The Seascapes

The Seascapes

Monday, November 28, 2011

The “Butterfish Smackdown” IV. Our modeling approach

Butterfish are generally more abundant in trawl collections on the bottom during the day than at night based on our generalized additive habitat model constructed with the fisherman using NOAA fisheries independent biological surveys of the Mid Atlantic Bight.  The response of butterfish abundance on the bottom to solar elevation reflects a classic diel vertical migration many animals undertake into surface waters at night and to deeper waters during the daytime.

We are using a statistical approach to develop a butterfish habitat model with the squid fisherman.  Ecological models come in flavors ranging from mental, to statistical, to mechanistic.  Mental models are educated guesses about how we think something like a butterfish works, while mechanistic models are formal mathematical or logical descriptions put in terms of vital rates and interactions of constituent parts.  A mechanistic butterfish habitat model might have equations describing habitat preferences based upon relationships between growth and food and temperature, predators and the availability of refuges in the water column including those achieved by vertical migration, as well as swimming and sensory capabilities that limit the animals ability to detect and reach safe habitats also good for growth at more distant locations.  Tradeoffs like trading a little growth for survival can might also be included in a mechanistic model.  Our approach lies halfway between and uses statistical techniques and data, in this case catches of butterfish and ocean habitat data, to describe relationships between butterfish abundance and habitat characteristics.  We are, however, primarily using our mental models to determine the relationships to explore with our statistical models.  Mental models are often discounted in science.  But good fisherman have accurate mental models of the behaviors of fish in the ocean—otherwise they go broke.  Ecologists and oceanographers also have accurate models of the ocean and its animals in their heads too, but they may emphasize different relationships. We have combined forces to build the best possible statistical habitat model for butterfish, which is the hypothesis we will test in our field evaluation experiment 13 days from now.  If the test goes well we can begin to work together to develop mechanistic models of habitat relationships for butterfish and other species.

Our modeling approach involves building a statistical species niche model for butterfish using NOAA fisheries independent biological surveys and ocean observations.  Our statistical modeling is guided by hypotheses (=mental models) of fisherman, ecologists and oceanographers about the way butterfish respond to characteristics of the ocean.   The resulting statistical niche model will be used to project butterfish habitat in space and time as a “nowcast”.  In our evaluation experiment we will use this “nowcast” to test the validity of our butterfish niche model.
So what are the limitations of our approach?  Firstly, we may fail to consider important butterfish habitat relationships that scientists and fisherman don’t fully appreciate yet.  We have tried to avoid this by doing preliminary analyses using most of the habitat data available to us.  Many of the other limitations to our approach have to do with the data available to us. We are using surface observations of the ocean measured with satellites and high frequency radar sensors to describe the habitat of fish that live in the water column. We are also using infrequently collected seasonal bottom trawl surveys to statistically model the habitat of an animal that spends allot of time up in the water column. And we are making predictions outside of the period of the data we used to "train" the model.  The day is coming when we will be able to use acoustic measurements of the animals and 3 dimensional oceanographic models to describe distributions of butterfish in the water column in relation to subsurface habitat features, but we are not quite there yet.  Our models also assume that the best habitat is located where the animals are most abundant as measured by trawls.  There are a number of reasons why this assumption can be incorrect. The abundance of fish in trawls cannot be used to distinguish migration corridors that many animals move through quickly from areas in which fewer individuals take up longer term residency because habitat resources support their long term requirements for survival and growth.  Habitat models based on abundance also assume that organisms evaluate habitat quality accurately, without perceptual and movement constraints, and therefore reach abundances at equilibrium with habitat carrying capacity without any time delays.  This is probably rarely the case, particularly in the Autumn in regions like the Mid-Atlantic Bight where important habitat features are extremely variable in time and space and many of the animals are highly migratory.  So there are all sorts of reasons not to do this.  But science never happens unless you take a risk.  It’s just good to be aware of all the assumptions involved in the approach you take from the outset.

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