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  1. AU="Hanks, Ephraim M"
  2. AU="Ruiz-Narvaez, Edward A"
  3. AU="Krzysztof Kamiński"
  4. AU="Sharma, Ishna"
  5. AU="Warner, Brit"
  6. AU="JOCHEN SCHÖNGART"
  7. AU="Curdy, Nicolas"
  8. AU="Nkfusai, Claude Ngwayu"
  9. AU="Peng, Yonghan"
  10. AU="Decker, Miriam"
  11. AU="Campbell, Kerry"
  12. AU="Le Deley, Marie-Cécile" AU="Le Deley, Marie-Cécile"
  13. AU="Guan, Shu"

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  1. Artikel: A Sample Covariance-Based Approach For Spatial Binary Data

    Zarmehri, Sahar / Hanks, Ephraim M / Lin, Lin

    Journal of agricultural, biological, and environmental statistics. 2021 June, v. 26, no. 2

    2021  

    Abstract: The field of landscape genetics enables the study of infectious disease dynamics by connecting the landscape features with evolutionary changes. Quantifying genetic correlation across space is helpful in providing insight into the rate of spread of an ... ...

    Abstract The field of landscape genetics enables the study of infectious disease dynamics by connecting the landscape features with evolutionary changes. Quantifying genetic correlation across space is helpful in providing insight into the rate of spread of an infectious disease. We investigate two genetic patterns in spatially referenced single-nucleotide polymorphisms (SNPs): isolation by distance and isolation by resistance. We model the data using a Generalized Linear Mixed effect Model (GLMM) with spatially referenced random effects and provide a novel approach for estimating parameters in spatial GLMMs. In this approach, we use the links between binary probit models and bivariate normal probabilities to directly compute the model-based covariance function for spatial binary data. Parameter estimation is based on minimizing sum of squared distance between the elements of sample covariance and model-based covariance matrices. We analyze data including Brucella Abortus SNPs from spatially referenced hosts in the Greater Yellowstone Ecosystem.
    Schlagwörter Brucella melitensis biovar Abortus ; covariance ; data analysis ; ecosystems ; genetic correlation ; infectious diseases ; landscape genetics ; landscapes ; models
    Sprache Englisch
    Erscheinungsverlauf 2021-06
    Umfang p. 220-249.
    Erscheinungsort Springer US
    Dokumenttyp Artikel
    ZDB-ID 1324615-x
    ISSN 1537-2693 ; 1085-7117
    ISSN (online) 1537-2693
    ISSN 1085-7117
    DOI 10.1007/s13253-020-00424-0
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  2. Artikel: Bayesian analysis of spatial generalized linear mixed models with Laplace moving average random fields

    Walder, Adam / Hanks, Ephraim M

    Computational statistics & data analysis. 2019 Sept. 30,

    2019  

    Abstract: Gaussian random field (GRF) models are widely used in spatial statistics to capture spatially correlated error. Gaussian processes can easily be replaced by the less commonly used Laplace moving averages (LMAs) in spatial generalized linear mixed models ( ...

    Abstract Gaussian random field (GRF) models are widely used in spatial statistics to capture spatially correlated error. Gaussian processes can easily be replaced by the less commonly used Laplace moving averages (LMAs) in spatial generalized linear mixed models (SGLMMs). LMAs are shown to offer improved predictive power when the data exhibits localized spikes in the response. Further, SGLMMs with LMAs are shown to maintain analogous parameter inference and similar computing to Gaussian SGLMMs. A novel discrete space LMA model for irregular lattices is proposed, along with conjugate samplers for LMAs with georeferenced and areal support. A Bayesian analysis of SGLMMs with LMAs and GRFs is conducted over multiple data support and response types.
    Schlagwörter Bayesian theory ; georeferencing ; models ; normal distribution ; samplers
    Sprache Englisch
    Erscheinungsverlauf 2019-0930
    Erscheinungsort Elsevier B.V.
    Dokumenttyp Artikel
    Anmerkung Pre-press version
    ZDB-ID 1478763-5
    ISSN 0167-9473
    ISSN 0167-9473
    DOI 10.1016/j.csda.2019.106861
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  3. Artikel: Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks

    Hanks, Ephraim M

    Journal of the American Statistical Association. 2017 Apr. 3, v. 112, no. 518

    2017  

    Abstract: We present an approach for modeling areal spatial covariance in observed genetic allele data by considering the stationary (limiting) distribution of a spatio-temporal Markov random walk model for gene flow. This stationary distribution corresponds to an ...

    Abstract We present an approach for modeling areal spatial covariance in observed genetic allele data by considering the stationary (limiting) distribution of a spatio-temporal Markov random walk model for gene flow. This stationary distribution corresponds to an intrinsic simultaneous autoregressive (SAR) model for spatial correlation, and provides a principled approach to specifying areal spatial models when a spatio-temporal generating process can be assumed. We apply the approach to a study of spatial genetic variation of trout in a stream network in Connecticut, USA.
    Schlagwörter alleles ; covariance ; equations ; gene flow ; genetic variation ; models ; streams ; trout ; Connecticut
    Sprache Englisch
    Erscheinungsverlauf 2017-0403
    Umfang p. 497-507.
    Erscheinungsort Taylor & Francis
    Dokumenttyp Artikel
    ZDB-ID 2064981-2
    ISSN 1537-274X ; 0003-1291 ; 0162-1459
    ISSN (online) 1537-274X
    ISSN 0003-1291 ; 0162-1459
    DOI 10.1080/01621459.2016.1224714
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  4. Artikel ; Online: A Mechanistic Model of Annual Sulfate Concentrations in the United States

    Wikle, Nathan B. / Hanks, Ephraim M. / Henneman, Lucas R. F. / Zigler, Corwin M.

    Journal of the American Statistical Association. 2022 Sept. 14, v. 117, no. 539 p.1082-1093

    2022  

    Abstract: Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we ... ...

    Abstract Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we develop a mechanistic model which we use to estimate, with uncertainty, the contribution of ambient sulfate concentrations attributable specifically to sulfur dioxide (SO₂) emissions from individual coal-fired power plants in the central United States. We propose a multivariate Ornstein–Uhlenbeck (OU) process approximation to the dynamics of the underlying space-time chemical transport process, and its distributional properties are leveraged to specify novel probability models for spatial data that are viewed as either a snapshot or time-averaged observation of the OU process. Using US EPA SO₂ emissions data from 193 power plants and state-of-the-art estimates of ground-level annual mean sulfate concentrations, we estimate that in 2011—a time of active power plant regulatory action—existing flue-gas desulfurization (FGD) technologies at 66 power plants reduced population-weighted exposure to ambient sulfate by 1.97 μg/m³ (95% CI: 1.80–2.15). Furthermore, we anticipate future regulatory benefits by estimating that installing FGD technologies at the five largest SO₂-emitting facilities would reduce human exposure to ambient sulfate by an additional 0.45 μg/m³ (95% CI: 0.33–0.54). Supplementary materials for this article are available online.
    Schlagwörter United States Environmental Protection Agency ; air pollution ; air quality ; coal ; flue gas desulfurization ; humans ; mechanistic models ; power plants ; space and time ; spatial data ; sulfates ; sulfur dioxide ; uncertainty ; Ornstein-Uhlenbeck process ; SDEs ; Space-time processes ; Spatial statistics
    Sprache Englisch
    Erscheinungsverlauf 2022-0914
    Umfang p. 1082-1093.
    Erscheinungsort Taylor & Francis
    Dokumenttyp Artikel ; Online
    ZDB-ID 2064981-2
    ISSN 1537-274X
    ISSN 1537-274X
    DOI 10.1080/01621459.2022.2027774
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  5. Artikel: A novel quantitative framework for riverscape genetics

    White, Shannon L / Hanks, Ephraim M / Wagner, Tyler

    Ecological applications. 2020 Oct., v. 30, no. 7

    2020  

    Abstract: Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To ... ...

    Abstract Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation‐by‐resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout (Salvelinus fontinalis) in north central Pennsylvania, USA. Using the fixation index (FST) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top‐ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.
    Schlagwörter Salvelinus fontinalis ; autocorrelation ; case studies ; covariance ; gene flow ; genetic distance ; landscape genetics ; models ; regression analysis ; riparian areas ; rivers ; stream flow ; Pennsylvania
    Sprache Englisch
    Erscheinungsverlauf 2020-10
    Erscheinungsort John Wiley & Sons, Ltd
    Dokumenttyp Artikel
    Anmerkung NAL-AP-2-clean ; JOURNAL ARTICLE
    ZDB-ID 1074505-1
    ISSN 1939-5582 ; 1051-0761
    ISSN (online) 1939-5582
    ISSN 1051-0761
    DOI 10.1002/eap.2147
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  6. Artikel: A novel quantitative framework for riverscape genetics.

    White, Shannon L / Hanks, Ephraim M / Wagner, Tyler

    Ecological applications : a publication of the Ecological Society of America

    2020  Band 30, Heft 7, Seite(n) e02147

    Abstract: Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To ... ...

    Abstract Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation-by-resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout (Salvelinus fontinalis) in north central Pennsylvania, USA. Using the fixation index (F
    Mesh-Begriff(e) Animals ; Ecosystem ; Pennsylvania ; Rivers ; Trout/genetics
    Sprache Englisch
    Erscheinungsdatum 2020-06-01
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 1074505-1
    ISSN 1939-5582 ; 1051-0761
    ISSN (online) 1939-5582
    ISSN 1051-0761
    DOI 10.1002/eap.2147
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  7. Artikel: A Mechanistic Model of Annual Sulfate Concentrations in the United States.

    Wikle, Nathan B / Hanks, Ephraim M / Henneman, Lucas R F / Zigler, Corwin M

    Journal of the American Statistical Association

    2022  Band 117, Heft 539, Seite(n) 1082–1093

    Abstract: Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we ... ...

    Abstract Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we develop a mechanistic model which we use to estimate, with uncertainty, the contribution of ambient sulfate concentrations attributable specifically to sulfur dioxide (SO
    Sprache Englisch
    Erscheinungsdatum 2022-03-17
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2064981-2
    ISSN 1537-274X ; 0162-1459 ; 0003-1291
    ISSN (online) 1537-274X
    ISSN 0162-1459 ; 0003-1291
    DOI 10.1080/01621459.2022.2027774
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  8. Artikel ; Online: Machine learning for modeling animal movement.

    Wijeyakulasuriya, Dhanushi A / Eisenhauer, Elizabeth W / Shaby, Benjamin A / Hanks, Ephraim M

    PloS one

    2020  Band 15, Heft 7, Seite(n) e0235750

    Abstract: Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior and ...

    Abstract Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior and to fill in missing tracking data. Machine Learning and Deep learning algorithms are powerful and flexible predictive modeling tools but have rarely been applied to animal movement data. In this study we present a general framework for predicting animal movement that is a combination of two steps: first predicting movement behavioral states and second predicting the animal's velocity. We specify this framework at the individual level as well as for collective movement. We use Random Forests, Neural and Recurrent Neural Networks to compare performance predicting one step ahead as well as long range simulations. We compare results against a custom constructed Stochastic Differential Equation (SDE) model. We apply this approach to high resolution ant movement data. We found that the individual level Machine Learning and Deep Learning methods outperformed the SDE model for one step ahead prediction. The SDE model did comparatively better at simulating long range movement behaviour. Of the Machine Learning and Deep Learning models the Long Short Term Memory (LSTM) individual level model did best at long range simulations. We also applied the Random Forest and LSTM individual level models to model gull migratory movement to demonstrate the generalizability of this framework. Machine Learning and deep learning models are easier to specify compared to traditional parametric movement models which can have restrictive assumptions. However, machine learning and deep learning models are less interpretable than parametric movement models. The type of model used should be determined by the goal of the study, if the goal is prediction, our study provides evidence that machine learning and deep learning models could be useful tools.
    Mesh-Begriff(e) Algorithms ; Animal Migration/physiology ; Animals ; Ants/physiology ; Deep Learning ; Machine Learning ; Models, Statistical ; Neural Networks, Computer
    Sprache Englisch
    Erscheinungsdatum 2020-07-27
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0235750
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  9. Artikel ; Online: Social fluidity mobilizes contagion in human and animal populations.

    Colman, Ewan / Colizza, Vittoria / Hanks, Ephraim M / Hughes, David P / Bansal, Shweta

    eLife

    2021  Band 10

    Abstract: Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By ... ...

    Abstract Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as
    Mesh-Begriff(e) Animals ; Basic Reproduction Number ; Behavior, Animal ; Communicable Diseases/transmission ; Disease Outbreaks ; Humans ; Models, Theoretical ; Social Behavior
    Sprache Englisch
    Erscheinungsdatum 2021-07-30
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.62177
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  10. Artikel: A Dynamic Individual-Based Model for High-Resolution Ant Interactions

    Wikle, Nathan B / Hanks, Ephraim M / Hughes, David P

    Journal of agricultural, biological, and environmental statistics. 2019 Dec., v. 24, no. 4

    2019  

    Abstract: Ant feeding interactions (i.e., trophallaxis events) are thought to regulate the flow of nutrients and disease within a colony. Consequently, there is great interest in learning which environmental and behavioral factors drive ant trophallaxis. In this ... ...

    Abstract Ant feeding interactions (i.e., trophallaxis events) are thought to regulate the flow of nutrients and disease within a colony. Consequently, there is great interest in learning which environmental and behavioral factors drive ant trophallaxis. In this paper, we analyze ant trophallaxis behavior in a colony of 73 carpenter ants, observed at 1-s intervals over a period of 4 h. The data represent repeated observations from a dynamic contact network; however, traditional statistical analyses of network models are ill-suited for data observed at such high temporal resolution. We present a model for high-resolution longitudinal network data, where the network is assumed to be a time inhomogeneous, continuous-time Markov chain, with transition rates modeled as a function of time-varying individual and pairwise biological covariates. In particular, the high temporal resolution of the data leads to a tractable likelihood function, and likelihood-based inference procedures are utilized to explain which biological factors drive contact. Our results reveal how differences in ant social castes and individual behaviors, such as ant speed and activity levels, influence patterns of ant trophallaxis in the colony. Supplementary materials accompanying this paper appear online.
    Schlagwörter carpenter ants ; learning ; Markov chain ; models ; nutrients ; statistical analysis ; trophallaxis
    Sprache Englisch
    Erscheinungsverlauf 2019-12
    Umfang p. 589-609.
    Erscheinungsort Springer US
    Dokumenttyp Artikel
    ZDB-ID 1324615-x
    ISSN 1537-2693 ; 1085-7117
    ISSN (online) 1537-2693
    ISSN 1085-7117
    DOI 10.1007/s13253-019-00363-5
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