Climate Change: Does an Earlier Spring Effect the Flowering Date of the Colorado Blue Columbine?
Conservation
Biology
Spring 2021
Introduction:
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Climate change is affecting ecosystems and biodiversity all around the globe and studying phenology can give insight into how those effects are manifesting. Phenology is the study of cyclic and seasonal natural phenomena and can be an indicator of how warming temperatures in the state of Colorado may impact the flowering time of the Colorado Blue Columbine, Aquilegia coerulea. A study on cranberry flowering is southern Massachusetts, found the flowering time to be about 2 days earlier per each 1°C increase in the average May temperature (Ellwood et al . 2014). When studying the phenology of Aquilegia coerulea, it is important to note the variation in the ecosystems in which the species occurs naturally. In a recent study, the change in flowering time for various species was found to be not only dependent on temperature but also on altitude, with high elevation individuals showing no significant shift of flowering time in response to the changing climate (Rafferty et al. 2020). It is possible the ordinal date of flowering times for the Colorado Blue Columbine may vary significantly throughout various regions at different altitudes, as well as be affected by be affected by the warming temperatures. The rapid responses of flowering time to the warming temperatures due to phenotypic plasticity (Brunet & Larson-Rabin 2012). While there have been studies focusing on the effects of climate change on the alpine species, Aquilegia coerulea’s response has not been widely studied. The null hypothesis regarding the statistical analysis is that ordinal flowering date has no statistically significant relationship with year collected. The alternative hypothesis is the year will have a relationship with flowering date. The most likely alternative hypothesis is the ordinal date will be earlier due to climate change and warming temperatures, and the least likely alternative hypothesis would be the flowering time occurs later.
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Methods:
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The data of the collection dates for Aquilegia coerulea were obtained from the Rocky Mountain Herbarium, with only the data for flowering plants and flowering with fruiting utilized for statistical analysis. Once filtered to narrow down the data to only species in Colorado, the ordinal date of flowering for each data point was calculated. Year collected was the independent variable, and the ordinal date was the dependent variable. Once graphed using a scatter plot, linear regression was used to determine the P value – determining the likelihood that observed differences could be due to random chance– as well as the R2 value – representing the degree to which the two variables explain each other. The use of linear regression allows for the determination of the statistical significance between the year collected and the ordinal date by modeling the relationship between the two variables using a linear equation. If the P value is less than 0.05, meaning there is only a 5% chance the pattern is due to random chance, the null hypothesis can be rejected.
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Results:
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Figure 1, shown below, depicts the year collected plotted against the recorded ordinal date for flowering in Aquilegia coerulea, analyzed using linear regression. After statistical analysis, the data (n=458) produced a p value of P=0.4274 and R2=0.001. The line of best fit produced a line with a slope of m=-0.0228, as shown by the equation y= 242.9 – 0.0228x, indicates an insignificant rate of change over time.
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Figure 1. Aquilegia coerulea (n=458) ordinal flowering date in Colorado from 1892-2014.
Discussion:
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After plotting the ordinal date against the year collected, and using linear regression to analyze the scatter plot, there is failure to reject the null hypothesis. This means that the year collected has no effect on the ordinal date for the occurrence of flowering in Aquilegia coerulea in the state of Colorado. Additionally, the rejection of the null hypothesis indicates there is no significant change in the flowering time of the Aquilegia coerulea in response to warmer temperatures due to climate change. The results of the statistical analysis fail to reject the null hypothesis due to the P value of P=0.4274, which is well above the criteria to reject the null hypothesis of p<0.05. Furthermore, the R squared value of R2=0.001 indicates that the independent variable – year collected – does not explain the variation of the dependent variable – ordinal date. Considering the rejection of the null hypothesis, it can be concluded that in this data set, climate change has no effect on the flowering time of the Colorado Blue Columbine.
It is important to consider that the flowering data was collected over the entire state, meaning there are many other factors that could contribute to the flowering ordinal date. These factors could include altitude, leading to lower temperatures. Additionally, if the plant is growing on an alpine slope, the north-facing side will receive less sunlight, which could also affect the ordinal date of flowering for the Colorado Blue Columbine. Due to the large data set (n=458), it is unlikely a type II error occurred, and the failure to reject the null hypothesis is correct.
In conclusion, to properly track the effects of climate change on the ordinal date for flowering of for Aquilegia coerulea, the observed species must be in one concentrated area. This would minimize the variation seen in this data set and would improve the understanding on the changing climates effects on Colorado’s state flower. This method would also improve the impact of evelvation on flowering time on Aquilegia coerulea.
References:
Aquilegia coerulea [Internet]. [updated 2021 April 20]. Laramie (WY): University of Wyoming,
Department of Botany; [cited 2021 April 20]. Available from
Brunet, J., Larson-Rabin, Z. 2012. The Response of flowering time to global warming in high-
altitude plant: the impact of genetics and the environment. Botany 90(4): 319-326.
Ellwood ER, Playfair SR, Polgar CA, Primack RB. 2014. Cranberry flowering times and climate
change in southern Massachusetts. Int J Biometeorol 58(7): 1693-1697.
Rafferty NE, Diez JM, Bertelsen CD. 2020. Climate Change: Flowering time may be shifting in
surprising ways. Current Biology 30(3): 432-441.
