentioned in the post is going.
One argument against any move to not collect new palaeo sequence data and rely upon existing data to address new question is related to Maslow’s Hammer; if the only tool you have is a hammer, you treat all jobs as if they were a nail. There are only a subset of questions we might like to investigate with existing palaeo data because it was collected in ways not amenable to answering the particular questions we now want to ask. You either end up forcing the data or changing the analysis to meat the data, or you don’t answer that question and work on something else.
For example, there aren’t many records that are truly high resolution, high enough to start talking about testing ecological theory of critical transitions or timescales are ecological stability/resilience. In part this is because of the nature of sediments, but generally the majority of existing sequences were collected for different reasons and hence effort was expended on the whole sequence, not on specific sections where say climate was changing.
This is not just a pollen/palaeoecological issue. I worked on two EU funded projects recently and know of others, where a lot of money was spent on collating existing data into big databases for freshwater ecology, etc. One even explicitly forbab the consortium members from collecting new primary biodiversity data – although some people managed to work around those issues. Ignoring that many of these products aren’t open except to the consortia that produced them, there are two big issues here. No or very little new work was done, and the analyses of these data was often hampered or constrained by the inadequacies of the data. Invariably, as Bob O’Hara has just been pointing out at the BES Macroecology meeting, the ways the individual datasets were sampled is not accounted for in the analysis of the larger whole.
I do wonder what the utility of such activities really was, beyond collating the data. How applicable are the results.
I believe that the truly novel ideas will come from those people collecting new, tailored data for specific tasks and not those collecting entire new sequences just to fill in maps or those looking at existing data. I’m sure some good work can be done on existing data, I’m just not convinced yet that there are enough people out there that can use existing data appropriately and account for the sampling issues etc. One of the very interesting things PalEON is doing is that they have a great team from a range of disciplines including the statisticians (or statistically-minded people) that will hopefully be able to address such issues in their analyses.
Perhaps I’m bitter; I’ve been in the position where I wasn’t involved in designing the research programmes that claimed they would do wonderful things with all these existing data, but I was the poor sap who had to do things with them. It is certainly not something I want to be doing for the rest of my career!
The same situation is certainly true in neo-ecology/biogeography. Filling the gaps in the large databases is as important as analyzing them, but it is significantly less rewarding (in terms of publications and impact). I really like your education/training model, though as a researcher at an undergrad institution, I would argue that you should have the undergrads testing (and even generating) hypotheses as well, and getting them used to working with large databases along with the samples. Thanks for a great post.
I think you’re absolutely right about undergraduates– the ones doing theses, at the very least. I’m a big fan of undergraduate research, and including undergraduates in the publication process (there have been undergraduates on all of the papers I generated as a grad student).
Pollen analysis is alive and well in Geography departments in the UK, at least! My colleague Richard Middleton and I have developed some software for getting students started on pollen identification, which makes teaching a larger class a lot easier and seems to go down well with students! Some basic info online at:
do email me if you might be interested in using it, it’s freely available for academic colleagues!
Thanks so much for the link to the resource! I’ll definitely be checking it out.
I’d be interested, Jane, but the link doesn’t work. Can you check it ?
I don’t really have an answer to the questions you pose … but I have a question for you and other paleoecologists who might be reading. I’m a geologist and tangentially related to this field, but it seems as if the ‘paleo’ that you are referring to here only goes back so far (e.g., in this post you only mention radiocarbon). How deep into deep time does the field of ‘paleoecology’ as you see it typically go? For example, is someone reconstructing Earth-surface environments, including the ecosystem, in the Devonian doing paleoecology? Going down the temporal rabbit hole to >10^6 yr timescales means that the robust, statistical/quantitative tools you are talking about work differently, if at all, because of the comparably low temporal resolution provided by U-Pb geochronology or evolution (fossil biozones). In some cases, we are forced to look at relative temporal statistics (e.g., in context of stratigraphic depth/thickness with no age model) because that’s what we got!
I suppose my question is simply semantics about overlapping disciplines … but it’s something I was curious about.
There are definitely deeper time paleoecologists. The vast majority of folks I interact with (and, I’d guess, the majority of folks in the field) are Quaternarists– and, really, late Pleistocene and Holocene specialists. It’s certainly harder to get into ecological processes in deeper timescales, although very cool work is done on things like food webs with isotopes. There are some things that paleontologists do that I’d personally classify more as evolutionary ecology or biogeography than paleoecology per se.
Thanks. I find this topic interesting b/c a colleague and I are in the midst of writing a paper about bridging timescales in Earth-surface processes research. We’ve both worked with Holocene archives with centennial resolution, but also with sediments that are 100s of millions of years old. We see a similar breakdown of Quaternary specialists and ‘deep time’ researchers (this also exists in oceanography/paleoceanography). It really comes down to the attainable temporal resolution and broader context (e.g., with Quaternary archives you may definitively know you have lake sediments, whereas in the Paleozoic that’s a crucial interpretation with uncertainty). Anyway, thanks again.
There are disconnects, because what is signal at one time scale is noise at another – or to put it another way were are probably looking at signals dominated by different forcing factors.
When I started at Hull, a geologically trained colleague told me with great enthusiasm about a synchronous mid-Holocene event he’d registered in multiple cores, and asked if I’d be interested in doing some palynology around the event horizon. Turned out his idea of synchronous was defined by three 14-C dates spread across three millennia and other cores which ahd the same stratigraphy – which led to an interesting discussion about exactly what synchroneity means at different scales.
These questions interest me, too, but at the smaller end – I’m very interested in the practicalities of how Holocene palaeoecologists talk to ecologists/conservation biologists, so my scale change is from 1000s of years to 1-10 year scales.
I think that one of the issues with linking palynology between the timescales is partly one of intent. In the Quaternary, by and large, we have a good sense of which plants (and fungi) produced the spores and pollen that we find in our samples. We can go out and observe the plants and fungi ‘in the wild’, understand their relations to climate and community, and then make inference from assemblages obtained from sediment. As we move back further in time, this sort of relationship breaks down. There are many species described only by the morphology of their pollen,
A great example is Aquilapollenites (a pollen grain that looks a bit like an eagle), which has numerous morphotypes in the Maestrichtian (at least, I’m not sure exactly sure how long it hangs around). The problem is, we have no idea what kind of plant it came from, or at least, we have very little sense of it, and there’s lots of palynomorphs like that in deeper time. Stratgraphic palynology is most often used as a stratigraphic tool (particularly by oil companies, to guide them in their drilling), and less so for paleoecological reconstruction for exactly this reason. Effectively, the problem with pollen is that it is separated from the parent plant. While we can calibrate it in a modern context, the further down the rabbit hole we go, the harder it becomes to calibrate.
But, to echo Jacquelyn, pollen isn’t dead, and there’s lots of great work being done with these stratigraphic records. It’s just a question of what the appropriate questions and analytical tools are, and they’re different across different timescales.