Henry R Scharf

Interests

Research

Bayesian statistics; Spatio-temporal statistics; Computational statistics; Ecological and environmental applications

Courses

Scholarly Contributions

Journals/Publications

• Ellwanger, A. L., Morrow, K. S., Dhawale, A. K., Scharf, H. R., Ngakan, P. O., & Riley, E. P. (2026). Balancing risk and reward: Toward resilient human–primate coexistence in a rapidly changing environment in Sulawesi, Indonesia. Ambio, 55(Issue 5). doi:10.1007/s13280-026-02348-4
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  This study explores human–moor macaque (Macaca maura) coexistence in Sulawesi, Indonesia, using resilience thinking to assess temporal patterns of coadaptation amidst stressors such as provisioning and road construction. Comparing data from 2016–2017 to 2023–2025, we examine changes in provisioning patterns, macaque roadside use, and people’s perceptions of macaques to evaluate factors that may test the system’s resilience. Our results show that although provisioning frequency has remained stable, hand-feeding is increasingly common and macaques have increased their use of roadside habitat. Additionally, people’s perceptions have shifted from excitement and novelty to fear and normalization. Decreasing tolerance, coupled with increased risks associated with roadside behavior, highlights the system’s potential to transition to a state incompatible with coexistence. Our results can be leveraged to promote resilient coexistence, e.g., interventions that enable safer roadside crossing for the macaques and community outreach programs that make use of people’s empathy for the macaques’ welfare.
• McFadden, A., Stowe, D., Riggan, P., Tissell, R., O'Leary, J., & Scharf, H. R. (2024). Estimating Fire Radiative Energy Density with Repeat-Pass Aerial Thermal-Infrared Imaging of Actively Progressing Wildfires. Fire, 7(6), 179.
• Scharf, H., Schierbaum, J., Matsumoto, H., & Assal, T. (2024). Predicting species-level vegetation cover using large satellite imagery data sets. Journal of Agricultural, Biological, and Environmental Statistics, 31, 60–79. doi:10.1007/s13253-024-00639-5
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  Accurate information on the distribution of vegetation species is used as a proxy for the health of an ecosystem, a currency of international environmental treaties, and a necessary planning tool for forest preservation and rehabilitation, to name just a few of its applications. However, direct, extensive observation of vegetation across large geographic regions can be very expensive. The extensive coverage and high temporal resolution of remote sensing data collected by satellites like the European Space Agency’s Sentinel-2 system could be a critical component of a solution to this problem. We propose a hierarchical model for predicting vegetation cover that incorporates high resolution satellite imagery, landscape characteristics such as elevation and slope, and direct observation of vegetation cover. Besides providing model-based predictions of vegetation cover with accompanying uncertainty quantification, our proposed model offers inference about the effects of landscape characteristics on vegetation type. Implementation of the model is computationally challenging due to the volume and spatial extent of data involved. Thus, we propose an efficient, approximate method for model fitting that is able to make use of all available observations. We demonstrate our approach with an application to the distribution of three post-fire resprouting deciduous species in the Jemez Mountains of New Mexico.Supplementary materials accompanying this paper appear on-line.
• Trinidad, J., Scharf, H. R., Ngakan, P. O., & Riley, E. P. (2025). Roadside Dining: The Collective Movement Behavior of Sulawesi Moor Macaques in a Provisioning Context. American Journal of Primatology, 87(1), e23727.
• Boulil, Z. L., Durban, J. W., Fearnbach, H., Joyce, T. W., Leander, S. G., & Scharf, H. R. (2023). Detecting changes in dynamic social networks using multiply-labeled movement data. Journal of Agricultural, Biological and Environmental Statistics, 28(2), 243--259.
• Scharf, H., Schierbaum, J., Matsumoto, H., & Assal, T. (2024). Predicting species-level vegetation cover using large satellite imagery data sets. Journal of Agricultural, Biological and Environmental Statistics, 1-20.
• Williams, P. J., Lu, X., Scharf, H. R., & Hooten, M. B. (2023). Embracing asymmetry in nature: How to account for skewness in ecological data. Ecological Informatics, 75, 102085.
• Raiho, A. M., Scharf, H. R., Roland, C. A., Swanson, D. K., Stehn, S. E., & Hooten, M. B. (2022). Searching for refuge: A framework for identifying site factors conferring resistance to climate-driven vegetation change. Diversity and Distributions, 28(4), 793--809.
• Scharf, H. (2022). Local Indicators of Spatial Association (LISA). Wiley StatsRef: Statistics Reference Online, 1--9.
• Scharf, H. R., Lu, X., Williams, P. J., & Hooten, M. B. (2022). Constructing flexible, identifiable and interpretable statistical models for binary data. International Statistical Review, 90(2), 328--345.
• Scharf, H. R., Raiho, A. M., Pugh, S., Roland, C. A., Swanson, D. K., Stehn, S. E., & Hooten, M. B. (2022). Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity. Biometrics, 78(4), 1427--1440.
• Reimer JR, ., Arroyo-Esquivel, J., Jiang, J., Scharf, H. R., Wolkovich, E. M., Zhu, K., & Boettiger, C. (2021). Noise can create or erase long transient dynamics. Theoretical Ecology, 14(4), 685--695.
• Scharf, H. (2021). Statistical Analysis of Animal Movement: Understanding Behavior Through Hierarchical Parametric Models. NOTICES OF THE AMERICAN MATHEMATICAL SOCIETY, 68(6).
• Scharf, H. (2021). Statistical analysis of animal movement: Understanding behavior through hierarchical parametric models. Notices of the American Mathematical Society, 68(Issue 6). doi:10.1090/noti2293
• Scharf, H. R., & Buderman, F. E. (2020). Animal movement models for multiple individuals. Wiley Interdisciplinary Reviews: Computational Statistics, 12(6), e1506.
• Hooten, M. B., Scharf, H. R., & Morales, J. M. (2019). Running on empty: recharge dynamics from animal movement data. Ecology letters, 22(2), 377--389.
• Scharf, H. R., Hooten, M. B., Wilson, R. R., Durner, G. M., & Atwood, T. C. (2019). Accounting for phenology in the analysis of animal movement. Biometrics, 75(3), 810--820.
• Hooten, M. B., Scharf, H. R., Hefley, T. J., Pearse, A. T., & Weegman, M. D. (2018). Animal movement models for migratory individuals and groups. Methods in Ecology and Evolution, 9(7), 1692--1705.
• Scharf, H. R., Hooten, M. B., Johnson, D. S., & Durban, J. W. (2018). Process convolution approaches for modeling interacting trajectories. Environmetrics, 29(3), e2487.
• Hefley, T. J., Broms, K. M., Brost, B. M., Buderman, F. E., Kay, S. L., Scharf, H. R., Tipton, J. R., Williams, P. J., & Hooten, M. B. (2017). The basis function approach for modeling autocorrelation in ecological data. Ecology, 98(3), 632--646.
• Scharf, H., Hooten, M. B., & Johnson, D. S. (2017). Imputation approaches for animal movement modeling. Journal of Agricultural, Biological and Environmental Statistics, 22, 335--352.
• Scharf, H., Hooten, M., Fosdick, B. K., Johnson, D. S., London, J. M., & Durban, J. W. (2016). Dynamic social networks based on movement. The Annals of Applied Statistics, 10(4), 2182--2202.

Presentations

• Scharf, H. R. (2024). A strategy for avoiding particle degeneracy in recursive Bayesian inference. Department Seminar. Pennsylvania State University.
• Scharf, H. R. (2024). Predicting species-level vegetation cover using large satellite imagery
  data sets. Department Seminar. Pennsylvania State University.