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For Malin to do on Amphiprion workstation. Will be for 2006-2100.

@larogers123
Something for us to ponder: should we project a species' model to a region in which the species has never been observed? We could restrict it to the same ocean, however.

The benefits are that we might capture new species moving into a region. The downside is that it's pretty far outside historical conditions to which our model has been fit. We'd have to come up with a dummy meanbiomass for the new region (which wouldn't be hard).

@larogers123
Lauren and team will fill in SST data for Newfoundland and WCAnn surveys (these data are missing) using global datasets.

We currently deal with seasons and regions inconsistently. For instance, the NEUS is divided into two "regions", i.e. surveys, which represent the two seasons surveys are conducted. Other surveys are not divided by season although they occur over multiple seasons. And the US West Coast is divided into two surveys that cover approx the same region and seasons, but in different years and possibly (?) with different gears.

This matters for model fitting details, particularly how we calculate a yearly mean biomass and whether we need different intercepts for each season. I/we need to think through this a bit more thoroughly to be sure we're not making a mistake.

Currently in 7_rangeprojection.r, projections of CPUE (by wt) are made on a lat/lon grid. This is essentially the projected catch per unit of survey effort (~= survey haul) were a haul to be made in that particular place.

We then take the average of all projections within a larger grid box to calculate an average projected CPUE per cell (essentially accounting for the differences in rugosity within that cell).

For points that fall on land, rather than setting their CPUE to zero (as is currently in the code), I think we should just omit them from the average. Setting them to zero implies that our final output raster will be equivalent to a total catch within that cell (thus the catch within these coastal cells is lower in total), but I've rather been thinking of that raster as showing the relative density of fish. In this case, giving points on land a value of zero makes it look like the density of fish in the coastal areas is lower than otherwise.

It would be great to get from a density index (CPUE) to an absolute number, but I'm not sure how to do that. Possibly we could dig in to assessments (for assessed species), assume catchability for non-assessed species), and calculate area swept for a haul, etc. to get up to total abundance in an area, but would we gain much by doing this?

This should become clear at the next stage, so I'm flagging the points-on-land issue for now. To remove from average, just drop them from rugos object before merging with clim object.

We can do this once #2 is done