The notion of using prior models, or data, with Bayesian statistics, or looking at the best fit model for your data with AIC, seems so necessary for scientific relevance that it is a wonder that not everyone is using it. First, let me back up and bring us to the perspective of ocean data. Working with any marine mammal provides special challenges on multiple levels, especially when trying to look at a species as a whole. For one thing, if we are trying to look at a specific functionality, for example, the hearing range of a sperm whale, how do we even start to collect that data? We cannot bring these animals into a lab to do any sort of testing like we can for, say, dogs. Even if we were to miraculously be able to do so, such as with dolphins and killer whales in captivity, we are then only testing the few individuals that have been in captivity, who are in a completely different environment than wild animals in their natural habitat, both of which now have a variety of different parameters they are exposed to.
OK, so then let’s say we somehow invent a great way to test the hearing of an individual whale in the wild- in the great wide ocean. Now we have the challenge of finding them. We’re talking about trying to first find, and then test, animals that spend the majority of their time underwater. Not to mention most marine mammals migrate and move great distances, with their entire habitat coverage spanning past continent ranges. While this is a bit unrealistic in the idea of testing, the same questions still apply to realistic applications such as stock assessments. When we talk about modeling stock assessments to estimate how many individuals there are of a species, or population, it seems necessary to incorporate the newer methods of statistics to be even remotely close to real numbers. We cannot (and do not) go out and model population numbers based on one survey and one survey only, and so must use past surveys and past models to help arrive to truthful estimates.
Even with incorporating prior knowledge, data, and models, I still wonder how we can ever know for sure if what we model is close to the truth? It’s often overwhelming to try to incorporate all the factors that address all the questions appropriately. In my marine mammal acoustic research, the biological and logical factors to consider seem endless: first, are the animals even present and passing through the area where we are collecting data? Are they vocalizing? Are they vocalizing loud enough to be picked up by the recorders? Is the individual or group I am hearing typical of the population?
As we evolve in science and technology, the ease and ability of collecting data improves. Yet, to arrive close to the truth in our modelings, our need to incorporate and consider the past and present cannot be ignored. In many cases, like our ocean, I am not sure we’ll ever be able to get the absolute truth, but we can certainly build upon past models and continue to move forward to get better at our predictions.