The question of determining what is and what is not suitable habitat can be tackled from many angles. My tool of choice is an empirical "species distribution model" (SDM), also known as an "ecological niche model", "environmental niche model", "habitat model", and by several other phrases. The name of the game is to gather environmental data from the location (and in my case time) of known species occurrence. By associating environmental data with species occurrence, one can build a characterization of the response of the species to its environment. By applying this characterization to spatial environmental datasets, environmental "suitability" can be estimated over a broader area or in a different time than that of the species occurrences.
I'm using several years of right whale sightings, along with satellite- and model-derived physical and biotic environmental data (e.g. previously posted copepod model output), to produce weekly estimates of right whale habitat suitability. I'm in the model validating phase, in which I build a model with data from years 1,2,...,n-1, and test it with data from year n. I refer to this as an "out of sample" test. A common alternative to this method of model validation is to randomly select and remove a subset of occurrences from all n years, and test the model using these occurrence locations. The former method is a considerably harder task for a model, but I think it is a better for my purposes because it measures the model's performance in the face of inter-annually varying response of a species to its environment.
For certain years I can (using Maxent) build good models the springtime distribution of right whales in the Cape Cod Bay and Great South Channel critical habitats. Pictured is a plot of the receiver operator characteristic curve, and the area underneath that curve (AUC). AUC=0.5 indicates that the model is no better than random, AUC=1.0 indicates that the model is perfect (always placing species occurrences in "good" habitat). An AUC score greater than about 0.75 is generally considered to be the cut-off for a useful model. I built this model with springtime data from 2002, 2003, 2004, 2006, and I tested it with data from 2005. AUC=0.846. Not bad.
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