Category Archives: Paleo-data

Oak Trees and Climate in the Midwest

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Post by Kelly Heilman, a graduate student with Jason McLachlan at the University of Notre Dame

If trees could talk…

Every year, oak trees in the Midwest awaken in spring, to spread their leaves to grow through the summer, and settle down for a long winter’s rest in the fall. As we watch this seasonal cycle, trees warn us of the coming of winter as they shed their leaves, putting on a glorious show of red, gold and orange. At times, trees tell us to slow down by inviting us into their expansive shade to rest, read a book, and listen the birds sing in the distance. However, to many dendrochronologists, trees talk about something else–their records of the past. The patterns of each season’s tree growth are recorded in the annual rings of trees, and are protected like a memory beneath the tree’s bark. These annual variations in tree rings provide researchers with information on the responses of trees to climate variations, as well as to other stresses that trees face, such as competition with their neighbors for light, and fire disturbances. All this makes tree ring records particularly useful tools for looking into the past. So, in a way trees can talk . . . but it takes some effort for us to listen to them.

Trees in the temperate zone, such as this Bur Oak, record annual rings of growth, allowing us to count their rings to get tree age, and correlate growth with climate records. Pictured above is a 5-mm wide core sample from a tree at Bonanza prairie SNA (Scientific and Natural Area) in Minnesota, with the bark pictured on the left and the center of the tree on the right. (Click on image for a larger view.)

Looking at the past responses of ecosystems to climate variations through the lens of tree rings can provide a better understanding of how these ecosystems might respond to future changes. My research focuses broadly on the savanna-forest boundary in the Midwest, both on the environmental conditions that form this boundary and how environmental changes impact these savannas and forests. Over the last century, humans have altered the landscape in the Midwest, through large scale agriculture, land-use change, fire suppression, changes in CO2 and climate shifts (Goring et al. 2016, Rhemtulla et al. 2007). Many of these changes have likely affected the growth, survival, and climate sensitivity of trees, which could impact the trajectory of forests in the future. Therefore, I set out to quantify how savanna and forest trees functioned both in the past, and on the modern landscape. Using annual growth increments recorded in tree rings, my objective was to quantify how both modern and past ecosystems functioned (in terms of how much carbon they uptake and store in their annual growth rings), and to determine if and how tree growth patterns vary across temperature, precipitation, and soil gradients.

Thoughts from the field (don’t forget your bug spray and sunscreen):

To view the annual rings of tree growth, we collected tree core samples from sites across the historic savanna-forest boundary in the Midwest from several Minnesota Scientific and Natural Areas (SNAs), Minnesota State Parks, State Parks in Iowa and Missouri, as well as several sites within McHenry County Conservation District in Illinois (see map). In my sampling design, I aimed to capture the growth responses of young and old trees in both savannas and forests, across the wet-to-dry climate gradient in the Midwest. We targeted several Oak species, including Bur Oak (Quercus macrocarpa), White Oak (Quercus alba), Red Oak (Quercus rubra), and Chinkapin Oak (Quercus muehlenbergii), but also sampled several eastern forest species as well. With the help of fellow Paleonistas (Ann Raiho, Monika Shea) and field technicians Evan Welsh and Santi Thompson, we sampled tree cores at 23 different sites during the summers of 2015 and 2016. Coring trees can be monotonous, physically difficult, and relaxing all at the same time. Lucky for us, we got to go to some beautiful places across the Midwest.

Map of all the sites where we collected tree core samples from during 2015 and 2016. Background color represents mean annual precipitation (MAP) of the region obtained from PRISM climate data. Tree cores were collected from savannas and forests that occur along the historic prairie-forest boundary in the Midwest. (Click on image for a larger view.)

Coring a large Bur Oak tree in a savanna at Maplewood State Park, Minnesota.

Bur Oak acorn from a young tree located in St. Croix savanna SNA in Minnesota

Working out in the field gave us opportunities to see some awesome ecosystems and sunsets. Looking out onto to prairie from a savanna at Glacial Lakes State Park, MN, the tallest plants were not trees, but tallgrass prairie plants, such as the Big Bluestem, or “turkey foot” (Andropodon gerardii) pictured here.

An open savanna and prairie complex at Mound Prairie SNA, in Minnesota.

The Oak savanna canopy is sparse compared to a closed forest, letting in plenty of light for understory grasses and forbs to grow.

After driving a couple thousand miles total, spending over 30 nights in a tent in 2015 and 2016, and battling what seemed like an infinite amount of mosquitoes and ticks, we headed back to the lab to measure the width of each annual tree ring, and determine how climate affects Midwestern oak tree growth.

Back at the Lab:

Once we returned from the field, the cores were glued to wooden mounts, sanded, counted and measured. This work was done with the help of several awesome undergraduate students over the last two years, including: Jacklyn Cooney, Clare Buntrock, Santi Thompson, and Da Som Kim. Once the cores were measured and cross-dated with each other (using common “marker” years of extremely low growth, such as the 1934 Dust bowl drought, to double check our measurements), we have a temporal record of growth fluctuations for each site.

What climate factor affects oak tree growth in savannas and forests?

Tree growth responds strongly to the most limiting factor to their growth.

For example, in water limited regions of Southwestern North America, tree growth is highly correlated with interannual precipitation and drought, often making tree ring records from these regions good candidates for precipitation reconstructions (Charney et al. 2016, Peterson 2014). However, in many Eastern North American forests, water availability for growth is not a huge limiting factor, and tree growth is more sensitive to summer temperatures, drought, and light availability (Peterson 2014, Charney et al. 2016).  The savanna-forest boundary in the Midwest is located between these Eastern closed forests and the more water limited prairies to the West. Therefore, tree species that occur along this boundary are often thought to exist at the edge of their theoretical and climatic range boundaries, and theoretically could respond strongly to moisture stress or temperature stress. The historic transition from open prairie to savanna to forests occurred at a range of precipitation and temperature climatic envelopes; this transition zone in Minnesota had low mean annual precipitation (300-600 mm/year), and much higher mean annual precipitation in Indiana & Illinois (700-1200). Therefore, I originally hypothesized that these western savannas and forests may respond more strongly to low precipitation and drought than Eastern savannas and forests.

Contrary to my original hypotheses, oak tree ring growth is not primarily controlled by precipitation in oak trees near the savanna-forest boundary. Rather, tree growth at most sites is strongly linked to summer drought severity and summer temperatures. The negative impacts of drought on tree ring growth are likely mediated by temperature-induced drought stress, as suggested by the strong negative correlations with minimum and maximum June and July temperatures at almost all sites. While growth at some sites is mildly correlated to late summer precipitation, these places tend to have sandy soils, suggesting that future decreases in precipitation could have larger negative consequences for tree growth on sites with sandy soils. Interestingly, despite low moisture availability, sites with the lowest mean annual rainfall were only weakly correlated with monthly precipitation, suggesting that perhaps these systems are relying heavily on deeper groundwater sources for water. However, sites with low rainfall, such as Bonanza Prairie SNA in Minnesota, do have strong sensitivity to drought indices (Palmer Drought Severity Index, PDSI) and temperature indicating that high temperature drought stress, rather than water stress due to low precipitation is more important in this ecosystem.

Correlations with monthly climate indicate that Oak trees at most sites are most sensitive to summer drought index and summer temperatures, but few are strongly sensitive to monthly precipitation. Red colored sites have lower mean annual precipitation, and blue sites have higher mean annual precipitation. A). Tree growth at all sites is most strongly correlated to summer drought (Palmer Drought Severity Index is positive in non-drought periods and negative in drought periods). B). July precipitation is only weakly correlated with growth at some sites. C). Tree growth is somewhat negatively correlated with summer maximum temperatures. (Click on image for a larger view.)

Have growth sensitivities changed over time?

The climate-growth relationship is often assumed to be constant for the purposes of climate reconstructions. However, recent dendroecological studies recognize that growth-climate relationships may change due to shifts in climate seasonality, changes in tree size class, tree competition, and possibly due to increases in atmospheric CO2 (Voelker et al. 2006). In theory, higher levels of CO2 in the atmosphere can enhance tree growth by increasing CO2 available for photosynthesis in the leaf, without changing stomatal conductance (gs, the amount of water that moves through the stomata). This results in an increase in plant Water Use Efficiency (WUE), or the amount of carbon taken up per unit of water used, which could help reduce the impacts of drought on trees (McCarroll and Loader 2004). While the effect of atmospheric CO2 on tree growth is still largely debated, past researchers found that Bur Oak (Quercus macrocarpa) trees in Western Minnesota have become less sensitive to drought since the beginning of the 20th century, and mortality due to drought has decreased  as well (Wyckoff and Bowers 2009). Additionally, Voelker et al. (2006) found that the positive effects on growth that may result from increased atmospheric CO2 likely decline with tree age. My sampling effort has extended the spatial range of oak sampling in the Midwest, allowing us to test whether a change in the growth–drought relationship over the 20th century is regional and if it has occurred in different oak species and site conditions.

With our data across the Midwest, we find preliminary evidence supporting a change in growth sensitivity to climate. Trees across the Midwest were less sensitive to drought after 1950, and younger trees established under high CO2 were also less sensitive to drought than older trees.

These results are consistent with the previous work in Minnesota (Wyckoff and Bowers 2009), and with a positive enhancement of CO2. In two of the three closed forest sites sampled, we find no difference in the growth-drought sensitivity over time, suggesting that savanna trees, but not forest trees have become less susceptible to drought in the region. While the stand structure (open savanna or closed forest) may help explain where we see shifts in growth-climate sensitivity, species sampled may also play a role, as well as the mean annual precipitation and temperature. To specifically test whether CO2 enhancement is driving the decreased drought sensitivity, I am currently working on a project that tests to see if the composition of carbon isotopes recorded within annual tree rings have changed. The ratio of heavy to light carbon isotopes can be used to quantify plant Water Use Efficiency, which will increase over time if CO2 has a net positive effect on tree growth.

Up Next…

This project is still ongoing and there are several questions that I am still exploring. I am continuing to work on a formal analysis of the tree ring growth data, and look more at species-specific sensitivities to climate, since the preliminary analyses focus on site specific responses.

If growth and sensitivity of growth to climate changes over time, I want to know if it is due to the effects of CO2, or some other factor affecting forest growth. This next year, I will be spending a lot of time in the lab quantifying stable carbon isotopes, to determine if plant WUE increases result in the decrease in drought sensitivity over time.

 

References:
Charney, N. D., et al. (2016). Observed forest sensitivity to climate implies large changes in 21st century North American forest growth. Ecology Letters, 19(9), 1119–1128. https://doi.org/10.1111/ele.12650

Goring, S. J., et al. (2016). Novel and Lost Forests in the Upper Midwestern United States, from New Estimates of Settlement-Era Composition, Stem Density, and Biomass. PLOS ONE, 11(12), e0151935. https://doi.org/10.1371/journal.pone.0151935

McCarroll, D., & Loader, N. J. (2004). Stable isotopes in tree rings. Quaternary Science Reviews, 23(7–8), 771–801. https://doi.org/10.1016/j.quascirev.2003.06.017

Peterson, D. L. (2014). Climate Change and United Steates Forests. In Climate Change and United States Forests. Springer. Retrieved from http://www.springer.com/us/book/9789400775145

Rhemtulla, J. M., et al. (2007). Regional land-cover conversion in the U.S. upper Midwest: magnitude of change and limited recovery (1850–1935–1993). Landscape Ecology, 22(1), 57–75. https://doi.org/10.1007/s10980-007-9117-3

Voelker, S. L., et al. (2006). Historical CO2 Growth Enhancement Declines with Age in Quercus and Pinus. Ecological Monographs, 76(4), 549–564.

Wyckoff, P. H., & Bowers, R. (2010). Response of the prairie–forest border to climate change: impacts of increasing drought may be mitigated by increasing CO2. Journal of Ecology, 98(1), 197–208. https://doi.org/10.1111/j.1365-2745.2009.01602.x

Identifying Local Fire Events From Sediment Charcoal Records Via Regularization

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Post by Malcolm Itter, a graduate student with Andrew Finley at Michigan State University. Malcolm received an Outstanding Student Paper Award for this work at AGU 2016!

Charcoal particles deposited in lake sediments during and following wildland fires serve as records of local to regional fire history. As paleoecologists, we would like to apply these records to understand how fire regimes, including fire frequency, size, and severity, vary with climate and regional vegetation on a centennial to millennial scale. Sediment charcoal deposits arise from several sources including: 1) direct transport during local fires; 2) surface transport via wind and water of charcoal deposited within a lake catchment following regional fires; 3) sediment mixing within the sample lake concentrating charcoal in the lake center. A common challenge when using sediment charcoal records is the need to separate charcoal generated during local fire events from charcoal generated from regional and secondary sources. Recent work by PalEON collaborators including myself, Andrew Finley, Mevin Hooten, Phil Higuera, Jenn Marlon, Ryan Kelly, and Jason McLachlan applies statistical regularization to separate local and regional charcoal deposition allowing for inference regarding local fire frequency and regional fire dynamics. Here we describe the general concept of regularization as it relates to paleo-fire reconstruction. Additional details can be found in Itter et al. (Submitted).

Figure 1: Illustration of theoretical charcoal deposition to a lake if charcoal particles arising from regional fires were distinguishable from particles arising from local fires (in practice, charcoal particles from different sources are indistinguishable). The figure does not depict charcoal arising from secondary sources such as surface water runoff or sediment mixing.

Figure 1 illustrates primary and regional charcoal deposition to a sample lake. We can think of charcoal deposition to a sample lake as being driven by two independent processes in time: a foreground process driving primary charcoal deposition during local fires, and a background process driving regional and secondary charcoal deposition. In practice, charcoal particles arising from different sources are indistinguishable in sediment charcoal records. We observe a single charcoal count over a fixed time interval. Direct estimation of foreground and background processes is not possible without separate background and foreground counts. We overcome the lack of explicit background and foreground counts by making strong assumptions about the nature of the background and foreground processes. Specifically, we assume the background process is smooth, exhibiting low-frequency changes over time, while the foreground process is highly-variable, exhibiting high-frequency changes in charcoal deposition rates associated with local fires. These assumptions follow directly from a long line of paleoecological research, which partitions charcoal into: 1) a background component that reflects regional charcoal production varying as a function of long-term climate and vegetation shifts; 2) a peak component reflecting local fire events and measurement error.

We use statistical regularization to ensure the assumption regarding the relative smoothness and volatility of the background and foreground processes is met. Under regularization, we seek the solution to an optimization problem (such as maximizing the likelihood of a parameter) subject to a constraint. The purpose of the constraint, in the context of Bayesian data analysis, is to bound the posterior distribution to some reasonable range. In this way, the constraint resembles an informative prior distribution. Additional details on statistical regularization can be found in Hobbs & Hooten (2015) and Hooten & Hobbs (2015).

In the context of sediment charcoal records, we model two deposition processes under the constraint that the background process is smooth, while the foreground process is volatile. We use unique sets of regression coefficients to model the background and foreground processes. Both sets of regression coefficients are assigned prior distributions, but with different prior variances. The prior variance for the foreground coefficients is much larger than the prior variance for the background coefficients. The prior variance parameters serve as the regulators (equivalent to a penalty term in Lasso or ridge regression) and force the background process to be smooth, while allowing the foreground process to be sufficiently flexible to capture charcoal deposition from local fires.

Figure 2: Model results for Screaming Lynx Lake, Alaska. Upper panel indicates observed charcoal counts along with the posterior mean charcoal count (blue line). Middle panel illustrates posterior mean foreground (orange line) and background (black line) deposition processes. Lower panel plots posterior mean probability of fire estimates for each observed time interval (black line) along with the upper and lower bounds of the 95 percent credible interval (gray shading) and an optimized local fire threshold (red line).

Figure 2 shows the results of regularization separation of background and foreground deposition processes from a single set of charcoal counts for Screaming Lynx Lake in Alaska. The probability of fire values presented in the lower panel of Figure 2 follow from the ratio of the foreground process relative to the sum of the background and foreground processes. We would not be able to identify the background and foreground processes without the strong assumption on the dynamics of the processes over time and the corresponding regularization. The benefits of using such an approach to model sediment charcoal deposition are: 1) our model reflects scientific understanding of charcoal deposition to lakes during and after fire events; 2) we are able to identify local fire events from noisy sediment charcoal records; 3) the background process provides a measure of regional fire dynamics, which can be correlated with climate and vegetation shifts over time.

References
1. Hobbs, N.T., Hooten, M.B. 2015. Bayesian Models: A Statistical Primer for Ecologists. Princeton University Press, Princeton, NJ.
2. Hooten, M.B., Hobbs, N.T. 2015. A guide to Bayesian model selection for ecologists. Ecololgical Monographs, 85, 3-28.
3. Itter, M.S., Finley A.O., Hooten, M.B., Higuera, P.E., Marlon, J.R., Kelly, R., McLachlan, J.S. (Submitted). A model-based approach to wildland fire reconstruction using sediment charcoal records. arXiv:1612.02382

State Data Assimilation and PalEON

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Post by Michael Dietze and Ann Raiho

What is state data assimilation (SDA)?

SDA is the process of using observed data to update the internal STATE estimates of a model, as opposed to using data for validation or parameter calibration. The exact statistical methods vary, but generally this involves running models forward, stopping at times where data were observed, nudging the model back on track, and then restarting the model run (Figure 1). The approached being employed by the modeling teams in PalEON are all variations of ENSEMBLE based assimilation, meaning that in order to capture the uncertainty and variability in model predictions, during the analysis step (i.e. nudge) we update both the mean and the spread of the ensemble based on the uncertainties in both the model and the data. Importantly, we don’t just update the states that we observed, but we also update the other states in the model based on their covariances with the states that we do observe. For example, if we update composition based on pollen or NPP based on tree rings, we also update the carbon pools and land surface fluxes that co-vary with these.

Figure 1. Schematic of how state data assimilation works. From an initial state (shown as pink in the Forecast Step) you make a prediction (blue curve in the Analysis step). Then compare your data or new observation (green in the Analysis step) to the model prediction (blue) and calculate an updated state (pink in the Analysis step).

There are many components in the PalEON SDA and many people are involved. In all methods being employed by PalEON modeling teams, the uncertainty in the meteorological drivers is a major component of the model ensemble spread. Christy Rollinson has developed a workflow that generates an ensemble of ensembles of meteorological drivers – first she starts with an ensemble of different GCM’s that have completed the ‘last millennia’ run (850-1850 AD) and then downscales each GCM in space and time, generating an ensemble of different meteorological realizations for each GCM that propagates the downscaling uncertainty. John Tipton and Mevin Hooten then update this ensemble of ensembles, providing weights to each based on their fidelity with different paleoclimate proxies over different timescales. In addition to the meteorological realizations, some of the techniques being employed also accommodate model parameter error and model process error (which is like a ‘residual’ error after accounting for observation error in the data).

Why are we doing SDA in PalEON?

In the PalEON proposals we laid out four high-level PalEON objectives: Validation, Inference, Initialization, and Improvement. Our previous MIP (Model Intercomparison Project) activities at the site and regional scale were focuses specifically on the first of these, Validation. By contrast, SDA directly informs the next two (Inference, Initialization). Both the SDA and the MIP indirectly support the fourth (Improvement).

In terms of Inference, the central idea here is to formally fuse models and data to improve our ability to infer the structure, composition, and function of ecosystems on millennial timescales. Specifically, by leveraging the covariances between observed and unobserved states we’re hoping that models will help us better estimate what pre- and early-settlement were like, in particular for variables not directly related to our traditional paleo proxies (e.g. carbon pools, GPP, NEE, water fluxes, albedo). The last millennium is a particularly important period to infer as it’s the baseline against which we judge anthropogenic impacts, but we lack measurements for many key variables for that baseline period. We want to know how much we can reduce the uncertainty about that baseline.

In terms of Initialization, a key assumption in many modeling exercises (including all CMIP / IPCC projections) is that we can spin ecosystems up to a presettlement ‘steady state’ condition. Indeed, it is this assumption that’s responsible for there being far less model spread at 1850 than for the modern period, despite having far greater observations for the modern. However, no paleoecologist believes the world was at equilibrium prior to 1850. Our key question is “how much does that assumption matter?” Here we’re using data assimilation to force models to follow the non-equilibrium trajectories they actually followed and assessing how much impact that has on contemporary predictions.

Finally, SDA gives us a new perspective on model validation and improvement. In our initial validation activity, as well as all other MIPs and most other validation activities, if a model gets off to a wrong start, it will generally continue to perform poorly thereafter even if it correctly captures processes responsible for further change over time. Here, by continually putting the model back ‘on track’ we can better assess the ability of models to capture the system dynamics over specific, fixed time steps and when in time & space it makes reasonable vs unreasonable predictions.

SDA Example

Figure 2 shows a PalEON SDA example for a 30 year time period using tree ring estimates of aboveground biomass for four tree species from data collected at UNDERC and a forest gap model called LINKAGES.  The two plots show the tree ring data for hemlock and yellow birch in green, the model prediction in purple and the pink is how the data “nudge” the model. The correlation plot on the right represents the process error correlation matrix.  That is, it shows what correlations are either missing in LINKAGES or are over represented. For example, the negative correlations between hemlock vs. yellow birch and cedar suggest there’s a negative interaction between these species that is stronger than LINKAGES predicted, while at the same time yellow birch and cedar positively covary more than LINKAGES predicted. One interpretation of this is that hemlock is a better competitor in this stand, and yellow birch and cedar worse, than LINKAGES would have predicted. Similarly, the weak correlations of all other species with maple doesn’t imply that maples are not competing, but that the assumptions built into LINKAGES are already able to capture the interaction of this species with its neighbors.

Figure 2. SDA example of aboveground biomass in the LINKAGES gap model. The left and middle plots are the biomass values through time given the data (green), the model predictions (purple), and the updated model-data output (pink). The plot on the right is a correlation plot representing the process error correlation matrix.

 

Expert Elicitation to Interpret Pollen Data

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

Fossil pollen counts from sediments collected from bogs, lakes, or forest hollows tell us something about the composition of surrounding forests (read more about fossil pollen here and here). In a sediment core, pollen samples from multiple depths tell us about changes in these surrounding forests over time. Barring some rare and complex geophysical events, going deeper means going back in time. With some simplifying assumptions about how pollen travels from tree to sediment we can use counts of sediment pollen grains to quantitatively reconstruct forests of the past.

However, correlating depth with time, or aging the sediment, is a difficult problem. Sediment accumulates at rates that vary through time, resulting in non-linear age-depth relationships. This means that knowing the sampling year – or the age of the surface sediment – is not enough to reliably estimate the ages of samples from further down in the sediment. This lack of information is solved with radiometric dating. Small pieces of plant material from the surrounding environment find their way into the sediment; these are macrofossils. Isotope signatures from these macrofossils can be used to determine their approximate age, and provide us with additional age-depth data points. Age-depth models can be constructed from these age-depth data points.

Another way to link depth with age is to look for signatory changes in pollen representation over time. Hallmark changes in the representation of indicator taxa allow scientists to associate sediment depths with events whose dates (ages) are roughly known. In the upper midwestern US, European settlement led to significant land-use changes which resulted in increases in several agricultural indicator taxa, including ambrosia (i.e., ragweed) and rumex (i.e., docks and sorrels) (Figure 1). This change in pollen representation makes it possible to identify pre- and post-settlement depths in a pollen sediment core. This matters because some scientists (including some of us on PalEON) hypothesize that major land-use changes probably caused big changes in the pollen-vegetation relationship. Were these anthropogenically-induced changes in the pollen-vegetation relationship greater than what we would expect without this external forcing? We don’t know, and might never know.

Images of A) ragweed and B) sheep sorrel.

Images of A) ragweed and B) sheep sorrel.

Nevertheless, we want to identify what we often refer to as the settlement horizon in the pollen records for at least two reasons. First, it allows us to compare pollen from the time of European settlement with public land survey records. Second, it is often used as an additional age-depth data point in the construction of age-depth models. But how easy is it to identify this settlement horizon? Recent work shows it is not as easy as one might have thought.

The unofficial PalEON mantra is that it is better to be correct and uncertain than certain and wrong. This line of thought led us to conduct an experiment using expert elicitation, where experts were tasked with identifying the settlement horizon in pollen records from the upper midwest. Four experts each considered 185 pollen records from the upper midwest USA. For 59 pollen records the experts agreed on the location of the settlement horizon (Figure 2). For the remaining records, there was some level of disagreement (Figure 3). This is not surprising, but does highlight the importance of thinking about uncertainty. Does this mean that we should disregard all previous attempts to identify the settlement horizon? The answer to this is a resounding no. The moral from all of this is that understanding your data is critical; understand its uncertainty and how this impacts your work. In the age of big-data and data-sharing, it becomes more difficult to really know your data, but the payoff is sound science. Know your data, and know it well.

To learn more about how we use results from the expert elicitation exercise referred to above, check out our recent Dawson et al. 2016 paper in Quaternary Science Reviews where we calibrate the pollen-vegetation relationship. Elicitation results have also been used to redefine controls for a new suite of age-depth models (Goring et al., in prep), which will in turn be used to assign dates to pollen samples used in vegetation reconstructions (Dawson et al, in prep).

 

Figure 2. Example of a pollen diagram from a site where experts were in complete agreement on the location of the representative pre-settlement sample. Samples identified by experts as pre-settlement are indicated by the dashed lines.

Figure 2. Example of a pollen diagram from a site where experts were in complete agreement on the location of the representative pre-settlement sample. Samples identified by experts as pre-settlement are indicated by the dashed lines.

Pollen Diagram Figure 3

Figure 3. Example of a pollen diagram from a site where experts were in complete disagreement on the location of the representative pre-settlement sample. Samples identified by experts as pre-settlement are indicated by the dashed lines.

References

  1. Dawson, Paciorek, McLachlan, Goring, Williams, Jackson. 2016. Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data. Quaternary Science Reviews.137: 156-175. 
  2. Goring, Dawson, Grimm, et al. Semi-automated age model development for large scale databases. 2016. In prep for submission to Open Quaternary.
  3. Dawson, Paciorek, McLachlan, Goring, Williams, Jackson. 2016. Pre-industrial baseline variation of upper midwestern US vegetation. In prep for submission to Quaternary Science Reviews.

Synthesizing Fire-History Records to Understand Fire-Regime Variability Across Alaska

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Post by Tyler Hoecker graduate student with Philip Higuera at the University of Montana. Tyler received an Outstanding Student Paper Award when he presented this work at AGU 2015!

Over the past two decades the paleoecological research community has amassed dozens of sediment cores from across Alaska. These long-term records contain a range of clues about the character of ecosystems and climate that existed deep in the past. Some records extend back as far as 14,000 years, and have been used to reconstruct millennial-scale vegetation, climate and disturbance dynamics. For example, Higuera et al. (2009) identified the marked increase in biomass burning and fire frequency following the transition from forest-tundra to modern black-spruce dominated boreal forest ca. 5000-6000 years ago using cores from four lakes in the south-central Brooks Range (Figure 1).

Figure 1

Figure 1. Paleocharcoal record from a south-central Brooks Range lake presented in Higuera et al. 2009. Vertical bars in the top panel show the charcoal accumulation rate (CHAR; pieces cm-2 year-1) over the past 7,000 years, and crosses indicate peaks in CHAR that are inferred to represent local fire events. Bottom panel shows peak magnitude (pieces cm-2 peak-1) delineated by vegetation zone.

More recent work has focused in on the past two millennia to understand the sensitivity of fire regimes in the boreal forest to centennial-scale climate change, including the Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA) (Kelly et al., 2013). Persisting from ca. 850-1200 A.D. (750-1100 calibrated years before present), the MCA has been proposed as a rough analog to modern and predicted climate warming. This work identified significant increases in biomass burning during the MCA based on 14 cores from the Yukon Flats region of boreal forest, likely in response to regional warming. Biomass burning tapered off before LIA cooling began, hypothesized to reflect negative feedbacks from fuel limitations (Kelly et al., 2013).

The synthesis work I presented at AGU built on the work of Kelly et al. (2013) and focused on characterizing centennial-scale variability over the past two millennia in an additional 12 published fire-history records from elsewhere in the Alaskan boreal forest and tundra (Copper River Basin, Brooks Range, Noatak River Watershed). I looked at these records as Alaskan-wide (n=26) and regional composites (n=14 in Yukon Flats and n=4 elsewhere).

Figure 2

Figure 2. Maps of new and published paleocharcoal records used in the synthesis. 26 paleocharcoal records were analyzed as an Alaskan-wide composite, and as ecoregional composites (colored polygons). 8 new records were collected in the Kuskokwim Mountains ecoregion of interior boreal forest (dark green polygon, westernmost points) in June 2015. Wildfire burn perimeters since 1939 are shown in red.

Each of the composite records demonstrates the pronounced high-frequency variability in fire activity, but long-term trends were also revealed. As an Alaskan-wide composite, this analysis suggests a relatively cohesive increase in biomass burning during the MCA, and a reduction during the LIA, largely reflecting the influence of the Yukon Flats records (Figure 3).

Figure 3

Figure 3. Alaskan-wide composite of 26 paleocharcoal records. Top panel shows the Z-score of charcoal accumulation rate (CHAR; pieces cm-2 year-1) over the last 2500 years. Thick black line represents a 500-year mean, thin black line represents a 100-year mean, and gray band indicates bootstrapped 90% confidence intervals around the 500-year mean. Colored bars indicate the approximate persistence of the Medieval Climate Anomaly (MCA) and Little Ice Age (LIA). Bottom panel indicates the number of records contributing to the composite through time (22-26).

When considered as regional composites (Figure 4) variability across Alaska emerges. At this scale the Yukons Flats, Copper River Basin, and Noatak River Watershed all show a sensitivity to warming, with some variability in the timing of this response. However, the Brooks Range sites show little low-frequency variability during either the MCA or LIA. This suggests that vegetation feedbacks or regional-scale controls act to moderate the impact of warming on biomass burning. Alternatively, climate forcing could have been uneven across Alaska as this scale, and/or climate forcing may not have been substantial enough in some regions to elicit a response in fire regimes. The sensitivity in observed paleocharcoal records may also be a function of sample size; centennial-scale change in some regions may be subtler than can be detected with only four lake cores.

Figure 4

Figure 4. Regional composite paleocharcoal records. Time series are labeled by region (number of records). Each shows the Z-score of charcoal accumulation rate (CHAR; pieces cm-2 year-1) over the last 2500 years. Thick black line represents a 500-year mean, thin black line represents a 100-year mean, and gray band indicates bootstrapped 90% confidence intervals around the 500-year mean. Colored bars indicate the approximate persistence of the Medieval Climate Anomaly (MCA) and Little Ice Age (LIA).

Untangling the potential mechanisms driving the variability across time and space will require similar analyses with larger datasets. For my PalEON-supported MS thesis I will incorporate eight new lake-sediment records collected in 2015 to assess fire-regime sensitivity in a climatically distinct region (Figure 2). This network of lakes will lend itself to detecting the relatively short-term variability of the MCA and LIA. Spatially explicit reconstructions of climate over this time period will also help to explain the causes of variability in biomass burning, and improve our understanding of the relative importance of climate and vegetation dynamics in driving fire regime trend.

Citations
Higuera et al. 2009. Vegetation mediated the impacts of postglacial climate change on fire regimes in the south-central Brooks Range, Alaska. Ecological Monographs 79(2): 201-219

Kelly et al. 2013. Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. Proceedings of the National Academy of Sciences 110(32): 13055-13060

Empirically Reconstructing Biophysics with Remote Sensing Data

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Post by Bethany Blakely graduate student with Adrian Rocha and Jason McLachlan at the University of Notre Dame

Biophysics is important . . . but only recently!

The exchange of energy between the land surface and the atmosphere (biophysics) plays a huge role in local and global climate. The polar ice-albedo feedback, where snowmelt reduces albedo and further accelerates melting, is an example that most readers will be familiar with. Vegetation is an important mediator of biophysical change (Figure 1). Changes in stature, phenology, water use, and other vegetation characteristics alter the exchange of energy between a vegetated surface and the surrounding atmosphere. Despite the potential climatic importance of these effects, they were often neglected in early efforts to understand vegetation-climate interactions. One contributor to the relative neglect of biophysics in past climate science may be its temporospatial misalignment with vegetation processes. Surface-atmosphere energy exchanges happen rapidly and at a local scale, making them hard to rectify with annual to centennial changes in landscape and climate.

Figure 1

Figure 1. Major ways in which vegetation affects surface-atmosphere exchange of energy. CRO = crops, DBF = deciduous broadleaf forests, ENF = evergreen needleleaf forests, GRA = grassland. Forested land tends to absorb more energy than non-forested land due to lower albedo but dissipates a larger portion of that energy through evaporation, limiting temperature increase. Zhao et al 2014

 

Remote sensing to the rescue?

Remote sensing is a useful tool for bridging the gap between biophysical and landscape processes. It offers a great deal flexibility in scale and is well suited to represent the kinds of things that matter for biophysics. After all, remote sensing satellites literally measure outputs of energy from the land surface. Since the processed data products from these measurements are standardized at relatively fine temporospatial resolution but collected over annual to decadal periods of time, they facilitate highly data-informed generalizations about fine-scale processes.

The biophysics of the past

I’ve been spending the past two years using “okay, mostly learning to use” remote sensing data to empirically link vegetation and biophysics. The goal is to understand how vegetation change since European settlement has altered the biophysics of the land surface, and how that might affect climate.

Fig 2

Figure 2: PLS pre-settlement vegetation and MODIS modern vegetation

I used 10-year averages of MODIS remote sensing data to create regressions linking vegetation type to two important biophysical properties: albedo and surface temperature. I then projected that relationship onto the vegetation of the past. Think 30,000-color paint-by-number grid cells.

Fig 3

Figure 3: Differences (modern vs historic) in albedo and surface temperature

The biophysical changes in the land surface are striking. Typical albedo has increased in the winter – there are less trees (because of logging, urban expansion, etc.) to cover up the snow – but is mostly unchanged in the growing season. Surface temperature has decreased in winter but increased in summer. This trend is particularly interesting because it seems to suggest a loss of temperature regulation with the loss of forests; the modern surface is colder in the cold season and warmer in the warm season. For more detail on my findings, check out my recent AGU poster. Although we can’t know exactly how a centuries-gone landscape exchanged energy with its atmosphere, remote sensing data offers a way to construct useful empirical baseline for changes in vegetation biophysics. In addition to offering its own scientific insights, this work could serve as an interesting comparison to outputs from Paleon Models, a role which I plan to participate in on in the upcoming site level model intercomparison project.

Reconstructing Multivariate Climate Using A Mechanistic Tree Ring Model

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Post by John Tipton, statistics graduate student with Mevin Hooten at Colorado State University

Statistical Challenges of Paleoclimate Reconstructions
The ability to reconstruct paleoclimate from proxy data is important for understanding how climate has changed in the past and to allow exploration into how changing climate influences ecological processes. Statistical reconstructions of paleoclimate have unique challenges because proxy data are noisy, indirect observations of climate. Thus, any statistical model must address the following challenges: change of temporal support, sparse data, and the prediction of unobserved climate variables. When reconstructing climate from tree ring widths, the change of temporal support arises because our climate data are monthly average temperature and log total precipitation, whereas tree ring growth is measured on an annual scale. Therefore, any statistical model must account for this temporal misalignment. To overcome estimation issues common in sparse data scenarios, many previous reconstructions used linear statistical methods with constraints to regress the tree ring widths onto climate. For a multivariate climate reconstruction (e.g., temperature and precipitation), predicting paleoclimate using linear regression requires the inversion of a many to one functional that has potentially infinite solutions. Thus, multivariate climate reconstructions from univariate tree ring width time series are not commonly performed.

Mechanistic Models – A Promising Alternative
There is a need for rigorous statistical multivariate climate reconstructions, and hence, we developed an alternative to using linear statistical methods (Tipton et. al., In Press), using a mechanistic, biologically motivated model that “grows” tree rings to approximate the true growth process. By growing tree ring widths on a monthly time step, the mechanistic model aligns the monthly climate data with annual tree ring width data. Extending the mechanistic model to allow each tree species to have differential response to climate provides strong constraints on possible climate scenarios, ameliorating the difficulties that arise from having too many unknowns. We perform Bayesian inference to generate a probabilistic reconstruction that allows visual exploration of uncertainties. In contrast, many paleoclimate reconstructions generate point estimates that are not probabilistic in nature. The probabilistic reconstructions provide auxillary information that can be used to determine at what time periods the reconstruction is informative. Unfortunately, the use of a mechanistic growth model comes at a computational cost, thus we fit our model using Markov Chain Monte Carlo with compiled C++ code to increase computation speed.

Reconstruction of T and P – at the Same Time!
Our motivating goal was to generate a reconstruction of spatially explicit climate (temperature and precipitation) in the Northeastern United States that can be used to better understand how vegetation patterns have changed due to both climate change and direct human activity. Our work focuses on the Hudson Valley of New York, although in future work this model framework could be extended to the entire Northeastern United States. We focus on the Hudson Valley because there has been previous efforts to reconstruct the Palmer Drought Severity Index (PDSI), a combination of temperature and precipitation, which we can directly compare to our reconstruction, exploring the benefits and costs of different modeling frameworks. Figure 1 shows our joint temperature and precipitation reconstruction with the darkness of the shaded areas proportional to the probabilistic model predictions. For comparison, the black line in the log precipitation plot represents the previous centered and scaled PDSI reconstruction. Interestingly, there is little learning about temperature from our model (although the uncertainties are reasonable) while the log precipitation reconstruction is highly correlated (r=0.72) with the preexisting PDSI reconstruction. This result is in line with ecological expectations – drought in the Hudson Valley is strongly associated with precipitation (Pederson et al., 2015). When comparing our reconstruction to the previous efforts, our method has the added benefit of providing uncertainty estimates that illuminate where in time the reconstruction is informative without relying on statistically improper scoring rules like RE and CE commonly used in the paleoclimate literature. The use of proper scoring rules for assessing predictive ability is vital, because improper scoring rules can lead to incorrect inference about predictive skill.

Figure 1. Plot of probabilistic reconstruction of temperature and log precipitation using a mechanistic tree ring growth model. The reconstructions are shaded according to posterior predictive probabilities with the dotted lines giving the 95% credible interval. The solid black line in the log precipitation plot is a centered and scaled reconstruction of PDSI using the same data. The black lines at the far right of each reconstruction are the observational records.

Figure 1. Plot of probabilistic reconstruction of temperature and log precipitation using a mechanistic tree ring growth model. The reconstructions are shaded according to posterior predictive probabilities with the dotted lines giving the 95% credible interval. The solid black line in the log precipitation plot is a centered and scaled reconstruction of PDSI using the same data. The black lines at the far right of each reconstruction are the observational records.

What Did We Learn?
Reconstructing climate based on ecological proxies is tricky! Our simulations showed that the model we developed can reconstruct multivariate climate with great skill when tree growth responds to both temperature and precipitation. However, in the Hudson Valley, and many other temperate regions, trees respond mainly to precipitation and the bulk of tree growth occurs in a very limited range of temperatures. Thus, while the reconstruction of precipitation in these regions is both accurate and precise, the reconstruction of temperature is inherently more uncertain. The main benefit of a fully rigorous approach for obtaining the reconstructions is that the proper uncertainty can then be factored in to our scientific understanding of the climate process as well as accounted for in other modeling efforts (e.g., ecosystem computer models that depend on historic climate reconstructions).

References
Pederson, N., A.W. D’Amato, J.M. Dyer, D.R. Foster, D. Goldblum, J.L. Hart, A.E. Hessl, L.R. Iverson, S.T. Jackson, and D. Martin-Benito. (2015). Climate remains an important driver of post-European vegetation change in the eastern United States. Global Change Biology, 21(6), 2105-2110.

Tipton, J.R., M.B. Hooten, N. Pederson, M.P. Tingley, and D. Bishop. (In Press).
Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models. Environmetrics.

Models Part 3: Using Ecosystem Models to Advance Ecology

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Post by Christine Rollinson, Post-doc at Boston University working with Michael Dietze.

After a bit of a break and some time back out in the woods, for the PalEON blog and myself, it’s time to wrap up our series of posts on the ecosystem modeling side of PalEON.  In the first post, we talked about why a paleoecological research group is using ecosystem models.  The second post took us behind the curtain and down the rabbit hole to describe a bit of the how of the modeling process.  Now it’s time to take a step back again and talk about what we are learning from the PalEON Model Inter-comparison Project (MIP).

Figure 1

Above is a figure showing aboveground biomass at Harvard Forest from the different PalEON MIP models. The first question most people want know is: Which model is right (or at least closest to reality)?  Well, the fact of the matter is, we don’t really know.  We know that modern biomass at the Harvard Forest is around 100 Mg C per hectare.  Most models are in the right ballpark, but each has a very different path it took to get there.  When we go back in time beyond the past few decades the data available to validate most of these models is extremely sparse.  PalEON has gathered pollen samples at Harvard forest and other MIP sites as well as pre-settlement vegetation records for most of the northeast.  The settlement vegetation records have provided a biomass benchmark for some of our western MIP sites, but aside from that, all other pre-1700 information currently available for comparison with models is based on forest composition.

So how do we evaluate these different models when we don’t actually know what these forests were like 1,000 years ago?  In the absence of empirical benchmarks, we are left comparing the models to each other.  A typical approach would then be to look at each model and try to explain why it has a particular pattern or is different from another model.  For example, why does ED have that giant spike in the 11th century? (We’re still trying to figure that one out; see previous post on debugging.)

Figure 2: The PalEON MIP isn’t the only model comparison project to see models predicting vastly different conditions in time periods that have no data available for comparison. This figure shows similar problems in CMIP51.

Figure 2: The PalEON MIP isn’t the only model comparison project to see models predicting vastly different conditions in time periods that have no data available for comparison. This figure shows similar problems in CMIP51.

 

 

 

 

 

 

 

 

 

 

 

The purpose of a multi-model comparison doesn’t necessarily need to be identifying a single “best” or most accurate model.  As an ecologist with PalEON, my goal is to avoid dissecting individual models and instead focus on the big picture and answer questions where output from a single model would be insufficient.

I like to view the PalEON MIP in the same way I would view an empirical-based study that is trying to find cohesive ecological patterns across many field sites or ecosystems.  An incredible about of detailed information can be gleaned from a study based in a single site or region, but these types of studies have their limitations.  Single-site studies have extraordinarily detailed information about plant physiology, ecosystem interactions, and direct effects of warming through experimentation. I think working with a single ecosystem model is very similar to performing a single-site field study.  With one model, you can run simulations to identify potential cause-and-effect relationships, but at the end of the day, the observed response may only be an artifact of that particular model’s structure and not representative of what happens in the real world.

Working with a multi-model comparison is like working with a continental- or global-scale data set.  There’s always a temptation to dig into the story of indvidual sites (or in this case models).  Each site or model has it’s own interesting quirks, stories, and contributions to ecology that a researcher could easily use to build a career (as many have). However, if we think about ideas that have formed the fundamentals of ecology, ideas such as natural selection, carrying capacity, or biogeography, these ideas are those that apply across taxa and ecosystems.  To draw the parallel with modeling, if we want to move away from a piecemeal approach to ecosystem modeling and find synthetic trends, test ecological theories, and identify areas of greatest uncertainty, we need to draw comparisons across many models.  The PalEON MIP, with detailed output from more than 10 models on ecosystem structure and function from millenial-length runs at six sites, is the perfect model data set to look at these sort of questions.

Figure 3

Current PalEON MIP analyses are investigating a wide range of ecological patterns and processes such as the role of temporal scale in understanding causes of ecosystem variation, transitions in vegetation composition, and ecological resilience after extreme events.  With the PalEON MIP, we are first identifying patterns that we know exist in nature such as concurrent shifts in climate and species composition or a given sensitivity of NPP to temperature. We can then find out if and when these patterns occur in the model and only then, look at similarities or differences among the models that might explain patterns of model behavior across models.  Additionally, my personal ecological research interests have led me to use these models to test conceptual ecological theories regarding the role of temporal scale in paleoecological data that have thus far been difficult to assess with empirical data (Fig. 4).

Figure 4

If there’s one point I want you to take away from this series on ecological modeling, it is this: ecosystem models are tools that let us test ecological theories in ways not possible with empirical data.  Models allow us to test our understanding of individual ecosystem processes as well as how those processes scale across space and time.  Because ecosystem models are representations of how we think the world works, modelers aren’t just programmers; modelers are ecologists too!

I want to take this opportunity to thank all of the PalEON team and collaborators and particularly the PalEON MIP participants for contributing runs and helping get me up to speed on ecological modeling.  Keep your eyes out at ESA next month for PalEON-related talks including this session where you can hear several PalEON-related talks including some actual MIP results!

  1. P. Friedlingstein et al., Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511-526 (2014). 

Edge of the Prairie

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Posted by Jody Peters, PalEON Program Manager

Prior to major land use changes, the tall grass prairie was a widespread ecotone in North America extending east into Minnesota, Illinois and Indiana.  Caitlin Broderick, a

Figure 1. Study area. Townships within the Yellow River watershed.

University of Notre Dame undergraduate working in the McLachlan lab, wanted to learn more about the edge of the prairie this spring semester.  She could have listened to NPR’s Garrison Keillor describe life in Lake Wobegone on the edge of the prairie in Minnesota.  But given that Notre Dame has been compiling historical records of trees from Indiana and Illinois, she decided to look at bit closer to home and focused on townships that are within the Yellow River watershed.  Coincidentally, this watershed is split almost in two by what is currently US 31 and what was historically known in the Public Land Survey (PLS) notes as the Michigan Road Lands. (Fig 1).

The following are some of the results Caitlin presented at the University of Notre Dame College of Science Joint Annual Spring Meeting.

Prairie_Fig2Currently, the Yellow River region is dominated by agriculture and deciduous forests (Fig 2).  However, from a 1935 depiction of the extent of the prairie prior to Euro-American settlement, prairie ecosystems extended into northwest Indiana including into the Yellow River region (Fig 3; Transeau 1935).  Caitlin explored what the edge of the prairie in the Yellow River region looked like during 1829-1837 using historical forest data obtained from PLS.Prairie_Fig3The survey notes provide identification of 1-2 trees, their diameters and distances from corner posts set every mile in each township (n=30 townships; 1055 corners). For this study, trees at each corner were categorized as “Oak” (only oaks were present), “Other” (22 non-oak tree taxa which were dominated by beech, ash, maple and elms), or “Oak + Other” (a combination of oak and non-oak trees).  There were also corners that were categorized as “Water” (in lakes, rivers, creeks, etc.), “No Tree” (had a post set in a mound but with no trees nearby), or “No Data” (corners with no information provided in the notes).

Caitlin used Arc GIS to map the tree composition at each corner (Fig 4).  Much to our surprise, she did not see many corners with no trees in the areas of Transeau’s prairie in the Yellow River region.  However, there was a striking pattern in the distribution of the trees, with the majority of the trees to the west of the Michigan Road Lands being oaks and those to the east being other hardwoods.

Prairie_Fig4

In addition to examining tree composition, Caitlin wanted to look at the structure of the trees and the physical environment across the region. To better define the two groups of trees (Oaks to the west and Other hardwoods to the east), Caitlin created buffers around individual corners and dissolved the buffers with matching tree classifications to create two groups of trees (Fig 5A). Comparing the trees in the two groups, Caitlin found that there was no difference in tree diameter (mean (cm)± se; Oak: 40.9± 0.5; Other: 40.2± 0.6). However, the trees in the Oak group were significantly further from the corner posts compared to the Other group (mean (m)± se; Oak: 36.2± 4.3; Other: 13.6± 2.4; p<0.001).  Given that there was a difference in the tree composition and distance from the corners, Caitlin looked at climatic and physical features that could help explain this difference between the oaks of the west and the other hardwoods in the east.  There was no ecological difference in either temperature or precipitation (mean± se; Temperature (ºC): Oak: 9.88± 0.003; Other: 9.89± 0.003; Precipitation (mm): Oak: 1010± 0.59; Other: 1007± 0.74).  However, although northern Indiana is quite flat, as with the tree composition, there was a striking difference in the pattern of elevation change across the Yellow River region that mirrored the change in the tree distribution. Trees in the Oak group were at significantly lower elevation compared to trees in the Other group (mean (m)± se; Oak: 225.5± 0.7, Other: 251.0± 0.6; p<0.01; Fig 5B).

Prairie_Fig5ab

IPrairie_Fig6n addition to exploring what the historical prairie-forest boundary looked like, Caitlin compared the National Commodity Crop Productivity Index (NCCPI) to see if there is a lasting impact on the current crop production by the physical factors that contributed to the differences in historical tree composition and structure.  The NCCPI models the ability of soils, landscapes, and climates to foster non-irrigated commodity crop productivity (Dobos et al. 2012). Caitlin found there was in fact, a difference between NCCPI of the two groups with crop productivity being significantly lower in the western area historically dominated by low density Oak tree communities (Fig 6).

Typically when people think of prairies, they think of open expanses of grass.  However, over the spring semester Caitlin found that in the Yellow River region of northern Indiana, the prairie described by Transeau actually looked more like a low density oak community compared to the closed forests further east.  While at first glance we can no longer see the prairie’s edge in the current vegetation of northern Indiana (Fig 2), when Caitlin looked closer, the factors that once controlled the boundary between the closed forests of the east and the open savannas moving west may still be at work limiting crop production in what was once savanna compared to production in the historically wooded uplands (although what these factors specifically are is still an open question).

As a fun follow-up to Caitlin’s research, nine of us took a lab canoe trip down the Yellow River. Although we didn’t see the same striking pattern of oaks to the west and other hardwoods to the east, as we drove the 50 minutes south on US 31 to get to the River, we did see small stands of hardwoods intermingled with a number of lone oaks standing in the middle of corn and soybean fields.  On our way to the ice cream store after our canoe trip, we also saw some of the lasting impacts of the oak savannas as we drove through Burr Oak, Indiana.  According to McDonald (1908), the village plat was filed in 1882 and was “nearly in the center of what is known as the “Burr Oak Flats”, which is as beautiful and productive a region as can be found anywhere.”

The trip down the Yellow River was a wonderful way to get outside on a beautiful spring day and experience firsthand the watershed/ecosystem that Caitlin has spent this spring studying in the lab.  The river was an easy enough paddle that novice canoers would be comfortable, but had enough downed trees to provide excitement for the more experienced canoer.  If you are ever in the area, connect with the Little Bit Canoe Rental. They do a wonderful job providing canoes and transport. Here are a couple of pictures of the fun!

Prairie_Fig7

 

 

References

Dobos, R.R., H.R. Sinclair, Jr., M.P. Robotham. 2012. User Guide for the National Commodity Crop Productivity Index (NCCPI), Version 2.0. Access pdf at the bottom of the page here.

McDonald, D. 1908. A Twentieth Century History of Marshall County, Indiana, Volume 1. Lewis Publishing Company, pg 134. Online access here.

Transeau, E. N. 1935. The Prairie Peninsula. Ecology 16(3): 423-437.

Pollen Dispersal II: Quantitative Reconstructions

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Post by Kevin Burke, Graduate Student at the University of Wisconsin, Madison working with Jack Williams.  

Since Lennart Von Post presented the first modern pollen diagram in 1916, the palynological community has been working to better understand the relationship between pollen abundances recovered from sediments, and the vegetation that produces this pollen.1,2 Throughout the twentieth century great strides have been made in quantitatively reconstructing past vegetation from relative pollen percentages. Mechanistic transport models by Colin Prentice and Shinya Sugita use taxonomic abundance and pollen productivity to estimate pollen source area, while other recent work by PalEON member Andria Dawson study this relationship using sophisticated Bayesian hierarchical models.3,4 However, these approaches all tend to use simplifying assumptions,  including constant, uniform wind speeds, and a circular source area. Our work here seeks to challenge and improve upon some of these underlying assumptions by developing a pollen transport model that includes realistic, variable wind speeds and directions.  Moreover, we are using the historical estimates of vegetation developed by the PalEON project.

Using North American Regional Reanalysis (NARR) wind data, a vegetation dataset of tree composition pre-Euro-American settlement , fossil pollen records from the Neotoma Paleoecology Database, and taxon-specific measurements of settling velocities for pollen grains, we are producing improved estimates of pollen loading (in grains per square meter) for lakes across the Prairie-Forest ecotone in the upper Midwest.  This work recently won an Outstanding Student Paper Award from the 2014 Fall Meeting of the American Geophysical Union.

Fig1

While the source area is largely governed by presence/absence of taxa in the vegetation dataset, things become much more interesting when looking at the ratio of relative proportion of pollen loading to relative proportion of trees per grid cell. We see that some taxa are much better represented in the pollen records given the number of trees on the landscape.

Fig2

We can also see that whether a given taxon is over- or underrepresented varies spatially, based on distance to lake and the other taxa present in a given grid cell.  These results shed light on the potential magnitude of a regional pollen source area, and help elucidate the relationship between pollen in a sediment core and trees on the landscape.  Ultimately, this kind of work can be used to build more accurate reconstructions of past vegetation from fossil pollen records.  From there, these paleovegetation reconstructions can be used to better constrain and improve terrestrial ecosystem models – a central goal of the PalEON project.

To see more results, including preliminary comparisons to pollen counts from sediment cores, you can check out our American Geophysical Union poster here.

References:

  1. Fries, M. Review of Palaeobotany and Palynology, 1967.
  2. Marquer et al. Quaternary Science Reviews, 2014.
  3. Prentice, C. Quaternary Research, 1985.
  4. Sugita, S. Quaternary Research, 1993.