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Featured researches published by Kung Sik Chan.


Proceedings of the Royal Society of London B: Biological Sciences | 2003

Review article. Studying climate effects on ecology through the use of climate indices: the North Atlantic Oscillation, El Niño Southern Oscillation and beyond

Nils Chr. Stenseth; Geir Ottersen; James W. Hurrell; Atle Mysterud; Mauricio Lima; Kung Sik Chan; Nigel G. Yoccoz; Bjørn Ådlandsvik

Whereas the El Niño Southern Oscillation (ENSO) affects weather and climate variability worldwide, the North Atlantic Oscillation (NAO) represents the dominant climate pattern in the North Atlantic region. Both climate systems have been demonstrated to considerably influence ecological processes. Several other large–scale climate patterns also exist. Although less well known outside the field of climatology, these patterns are also likely to be of ecological interest. We provide an overview of these climate patterns within the context of the ecological effects of climate variability. The application of climate indices by definition reduces complex space and time variability into simple measures, ‘packages of weather’. The disadvantages of using global climate indices are all related to the fact that another level of problems are added to the ecology–climate interface, namely the link between global climate indices and local climate. We identify issues related to: (i) spatial variation; (ii) seasonality; (iii) non–stationarity; (iv) nonlinearity; and (v) lack of correlation in the relationship between global and local climate. The main advantages of using global climate indices are: (i) biological effects may be related more strongly to global indices than to any single local climate variable; (ii) it helps to avoid problems of model selection; (iii) it opens the possibility for ecologists to make predictions; and (iv) they are typically readily available on Internet.


Proceedings of the National Academy of Sciences of the United States of America | 2006

Plague dynamics are driven by climate variation

Nils Chr. Stenseth; Noelle I. Samia; Hildegunn Viljugrein; Kyrre L. Kausrud; Michael Begon; Stephen Davis; Herwig Leirs; Vladimir M. Dubyanskiy; Jan Esper; Vladimir S. Ageyev; Nikolay L. Klassovskiy; Sergey B. Pole; Kung Sik Chan

The bacterium Yersinia pestis causes bubonic plague. In Central Asia, where human plague is still reported regularly, the bacterium is common in natural populations of great gerbils. By using field data from 1949–1995 and previously undescribed statistical techniques, we show that Y. pestis prevalence in gerbils increases with warmer springs and wetter summers: A 1°C increase in spring is predicted to lead to a >50% increase in prevalence. Climatic conditions favoring plague apparently existed in this region at the onset of the Black Death as well as when the most recent plague pandemic arose in the same region, and they are expected to continue or become more favorable as a result of climate change. Threats of outbreaks may thus be increasing where humans live in close contact with rodents and fleas (or other wildlife) harboring endemic plague.


Proceedings of the National Academy of Sciences of the United States of America | 2011

Dynamics of the plague–wildlife–human system in Central Asia are controlled by two epidemiological thresholds

Noelle I. Samia; Kyrre L. Kausrud; Hans Heesterbeek; Vladimir S. Ageyev; Michael Begon; Kung Sik Chan; Nils Chr. Stenseth

Plague (caused by the bacterium Yersinia pestis) is a zoonotic reemerging infectious disease with reservoirs in rodent populations worldwide. Using one-half of a century of unique data (1949–1995) from Kazakhstan on plague dynamics, including data on the main rodent host reservoir (great gerbil), main vector (flea), human cases, and external (climate) conditions, we analyze the full ecoepidemiological (bubonic) plague system. We show that two epidemiological threshold quantities play key roles: one threshold relating to the dynamics in the host reservoir, and the second threshold relating to the spillover of the plague bacteria into the human population.


Journal of the royal statistical society series b-methodological | 1990

On likelihood ratio tests for threshold autoregression

Kung Sik Chan; Howell Tong


Geo-Temas | 2011

Overfishing of top predators eroded the resilience of the Black Sea system regardless of the climate andanthropogenic conditions

Marcos Llope; Georgi M. Daskalov; Tristan Rouyer; Vesselina Mihneva; Kung Sik Chan; Alexander N. Grishin; N. Chr. Stenseth


Biometrika | 2007

A generalized threshold mixed model for analyzing nonnormal nonlinear time series, with application to plague in Kazakhstan

Noelle I. Samia; Kung Sik Chan; Nils Chr. Stenseth


Biometrika | 2004

Testing for multimodality with dependent data

Kung Sik Chan


Biometrika | 2011

Maximum likelihood estimation of a generalized threshold stochastic regression model

Noelle I. Samia; Kung Sik Chan


Biometrics | 2004

Testing for Common Structures in a Panel of Threshold Models

Kung Sik Chan; Howell Tong; N. Chr. Stenseth


Archive | 2004

A note on testing for multi-modality with dependent data

Howell Tong; Kung Sik Chan

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Howell Tong

London School of Economics and Political Science

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Adrian P. Gee

Center for Cell and Gene Therapy

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Borje S. Andersson

University of Texas MD Anderson Cancer Center

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Chitra Hosing

University of Texas MD Anderson Cancer Center

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D. Couriel

University of Texas at Austin

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E. Shpall

University of Texas at Austin

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