In a New Light

Post by Neil Pederson, Senior Ecologist at Harvard Forest
Reposted from The BroadLeaf Papers

We all love the colors of autumn. Fall brings to mind the vivid reds, oranges, yellows, and deep purples of September and October. By November in the Northeast, the leaves are gone and the sky often tilts into various shades of pale grey. The weather can be bone-chilling in a damp kind of way. It can be a bad time to be in the field. November in New England was the closest I’ve ever been to hypothermia. I now relish fieldwork in November, however, because of its light. November Light has helped me see things in a new way.

Filtered Light. Photo: N. Pederson

Filtered Light. Photo: N. Pederson

The first time I experienced the long-lasting glow of November Light was late in my dissertation field campaign. I recently had some great luck with a collection from an old-growth forest and wanted to see if I could squeeze out a few more diamonds before I called it a dissertation (and work would be lit by fluorescent light).

Kevin was my most reliable volunteer field assistant. I could call at a moment’s notice to see if he wanted to hit the field. He always said yes.

We bolted to southeast Pennsylvania and the weather was on our side. Blue skies and warm temperatures. We scoured this tiny patch of old forest to see if I had missed much on a prior trip. Soon after a brief lunch, it became apparent that we had done about all that was possible in that forest and we were slipping into lazy. So, we leaned back, chatted, and stared at the vernal roof.

At some point I kept checking the time on my GPS. My eyes kept telling me it was getting late. In reality, it was just approaching mid-afternoon. It dawned on me that angle of the Earth in that part of the Northern Hemisphere was delivering us an ever-lasting gobstopper dose of diffuse light. It felt like “sunset” above the Arctic Circle during summer. The light was low and hitting at all kinds of slanted angles. Colors glowed. It was glorious.

At the same time, it dawned on us that we were south of the last glaciation. Elk, woolly mammoth, and other megafauna likely used the game trails we were using that day more than a 100,000 years ago. More glory.

Bronzed Canopy. Photo: N. Pederson

Bronzed Canopy. Photo: N. Pederson

Just this past week we rolled up our field tapes for the last time during the 2014 PalEON season. It was a glorious feeling. Putting in ecological plots for tree-ring analysis is long and rather repetitive work. It is exhausting in a deeply different way than to reconstruct climate from tree rings. It was nice to know we had done a ton of work this year and that we were done. I imagine farmers get these feelings this time of year, too. As I dropped a coiled field tape into a backpack, it was instantly satisfying. We were putting our loyal field equipment down for a long winter’s rest.

Dan Bishop and Javier Martin Fernandez sampling in November Light. Photo: N. Pederson

Dan Bishop and Javier Martin Fernandez sampling in November Light. Photo: N. Pederson

We scheduled this last field campaign more than a month in advance. It is risky scheduling that far in advance in central New England this late in the year. But, after a Nor’Easter and a cold snap, the atmosphere shifted in our favor.

Blue skies. Brilliant Fagaceae colors. Stark contrast of a wide range of brightly-lit yellow leaves with the dark bark of red oak… and black oak?

While installing ‘nests’ around an older plot, or as a rather poetic colleague termed it, installing ‘doughnuts’, I ‘discovered’ a new species. Of course, black oak (Quercus velutina) was always there. It was just not talked about as much and, being hard to identify and often hybridizing with northern red oak Quercus rubra), it is often put in the red oak category. But, there it was, right in front our eyes.

Black oak reaching for the upper canopy among towering northern red oaks. Photo: N. Pederson

Black oak reaching for the upper canopy among towering northern red oaks. Photo: N. Pederson

Black oak bark. Photo: N. Pederson

Black oak bark. Photo: N. Pederson

Perhaps it was the November Light that made it ‘appear’? Maybe it was the showering of diffuse, angled light that made black oak jump out of the forest. Whatever it was, I now saw black oak everywhere. It wasn’t, of course. It was often red oak borrowing some of the velutinous traits of its sleeker, rarer cousin.

The glorious nature of November returned this past week and I saw these forests in a new light.

Canopy dominant red oak. Photo: N. Pederson

Canopy dominant red oak. Photo: N. Pederson

Classic red oak bark, bronzed. Photo: N. Pederson

Classic red oak bark, bronzed. Photo: N. Pederson

Oh there are some conifers in this forest (Pinus strobus). Photo: N. Pederson

Oh there are some conifers in this forest (Pinus strobus). Photo: N. Pederson

Glowing American beech (Fagus grandifolia). Photo: N. Pederson

Glowing American beech (Fagus grandifolia). Photo: N. Pederson

American beech. Photo: N. Pederson

American beech. Photo: N. Pederson

It comes in bronze, too. Photo: N. Pederson

It comes in bronze, too. Photo: N. Pederson

Fading Light. Photo: N. Pederson

Fading Light. Photo: N. Pederson

Late November Light. Photo: N. Pederson

Late November Light. Photo: N. Pederson

Pollen Dispersal I: Why We Get Sediment Pollen

Post by Andria Dawson, Post-Doc at University of California-Berkeley

Pollen and seed dispersal are important reproductive processes in plants, and in part determines the abundance and extent of a species. With a recent push to understand how species will respond to global climate change, dispersal ecology has gained increasing interest. We really want to know if there is a dispersal limitation, or if species can migrate quickly enough to maintain survival in a changing environment. Addressing this question presents challenges, many of which arise from trying to understand and quantify dispersal in ecosystems that are changing in both space and time (Robledo-Arnuncio 2014).
Pollen
In PalEON, one of our efforts involves estimating the past relative abundance of vegetation from fossil pollen records. To do this, we need to quantify the pollen-vegetation relationship, which includes modelling pollen dispersal processes.

For many trees, including those we study in the PalEON domain, pollination is exclusively (or at least predominantly) anemophilous (carried out by wind). In angiosperms, wind pollination is thought to have evolved as an alternative means of reproduction for use when animal pollinators are scarce (Cox 1991). It was previously understood that wind pollination was less efficient than insect pollination (Ackerman 2000), but Friedman and Barrett (2009) show that may not be the case. To estimate the efficiency of wind pollination, they compared pollen captured by stigmas to pollen produced, and found that the mean pollen capture was about 0.32%, which is similar to estimates of animal pollination efficiency. Although both dispersal vectors are comparable with respect to efficiency, these quantities indicate that both are still pretty inefficient – this is great news for paleoecologists! Some of the pollen that is not captured ends up in the sediment.

Now we know that we expect to find pollen in the sediment, but how do we begin to quantify how much pollen of any type we expect to find at a given location? The route a pollen grain takes to arrive at its final location is governed by atmopsheric flow dynamics (among other things). These dynamics are complicated by landscape heterogeneity and climate, and differ among and within individuals because not all pollen grains are created equal. However, we usually aren’t as interested in the route as we are in the final location – in particular, we want to know the dispersal location relative to the source location. The distributions of dispersal locations from a source defines a dispersal kernel which can be empirically estimated with dispersal location data. Often these kernels are modelled using parametric distributions, usually isotropic, and often stationary with respect both space and time. Are these approximations adequate? If so, at what scale? These are some of the questions we hope to address by using Bayesian hierarchical modelling to quantify the pollen vegetation relationship in the Upper Midwest.

References
1. Robledo-Arnuncio, JJ, et al. Movement Ecology, 2014.
2. Cox, PA. Philosophical Transactions of the Royal Society B: Biological Sciences, 1991.
3. Friedman, J, Barrett SCH. Annals of Botany, 2009.
4. Ackerman, JD. Plant Systematics and Evolution, 2000.

Sneak Peek at Results for Tree Composition Pre-Euro-American Settlement (ca. 1700-1850 AD)

Posted by Jody Peters with input from Simon Goring and Chris Paciorek

Just as many trees make up a mighty forest, many participants are needed to pull together and analyze data for PalEON.  Together we gain a deeper understanding of past forest dynamics, and use this knowledge to improve long-term forecasting capabilities.  Major components needed to understand past forest dynamics are tree composition, density and biomass prior to Euro-American settlement. In true macrosystems ecology fashion, over the past 3 years (and in some cases longer) individuals from multiple institutions (see Table and Figure captions, and Figure 3 here) have been working on collecting the data and developing a statistical multinomial model for tree composition in the Northeast and Midwest United States.  Our first task has been to estimate percent composition for several of the dominant forest taxa, and to provide reliable estimates of uncertainty.

We are excited to announce we have finally collected enough data to run the model across the entire northeastern United States!  Figure 1 provides an example of the composition results and associated uncertainty for beech and pine.  In addition to these two genera we have similar results for taxa such as oak, birch, hickory, maple, spruce, etc.  We can use these results to compare the pre-European Settlement forest composition to modern forests from US Forest Service Forest Inventory Assessment data as well as those extending 2000 years into the past using pollen data and STEPPS2 analyses (see this University of Wisconsin Press Release).  As we move forward with this project we will continue to update our datasets that have dispersed sampling (e.g., Indiana, Illinois and Ohio: Table 1) and we are in the process of developing maps of estimated density and biomass by tree taxon.

Stay tuned as more results come in and as the manuscripts get published!

 

Figure 1. Estimated composition (top maps) and associated uncertainty (bottom maps) created March 2014. Estimates come from a spatial multinomial model on an 8 km Albers grid, developed by Andy Thurman from the University of Iowa and Chris Paciorek from the University of California, Berkeley. The MCMC was run for 150,000 iterations, with the first 25,000 discarded as burn-in, and the remaining iterations subsampled (to save on storage and computation) to give 500 posterior draws.

Figure 1. Estimated composition (top maps) and associated uncertainty (bottom maps) created March 2014. Estimates come from a spatial multinomial model on an 8 km Albers grid, developed by Andy Thurman from the University of Iowa and Chris Paciorek and Andria Dawson from the University of California, Berkeley. The MCMC was run for 150,000 iterations, with the first 25,000 discarded as burn-in, and the remaining iterations subsampled (to save on storage and computation) to give 500 posterior draws.
Click on the image for a bigger, clearer picture.

 

 

 

 

 

 

 

 

 

 

 

 

Table 1. Source of tree data from Public Land Surveys from locations in the Northeast and Midwest United States. The “Sampling” column indicates when data came from the entire location (complete) or from a dispersed sample of townships or towns within the location.

LocationSamplingSource
MinnesotaCompleteDavid Mladenoff(University of Wisconsin - Madison)
WisconsinCompleteDavid Mladenoff(University of Wisconsin - Madison)
Michigan's Upper Peninsula & northern Lower PeninsulaCompleteMichigan Department of Natural Resources; David Mladenoff
Michigan's southern Lower PeninsulaDispersedJack Williams & Simon Goring
(University of Wisconsin - Madison)
IllinoisDispersedJason McLachlan (University of Notre Dame)
Illinois Chicago areaCompleteMarlin Bowles & Jenny McBride (The Morton Arboretum)
Illinois St. Louis areaCompleteDick Brugam (Southern Illinois University) & Paul Kilburn (Jefferson County Nature Association)
IndianaDispersedJason McLachlan (University of Notre Dame)
OhioDispersedCharles Cogbill
New England, New Jersey, New York and PennsylvaniaDispersedCharles Cogbill

PalEON on TV

Posted by Jody Peters, PalEON Program Manager

The elevator pitch (a 30 second to 2 min synopsis of your research) is critical for sharing science with other scientists and the general public. However, developing this pitch usually does not come naturally to most people. It is something that needs to be practiced. Recently Jason McLachlan and Sam Pecararo from the University of Notre Dame, had the opportunity to practice their pitches in featured segments on Outdoor Elements, a show on our local PBS station. Not only did Jason and Sam have to prepare their elevator pitch, but they also had to come up with visual props that would be interesting to view on TV.  We think they both did a great job condensing their science stories into a few minutes!

Jason’s segment, Paleobotany & Climate Change, originally aired on Feb 9, 2014 and focused on PalEON in general and specifically described some of our work with tree data from the Public Land Survey. After he was taped for this segment last fall, Jason wrote a blog post about what he wished he would have said. Compare what he wished he would have said to what actually was aired!

JasonOutdoorElements

Sam’s segment, Tree Coring, originally aired on February 16, 2014 and featured Sam coring a tree and talking about using tree rings to get an idea of how climate or other environmental variables influence tree growth.

SamOutdoorElements

Check out these segments to see Jason and Sam’s elevator pitch for some of the work of PalEON! Click on the links or photos above and scroll down to where it says “Play segment” to view. Each segment is approximately 7 minutes long.

 

Self thin you must

yoda

Post by Dave Moore, Professor at The University of Arizona
This post also appeared on the Paleonproject Tumblr

We spent a lot of time last week in Tucson discussing sampling protocols for PalEON’s tree ring effort that will happen this summer. The trouble is that trees (like other plants) will self thin over time and when we collect tree cores to recreate aboveground biomass increment we have to be careful about how far back in time we push our claims. Bonus points if you can explain the photo in ecological terms! I stole it from Rachel Gallery’s Ecology class notes.

Neil Pederson and Amy Hessl will be taking the lead in the North East while Ross Alexander working with Dave Moore and Val Trouet (LTRR) will push our sampling into the Midwest and beyond the PalEON project domain westwards. This is a neat collaboration between the PalEON project and another project funded by the DOE. Francesc Montane and Yao Liu who recently joined my lab will be helping to integrate these data into the Community Land Model. Also Mike Dietze‘s group will be using the ED model to interpret the results.

Because we want to integrate these data into land surface models we need to have a robust statistical framework so we had some equally robust discussions about statistical considerations with Chris Paciorek and Jason McLachlan and other members of the PalEON team.

Forests in a Changing Climate

Post by Andria Dawson, University of Notre Dame/University of California, Berkeley Postdoc

fall_forest3

Forests play an important role in the global carbon cycle by storing and releasing carbon through processes such as establishment, growth, mortality, and disturbance. Forests can be carbon sources if they release more carbon than they absorb, or carbon sinks if they absorb more than they release. Knowing that forests affect the carbon budget, it is natural to ask about the interactions between forests and the changing climate. Do forests mitigate climate change? The answer to this question is seemingly complex. Here are a few of the many reasons why…

Albedo

Some of the solar radiation that reaches the Earth is absorbed, while some is reflected. The reflectivity of a surface, or albedo, is some measure of the whiteness of a surface. Snow has a high albedo, while open ocean has a low albedo. Forests typically have a low albedo. To keep the earth cool, all we need to do is absorb less of this incoming radiation. Researchers at Dartmouth college found that in regions where snow is common and forest productivity is low, it is beneficial to the economy and the climate to clear those forests, which modulates the temperature by increasing albedo [1].

Carbon dioxide

Trees use carbon dioxide to photosynthesize. As atmospheric CO2 increases, trees are expected to experience increased growth, at least up to a point. CO2 is absorbed through the stomata, but while these stomata are open and readily absorbing CO2, they are also allowing the tree to lose moisture. When CO2 is more readily available, trees don’t have to open their stomata as wide to absord it. This leads to less moisture loss through the stomata, leaving the tree with additional resources for other processes, such as growth [2]. And in turn, increased growth leads to increased CO2 consumption. But only up to a point.

Terpenes

Forests interact with the atmosphere by releasing biological aerosols as well as compounds known as terpenes. Terpenes react and form aerosols, forming clouds, which in part determines how much light is reflected back to space. Spracklen et al. found that terpenes from a simulated pine forest increased cloud thickness, causing an additional 5% of solar radiation to be reflected back to space [3].

Changes in natural disturbance regimes

Climate change has the potential to affect disturbance regimes. Dale et al. succinctly wrote that “climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides” [4]. These disturbance events affect forests in different ways, from causing widespread mortality to causing changes in structure, composition, and function.

How to make sense of all of this?

The take home message is that an important relationship exists between forests and climate. The cumulative effect of these feedback mechanisms are difficult to disentangle, and further collaborative research based on ecosystem and atmospheric models confronted with data are key as we move forward. PalEON is one such collaborative effort, drawing from talents to work towards a better understanding of forest systems.

References
[1] Updated citation as of 4-17-14:Lutz, David A., Howarth, Richard B., “Valuing albedo as an ecosystem service: implications for forest management. Climatic Change (2014): doi:10.1007/s10584-014-1109-0
Original citation: Dartmouth College. “Can cutting trees help fight global warming? More logging, deforestation may better serve climate in some areas, study finds.” ScienceDaily. ScienceDaily, 5 December 2013.

[2] Keenan, Trevor F., et al. “Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise.” Nature 499.7458 (2013): 324-327.

[3] Spracklen, Dominick V., Boris Bonn, and Kenneth S. Carslaw. “Boreal forests, aerosols and the impacts on clouds and climate.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366.1885 (2008): 4613-4626.

[4] Dale, Virginia H., et al. “Climate Change and Forest Disturbances Climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides.” BioScience 51.9 (2001): 723-734.

The Invasion of the Zombie Maples

Post by Ana Camila Gonzalez, Undergraduate Researcher with Neil Pederson and the Tree Ring Laboratory at Columbia’s Lamont-Doherty Earth Observatory

As an undergraduate student interning at the Tree Ring Lab at Lamont-Doherty Earth Observatory, my involvement with PalEON has been rather localized to the data production side of things. My knowledge on the dynamics of climate and the models involved in forecasting future climate change is obviously limited as a second-year student. My knowledge on how frustrating it can be to cross-date the rings in Maple trees, however, is more extensive.

This past summer I was able to join the Tree Ring Lab on a fieldwork trip to Harvard Forest in Petersham, MA. My main task was to map each plot where we cored, recording the species of each tree cored, its distance to the plot center, its DBH, its canopy position, its compass orientation, and any defining characteristics (the tree was rotten, hollow, had two stems, etc.). The forest was beautiful, but it became more beautiful every time I wrote down the letters QURU (Quercus rubra)I had plenty of experience with oaks, and knew that they did not often create false or missing rings and are thus a fairly easy species to cross-date. I shuddered a little every time I had to write down BEAL (Betula alleghaniensis), however, since I had looked at a few yellow birches before and knew the rings were sometimes impossible to see let alone cross-date. I had no reaction to the letters ACRU (Acer rubrum), however, since I had never looked at a red maple core before. I was happy that it was a tree I could easily identify, and so I didn’t mind that the letters kept coming up. Had I known what was to come, I would’ve found a way to prevent anyone from putting a borer to a red maple.

At first, the maples seemed to be my friends. The rings were sensitive enough that multiple marker years helped me figure out where the missing rings where, what was false and what was real. I morbidly became a fan of the gypsy moth outbreak of 1981, because in many cases (but not all) it produced a distinct white ring that marked that year very clearly. This was definitely challenging, as the trees also seemed to be locally sensitive (a narrow ring in one tree might not at all be present in another) but all in all it seemed to be going well.

And then came the Zombie Maples.

Fig (a) Anatomy of a White Ring: Above is a core collected in 2003. It was alive. The white ring in the center of the image is 1981, the year of the regional gypsy moth outbreak in New York and New England.

That white ring you’re seeing above is the characteristic 1981 ring from a Zombie Maple cored in 2003. After that ring we can only see four rings – but this tree is alive, which means that there should be 13 rings after 1990 (Fig b). This means approximately 10 are missing.

Fig (b) Anatomy of a Zombie Maple: Above is a core collected in 2003. It was alive. The 1990 ring is marked in the image just right of center. There should be 13 rings between 1990 and the bark. You can only see four. Is it Alive? Is it Dead? Eek! It is a Zombie!!

This kind of suppression in the last two decades was present in multiple cores, and it made many perfectly alive trees seem like they should have been dead. Nine rings missing in a little over one millimeter. We see even more severe cases in our new collection: 15 rings where there should be 30 rings in about 2 millimeters – how is this tree supporting itself?

Cross-dating these cores took a lot longer than planned, and at times I was tempted to pretend my box of maples went missing, but afterwards I felt I was a much stronger cross-dater, and I’m realizing more and more that this really matters. If you’re going to base a model off of data that involves ring-width measurements from particular years, you better make sure you have the right years. What if we didn’t know the gypsy moth outbreak occurred in 1981, and somebody counting the rings back on the Zombie maple core above was led to believe it occurred in 1996? Our understanding of the trigger for this event would be incorrect because we would be looking for evidence from the wrong decade.

In a way, the Maples are still my friends. They were almost like the English teacher in high school who graded harshly who you didn’t appreciate until you realized how much better your writing had become.

PLS around Notre Dame

Posted by Jody Peters

The crew at Notre Dame is in the process of entering PLS data for Indiana. Recently we focused on the area around Notre Dame and South Bend. Here is the area as depicted in the 1829 PLS survey, 13 years before Notre Dame’s founding.
It has been interesting to learn about the changes that have taken place around campus. While the major roads around Notre Dame (Angela, Douglas and Juniper) were originally based off the PLS survey, all three roads have had sections of them moved.


In the 1829 survey, the two lakes on campus were drawn as one. We aren’t sure if this is because the surveyor was in a hurry and didn’t take time to explore the lakes further or if he was surveying in the winter when the lakes were ice covered. But from historical documents at Notre Dame there are a number of references to two lakes, although the lakes water levels have been known to fluctuate and at some points were quite high making the area between the lakes quite swampy.
At the time of the PLS survey the Notre Dame area was dominated by oaks. But another tree found in the area that was of interest to the lab was the pepperage tree. The pepperage tree, or more commonly spelled, pepperidge is also known as sour gum, black gum or black tupelo. As a side note, the pepperidge tree is where Pepperidge Farms get their name (got to love those goldfish crackers)! To learn more about pepperidge trees, check out this great site. Keep up to date with ND’s data entry progress by clicking on Indiana map at our Settlement Vegetation Site.

 

PalEON Settlement-era Vegetation Meeting

Jack Williams hosted a meeting on settlement-era vegetation at the University of Wisconsin from Oct 11-12, 2011. Attendees discussed the details of the orginal land survey records, plans for the extension of data collection to Ohio, Indiana, and Illinois, and methods for their analysis and integration into modeling efforts.