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Peer Reviewed Publications

2017

Itter, M.S., A.O. Finley, A.W. D’Amato, J.R. Foster and J.B. Bradford (Accepted). Variable effects of climate on forest growth in relation to ecosystem state. Ecological Applications.

Goring, S. J., and J. W. Williams (Accepted). Effect of historic land-use, pathogens, and climate change on tree-climate relationships in the northern United States, Ecology Letters.

Rollinson, C.R., Y. Liu, A. Raiho, D.J.P. Moore, J. McLachlan, D.A. Bishop, A. Dye, J. Hatala Matthes, A. Hessl, T. Hickler, N. Pederson, B. Poulter, T. Quaife, K. Schaefer, J. Steinkamp, M.C.Dietze. 2017. Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America. Global Change Biology. doi:10.1111/gcb.13626. Abstract

Tipton, J., M. Hooten, S. Goring. 2017. Reconstruction of spatiotemporal temperature processes from sparse historical records using probabilistic principal component regression. Advances in Statistical Climatology, Meteorology, and Oceanography 3:1-16. doi:10.5194/ascmo-3-1-2017. Abstract

2016

Goring, S.J., D.J. Mladenoff, C.V. Cogbill, S. Record, C.J. Paciorek, S.T. Jackson, M.C. Dietze, A. Dawson, J. Hatala Matthes, J.S. McLachlan, J.W. Williams. 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. doi:10.1371/journal.pone.0151935. Paper

Kujawa, E.R., S. Goring, A. Dawson, R. Calcote, E.C. Grimm, S.C. Hotchkiss, S.T. Jackson, E.A. Lynch, J. McLachlan, J. St-Jacques, C. Umbanhowar Jr., and J.W. Williams. 2016. The effects of anthropogenic land cover change on pollen-vegetation relationships in the American Midwest. Anthropocene 15: 60-71.  Abstract

Dye, A, A Barker-Plotkin, D Bishop, N Pederson, B Poulter and A Hessl. 2016. Comparing tree-ring and permanent plot estimates of aboveground net primary production in three Eastern U.S. forests. Ecosphere 7(9):e01454.10.1002/ecs2.1454. Paper

Deines JM, D Williams, Q Hamlin, JS McLachlan. 2016. Changes in regional forest composition in Ohio between Euro-American settlement and the present. American Midland Naturalist 176(2): 247-271. Abstract

Dawson, A., et al. 2016. Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data. Quaternary Science Reviews 137: 156-175. Abstract

Matthes, J.H., et al. 2016. Benchmarking historical CMIP5 plant functional types across the Upper Midwest and Northeastern United States. Journal of Geophysical Research: Biogeosciences. doi:10.1002/2015JG003175. Paper

Paciorek C.J., et al. 2016. Statistically-estimated tree composition for the Northeastern United States at Euro-American Settlement. PLoS ONE 11(2): e0150087. doi:10.1371/journal.pone.0150087. Paper

Tipton, J., M.B. Hooten, N. Pederson, M. Tingley, and D. Bishop. (2015). Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models. Environmetrics. DOI: 10.1002/env.2368. Abstract

Marlon, J.R., Kelly, R., A-L Daniau, B. Vanniere, M.J. Power, P. Bartlein,P. Higuera, O. Blarquez, S. Brewer, T. Brucher, A. Feurdean, G. Gil-Romera, V. Iglesias, S.y. Maezumi, B. Magi, C.J.C. Mustaphi, T. Zhihai. 2016. Reconstructions of biomass burning from sediment charcoal records to improve data-model comparisons. Biogeosciences 13:3225-3244. doi:10.5194/bg-13-3225-2016. Abstract

2015

Bishop, D. & N. Pederson. 2015. Regional variation of rainless day frequency across a subcontinental hydroclimate gradient. Journal of Extreme Events, 2(2): 1550007. DOI: 10.1142/S2345737615500074. Abstract

Hobbs, N.T. & M.B. Hooten. 2015. Bayesian Models: A Statistical Primer for Ecologists. Princeton University Press.  Book details here.

Goring, S., et al. 2015. neotoma: A Programmatic Interface to the Neotoma Paleoecological Database; Open Quaternary. DOI: http://doi.org/10.5334/oq.ab

Jackson, S.T. & J.L Blois. 2015. Community ecology in a changing environment: Perspectives from the Quaternary. Proceedings of the National Academy of Science, 112(16): 4915-4921. Abstract

Hooten, M.B. & N.T. Hobbs. 2015. A guide to Bayesian model selection for ecologists. Ecological Monographs, 85: 3-28. Abstract

Walsh, MK, JR Marlon, S Goring, KJ Brown, D Gavin. 2015. A regional perspective on Holocene on fire-climate-human interactions in the Pacific Northwest. Annals of the Association of American Geographers. 105(6): 1135-1157. Abstract

Finley, A.O., S. Banerjee, A.E. Gelfand. 2015. spBayes for large univariate and multivariate point-referenced spatio-temporal data models. Journal of Statistical Software, 63(13):1-28. Abstract

Datta, A., S. Banerjee, A.O. Finley, A.E. Gelfand. 2015. Hierarchical Nearest-Neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association. DOI: 10.1080/01621459.2015.1044091. Abstract

Zennaro, P., et al. 2015. Europe on fire three thousand years ago: Arson or climate?. Geophysical Research Letters, 42, 5023–2033. doi: 10.1002/2015GL064259. Paper

2014

Blarquez, O., et al. 2014. Paleofire: An R package to analyse sedimentary charcoal records from the Global Charcoal Database to reconstruct past biomass burning. Computers & Geosciences, 72: 255-261. Abstract

Finley, A.O, et al. 2014. Dynamic spatial regression models for space-varying forest stand tables. Environmetrics, 25: 596-609.

Feng, X., et al. 2014. Composite likelihood estimation for spatial ordinal data and spatial proportional data with zero/one values. Environmetrics. 25(8):571-583. DOI: 10.1002/env.2306. Abstract

Finley, A.O., S. Banerjee, and B.D. Cook. 2014. Bayesian hierarchical models for spatially misaligned data in R. Methods in Ecology and Evolution. 5(6):514-523. Abstract

Articles in a Special Issue of Frontiers in Ecology and the Environment on Macrosystem Ecology

  1. Goring, S.J., et al. 2014. Improving the culture of interdisciplinary collaboration in ecology by expanding measures of success. Frontiers in Ecology and the Environment. 12(1): 39-47. DOI: 10.1890/120370
  2. Cheruvelil KS, et al. 2014. Creating and maintaining high-performing collaborative research teams: the importance of diversity and interpersonal skills. Frontiers in Ecology and the Environment 12(1): 31-38. DOI: 10.1890/130001
  3. Heffernan JB, et al. 2014. Macrosystems ecology: understanding ecological patterns and processes at continental scales. Frontiers in Ecology and the Environment 12(1): 5-14. DOI: 10.1890/130017
  4. Levy O, et al. 2014. Approaches to advance scientific understanding of macrosystems ecology. Frontiers in Ecology and the Environment 12(1): 15-23. DOI: 10.1890/130019

2013

Record, S., et al. 2013. Should species distribution models account for spatial autocorrelation? A test of model projections across eight millennia of climate change. Global Ecology and Biogeography, 22:760–771. Abstract

Marsicek JP, et al. 2013.  Moisture and temperature changes associated with the mid-Holocene Tsuga decline in the northeastern United States. Quaternary Science Reviews 80: 129-142.

Day LT, et al. 2013.  Analysis of hemlock pollen size in Holocene lake sediments from New England. Quaternary Research 79: 362-365.

Swanson, A., et al. 2013. Spatial regression methods capture prediction uncertainty in species distribution model projections through time. Global Ecology and Biogeography, 22:242–251. Abstract

Paciorek CJ. 2013. Spatial models for point and areal data using Markov random fields on a fine grid. Electronic Journal of Statistics 7:946-972. Abstract

Clifford, M.J. & R.K. Booth. 2013. Increased fire probability of fire during late Holocene droughts in northern New England. Climate Change 119: 693-704.  Abstract

Dietze, M.C., D.S. LeBauer & R. Kooper. 2013. On improving the communication between models and data. Plant, Cell & Environment, 36(9):1575-1585.    Abstract

2012

Goring, S.J., et al. 2012. Deposition times in the northeastern United States during the Holocene: establishing valid priors for Bayesian age models. Quaternary Science Reviews 48: 54-60. Abstract

Jackson, S.T. 2012. Representation of flora and vegetation in Quaternary fossil assemblages: known and unknown knowns and unknowns. Quaternary Science Reviews 49:1-15. Abstract

Kumar, J., et al. 2012. Sub-daily statistical downscaling of meteorological variables using neural networks. Procedia Computer Science 9:887-896. Abstract

Paciorek, C. P. & J. S. McLachlan. 2009. Mapping Ancient Forests: Bayesian Inference for Spatio-Temporal Trends in Forest Composition Using the Fossil Pollen Proxy Record. Journal of the American Statistical Association. 104(486): 608-622. doi:10.1198/jasa.2009.0026. Abstract

Recent Posts

Identifying Local Fire Events From Sediment Charcoal Records Via Regularization

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

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