Erin Mary Schliep

ASSESSING MARINE MAMMAL ABUNDANCE: A NOVEL DATA FUSION

ANNALS OF APPLIED STATISTICS, 18(4), 3071–3090.

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Custer, C. A., North, J. S., Schliep, E. M., Verhoeven, M. R., Hansen, G. J. A., & Wagner, T. (2024, June 20). ECOLOGY, Vol. 6.

A Review of Data-Driven Discovery for Dynamic Systems

North, J. S., Wikle, C. K., & Schliep, E. M. (2023, September 29). INTERNATIONAL STATISTICAL REVIEW, Vol. 9.

Accounting for spatiotemporal sampling variation in joint species distribution models

North, J. S., Schliep, E. M., Hansen, G. J. A., Kundel, H., Custer, C. A., Mclaughlin, P., & Wagner, T. (2023, November 28). JOURNAL OF APPLIED ECOLOGY, Vol. 11.

Predicting climate change impacts on poikilotherms using physiologically guided species abundance models

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 120(15).

A Bayesian Approach for Data-Driven Dynamic Equation Discovery

Journal of Agricultural, Biological and Environmental Statistics, 8.

Impact of biopower generation on eastern US forests

Environment, Development and Sustainability, 3.

Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data

Journal of Quantitative Analysis in Sports, 17(3), 241–254.

Long‐term spatial modelling for characteristics of extreme heat events

Journal of the Royal Statistical Society: Series A (Statistics in Society), 184(3), 1070–1092.

Seasonal temperature–moisture interactions limit seedling establishment at upper treeline in the Southern Rockies

Ecosphere, 12(6).

Spatio-temporal analysis of the extent of an extreme heat event

Stochastic Environmental Research and Risk Assessment, 36(9), 2737–2751.

Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes

The Annals of Applied Statistics, 14(4).

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

Ecological Applications, 30(6).

Improved understanding and prediction of freshwater fish communities through the use of joint species distribution models

Canadian Journal of Fisheries and Aquatic Sciences, 77(9), 1540–1551.

On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics

Environmetrics, 32(6).

Comparison of total nitrogen data from direct and Kjeldahl‐based approaches in integrated data sets

Limnology and Oceanography: Methods, 17(12), 639–649.

Identifying and characterizing extrapolation in multivariate response data

PLOS ONE, 14(12), e0225715.

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

Limnology and Oceanography Letters, 5(2), 228–235.

Is body appreciation a mechanism of depression and anxiety? An investigation of the 3-Dimensional Body Appreciation Mapping (3D-BAM) intervention

Mental Health & Prevention, 14, 200158.

Asymmetric biotic interactions and abiotic niche differences revealed by a dynamic joint species distribution model

Ecology, 99(5), 1018–1023.

Bayesian Inference and Computing for Spatial Point Patterns

In NSF-CBMS Regional Conference Series in Probability and Statistics.

Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales

Limnology and Oceanography, 63(6), 2372–2383.

Comments on: Process modeling for slope and aspect with application to elevation data maps

TEST, 27(4), 778–782.

Disturbance and Vegetation Dynamics in Earth System Models: Workshop Report

Joint Temporal Point Pattern Models for Proximate Species Occurrence in a Fixed Area Using Camera Trap Data

Journal of Agricultural, Biological and Environmental Statistics, 23(3), 334–357.

Velocities for spatio-temporal point patterns

Spatial Statistics, 29, 204–225.

Alternating Gaussian process modulated renewal processes for modeling threshold exceedances and durations

Stochastic Environmental Research and Risk Assessment, 32(2), 401–417.

Assessing the joint behaviour of species traits as filtered by environment

Methods in Ecology and Evolution, 9(3), 716–727.

Biomass prediction using a density-dependent diameter distribution model

The Annals of Applied Statistics, 11(1).

Joint species distribution modelling for spatio‐temporal occurrence and ordinal abundance data

Global Ecology and Biogeography, 27(1), 142–155.

Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes

Bayesian Analysis, 12(4).

Spatial statistics and Gaussian processes: A beautiful marriage

Spatial Statistics, 18, 86–104.

A data fusion approach for spatial analysis of speciated PM_2.5across time

Environmetrics, 26(8), 515–525.

Autoregressive spatially varying coefficients model for predicting daily PM<sub>2.5</sub> using VIIRS satellite AOT

Advances in Statistical Climatology, Meteorology and Oceanography, 1(1), 59–74.

Data augmentation and parameter expansion for independent or spatially correlated ordinal data

Computational Statistics & Data Analysis, 90, 1–14.

Modeling change in forest biomass across the eastern US

Environmental and Ecological Statistics, 23(1), 23–41.

Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification

Environmetrics, 26(4), 243–254.

Stochastic Modeling for Velocity of Climate Change

Journal of Agricultural, Biological, and Environmental Statistics, 20(3), 323–342.

Modeling individual tree growth by fusing diameter tape and increment core data

Environmetrics, 25(8), 610–620.

Multilevel Latent Gaussian Process Model for Mixed Discrete and Continuous Multivariate Response Data

Journal of Agricultural, Biological, and Environmental Statistics, 18(4), 492–513.

The Distribution of European Corn Borer (Lepidoptera: Crambidae) Moths in Pivot-Irrigated Corn

Journal of Economic Entomology, 106(5), 2084–2092.

A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling

Extremes, 13(2), 219–239.