Content

I work with the Fish Forever initiative (Fish Forever), a partnership between the Sustainable Fisheries Group at UCSB, Rare, and Environmental Defense Fund, aimed at improving management of small-scale fisheries in the developing tropics through the use of TURF-reserves.

One of the most critical challenges we face is assessing and managing fisheris in the data- and resource-limited situations where we work. To address this, we are using data-limited assessment techniques to creatively work with the data that we do have. However, no single technique is perfect, so we are therefore using multiple techniques to examine each fishery so that we can have a more complete understanding of how the fishery is performing. I therefore have two main research questions:

  1. How can multiple data-limited assessment techniques, which might each give conflicting indications for how a fishery is doing, be interpreted and synthesized to make better management decisions for a fishery?
  2. How does including uncertainty, both in measurement error and life history information error, affect the interpretation and synthesis of multiple indicators?

Techniques

To address this question, I will apply multiple data-limited assessment techniques to a single data set including:

Data

The data set I will be using is from Karimunjawa National Park, Indonesia. This site is a prototype site for the Fish Forever intervention. The data set runs from 2010-2015 and includes catch, effort, and length-composition data. Wildlife Conservation Society (WCS) and Rare contributed to the data collection of this data set.

Data Wrangling

Working in R and the terminal

# present working directory
getwd()

# change working directory
setwd('.')

# list files
list.files()

# list files that end in '.jpg'
list.files(pattern=glob2rx('*.jpg'))

# file exists
file.exists('test.png')

setwd('students')

Installing packages

# Run this chunk only once in your Console
# Do not evaluate when knitting Rmarkdown

# list of packages
pkgs = c(
  'readr',        # read csv
  'readxl',       # read xls
  'dplyr',        # data frame manipulation
  'tidyr',        # data tidying
  'nycflights13', # test dataset of NYC flights for 2013
  'gapminder')    # test dataset of life expectancy and popultion

# install packages if not found
for (p in pkgs){
  if (!require(p, character.only=T)){
    install.packages(p)
  }
}

Reading in csvs with utils::read.csv

d = read.csv('../data/r-ecology/species.csv')
d
head(d)
summary(d)

Reading in csvs with readr::read_csv

library(readr)

d = read_csv('../data/r-ecology/species.csv')
d
head(d)
summary(d)

Formatting tables with dplry::tbl_df

library(readr)
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
d = read_csv('../data/r-ecology/surveys.csv') %>%
  tbl_df() %>%
  select(species_id,year) %>%
  #filter(species_id == "NL") %>%
  group_by(species_id,year) %>%
  summarize(count = n())
d
## Source: local data frame [535 x 3]
## Groups: species_id [?]
## 
##    species_id  year count
##         (chr) (int) (int)
## 1          AB  1980     5
## 2          AB  1981     7
## 3          AB  1982    34
## 4          AB  1983    41
## 5          AB  1984    12
## 6          AB  1985    14
## 7          AB  1986     5
## 8          AB  1987    35
## 9          AB  1988    39
## 10         AB  1989    31
## ..        ...   ...   ...
head(d)
## Source: local data frame [6 x 3]
## Groups: species_id [1]
## 
##   species_id  year count
##        (chr) (int) (int)
## 1         AB  1980     5
## 2         AB  1981     7
## 3         AB  1982    34
## 4         AB  1983    41
## 5         AB  1984    12
## 6         AB  1985    14
summary(d)
##   species_id             year          count       
##  Length:535         Min.   :1977   Min.   :  1.00  
##  Class :character   1st Qu.:1984   1st Qu.:  3.00  
##  Mode  :character   Median :1990   Median : 19.00  
##                     Mean   :1990   Mean   : 66.45  
##                     3rd Qu.:1996   3rd Qu.: 65.00  
##                     Max.   :2002   Max.   :892.00
glimpse(d)
## Observations: 535
## Variables: 3
## $ species_id (chr) "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB...
## $ year       (int) 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 198...
## $ count      (int) 5, 7, 34, 41, 12, 14, 5, 35, 39, 31, 27, 15, 10, 9,...