In brouwern/lloyd2016: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Introduction

Citation

Lloyd et al. 2016. Assessing conservation status of resident and migrant birds on Hispaniola with mist-netting. PeerJ. https://peerj.com/articles/1541/

Preliminaries

Load Libraries

library(reshape2)
library(here)
library(lubridate)

Load data

The here() function of the here package is used to construct the correct file path for the raw data

file. <- ""
path. <- 

Getting to know the data

Check to make sure it work

file.exists(path.)

Now load the actualy data

Check out data structure

Use basic R functdions to get to know the data

dim(dat)
names(dat)
summary(factor(dat$Date))

Check out data layout

Make Table 2

• Table 2 of Lloyd et al. 2016 shows the total number of UNIQUE individuals captured of each species
• They have some inconsistencies in their data coding that make this hard to reproduce, but I can get close
• KEY: only use Repeat.New == 1 (2 = recaps)

TODO:display Lloy'd et al's original table here to show what end product is

Note: throughout the paper the paper the authors exclude some additionally sampling dates

"To minimize the potentially confounding effects of seasonal variation in abundance and bird behavior that may affect capture rate, this analysis does not include data collected during the May, November, and December visits." (Lloyd et al. 2016)

Species total per year

First, calc total for each species for each year.

Subset used for data analysis:

dat$Year <- as.numeric(dat$Year)
i.07.ff <- which(dat$Year > 1996 &    #Exclude 1996
                dat$Repeat.New == 1 & #exclude ALL recaps
                dat$Mo %in% c(1,2,3) &  #use only focal months
                dat$effort == "constant") #use only data from constant-effort netting

Reshape the data. Note: I use older data manipulation package reshape2 rather than the Tidyverse's tools like dplyr.

cast1 <- reshape2::dcast(data = dat[i.07.ff,],
               formula = Species + Year  ~ ID,
               value.var = "Species",
               fun.aggregate = length)

dim(cast1)#262 1652? #current = 257 1486

Look at the output: each individual is represented by a column, with typically a 1 in a row for each year they were captured. If they were captured more than once within a year then there should be a value greatter than 1.

dim(cast1)
head(cast1[,1:10])
summary(cast1[,1:10])

Recode 0/1

Recode any values >1 as 1 to indicate just that a bird was captured at least once.

fx01 <- function(x){ifelse(x > 0,1,0)}

cast1[,-c(1:2)] <- apply(cast1[,-c(1:2)],2,fx01)

Calculate total caps

ann.tot <- apply(cast1[,-c(1:2)],1,sum)
ann.tot <- cbind(cast1[,c(1:2)],ann.tot)

Cast to total across years

spp.tot <- reshape2::dcast(data  = ann.tot,
            formula = Species ~ .,
            value.var = "ann.tot",
            fun.aggregate = sum)

      names(spp.tot)[2] <- "spp.total"

Save table 2

#save output
write.csv(spp.tot,
          row.names = F,
                file = f.)

Compare original with reproduced table 2

Not implemented; previous version of spreadsheet in /tables folder.

    # #Load original of table
    # Lloyd.table2 <- read.csv(file = "./data_Lloyd_PeerJ2016/Lloyd_table2_CSV.csv")
    # 
    # #fix name in reproduced version
    # names(spp.tot)[1] <- "spp"
    # 
    # #merge
    # table2.compare <- merge(spp.tot,Lloyd.table2, all = T)
    # 
    # #difference in counts
    # table2.compare$diff <- with(table2.compare,
    #                             Lloyd.N - spp.total)

Look at output

# table2.compare[,c("spp","species",
#                   "spp.total","Lloyd.N",
#                   "diff","B.yr")]
# 
# sum(table2.compare$spp.total)
# sum(table2.compare$Lloyd.N,na.rm = T)

Save merged table

# write.csv(table2.compare,row.names = F,
#           file = "./output_tables/table2_compare.csv")

Reshape Data For Regression Analyses

Look at months when sampling occured

table(as.numeric(dat$Mo),dat$Year)

Set up index for working data

• only 1997 and following years
• only use data from Jan, Feb and March
• only use constant effort data

NOTE: There is a random year when they also sampled in May - look into this (see above)

dat$Year <- as.numeric(dat$Year)
i.07.all <- which(dat$Year > 1996 & 
                #dat$Repeat.New == 1 &
                dat$Mo %in% c(1,2,3) &       #originally dat$Mo != 11 &
                dat$effort == "constant")

Check

summary(factor(dat$Year)[i.07.all])

"Cast by Location: PUVI vs. PALO"

This actually casts by ID... not sure why discrepencay in how I describe it and what I did, so I put the title in " "

with(dat, table(Session, Mo,Year))

summary(dat$Session)
summary(factor(dat$Mo))
summary(factor(dat$Year))
summary(factor(dat$Session))

with(dat, table(Year,Session))

cast.by.site <- dcast(data = dat[i.07.all,],
               formula = Species + Year + Location + Session  ~ ID,
               value.var = "Species",
               fun.aggregate = length)


dim(cast.by.site)

#df is very wide b/c there is a row for each individual!

summary(factor(cast.by.site$Year))
summary(factor(cast.by.site$Session))

with(cast.by.site, table(Year,Session,Location))

Recode 0/1

this allows total count of unique individuals to be determined

fx01 <- function(x){ifelse(x > 0,1,0)}
names(cast.by.site)[1:5]
cast.by.site[,-c(1:4)] <- apply(cast.by.site[,-c(1:4)],2,fx01)

summary(factor(cast.by.site$Year))
dim(cast.by.site)

Calcualte total captured for a given year w/in a site

spp.ann.tot <- apply(cast.by.site[,-c(1:4)],1,sum)
length(spp.ann.tot)

spp.ann.tot <- cbind(cast.by.site[,c(1:4)],
                     spp.ann.tot)
dim(spp.ann.tot)

summary(factor(spp.ann.tot$Year))

Check

length(unique(spp.ann.tot$Species))
unique(spp.ann.tot$Year)


with(spp.ann.tot, table(Year,Session,Location))

Add years when no birds were caught

Note that in its current format, there is a row of data ONLY if a species was captured at least once - there are no zeros for the year it wasn't captured

summary(spp.ann.tot$spp.ann.tot ) #min value == 1

Need to add zeros. First, make a dataframe that has a row for every species for every year for both sites. We'll use the handy expand.grid() functions will make all combinations of species-locations-years

all.spp <- unique(spp.ann.tot$Species)
length(all.spp)


Location <- unique(spp.ann.tot$Location)

#remove bad location
Location <- as.character(Location[-3])


Year <- unique(spp.ann.tot$Year)[order(unique(spp.ann.tot$Year))]


#session 1
empty.datframe.sess1 <- expand.grid(Species = all.spp,
                              Year = Year,
                              Location = Location,
                              Session = 1)

length(all.spp)*length(Year)*length(Location)
with(empty.datframe.sess1,
     table(Year, Location))

#session 2
## a 2nd sampling sessions occured at PUVI in March in just these 2 years
empty.datframe.sess2 <- expand.grid(Species = all.spp,
                              Year = c(2007,
                                       2008),
                              Location = "PUVI",
                              Session = 2)


dim(empty.datframe.sess1)  #884 4
dim(empty.datframe.sess2)  #68 4

empty.datframe <- rbind(empty.datframe.sess1,
                              empty.datframe.sess2)

dim(empty.datframe) #952 4

#952 = 884 + 68

There are 884 species-year-site combinations. There is extra stuff for sessions 2

length(all.spp)*length(Location)*length(Year)  #884

Merge the real data with the "empty dataframe. First, I'll show a problematic merge, then I'll correct it

    bad.merge <- merge(spp.ann.tot,empty.datframe)

The orignal and new dataframes are the same size; rows have not been inserted where there are currently zeros

    dim(bad.merge)
    dim(spp.ann.tot)

Add "all = T" to insert NAs into the dataframe where there currently are no entries (ie, years when a species wans't captured currently don't occur)

spp.ann.tot.b <- merge(spp.ann.tot,
                   empty.datframe,
                   all = T)

dim(spp.ann.tot.b)

Check the size; its now as big as the "empty.datafrme"

dim(spp.ann.tot.b)

summary(factor(spp.ann.tot.b$Session))

with(spp.ann.tot.b,table(Year, Session,Location))



summary(spp.ann.tot.b)

Change NAs to zeros

There now should be NAs inserted into the abundance column in years when a species wasn't observed at a particular site

summary(spp.ann.tot.b$spp.ann.tot)

Convert those NAs to zeros

i.NA <- which(is.na(spp.ann.tot.b$spp.ann.tot) == TRUE)
spp.ann.tot.b[i.NA,"spp.ann.tot"] <- 0

Check that zeros have been added

summary(spp.ann.tot.b$spp.ann.tot)

summary(factor(spp.ann.tot.b$Year))

with(spp.ann.tot.b,
     table(Year,Session))

with(spp.ann.tot.b,
     table(Year,Session,Location))

Load effort data

From Table 1 in Lloyd et al 2016. Note that for 2007 and 2009 there were additional sampling sessions in March for one Location

#data directory
d2 <- paste(d.working,"data/data_in", sep = "")

effort <- read.my.csv(file. = "Lloyd_table1_effort",
                      rel.dir. = d2)

Merge count data and effort

#remove stray NA

# i.NA <- which(spp.ann.tot.b$Location == "")
# spp.ann.tot.b <- spp.ann.tot.b[-i.NA,]

spp.ann.tot.b$Location <- factor(as.character(spp.ann.tot.b$Location))


#merge data
spp.ann.tot2<- merge(spp.ann.tot.b,
                     effort,
                     all = T)


dim(spp.ann.tot.b) == dim(spp.ann.tot2)

dim(spp.ann.tot.b) 
dim(spp.ann.tot2)
summary(spp.ann.tot2)

NOTE: things don't line up perfectly b/c of 2 years with zero effort and a few random NAs

Remove date column

i.date.col <- which(names(spp.ann.tot2) == "date")
spp.ann.tot2 <- spp.ann.tot2[,-i.date.col]

with(spp.ann.tot2, 
     table(Year, Location))

i.PALO <- which(spp.ann.tot2$Location == "PALO")

with(spp.ann.tot2[i.PALO,], 
     table(Year, Location))

Remove bad location

i.bad.loc <- which(spp.ann.tot2$Location == "")

spp.ann.tot2 <- spp.ann.tot2[-i.bad.loc,]

Set abundance of NA when net hours = 0

i.net.zero <- which(spp.ann.tot2$net.hours == 0)

spp.ann.tot2$spp.ann.tot[i.net.zero] <- NA

Save final cleaned data

d. <- paste(".",d.working,"data/data_out",sep = "")
f. <- paste("./",d.,"/Lloyd_data_cleaned.csv", sep = "")
write.csv(spp.ann.tot2, 
          row.names = F,
          file = f.)

spp.ann.tot2[which(spp.ann.tot2$Species == "GTGT" &
                     spp.ann.tot2$Location == "PUVI"), ]

spp.ann.tot2[which(spp.ann.tot2$Species == "WEWA" & spp.ann.tot2$spp.ann.tot > 0),]

Recaps per year

dat[which(dat$Species == "WEWA"),]

i.WEWA <- which(dat$Species == "WEWA" & dat$Year > 1996 & 
                dat$Repeat.New == 1 & #exclude ALL recaps
                dat$Mo %in% c(1,2,3) &  #previous was "!= 11"
                dat$effort == "constant")

cast.WEWA <- dcast(data = dat[i.WEWA,],
               formula = Species + Year  ~ ID,
               value.var = "Species",
               fun.aggregate = length)

Recode 0/1

Recode to pres/abs for

fx01 <- function(x){ifelse(x > 0,1,0)}

cast.WEWA[,-c(1:2)] <- apply(cast.WEWA[,-c(1:2)],2,fx01)

Calculate total caps

ann.tot.WEWA <- apply(cast.WEWA[,-c(1:2)],1,sum)
ann.tot.WEWA <- cbind(cast.WEWA[,c(1:2)],ann.tot.WEWA)

Cast to total across years

spp.tot.WEWA <- dcast(data  = ann.tot.WEWA,
      formula = Species ~ .,
      value.var = "ann.tot.WEWA",
      fun.aggregate = sum)

names(spp.tot.WEWA)[2] <- "spp.total"

brouwern/lloyd2016 documentation built on June 8, 2020, 12:36 a.m.