@article{custer_north_schliep_verhoeven_hansen_wagner_2024, title={Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model}, volume={6}, ISSN={["1939-9170"]}, DOI={10.1002/ecy.4362}, abstractNote={Abstract Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species–environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold‐adapted species was predicted to have high probabilities of extirpation across its current range—with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.}, journal={ECOLOGY}, author={Custer, Christopher A. and North, Joshua S. and Schliep, Erin M. and Verhoeven, Michael R. and Hansen, Gretchen J. A. and Wagner, Tyler}, year={2024}, month={Jun} }
 @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={Summary}, 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={Abstract}, 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}, 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}, 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={Abstract}, 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={Abstract}, 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}, 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={Abstract}, 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={Abstract}, 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={Abstract}, 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={Abstract}, 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={Abstract}, 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={Abstract}, 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={Abstract}, 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={Abstract}, 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} }