Collaborative Research: NRI: Ocean-Powered Robots for Autonomous Offshore Aquaculture. Looking forward to working with Lei and Yaling! and I’m recruiting a graduate student for this project.
***Zhou, C., Brothers,N., Browder, J., and Jiao, Y. 2020. Seabird bycatch loss rate variability in pelagic longline fisheries. Biological Conservation. (accepted)
check back later for the online version
Li, M., Jiao, Y., Xu, B., Zhang, C., Xue, Y., Ren, Y. 2020. Spatial analyses of the influence of autocorrelation on seasonal diet composition of a marine fish species. Fisheries Research. 228, 105563. https://doi.org/10.1016/j.fishres.2020.105563
Hilling, C.D., Jiao, Y., Bunch, A.J., and Phelps, Q.E. 2020. A simulation study to evaluate biases in population characteristics estimation associated with varying bin numbers in size-based age subsampling. North American Journal of Fisheries Management https://doi.org/10.1002/nafm.10429
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**Bi, R., Jiao, Y., Bakka, H., and Browder, J. 2020. Long-term climate ocean oscillations inform seabird bycatch from pelagic longline fishery. ICES Journal of Marine Science. (online now)
here is the link https://doi.org/10.1093/icesjms/fsz255
Ma, Q., Jiao, Y., Ren, Y., and Xue, Y. 2019. Population dynamics modelling with spatial heterogeneity for yellow croaker (Larimichthys polyactis) along the coast of China. Acta Oceanologica Sinica. (accepted)
**Li, M., Jiao Y., Bi, R., Ren, Y. 2020. Population status and distribution of whitespotted conger (Conger myriaster) in Yellow Sea: an important migratory species along coastal China with limited data. Fisheries Oceanography. 29:32-45. https://doi.org/10.1111/fog.12444
Zhou, C., Jiao, Y., and Browder, J. 2019. How much do we know about seabird bycatch in pelagic longline fisheries? A simulation study on the potential bias caused by the usually unobserved portion of seabird bycatch. PLOS ONE (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220797)
Zhou, C., Jiao, Y., and Browder, J. 2019. K-aggregated transformation of discrete distributions improves modeling count data with excess ones. Ecological Modelling (accepted https://authors.elsevier.com/a/1ZIPN15DJ~xLzr )