@article{north_wikle_schliep_2023, title={A Review of Data-Driven Discovery for Dynamic Systems}, volume={9}, ISSN={["1751-5823"]}, DOI={10.1111/insr.12554}, abstractNote={Many real‐world scientific processes are governed by complex non‐linear dynamic systems that can be represented by differential equations. Recently, there has been an increased interest in learning, or discovering, the forms of the equations driving these complex non‐linear dynamic systems using data‐driven approaches. In this paper, we review the current literature on data‐driven discovery for dynamic systems. We provide a categorisation to the different approaches for data‐driven discovery and a unified mathematical framework to show the relationship between the approaches. Importantly, we discuss the role of statistics in the data‐driven discovery field, describe a possible approach by which the problem can be cast in a statistical framework and provide avenues for future work.}, journal={INTERNATIONAL STATISTICAL REVIEW}, author={North, Joshua S. and Wikle, Christopher K. and Schliep, Erin M.}, year={2023}, month={Sep} }
 @article{north_schliep_hansen_kundel_custer_mclaughlin_wagner_2023, title={Accounting for spatiotemporal sampling variation in joint species distribution models}, volume={11}, ISSN={["1365-2664"]}, DOI={10.1111/1365-2664.14547}, abstractNote={


Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accounting for these biases is critical for making inferences.

We develop a joint species distribution model (JSDM) that accounts for varying sampling conditions due to the environment and time of sampling when estimating relative abundance. The novelty of our JSDM is that we explicitly model sampling effort as the product of known quantities based on time and gear type and an unknown functional relationship to capture seasonal variation in species life history.

We use the model to study relative abundance of six freshwater fish species across the state of Minnesota, USA. Our model enables estimates of relative abundance to be compared both within and across species and lakes, and captures the inconsistent sampling present in the data. We discuss how gear type, water temperature and day of the year impact catchability for each species at the lake level and throughout a year. We compare our estimates of relative abundance to those obtained from a model that assumes constant catchability to highlight important differences within and across lakes and species.

Synthesis and applications: Our method illustrates that assumptions relating indices of abundance to observed catch data can greatly impact model inferences derived from JSDMs. Specifically, not accounting for varying sampling conditions can bias inference of relative abundance, restricting our ability to detect responses to management interventions and environmental change. While our focus is on freshwater fisheries, this model architecture can be adopted to other systems where catchability may vary as a function of space, time and species.

}, journal={JOURNAL OF APPLIED ECOLOGY}, author={North, Joshua S. and Schliep, Erin M. and Hansen, Gretchen J. A. and Kundel, Holly and Custer, Christopher A. and Mclaughlin, Paul and Wagner, Tyler}, year={2023}, month={Nov} }
 @article{wagner_schliep_north_kundel_custer_ruzich_hansen_2023, title={Predicting climate change impacts on poikilotherms using physiologically guided species abundance models}, volume={120}, ISSN={["1091-6490"]}, DOI={10.1073/pnas.2214199120}, abstractNote={Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.}, number={15}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, author={Wagner, Tyler and Schliep, Erin M. and North, Joshua S. and Kundel, Holly and Custer, Christopher A. and Ruzich, Jenna K. and Hansen, Gretchen J. A.}, year={2023}, month={Apr} }
 @article{north_wikle_schliep_2022, title={A Bayesian Approach for Data-Driven Dynamic Equation Discovery}, volume={8}, ISSN={1085-7117 1537-2693}, url={http://dx.doi.org/10.1007/s13253-022-00514-1}, DOI={10.1007/s13253-022-00514-1}, journal={Journal of Agricultural, Biological and Environmental Statistics}, publisher={Springer Science and Business Media LLC}, author={North, Joshua S. and Wikle, Christopher K. and Schliep, Erin M.}, year={2022}, month={Aug} }
 @article{mirzaee_mcgarvey_aguilar_schliep_2022, title={Impact of biopower generation on eastern US forests}, volume={3}, ISSN={1387-585X 1573-2975}, url={http://dx.doi.org/10.1007/s10668-022-02235-4}, DOI={10.1007/s10668-022-02235-4}, abstractNote={Abstract Biopower, electricity generated from biomass, is a major source of renewable energy in the US. About ten percent of US non-hydro renewable electricity in 2020 was generated from biomass. Despite significant growth in woody biomass use for electricity in recent decades, a systematic assessment of associated impacts on forest resources is lacking. This study assessed associations between biopower generation, and selected timberland structure indicators and carbon stocks across 438 areas surrounding wood-using and coal-burning power plants in the Eastern US from 2005 to 2017. Timberland areas around plants generating biopower were associated with more live and standing-dead trees, and carbon in their respective stocks, than comparable areas of neighboring plants only burning coal. We also detected an inverse association between the number of biopower plants and number of live and dead trees, and respective carbon stocks. We discerned an upward temporal trajectory in carbon stocks within live trees with continued biopower generation. We found no significant differences related to the amount of MWh biopower generation within the analysis areas. Net impacts of biopower descriptors on timberland attributes point to a positive trend in selected ecological conditions and carbon balances. The upward temporal trend in carbon stocks with longer generation of wood-based biopower may point to a plausibly sustainable contribution to the decarbonization of the US electricity sector.}, journal={Environment, Development and Sustainability}, publisher={Springer Science and Business Media LLC}, author={Mirzaee, Ashkan and McGarvey, Ronald G. and Aguilar, Francisco X. and Schliep, Erin M.}, year={2022}, month={Mar} }
 @article{schliep_schafer_hawkey_2021, title={Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data}, volume={17}, ISSN={2194-6388 1559-0410}, url={http://dx.doi.org/10.1515/jqas-2020-0051}, DOI={10.1515/jqas-2020-0051}, abstractNote={Abstract Subjective wellness data can provide important information on the well-being of athletes and be used to maximize player performance and detect and prevent against injury. Wellness data, which are often ordinal and multivariate, include metrics relating to the physical, mental, and emotional status of the athlete. Training and recovery can have significant short- and long-term effects on athlete wellness, and these effects can vary across individual. We develop a joint multivariate latent factor model for ordinal response data to investigate the effects of training and recovery on athlete wellness. We use a latent factor distributed lag model to capture the cumulative effects of training and recovery through time. Current efforts using subjective wellness data have averaged over these metrics to create a univariate summary of wellness, however this approach can mask important information in the data. Our multivariate model leverages each ordinal variable and can be used to identify the relative importance of each in monitoring athlete wellness. The model is applied to professional referee daily wellness, training, and recovery data collected across two Major League Soccer seasons.}, number={3}, journal={Journal of Quantitative Analysis in Sports}, publisher={Walter de Gruyter GmbH}, author={Schliep, Erin M. and Schafer, Toryn L. J. and Hawkey, Matthew}, year={2021}, month={May}, pages={241–254} }
 @article{schliep_gelfand_abaurrea_asín_beamonte_cebrián_2021, title={Long‐term spatial modelling for characteristics of extreme heat events}, volume={184}, ISSN={0964-1998 1467-985X}, url={http://dx.doi.org/10.1111/rssa.12710}, DOI={10.1111/rssa.12710}, abstractNote={There is increasing evidence that global warming manifests itself in more frequent warm days and that heat waves will become more frequent. Presently, a formal definition of a heat wave is not agreed upon in the literature. To avoid this debate, we consider extreme heat events, which, at a given location, are well‐defined as a run of consecutive days above an associated local threshold. Characteristics of extreme heat events (EHEs) are of primary interest, such as incidence and duration, as well as the magnitude of the average exceedance and maximum exceedance above the threshold during the EHE. Using approximately 60‐year time series of daily maximum temperature data collected at 18 locations in a given region, we propose a spatio‐temporal model to study the characteristics of EHEs over time. The model enables prediction of the behaviour of EHE characteristics at unobserved locations within the region. Specifically, our approach employs a two‐state space–time model for EHEs with local thresholds where one state defines above threshold daily maximum temperatures and the other below threshold temperatures. We show that our model is able to recover the EHE characteristics of interest and outperforms a corresponding autoregressive model that ignores thresholds based on out‐of‐sample prediction.}, number={3}, journal={Journal of the Royal Statistical Society: Series A (Statistics in Society)}, publisher={Wiley}, author={Schliep, Erin M. and Gelfand, Alan E. and Abaurrea, Jesús and Asín, Jesús and Beamonte, María A. and Cebrián, Ana C.}, year={2021}, month={Jun}, pages={1070–1092} }
 @article{bailey_elliott_schliep_2021, title={Seasonal temperature–moisture interactions limit seedling establishment at upper treeline in the Southern Rockies}, volume={12}, ISSN={2150-8925 2150-8925}, url={http://dx.doi.org/10.1002/ecs2.3568}, DOI={10.1002/ecs2.3568}, abstractNote={. Over recent decades, sharply rising temperatures without an accompanying increase in precipitation have created widespread heat-induced drought stress, or hotter drought. Tree-ring reconstructions have discovered signi fi cant declines in seedling establishment from hotter drought across montane and subalpine forest belts during this time, yet comparable studies at upper treeline are non-existent. In this study, we reconstruct annual patterns of seedling establishment at upper treeline in the Southern Rocky Mountains to test the hypotheses that establishment is governed by temperature – moisture interactions and that slope aspect mediates the in fl uence of hotter drought. To test these hypotheses, we destructively sampled seedlings along a network of six study sites to reconstruct annual patterns of establishment on opposite north-facing and south-facing slopes. Results from this research can be summarized into two main points with respect to the in fl uence of climate on seedling establishment at upper treeline over approximately the last three decades (1991 – 2019). First, temperature – moisture interactions throughout the year play a crucial role in facilitating successful seedling establishment. Second, and perhaps most striking, is the complete lack of establishment at all sites over the past decade. This could signify that a threshold has been surpassed and conditions are now beyond the climatic optimum for successful seedling establishment above treeline moving forward. These results expand upon similar fi ndings from forests at lower elevations, introducing the likelihood that seedling establishment along the entire mountain forest belt of the Southern Rocky Mountains is being impacted by hotter drought. This means that any declines in the rate of seedling establishment across montane and subalpine forests will not be offset by increased recruitment at treeline.}, number={6}, journal={Ecosphere}, publisher={Wiley}, author={Bailey, Sydney N. and Elliott, Grant P. and Schliep, Erin M.}, year={2021}, month={Jun} }
 @article{cebrián_asín_gelfand_schliep_castillo-mateo_beamonte_abaurrea_2021, title={Spatio-temporal analysis of the extent of an extreme heat event}, volume={36}, ISSN={1436-3240 1436-3259}, url={http://dx.doi.org/10.1007/s00477-021-02157-z}, DOI={10.1007/s00477-021-02157-z}, abstractNote={Abstract Evidence of global warming induced from the increasing concentration of greenhouse gases in the atmosphere suggests more frequent warm days and heat waves. The concept of an extreme heat event (EHE), defined locally based on exceedance of a suitable local threshold, enables us to capture the notion of a period of persistent extremely high temperatures. Modeling for extreme heat events is customarily implemented using time series of temperatures collected at a set of locations. Since spatial dependence is anticipated in the occurrence of EHE’s, a joint model for the time series, incorporating spatial dependence is needed. Recent work by Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) develops a space-time model based on a point-referenced collection of temperature time series that enables the prediction of both the incidence and characteristics of EHE’s occurring at any location in a study region. The contribution here is to introduce a formal definition of the notion of the spatial extent of an extreme heat event and then to employ output from the Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) modeling work to illustrate the notion. For a specified region and a given day, the definition takes the form of a block average of indicator functions over the region. Our risk assessment examines extents for the Comunidad Autónoma de Aragón in northeastern Spain. We calculate daily, seasonal and decadal averages of the extents for two subregions in this comunidad. We generalize our definition to capture extents of persistence of extreme heat and make comparisons across decades to reveal evidence of increasing extent over time.}, number={9}, journal={Stochastic Environmental Research and Risk Assessment}, publisher={Springer Science and Business Media LLC}, author={Cebrián, Ana C. and Asín, Jesús and Gelfand, Alan E. and Schliep, Erin M. and Castillo-Mateo, Jorge and Beamonte, María A. and Abaurrea, Jesús}, year={2021}, month={Dec}, pages={2737–2751} }
 @article{schliep_collins_rojas-salazar_lottig_stanley_2020, title={Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes}, volume={14}, ISSN={1932-6157}, url={http://dx.doi.org/10.1214/20-aoas1371}, DOI={10.1214/20-aoas1371}, abstractNote={Concentrations of nitrogen provide a critical metric for under1 standing ecosystem function and water quality in lakes. However, 2 varying approaches for quantifying nitrogen concentrations may bias 3 the comparison of water quality across lakes and regions. Different 4 measurements of total nitrogen exist based on its composition (e.g., 5 organic versus inorganic, dissolved versus particulate), which we re6 fer to as nitrogen species. Fortunately, measurements of multiple ni7 trogen species are often collected, and can therefore be leveraged 8 together to inform our understanding of the controls on total nitro9 gen in lakes. We develop a multivariate hierarchical statistical model 10 that fuses speciated nitrogen measurements obtained across multiple 11 methods of reporting in order to improve our estimates of total nitro12 gen. The model accounts for lower detection limits and measurement 13 error that vary across lake, species, and observation. By modeling spe14 ciated nitrogen, as opposed to previous efforts that mostly consider 15 only total nitrogen, we obtain more resolved inference with regard 16 to differences in sources of nitrogen and their relationship with com17 plex environmental drivers. We illustrate the inferential benefits of 18 our model using speciated nitrogen data from the LAke GeOSpatial 19 and temporal database (LAGOS). 20}, number={4}, journal={The Annals of Applied Statistics}, publisher={Institute of Mathematical Statistics}, author={Schliep, Erin M. and Collins, Sarah M. and Rojas-Salazar, Shirley and Lottig, Noah R. and Stanley, Emily H.}, year={2020}, month={Dec} }
 @article{soranno_cheruvelil_liu_wang_tan_zhou_king_mccullough_stachelek_bartley_et al._2020, title={Ecological prediction at macroscales using big data: Does sampling design matter?}, volume={30}, ISSN={1051-0761 1939-5582}, url={http://dx.doi.org/10.1002/eap.2123}, DOI={10.1002/eap.2123}, abstractNote={Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially-extensive dataset to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8 million km2 area. We estimated model predictive performance for different subsets of the dataset to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in datasets from some forms of targeted sampling may limit predictive performance, compiling existing spatially-extensive datasets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.}, number={6}, journal={Ecological Applications}, publisher={Wiley}, author={Soranno, Patricia A. and Cheruvelil, Kendra Spence and Liu, Boyang and Wang, Qi and Tan, Pang‐Ning and Zhou, Jiayu and King, Katelyn B. S. and McCullough, Ian M. and Stachelek, Jemma and Bartley, Meridith and et al.}, year={2020}, month={Apr} }
 @article{wagner_hansen_schliep_bethke_honsey_jacobson_kline_white_2020, title={Improved understanding and prediction of freshwater fish communities through the use of joint species distribution models}, volume={77}, ISSN={0706-652X 1205-7533}, url={http://dx.doi.org/10.1139/cjfas-2019-0348}, DOI={10.1139/cjfas-2019-0348}, abstractNote={Two primary goals in fisheries research are to (i) understand how habitat and environmental conditions influence the distribution of fishes across the landscape and (ii) make predictions about how fish communities will respond to environmental and anthropogenic change. In inland, freshwater ecosystems, quantitative approaches traditionally used to accomplish these goals largely ignore the effects of species interactions (competition, predation, mutualism) on shaping community structure, potentially leading to erroneous conclusions regarding habitat associations and unrealistic predictions about species distributions. Using two contrasting case studies, we highlight how joint species distribution models (JSDMs) can address the aforementioned deficiencies by simultaneously quantifying the effects of abiotic habitat variables and species dependencies. In particular, we show that conditional predictions of species occurrence from JSDMs can better predict species presence or absence compared with predictions that ignore species dependencies. JSDMs also allow for the estimation of site-specific probabilities of species co-occurrence, which can be informative for generating hypotheses about species interactions. JSDMs provide a flexible framework that can be used to address a variety of questions in fisheries science and management.}, number={9}, journal={Canadian Journal of Fisheries and Aquatic Sciences}, publisher={Canadian Science Publishing}, author={Wagner, Tyler and Hansen, Gretchen J.A. and Schliep, Erin M. and Bethke, Bethany J. and Honsey, Andrew E. and Jacobson, Peter C. and Kline, Benjamen C. and White, Shannon L.}, year={2020}, month={Sep}, pages={1540–1551} }
 @article{north_schliep_wikle_2020, title={On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics}, volume={32}, ISSN={1180-4009 1099-095X}, url={http://dx.doi.org/10.1002/env.2665}, DOI={10.1002/env.2665}, abstractNote={Statistical methods are required to evaluate and quantify the uncertainty in environmental processes, such as land and sea surface temperature, in a changing climate. Typically, annual harmonics are used to characterize the variation in the seasonal temperature cycle. However, an often overlooked feature of the climate seasonal cycle is the semi‐annual harmonic, which can account for a significant portion of the variance of the seasonal cycle and varies in amplitude and phase across space. Together, the spatial variation in the annual and semi‐annual harmonics can play an important role in driving processes that are tied to seasonality (e.g., ecological and agricultural processes). We propose a multivariate spatiotemporal model to quantify the spatial and temporal change in minimum and maximum temperature seasonal cycles as a function of the annual and semi‐annual harmonics. Our approach captures spatial dependence, temporal dynamics, and multivariate dependence of these harmonics through spatially and temporally varying coefficients. We apply the model to minimum and maximum temperature over North American for the years 1979–2018. Formal model inference within the Bayesian paradigm enables the identification of regions experiencing significant changes in minimum and maximum temperature seasonal cycles due to the relative effects of changes in the two harmonics.}, number={6}, journal={Environmetrics}, publisher={Wiley}, author={North, Joshua S. and Schliep, Erin M. and Wikle, Christopher K.}, year={2020}, month={Dec} }
 @article{stanley_rojas‐salazar_lottig_schliep_filstrup_collins_2019, title={Comparison of total nitrogen data from direct and Kjeldahl‐based approaches in integrated data sets}, volume={17}, ISSN={1541-5856 1541-5856}, url={http://dx.doi.org/10.1002/lom3.10338}, DOI={10.1002/lom3.10338}, abstractNote={There are multiple protocols for determining total nitrogen (TN) in water, but most can be grouped into direct approaches (TN‐d) that convert N forms to nitrogen‐oxides (NOx) and combined approaches (TN‐c) that combine Kjeldahl N (organic N +NH3) and nitrite+nitrate (NO2+NO3‐N). TN concentrations from these two approaches are routinely treated as equal in studies that use data derived from multiple sources (i.e., integrated data sets) despite the distinct chemistries of the two methods. We used two integrated data sets to determine if TN‐c and TN‐d results were interchangeable. Accuracy, determined as the difference between reported concentrations and the most probable value (MPV) of reference samples, was high and similar in magnitude (within 3.5–4.5% of the MPV) for both methods, although the bias was significantly smaller at low concentrations for TN‐d. Detection limits and data flagged as below detection suggested greater sensitivity for TN‐d for one data set, while patterns from the other data set were ambiguous. TN‐c results were more variable (less precise) by many measures, although TN‐d data included a small fraction of notably inaccurate results. Precision of TN‐c was further compromised by propagated error, which may not be acknowledged or detectable in integrated data sets unless complete metadata are available and inspected. Finally, concurrent measures of TN‐c and TN‐d in lake samples were extremely similar. Overall, TN‐d tended to be slightly more accurate and precise, but similarities in accuracy and the near 1 : 1 relationship for concurrent TN‐d and TN‐c measurements support careful use of data interchangeably in analyses of heterogeneous, integrated data sets.}, number={12}, journal={Limnology and Oceanography: Methods}, publisher={Wiley}, author={Stanley, Emily H. and Rojas‐Salazar, Shirley and Lottig, Noah R. and Schliep, Erin M. and Filstrup, Christopher T. and Collins, Sarah M.}, year={2019}, month={Oct}, pages={639–649} }
 @article{bartley_hanks_schliep_soranno_wagner_2019, title={Identifying and characterizing extrapolation in multivariate response data}, volume={14}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0225715}, DOI={10.1371/journal.pone.0225715}, abstractNote={Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. 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 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 propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. 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 outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences.}, number={12}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Bartley, Meridith L. and Hanks, Ephraim M. and Schliep, Erin M. and Soranno, Patricia A. and Wagner, Tyler}, editor={Daniels, Bryan CEditor}, year={2019}, month={Dec}, pages={e0225715} }
 @article{wagner_lottig_bartley_hanks_schliep_wikle_king_mccullough_stachelek_cheruvelil_et al._2019, title={Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data}, volume={5}, ISSN={2378-2242 2378-2242}, url={http://dx.doi.org/10.1002/lol2.10134}, DOI={10.1002/lol2.10134}, abstractNote={Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land‐use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.}, number={2}, journal={Limnology and Oceanography Letters}, publisher={Wiley}, author={Wagner, Tyler and Lottig, Noah R. and Bartley, Meridith L. and Hanks, Ephraim M. and Schliep, Erin M. and Wikle, Nathan B. and King, Katelyn B. S. and McCullough, Ian and Stachelek, Jemma and Cheruvelil, Kendra S. and et al.}, year={2019}, month={Dec}, pages={228–235} }
 @article{ramseyer winter_landor_teti_morris_schliep_pevehouse-pfeiffer_pekarek_2019, title={Is body appreciation a mechanism of depression and anxiety? An investigation of the 3-Dimensional Body Appreciation Mapping (3D-BAM) intervention}, volume={14}, ISSN={2212-6570}, url={http://dx.doi.org/10.1016/j.mph.2019.200158}, DOI={10.1016/j.mph.2019.200158}, abstractNote={Body appreciation is related to numerous mental health outcomes, including depression and anxiety. This pilot study investigated the effects of an intervention, 3-Dimensional Body Appreciation Mapping (3D-BAM), developed to improve body image, depression, and anxiety by using 3D scanning technology to train participants to focus on ways they appreciate their bodies. Eighty-nine emerging adult women (Mage = 20.64) participated in the intervention and completed body image and mental health measures at baseline, pre/post-intervention, and 3-month follow up. For the intervention, participants digitally “painted” body parts of their personalized 3D avatar that they believed lived up to the cultural image of women, and that they appreciated for their appearance, utility, and role in interpersonal relationships. Following the intervention, participants reported increased body appreciation over time. Depression and anxiety decreased, but the reduction cannot be attributed to the intervention. However, body appreciation had a significant negative effect on depression and anxiety. These preliminary findings illustrate how utilizing 3D scanning technology to focus on body appreciation can improve body image among emerging adult women and reduce pathology.}, journal={Mental Health & Prevention}, publisher={Elsevier BV}, author={Ramseyer Winter, Virginia and Landor, Antoinette M. and Teti, Michelle and Morris, Kristen and Schliep, Erin M. and Pevehouse-Pfeiffer, Danielle and Pekarek, Emily}, year={2019}, month={Jun}, pages={200158} }
 @article{schliep_gelfand_2019, title={Velocities for spatio-temporal point patterns}, volume={29}, ISSN={2211-6753}, url={http://dx.doi.org/10.1016/j.spasta.2018.12.007}, DOI={10.1016/j.spasta.2018.12.007}, abstractNote={Point patterns gathered over space and time are receiving increasing attention in the literature. Examples include incidence of disease events, incidence of insurgent activity events, or incidence of crime events. Point pattern models can attempt to explain these events. Here, a log Gaussian Cox process specification is used to learn about the behavior of the intensity over space and time. Our contribution is to expand inference by introducing the notion of the velocity of a point pattern. We develop a velocity at any location and time within the region and period of study. These velocities are associated with the evolution of the intensity driving the spatio-temporal point pattern, where this intensity is a realization of a stochastic process. Working with directional derivative processes, we are able to develop derivatives in arbitrary directions in space as well as derivatives in time. The ratio of the latter to the former provides a velocity in that direction at that location and time, i.e., speed of change in intensity in that direction. This velocity can be interpreted in terms of speed of change in chance for an event. The magnitude and direction of the minimum velocity provides the slowest speed and direction of change in chance for an event. We use a sparse Gaussian process model approximation to expedite the demanding computation for model fitting and gradient calculation. We illustrate our methodology with a simulation for proof of concept and with a spatio-temporal point pattern of theft events in San Francisco, California in 2012.}, journal={Spatial Statistics}, publisher={Elsevier BV}, author={Schliep, Erin M. and Gelfand, Alan E.}, year={2019}, month={Mar}, pages={204–225} }
 @article{schliep_gelfand_mitchell_aiello‐lammens_silander_2018, title={Assessing the joint behaviour of species traits as filtered by environment}, volume={9}, ISSN={2041-210X 2041-210X}, url={http://dx.doi.org/10.1111/2041-210x.12901}, DOI={10.1111/2041-210x.12901}, abstractNote={Understanding and predicting how species traits are shaped by prevailing environmental conditions is an important yet challenging task in ecology. Functional trait‐based approaches can replace potentially idiosyncratic species‐specific response models in learning about community behaviour across environmental gradients. Customarily, models for traits given environment consider only trait means to predict species and functional diversity, as intra‐taxon variability in traits is often thought to be negligible. A growing body of literature indicates that intra‐taxon trait variability is substantial and critical in structuring plant communities and assessing ecosystem function. We propose flexible joint trait distribution models given environment and across species that incorporate intra‐taxon variability as well as inter‐site/plot variability. Using a Bayesian framework, our joint trait distribution models allow for mixed continuous, binary and ordinal trait variables and incorporate dependence among traits enabling both joint and conditional trait prediction at unobserved sites. The models can be used to inform about the well‐known fourth‐corner problem, which attempts to interpret trait‐by‐environment matrices. We demonstrate the utility of our methodology through joint predictive trait distributions for individual species as well as joint community‐weighted trait distributions for environments while incorporating intra‐taxon trait variability. Explicit details on the probabilistic interpretations of the random trait‐by‐environment matrices obtained arising under our model are also provided to address the fourth‐corner problem. Finally, our joint trait distribution model is applied to simulated and real vegetation data collected from the Greater Cape Floristic Region of South Africa. The proposed methodology places a fully model‐based foundation on explaining intra‐taxon trait variation given environment. It extends the utility and interpretability of commonly applied techniques for investigating community‐weighted traits and illuminates randomness in the fourth‐corner problem.}, number={3}, journal={Methods in Ecology and Evolution}, publisher={Wiley}, author={Schliep, Erin M. and Gelfand, Alan E. and Mitchell, Rachel M. and Aiello‐Lammens, Matthew E. and Silander, John A., Jr.}, editor={Peres‐Neto, PedroEditor}, year={2018}, month={Mar}, pages={716–727} }
 @article{lany_zarnetske_schliep_schaeffer_orians_orwig_preisser_2018, title={Asymmetric biotic interactions and abiotic niche differences revealed by a dynamic joint species distribution model}, volume={99}, ISSN={0012-9658 1939-9170}, url={http://dx.doi.org/10.1002/ecy.2190}, DOI={10.1002/ecy.2190}, abstractNote={A species' distribution and abundance are determined by abiotic conditions and biotic interactions with other species in the community. Most species distribution models correlate the occurrence of a single species with environmental variables only, and leave out biotic interactions. To test the importance of biotic interactions on occurrence and abundance, we compared a multivariate spatiotemporal model of the joint abundance of two invasive insects that share a host plant, hemlock woolly adelgid (HWA; Adelges tsugae) and elongate hemlock scale (EHS; Fiorina externa), to independent models that do not account for dependence among co-occurring species. The joint model revealed that HWA responded more strongly to abiotic conditions than EHS. Additionally, HWA appeared to predispose stands to subsequent increase of EHS, but HWA abundance was not strongly dependent on EHS abundance. This study demonstrates how incorporating spatial and temporal dependence into a species distribution model can reveal the dependence of a species' abundance on other species in the community. Accounting for dependence among co-occurring species with a joint distribution model can also improve estimation of the abiotic niche for species affected by interspecific interactions.}, number={5}, journal={Ecology}, publisher={Wiley}, author={Lany, Nina K. and Zarnetske, Phoebe L. and Schliep, Erin M. and Schaeffer, Robert N. and Orians, Colin M. and Orwig, David A. and Preisser, Evan L.}, year={2018}, month={Apr}, pages={1018–1023} }
 @book{gelfand_schliep_2018, title={Bayesian Inference and Computing for Spatial Point Patterns}, ISBN={9780940600850}, ISSN={2329-0978}, url={http://dx.doi.org/10.1214/cbms/1530065028}, DOI={10.1214/cbms/1530065028}, journal={NSF-CBMS Regional Conference Series in Probability and Statistics}, publisher={Institute of Mathematical Statistics and American Statistical Association}, author={Gelfand, Alan E. and Schliep, Erin M.}, year={2018} }
 @article{wagner_schliep_2018, title={Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales}, volume={63}, ISSN={0024-3590 1939-5590}, url={http://dx.doi.org/10.1002/lno.10944}, DOI={10.1002/lno.10944}, abstractNote={Empirical nutrient models that describe lake nutrient, productivity, and water clarity relationships among lakes play a prominent role in limnology. Landscape‐based regressions are also used to understand macroscale variability of lake nutrients, clarity, and productivity (hereafter referred to as nutrient‐productivity). Predictions from both models are used to inform eutrophication management globally. To date, these two classes of models are generally conducted separately, which ignores the known dependencies among nutrient‐productivity variables. We present a statistical model that integrates nutrient‐productivity and landscape‐based regressions—where lake nutrients, productivity, and clarity variables are modeled jointly. We fitted a joint nutrient‐productivity model to over 7000 lakes with three nutrients (total phosphorus, total nitrogen, nitrate concentrations), chlorophyll a concentrations, and Secchi disk depth as response variables and landscape features as predictor variables. Because lakes in different regions respond to landscape features differently, we focused our analysis on two subregions with different dominant land uses, the agricultural Midwest and the forested Northeast U.S. Predictive performance was enhanced by modeling nutrient‐productivity variables jointly. We also found strong evidence that nutrient‐productivity variables were coupled, and that only nitrate may be decoupled from other nutrient‐productivity variables in the forested region. We speculate that these regional differences may be related to differences in the strength of biogeochemical cycles and stoichiometric controls between these regions. Jointly modeling nutrient‐productivity variables in lakes effectively integrates the two dominant approaches for studying lakes nutrient‐productivity relationships and provides novel insight into macroscale patterns of the coupling of nutrients, chlorophyll, and water clarity in lakes.}, number={6}, journal={Limnology and Oceanography}, publisher={Wiley}, author={Wagner, Tyler and Schliep, Erin M.}, year={2018}, month={Jul}, pages={2372–2383} }
 @article{schliep_2018, title={Comments on: Process modeling for slope and aspect with application to elevation data maps}, volume={27}, ISSN={1133-0686 1863-8260}, url={http://dx.doi.org/10.1007/s11749-018-0620-4}, DOI={10.1007/s11749-018-0620-4}, number={4}, journal={TEST}, publisher={Springer Science and Business Media LLC}, author={Schliep, Erin M.}, year={2018}, month={Nov}, pages={778–782} }
 @book{clark_kueppers_stover_wyckoff_schliep_2018, title={Disturbance and Vegetation Dynamics in Earth System Models: Workshop Report}, url={http://dx.doi.org/10.2172/1616531}, DOI={10.2172/1616531}, abstractNote={This report summarizes discussion and outcomes from the March 2018 workshop, Disturbance and Vegetation Dynamics in Earth System Models, sponsored by the Office of Biological and Environmental Research (BER) within the U.S. Department of Energy Office of Science. The goals of this workshop, held in Gaithersburg, Maryland, were to (1) identify key uncertainties in current dynamic vegetation models limiting the ability to adequately represent vegetation in Earth System Models (ESMs) and (2) identify and prioritize research directions that can improve models, including forest structural change and feedbacks and responses to disturbance. Failure to capture disturbance dynamics and feedbacks limits the utility of ESMs for predictive understanding and application to societally important problems. This workshop considered (a) dynamic processes that significantly affect terrestrial ecosystems and the coupled Earth system and (b) the data constraints and modeling challenges important for future progress. There were three dominant workshop conclusions: vegetation changes, including disturbance- driven changes, are affecting climate and natural resources; these impacts are expected to increase in the future; and, yet, current models have insufficient data and process representations to adequately predict these changes.}, institution={Office of Scientific and Technical Information (OSTI)}, author={Clark, James and Kueppers, Lara and Stover, Daniel and Wyckoff, Peter and Schliep, Erin}, year={2018}, month={Nov} }
 @article{schliep_gelfand_clark_kays_2018, title={Joint Temporal Point Pattern Models for Proximate Species Occurrence in a Fixed Area Using Camera Trap Data}, volume={23}, ISSN={1085-7117 1537-2693}, url={http://dx.doi.org/10.1007/s13253-018-0327-8}, DOI={10.1007/s13253-018-0327-8}, number={3}, journal={Journal of Agricultural, Biological and Environmental Statistics}, publisher={Springer Science and Business Media LLC}, author={Schliep, Erin M. and Gelfand, Alan E. and Clark, James S. and Kays, Roland}, year={2018}, month={Jun}, pages={334–357} }
 @article{schliep_lany_zarnetske_schaeffer_orians_orwig_preisser_guisan_2018, title={Joint species distribution modelling for spatio‐temporal occurrence and ordinal abundance data}, volume={27}, ISSN={1466-822X 1466-8238}, url={http://dx.doi.org/10.1111/geb.12666}, DOI={10.1111/geb.12666}, abstractNote={Aim 
Species distribution models are important tools used to study the distribution and abundance of organisms relative to abiotic variables. Dynamic local interactions among species in a community can affect abundance. The abundance of a single species may not be at equilibrium with the environment for spreading invasive species and species that are range shifting because of climate change. 
 
Innovation: We develop methods for incorporating temporal processes into a spatial joint species distribution model for presence/absence and ordinal abundance data. We model non-equilibrium conditions via a temporal random effect and temporal dynamics with a vector-autoregressive process allowing for intra- and interspecific dependence between co-occurring species. The autoregressive term captures how the abundance of each species can enhance or inhibit its own subsequent abundance or the subsequent abundance of other species in the community and is well suited for a ‘community modules’ approach of strongly interacting species within a food web. R code is provided for fitting multispecies models within a Bayesian framework for ordinal data with any number of locations, time points, covariates and ordinal categories. 
 
Main conclusions 
We model ordinal abundance data of two invasive insects (hemlock woolly adelgid and elongate hemlock scale) that share a host tree and were undergoing northwards range expansion in the eastern U.S.A. during the period 1997–2011. Accounting for range expansion and high inter-annual variability in abundance led to improved estimation of the species–environment relationships. We would have erroneously concluded that winter temperatures did not affect scale abundance had we not accounted for the range expansion of scale. The autoregressive component revealed weak evidence for commensalism, in which adelgid may have predisposed hemlock stands for subsequent infestation by scale. Residual spatial dependence indicated that an unmeasured variable additionally affected scale abundance. Our robust modelling approach could provide similar insights for other community modules of co-occurring species.}, number={1}, journal={Global Ecology and Biogeography}, publisher={Wiley}, author={Schliep, Erin M. and Lany, Nina K. and Zarnetske, Phoebe L. and Schaeffer, Robert N. and Orians, Colin M. and Orwig, David A. and Preisser, Evan L. and Guisan, Antoine}, year={2018}, month={Jan}, pages={142–155} }
 @article{schliep_gelfand_holland_2017, title={Alternating Gaussian process modulated renewal processes for modeling threshold exceedances and durations}, volume={32}, ISSN={1436-3240 1436-3259}, url={http://dx.doi.org/10.1007/s00477-017-1417-9}, DOI={10.1007/s00477-017-1417-9}, abstractNote={It is often of interest to model the incidence and duration of threshold exceedance events for an environmental variable over a set of monitoring locations. Such data arrive over continuous time and can be considered as observations of a two-state process yielding, sequentially, a length of time in the below threshold state followed by a length of time in the above threshold state, then returning to the below threshold state, etc. We have a two-state continuous time Markov process, often referred to as an alternating renewal process. The process is observed over a truncated time window and, within this window, duration in each state is modeled using a distinct cumulative intensity specification. Initially, we model each intensity over the window using a parametric regression specification. We extend the regression specification adding temporal random effects to enrich the model using a realization of a log Gaussian process over time. With only one type of renewal, this specification is referred to as a Gaussian process modulated renewal process. Here, we introduce Gaussian process modulation to the intensity for each state. Model fitting is done within a Bayesian framework. We clarify that fitting with a customary log Gaussian process specification over a lengthy time window is computationally infeasible. The nearest neighbor Gaussian process, which supplies sparse covariance structure, is adopted to enable tractable computation. We propose methods for both generating data under our models and for conducting model comparison. The model is applied to hourly ozone data for four monitoring sites at different locations across the United States for the ozone season of 2014. For each site, we obtain estimated profiles of up-crossing and down-crossing intensity functions through time. In addition, we obtain inference regarding the number of exceedances, the distribution of the duration of exceedance events, and the proportion of time in the above and below threshold state for any time interval.}, number={2}, journal={Stochastic Environmental Research and Risk Assessment}, publisher={Springer Science and Business Media LLC}, author={Schliep, Erin M. and Gelfand, Alan E. and Holland, David M.}, year={2017}, month={Apr}, pages={401–417} }
 @article{schliep_gelfand_clark_tomasek_2017, title={Biomass prediction using a density-dependent diameter distribution model}, volume={11}, ISSN={1932-6157}, url={http://dx.doi.org/10.1214/16-aoas1007}, DOI={10.1214/16-aoas1007}, abstractNote={Prediction of aboveground biomass, particularly at large spatial scales, is necessary for estimating global-scale carbon sequestration. Since biomass can be measured only by sacrificing trees, total biomass on plots is never observed. Rather, allometric equations are used to convert individual tree diameter to individual biomass, perhaps with noise. The values for all trees on a plot are then summed to obtain a derived total biomass for the plot. Then, with derived total biomasses for a collection of plots, regression models, using appropriate environmental covariates, are employed to attempt explanation and prediction. Not surprisingly, when out-of-sample validation is examined, such a model will predict total biomass well for holdout data because it is obtained using exactly the same derived approach. Apart from the somewhat circular nature of the regression approach, it also fails to employ the actual observed plot level response data. At each plot, we observe a random number of trees, each with an associated diameter, producing a sample of diameters. A model based on this random number of tree diameters provides understanding of how environmental regressors explain abundance of individuals, which in turn explains individual diameters. We incorporate density dependence because the distribution of tree diameters over a plot of fixed size depends upon the number of trees on the plot. After fitting this model, we can obtain predictive distributions for individuallevel biomass and plot-level total biomass. We show that predictive distributions for plot-level biomass obtained from a density-dependent model for diameters will be much different from predictive distributions using the regression approach. Moreover, they can be more informative for capturing uncertainty than those obtained from modeling derived plot-level biomass directly. We develop a density-dependent diameter distribution model and illustrate with data from the national Forest Inventory and Analysis (FIA) database. We also describe how to scale predictions to larger spatial regions. Our predictions agree (in magnitude) with available wisdom on mean and variation in biomass at the hectare scale.}, number={1}, journal={The Annals of Applied Statistics}, publisher={Institute of Mathematical Statistics}, author={Schliep, Erin M. and Gelfand, Alan E. and Clark, James S. and Tomasek, Bradley J.}, year={2017}, month={Mar} }
 @article{taylor-rodríguez_kaufeld_schliep_clark_gelfand_2017, title={Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes}, volume={12}, ISSN={1936-0975}, url={http://dx.doi.org/10.1214/16-ba1031}, DOI={10.1214/16-ba1031}, abstractNote={Species distribution models are used to evaluate the variables that affect the distribution and abundance of species and to predict biodiversity. Historically, such models have been fitted to each species independently. While independent models can provide useful information regarding distribution and abundance, they ignore the fact that, after accounting for environmental covariates, residual interspecies dependence persists. With stacking of individual models, misleading behaviors, may arise. In particular, individual models often imply too many species per location. Recently developed joint species distribution models have application to presence–absence, continuous or discrete abundance, abundance with large numbers of zeros, and discrete, ordinal, and compositional data. Here, we deal with the challenge of joint modeling for a large number of species. To appreciate the challenge in the simplest way, with just presence/absence (binary) response and say, S species, we have an S-way contingency table with 2 cell probabilities. Even if S is as small as 100 this is an enormous table, infeasible to work with without some structure to reduce dimension. We develop a computationally feasible approach to accommodate a large number of species (say order 10) that allows us to: 1) assess the dependence structure across species; 2) identify clusters of species that have similar dependence patterns; and 3) jointly predict species distributions. To do so, we build hierarchical models capturing dependence between species at the first or “data” stage rather than at a second or “mean” stage. We employ the Dirichlet process for clustering in a novel way to reduce dimension in the joint covariance structure. This last step makes computation tractable. We use Forest Inventory Analysis (FIA) data in the eastern region of the United States to demonstrate our method. It consists of presence–absence measurements for 112 tree species, observed east of the Mississippi. As a proof of concept for our dimension reduction approach, we also include simulations using continuous and binary data. ∗Postdoctoral Associate, SAMSI/Duke University, Research Triangle Park, NC 27709, taylor-rodriguez@samsi.info †Postdoctoral Researcher, SAMSI/North Carolina State University, Research Triangle Park, NC 27709, kkaufeld@samsi.info ‡Assistant Professor, Department of Statistics, University of Missouri, Columbia, MO 65211, schliepe@missouri.edu §Professor, Nicholas School of the Environment, Department of Statistical Science, Duke University, Durham, NC 27708, jimclark@duke.edu ¶Professor, Department of Statistical Science, Duke University, Durham, NC 27708, alan@stat.duke.edu c © 2017 International Society for Bayesian Analysis DOI: 10.1214/16-BA1031 940 Joint Species Distribution Modeling with Dirichlet Processes}, number={4}, journal={Bayesian Analysis}, publisher={Institute of Mathematical Statistics}, author={Taylor-Rodríguez, Daniel and Kaufeld, Kimberly and Schliep, Erin M. and Clark, James S. and Gelfand, Alan E.}, year={2017}, month={Dec} }
 @article{gelfand_schliep_2016, title={Spatial statistics and Gaussian processes: A beautiful marriage}, volume={18}, ISSN={2211-6753}, url={http://dx.doi.org/10.1016/j.spasta.2016.03.006}, DOI={10.1016/j.spasta.2016.03.006}, abstractNote={Spatial analysis has grown at a remarkable rate over the past two decades. Fueled by sophisticated GIS software and inexpensive and fast computation, collection of data with spatially referenced information has increased. Recognizing that such information can improve data analysis has led to an explosion of modeling and model fitting. The contribution of this paper is to illustrate how Gaussian processes have emerged as, arguably, the most valuable tool in the toolkit for geostatistical modeling. Apart from the simplest versions, geostatistical modeling can be viewed as a hierarchical specification with Gaussian processes introduced appropriately at different levels of the specification. This naturally leads to adopting a Bayesian framework for inference and suitable Gibbs sampling/Markov chain Monte Carlo for model fitting. Here, we review twenty years of modeling work spanning multivariate spatial analysis, gradient analysis, Bayesian nonparametric spatial ideas, directional data, extremes, data fusion, and large spatial and spatio-temporal datasets. We demonstrate that Gaussian processes are the key ingredients in all of this work. Most of the content is focused on modeling with examples being limited due to length constraints for the article. Altogether, we are able to conclude that spatial statistics and Gaussian processes do, indeed, make a beautiful marriage.}, journal={Spatial Statistics}, publisher={Elsevier BV}, author={Gelfand, Alan E. and Schliep, Erin M.}, year={2016}, month={Nov}, pages={86–104} }
 @article{rundel_schliep_gelfand_holland_2015, title={A data fusion approach for spatial analysis of speciated PM<sub>2.5</sub>across time}, volume={26}, ISSN={1180-4009}, url={http://dx.doi.org/10.1002/env.2369}, DOI={10.1002/env.2369}, abstractNote={PM2.5 exposure is linked to a number of adverse health effects such as lung cancer and cardiovascular disease. However, PM2.5 is a complex mixture of different species whose composition varies substantially in both space and time. An open question is how these constituent species contribute to the overall negative health outcomes seen from PM2.5 exposure. To this end, the Environmental Protection Agency as well as other federal, state, and local organization monitor total PM2.5 along with its primary species on a national scale. From an epidemiological perspective, there is a need to develop effective methods that will allow for the spatially and temporally sparse observations to be used to predict exposures for locations across the entire United States.}, number={8}, journal={Environmetrics}, publisher={Wiley}, author={Rundel, Colin W. and Schliep, Erin M. and Gelfand, Alan E. and Holland, David M.}, year={2015}, month={Oct}, pages={515–525} }
 @article{schliep_gelfand_holland_2015, title={Autoregressive spatially varying coefficients model for predicting daily PM<sub>2.5</sub> using VIIRS satellite AOT}, volume={1}, ISSN={2364-3587}, url={http://dx.doi.org/10.5194/ascmo-1-59-2015}, DOI={10.5194/ascmo-1-59-2015}, abstractNote={Abstract. There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.}, number={1}, journal={Advances in Statistical Climatology, Meteorology and Oceanography}, publisher={Copernicus GmbH}, author={Schliep, E. M. and Gelfand, A. E. and Holland, D. M.}, year={2015}, month={Dec}, pages={59–74} }
 @article{schliep_hoeting_2015, title={Data augmentation and parameter expansion for independent or spatially correlated ordinal data}, volume={90}, ISSN={0167-9473}, url={http://dx.doi.org/10.1016/j.csda.2015.03.020}, DOI={10.1016/j.csda.2015.03.020}, abstractNote={Data augmentation and parameter expansion can lead to improved iterative sampling algorithms for Markov chain Monte Carlo (MCMC). Data augmentation allows for simpler and more feasible simulation from a posterior distribution. Parameter expansion accelerates convergence of iterative sampling algorithms by increasing the parameter space. Data augmentation and parameter-expanded data augmentation MCMC algorithms are proposed for fitting probit models for independent ordinal response data. The algorithms are extended for fitting probit linear mixed models for spatially correlated ordinal data. The effectiveness of data augmentation and parameter-expanded data augmentation is illustrated using the probit model and ordinal response data, however, the approach can be used broadly across model and data types.}, journal={Computational Statistics & Data Analysis}, publisher={Elsevier BV}, author={Schliep, Erin M. and Hoeting, Jennifer A.}, year={2015}, month={Oct}, pages={1–14} }
 @article{schliep_gelfand_clark_zhu_2015, title={Modeling change in forest biomass across the eastern US}, volume={23}, ISSN={1352-8505 1573-3009}, url={http://dx.doi.org/10.1007/s10651-015-0321-z}, DOI={10.1007/s10651-015-0321-z}, number={1}, journal={Environmental and Ecological Statistics}, publisher={Springer Science and Business Media LLC}, author={Schliep, Erin M. and Gelfand, Alan E. and Clark, James S. and Zhu, Kai}, year={2015}, month={Jun}, pages={23–41} }
 @article{hanks_schliep_hooten_hoeting_2015, title={Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification}, volume={26}, ISSN={1180-4009 1099-095X}, url={http://dx.doi.org/10.1002/env.2331}, DOI={10.1002/env.2331}, abstractNote={In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa. Copyright © 2015 John Wiley & Sons, Ltd.}, number={4}, journal={Environmetrics}, publisher={Wiley}, author={Hanks, Ephraim M. and Schliep, Erin M. and Hooten, Mevin B. and Hoeting, Jennifer A.}, year={2015}, month={Feb}, pages={243–254} }
 @article{schliep_gelfand_clark_2015, title={Stochastic Modeling for Velocity of Climate Change}, volume={20}, ISSN={1085-7117 1537-2693}, url={http://dx.doi.org/10.1007/s13253-015-0210-9}, DOI={10.1007/s13253-015-0210-9}, number={3}, journal={Journal of Agricultural, Biological, and Environmental Statistics}, publisher={Springer Science and Business Media LLC}, author={Schliep, Erin M. and Gelfand, Alan E. and Clark, James S.}, year={2015}, month={Jun}, pages={323–342} }
 @article{schliep_dong_gelfand_li_2014, title={Modeling individual tree growth by fusing diameter tape and increment core data}, volume={25}, ISSN={1180-4009}, url={http://dx.doi.org/10.1002/env.2324}, DOI={10.1002/env.2324}, abstractNote={Tree growth estimation is a challenging task as difficulties associated with data collection and inference often result in inaccurate estimates. Two main methods for tree growth estimation are diameter tape measurements and increment cores. The former involves repeatedly measuring tree diameters with a cloth or metal tape whose scale has been adjusted to give diameter readings directly. This approach has the advantage that diameters can be measured rapidly. However, because of the substantial error involved during tape measurements, negative diameter increments are often observed. Alternatively, annual radius increment data can be obtained by taking tree cores and averaging repeated measurements of the ring widths. Acquiring and analyzing tree cores is a time‐consuming process, and taking multiple cores may have adverse effects on tree health. Therefore, radius increment data are typically only available for a subset of trees within a stand. We offer a fusion of the data sources, which enables us to accommodate missingness and to borrow strength across individuals. It enables individual tree‐level inference as well as average or stand level inference. Our model recognizes that tree growth in a given year depends upon tree size at the start of the year as well as levels of appropriate covariates operating in that year. We apply our modeling to a fairly large dataset taken from two forest stands at Coweeta Hydrologic Laboratory in the southern Appalachians collected from 1991 to 2011. Copyright © 2014 John Wiley & Sons, Ltd.}, number={8}, journal={Environmetrics}, publisher={Wiley}, author={Schliep, Erin M. and Dong, Tracy Qi and Gelfand, Alan E. and Li, Fan}, year={2014}, month={Dec}, pages={610–620} }
 @article{schliep_hoeting_2013, title={Multilevel Latent Gaussian Process Model for Mixed Discrete and Continuous Multivariate Response Data}, volume={18}, ISSN={1085-7117 1537-2693}, url={http://dx.doi.org/10.1007/s13253-013-0136-z}, DOI={10.1007/s13253-013-0136-z}, abstractNote={We propose a Bayesian model for mixed ordinal and continuous multivariate data to evaluate a latent spatial Gaussian process. Our proposed model can be used in many contexts where mixed continuous and discrete multivariate responses are observed in an effort to quantify an unobservable continuous measurement. In our example, the latent, or unobservable measurement is wetland condition. While predicted values of the latent wetland condition variable produced by the model at each location do not hold any intrinsic value, the relative magnitudes of the wetland condition values are of interest. In addition, by including point-referenced covariates in the model, we are able to make predictions at new locations for both the latent random variable and the multivariate response. Lastly, the model produces ranks of the multivariate responses in relation to the unobserved latent random field. This is an important result as it allows us to determine which response variables are most closely correlated with the latent variable. Our approach offers an alternative to traditional indices based on best professional judgment that are frequently used in ecology. We apply our model to assess wetland condition in the North Platte and Rio Grande River Basins in Colorado. The model facilitates a comparison of wetland condition at multiple locations and ranks the importance of in-field measurements.}, number={4}, journal={Journal of Agricultural, Biological, and Environmental Statistics}, publisher={Springer Science and Business Media LLC}, author={Schliep, Erin M. and Hoeting, Jennifer A.}, year={2013}, month={Apr}, pages={492–513} }
 @article{merrill_walter_peairs_schleip_2013, title={The Distribution of European Corn Borer (Lepidoptera: Crambidae) Moths in Pivot-Irrigated Corn}, volume={106}, ISSN={0022-0493 0022-0493}, url={http://dx.doi.org/10.1603/ec12358}, DOI={10.1603/ec12358}, abstractNote={ABSTRACT 
 The European corn borer, Ostrinia nubilalis (Hübner), is a damaging pest of numerous crops including corn, potato, and cotton. An understanding of the interaction between O. nubilalis and its spatial environment may aid in developing pest management strategy. Over a 2-yr period, ≈8,000 pheromone trap catches of O. nubilalis were recorded on pivot-irrigated corn in northeastern Colorado. The highest weekly moth capture per pivot-irrigated field occurred on the week of 15 July 1997 at 1,803 moths captured. The lowest peak moth capture per pivot-irrigated field was recorded on the week of 4 June 1998 at 220 moths captured. Average trap catch per field ranged from ≈1.6 moths captured per trap per week in 1997 to ≈0.3 moths captured per trap per week in 1998. Using pheromone trap moth capture data, we developed a quantified understanding of the spatial distribution of adult male moths. Our findings suggest strong correlations between moth density and adjacent corn crops, prevailing wind direction, and an edge effect. In addition, directional component effects suggest that more moths were attracted to the southwestern portion of the crop, which has the greatest insolation potential. In addition to the tested predictor variables, we found a strong spatial autocorrelation signal indicating positive aggregations of these moths and that males from both inside and outside of the field are being attracted to within-field pheromone traps, which has implications for refuge strategy management.}, number={5}, journal={Journal of Economic Entomology}, publisher={Oxford University Press (OUP)}, author={Merrill, Scott C. and Walter, Shawn M. and Peairs, Frank B. and Schleip, Erin M.}, year={2013}, month={Oct}, pages={2084–2092} }
 @article{schliep_cooley_sain_hoeting_2010, title={A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling}, volume={13}, ISSN={1386-1999 1572-915X}, url={http://dx.doi.org/10.1007/s10687-009-0098-2}, DOI={10.1007/s10687-009-0098-2}, abstractNote={We analyze output from six regional climate models (RCMs) via a spatial Bayesian hierarchical model. The primary advantage of this approach is that the statistical model naturally borrows strength across locations via a spatial model on the parameters of the generalized extreme value distribution. This is especially important in this application as the RCM output we analyze have extensive spatial coverage, but have a relatively short temporal record for characterizing extreme behavior. The hierarchical model we employ is also designed to be computationally efficient as we analyze RCM output for nearly 12000 locations. The aim of this analysis is to compare the extreme precipitation as generated by these RCMs. Our results show that, although the RCMs produce similar spatial patterns for the 100-year return level, their characterizations of extreme precipitation are quite different. Additionally, we examine the spatial behavior of the extreme value index and find differing spatial patterns for the point estimates for the RCMs. However, these differences may not be significant given the uncertainty associated with estimating this parameter.}, number={2}, journal={Extremes}, publisher={Springer Science and Business Media LLC}, author={Schliep, Erin M. and Cooley, Daniel and Sain, Stephan R. and Hoeting, Jennifer A.}, year={2010}, pages={219–239} }