June 2009 Archives

Optimal foraging habits of a graduate student

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As a biologist, there is always a temptation to try and identify (and quantify) the motives behind everyday life decisions. The analyses of everyday life decisions allow insight into both the human mind and the processes which guide our decisions. With such knowledge, we can make informed decisions for ourselves and others and try to maximize our responses given the likelihood of another's response. But I'm getting too abstract here. Let's look at a more concrete example.
    One skill which no good scientist is without, is that of optimal foraging. Especially when said scientist is in their graduate school years. One could argue that scientists in the Marine realm are particularly good at this, anecdotally, I would concur.
     What goes into the decision of a grad student's foraging optimization? We'll start with a basic benefit/cost of food equation? (click
View image for larger version) and an accompanying example:

Say we're offered a free salad, nice fresh greens with some balsamic dressing:
calories = +
nutrition = +
relative_importance=.5 (neutral on scale of 0=low to 1=high)
satisfaction = +
dollar_cost = $0.00
effort = - (walking to the kitchen)
likelihood_of_success= 100 / [timegone- timenow / timegone- timefirst available]
    which is to say, the sooner the food is acquired from time of availability, the higher  
    likelihood  of success.
time_cost = no additional time cost beyond travel to and from salad and consuming     salad.
opportunity_cost= we'll say small. you have a model running and were just reading
    the latest science magazine.

In the above example, the benefit is decidedly positive, and thus, barring complications, our decision to forage is a go. Eat.

But if we tweak a few parameters (terms included in the above equation) the answer may not be so clear: say the caloric intake was only 300C and the effort required was a two hour bike race. A 2hr bike race can drain 2000 calories easily. 300 - 2000 = -1700. For the benefit analysis to be positive, there would need to be some serious satisfaction involved (assuming for the example dollar-cost remains at zero) for a go-ahead forage decision.

Or, say the salad above cost $10 and for the an equivalent calorie input and effort one could get a hamburger for $5. Given a minimal change in satisfaction, the hamburger returns a relatively higher benefit value and is a better choice.

I'm guessing by now, you get the idea of quantifying the decision making process. You might wonder, are other organisms faced with the same decisions? Yes. I'm so glad you wondered that, too because I think about it all the time. Whales, for example, don't have to worry about whether or not they have enough cash in their bank account to pay for dinner, but they do have to worry about whether or not they have the energy stores to travel to a feeding ground. We might also add to a whale's decision equation, likelihood of survival (from predation or starvation) or whether a food choice is timed correctly in the year. Whales, humans, and many other organisms have come up with some intriguing complex ways to determine their foraging behavior. Humans might spend over $100 on a dinner to impress a potential mate. Angler fish have elaborate bioluminescent lures to attract prey (http://en.wikipedia.org/wiki/Anglerfish). Whale may follow internal waves to find their dinner(see post in the archives on the Moore and Lien 2008 paper).
     We understand that every organism needs food of some kind. But figuring out how and why different organisms obtain food the way they do can be a career long adventure.  

note: I haven't formally searched the literature on related papers. Feel free to post related citations as comments. Otherwise, I'll add some when I look them up.

Weather forecasters enjoy (or lament) the gratification of finding out how good their forecasts are just a few days after making them.  Ecosystem forecasters, on the other hand, often have to wait a long time before we know.  One of our forecasts is for the arrival date of right whales in the Great South Channel critical habitat.  Each year we try to predict when the whales will arrive in large numbers to feed there.  Then we wait, sometimes for years, for the data to come in, so that we can see which predictions are correct, and which need to be re-examined, and why.

We now have the right whale sightings data from 2007 and 2008, so the moment of truth is at hand.  I haven't dug into the analysis yet, but here is the first cut.  Our 2007 prediction was very close, falling just a few days from the actual arrival date, and falling well within the predicted window (see figure).  Our 2008 prediction was later than the arrival date.  This appears to be driven by a period in mid March when there were lots of whales, followed by a lull.  Use of the habitat picked up again in mid April, and persisted from there.  Our model appeared to pick up on this later arrival, missing the brief but intense party in mid March.  It'll be interesting to dig into this further when we start our analysis, and see if we can shed light on what was happening in mid March.

Figure  Right whale sightings per unit effor in the Great South Channel for 1998-2008.  Black line indicates the arrival date computed from the data (day at which sightings per unit effort crosses 0.01).  Horizontal boxes indicate the arrival date forecast for each year.  Figure is preliminary.

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