Geology topics

John Tipton

Publications and source records attributed to John Tipton.

4 recordsLinked to original sources

USGS2019

Comparing and improving methods for reconstructing peatland water-table depth from testate amoebae

Proxies that use changes in the composition of ecological communities to reconstruct temporal changes in an environmental covariate are commonly used in paleoclimatology and paleolimnology. Existing methods, such as weighted averaging and modern analog technique, relate compositional data to the covariate in very simple ways, and different methods are seldom compared systematically. We present a new Bayesian model that better represents the underlying data and the complexity in the relationships between species’ abundances and a paleoenvironmental covariate. Using testate amoeba-based reconstructions of water-table depth as a test case, we systematically compare new and existing models in a cross-validation experiment on a large training dataset from North America. We then apply the different models to a new 7500-year record of testate amoeba assemblages from Caribou Bog in Maine and compare the resulting water-table depth reconstructions. We find that Bayesian models represent an improvement over existing methods in three key ways: more complete use of the underlying compositional data, full and meaningful treatment of uncertainty, and clear paths toward methodological improvements. Furthermore, we highlight how developing and systematically comparing methods leads to an improved understanding of the proxy system. This paper focuses on testate amoebae and water-table depth, but the framework and ideas are widely applicable to other proxies based on compositional data.

Continental United States and Alaska↗

USGS2018

Past and future global transformation of terrestrial ecosystems under climate change

Impacts of global climate change on terrestrial ecosystems are imperfectly constrained by ecosystem models and direct observations. Pervasive ecosystem transformations occurred in response to warming and associated climatic changes during the last glacial-to-interglacial transition, which was comparable in magnitude to warming projected for the next century under high-emission scenarios. We reviewed 594 published paleoecological records to examine compositional and structural changes in terrestrial vegetation since the last glacial period and to project the magnitudes of ecosystem transformations under alternative future emission scenarios. Our results indicate that terrestrial ecosystems are highly sensitive to temperature change and suggest that, without major reductions in greenhouse gas emissions to the atmosphere, terrestrial ecosystems worldwide are at risk of major transformation, with accompanying disruption of ecosystem services and impacts on biodiversity.

Science↗

USGS2017

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio-temporal temperature from a very sparse historical record.

Advances in Statistical Climatology, Meteorology a↗

USGS2015

Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.

Environmetrics↗