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223 results on '"Soranno, Patricia A."'

1. Identifying and characterizing extrapolation in multivariate response data

Author

Bartley, Meridith L, Hanks, Ephraim M, Schliep, Erin M, Soranno, Patricia A, and Wagner, Tyler

Subjects
Statistics - Applications
Abstract

Extrapolation is defined as making predictions beyond the range of the data used to estimate a statistical model. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are more and more common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we illustrate novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees., Comment: 28 pages, 2 supplementary files, 6 main figures, 2 supplementary figures, 2 supplementary tables

Published
2019
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2. Spatially Constrained Spectral Clustering Algorithms for Region Delineation

Author

Yuan, Shuai, Tan, Pang-Ning, Cheruvelil, Kendra Spence, Collins, Sarah M., and Soranno, Patricia A.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Regionalization is the task of dividing up a landscape into homogeneous patches with similar properties. Although this task has a wide range of applications, it has two notable challenges. First, it is assumed that the resulting regions are both homogeneous and spatially contiguous. Second, it is well-recognized that landscapes are hierarchical such that fine-scale regions are nested wholly within broader-scale regions. To address these two challenges, first, we develop a spatially constrained spectral clustering framework for region delineation that incorporates the tradeoff between region homogeneity and spatial contiguity. The framework uses a flexible, truncated exponential kernel to represent the spatial contiguity constraints, which is integrated with the landscape feature similarity matrix for region delineation. To address the second challenge, we extend the framework to create fine-scale regions that are nested within broader-scaled regions using a greedy, recursive bisection approach. We present a case study of a terrestrial ecology data set in the United States that compares the proposed framework with several baseline methods for regionalization. Experimental results suggest that the proposed framework for regionalization outperforms the baseline methods, especially in terms of balancing region contiguity and homogeneity, as well as creating regions of more similar size, which is often a desired trait of regions.

Published
2019

3. Ecological prediction at macroscales using big data : Does sampling design matter?

Author

Soranno, Patricia A., Cheruvelil, Kendra Spence, Liu, Boyang, Wang, Qi, Tan, Pang-Ning, Zhou, Jiayu, King, Katelyn B. S., Mccullough, Ian M., Stachelek, Joseph, Bartley, Meridith, Filstrup, Christopher T., Hanks, Ephraim M., Lapierre, Jean-Francaois, Lottig, Noah R., Schliep, Erin M., Wagner, Tyler, and E. Webster, Katherine

Published
2020

16. A continuous classification of the 476,697 lakes of the conterminous USbased on geographic archetypes

Author

Lapierre, Jean‐Francois, Webster, Katherine E., Hanks, Ephraim M., Wagner, Tyler, Soranno, Patricia A., McCullough, Ian M., Reinl, Kaitlin L., Domka, Marcella, and Lotting, Noah R.

Abstract

A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad‐scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes ≥ 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophyll a(Chl a), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chl a) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change.

Published
2023
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21. Macrosystems ecology: understanding ecological patterns and processes at continental scales

Author

Heffernan, James B, Soranno, Patricia A, Angilletta, Michael J, Buckley, Lauren B, Gruner, Daniel S, Keitt, Tim H, Kellner, James R, Kominoski, John S, Rocha, Adrian V, Xiao, Jingfeng, Harms, Tamara K, Goring, Simon J, Koenig, Lauren E, McDowell, William H, Powell, Heather, Richardson, Andrew D, Stow, Craig A, Vargas, Rodrigo, and Weathers, Kathleen C

Published
2014

47. Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Author

Wagner, Tyler, primary, Lottig, Noah R., additional, Bartley, Meridith L., additional, Hanks, Ephraim M., additional, Schliep, Erin M., additional, Wikle, Nathan B., additional, King, Katelyn B. S., additional, McCullough, Ian, additional, Stachelek, Jemma, additional, Cheruvelil, Kendra S., additional, Filstrup, Christopher T., additional, Lapierre, Jean Francois, additional, Liu, Boyang, additional, Soranno, Patricia A., additional, Tan, Pang‐Ning, additional, Wang, Qi, additional, Webster, Katherine, additional, and Zhou, Jiayu, additional

Published
2019
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