# NOTE: the function my.percent() is locally stored on my computer (Hugo) and won't work remotely

# source("C:/Users/hdmfla/Google Drive/R/Rfunctions/my.percent.R")
# 2020-07-03 HF: Copying the contents of the above-mentioned file here:
my.percent <- function(x,precision=1,symbol=TRUE) {
    if ( length(x) == 0 )
        return(character())
    x <- round((x*100), precision)
    if( symbol ){
	    return(paste0(x, "%"))
	} else {
		return(x)
	}
}
# end of file.

# source("C:/Users/hdmfla/Google Drive/R/Rfunctions/expand.line.R")
# 2020-07-03 HF: Copying the contents of the above-mentioned file here:
expand.line <- function(x1,x2,y1,height=0,y2=y1){
		y3 <- max(c(y1,y2)) + height 
		output <- data.frame(x = c(x1,x1,x2,x2), y = c(y1,y3,y3,y2))
	return(output)
}
# end of file.

# source("C:/Users/hdmfla/Google Drive/R/Rblocks/colorblind.R")
# 2020-07-03 HF: Copying the contents of the above-mentioned file here:
cbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999")
names(cbPalette) <- c("Orange","Blue","Green","Yellow","Darkblue","Darkorange","Pink","Grey")
cb <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999")
# end of file.

title <- read.csv("Raw_files/new_title_layer.csv")
abstract <- read.csv("Raw_files/abstract_layer.csv")
fulltext <- read.csv("Raw_files/text_layer.csv")
papers <- read.csv("Raw_files/database_rockfish_free.csv")

library(scales) 
library(ggplot2)

# Screening results
	# 1.1) preparing the title layer

		# title <- read.csv("Raw_files/new_title_layer.csv")
		
		#	a <- match(title$Title,old.title$Title)[!is.na(match(title$Title,old.title$Title))]
		#	b <- match(order.title$Title,new.title$Title)[!is.na(match(order.title$Title,new.title$Title))]

		#	don't forget to check column consistency
		#	new.title[b,c(9,12:16)] <- order.title[a,c(8:13)]

		#	new.title[decision,c(9,12:16)] <- temp[decision,c(9,12:16)]

		#  [1] "Title"       "Author"      "Year"        "Country"     "sco"
		#  [6] "wos"         "dtu"         "E_title"     "M_eco"       "M_species"
		# [11] "M_location"  "M_off.topic" "M_other"   

		title.total <- nrow(title)

		E.title.total <- sum(title$E_title)

		E.title.eco <- sum(title$M_eco)
		E.title.species <- sum(title$M_species)
		E.title.local <- sum(title$M_location)
		E.title.off <- sum(title$M_off.topic)
		E.title.other <- sum(title$M_other)

		# this fragment is used to test relationships between columns
		#output <- vector()
		#for( i in 1:nrow(title) ){
		#	output[i] <- title[i,5] == 1 & title[i,8]== 1
		#}
		#sum(output)
		##

		# dtu
		# 3761 total
		# 3446 excluded (91.62)
		# 2011 off-topic (58.36 of the exclusions) (78.07 of the off-topics)
		
		# sco
		# 1731 total
		# 1504 excluded (86.89)
		# 


		title.total 							# 2333
		E.title.total; E.title.total/title.total			# 2036 	87.27
		E.title.eco; E.title.eco/E.title.total			# 113 	05.55
		E.title.species; E.title.species/E.title.total	# 715 	35.12
		E.title.local; E.title.local/E.title.total		# 80 	03.93
		E.title.off; E.title.off/E.title.total			# 1100	54.03
		E.title.other; E.title.other/E.title.total 		# 28 	01.38


		sum(E.title.eco,E.title.species,E.title.local,E.title.off,E.title.other)


		## ERROR TESTING
			# not included but no reason
				output <- vector()

				for( i in 1:nrow(title) ){
					decision <- title[i,8]==1 & sum(title[i,11:15])==0
					if( decision ){
						output[i] = 1
					} else {
						output[i] = 0
					}
				}

				sum(output)

			# double exclusions
				output <- vector()

				for( i in 1:nrow(title) ){
					if( sum(title[i,11:15])>1 ){
						output[i] = 1
					} else {
						output[i] = 0
					}
				}

				sum(output)

			# included but excluded
				output <- vector()

				for( i in 1:nrow(title) ){
					decision <- title[i,8]==0 & sum(title[i,11:15])>0
					if( decision ){
						output[i] = 1
					} else {
						output[i] = 0
					}
				}

				sum(output)
			##
		##

	# 2.1) preparing the abstract layer

		# abstract <- read.csv("Raw_files/abstract_layer.csv")
		
		abstract.total <- nrow(abstract)

		E.abstract.total <- sum(abstract$E_abstract)

		E.abstract.eco <- sum(abstract$M_eco)
		E.abstract.species <- sum(abstract$M_species)
		E.abstract.local <- sum(abstract$M_location)
		E.abstract.off <- sum(abstract$M_off.topic)
		E.abstract.other <- sum(abstract$M_other)

		# this fragment is used to test relationships between columns
		#output <- vector()
		#for( i in 1:nrow(abstract) ){
		#	output[i] <- abstract[i,5] == 1 & abstract[i,8]== 1
		#}
		#sum(output)
		##

		# dtu
		# 3761 total
		# 3446 excluded (91.62)
		# 2011 off-topic (58.36 of the exclusions) (78.07 of the off-topics)
		
		# sco
		# 1731 total
		# 1504 excluded (86.89)
		# 


		abstract.total 							# 297
		E.abstract.total; E.abstract.total/abstract.total			# 136	45.79
		E.abstract.eco; E.abstract.eco/E.abstract.total			# 21 	15.44
		E.abstract.species; E.abstract.species/E.abstract.total	# 44	32.35
		E.abstract.local; E.abstract.local/E.abstract.total		# 20	14.70
		E.abstract.off; E.abstract.off/E.abstract.total			# 34	25.00
		E.abstract.other; E.abstract.other/E.abstract.total 		# 17	12.50


		sum(E.abstract.eco,E.abstract.species,E.abstract.local,E.abstract.off,E.abstract.other)


		## ERROR TESTING
		# not included but no reason
			output <- vector()

			for( i in 1:nrow(abstract) ){
				decision <- abstract[i,9]==1 & sum(abstract[i,11:15])==0
				if( decision ){
					output[i] = 1
				} else {
					output[i] = 0
				}
			}

			sum(output)

		# double exclusions
			output <- vector()

			for( i in 1:nrow(abstract) ){
				if( sum(abstract[i,11:15])>1 ){
					output[i] = 1
				} else {
					output[i] = 0
				}
			}

			sum(output)

		# included but excluded
			output <- vector()

			for( i in 1:nrow(abstract) ){
				decision <- abstract[i,9]==0 & sum(abstract[i,11:15])>0
				if( decision ){
					output[i] = 1
				} else {
					output[i] = 0
				}
			}

			sum(output)
		##
	##

	# 3.1) preparing the full text layer

		# fulltext <- read.csv("Raw_files/text_layer.csv")
		
		text.total <- nrow(fulltext)

		E.text.total <- sum(fulltext$E_text)

		E.text.eco <- sum(fulltext$M_eco)
		E.text.species <- sum(fulltext$M_species)
		E.text.local <- sum(fulltext$M_location)
		E.text.off <- sum(fulltext$M_off.topic)
		E.text.other <- sum(fulltext$M_other)

		# this fragment is used to test relationships between columns
		#output <- vector()
		#for( i in 1:nrow(fulltext) ){
		#	output[i] <- fulltext[i,5] == 1 & fulltext[i,8]== 1
		#}
		#sum(output)
		##

		text.total 							# 161
		E.text.total; E.text.total/text.total				# 116 (72%)
		E.text.eco; E.text.eco/E.text.total					# 16 	(14%)
		E.text.species; E.text.species/E.text.total	# 24	(21%)
		E.text.local; E.text.local/E.text.total			# 57	(49%)
		E.text.off; E.text.off/E.text.total					# 08	(7%)
		E.text.other; E.text.other/E.text.total 		# 11	(9%)
		sum(E.text.eco,E.text.species,E.text.local,E.text.off,E.text.other) #check
	##

# ggplot2
	plot.values<- c(
		title.total <- nrow(title)
		,E.title.total <- sum(title$E_title)
		,E.title.eco <- sum(title$M_eco)
		,E.title.species <- sum(title$M_species)
		,E.title.local <- sum(title$M_location)
		,E.title.off <- sum(title$M_off.topic)
		,E.title.other <- sum(title$M_other)
		#
		,abstract.total <- nrow(abstract)
		,E.abstract.total <- sum(abstract$E_abstract)
		,E.abstract.eco <- sum(abstract$M_eco)
		,E.abstract.species <- sum(abstract$M_species)
		,E.abstract.local <- sum(abstract$M_location)
		,E.abstract.off <- sum(abstract$M_off.topic)
		,E.abstract.other <- sum(abstract$M_other)
		#
		,text.total <- nrow(fulltext)
		,E.text.total <- sum(fulltext$E_text)
		,E.text.eco <- sum(fulltext$M_eco)
		,E.text.species <- sum(fulltext$M_species)
		,E.text.local <- sum(fulltext$M_location)
		,E.text.off <- sum(fulltext$M_off.topic)
		,E.text.other <- sum(fulltext$M_other)
		)
	
	plot.notes <- c(
		""
		,my.percent(E.title.total/title.total)
		,paste(my.percent(E.title.eco/E.title.total),"*",sep="")
		,paste(my.percent(E.title.species/E.title.total),"*",sep="")
		,paste(my.percent(E.title.local/E.title.total),"*",sep="")
		,paste(my.percent(E.title.off/E.title.total),"*",sep="")
		,paste(my.percent(E.title.other/E.title.total),"*",sep="")
		,""
		,my.percent(E.abstract.total/abstract.total)
		,paste(my.percent(E.abstract.eco/E.abstract.total),"*",sep="")
		,paste(my.percent(E.abstract.species/E.abstract.total),"*",sep="")
		,paste(my.percent(E.abstract.local/E.abstract.total),"*",sep="")
		,paste(my.percent(E.abstract.off/E.abstract.total),"*",sep="")
		,paste(my.percent(E.abstract.other/E.abstract.total),"*",sep="")
		,""
		,my.percent(E.text.total/text.total)
		,paste(my.percent(E.text.eco/E.text.total),"*",sep="")
		,paste(my.percent(E.text.species/E.text.total),"*",sep="")
		,paste(my.percent(E.text.local/E.text.total),"*",sep="")
		,paste(my.percent(E.text.off/E.text.total),"*",sep="")
		,paste(my.percent(E.text.other/E.text.total),"*",sep="")
		)

	plot.x <- rep(c("Total"
		,"Excluded"
		,"Ecosystem"
		,"Species"
		,"Location"
		,"Off-topic"
		,"Other"
		), 3
	)

	PlotData <- data.frame(Value=plot.values
		,Labels=factor(plot.x,levels=c("Total","Excluded","Ecosystem","Species","Location","Off-topic","Other"))
		,Color=rep(c(rep(cbPalette[[2]],2),rep(cbPalette[[3]],5)),3)
		,Note=plot.notes,Note.x=rep(c(1:7),3),Note.y=c(plot.values[1:7]+90,plot.values[8:14]+10,plot.values[15:21]+5)
		,Level=factor(rep(c("a) Title","b) Abstract","c) Full text"),each=7),levels=c("a) Title","b) Abstract","c) Full text"))
		)

	p <- ggplot(PlotData,aes(y=Value, x=Labels))
	p <- p + geom_bar(stat="identity", fill=PlotData$Color)
	p <- p + geom_text(aes(x=Note.x,y=Note.y,label=Note),size=2)
	p <- p + facet_wrap(.~Level, scales="free_y")
	p <- p + theme_bw()
	p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1.1))
	p <- p + scale_y_continuous(expand = c(0.0, 0.00))
	p <- p + labs(x="", y="Number of papers")
	p <- p + theme(panel.grid.minor.x = element_blank()
			      ,panel.grid.major.x = element_blank()
			      )
	p

ggsave("exclusion_process.eps",width=8,height=3)
ggsave("exclusion_process.png",width=8,height=3)

rm(list=ls()[grep("E.",ls())])
rm(abstract.total,plot.labels,plot.notes,plot.values,plot.x,text.total,title.total)

#...................
# Analyses

papers <- read.csv("Raw_files/database_rockfish_free.csv")

# --

# recipient <- vector()
# for(i in 58:128){
# 	recipient[length(recipient)+1] <- sum(papers[,i])
# }

# names(recipient) <- colnames(papers)[58:128]

# to.plot <- recipient[rev(order(recipient))]

# png("Figures/per.species.png",height=400, width=400, unit="px", bg="transparent")
# par(mar=c(4,4,1,1)
# 	,las=2
# 	)
# barplot(to.plot[1:10]
# 	,ylab="Nº of accepted papers"
# 	,col="royalblue"
# 	)
# dev.off()


# --

recipient <- vector()
for(i in c(39:51) ){
	recipient[length(recipient)+1] <- sum(papers[,i])
}

labels <- c("Biomass", "Density", "Biodiversity", "Condition", "Mortality", "Growth", "Feeding", "Spawning", "Nursery", "Settling", "Diel cycle", "Site fidelity", "Migration")
my.colors <- c(rep(cbPalette[1],3)
	,rep(cbPalette[2],4)
	,rep(cbPalette[3],3)
	,rep(cbPalette[4],3))

PlotData <- data.frame(Values=recipient
	,x=labels,Groups=c(rep("Community-related",3),rep("Fitness-related",4),rep("Reproduction-related",3),rep("Distribution-related",3))
	)
PlotData$x <- with(PlotData, factor(x,levels=x[rev(order(recipient))]))

p <- ggplot(PlotData,aes(x=x,y=Values,fill=Groups))
p <- p + geom_bar(stat="identity")
p <- p + theme_bw()
p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1.1))
p <- p + scale_y_continuous(limits=c(0,26), expand = c(0.0, 0.00))
p <- p + labs(x="", y="Number of papers")
p <- p + theme(panel.grid.minor.x = element_blank()
	,panel.grid.major.x = element_blank()
	,legend.title = element_blank()
  ,legend.position = c(.98, .98)
	,legend.justification = c("right", "top")
	,legend.box.just = "right"
	,legend.margin = margin(2, 6, 2, 2) # top, left, down, right
  ,legend.background = element_rect(fill="white",linetype="solid", colour ="black", size=0.35)
	)
p <- p + scale_fill_manual(values = c("Community-related"=cbPalette[[1]]
	,"Fitness-related"=cbPalette[[2]]
	,"Reproduction-related"=cbPalette[[3]]
	,"Distribution-related"=cbPalette[[4]]
	))
p

ggsave("per_variable.png",width=4,height=3)
ggsave("per_variable.pdf",width=4,height=3)

#png("Figures/per.variable.png",height=300, width=400, unit="px", bg="white")

# postscript("Figures/per_variable_updated.eps",height=3.5, width=5.5, bg="transparent", horizontal=F)
# 	par(mar=c(4.5,4,1,0)
# 		,las=2
# 		)
# 	xx <- barplot(to.plot
# 		,axes = FALSE
# 		,axisnames = FALSE
# 		,ylab="Nº of reviewed papers"
# 		,col=my.colors[rev(order(recipient))]
# 		,ylim=c(0,30)
# 		)
# 	text(xx
# 		,par("usr")[3]
# 		,labels = labels[rev(order(recipient))]
# 		,srt = 45
# 		,adj = c(1.1,1.1)
# 		,xpd = TRUE
# 	#	,cex = 0.8
# 	)
# 	axis(2
# 		,las=2
# 	#	,cex.axis=0.8
# 	)
# 	legend(xx[7],30
# 		,c("Community-related","Fitness-related","Reproduction-related","Distribution-related")
# 		,fill=unique(my.colors)
# 		)
# dev.off()

# --
#pre-tables
my.list <- list(papers[papers$GadMor ==1,]
			   ,papers[papers$CluHar ==1,]
			   ,papers[papers$PolVir ==1,]
			   ,papers[papers$MulSur ==1,]
			   ,papers[papers$MerMer ==1,]
			   ,papers[papers$PolPol ==1,]
			   ,papers[papers$PlePla ==1,]
			   ,papers[papers$SolSol ==1,]
			   )

names(my.list) <- c("GadMor","CluHar","PolVir","MulSur","MerMer","PolPol","PlePla","SolSol")

# Substrate table
recipient <- matrix(nrow=9,ncol=9)
the.column <- c("H_Natural","H_Artificial","H_intentional","H_colateral","H_platform","H_wind","H_shipwreck")

for( i in 1:8 ){
	divider <- nrow(my.list[[i]])
	for( k in 1:7 ){
		a <- sum(my.list[[i]][,the.column[k]])
		recipient[k,i] <- paste(a,"(",my.percent(a/divider,symbol=F),")",sep="")
	}
		other <- sum(my.list[[i]][,"H_seawall"],my.list[[i]][,"H_riprap"],my.list[[i]][,"H_other"]!=0)
		recipient[8,i] <- paste(other,"(",my.percent(other/divider,symbol=F),")",sep="")
		recipient[9,i] <- paste(divider,"(",my.percent(divider/nrow(papers),symbol=F),")",sep="")
}

for( k in 1:7 ){
	recipient[k,9] <- paste(sum(papers[,the.column[k]]),"(",my.percent(sum(papers[,the.column[k]])/nrow(papers),symbol=F),")",sep="")
}

recipient[8,9] <- paste(sum(papers$H_seawall,papers$H_riprap,papers$H_other!=0),"(",my.percent(sum(papers$H_seawall,papers$H_riprap,papers$H_other!=0)/nrow(papers),symbol=F),")",sep="")

recipient[9,9] <- nrow(papers)

to.add <- c("1) Nat. Reefs","2) Art. Reefs","2.1) Intentional","2.2) De facto","2.2.a) Platforms","2.2.b) Wind Farms","2.2.c) Shipwrecks","2.2.d) Other","Total")
to.print <- cbind(to.add,as.data.frame(recipient))

colnames(to.print) <- c("NA",names(my.list),"Total")

print(
    xtable::xtable(to.print,digits=0),
    include.rownames=F,
    include.colnames=T,
    hline.after = NULL,
    floating=F,
    only.contents=T,
    print.results=T
) 


# Variable table
recipient <- matrix(nrow=14,ncol=9)
the.column <- colnames(papers[,39:51])

for( i in 1:8 ){
	divider <- nrow(my.list[[i]])
	for( k in 1:13 ){
		a <- sum(my.list[[i]][,the.column[k]])
		recipient[k,i] <- paste(a,"(",my.percent(a/divider,symbol=F),")",sep="")
	}
	recipient[14,i] <- paste(divider,"(",my.percent(divider/nrow(papers),symbol=F),")",sep="")
}
for( k in 1:13 ){
	recipient[k,9] <- paste(sum(papers[,the.column[k]]),"(",my.percent(sum(papers[,the.column[k]])/nrow(papers),symbol=F),")",sep="")
}

recipient[14,9] <- nrow(papers)

to.add <- c("Biomass", "Density", "Biodiversity", "Condition", "Mortality", "Growth", "Feeding", "Spawning", "Nursery", "Settling", "Diel cycle", "Site fidelity", "Migration", "Total")
to.print <- cbind(to.add,as.data.frame(recipient))

colnames(to.print) <- c("NA", names(my.list),"Total")

print(
    xtable::xtable(to.print,digits=0),
    include.rownames=F,
    include.colnames=T,
    hline.after = NULL,
    floating=F,
    only.contents=T,
    print.results=T
) 


# new barplot

# to.plot <- with(papers,c(sum(H_Natural)
# 						,sum(H_Artificial)
# 						,NA
# 						,sum(H_intentional)
# 						,sum(H_colateral)
# 						,NA
# 						,sum(H_wind)
# 						,sum(H_shipwreck)
# 						,sum(H_platform)
# 						,4 # 3 Others + 1 riprap
# 						))
# # apply(papers[,30:37],2,sum)
# # sum(papers[,38]!=0)


# labels <- c("Natural","Artificial",""
# 	,"Intentional","De facto",""
# 	,"Wind farms","Shipwrecks","Oil platforms","Other")
# colors <- c(rep("lightblue",2)
# 		,NA
# 		,rep("olivedrab1",2)
# 		,NA
# 		,rep("lightgreen",4)
# 		)

# postscript("Figures/per_substrate_updated.eps",height=3.5, width=5.5, bg="transparent", horizontal=F)
# 	par(mar=c(5,4,1,0)) #bottom, left, top and right 
# 	xx <- barplot(to.plot
# 		,axes = FALSE
# 		,axisnames = FALSE
# 		,ylab="Number of Papers"
# 		,ylim = c(0,30)
# 		,col = colors
# 	)
# 	text(xx
# 		,par("usr")[3]
# 		,labels = labels
# 		,srt = 45
# 		,adj = c(1.1,1.1)
# 		,xpd = TRUE
# 	#	,cex = 0.8
# 	)
# 	axis(2
# 		,las=2
# 	#	,cex.axis=0.8
# 	)
# 	## Add text at top of bars
# 	text(x = xx
# 		,y = to.plot
# 		,label = my.percent(to.plot/45)
# 		,pos = 3
# 	#	,cex = 0.7
# 	)	
# 	h1 <- max(to.plot[1:2])
# 	h2 <- max(to.plot[4:5])
# 	lines(x=c(xx[4]-.5,xx[5]+0.5),y=rep(h1,2),lty=1)
# 	lines(x=rep(xx[5]+0.5,2),y=c(h1,h1-2),lty=1)
# 	lines(x=rep(xx[4]-0.5,2),y=c(h1,h1-2),lty=1)
# 	lines(x=c(xx[7]-.5,xx[10]+0.5),y=rep(h2,2),lty=1)
# 	lines(x=rep(xx[10]+0.5,2),y=c(h2,h2-2),lty=1)
# 	lines(x=rep(xx[7]-0.5,2),y=c(h2,h2-2),lty=1)
# 	rm(h1,h2)
# 	text(x = c(xx[4]+0.6, xx[8]+0.6)
# 		,y =  to.plot[c(2,5)]
# 		,label = c("Artificial","De facto")
# 		,pos = 3
# 	#	,cex = 1
# 		,font = c(1,3)
# 	)	
# dev.off()


PlotData <- data.frame(
	Values=with(papers,c(sum(H_Natural)
		,sum(H_Artificial)
		,0
		,sum(H_intentional)
		,sum(H_colateral)
		,0
		,sum(H_wind)
		,sum(H_shipwreck)
		,sum(H_platform)
		,4 # 3 Others + 1 riprap
		))
	,x=letters[1:10]
	,x.labels=c("Natural","Artificial",""
		,"Intentional","De facto",""
		,"Wind farms","Shipwrecks","Oil platforms","Other")
	,Colors=c(rep(cbPalette[[1]],2)
		,NA
		,rep(cbPalette[[2]],2)
		,NA
		,rep(cbPalette[[3]],4)
		)
	)

PlotData$Note <- my.percent(PlotData$Values/45)
PlotData$Note[c(3,6)] <- ""


p <- ggplot(PlotData,aes(x=x,y=Values))
p <- p + geom_bar(stat="identity", fill=PlotData$Colors)
p <- p + scale_y_continuous(limits=c(0,30), expand = c(0.0, 0.00))
p <- p + scale_x_discrete(breaks=letters[c(1,2,4,5,7:10)],labels=PlotData$x.labels[c(-3,-6)])
p <- p + theme_bw()
p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1.1))
p <- p + labs(x="", y="Number of papers")
p <- p + geom_line(data=expand.line(3.5,5.5,21,height=0.5),aes(x=x,y=y))
p <- p + geom_line(data=expand.line(6.5,10.5,14,height=0.5),aes(x=x,y=y))
p <- p + geom_text(x=4.5,y=22.5,label="Artificial",size=2)
p <- p + geom_text(x=8.5,y=15.5,label="de facto",fontface = "italic",size=2)
p <- p + geom_text(aes(x=x,y=Values+1,label=Note),size=2)
p <- p + theme(panel.grid.minor.x = element_blank()
	,panel.grid.major.x = element_blank()
	)
p

ggsave("per_substrate.png",width=4,height=3)
ggsave("per_substrate.pdf",width=4,height=3)

t(t(colnames(papers)))
sum(apply(papers[,39:41],1,sum)>0)/nrow(papers)



# 2019-07-21 comment by Dave and Josianne
# Dave: I think that, in general, the Results do not spend enough time describing 
# interesting results for each ofthe 4 categories of consideration.  For example, 
# add interesting and specific examples of how the various components of a given 
# category (e.g., examples within specieis distribitions, or community, etc.) vary 
# between hardbottom versus other habitats, or within hardbottoms for different species. 
#
# Josianne: Hugo, from your xcel file can you find if thefish community is 
# similar/different in the different catagoreis in figure 4 below. this could be added 
# to the text to provide more information for results to respond to DAve's comment 17.
#

# bergstrom2013	A 	N
# eel 					1		1
# cod 					1		1
# cod 					1		1
# whiting				1		0
# Pflesus				1		1
# Pplatessa			1		1
# Sole					1		1

# bertoncini2010 no significant differences between AR and natural R

# borges2007 - natural only
# AmmSpp
# CapApe
# EngEnc
# MulSur
# SarPil
# TraTra

# castege2016 - artificial only
# DicLab
# EngEnc
# MalVil
# MulSur
# PolPol
# SarPil
# ScoRho
# SolSol
# TraTra

# Fujii2015 - artificial only
# BroBro
# GadMor
# MelAeg
# PolVir

# griffin2016 - artificial only
# MerMer
# ScyCan
# ScySte
# SolSol

# henriques2013 - Natural only
# DicLab
# MulSur
# PagBog
# PolPol

# hunter2009
# GadMor	non significant differences
# PolPol	non significant differences
# PolVir not present in the table