Q1. Is there a difference in the abundance/diversity of birds observed using the two environments?

Data loading

library(readxl)
q1 <- read_excel("data/Copy of 2022 BOL2024 Week9 Data Entry THURS.xlsx", sheet = 2)
## New names:
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
View(q1)
rgreen <- q1$RGreen
rgrey <- q1$RGrey
agreen <- q1$AGreen
agrey <- q1$AGrey

Richness normality

shapiro.test(rgreen)
## 
##  Shapiro-Wilk normality test
## 
## data:  rgreen
## W = 0.82872, p-value = 4.23e-11
shapiro.test(rgrey)
## 
##  Shapiro-Wilk normality test
## 
## data:  rgrey
## W = 0.7781, p-value = 7.459e-13

Abundance normality

shapiro.test(agreen)
## 
##  Shapiro-Wilk normality test
## 
## data:  agreen
## W = 0.68194, p-value = 1.561e-15
shapiro.test(agrey)
## 
##  Shapiro-Wilk normality test
## 
## data:  agrey
## W = 0.70966, p-value = 7.92e-15

Data non-normal, perform Wilcoxon signed-rank test

Richness Wilcoxon

  • H0: no difference in the mean species richness in green & grey habitats
  • H1: significant difference in the mean species richness in green & grey habitats
wilcox.test(rgreen, rgrey, paired=TRUE)
## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  rgreen and rgrey
## V = 2882.5, p-value = 6.801e-05
## alternative hypothesis: true location shift is not equal to 0

p<0.05, reject H0, thus significant difference in mean sp. richness in green & grey

Abundance Wilcoxon

wilcox.test(agreen, agrey, paired=TRUE)
## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  agreen and agrey
## V = 3965.5, p-value = 2.19e-05
## alternative hypothesis: true location shift is not equal to 0

p<0.05, reject H0, thus significant difference in mean sp. abundance in green & grey

ie. to answer Q1, there is a significant difference between species richness and species abudance in both habitats.

Q2. Do native and introduced birds use green and grey space differently?

library(readxl)
q2r <- read_excel("data/Copy of 2022 BOL2024 Week9 Data Entry THURS.xlsx", sheet = 3)
View(q2r)
q2r$Habitat <- as.factor(q2r$Habitat)
q2r$Origin <- as.factor(q2r$Origin)
str(q2r)
## tibble [264 × 3] (S3: tbl_df/tbl/data.frame)
##  $ Habitat : Factor w/ 2 levels "Green","Grey": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Origin  : Factor w/ 2 levels "Introduced","Native": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Richness: num [1:264] 1 0 1 2 0 1 1 0 1 1 ...
rhab <- q2r$Habitat
rorigin <- q2r$Origin
rich <- q2r$Richness

2-way ANOVA (richness v origin v habitat)

r.aov <- aov(rich ~ rhab + rorigin + rhab : rorigin, data = q2r)
summary(r.aov)
##               Df Sum Sq Mean Sq F value   Pr(>F)    
## rhab           1  13.64   13.64   12.54 0.000473 ***
## rorigin        1 101.95  101.95   93.71  < 2e-16 ***
## rhab:rorigin   1  18.53   18.53   17.03 4.95e-05 ***
## Residuals    260 282.84    1.09                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  • habitat p-value significant, ie. lvls of habitat (green & grey) are associated w/ significantly diff. species richness
  • origin p-value significant, ie. lvls of origin (native & introduced) are associated w/ significantly diff. spp. richness
library(readxl)
q2a <- read_excel("data/Copy of 2022 BOL2024 Week9 Data Entry THURS.xlsx", sheet = 4)
View(q2a)
q2a$Habitat <- as.factor(q2r$Habitat)
q2a$Origin <- as.factor(q2r$Origin)
str(q2r)
## tibble [264 × 3] (S3: tbl_df/tbl/data.frame)
##  $ Habitat : Factor w/ 2 levels "Green","Grey": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Origin  : Factor w/ 2 levels "Introduced","Native": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Richness: num [1:264] 1 0 1 2 0 1 1 0 1 1 ...
ahab <- q2a$Habitat
aorigin <- q2a$Origin
abun <- q2a$Abundance

2-way ANOVA (abundance v origin v habitat)

a.aov <- aov(abun ~ ahab + aorigin + ahab : aorigin, data = q2a)
summary(a.aov)
##               Df Sum Sq Mean Sq F value   Pr(>F)    
## ahab           1    325   325.2   14.80 0.000150 ***
## aorigin        1    286   285.5   13.00 0.000374 ***
## ahab:aorigin   1    367   367.4   16.72 5.78e-05 ***
## Residuals    260   5713    22.0                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  • habitat p-value significant, ie. lvls of habitat (green & grey) are associated w/ significantly diff. spp. abundance
  • origin p-value significant, ie. lvls of origin (native & introduced) are associated w/ significantly diff. spp. abundance

Q3. Was there variation in the abundance/diversity due to survey method?

Data loading

library(readxl)
q3 <- read_excel("data/Copy of 2022 BOL2024 Week9 Data Entry THURS.xlsx", sheet = 5)
View(q3)

rpt <- q3$RPoint
rtrans <- q3$RTrans
apt <- q3$APoint
atrans <- q3$ATrans

Wilcoxon richness v method

wilcox.test(rpt, rtrans, paired=TRUE)
## Warning in wilcox.test.default(rpt, rtrans, paired = TRUE): cannot compute exact
## p-value with ties
## Warning in wilcox.test.default(rpt, rtrans, paired = TRUE): cannot compute exact
## p-value with zeroes
## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  rpt and rtrans
## V = 136, p-value = 0.0003821
## alternative hypothesis: true location shift is not equal to 0

Wilcoxon abundance v method

wilcox.test(apt, atrans, paired=TRUE)
## Warning in wilcox.test.default(apt, atrans, paired = TRUE): cannot compute exact
## p-value with ties
## Warning in wilcox.test.default(apt, atrans, paired = TRUE): cannot compute exact
## p-value with zeroes
## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  apt and atrans
## V = 278.5, p-value = 0.001434
## alternative hypothesis: true location shift is not equal to 0