Yan Jiao

Yan Jiao, Associate Professor


B.S., Ocean University of China (1993)
M.S., Ocean University of China (1997)
Ph.D., Memorial University of Newfoundland (2004)

Dr. Yan Jiao’s Lab

Email: yjiao@vt.edu
Office: 110 Cheatham Hall
Phone: (540) 231-5749

Department of Fish and Wildlife Conservation
310 West Campus Drive
Virginia Tech,Cheatham Hall, Room 106 (MC 0321)
Blacksburg, VA 24061


My research interests are to explain the nature of aquatic species and manage them as appropriate as we can in a probabilistic way. Specifically, I am working on: Population dynamics and stock assessment; Risk analysis; Fisheries management (decision analysis, adaptive management); Fishery ecology; Statistical computing. The types of models we work on include stock recruitment, statistical catch-at-age, matrix models, generalized linear/additive models, spatial-temporal modeling, hierarchical modeling, measurement error (error-in-variable) models, time series models, multi-species models, Bayesian modeling, quantitative risk assessment. Specific species of interest include of commercial and recreational fish species, species under conservation or invasive.

Recent research focus: spatial-temporal dynamics and its modeling in fisheries; ecosystem modeling (climate driven population dynamics modeling; quantification of species interaction)


Courses Taught:

  • Advanced Quantitative Methods in Fisheries and Conservation Biology (FiW 6004)
  • Fish Population Dynamics and Modeling (FiW 5514)
  • Risk Assessment and Decision Analysis for Fisheries and Conservation Biology (FiW 6514)
  • Marine Ecology (FiW 4624)
  • Advanced Marine Ecology (FiW 5624G)
  • Catch Rate Standardization in Fisheries (FiW 5984)
  • Special Topics on Fishery Resources and Fishery Management

Current Research Projects:

    2016-2018: Verification of natural mortality estimation of Walleye in Lake Erie based on integrated Bayesian statistical catch-at-age model. (PI), GLFC.

    There are multiple concerns on walleye stock assessment. Among these concerns, natural mortality has been found to be one of the most uncertain and most influential parameters in fisheries stock assessment which needs to be addressed. Natural mortality greatly affects fisheries management decision making through its influence on Biological Reference Points, population size and fishing mortality. Bayesian estimators will be developed to solve the SCA models with different M submodels. A simulation study will be further developed to verify the robustness of the supported hypothesis on M, its estimation accuracy based on the data available, and management implications of using inappropriate hypotheses about M.

    2013-2016: Integration of spatial stock structure and multiple stocks into stock assessment for yellow perch in Lake Erie. (PI), OCFA.

    Yellow perch in Lake Erie is one of the major economic species in this ecosystem. Its stock assessment quality relies on the exploration and quantitative interpretation on its spatial dynamics, which include stock structure, movement across management units and the appropriate reflection of the hypotheses on stock structure and movement in the currently used statistical catch-at-age models. In this project, both species distribution data and tag-recapture data will be used to analyze the stock structure and the movement of this species. A simulation will further be used to explore the influence of the hypotheses on spatial structure and movement.

    2012-2017: Dynamics and role of Blue Catfish Ictalurus furcatus in Tidal Rivers of Virginia. (co- PI with Don Orth (PI)), Virginia Department of Fish and Inland Fisheries.

    Blue catfish is not native to Atlantic coastal rivers. They were stocked in the 1970s for the purpose of establishing a new fishery. This project is to better model the trend of blue catfish in these tidal rivers, simulate its population dynamics after entering and adapting into the ecosystem, and explore its potential effects on the other species in this ecosystem. The project includes a multi-year diet sampling program, modeling efforts on population trends and life history variation using Bayesian hierarchical models, and control strategy evaluation through simulation.

    2015-2019: Estimating seabird bycatch of the US pelagic longline fleet in the western north Atlantic. (PI), NOAA.

    The U.S. Atlantic pelagic longline fleet operates over a large area, but seabird bycatch is confined mainly to the part of the fishing region along the eastern U.S. seaboard from approximately Cape Hatteras north. Analyses to date have not made full use of this knowledge. This project proposed to predict the annual total seabird bycatch of the U.S. Atlantic pelagic longline fleet, 1992 - present, based on observations of bycatch in the pelagic observer program for the same period, logbook effort data for the same period, and lessons learned in previous analyses. It also proposed to predict the annual seabird bycatch for a subset of the U.S. Atlantic pelagic longline fleet applying effort in areas of relatively high seabird bycatch.


Select Recent Publications:

* students and postdocs under my supervision.

  • Jiao, Y., O'Reilly, R., Smith, E., and Orth, D. 2016. Integrating spatial synchrony/asynchrony of population distribution into stock assessment models: a spatial hierarchical Bayesian statistical catch-at-age approach. ICES Journal of Marine Science. doi: 10.1093/icesjms/fsw036 (Editor's Choice)
  • *Li, Y., Jiao, Y., and Browder, J. 2016. Modeling spatially-varying ecological relationships using geographically weighted generalized linear model: a simulation study based on longline seabird bycatch. Fisheries Research. 181: 14-24
  • *Li, Y., Jiao, Y., and Browder, J. 2016. Assessment of seabird bycatch in the U.S. Atlantic pelagic longline fishery, with an extra exploration on modeling spatial variation. ICES Journal of Marine Science. (accepted)
  • Houston, C., Rypel, A., Jiao, Y., Haas, C., and Gorman, T. 2016. Hindcasting historical breeding conditions for an endangered salamander in ephemeral wetlands of the Southeastern USA: implications of climate change. PLOS ONE. DOI: 10.1371/journal.pone.0150169
  • *Liu, C., Wan, R., Jiao, Y., and Reid, K. 2016. Exploring nonstationary and scale-dependent relationships between walleye (Sander vitreus) distribution and habitat variables in Lake Erie. Marine and Freshwater Research. http://dx.doi.org/10.1071/MF15374
  • *Li, Y., and Jiao, Y. 2015. Modeling spatial patterns of rare species using eigenfunction-based spatial filters: an example of modified delta model for zero-inflated data. Ecological Modeling. 299:51-63
  • Stich, D., Jiao, Y., and Murphy, B. 2015. Life, death, and resurrection: accounting for state uncertainty in survival estimation from tagged grass carp. North American Journal of Fisheries Management. 35:321-330
  • *Li, Y., and Jiao, Y. 2015. Evaluation of stocking strategies for endangered white abalone using a hierarchical demographic model. Ecological Modeling. 299:14-22
  • *Wang, Y. and Jiao, Y. 2015. Estimating time-based instantaneous total mortality rate based on the age-structured abundance index. Chinese Journal of Oceanology and Limnology. 32:1-18
  • *Hua, D., Jiao, Y., Neves, R.J., Jones, J. 2015. Using PIT tags to assess individual heterogeneity in a mark-recapture study of laboratory-reared juveniles of the endangered Cumberlandian combshell (Epioblasma brevidens). Ecology and Evolution. 5:1076-1087.



Last updated May , 2016