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Title: Incorporating variability in simulations of seasonally forced phenology using integral projection models

Authors:
ORCiD logo [1];  [2];  [3];  [1];  [1]
  1. Earth and Environmental Science Division, Los Alamos National Laboratory, Los Alamos NM USA
  2. Department of Entomology, University of Minnesota, St Paul MN USA
  3. Pacific Northwest National Laboratory, Richland WA USA
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1413516
Report Number(s):
PNNL-SA-129909
Journal ID: ISSN 2045-7758
Grant/Contract Number:
AC05-76RL01830; XWDM00
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Ecology and Evolution
Additional Journal Information:
Journal Name: Ecology and Evolution; Journal ID: ISSN 2045-7758
Publisher:
Wiley
Country of Publication:
United States
Language:
English

Citation Formats

Goodsman, Devin W., Aukema, Brian H., McDowell, Nate G., Middleton, Richard S., and Xu, Chonggang. Incorporating variability in simulations of seasonally forced phenology using integral projection models. United States: N. p., 2017. Web. doi:10.1002/ECE3.3590.
Goodsman, Devin W., Aukema, Brian H., McDowell, Nate G., Middleton, Richard S., & Xu, Chonggang. Incorporating variability in simulations of seasonally forced phenology using integral projection models. United States. doi:10.1002/ECE3.3590.
Goodsman, Devin W., Aukema, Brian H., McDowell, Nate G., Middleton, Richard S., and Xu, Chonggang. 2017. "Incorporating variability in simulations of seasonally forced phenology using integral projection models". United States. doi:10.1002/ECE3.3590. https://www.osti.gov/servlets/purl/1413516.
@article{osti_1413516,
title = {Incorporating variability in simulations of seasonally forced phenology using integral projection models},
author = {Goodsman, Devin W. and Aukema, Brian H. and McDowell, Nate G. and Middleton, Richard S. and Xu, Chonggang},
abstractNote = {},
doi = {10.1002/ECE3.3590},
journal = {Ecology and Evolution},
number = ,
volume = ,
place = {United States},
year = 2017,
month =
}

Journal Article:
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  • Phenology models are becoming increasingly important tools to accurately predict how climate change will impact the life histories of organisms. We propose a class of integral projection phenology models derived from stochastic individual-based models of insect development and demography. Our derivation, which is based on the rate summation concept, produces integral projection models that capture the effect of phenotypic rate variability on insect phenology, but which are typically more computationally frugal than equivalent individual-based phenology models. We demonstrate our approach using a temperature-dependent model of the demography of the mountain pine beetle (Dendroctonus ponderosae Hopkins), an insect that kills maturemore » pine trees. This work illustrates how a wide range of stochastic phenology models can be reformulated as integral projection models. Due to their computational efficiency, these integral projection models are suitable for deployment in large-scale simulations, such as studies of altered pest distributions under climate change.« less
  • Phenology models are becoming increasingly important tools to accurately predict how climate change will impact the life histories of organisms. We propose a class of integral projection phenology models derived from stochastic individual-based models of insect development and demography. Our derivation, which is based on the rate summation concept, produces integral projection models that capture the effect of phenotypic rate variability on insect phenology, but which are typically more computationally frugal than equivalent individual-based phenology models. We demonstrate our approach using a temperature-dependent model of the demography of the mountain pine beetle (Dendroctonus ponderosae Hopkins), an insect that kills maturemore » pine trees. This work illustrates how a wide range of stochastic phenology models can be reformulated as integral projection models. Due to their computational efficiency, these integral projection models are suitable for deployment in large-scale simulations, such as studies of altered pest distributions under climate change.« less
  • Purpose: The GATE Monte Carlo simulation toolkit is used for the implementation of realistic PET simulations incorporating tumor heterogeneous activity distributions. The reconstructed patient images include noise from the acquisition process, imaging system's performance restrictions and have limited spatial resolution. For those reasons, the measured intensity cannot be simply introduced in GATE simulations, to reproduce clinical data. Investigation of the heterogeneity distribution within tumors applying partial volume correction (PVC) algorithms was assessed. The purpose of the present study was to create a simulated oncology database based on clinical data with realistic intratumor uptake heterogeneity properties.Methods: PET/CT data of seven oncologymore » patients were used in order to create a realistic tumor database investigating the heterogeneity activity distribution of the simulated tumors. The anthropomorphic models (NURBS based cardiac torso and Zubal phantoms) were adapted to the CT data of each patient, and the activity distribution was extracted from the respective PET data. The patient-specific models were simulated with the Monte Carlo Geant4 application for tomography emission (GATE) in three different levels for each case: (a) using homogeneous activity within the tumor, (b) using heterogeneous activity distribution in every voxel within the tumor as it was extracted from the PET image, and (c) using heterogeneous activity distribution corresponding to the clinical image following PVC. The three different types of simulated data in each case were reconstructed with two iterations and filtered with a 3D Gaussian postfilter, in order to simulate the intratumor heterogeneous uptake. Heterogeneity in all generated images was quantified using textural feature derived parameters in 3D according to the ground truth of the simulation, and compared to clinical measurements. Finally, profiles were plotted in central slices of the tumors, across lines with heterogeneous activity distribution for visual assessment.Results: The accuracy of the simulated database was assessed against the original clinical images. The PVC simulated images matched the clinical ones best. Local, regional, and global features extracted from the PVC simulated images were closest to the clinical measurements, with the exception of the size zone variability and the mean intensity values, where heterogeneous tumors showed better reproducibility. The profiles on PVC simulated tumors after postfiltering seemed to represent the more realistic heterogeneous regions with respect to the clinical reference.Conclusions: In this study, the authors investigated the input activity map heterogeneity in the GATE simulations of tumors with heterogeneous activity distribution. The most realistic heterogeneous tumors were obtained by inserting PVC activity distributions from the clinical image into the activity map of the simulation. Partial volume effect (PVE) can play a crucial role in the quantification of heterogeneity within tumors and have an important impact on applications such as patient follow-up during treatment and assessment of tumor response to therapy. The development of such a database incorporating patient anatomical and functional variability can be used to evaluate new image processing or analysis algorithms, while providing control of the ground truth, which is not available when dealing with clinical datasets. The database includes all images used and generated in this study, as well as the sinograms and the attenuation phantoms for further investigation. It is freely available to the interested reader of the journal at http://www.med.upatras.gr/oncobase/.« less
  • To study the effect of spatial variability of sediment hydraulic properties on multiphase flow, oil infiltration into a hypothetical glacial outwash aquifer, followed by oil extraction, was simulated using a cross-sectional multiphase flow model. The analysis was simplified by neglecting capillary hysteresis. The first simulation used a uniform mean permeability and mean retention curve. This was followed by 50 Monte Carlo simulations conducted using 50 spatially variable permeability realizations and corresponding spatially variable retention curves. For the type of correlation structure considered in this study, which is similar to that of glacial outwash deposits, use of mean hydraulic properties reproducesmore » the ensemble average oil saturation distribution obtained from the Monte Carlo simulations. However, spatial variability causes the oil saturation distribution in an individual oil lens to differ significantly from that of the mean lens. Oil saturations at a given location may be considerably higher than would be predicted using uniform mean properties. During cleanup by oil extraction from a well, considerably more oil may remain behind in the heterogeneous case than in the spatially uniform case.« less