Students and Staff

In my doctoral study at SAFS, I vow
To learn, become enlightened, and see:
Oh, dear, yearling Chinook, where art thou?
After the gauntlet of dams, how fair thee?

With challenge trials and survival curves,
Despite your evasion of becoming hors d'œuvres,
I estimate your vitality and survival capacity,
To take a stab at this elusive “latent mortality.”

From Lower Granite Dam on the Snake River,
To Bonneville Dam on the Columbia River,
How is it that barging or in-river migration
Causes there forth lifetime differentiation?

On the short ride, is it the lost opportunity to grow,
That makes you more susceptible to predatory woe?
Or, is it the high density of fish, including piscivore,
That increases transmission of diseases, galore!

Without the luxury of a motorized vessel,
Is it stressful while through the dams you wrestle?
Or, is it the degree days that you accumulate,
That physiologically determine your fate?

So, in a couple more years, hopefully less,

I will provide something more than an educated guess,
In a thesis all bound in a pretty book,
Of how prior experience affects future survival, in Chinook.

I'm working to create a model of the oceanic phase of Pacific salmon migration, which is poorly understood in comparison with freshwater migration. Previous models have examined the oceanic migration of salmon, but have generally focused on one cue such as compass orientation. The model combines advection by ocean currents with fish swimming behavior to model trajectories of salmon. The behavior rules model how fish process information using multiple gradients and signals based on realistic fish sensory abilities, and the ocean currents are provided by a numerical ocean model. The model will be used to examine spring run Chinook salmon returning from the high seas to the Columbia River, including the effects of interannual variability in ocean currents and what variability among stocks in arrival timing suggests in terms of probable ocean distributions.

I've also been involved with a project to create a mathematical model of decision-making with respect to predicting an animal's response to a stimulus based on past experiences. The model combines a short-term memory stream and a long-term memory stream, which are weighted based on the estimated errors and the recency of information. The model has been successfully applied to several experimental data sets.

I’m currently working on a model that is used to understand population survival. This model subsumes various mechanisms into a single measure called “vitality” which represents the survival capability for a living organism. The model quantifies the sources of mortality into vitality-dependent and -independent parts and characterizes the vitality-dependent part in terms of initial and evolving heterogeneities. In general, it provides an accessible tool to decompose any survival curves into four pieces: intrinsic mortality related to the senescence rate, extrinsic mortality related to the accidental death rate as well as two sources of heterogeneities among a population. Through the lens of the partition, we are able to better understand the underlining biological and ecological mechanisms that shape the survival curves. 

This model has been successfully applied to real mortality data including insects, fish and human beings and has been used to explore several classic problems in demography and ecology: 1) medfly demographic paradox, 2) effect of diet restriction on longevity, 3) cross-life stage effects on survival curves, and 4) mortality plateaus. Future applications will include explaining the age-specific patterns of human mortality rates, examining the double-bump survival curves of fish data, and hopefully having something to do with the evolution theory.
I also spent two months in World Health Organization working on explaining the cancer incidence plateaus with the model.

Two papers have been published to explain this model:

• Li, T. and Anderson, J.J. 2009. The vitality model: a way to understand population survival and demographic heterogeneity. Theoretical population biology.
• Anderson, J.J., Gildea, M.C., Williams, D.W., and Li, T. 2008. Linking growth, survival and heterogeneity through stochastic vitality. Am. Nat. 171, No.1, E-article.

A routine of estimating the vitality parameters is also developed which is available online: http://www.cbr.washington.edu/vitality/.