Dear List,

 

I am wondering if the methodological approach I am taking is correct and
would be very grateful if people could comment and make suggestions, much
appreciated.

 

I have developed the best model ( AIC model selection) using oceanographic
data ( i.e. SST, chlorophyll, NPP...x6) to predict reported fisheries catch
for 52 countries.

 

I then extract the residuals from the model and anything positive has a
higher catch than would be predicted given the level of productivity in the
country, with the opposite being true. 

 

What I want to do is:

 

1.       Regress a suite of ecological and socioeconomic variables against
the residuals from the oceanographic model to determine which factors cause
some countries to be above and some below. I.E as trophic level increase the
residuals become increasingly negative.

2.       Ideally ( and I am unsure how or if it is possible) work out for
each country which variables/s cause the poor fit of that country to the
oceanographic model.

 

Thanks in advance for any suggestions / possible methods.

 

Chris 

 

P.S - Below is the type of conclusions I am drawing

 

There are a number of reasons why some countries have higher / lower catch
than you would expect.

 

For example if the target fishery is a high trophic level species then the
link between primary productivity and catch will be lesser than if the
species was a lower trophic level ( transfer efficiency etc etc)- resulting
in a negative residual.  Alternatively it maybe that the area is being
overfished i.e. the north sea meaning more fish are being caught in that
region than it can sustain - resulting in a high positive residual (as
predicted by the model) 

 

In reality it is likely a combination of this plus other, however some
factors will be relevant to others i.e. Somalia has a really low catch
compared to its productivity likely due to piracy and poor reporting of
statistics. 

 

 

 


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