## Wednesday, April 8, 2015

### Map projection cheat sheet

Here's a cheat sheet for map projection settings for the mapproject function (mapproj package). I've repeated the same general procedure for each map: 1. create blank world map, 2. project colored polygons, 3. overlay country boundaries. Some specifications were needed for individual map projections (i.e. different orientation, lat/lon limits, additional parameters). Most projections work well as long as the limits and orientation are valid - in a few cases country boundaries ('square' and 'hex') were wrapped incorrectly. I was unable to get the two spheroid projections working due to the escape character in their names ('sp\_mercator', 'sp\_albers') (maybe someone has a suggestion here?).

In addition to the maps and mapproj packages, the sinkr package is also used for the calculation of global distance, value to color conversion, and polygon creation.

## Thursday, April 2, 2015

### Add text using relative plot region coordinates

Here is a simple but useful function for adding text to a plot device using the relative x, y coordinates (i.e. between 0 and 1). I found myself programming these few lines repeatedly and decided to finally bundle it into a function, called reltext(). The function can be found below, and is also available in the sinkr package (https://github.com/marchtaylor/sinkr).

### Exploring fishing harvest feedback policies in R using vector optimization

Walters and Martell (2004, Section 3.2) present an interesting example of harvest feedback policy exploration using basic spreadsheet techniques and optimization procedures (e.g. Solver in Excel, or Optimizer in Quattro Pro). For a given species, parameters describing growth, mortality, and reproduction/recruitment are used to model the population's dynamics. Given a random series of environmental effects to recruitment parameters (survival and carrying capacity) the optimization procedure can be used to determine the best series of fishing mortality (F) values given a cost function (e.g. maximize the sum of: harvest yield, log-transformed yield, discounted yield):

In this example, for a hypothetical population of tilapia, yearly recruitment survival varies by ca. +/-30%, while recruitment carrying capacity has two periods of poor recruitment (-50%, e.g. due to a regime shift) during the simulation years 20-35 and 60-75, as indicated by the shaded areas. The optimal F series are similar for maximizing yield or discounted yield (discount rate = 5%) likely due to the fact that tilapia is a fast-growing, high fecundity species and can rebound quickly following intensive fishing pressure. Maximizing log-transformed biomass is the "risk-averse" strategy, whereby peaks in harvest are less highly valued, and fluctuations in fishing pressure (and yield) are dampened:

Interesting patterns emerge, e.g. one should increase fishing pressure at the beginning of a "bad" recruitment regime (grey regions) and reduce fishing at the beginning a "good" recruitment regime. In both cases, this speeds the trajectory of the stock to it's new equilibrium and takes advantage of soon to be lost or gained harvestable biomass. In other words, fish hard before bad times so as not to "waste" a large spawning stock, and lay off of fishing before good times so as to make full use of the spawning potential. Martell and Walters (2004) state,
These anticipatory behaviors are a bizarre reversal of our usual prescriptions about how to hedge against uncertain future changes in productivity and would almost certainly never be acceptable as management recommendations in practical decision settings.
The figure at the top of the post shows the relationship between biomass and yield during the optimal scenarios.  The relationship is most strongly linear for the log(Yt) optimization, but a relationship is evident for all three scenarios. One could imagine using the coefficients of this regression as a general harvesting strategy; x-intercept would be the minimum biomass before harvesting is allowed, and the slope gives the exploitation rate (E = harvest/biomass). One would need to program this strategy to be able to make the comparison to the optimal harvested values, but even the simplified strategy of using a constant exploitation rate from the regression (e.g. E = 0.34, 0.23, 0.37) results in 87, 77, and 90% of the optimal solution for sum(Yt), sum(log(Yt)), and sum(discounted Yt), respectively. These levels of performance (and higher) are typical for most examples explored by the approach (Walters and Parma, 1996).

Finally, the authors also demonstrate the situation where all sizes are vulnerable to fishing (as opposed to a knife-edge selection of individuals above a given minimum size, as in the above example). Here the sum(Yt) optimization results in a "pulse-fishing" strategy whereby the stock is fished hard (usually E > 0.5) for 1-2 years, followed by 1-2 years of recovery where no fishing is allowed. This pattern results from "growth overfishing", whereby unselective harvesting wastes a large part of the biomass that is still growing rapidly:

The examples use the stockSim() and optim.stockSim() functions of the R package fishdynr. Instructions for direct installation from GitHub can be found here: https://github.com/marchtaylor/fishdynr. Vector optimization of the F series is computed with the optim() function (stats package).

References:
- Walters, C. J., Martell, S. J., 2004. Fisheries ecology and management. Princeton University Press.
- Walters, C., Parma, A. M., 1996. Fixed exploitation rate strategies for coping with effects of climate change. Canadian Journal of Fisheries and Aquatic Sciences, 53(1), 148-158.

Script to reproduce the example: