Estimating the benefit of green infrastructure to urban ecosystems: A synthesis and case-study
library(tidyverse)
library(PRISMAstatement)Global urbanization continues unabated, with more than 50% of the worlds’ population living in cities. Cities are conventionally viewed as a threat to local biodiversity because natural habitat is replaced with development. However, more recently, there is greater acknowledgement from the public and private sectors that supporting local environments sustains critical ecosystem services, which in turn improves human health and biodiversity conservation. Consequently, urban planning and design has shifted towards green infrastructure (GI), such as green roofs and retention ponds, to increase connections between city and nature in an era of climate change. The contribution of GI to some ecosystem services has been proven (e.g. stormwater management, building cooling), but the contribution to biodiversity conservation remains unspecified. Using a systematic literature review, this project will (i) determine effect estimates that relate different GI types and characteristics to the impacts on natural systems, and (ii) compile relevant data to develop different implementation scenarios GI for Toronto and region. This study will inform natural system planning and improve quantification of GI on urban ecosystems. Findings from this research will have global ramifications that allow city planners to optimize GI implementation for sustainable development and decrease the impacts of cities on natural systems.
Objectives
- A meta-analysis of the literature summarizing the effect of green infrastructure on natural systems.
- Using Toronto, Ontario as a case study, develop a tool that can communicates the effectiveness of different GI implementation for biodiversity conservation.
Expected Deliverables
- A peer-reviewed journal article that is a meta-analysis from objective 1.
- A tool or data analysis that projects different scenarios of green infrastructure implementation for the City of Toronto.
Timeline
| date | task |
|---|---|
| June 18 | Begin meeting with staff and MacIvor lab to set out workplan |
| June 25 | Begin literature review and data extraction |
| July 2 | Aggregate available data for GI analysis in Toronto |
| July 3 | Complete meetings with TRCA staff on relevant considerations for the project |
| July 9 | Determine important parameters for modelling GI in Toronto |
| August 20 | Complete collection and review of relevant articles |
| September 3 | Conduct meta-analysis on available data |
| September 10 | Propose candidate models for quantifying GI effects for natural systems |
| Sept 24 | Model validation and begin writting manuscript |
| Oct 15 | Complete a draft of manuscript and finalize model |
Literature Review - 1. Search
A systematic literature search will be conducted using Web of Scienc for all articles between 1980 and 2018. This time frame was chosen because it captures the majority of the literature on green infrastructure and with articles that have some measured estimates. The review will include all studies globally. The intended purpose of this search is to capture all articles that have documented both green infrastructure implementation and a measure of natural systems. The search terms that will be used are:
*green infrastructure OR low*impact development OR Sustainable Drainage System* OR Water Sensitive Urban Design OR green*roof AND *diversity OR species OR ecosystem OR ecology OR habitat* OR co-benefit
These terms have returned 871 results (as of June 2018)
Revise list
search1.1 <- read.csv("data/WOS-lit.csv")
search1.2 <- read.csv("data/WoSPart3-July_4_2018.csv")
net.difference <- anti_join(search1.2, search1.1, by = "DOI")
net.difference <- net.difference %>% select(Title, DOI) #to simplify for a look
nrow(net.difference) #count of number of differences from consecutive search## [1] 182## 182 papers to be added by including revised terms
## Select those articles and join with other dataset
net.difference <- anti_join(search1.2, search1.1, by = "DOI")
updated.search <- rbind(search1.1, net.difference)
#write.csv(updated.search, "data/WOS-lit.updated.csv")Adding revised terms from July 3rd meeting added 182 papers Total articles returned = 1,053 (as of July 2018)
## Adding terms for naturalized pond and pollinator garden
search1.2 <- read.csv("data/WOS-lit.updated.csv")
search1.3 <- read.csv("data/WoSPart4-July_11_2018.csv")
net.difference <- anti_join(search1.3, search1.2, by = "DOI")
net.difference <- net.difference %>% select(Title, DOI) #to simplify for a look
nrow(net.difference) #count of number of differences from consecutive search## [1] 0## 213 papers to be added by including revised terms
## Select those articles and join with other dataset
net.difference <- anti_join(search1.3, search1.2, by = "DOI")
updated.search <- rbind(search1.2, net.difference)
#write.csv(updated.search, "data/WOS-lit.updated.csv")Adding revised terms from July 3rd meeting added 213 papers Total articles returned = 1,224 (as of July 2018)
Reviewers comments to add papers
search1.4 <- read.csv("data/WOS-lit.updated.csv")
reviewer <- read.csv("data//reviewer.updated2019.csv")
net.difference <- anti_join( reviewer, search1.4, by = "DOI")## Warning: Column `DOI` joining factors with different levels, coercing to
## character vectornet.difference <- net.difference %>% select(Title, DOI) #to simplify for a look
nrow(net.difference) #count of number of differences from consecutive search## [1] 579str(net.difference)## 'data.frame': 579 obs. of 2 variables:
## $ Title: Factor w/ 720 levels "A Comparative Approach to Artificial and Natural Green Walls According to Ecological Sustainability",..: 446 437 372 474 429 262 203 697 448 153 ...
## $ DOI : Factor w/ 720 levels "","10.1002/(SICI)1099-1085(20000415)14:5<867::AID-HYP975>3.0.CO;2-5",..: 618 407 620 458 404 545 2 406 164 588 ...write.csv(net.difference, "new.papers.csv", row.names=FALSE)Literature Review - 2. Sort
This steps includes a. checking for duplicating, b. reviewing each instance for relevancy, c. consistently identifying and documenting exclusion criteria. Outcomes include a list of publications to be used for synthesis, a library of pdfs, and a PRISMA report to ensure the worflow is transparent and reproducible. Papers were excluded with the following characteristics:
- Not emperical study (e.g. review, book chapter)
- Irrelevant categories (e.g. political science, law, sports tourism, art)
evidence <- read.csv("data/evidence.updated.csv")
### Identify studies that were excluded
excludes <- evidence %>% group_by(reason) %>% count(exclude) %>% filter(reason!="")
ggplot(excludes, aes(x=reason, y=n)) + geom_bar(stat="identity") + coord_flip()
### Proportion excluded
excludes %>% mutate(percent=n/1140*100) %>% data.frame(.)## reason exclude n percent
## 1 city or greenspace planning y 136 11.9298246
## 2 civil engineering y 69 6.0526316
## 3 climate adaptation y 38 3.3333333
## 4 conceptual framework y 55 4.8245614
## 5 ecology study n 1 0.0877193
## 6 ecology study y 246 21.5789474
## 7 economics y 32 2.8070175
## 8 energy y 6 0.5263158
## 9 food security/agriculture n 1 0.0877193
## 10 food security/agriculture y 64 5.6140351
## 11 GI technology n 1 0.0877193
## 12 GI technology y 135 11.8421053
## 13 industrial ecology y 24 2.1052632
## 14 methods y 1 0.0877193
## 15 modelling y 79 6.9298246
## 16 no comparison y 10 0.8771930
## 17 policy y 63 5.5263158
## 18 regulatory/ecosystem services y 393 34.4736842
## 19 review n 1 0.0877193
## 20 review y 105 9.2105263
## 21 social impacts/human health y 115 10.0877193
## 22 urban ecology/human disturbance y 112 9.8245614## frequency of study
year.rate <- evidence %>% group_by(Year) %>% summarize(n=length(Year))
ggplot(tail(year.rate,20)) + geom_bar(aes(x=Year, y=n), stat="identity") + ylab("number of published studies") +xlab("year published") +theme(text = element_text(size=16))
Papers processed - Progress
## Completed so far
prog <- sum(evidence$exclude!="")
prog## [1] 1845## Remaining
total <- nrow(evidence)
total## [1] 1845setTxtProgressBar(txtProgressBar(0,total, style = 3), prog)##
|
| | 0%
|
|=================================================================| 100%Initial pass for relevant papers complete.
Description of studies
GI.type <- evidence %>% group_by(GI.type) %>% count(exclude) %>% filter(GI.type!="")
ggplot(GI.type, aes(x=GI.type, y=n)) + geom_bar(stat="identity") + coord_flip()
Representations of relevant GI types found in papers
Prisma report
## total number of papers found
nrow(evidence)## [1] 1845## number of papers found outside of WoS
other <- read.csv("data/other.sources.csv")
nrow(other)## [1] 28## number of articles excluded
excludes <- evidence %>% filter(exclude=="y")
nrow(excludes)## [1] 1683## relevant papers
review <- evidence %>% filter(exclude!="y")
nrow(review)## [1] 162## papers for meta
meta <- evidence %>% filter(meta.=="yes")
nrow(meta)## [1] 132prisma(found = 1855,
found_other = 28,
no_dupes = 1883,
screened = 1883,
screen_exclusions = 0,
full_text = 1883,
full_text_exclusions = 1721,
qualitative = 162,
quantitative = 33,
width = 800, height = 800)## Loading required namespace: DiagrammeRLiterature Review - 3. Synthesis
The research questions we are exploring:
- What are the patterns of GI studies globally
- How does green infrastructure compare to conventional “grey” equivalents (e.g. green roof to conventional roof)?
- How does green infrastructure compare to its natural equivalents (e.g. retention ponds )?
- What features of green infrastructure can improve the quality of natural systems?
Patterns of GI Studies Globally
require(ggmap)
### Start with base map of world
mp <- NULL
mapWorld <- borders("world", colour="gray50", fill="gray50") # create a layer of borders
mp <- ggplot() + mapWorld
## colorblind-friendly palette
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7","#000000")
meta <- read.csv("data//evidence.updated.csv")
meta <- subset(meta, GI.type!="")
## plot points on top
mp <- mp+ geom_point(data=meta , aes(x=lon, y=lat, color=GI.type), size=2) + scale_colour_manual(values=cbPalette)+
theme(legend.position="bottom", text = element_text(size=20))
mp
## Number of studies extracted from online data
occurdat<-list.files("data//MS.data",pattern=".csv$",full=T)
length(occurdat)## [1] 78## 70 Studies found with usable data for synthesisFrequency of GI types and taxa
meta <- read.csv("data//Master.GI.Datasets.csv")
freq.GI <- meta %>% filter(Infrastructure!="grey" & Habitat!="Natural") %>% group_by(GI.type, Taxa.simplified) %>% summarize(n=length(unique(Study))) %>% data.frame(.)
table.GI <- freq.GI %>% spread(Taxa.simplified, n, fill=0)
#write.csv(table.GI, "Table.GI.csv")Green infrastructure comparison to conventional
## load master datasets
meta <- read.csv("data//Master.GI.Datasets.csv")
## Omit repo 3 because duplicated with study 1305 and remove repo-9 because not equivalent GI comparisons. Removed Repo 3 because compare roof with ground. Omitted 1307 because duplicate with 1776
meta <- meta %>% filter(Study != "repo.3" & Study!="repo.9" & Study!="repo.1" & Study != 1307)
## Drop relative abundance because difference = 0
meta <- meta %>% filter(Estimate!="Relative.Abundance")
## convert SE to SD
meta[meta$Stat == "se", "Value"] <- meta[meta$Stat == "se", "Value"]*sqrt(meta[meta$Stat == "se", "replicate"])
meta[meta$Stat == "se", "Stat"] <- "sd"
## Load packages and functions
library(reshape2)
library(metafor)
source("meta.eval.r") ## Multiple aggregate
## Create Unique identifier column
meta2 <- meta
meta2[,"UniqueSite"] <- paste(meta2$Study, meta2$Taxa.simplified, meta2$GI.compare, meta$Estimate, sep="-")
meta3 <- meta2 %>% filter(Infrastructure != "natural") %>% filter()
## Use function to extract summary statistics for comparisons
## meta.eval arguments are (meta.data, compare, ids , stats)
Infra.compare <- meta.eval(meta3, Infrastructure, UniqueSite, Stat )
## Combine the lists into same dataframe
## Rename Columns in second dataframe
Infra.stat <- Infra.compare[[2]] ## extracted statistics
names(Infra.stat) <- c("UniqueSite","green_mean","green_sd","grey_mean","grey_sd","green_n","grey_n") ## rename columns to match
Infra.raw <- Infra.compare[[1]] ## calculated statistics from raw values
## Join two dataframes
meta.stat <- rbind(Infra.raw, Infra.stat[, names(Infra.raw)])
meta.ready <- escalc(n1i = grey_n, n2i = green_n, m1i = grey_mean, m2i = green_mean, sd1i = grey_sd, sd2i = green_sd, data = meta.stat, measure = "SMD", append = TRUE)
## separate out the identifiers
siteID <- matrix(unlist(strsplit(meta.ready$UniqueSite,"-")),ncol=4, byrow=TRUE)
siteID <- data.frame(siteID) ## recreate as dataframe
colnames(siteID) <- c("Study","taxa","GI.compare","measure") ## add column names
meta.ready <- cbind(data.frame(meta.ready), siteID)
#random-effects meta-analysis for green infrastructure vs grey
m1 <- rma(yi=yi, vi=vi, data = meta.ready)
summary(m1) ##
## Random-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -24.1396 48.2792 52.2792 53.5573 53.3701
##
## tau^2 (estimated amount of total heterogeneity): 1.4422 (SE = 0.6306)
## tau (square root of estimated tau^2 value): 1.2009
## I^2 (total heterogeneity / total variability): 90.13%
## H^2 (total variability / sampling variability): 10.14
##
## Test for Heterogeneity:
## Q(df = 14) = 84.1182, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.9963 0.3340 -2.9835 0.0029 -1.6509 -0.3418 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#mixed-effects meta-analysis for green infrastructure vs grey
m2 <- rma(yi=yi, vi=vi, mods=~GI.compare-1, data = meta.ready)
summary(m2) ##
## Mixed-Effects Model (k = 15; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -15.8804 31.7607 43.7607 45.5762 71.7607
##
## tau^2 (estimated amount of residual heterogeneity): 1.0449 (SE = 0.5615)
## tau (square root of estimated tau^2 value): 1.0222
## I^2 (residual heterogeneity / unaccounted variability): 86.95%
## H^2 (unaccounted variability / sampling variability): 7.66
##
## Test for Residual Heterogeneity:
## QE(df = 10) = 49.9265, p-val < .0001
##
## Test of Moderators (coefficient(s) 1:5):
## QM(df = 5) = 19.9972, p-val = 0.0013
##
## Model Results:
##
## estimate se zval pval ci.lb
## GI.comparegarden 1.3477 1.1626 1.1592 0.2464 -0.9309
## GI.compareretention pond -0.8800 0.6308 -1.3950 0.1630 -2.1163
## GI.compareroadsides -3.5728 1.2506 -2.8568 0.0043 -6.0239
## GI.compareroof -1.1348 0.4365 -2.6001 0.0093 -1.9903
## GI.comparewall -0.8206 0.6141 -1.3362 0.1815 -2.0242
## ci.ub
## GI.comparegarden 3.6264
## GI.compareretention pond 0.3564
## GI.compareroadsides -1.1216 **
## GI.compareroof -0.2794 **
## GI.comparewall 0.3831
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Produce a forest plot to determine the effect sizes for each study
forest(m1, slab = meta.stat$UniqueSite)
## Check for publication bias
## The symetrical distriubtion suggests there is no publication bias
funnel(m1)
## Calculate rosenthals Failsafe number
fsn(yi, vi, data=meta.ready)##
## Fail-safe N Calculation Using the Rosenthal Approach
##
## Observed Significance Level: <.0001
## Target Significance Level: 0.05
##
## Fail-safe N: 432### plot Forest plot with each subgroup
res.w <- rma(yi=yi, vi=vi, data = meta.ready,
subset=(GI.compare=="wall"))
res.r <- rma(yi=yi, vi=vi, data = meta.ready,
subset=(GI.compare=="roof"))
res.p <- rma(yi=yi, vi=vi, data = meta.ready,
subset=(GI.compare=="retention pond"))
res.rd <- rma(yi=yi, vi=vi, data = meta.ready,
subset=(GI.compare=="roadsides"))
# ## generate plot with spaces inbetween
# forest(m1, atransf=exp, cex=0.75, ylim=c(-1, 24),
# order=order(meta.ready$GI.compare,meta.ready$taxa), rows=c(3:4,7,10:16,19:21),
# # mlab="RE model for all studies", psize=1, slab= paste(meta.ready$Study, meta.ready$taxa, meta.ready$measure))
#
# addpoly(res.w, row=18, cex=0.75, atransf=exp, mlab="RE model for green wall")
# addpoly(res.r, row= 9, cex=0.75, atransf=exp, mlab="RE model for green roof")
# addpoly(res.rd, row= 6, cex=0.75, atransf=exp, mlab="RE model for roadsides")
# addpoly(res.p, row= 2, cex=0.75, atransf=exp, mlab="RE model for retention ponds")Green infrastructure comparison to natural equivalent
## Create Unique identifier column
meta2 <- meta
meta2[,"UniqueSite"] <- paste(meta2$Study, meta2$Taxa.simplified, meta2$Nat.compare, meta2$Estimate, sep="-")
## Remove comparisons except urban and rural
meta2 <- meta2 %>% filter(Habitat == "urban" | Habitat == "natural") %>% filter (Nat.compare != "park") %>% filter(Study != 1156)
## Determine the number of comparisons available
compare.eval(meta2, Habitat, UniqueSite)## [1] 41## Use function to extract summary statistics for comparisons
## meta.eval arguments are (meta.data, compare, ids , stats)
nat.compare <- meta.eval(meta2, Habitat, UniqueSite, Stat )
## Combine the lists into same dataframe
## Rename Columns in second dataframe
nat.stat <- nat.compare[[2]] ## extracted statistics
names(nat.stat) <- c("UniqueSite","natural_mean","natural_sd","urban_mean","urban_sd","natural_n","urban_n") ## rename columns to match
nat.raw <- nat.compare[[1]] ## calculated statistics from raw values
## Join two dataframes
meta.stat <- rbind(nat.raw, nat.stat[, names(nat.raw)])
meta.ready <- escalc(n1i = urban_n, n2i = natural_n, m1i = urban_mean, m2i = natural_mean, sd1i = urban_sd, sd2i = natural_sd, data = meta.stat, measure = "SMD", append = TRUE)
## separate out the identifiers
siteID <- matrix(unlist(strsplit(meta.ready$UniqueSite,"-")),ncol=4, byrow=TRUE)
siteID <- data.frame(siteID) ## recreate as dataframe
colnames(siteID) <- c("Study","taxa","GI.type","measure") ## add column names
meta.ready <- cbind(data.frame(meta.ready), siteID)
#random-effects meta-analysis for urban GI vs natural
m1 <- rma(yi, vi, data = meta.ready)
summary(m1) ##
## Random-Effects Model (k = 40; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -54.1794 108.3588 112.3588 115.6860 112.6922
##
## tau^2 (estimated amount of total heterogeneity): 0.7070 (SE = 0.1976)
## tau (square root of estimated tau^2 value): 0.8408
## I^2 (total heterogeneity / total variability): 89.12%
## H^2 (total variability / sampling variability): 9.19
##
## Test for Heterogeneity:
## Q(df = 39) = 370.7101, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1979 0.1493 1.3262 0.1848 -0.0946 0.4905
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Natural vs Urban GI
## Produce a forest plot to determine the effect sizes for each study
forest(m1, slab = meta.stat$UniqueSite, order=order(meta.ready$GI.type,meta.ready$taxa))
## Check for publication bias
## The symetrical distriubtion suggests there is no publication bias
funnel(m1)
#mixed-effects meta-analysis for green infrastructure vs grey
m2 <- rma(yi=yi, vi=vi, mods=~GI.type-1, data = meta.ready)
summary(m2) ##
## Mixed-Effects Model (k = 40; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -41.5857 83.1713 97.1713 107.8559 101.4790
##
## tau^2 (estimated amount of residual heterogeneity): 0.4192 (SE = 0.1391)
## tau (square root of estimated tau^2 value): 0.6474
## I^2 (residual heterogeneity / unaccounted variability): 82.11%
## H^2 (unaccounted variability / sampling variability): 5.59
##
## Test for Residual Heterogeneity:
## QE(df = 34) = 213.6955, p-val < .0001
##
## Test of Moderators (coefficient(s) 1:6):
## QM(df = 6) = 25.7780, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval
## GI.typegreen roof vs. grassland -0.3186 0.4137 -0.7703 0.4411
## GI.typepond 0.2101 0.1570 1.3385 0.1807
## GI.typeroadsides vs forest 0.0737 0.3691 0.1997 0.8417
## GI.typeurban garden vs. forest -0.5099 0.3425 -1.4887 0.1366
## GI.typeurban garden vs. meadow 1.7735 0.7047 2.5168 0.0118
## GI.typewetland vs. bioswale 1.9298 0.5016 3.8474 0.0001
## ci.lb ci.ub
## GI.typegreen roof vs. grassland -1.1294 0.4921
## GI.typepond -0.0975 0.5177
## GI.typeroadsides vs forest -0.6497 0.7971
## GI.typeurban garden vs. forest -1.1812 0.1614
## GI.typeurban garden vs. meadow 0.3924 3.1546 *
## GI.typewetland vs. bioswale 0.9467 2.9129 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Calculate rosenthals Failsafe number
fsn(yi, vi, data=meta.ready)##
## Fail-safe N Calculation Using the Rosenthal Approach
##
## Observed Significance Level: <.0001
## Target Significance Level: 0.05
##
## Fail-safe N: 230Green infrastructure features in relation to measures of green infrastructure
## The area of green infrastructure
names(meta)[20:21] <- c("GI.area","height")
meta.area <- subset(meta, GI.area>0)
## omit Study 536 & 1304 because raw counts
meta.area <- subset(meta.area, Study != 536 & Study != 1304)
## Determine unique identifier
meta.area[,"UniqueSite"] <- paste(meta.area$Study, meta.area$Taxa.simplified, meta.area$Nat.compare, meta.area$Estimate, sep="-")
## Summarize average richness by area sizes
area.stat <- meta.area %>% filter(Stat=="count" | Stat=="mean") %>% filter(Estimate=="richness") %>% group_by(Study, GI.area, GI.type) %>% summarize(val=mean(Value)) %>% data.frame(.)
area.rich <- area.stat %>% filter(GI.type=="green roof" | GI.type=="green wall" | GI.type=="yards/home gardens" | GI.type=="public/community gardens")
library(ggplot2)
## Species richness per area
ggplot(area.rich, aes(x=GI.area, y=val, color=GI.type) ) + geom_point(size=3) + theme_bw() + scale_color_brewer(palette="Set2") + ylab("Average species richness") + xlab(expression("Average area of green infrastructure (m"^2*")"))+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + stat_smooth(method="lm", formula= y~poly(x,2),aes(x=GI.area, y=val), color="#181818", fill="#80808080", data=area.rich)
m1 <- lm(val~poly(GI.area,2), data=area.rich)
summary(m1)##
## Call:
## lm(formula = val ~ poly(GI.area, 2), data = area.rich)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3192 -0.8696 -0.0566 1.0480 7.7911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.8601 0.1859 26.141 < 2e-16 ***
## poly(GI.area, 2)1 9.9386 1.7929 5.543 2.94e-07 ***
## poly(GI.area, 2)2 -7.1637 1.7929 -3.996 0.000132 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.793 on 90 degrees of freedom
## Multiple R-squared: 0.3416, Adjusted R-squared: 0.3269
## F-statistic: 23.35 on 2 and 90 DF, p-value: 6.802e-09## Summarize average abundance by area sizes
area.stat <- meta.area %>% filter(Stat=="count" | Stat=="mean") %>% filter(Estimate=="abundance") %>% group_by(Study, GI.area, GI.type) %>% summarize(val=mean(Value)) %>% data.frame(.)
area.abd <- area.stat %>% filter(GI.type=="green roof" | GI.type=="green wall" | GI.type=="yards/home gardens" | GI.type=="public/community gardens") %>%
filter(GI.area<50000) %>% ## keep numbers approximately similar - removed outlier of 200,000
filter(Study != 1299 & Study != 1127)
## Species richness per area
ggplot(area.abd, aes(x=GI.area, y=val, color=GI.type) ) + geom_point(size=3) + theme_bw() + scale_color_brewer(palette="Set2") + ylab("Average abundance of species") + xlab(expression("Average area of green infrastructure (m"^2*")"))+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
m2 <- lm(val~GI.area, data=area.abd)
summary(m2)##
## Call:
## lm(formula = val ~ GI.area, data = area.abd)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.825 -21.094 1.306 10.658 82.693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.8430165 2.3709545 11.743 <2e-16 ***
## GI.area -0.0002494 0.0009327 -0.267 0.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.85 on 119 degrees of freedom
## Multiple R-squared: 0.0006004, Adjusted R-squared: -0.007798
## F-statistic: 0.07149 on 1 and 119 DF, p-value: 0.7896## Compare pH for abundance
ph.data <- meta %>% filter(pH>0) %>% filter(GI.type=="retention pond" | GI.type=="natural water")
ph.abd <- ph.data %>% filter(Estimate=="abundance" & Stat=="count")%>% group_by(pH, GI.type) %>% summarize(val=mean(Value)) %>% data.frame(.)
## plot richness against pH
ggplot(ph.abd, aes(x=pH, y=val, color=GI.type) ) + geom_point(size=3) + theme_bw() + scale_color_brewer(palette="Set2") + ylab("Average Richness") + xlab("Average pH of Green Infrastructure")+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
## Compare pH for occurrence
ph.occ <- ph.data %>% filter(Estimate=="occurrence")%>% group_by(pH, GI.type) %>% summarize(val=mean(Value)) %>% data.frame(.)
m1 <- glm(Value~ pH, family=binomial, data=ph.data %>% filter(Estimate=="occurrence"))
summary(m1)##
## Call:
## glm(formula = Value ~ pH, family = binomial, data = ph.data %>%
## filter(Estimate == "occurrence"))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8915 -0.8488 -0.8377 1.5229 1.6131
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.22413 1.45261 -0.154 0.877
## pH -0.08643 0.20324 -0.425 0.671
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 308.55 on 251 degrees of freedom
## Residual deviance: 308.37 on 250 degrees of freedom
## AIC: 312.37
##
## Number of Fisher Scoring iterations: 4## plot richness against pH
ggplot(ph.occ, aes(x=pH, y=val, color=GI.type) ) + geom_point(size=3) + theme_bw() + scale_color_brewer(palette="Set2") + ylab("Average Occurrence") + xlab("Average pH of Green Infrastructure")+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
## plot richness against height
h.rich <- meta %>% filter(height>0) %>% filter(Estimate=="richness") %>% filter(Stat=="count" | Stat=="mean") %>% group_by(height, GI.type) %>% summarize(val=mean(Value)) %>% data.frame(.)
ggplot(h.rich, aes(x=height, y=val, color=GI.type) ) + geom_point(size=3) + theme_bw() + scale_color_brewer(palette="Set2") + ylab("Average Richness") + xlab("Average Height of Green Infrastructure")+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
## plot abundance against height
h.abd <- meta %>% filter(height>0) %>% filter(Estimate=="abundance") %>% filter(Stat=="count" | Stat=="mean") %>% group_by(height, GI.type) %>% summarize(val=mean(Value)) %>% data.frame(.)
ggplot(h.abd, aes(x=height, y=val, color=GI.type) ) + geom_point(size=3) + theme_bw() + scale_color_brewer(palette="Set2") + ylab("Average Abundance") + xlab("Average Height of Green Infrastructure")+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
## Pond salinity
salt <- subset(meta, Salinity>0)
length(unique(salt$Salinity)) ## not enough samples for a meaningful comparison## [1] 4## Pond depth
deep <- subset(meta, depth..m.>0)
length(unique(deep$depth..m.)) ## not enough samples for a meaningful comparison## [1] 8Technical report analyses
meta <- read.csv("data//Master.GI.Datasets.csv")
spp.n <- meta %>% group_by(Genus) %>% summarize(n=length(Species)) %>% arrange(-n) %>% data.frame()
spp.n## Genus n
## 1 18401
## 2 Columba 2338
## 3 Passer 1843
## 4 Turdus 1001
## 5 Vanessa 699
## 6 Andrena 696
## 7 Bombus 555
## 8 Pieris 515
## 9 Lasioglossum 440
## 10 Mimus 367
## 11 Trifolium 351
## 12 Sturnidae 344
## 13 Prunella 331
## 14 Corvus 310
## 15 Sturnus 257
## 16 bromus 232
## 17 polygonum 232
## 18 Hylaeus 205
## 19 Rana 202
## 20 Nomada 199
## 21 Megachile 192
## 22 Cracticus 189
## 23 Cupido 180
## 24 Locustella 177
## 25 Osmia 165
## 26 Parus 164
## 27 Chaetura 158
## 28 Halictus 157
## 29 Anthochaera 139
## 30 unknown 139
## 31 Cyanistes 138
## 32 Larus 138
## 33 Sedum 132
## 34 Epipactis 130
## 35 Senecio 126
## 36 Asteraceae 124
## 37 Cheilosia 120
## 38 Veronica 117
## 39 bidens 116
## 40 Harpalus 116
## 41 ipomoea 116
## 42 melilotus 116
## 43 Mirini 116
## 44 plantago 116
## 45 poa 116
## 46 potentilla 116
## 47 solidago 116
## 48 symphyotrichum 116
## 49 trifolium 116
## 50 ulmus 116
## 51 Ranunculus 115
## 52 Acer 113
## 53 Chloris 111
## 54 Prunus 111
## 55 Sonchus 111
## 56 Mutusca 110
## 57 Trichoglossus 109
## 58 Nysius 107
## 59 Bellis 106
## 60 Sphecodes 105
## 61 Chaetedus 101
## 62 Solanum 101
## 63 Manorina 100
## 64 Geranium 99
## 65 Rosa 96
## 66 Schinus 90
## 67 Spilopelia 90
## 68 Pica 89
## 69 Amara 83
## 70 Grallina 83
## 71 Acridotheres 80
## 72 Bufo 79
## 73 Tanacetum 78
## 74 Euphorbia 77
## 75 Oxalis 76
## 76 Vicia 76
## 77 Primula 74
## 78 Sorbus 74
## 79 Eupeodes 73
## 80 Poa 73
## 81 Eristalis 72
## 82 Platycherius 72
## 83 Remaudiereana 72
## 84 Hypericum 70
## 85 Stellaria 69
## 86 Cirsium 68
## 87 Juncus 68
## 88 Viola 68
## 89 Cardamine 67
## 90 Aglais 66
## 91 Taraxacum 65
## 92 Lomandra 64
## 93 Platycercus 64
## 94 Coelioxys 63
## 95 Lamium 63
## 96 Goodenia 62
## 97 Plantago 62
## 98 Dianthus 61
## 99 Galium 60
## 100 Longitarsus 60
## 101 Medicago 60
## 102 Nabis 60
## 103 Ocyphaps 60
## 104 Stenophyella 60
## 105 acalypha 58
## 106 acer 58
## 107 ageratina 58
## 108 ailanthus 58
## 109 alliaria 58
## 110 Allium 58
## 111 amaranthus 58
## 112 ambrosia 58
## 113 arctium 58
## 114 artemisia 58
## 115 asclepias 58
## 116 calystegia 58
## 117 capsella 58
## 118 catalpa 58
## 119 celastrus 58
## 120 celtis 58
## 121 chamaesyce 58
## 122 chenopodium 58
## 123 cichorium 58
## 124 cirsium 58
## 125 clematis 58
## 126 commelina 58
## 127 convolvulus 58
## 128 conyza 58
## 129 cynodon 58
## 130 cyperus 58
## 131 dactylis 58
## 132 daucus 58
## 133 digitaria 58
## 134 duchesnea 58
## 135 echinochloa 58
## 136 elymus 58
## 137 erigeron 58
## 138 euonymus 58
## 139 festuca 58
## 140 galinsoga 58
## 141 glechoma 58
## 142 hedera 58
## 143 hypochaeris 58
## 144 juglans 58
## 145 juncus 58
## 146 lactuca 58
## 147 lepidium 58
## 148 lolium 58
## 149 lonicera 58
## 150 lotus 58
## 151 medicago 58
## 152 microstegium 58
## 153 morus 58
## 154 oxalis 58
## 155 parthenocissus 58
## 156 paulownia 58
## 157 phytolacca 58
## 158 platanus 58
## 159 quercus 58
## 160 rosa 58
## 161 rubus 58
## 162 rumex 58
## 163 securigera 58
## 164 senecio 58
## 165 setaria 58
## 166 sisymbrium 58
## 167 sonchus 58
## 168 taraxacum 58
## 169 toxicodendron 58
## 170 veronica 58
## 171 vicia 58
## 172 viola 58
## 173 vitis 58
## 174 Wahlenbergia 58
## 175 Bursaria 57
## 176 Eucalyptus 57
## 177 Raphanus 56
## 178 Anagallis 55
## 179 Polygonum 55
## 180 Hirundo 54
## 181 Pararge 53
## 182 Apis 52
## 183 Rosmarinus 52
## 184 Calystegia 51
## 185 Ligustrum 51
## 186 Rubus 51
## 187 Salvia 51
## 188 Spiraea 51
## 189 Convolvulus 50
## 190 Pelargonium 50
## 191 Sitona 50
## 192 Acacia 49
## 193 Lavandula 49
## 194 Stachys 49
## 195 Syrphus 49
## 196 Dasysyrphus 48
## 197 Galenia 48
## 198 Helophilus 48
## 199 Malva 48
## 200 Melanostoma 48
## 201 plant 48
## 202 Potentilla 48
## 203 Sidnia 48
## 204 Volucella 48
## 205 Xylota 48
## 206 Cardinalis 47
## 207 Epilobium 47
## 208 Festuca 47
## 209 Capsella 46
## 210 Myoporum 46
## 211 Canna 45
## 212 Colletes 45
## 213 Mentha 45
## 214 Ocirrhoe 45
## 215 Sylvia 45
## 216 Conyza 44
## 217 Lapsana 44
## 218 Glossopsitta 43
## 219 Anthidium 42
## 220 Chelostoma 42
## 221 Glechoma 42
## 222 Hyla 42
## 223 Limanarium 42
## 224 Melitta 42
## 225 Santolina 42
## 226 Cerastium 41
## 227 Cuspicona 41
## 228 Lissotriton 41
## 229 Melaleuca 41
## 230 Achillea 40
## 231 Olearia 40
## 232 Spinus 40
## 233 Buddleja 39
## 234 Carex 39
## 235 Cacatua 38
## 236 Plectranthus 38
## 237 Zenaida 38
## 238 Begonia 37
## 239 Iris 37
## 240 Lolium 37
## 241 Agapanthus 36
## 242 Cotoneaster 36
## 243 Leucophaeus 36
## 244 Nepeta 36
## 245 Orthotylinae 36
## 246 Picris 36
## 247 Pseudacris 36
## 248 Silene 36
## 249 Halticus 35
## 250 Rumex 35
## 251 Anthophora 34
## 252 Bombycilla 34
## 253 Dactylis 34
## 254 Fraxinus 34
## 255 Pardalotus 34
## 256 Petunia 34
## 257 Ajuga 33
## 258 Atriplex 33
## 259 Lysimachia 33
## 260 Melissodes 33
## 261 Tagetes 33
## 262 Viburnum 33
## 263 Aquilegia 32
## 264 Campanula 32
## 265 Dietes 32
## 266 Ficinia 32
## 267 Gahnia 32
## 268 Gaura 32
## 269 Haemorhous 32
## 270 Lotus 32
## 271 Papaver 32
## 272 Salix 32
## 273 Fringilla 31
## 274 Hieracium 31
## 275 Atheta 30
## 276 Centaurea 30
## 277 Lathyrus 30
## 278 Lonicera 30
## 279 Melospiza 30
## 280 Myosotis 30
## 281 Phylinae 30
## 282 Thymus 30
## 283 Aesculus 29
## 284 Argyranthemum 29
## 285 Chenopodium 29
## 286 Crepis 29
## 287 Eolophus 29
## 288 Heriades 29
## 289 Leucanthemum 29
## 290 Pentatomidae 29
## 291 Crataegus 28
## 292 Hosta 28
## 293 Lychnis 28
## 294 Ageratum 27
## 295 Alyssum 27
## 296 Anemone 27
## 297 Anthriscus 27
## 298 Chelidonium 27
## 299 Cornus 27
## 300 Digitalis 27
## 301 Lilium 27
## 302 Ribes 27
## 303 Syringa 27
## 304 Tilia 27
## 305 Urtica 27
## 306 Alliaria 26
## 307 Anisodactylus 26
## 308 Berberis 26
## 309 Ceratostigma 26
## 310 Chinoneides 26
## 311 Malus 26
## 312 Muscari 26
## 313 Rhododendron 26
## 314 Symphytum 26
## 315 Trachelospermum 26
## 316 Acanthiza 25
## 317 Duchesnea 25
## 318 Erysimum 25
## 319 Fuchsia 25
## 320 Hedera 25
## 321 Helichrysum 25
## 322 Hypera 25
## 323 Iberis 25
## 324 Ilex 25
## 325 Laburnum 25
## 326 Leontodon 25
## 327 Magnolia 25
## 328 Narcissus 25
## 329 Philonthus 25
## 330 Pulmonaria 25
## 331 Quedius 25
## 332 Solidago 25
## 333 Tachyporus 25
## 334 Tripleurospermum 25
## 335 Agrostis 24
## 336 Alchemilla 24
## 337 Aubretia 24
## 338 Baccha 24
## 339 Calendula 24
## 340 Chalcosyrphus 24
## 341 Cheilosa 24
## 342 Choisya 24
## 343 Chrysogaster 24
## 344 Circaea 24
## 345 Claytonia 24
## 346 Colpodes 24
## 347 Crompus 24
## 348 Deraeocorini 24
## 349 Dictyotus 24
## 350 Dicyphini 24
## 351 Dilompus 24
## 352 Epistrophe 24
## 353 Episyrphus 24
## 354 Erica 24
## 355 Eumerus 24
## 356 Eupolemus 24
## 357 Ferdinandea 24
## 358 Hebe 24
## 359 Heringia 24
## 360 Hyacinthoides 24
## 361 Inula 24
## 362 Itea 24
## 363 Large 24
## 364 Liriodendron 24
## 365 Matricaria 24
## 366 Meconopsis 24
## 367 Melangyna 24
## 368 Melanogaster 24
## 369 Meliscaeva 24
## 370 Merodon 24
## 371 Myathropa 24
## 372 Neoascia 24
## 373 Nigella 24
## 374 Orchis 24
## 375 Oxycarenus 24
## 376 Pansy 24
## 377 Parasyrphus 24
## 378 Phygelius 24
## 379 Pilosella 24
## 380 Platycheirus 24
## 381 Plinthisus 24
## 382 Polyommatus 24
## 383 Portevinia 24
## 384 Pseudofumaria 24
## 385 Rhinanthus 24
## 386 Rhingia 24
## 387 Rhipidura 24
## 388 Riponnensia 24
## 389 Sambucas 24
## 390 Scaeva 24
## 391 Small 24
## 392 Sphaerophoria 24
## 393 Syritta 24
## 394 Tingis 24
## 395 Ulex 24
## 396 Vanellus 24
## 397 Violet 24
## 398 Weigelia 24
## 399 Xanthogramma 24
## 400 Yellow 24
## 401 Bembidion 23
## 402 Galinsoga 23
## 403 Pinus 23
## 404 Artemisia 22
## 405 Baptisia 22
## 406 Correa 22
## 407 Hypochaeris 22
## 408 Orius 22
## 409 Verbena 22
## 410 Xerochrysum 22
## 411 Celtis 21
## 412 Lythrum 21
## 413 Aleochara 20
## 414 Anas 20
## 415 Eysarcoris 20
## 416 Mcateella 20
## 417 Phyllotreta 20
## 418 Protapion 20
## 419 Amorbus 19
## 420 Clematis 19
## 421 Falco 19
## 422 Nebria 19
## 423 Stephanitis 19
## 424 Amaranthus 18
## 425 Baclozygum 18
## 426 Coridromius 18
## 427 Emesinae 18
## 428 Lagerstraemia 18
## 429 Megachilie 18
## 430 Phaps 18
## 431 Ptilotula 18
## 432 Brentiscerus 17
## 433 Erythrina 17
## 434 Grevillea 17
## 435 Robinia 17
## 436 Abelia 16
## 437 Agelaius 16
## 438 Brachyscome 16
## 439 Dindymus 16
## 440 Ozothamnus 16
## 441 Parietaria 16
## 442 Pheropsophus 16
## 443 Sericornis 16
## 444 Synuchus 16
## 445 Agriotes 15
## 446 Atomaria 15
## 447 Cermatulus 15
## 448 Cryptocephalus 15
## 449 Cymodema 15
## 450 Froggattia 15
## 451 Gabrius 15
## 452 Gminatus 15
## 453 Mictis 15
## 454 Nerium 15
## 455 Platystethus 15
## 456 Sambucus 15
## 457 Stenus 15
## 458 Tychius 15
## 459 Ceratina 14
## 460 Certhia 14
## 461 Corvidae 14
## 462 Lamiaceae 14
## 463 Lepidium 14
## 464 Lithobates 14
## 465 Pterostichus 14
## 466 Rudbeckia 14
## 467 Ulmus 14
## 468 Lactuca 13
## 469 Malurus 13
## 470 Unknown 13
## 471 Agapostemon 12
## 472 Angiozanthos 12
## 473 Anischys 12
## 474 Bauhinia 12
## 475 Buchananiella 12
## 476 Cletus 12
## 477 Coranus 12
## 478 Creontiades 12
## 479 Cryptorhamphus 12
## 480 Culex 12
## 481 Dianella 12
## 482 Dicrotelus 12
## 483 Dieuches 12
## 484 Diplocysta 12
## 485 Echium 12
## 486 Eribotes 12
## 487 Eritingis 12
## 488 Euander 12
## 489 Eucera 12
## 490 Eurynysius 12
## 491 Gelonus 12
## 492 Germalus 12
## 493 Geum 12
## 494 Hesperiidae 12
## 495 Hydrangea 12
## 496 Koscocrompus 12
## 497 Lasioglossum 12
## 498 Lethaeini 12
## 499 Malandiola 12
## 500 Melanacanthus 12
## 501 Melia 12
## 502 Nezara 12
## 503 Notius 12
## 504 Oechalia 12
## 505 Oncocoris 12
## 506 Ontiscus 12
## 507 Penstemon 12
## 508 Pipistrellus 12
## 509 Portulaca 12
## 510 Pseudacris 12
## 511 Stelis 12
## 512 Stizocephalus 12
## 513 Stylogeocoris 12
## 514 Symphyotrichum 12
## 515 Thaumastocoris 12
## 516 Ulonemia 12
## 517 Zanessa 12
## 518 Agonum 11
## 519 Ailanthus 11
## 520 Asclepias 11
## 521 Betula 11
## 522 Ceratonia 11
## 523 Colias 11
## 524 Delonix 11
## 525 Deschampsia 11
## 526 Eupatorium 11
## 527 Eurema 11
## 528 Hordeum 11
## 529 Kickxia 11
## 530 Oedemera 11
## 531 Olea 11
## 532 Ophonus 11
## 533 Phylloscopus 11
## 534 Syntomus 11
## 535 Tamarix 11
## 536 Thuja 11
## 537 Agathis 10
## 538 Amischa 10
## 539 Amphimallon 10
## 540 Asiorestia 10
## 541 Bignoniaceae 10
## 542 Bledius 10
## 543 Brachychiton 10
## 544 Brachypera 10
## 545 Bradycellus 10
## 546 Calathus 10
## 547 Carpelimus 10
## 548 Cedrus 10
## 549 Ceutorhynchus 10
## 550 Chaetocnema 10
## 551 Chlorophytum 10
## 552 Corticarina 10
## 553 Cortinicara 10
## 554 Cyanapion 10
## 555 Danthonia 10
## 556 Diabolia 10
## 557 Dichanthelium 10
## 558 Elaphropus 10
## 559 Epuraea 10
## 560 Galeruca 10
## 561 Hakea 10
## 562 Harpephyllum 10
## 563 Hemitrichapion 10
## 564 Ionactis 10
## 565 Lagunaria 10
## 566 Lespedeza 10
## 567 Margarinotus 10
## 568 Melanophthalma 10
## 569 Monotoma 10
## 570 Morus 10
## 571 Musa 10
## 572 Ocypus 10
## 573 Onthophagus 10
## 574 Othius 10
## 575 Panicum 10
## 576 Platanus 10
## 577 Platydracus 10
## 578 Polygonia 10
## 579 Pseudoophonus 10
## 580 Psylliodes 10
## 581 Pycnanthemum 10
## 582 Schizachyrium 10
## 583 Scopaeus 10
## 584 Searsia 10
## 585 Sophora 10
## 586 Sorghastrum 10
## 587 Tasgius 10
## 588 Trema 10
## 589 Xantholinus 10
## 590 Bromus 9
## 591 Melecta 9
## 592 Mercurialis 9
## 593 Monarda 9
## 594 Mycelis 9
## 595 Psephotus 9
## 596 Angelonia 8
## 597 Anthropodium 8
## 598 Arctium 8
## 599 Augochlorella 8
## 600 Bougainvillea 8
## 601 Brachinus 8
## 602 Chlaenius 8
## 603 Clivia 8
## 604 Craspedia 8
## 605 Daucus 8
## 606 Dolichus 8
## 607 Fallopia 8
## 608 Foeniculum 8
## 609 Haplochlaenius 8
## 610 Lecanora 8
## 611 Lesticus 8
## 612 Megachile 8
## 613 Persicaria 8
## 614 Phyla 8
## 615 Pomaderris 8
## 616 Verbascum 8
## 617 Xylocopa 8
## 618 Accipiter 7
## 619 Badister 7
## 620 Caloplaca 7
## 621 Colluricincla 7
## 622 Coracina 7
## 623 Digitaria 7
## 624 Erigeron 7
## 625 Hylaeus 7
## 626 Phylidonyris 7
## 627 Poecilus 7
## 628 Rorippa 7
## 629 Sisymbrium 7
## 630 Aedes 6
## 631 Amorpha 6
## 632 Augochlora 6
## 633 Ballota 6
## 634 Chrysoceohalum 6
## 635 Dalea 6
## 636 Echinochloa 6
## 637 Elytrigia 6
## 638 Hoplitis 6
## 639 Kalanchoe 6
## 640 Linaria 6
## 641 Liriope 6
## 642 Loricera 6
## 643 Melanocanthus 6
## 644 Molothrus 6
## 645 Pachycephala 6
## 646 Pimelea 6
## 647 Podargus 6
## 648 Sagina 6
## 649 Serinus 6
## 650 Sherardia 6
## 651 Sinapis 6
## 652 Strelitzia 6
## 653 Tingidae 6
## 654 Zizia 6
## 655 Ablattaria 5
## 656 Acrotona 5
## 657 Acupalpus 5
## 658 Agrypnus 5
## 659 Alcea 5
## 660 Aloconota 5
## 661 Altica 5
## 662 Amarochara 5
## 663 Anacaena 5
## 664 Anaspis 5
## 665 Anotylus 5
## 666 Anthicus 5
## 667 Anthrenus 5
## 668 Anthribus 5
## 669 Aphodius 5
## 670 Arenaria 5
## 671 Arpedium 5
## 672 Arthrolips 5
## 673 Astenus 5
## 674 Bacidina 5
## 675 Baris 5
## 676 Bitoma 5
## 677 Bombus 5
## 678 Brachythecium 5
## 679 Brassica 5
## 680 Bruchus 5
## 681 Byrrhus 5
## 682 Cantharis 5
## 683 Carduelis 5
## 684 Cartodere 5
## 685 Cassida 5
## 686 Ceratapion 5
## 687 Cercyon 5
## 688 Chrysolina 5
## 689 Cidnopus 5
## 690 Cis 5
## 691 Clanoptilus 5
## 692 Coccinella 5
## 693 Corticaria 5
## 694 Corymbia 5
## 695 Cryptophagus 5
## 696 Cryptopleurum 5
## 697 Cteniopus 5
## 698 Curtimorda 5
## 699 Cyanocitta 5
## 700 Cynegetis 5
## 701 Cytilus 5
## 702 Dinaraea 5
## 703 Dolichosoma 5
## 704 Dorcus 5
## 705 Dryops 5
## 706 Enicmus 5
## 707 Ennearthron 5
## 708 Ephistemus 5
## 709 Erigone 5
## 710 Eucinetus 5
## 711 Eutrichapion 5
## 712 Fragaria 5
## 713 Gastrophysa 5
## 714 Gauropterus 5
## 715 Gymnetron 5
## 716 Gyrohypnus 5
## 717 Halyzia 5
## 718 Harmonia 5
## 719 Helophorus 5
## 720 Hibiscus 5
## 721 Hippodamia 5
## 722 Hirschfeldia 5
## 723 Hirticomus 5
## 724 Hispa 5
## 725 Hister 5
## 726 Holcus 5
## 727 Holotrichapion 5
## 728 Hoplia 5
## 729 Impatiens 5
## 730 Ischnopterapion 5
## 731 Ischnosoma 5
## 732 Lionychus 5
## 733 Lithocharis 5
## 734 Mecinus 5
## 735 Megasternum 5
## 736 Melissodes 5
## 737 Metopsia 5
## 738 Microlestes 5
## 739 Mycetoporus 5
## 740 Necrophorus 5
## 741 Neobisnius 5
## 742 Ocyusa 5
## 743 Olibrus 5
## 744 Omalium 5
## 745 Omonadus 5
## 746 Orchestes 5
## 747 Origanum 5
## 748 Otiorhynchus 5
## 749 Oulema 5
## 750 Oxyomus 5
## 751 Oxypoda 5
## 752 Oxystoma 5
## 753 Parocyusa 5
## 754 Parophonus 5
## 755 Peponapis 5
## 756 Physalis 5
## 757 Pityogenes 5
## 758 Plathyrinus 5
## 759 Platynaspis 5
## 760 Propylea 5
## 761 Proteinus 5
## 762 Psammodius 5
## 763 Psyllobora 5
## 764 Rabigus 5
## 765 Rhinusa 5
## 766 Rhyzobius 5
## 767 Saponaria 5
## 768 Sepedophilus 5
## 769 Sibinia 5
## 770 Simplocaria 5
## 771 Stegobium 5
## 772 Stelidota 5
## 773 Stenocarus 5
## 774 Stenolophus 5
## 775 Stenopterapion 5
## 776 Teretriorhynchites 5
## 777 Thanatophilus 5
## 778 Timarcha 5
## 779 Tinotus 5
## 780 Trechus 5
## 781 Trichopterapion 5
## 782 Trichosirocalus 5
## 783 Triepeolus 5
## 784 Tritoma 5
## 785 Tytthaspis 5
## 786 Valgus 5
## 787 Variimorda 5
## 788 Xyleborus 5
## 789 Zacladus 5
## 790 Zoosetha 5
## 791 Zorochros 5
## 792 Acanthus 4
## 793 Archilochus 4
## 794 Brachychitron 4
## 795 Brachypodium 4
## 796 Caligavis 4
## 797 Cassinia 4
## 798 Cisticola 4
## 799 Cistus 4
## 800 Coleonema 4
## 801 Dumetella 4
## 802 Eopsaltria 4
## 803 Equisetum 4
## 804 Erodium 4
## 805 Galaxiella 4
## 806 Helianthus 4
## 807 Limanium 4
## 808 Melissa 4
## 809 Nandina 4
## 810 Notiophilus 4
## 811 Nyctalus 4
## 812 Oenothera 4
## 813 Pelagonium 4
## 814 Soleirolia 4
## 815 Torilis 4
## 816 Tussilago 4
## 817 Verrucaria 4
## 818 Walckenaeria 4
## 819 Acanthorhynchus 3
## 820 Aegithalos 3
## 821 Aegopodium 3
## 822 Andrena 3
## 823 Antirrhinum 3
## 824 Astilbe 3
## 825 Barbarea 3
## 826 Borago 3
## 827 Calibrachoa 3
## 828 Candelariella 3
## 829 Carpinus 3
## 830 Casuarina 3
## 831 Centaurium 3
## 832 Cichorium 3
## 833 Cladonia 3
## 834 Cleome 3
## 835 Colleies 3
## 836 Coreopsis 3
## 837 Corydalis 3
## 838 Cosmos 3
## 839 Cucurbita 3
## 840 Dacelo 3
## 841 Dahlia 3
## 842 Diplocephalus 3
## 843 Diplotaxis 3
## 844 Dryopteris 3
## 845 Echinacea 3
## 846 Ficus 3
## 847 Gladiolus 3
## 848 Halictus 3
## 849 Hemerocallis 3
## 850 Herniaria 3
## 851 Heuchera 3
## 852 Isodontia 3
## 853 Kniphofia 3
## 854 Lantana 3
## 855 Lasius 3
## 856 Liatris 3
## 857 Linum 3
## 858 Lobelia 3
## 859 Luzula 3
## 860 Melilotus 3
## 861 Neochmia 3
## 862 Ocimum 3
## 863 Oedothorax 3
## 864 Papilio 3
## 865 Perovskia 3
## 866 Phlox 3
## 867 Pseudopanurgus 3
## 868 Quercus 3
## 869 Reseda 3
## 870 Rondeletia 3
## 871 Schistidium 3
## 872 Setaria 3
## 873 Setophaga 3
## 874 Spergularia 3
## 875 Strepera 3
## 876 Svastra 3
## 877 Taxus 3
## 878 Tenuiphantes 3
## 879 Tradescantia 3
## 880 Trapelia 3
## 881 Troglodytes 3
## 882 Vespula 3
## 883 Vinca 3
## 884 Weigela 3
## 885 Xanthoria 3
## 886 Zinnia 3
## 887 Agapostemon 2
## 888 Agastache 2
## 889 Amegilla 2
## 890 Anethum 2
## 891 Angophora 2
## 892 Anthidium 2
## 893 Arabidopsis 2
## 894 Araucaria 2
## 895 Artocarpus 2
## 896 Asplenium 2
## 897 Baeolophus 2
## 898 Bathyphantes 2
## 899 Bidens 2
## 900 Bryum 2
## 901 Calliopsis 2
## 902 Capsicum 2
## 903 Carpobrotus 2
## 904 Celastrina 2
## 905 Celosia 2
## 906 Centromerus 2
## 907 Ceratina 2
## 908 chinensis 2
## 909 Cinnamomum 2
## 910 Clubiona 2
## 911 Commelina 2
## 912 Coriandrum 2
## 913 Corylus 2
## 914 Cotula 2
## 915 Cucumis 2
## 916 Cyclamen 2
## 917 Cymbalaria 2
## 918 Datura 2
## 919 Dendrocopos 2
## 920 Dodecatheon 2
## 921 Enoplognatha 2
## 922 Epacris 2
## 923 Epeolus 2
## 924 Eragrostis 2
## 925 Eriobotrya 2
## 926 Falcol 2
## 927 Gagea 2
## 928 Galanthus 2
## 929 Galenis 2
## 930 Galeopsis 2
## 931 Hahnia 2
## 932 Heleborus 2
## 933 Hippolais 2
## 934 Holcopasites 2
## 935 Hoya 2
## 936 Humulus 2
## 937 Hylotelephium 2
## 938 Hypnum 2
## 939 Ichthyosaura 2
## 940 Icterus 2
## 941 Ipomoea 2
## 942 Jacaranda 2
## 943 Jasminum 2
## 944 Lablab 2
## 945 Lecania 2
## 946 Lecidella 2
## 947 Lepraria 2
## 948 Linyphia 2
## 949 Lipotriches 2
## 950 Lophostemon 2
## 951 Macropis 2
## 952 maxima 2
## 953 Megalurus 2
## 954 Meioneta 2
## 955 Melica 2
## 956 Melithreptus 2
## 957 Nemastoma 2
## 958 Nesoptilotis 2
## 959 Observed 2
## 960 Orthotrichum 2
## 961 Ozyptila 2
## 962 Parthenocissus 2
## 963 Pelophylax 2
## 964 Petrochelidon 2
## 965 Petroselinum 2
## 966 Phaeophyscia 2
## 967 Philanthus 2
## 968 Phoenicurus 2
## 969 Phoenix 2
## 970 Physcia 2
## 971 Phytolacca 2
## 972 Placynthiella 2
## 973 Platycodon 2
## 974 Plumbago 2
## 975 Populus 2
## 976 Porrhomma 2
## 977 Prionyx 2
## 978 Ratibida 2
## 979 regia 2
## 980 Salamandra 2
## 981 Sanguisorba 2
## 982 Saxifraga 2
## 983 Scabiosa 2
## 984 Scrophularia 2
## 985 Securigera 2
## 986 Sempervivum 2
## 987 Sorbaria 2
## 988 Sphex 2
## 989 Spirea 2
## 990 Spizella 2
## 991 Steatoda 2
## 992 Stokesia 2
## 993 Sutera 2
## 994 Syzygium 2
## 995 Tegenaria 2
## 996 Torenia 2
## 997 Tortula 2
## 998 Tragopogon 2
## 999 Triodanis 2
## 1000 Triturus 2
## 1001 Unidentified 2
## 1002 variegata 2
## 1003 Veronicastrum 2
## 1004 Vittadinia 2
## 1005 Westringia 2
## 1006 Zantedeschia 2
## 1007 Iphiclides 1
## 1008 Acarospo 1
## 1009 Aconitum 1
## 1010 Aethusa 1
## 1011 Afranthidium 1
## 1012 Ageratina 1
## 1013 Agrimonia 1
## 1014 Agroeca 1
## 1015 alba 1
## 1016 Aleurites 1
## 1017 alexandrae 1
## 1018 Alisma 1
## 1019 Aloxyria 1
## 1020 Amandinea 1
## 1021 Amblystegium 1
## 1022 Ambrosia 1
## 1023 Amelanchier 1
## 1024 americana 1
## 1025 Anchomenus 1
## 1026 Ancistrocerus 1
## 1027 Annona 1
## 1028 Anthophora 1
## 1029 Anyphaena 1
## 1030 Apera 1
## 1031 Apis 1
## 1032 Apocynum 1
## 1033 Araeoncus 1
## 1034 Archontophoenix 1
## 1035 Arrhenatherum 1
## 1036 Asaphidion 1
## 1037 Asparagus 1
## 1038 Asperula 1
## 1039 Aspicilia 1
## 1040 Aster 1
## 1041 Augochlora 1
## 1042 Avena 1
## 1043 azedarach 1
## 1044 babylonica 1
## 1045 Ballus 1
## 1046 Barbula 1
## 1047 Bembix 1
## 1048 benjamina 1
## 1049 Berteroa 1
## 1050 Bischofia 1
## 1051 Bombax 1
## 1052 Bryoerythrophyllum 1
## 1053 Bryonia 1
## 1054 Buellia 1
## 1055 burmanii* 1
## 1056 Buteo 1
## 1057 Buxus 1
## 1058 cajuputi ssp. Cumingiana 1
## 1059 Calamagrostis 1
## 1060 Calamintha 1
## 1061 Callistemon 1
## 1062 Calophasia 1
## 1063 campanulata 1
## 1064 camphora* 1
## 1065 Carabus 1
## 1066 Caragana 1
## 1067 Carduus 1
## 1068 Carica 1
## 1069 Caryota 1
## 1070 catappa 1
## 1071 ceiba 1
## 1072 Ceratinella 1
## 1073 Ceratodon 1
## 1074 Cerceris 1
## 1075 Chaenorhinum 1
## 1076 Chaenorrhinum 1
## 1077 Chaerophyllum 1
## 1078 Chalybion 1
## 1079 Chelone 1
## 1080 Chionodoxa 1
## 1081 Chondrilla 1
## 1082 Cicurina 1
## 1083 Cinn. 1
## 1084 Citrus 1
## 1085 Clausena 1
## 1086 Clauzadea 1
## 1087 Clivina 1
## 1088 Coccothraustes 1
## 1089 Cocos 1
## 1090 Coelioxys 1
## 1091 Coenogonium 1
## 1092 Coleus 1
## 1093 confusa 1
## 1094 Conium 1
## 1095 Convallaria 1
## 1096 Corynephorus 1
## 1097 Crabroninae 1
## 1098 Crateva 1
## 1099 Crocosmia 1
## 1100 Crocus 1
## 1101 Crossocerus 1
## 1102 cumini 1
## 1103 cunninghamiana 1
## 1104 cunninghamii 1
## 1105 Cupressus 1
## 1106 Cycas 1
## 1107 Cynodon 1
## 1108 Cystopteris 1
## 1109 Delphinium 1
## 1110 denudata 1
## 1111 Descurainia 1
## 1112 Desmodium 1
## 1113 Dicentra 1
## 1114 Didymodon 1
## 1115 Diervilla 1
## 1116 Dimocarpus 1
## 1117 Diplostyla 1
## 1118 Dipsacus 1
## 1119 Doellingeria 1
## 1120 Dolichovespula 1
## 1121 Draba 1
## 1122 Dracontomelon 1
## 1123 duperreanum 1
## 1124 Dypsis 1
## 1125 Dyschirius 1
## 1126 Dysdera 1
## 1127 Echinocystis 1
## 1128 Echinops 1
## 1129 Ectemnius 1
## 1130 Elaeagnus 1
## 1131 Elaeocarpus 1
## 1132 Elaphrus 1
## 1133 elastica 1
## 1134 elliottii 1
## 1135 Elymus 1
## 1136 Empidonax 1
## 1137 Entelecara 1
## 1138 Episinus 1
## 1139 equisetifolia 1
## 1140 Eranthis 1
## 1141 Eratigena 1
## 1142 Erigonella 1
## 1143 Ero 1
## 1144 Eryngium 1
## 1145 Estimated 1
## 1146 Eumenes 1
## 1147 Euodynerus 1
## 1148 Euonymus 1
## 1149 Euophrys 1
## 1150 Eurhynchium 1
## 1151 Euryopis 1
## 1152 Eutrochium 1
## 1153 Exoneura 1
## 1154 Fagus 1
## 1155 Ficaria 1
## 1156 Filago 1
## 1157 Floronia 1
## 1158 funebris 1
## 1159 Gaillardia 1
## 1160 Garrulus 1
## 1161 glutinosa* 1
## 1162 Glyptostrobus 1
## 1163 Gnathonarium 1
## 1164 Gomphrena 1
## 1165 Gonatium 1
## 1166 Gongylidium 1
## 1167 Gorytes 1
## 1168 granatum 1
## 1169 grandiflora 1
## 1170 Graphium 1
## 1171 Grimmia 1
## 1172 guajava 1
## 1173 Gypsophila 1
## 1174 hainanensis 1
## 1175 Haplodrassus 1
## 1176 Harpactea 1
## 1177 Helenium 1
## 1178 Heliopsis 1
## 1179 heptaphylla* 1
## 1180 Heracleum 1
## 1181 heterophylla 1
## 1182 heterophyllum* 1
## 1183 heterophyllus 1
## 1184 Homalictus 1
## 1185 Homalothecium 1
## 1186 Hyacinthus 1
## 1187 Hyperphyscia 1
## 1188 Hypocenomyce 1
## 1189 Hypogymnia 1
## 1190 Hyssopus 1
## 1191 indica 1
## 1192 jambos 1
## 1193 japonica 1
## 1194 javanica* 1
## 1195 Juniperus 1
## 1196 Knautia 1
## 1197 lansium 1
## 1198 Lavatera 1
## 1199 Lecidea 1
## 1200 Leimonis 1
## 1201 Leistus 1
## 1202 Lepthyphantes 1
## 1203 Lestica 1
## 1204 Licinus 1
## 1205 Ligularia 1
## 1206 Liocranum 1
## 1207 Litchi 1
## 1208 Litsea 1
## 1209 longan 1
## 1210 lutescens 1
## 1211 Lycopus 1
## 1212 Macaranga 1
## 1213 macrophyllus* 1
## 1214 madagascariensis 1
## 1215 Mahonia 1
## 1216 Mangifera 1
## 1217 mangium 1
## 1218 Manilkara 1
## 1219 massoniana* 1
## 1220 Matteuccia 1
## 1221 Michelia 1
## 1222 Micrargus 1
## 1223 microcarpa* 1
## 1224 Microneta 1
## 1225 Milleriana 1
## 1226 Mniotilta 1
## 1227 Moehringia 1
## 1228 moluccana 1
## 1229 Monobia 1
## 1230 Motacilla 1
## 1231 Muscicapa 1
## 1232 Myrmecina 1
## 1233 Neottiura 1
## 1234 Neriene 1
## 1235 Nicotiana 1
## 1236 nitidus ssp. lingnanensis 1
## 1237 nucifera 1
## 1238 Odiellus 1
## 1239 Odontites 1
## 1240 oleander 1
## 1241 Ononis 1
## 1242 Ornithogalum 1
## 1243 Osmia 1
## 1244 Oxybelus 1
## 1245 Palliduphantes 1
## 1246 papaya 1
## 1247 Pardosa 1
## 1248 Parmelia 1
## 1249 Passaloecus 1
## 1250 Pastinaca 1
## 1251 Paulownia 1
## 1252 Pelecopsis 1
## 1253 Pemphredon 1
## 1254 pensilis 1
## 1255 Peponapis 1
## 1256 Persea 1
## 1257 Petrorhagia 1
## 1258 Phaseolus 1
## 1259 Phedimus 1
## 1260 Philadelphus 1
## 1261 Phleum 1
## 1262 Phlyctis 1
## 1263 Pholcus 1
## 1264 Phrurolithus 1
## 1265 Physostegia 1
## 1266 Picea 1
## 1267 Picus 1
## 1268 Pimpinella 1
## 1269 pinnata* 1
## 1270 Pipilo 1
## 1271 Pittosporum 1
## 1272 Plagiomnium 1
## 1273 Plumeria 1
## 1274 Podocarpus 1
## 1275 Polistes 1
## 1276 Polycarpon 1
## 1277 Polygonatum 1
## 1278 Polyscias 1
## 1279 Polytrichum 1
## 1280 Ponera 1
## 1281 Pongamia 1
## 1282 Porina 1
## 1283 Porpidia 1
## 1284 Prinerigone 1
## 1285 Psenulus 1
## 1286 Pseudevernia 1
## 1287 Pseudotsuga 1
## 1288 Psidium 1
## 1289 Psilolechia 1
## 1290 Pterospermum 1
## 1291 Pulsatilla 1
## 1292 Punica 1
## 1293 Pyracantha 1
## 1294 Ramalina 1
## 1295 Ravenala 1
## 1296 revoluta 1
## 1297 Reynoutria 1
## 1298 Rhynchostegium 1
## 1299 Rilaena 1
## 1300 Robertus 1
## 1301 robusta 1
## 1302 roebelenii 1
## 1303 romanzoffiana 1
## 1304 Roystonea 1
## 1305 rubra 1
## 1306 Sarcogyne 1
## 1307 Satureja 1
## 1308 Satyrinae 1
## 1309 Scaevola 1
## 1310 Sceliphron 1
## 1311 Schefflera 1
## 1312 Scilla 1
## 1313 Scolia 1
## 1314 Scoliciosporum 1
## 1315 Scolopax 1
## 1316 Scorzoneroides 1
## 1317 Segestria 1
## 1318 Senna 1
## 1319 sinensis* 1
## 1320 Sitta 1
## 1321 sp. 1
## 1322 Spathodea 1
## 1323 Sphecius 1
## 1324 squamosa 1
## 1325 Stemonyphantes 1
## 1326 Stereocaulon 1
## 1327 Stipa 1
## 1328 surattensis 1
## 1329 Syagrus 1
## 1330 Symmorphus 1
## 1331 Symphoricarpos 1
## 1332 Syncarpia 1
## 1333 Tachytes 1
## 1334 tanarius var. tomentosa 1
## 1335 Terminalia 1
## 1336 Tetragonula 1
## 1337 Thalictrum 1
## 1338 Thyreus 1
## 1339 Toxomerus 1
## 1340 tripinnata 1
## 1341 Trochosa 1
## 1342 Trogulus 1
## 1343 Tropaeolum 1
## 1344 Troxochrus 1
## 1345 Trypoxylon 1
## 1346 Tsuga 1
## 1347 Tulipa 1
## 1348 unilocularis 1
## 1349 Valeriana 1
## 1350 Vezdaea 1
## 1351 viminalis 1
## 1352 Vitex 1
## 1353 Vulpia 1
## 1354 Vulpicida 1
## 1355 Xylocopa 1
## 1356 Xysticus 1
## 1357 Yulania 1
## 1358 zapota 1
## 1359 Zodarion 1
## 1360 Zora 1spp.nbird <- meta %>% filter(Taxa.simplified!="birds") %>% group_by(Genus) %>% summarize(n=length(Species)) %>% arrange(-n) %>% data.frame() %>% head()
spp.nbird## Genus n
## 1 18401
## 2 Columba 2338
## 3 Passer 1843
## 4 Turdus 1001
## 5 Vanessa 699
## 6 Andrena 696## Appendix Figures
meta <- read.csv("data//Master.GI.Datasets.csv")
## Omit repo 3 because duplicated with study 1305 and remove repo-9 because not equivalent GI comparisons. Removed Repo 3 because compare roof with ground
meta <- meta %>% filter(Study != "repo.3" & Study!="repo.9" & Study!="repo.1")
## Drop relative abundance because difference = 0
meta <- meta %>% filter(Estimate!="Relative.Abundance")
## pH
ph.count <- subset(meta, pH>0 & Genus != "")
ggplot(ph.count) + geom_density(aes(pH, fill=Genus), position="stack") + xlab("pH of retention pond") + ylab("frequency of occurrence")+theme_set(theme_bw(base_size = 22))+theme_bw()
se <- function(x) {sd(x)/sqrt(length(x))}
## Depth
depth <- subset(meta, depth..m.>0 )
depth <- depth %>% group_by(depth..m.) %>% summarize(avg=mean(Value), error=se(Value))
ggplot(depth, aes(x=depth..m., y=avg)) + geom_point( size=4) + geom_errorbar(data=depth, aes(ymin=avg-error, ymax=avg+error), width=0.03)+ xlab("depth of retention pond (m)") + ylab("average number of individuals observed")+theme_set(theme_bw(base_size = 22))+theme_bw()
## Height
high <- subset(meta, GI.height..m.>0 & Stat=="mean")
high <- filter(high, Estimate %in% c("abundance","richness"))
high <- high %>% group_by(GI.height..m.,Estimate) %>% summarize(Value=mean(Value))
ggplot(high) + geom_point(aes(x=GI.height..m., y=Value)) + facet_grid(~Estimate)+ xlab("height of green roof (m)") + ylab("average number observed")+theme_set(theme_bw(base_size = 22))+theme_bw()