Title: R3 Phenology Lab
Date: May 22nd 2018
Q1: List the three climate divisions that you analyzed. When analyzing your linear models, did your data meet the assumptions of the statistical analysis? Provide me with the p-value, adjusted R2 and the equation of the line for each division. How do you interpret the slope of each equation, p-values, and adjusted R2?
Climate Division 1 Data did meet assumptions of the statistical analysis P-value: 0.001139 Adjusted R2: 0.08176 Equation of line: m <- 0.011276 b <- -16.013345 x <- 2025 y = 6.820555 The slope of the equation is positive, so we can tell that there is a positive relationship between year and temp in Division 1. The p-value is less than 0.05 so there is a significant difference, and the adjusted R2 value isn’t that close to 1 so we can assume that the data is not all perfectly on the best fit line.
Climate Division 2 Data did meet assumptions of the statistical analysis P-value: 0.00502 Adjusted R2: 0.05933 Equation of line: m <- 0.009592 b <- -12.718121 x <- 2020 y = 6.57719 The slope of the equation is positive, so we can tell that there is a positive relationship between year and temp in Division 2. The p-value is less than 0.05 so there is a significant difference,and the adjusted R2 value isn’t that close to 1 so we can assume that the data is not all perfectly on the best fit line.
Climate Division 3 Data did meet assumptions of the statistical analysis P-value: 5.157e-05 Adjusted R2: 0.1279 Equation of line: m <- 0.013736 b <- -22.058435 x <- 2010 y = 5.550925 The slope of the equation is positive, so we can tell that there is a positive relationship between year and temp in Division 3. The p-value is less than 0.05 so there is a significant difference, and the adjusted R2 value isn’t that close to 1 so we can assume that the data is not all perfectly on the best fit line.
Q2: Interpret the temperature trends in your three climatic divisions? Do your climatic divisions agree or conflict? Explain your answer using sound climatological and ecological reasoning
In all of the climatic divisions there was a positive correlation between temperature rising as the year increased, so they all do agree.
Q3: Produce a plot with trend line for each of your climatic divisions. Attach and label in a separate document
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.4.4tempDivImport.df <- read.csv("C:\\Users\\gkrochmal\\Downloads\\temperatureDivisions.csv")
tempDiv <- subset(tempDivImport.df, division =="Div1") #subsetting data to choose a division
LM1 <- lm(temp ~ year, data=tempDiv) #naming a linear model
plot(LM1)
Climate Division 1
Climate Division 1
Climate Division 1
Climate Division 1
summary(LM1) #summary of linear model##
## Call:
## lm(formula = temp ~ year, data = tempDiv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2997 -1.0094 -0.0417 0.9290 3.1430
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -16.013345 6.592246 -2.429 0.01671 *
## year 0.011276 0.003377 3.339 0.00114 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.202 on 113 degrees of freedom
## Multiple R-squared: 0.08981, Adjusted R-squared: 0.08176
## F-statistic: 11.15 on 1 and 113 DF, p-value: 0.001139graphLine <- ggplot(data=tempDiv, aes(x=year, y=temp))
graphLine + theme_classic() + geom_line() + geom_smooth(method='lm', se=FALSE, color="red")
Climate Division 1
m <- 0.011276
b <- -16.013345
x <- 2025tempDivImport.df <- read.csv("C:\\Users\\gkrochmal\\Downloads\\temperatureDivisions.csv")
tempDiv <- subset(tempDivImport.df, division =="Div2") #subsetting data to choose a division
LM2 <- lm(temp ~ year, data=tempDiv) #naming a linear model
plot(LM2
)
Climate Division 2
Climate Division 2
Climate Division 2
Climate Division 2
summary(LM2) #summary of linear model##
## Call:
## lm(formula = temp ~ year, data = tempDiv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2842 -0.9977 -0.0807 0.8322 3.1626
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12.718121 6.543259 -1.944 0.05442 .
## year 0.009592 0.003352 2.862 0.00502 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.193 on 113 degrees of freedom
## Multiple R-squared: 0.06758, Adjusted R-squared: 0.05933
## F-statistic: 8.19 on 1 and 113 DF, p-value: 0.00502graphLine <- ggplot(data=tempDiv, aes(x=year, y=temp))
graphLine + theme_classic() + geom_line() + geom_smooth(method='lm', se=FALSE, color="red")
Climate Division 2
m <- 0.009592
b <- -12.718121
x <- 2020tempDivImport.df <- read.csv("C:\\Users\\gkrochmal\\Downloads\\temperatureDivisions.csv")
tempDiv <- subset(tempDivImport.df, division =="Div3") #subsetting data to choose a division
LM3 <- lm(temp ~ year, data=tempDiv) #naming a linear model
plot(LM3)
Climate Division 3
Climate Division 3
Climate Division 3
Climate Division 3
summary(LM3) #summary of linear model##
## Call:
## lm(formula = temp ~ year, data = tempDiv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.03210 -0.92518 -0.03144 0.85394 2.80087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -22.058435 6.370539 -3.463 0.000756 ***
## year 0.013736 0.003263 4.210 5.16e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.162 on 113 degrees of freedom
## Multiple R-squared: 0.1356, Adjusted R-squared: 0.1279
## F-statistic: 17.72 on 1 and 113 DF, p-value: 5.157e-05graphLine <- ggplot(data=tempDiv, aes(x=year, y=temp))
graphLine + theme_classic() + geom_line() + geom_smooth(method='lm', se=FALSE, color="red")
Climate Division 3
m <- 0.013736
b <- -22.058435
x <- 2010Q4: Write out the equation of line for each of your division. Compare and contrast the equation of the lines to your previous results climatic division results. Explain.
Division 1: m <- 0.011276 b <- -22.009867 x <- 2025 y = 6.820555
Division 2: m <- 0.009592 b <- -18.722904 x <- 2020 y = 0.824033
Division 3 m <- 0.013736 b <- -26.813392 x <- 2010 y = 0.652936
When comparing my temperature anomaly analysis to the climatic division analysis, there were similaries amongst the “m” values. The value of “m” did not change in either analysis. However, the y-intercept (B) did change the value decreased in the temperature anomaly analysis.
Q5: What are the interpretation advantages and disadvantages of plotting temperature anomalies compared to raw temperature data? Explain.
The advantages of a temperature anomaly is that it is the difference from the baseline temperature, and the factors like location or elevation are not so crucial in anomalies, so is more general. But this could be a disadvantage if you want to look at a more specific area including its characteristics, which you would use raw temperature for.
Q6: List the three flower species that you analyzed. When analyzing your linear models, did your data meet the assumptions of the statistical analysis? Provide me with the p-value, adjusted R2 , and the equation of the line for each species. How do you interpret the slope of each equation, p-values, and adjusted R2?
Clematis Virgin Data did meet assumptions of the statistical analysis P-value: 0.03124 Adjusted R2: 0.2116 Equation of line: m <- 4.902 b <- 126.108 This flower species has a positive slope, so there is a positive correlaation between the phenological response of flowers in relation to temperature. The p-value is less than 0.05 so there is a significant difference. The adjusted R2 value is not that close to 1 so we can assume that the data is not all on the best fit line.
Aquilegia canadensis Data did meet assumptions of the statistical analysis P-value: 0.0006816 Adjusted R2: 0.2377 Equation of line: m <- -3.1146 b <- 155.7332 This flower species had a negative slope, so there is a negative correlation between the phenological response of flowers in relation to temperature. The p-value is less than 0.05 so there is a significant difference. The asjuted R2 value is not that close to 1 so we can assume that the data is not all on the best fit line.
Cornus florida Data did meet assumptions of the statistical analysis P-value: 7.491e-05 Adjusted R2: 0.183 Equation of line: m <- -2.3231 b <- 145.6692 This flower species had a negative slope, so there is a negative correlation between the phenological response of flowers in relation to temperature. The p-value is less than 0.05 so there is a significant difference. The asjuted R2 value is not that close to 1 so we can assume that the data is not all on the best fit line.
Q7: Compare and contrast flowering time shifts with increasing in temperature for your three flowering plant species.
Cornus florida and Aquilegia canadensis are two flower species whose flowering time won’t neccessarily be impacted by the increasing temperature. However, Clematis Virgin has a positive relationship with flowering time and temperature increase so this flower species will be effected by the temperature change.
Q8: Based on your calculations, will your three species be equally well adapted to warming in Ohio? What impacts might this have on Ohio flowering plant species diversity? Explain
Clematis virgin is the only flower species that will have a harder time adapting to the warming in Ohio. This will reduce overall plant species diversity because not all flowers will be able to keep up with the changing temperature. Species Cornus florida and Aquilegia canadensis will be able to adapt to the warming in Ohio. The flowers that can adapt to the warming temperature may have a larger species abundance, and the overall diversity of that ecosystem will decrease.
Q9: Given your knowledge of flowering shifts with temperature in A. canadensis and C. florida as well as arrival time shifts with temperature in A. colubris and C. ladon, speculate on what effects climate warming might have on survival and reproduction in these species. How would species interactions change with a 1oC temperature increase? With a 3oC temperature increase?
Archilochus colubris used Aquilegia canadensis flowers as a nectar food resource so luckily this species of bird will not be effected by the warming temperature since the flower will be available at the same time it has always been. Celastrina ladon caterpillars feed on Cornus florida which also is not entirley effected by temperature warming so this butterfly species will still be able to feed on the flowers. The species interactions will not change dramastically with a 1C temperature increase, but a 3c temperature change may have more of a slight change.
Q10: Now you have a basic understanding of the complexity of organism phenology and climate change. How can we use this information during habitat restoration? What are some of the advantages and drawbacks of incorporating climate change predictions when devising plans for ecological restoration?
This lab was so helpful in my understanding of how all these factors actually tie together. After analyzing all of the data piece by piece then thinking about it as a whole I was able to use this to think about habitat restoration. Looking at temperature trend data we can see how that can effect the wintering of plants and when they will be up and blooming for spring, which we can then compare to see which macroorganisms like butterflys and birds will be effected. The advantages of incorporating climate change predictions is we can see which species we should look out for because the avaliable food or weather conditions that will be changing. However a drawback could be that we don’t want to devise a plan for restoration and not have the temperature change to the models we were basing it off of as a guess.