Content

I’m very interested in the effects of markets over fisheries management. My Group Project is focused in answering the overarching question What are the consequences of applying seafood certification programs to small-scale fisheries?, focusing on a Small scale fishery in Costa Rica. Our specific questions are:

Costa Rican Snapper fishery as a case study

The project is being developed in the North Pacific of the country, with two communities of small scale fishers who work with bottom long lines. Since 2010 the communities have been trying to obtain a certification of sustainability, in order to sell a premium price product. Currently they are going through a pre-assessment for FairTrade. Link to CRSeafood webpage (http://www2.bren.ucsb.edu/~crseafood/)

Techniques

In order to understand what affects the supply chain of snapper, we had to first map how is this system laid out. We did this through systems thinking. The following figure shows our preliminary results:

Data

Currently my team and I are working on developing a model that will show the effects of implementing a certification. We are evaluating these changes in terms of profits and biomass changes. Learning how to use Github and applying it to the creation of this model would be a great advantage. We are currently cleaning and organizing the data obtained, therefore there are no preliminary results regarding the bio-economic model of snapper. However, here is a short example of some of the data we have available.

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if (basename(getwd())!='students'){
  setwd('students')
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>>>>>>> 32c37993d2f0ec807a01e9a6d5b65e077dfb422a 
# read csv
d = read.csv('data/julianaherrera_pricessnapper.csv')
      
# output summary
summary(d)
##       Year          Month           Fishery                    Spp     
##  Min.   :1990   APR    : 34   Artisanal :240   pargo             : 12  
##  1st Qu.:1993   AUG    : 34   Industrial:168   pargo coliamarilla: 12  
##  Median :2000   DEC    : 34                    pargo pequeno     : 12  
##  Mean   :1999   FEB    : 34                    pargo seda        :372  
##  3rd Qu.:2004   JAN    : 34                                            
##  Max.   :2009   JUL    : 34                                            
##                 (Other):204                                            
##      Price    
##         :125  
##  500,00 :  4  
##  110,00 :  2  
##  650,00 :  2  
##  1000,00:  1  
##  1006,08:  1  
##  (Other):273

Data Wrangling

# loading packages
library(readxl)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
library(readr)

# loading data
pricedata <- read_csv('data/julianaherrera_pricessnapper.csv') 
  colnames(pricedata) <- tolower(colnames(pricedata))

fishdata <- read_excel('data/julianaherrera_catch.xlsx')
  colnames(fishdata) <- tolower(colnames(fishdata))
  
# Changing columns and rows
pricedata_2 <- pricedata[,c('year', 'fishery', 'price')] # limiting data to year, species and price only

pricedata_3 <- pricedata_2[pricedata_2$fishery == 'Artisanal'] 

pricedata_4 <- aggregate(fishery ~ year, data = pricedata_3, FUN='length') # counts values per year

 #Reshaping data 
tunadata <- (fishdata) %>%
  select(-total) %>%
  gather('month', 'catch', jan:dec) %>%
  filter(region == 'Golfito' & species == 'ATUN')