Who Participated?
- 435 Appleton Homes!
- Bee diversity and abundance data collected at 20 homes and 15 mowed urban green spaces
- we can extrapolate to the area and say that we protected between 19 and 24 acres of pollinator habiat across the city!
## Warning: Removed 120 rows containing missing values (geom_point).
What is the average size of a NoMow Lawn Participant?
- The average participant had a yard of 195 sq meters or 2100 sq. ft! WOW!
Were there more floral resourcess in NoMow Lawns?
- Mowed Areas had 36% fewer species and 34% lower flower denisty than mowed areas
#Floral Richness
kruskal.test(Floral_Richness ~ Mow_NoMow, data=NoMow)
##
## Kruskal-Wallis rank sum test
##
## data: Floral_Richness by Mow_NoMow
## Kruskal-Wallis chi-squared = 14.485, df = 1, p-value = 0.0001412
FR<- ggplot (NoMow, aes (x=Mow_NoMow, y=Floral_Richness)) + geom_boxplot()
ggplotly (FR)
#Floral Density
kruskal.test(Mean_Floral_Density ~ Mow_NoMow, data=NoMow)
##
## Kruskal-Wallis rank sum test
##
## data: Mean_Floral_Density by Mow_NoMow
## Kruskal-Wallis chi-squared = 16.828, df = 1, p-value = 4.092e-05
FD<- ggplot (NoMow, aes (x=Mow_NoMow, y=Mean_Floral_Density)) + geom_boxplot()
ggplotly (FD)
Are there more bees in NoMow homes compared to mowed greenspaces?
- YES! There are 4X as many bees in non-mowed lawns as in the mowed urban green spaces
bee.abund <- ggplot (NoMow, aes (x=Mow_NoMow, y=Bee_Abundance, fill=Mow_NoMow)) +
geom_boxplot (alpha=0.5, color="black") + theme_bw() +xlab("") + ylab ("Bee Abundance") +
scale_fill_manual (values=c("black", "#FFCC00")) +
annotate ("text", x=1, y=25, label="A", size=16) + theme (legend.position = "none")
ggplotly (bee.abund)
kruskal.test(Bee_Abundance ~ Mow_NoMow, data=NoMow)
##
## Kruskal-Wallis rank sum test
##
## data: Bee_Abundance by Mow_NoMow
## Kruskal-Wallis chi-squared = 19.722, df = 1, p-value = 8.959e-06
Are there more species of bees in NoMow homes compared to mowed greenspaces?
- YES! There are 3X as many bee species in non-mowed lawns as in the mowed urban green spaces
bee.rich <- ggplot (NoMow, aes (x=Mow_NoMow, y=Bee_Richness, fill=Mow_NoMow)) +
geom_boxplot (alpha=0.5, color="black") + theme_bw() +xlab("") + ylab ("Bee Richness") +
scale_fill_manual (values=c("black", "#FFCC00")) +
annotate ("text", x=1, y=10, label="B", size=16) + theme (legend.position = "none")
ggplotly (bee.rich)
kruskal.test(Bee_Richness ~ Mow_NoMow, data=NoMow)
##
## Kruskal-Wallis rank sum test
##
## data: Bee_Richness by Mow_NoMow
## Kruskal-Wallis chi-squared = 16.686, df = 1, p-value = 4.41e-05
What are the variables that best predict increases in abundance of bees?
- Area unmowed is the best predictor of abundances of bees
- Floral species richness is also a good predictor of bee abundance in unmowed (city parks)
NoMow_GLM_abund<-glm (Bee_Abundance ~ Mow_NoMow + NoMowArea + Mean_Floral_Density + Floral_Richness,
family="poisson", data=NoMow)
summary (NoMow_GLM_abund); stepAIC (NoMow_GLM_abund)
##
## Call:
## glm(formula = Bee_Abundance ~ Mow_NoMow + NoMowArea + Mean_Floral_Density +
## Floral_Richness, family = "poisson", data = NoMow)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3590 -0.6962 -0.0081 0.5849 3.4899
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7597283 0.2147124 3.538 0.000403 ***
## Mow_NoMowNoMow 1.3117012 0.2233405 5.873 4.28e-09 ***
## NoMowArea 0.0029964 0.0006842 4.380 1.19e-05 ***
## Mean_Floral_Density 0.0018254 0.0036037 0.507 0.612479
## Floral_Richness -0.0353788 0.0220109 -1.607 0.107982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 170.454 on 34 degrees of freedom
## Residual deviance: 59.062 on 30 degrees of freedom
## AIC: 201.26
##
## Number of Fisher Scoring iterations: 4
## Start: AIC=201.26
## Bee_Abundance ~ Mow_NoMow + NoMowArea + Mean_Floral_Density +
## Floral_Richness
##
## Df Deviance AIC
## - Mean_Floral_Density 1 59.319 199.51
## <none> 59.062 201.25
## - Floral_Richness 1 61.711 201.91
## - NoMowArea 1 76.893 217.09
## - Mow_NoMow 1 95.981 236.17
##
## Step: AIC=199.51
## Bee_Abundance ~ Mow_NoMow + NoMowArea + Floral_Richness
##
## Df Deviance AIC
## <none> 59.319 199.51
## - Floral_Richness 1 61.718 199.91
## - NoMowArea 1 77.194 215.39
## - Mow_NoMow 1 115.455 253.65
##
## Call: glm(formula = Bee_Abundance ~ Mow_NoMow + NoMowArea + Floral_Richness,
## family = "poisson", data = NoMow)
##
## Coefficients:
## (Intercept) Mow_NoMowNoMow NoMowArea Floral_Richness
## 0.795850 1.363919 0.002909 -0.032357
##
## Degrees of Freedom: 34 Total (i.e. Null); 31 Residual
## Null Deviance: 170.5
## Residual Deviance: 59.32 AIC: 199.5
area.abund<- ggplot (NoMow, aes (x=NoMowArea, y=Bee_Abundance, color=Mow_NoMow)) + geom_point() +
geom_smooth(method="lm") + theme_bw() + scale_color_manual(values=c("grey", "black")) +
xlab("Size of Now Mow Area in sq. meters") + ylab ("Bee Abundance")
ggplotly (area.abund)
## `geom_smooth()` using formula 'y ~ x'
floral.abund<-ggplot (NoMow, aes (x=Floral_Richness, y=Bee_Abundance, color=Mow_NoMow)) +
geom_point() + geom_smooth(method="loess") + theme_bw() +
scale_color_manual(values=c("grey","black")) + xlab("Number of Flowering Species in Lawn") +
ylab ("Bee Abundance")
ggplotly (floral.abund)
## `geom_smooth()` using formula 'y ~ x'
What are the variables that best predict increases in species richness of bees?
- Area unmowed is the best predictor of bee species richness!
NoMow_GLM_rich<-glm (Bee_Richness ~ Mow_NoMow + NoMowArea + Mean_Floral_Density + Floral_Richness,
family="poisson", data=NoMow)
summary (NoMow_GLM_rich); stepAIC (NoMow_GLM_rich)
##
## Call:
## glm(formula = Bee_Richness ~ Mow_NoMow + NoMowArea + Mean_Floral_Density +
## Floral_Richness, family = "poisson", data = NoMow)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7989 -0.4207 -0.1673 0.3986 1.6515
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3237348 0.2744211 1.180 0.23812
## Mow_NoMowNoMow 0.5351049 0.2875111 1.861 0.06272 .
## NoMowArea 0.0025685 0.0009689 2.651 0.00803 **
## Mean_Floral_Density 0.0052839 0.0051391 1.028 0.30386
## Floral_Richness 0.0160131 0.0293954 0.545 0.58593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 56.479 on 34 degrees of freedom
## Residual deviance: 21.879 on 30 degrees of freedom
## AIC: 146.98
##
## Number of Fisher Scoring iterations: 4
## Start: AIC=146.98
## Bee_Richness ~ Mow_NoMow + NoMowArea + Mean_Floral_Density +
## Floral_Richness
##
## Df Deviance AIC
## - Floral_Richness 1 22.173 145.27
## - Mean_Floral_Density 1 22.945 146.04
## <none> 21.880 146.98
## - Mow_NoMow 1 25.362 148.46
## - NoMowArea 1 28.472 151.57
##
## Step: AIC=145.27
## Bee_Richness ~ Mow_NoMow + NoMowArea + Mean_Floral_Density
##
## Df Deviance AIC
## - Mean_Floral_Density 1 23.595 144.69
## <none> 22.173 145.27
## - Mow_NoMow 1 26.508 147.61
## - NoMowArea 1 29.945 151.04
##
## Step: AIC=144.69
## Bee_Richness ~ Mow_NoMow + NoMowArea
##
## Df Deviance AIC
## <none> 23.595 144.69
## - NoMowArea 1 30.376 149.48
## - Mow_NoMow 1 38.171 157.27
##
## Call: glm(formula = Bee_Richness ~ Mow_NoMow + NoMowArea, family = "poisson",
## data = NoMow)
##
## Coefficients:
## (Intercept) Mow_NoMowNoMow NoMowArea
## 0.509296 0.778409 0.002487
##
## Degrees of Freedom: 34 Total (i.e. Null); 32 Residual
## Null Deviance: 56.48
## Residual Deviance: 23.59 AIC: 144.7
area.rich<- ggplot (NoMow, aes (x=NoMowArea, y=Bee_Richness, color=Mow_NoMow)) + geom_point() +
geom_smooth(method="loess") + theme_bw() + scale_color_manual(values=c("grey", "black")) +
xlab("Size of Now Mow Area in sq. meters") + ylab ("Bee Richness")
ggplotly (area.rich)
## `geom_smooth()` using formula 'y ~ x'
