Final copy of thesis

WHITMAN COLLEGE

Physiological and Phenotypic Responses of Mimulus cupreus and Mimulus luteus var. luteus to Elevated Concentrations of CO2

Jeremy Nolan

May 2016

TABLE OF CONTENTS

Abstract............................................................................................................................................3

Introduction..................................................................................................................................3-6

Methods…………………………………………………………...…………………….….….6-12

Experimental Design………………………………………………………………………6

Germination and Transplantation…………………………………………………....…….7

Control Conditions…………………………………………………………...………....…7

Experimental Conditions…………………………………………………………..…...….8

Physiological Data Collection………………………………………….……….……...….9

Phenotypic Data Collection…………………………………………………..……..…….9

Statistical Analysis of Physiological and Phenotypic Data……………….....….…….…10

Leaf Image Analysis………………………………………………………..…..........…..10

Results……………………………………………………………………………..…………13-21

Physiological Results…………………………………………………….……..…….….13

Phenotypic Results……………………………………………………….………..……..16

Leaf Image Analysis……………………………………………………….……….……18

Discussion……………………………………………………………………….…………....22-28

Physiological Responses to Experimental Treatment…………………...………….……22

Phenotypic Responses to Experimental Treatment………………………………..……..26

Leaf Image Analysis……………………………………………………………….…….27

Conclusion…………………………………………………………………….……...……….…29

Acknowledgements……………………………………………………………….……..……….29

Works Cited……...………………………………………………………...…………………29-32

2

ABSTRACT

Plants confront a variety of environmental stressors on a daily basis and must develop effective

physiological, phenotypic, and ecological responses in order to survive and propagate. We tested

the ability of Mimulus cupreus and Mimulus luteus var. luteus to respond to elevated levels of

atmospheric carbon dioxide by germinating one inbred line of each plant and propagating them

in normal and high CO2 conditions. We then measured three physiological traits, three

phenotypic traits, and performed a principal components analysis on leaf images in order to

determine how the two species respond to these stressful conditions. Our study found that

elevated levels of CO2 were associated with significant signs of physiological and phenotypic

stress across all measurements. These stress responses may be the result of a number of different

factors, which may have been the result of elevated levels of CO2, or the result of other

uncontrolled variations between our control and experimental conditions. Our results reflect the

fact that plant responses to environmental stressors are not always easy to predict, and that they

thus warrant continued study.

INTRODUCTION

As sessile organisms, plants have had to evolve varying degrees of phenotypic plasticity in

response to the many abiotic and biotic environmental stressors they face (Taiz 2010). Abiotic

factors such as air quality, water availability, soil nutrient content, salinity, light, wind, and

temperature, in addition to biotic factors such as predation and parasitism, can cause a variety of

physiological, metabolic, and reproductive effects in plants, and can impact overall species

fitness. As anthropogenic climate change continues to alter natural habitats and introduce new

stressors, it is pertinent that we research how plants respond and adapt to these stressors. Our

research focuses specifically on how two species of Mimulus respond when exposed to elevated

3

levels of CO2—an appropriate topic, given the mounting concern over the impact of rising

concentrations of global atmospheric CO2 on species (IPCC 2014), as well as the importance of

CO2 on plant life.

CO2 is the principle source of carbon for plants and a main component in the photosynthetic

pathway. Numerous studies, including long-term free-air carbon dioxide enrichment (FACE)

experiments, have found that elevated concentrations of CO2 can cause a wide range of

significant physiological and reproductive changes in plants, including: increased tolerance to

herbivory (Lau et al., 2009); shifts in reproductive allocation (Wang et al., 2015); increased

biomass and growth rates (Donohue et al., 2013, Nakamura et al., 2011, and Sheppard et al.,

2014); increased antioxidant and anthocyanin activity (Romero et al., 2007 and Wang et al.,

2003); and reduction of stomatal conductance and transpiration rate (Idso et al., 1987). While the

impacts of elevated CO2 have been relatively well-studied in a diverse array of crop species (Jin

et al., 2013, and Bunce 2001), it remains poorly understood in some scientifically important

plants, one of which is the genus Mimulus.

Mimulus is a widespread genus of herbaceous perennials that consists of 120 species distributed

across North and South

America, Australia, South

Africa, India, Madagascar, and

the Himalayas (Beardsley and

Olmstead, 2002). Due to its

quick germination time of 6-12

weeks, ease of propagation,

high seed production, and

4

Figure 1A (left) The evolutionary relationship between several Mimulus species, including M. cupreus and M. l. luteus Figure 1B (right) A map showing the range

of M. l. luteus (yellow) and M. cupreus (orange) (Cooley et al., 2011).

genotypic, phenotypic, and ecological variability, Mimulus is considered a model organism for

evolutionary and ecological genomic studies (Wu et al., 2007). This study utilizes Mimulus

cupreus and Mimulus luteus var. luteus, two closely related species that are endemic to the

Chilean Andes (see Fig. 1A and 1B on previous page). Mimulus cupreus is a predominantly self-

fertilizing species that lives at altitudes ranging from 900-2100m and produces orange and, less

frequently, yellow flowers. Mimulus luteus var. luteus is a yellow-flowered plant with a mixed

mating system, and is found from sea level to up to 3650m (Von Bohlen, 1995). Both M. cupreus

and M. l. luteus lend themselves well to scientific comparison due to their phenotypic and

geographic variation. While the physiology of other Mimulus species, including M. lewisii, M.

cardinalis, and M. guttatus, has been studied in greater depth (see Angert 2006, Decker 1959,

and Murren et al., 2006), the physiology of M. cupreus and M. l. luteus has not been extensively

studied (see Valentine 2014 and Wijnen 2015 for two previous student theses that do examine

their physiology). Studying the physiology of these two species would thus add a meaningful

contribution to current scientific literature.

The goal of our research is to understand how M. cupreus and M. l. luteus respond to elevated

concentrations of CO2. For our experiment we used one inbred line of M. cupreus and one of M.

l. luteus and propagated them in normal- and high-CO2 conditions (approximately two-times

ambient CO2, ~850ppm). We then analyzed three distinct physiological traits and three

phenotypic traits, and performed a principle components analysis of leaf images in order to

assess how both species responded to the elevation in CO2 concentration.

Given the potential variability of plant responses to environmental stressors, we formulated three

broad hypotheses that took into consideration the role of CO2 in the photosynthetic pathway, the

findings of previous high CO2 studies, and the geographic distribution of M. cupreus and M.

5

luteus. Our first hypothesis was that elevated CO2 concentrations would result in higher rates of

physiological activity in both M. cupreus and M. l. luteus. CO2 is a vital component in the

photosynthetic pathway; because of its important role, we thus proposed that an increase in CO2

concentrations would cause a subsequent increase in all physiological measurements that are

involved with, or are influenced by, the photosynthetic pathway. Our second hypothesis was that

the physiology and phenotypic of M. l. luteus would be less affected than M. cupreus by

increased CO2 concentrations. We proposed this hypothesis because M. l. luteus occupies a larger

geographic range than M. cupreus, and is thus presumably exposed to more environmental

variabilities. We believed that this increased geographic range and exposure would thus make it

less susceptible to shifts in atmospheric CO2 concentrations. Our third and final hypothesis was

that experimental CO2 treatment would cause phenotypic variations in both M. cupreus and M. l.

luteus. We based this hypothesis on the findings of several aforementioned studies that found

that increases in CO2 resulted in increased biomass and growth rates, as well as shifts in

antioxidant and anthocyanin concentrations.

METHODS

Experimental Design

The goal of this experiment was to investigate the physiological and phenotypic responses of M.

cupreus and M. l. luteus to elevated levels of carbon dioxide. The experiment spanned 7 ½ weeks

(6/18/2015-8/10/2015), with 2 ½ weeks of preliminary testing and setup. In order to assess the

physiological and phenotypic response of Mimulus to high-CO2 conditions, we examined seven

distinct elements, including: three physiological factors (rate of photosynthesis, rate of

transpiration, and stomatal conductance of water); three phenotypic factors (above ground fresh

6

mass, flower number, and leaf number); and leaf morphology and color, via a mathematical

procedure called principle components analysis (PCA).

Germination and Transplantation

One inbred line of M. cupreus and one inbred

line of M. l. luteus (Fig. 2) were planted in

72-well growth flats filled with dampened

Miracle Gro soil on 6/18/2015, and were

germinated in the rooftop greenhouse. Each

well received 20 seeds. On 7/2/2015,

individual seedlings were transplanted into 3” plastic pots and assigned to an experimental or

control growth group, with 45 M. cupreus and 45 M. l. luteus plants in each group. Control plants

remained in the rooftop greenhouse, while experimental plants grew in greenhouse conditions for

three more days before being moved into the high-CO2 chamber. Both control and experimental

plants were grown in Miracle-Gro soil and watered once daily.

Control Conditions

Temperature in the greenhouse fluctuated between 20°C-34°C, and plants received

approximately 16 hours of sunlight per day. Control plants were placed under a shade cloth from

3pm to 11am to replicate the light levels in the CO2 chamber. Under this cloth, control plants

received an average of 26μmol m-2s-1 PAR (photosynthetically active radiation) per hour. From

11am-3pm, the plants were taken out from under the shade cloth and placed in full sunlight for

testing, where they received an average of 84μmol m-2s-1 PAR per hour. Preliminary research

7

Flower Genotype Cross TypeM. cupreus CO42 Inbred

M. l. luteus EY7 Inbred

Figure 2 The two species of Mimulus used in this experiment

found that CO2 levels in the greenhouse were approximately 415ppm, approximately 12ppm

higher than the average global concentration of CO2.

Experimental Conditions

Temperature in the CO2 chamber was

constant at 27°C, and plants received

approximately 16 hours of light exposure at

an average of 62μmol m-2s-1 PAR per hour.

New lights were installed in the CO2

chamber on 7/9/2015 to increase the

amount of light received by the

experimental plants. Preliminary

research found that CO2 within the high-CO2 chamber dissipated at a rate of approximately

y=4100x-0.624. We used this known dissipation rate and designed a daily regimen involving two

separate CO2 treatments at specific concentrations (Fig. 3). From 9am to 11am, the plants were

placed in the chamber, and 1260ppm CO2 was pumped into the chamber once at 9am. The plants

were then removed from the chamber and placed in the greenhouse for testing from 11am to

3pm. The experimental plants were then placed back in the high-CO2 chamber from 3pm to 9am,

and 1803ppm CO2 was pumped into the chamber once at 3pm. By calculating the area under the

three curves, we found that this treatment would expose plants to an average daily value of

approximately 850ppm CO2—roughly double the measured ambient concentration of 415ppm

CO2 in the greenhouse.

8

0 2 4 6 8 10 12 14 16 18 20 22 240

500

1000

1500

2000

Daily Carbon Dioxide Variation

Time of DayPP

M C

O2

Figure 3 Daily carbon dioxide treatments for experimental plants. CO2 was pumped into chamber twice-daily (9am and 3pm)

Preliminary research found that carbon dioxide within the chamber dissipated at a rate of approximately y=4100x-0.624

Physiological Data Collection

The ADC Bioscientific LCi-SD Ultra Compact photosynthesis system was used from 7/29/2015

to 8/3/2015, between 1-3pm to measure the rate of photosynthesis and transpiration, boundary

layer resistance, and stomatal conductance of water. 23 M. cupreus and 23 M. l. luteus control

specimens were measured, as were 23 M. cupreus and 23 M. l. luteus experimental specimens.

The system was calibrated to greenhouse CO2 levels. Measuring these four physiological

characteristics took five minutes per plant. Before collecting data from each, the leaf chamber

was left open for one minute to calibrate it to greenhouse conditions. Once calibrated, a leaf was

inserted in the chamber for five minutes and measurements were taken at one minute intervals.

After the five consecutive one-minute measurements were taken, the leaf was removed and the

chamber was left open for one minute to reset to greenhouse conditions before inserting the next

leaf.

Phenotypic Data Collection

The number of days to flowering after seed planting was recorded for every plant that flowered

before the end of the experiment. Number of leaves was collected on 8/8/2015, two days before

the end of the experiment. Above-ground fresh biomass data were collected after leaf imaging by

weighing all above-ground biomass from each plant (including imaged leaf). Plants were then

discarded. Seven experimental M. cupreus, one control M. l. luteus, and four experimental M. l.

luteus were not included in analysis of phenotypic data because they failed to grow after being

transplanted.

9

Statistical Analysis of Physiological and Phenotypic Data

Physiological and phenotypic data were analyzed and organized into three separate categories:

physiological results, phenotypic results, and image analysis. Analysis of physiological traits was

done using SPSS (Armonk, NY v. 23.0) and Microsoft Excel (Redmond, WA 2010). Three

general linearized models were performed to determine levels of significance, with low versus

high CO2 treatment as the independent variables and the rate of photosynthesis, rate of

transpiration, and stomatal conductance of water as the dependent variables.

Phenotypic traits were also analyzed using SPSS and Microsoft Excel. Three generalized linear

models were run to determine levels of significance, with low versus high CO2 as the

independent variables and number of leaves, above-ground biomass, and days to flowering as the

dependent variables.

Leaf Image Analysis

On 8/8/2015 and 8/9/2015, we collected a single leaf

from each plant to use for further imaging analysis.

Leaves were chosen based on their length (under 1”)

so that they could fit within the field of view of the

microscope. Images were then taken with a dissecting

microscope and attached Nikon E4500 camera (Fig.

4). In order to minimize variation between images,

camera settings were manually adjusted until proper exposure was achieved. Camera settings

were then fixed to the following conditions: shutter speed was set to 1/250 second; f-stop was set

at f/2.6; and ISO was set to 100. Additionally, all images were the same size (640x480 pixels)

10

Figure 4 Setup used for leaf imaging and above-ground wet biomass collection. Dissecting microscope (with attached digital camera) and scale are to the right

of the computer.

Figure 5A An example of an original, unprocessed leaf image, in this case from an experimental M. cupreus.

Figure 5B The same image as 5A, now converted into greyscale.

Figure 5C The same image as Figures 5A and 5B, now with an overlaying black mask.

and were taken in sRGB (standard red, green, blue) color space—two requirements that were

necessary to perform a principle components analysis.

Image analysis was a multi-step process that required a variety of

different approaches that were each tailored to

the type of data we were examining. Our

original images (Fig. 5A) were first converted into two types of

images: greyscale (Fig. 5B) and masked (Fig.

5C). Greyscale images were used for

morphological analyses. This conversion allowed us to add

greater contrast to leaf edges, and to minimize

variations in ambient light temperature and

strength. Masked images were used for color analysis. Adding a

mask allowed us to more-closely analyze color variations between leaves, and helped to

minimize variations in leaf shape and image brightness.

We then imported our processed images into MatLab (Natick, MA v. 2015a) and ran a total of

four separate principal component analyses (PCA). PCA is a widely-used, non-biased

multivariate technique used to identify visual patterns (interchangeably referred to as “principal

components” or “factors”) in visual data. It is a powerful tool for phenotypic analysis because it

is able to accentuate regions of variance within visual data that, in many cases, are not

discernable to the naked eye. In examining visual data in such a broad manner, PCA does not

confine researchers to specific phenotypic parameters; rather, it allows researchers to broadly

examine the entire visual dataset for variance. PCA has been widely used within the biological

sciences to analyze and interpret a range of data types, including variations in morphology

11

(Holliday et al., 2013), color (Boback et al., 2010), genetic variation (Kloda et al., 2007), and

more.

At the simplest level, a PCA “describe[s] the variance in a set of multivariate data in terms of a

set of underlying orthogonal variables (principal components)” (Shuib et al., 2011). In the case

of this experiment, PCA served two purposes: one, to highlight similarities and differences in

leaf morphology; and two, to highlight similarities and differences in leaf color. A separate PCA

was required for each purpose. We thus ran a total of four independent PCAs: two that examined

both between-treatment leaf morphology and leaf color in M. cupreus, and two that examined

between-treatment leaf morphology and leaf color in M. l. luteus. For ease of interpretation, PCA

of leaf morphology was presented in two different forms: first, as a visual overlay of principal

components on leaf images; and second, as a graphic representation of principal components.

PCA of leaf color was presented as four “factor grids” that visualized the first three principle

components of color presence within the red, green and blue color spaces.

12

RESULTS

Physiological Results

Rate of photosynthesis varied significantly by treatment (p=0.001, Fig. 6, Table 1), but not by

species (p=0.250, Fig. 6, Table 1) or species-treatment interactions (p=0.659, Fig. 6, Table 1).

Both experimental M. cupreus and M. l. luteus had lower average rate of photosynthesis than

control M. cupreus and M. l. luteus.

13

0

1

2

3

4

5

6Series1

Mea

n R

ate

of P

hoto

synt

hesi

s (μ

mol

m-2

s-

1)

Control CO2 Experimental CO2

Species p=0.250Treatment p=0.001Interaction p=0.659

Figure 6 CO2 treatment significantly lowered the rate of photosynthesis in experimental subjects when compared to the control CO2 subjects (p=0.001). Error bars represent 95% confidence intervals.

Dependent Variable: Photosynthesis

Source

Type III Sum of

Squares df Mean Square F Sig.

Corrected Model 26.932a 3 8.977 4.107 .009

Intercept 2191.042 1 2191.042 1002.339 .000

Species 2.936 1 2.936 1.343 .250

Treatment 23.567 1 23.567 10.781 .001

Species * Treatment .428 1 .428 .196 .659

Error 192.362 88 2.186

Total 2410.336 92

Corrected Total 219.294 91

a. R Squared = .123 (Adjusted R Squared = .093)Table 1 Generalized linear model revealed the significant effect of treatment on photosynthetic rate.

Rate of transpiration varied significantly by species (p<0.001, Fig. 7, Table 2) and treatment

(p<0.001, Fig. 7, Table 2), but not by species-treatment interactions (p=0.694, Fig. 7, Table 2).

Both species had lower mean rates of transpiration in the experimental treatment compared to the

control. Mimulus luteus had higher overall rates of transpiration than M. cupreus under both

control and experimental conditions (Fig. 7).

Dependent Variable: Transpiration

SourceType III Sum of


Corrected Model 2.860a 3 .953 8.418 .000Intercept 213.037 1 213.037 1880.802 .000Species 2.148 1 2.148 18.965 .000Treatment .695 1 .695 6.132 .015Species * Treatment .018 1 .018 .156 .694Error 9.968 88 .113Total 225.865 92Corrected Total 12.828 91

a. R Squared = .223 (Adjusted R Squared = .196)Table 2 Generalized linear model revealed the significant effect of species and treatment on rates of transpiration.

14

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Series1

Mea

n R

ate

of T

rans

pira

tion

(mm

ol m

-2 s

-1)


Species p<0.001Treatment p<0.001Interaction p=0.694

Figure 7 Both species and CO2 treatment were found to have a significant impact on average rates of transpiration in Mimulus (p<0.001 and p<0.001, respectively). Experimental treatment significantly lowered the average rate of transpiration, and under both conditions M. l. luteus had a higher average rate of transpiration than M. cupreus. Error bars represent 95% confidence intervals.

Stomatal conductance of water varied significantly by species (p<0.001, Fig. 8, Table 3) and

treatment (p<0.001, Fig. 8, Table 3), but not by species-treatment interactions (p=0.597, Fig. 8,

Table 3). Species had lower average rates of stomatal conductance in the experimental treatment

compared to the control. Mimulus luteus had higher overall rates of stomatal conductance than

M. cupreus under both control and experimental conditions (Fig. 8).

Dependent Variable: Conductance

SourceType III Sum of


Corrected Model .025a 3 .008 22.906 .000Intercept .324 1 .324 874.523 .000Species .005 1 .005 13.336 .000CO2 .020 1 .020 55.099 .000Species * CO2 .000 1 .000 .282 .597Error .033 88 .000Total .382 92Corrected Total .058 91

a. R Squared = .438 (Adjusted R Squared = .419)

15

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09Series1

Mea

n St

omat

al C

ondu

ctan

ce (m

mol

m-2

s-

1)

Species p<0.001Treatment p<0.001Interaction p=0.597

Control CO2Experimental CO2

Figure 8 Both species and CO2 treatment were found to have a significant impact on average rates of stomatal conductance in Mimulus (p<0.001 and p<0.001, respectively). Experimental treatment significantly lowered the average rate of stomatal conductance, and under both conditions M. l. luteus had a higher average rate of transpiration than M. cupreus. Error bars represent 95% confidence intervals.

Table 3 Generalized linear model revealed the significant effects of both species and treatment on stomatal conductance of water.

Phenotypic Results

Above-ground fresh biomass varied significantly by treatment (p<0.001, Fig. 9, Table 4), but not

by species (p=0.789, Fig. 9, Table 4) or species-treatment interaction (p=0.370, Fig. 9, Table 4).

Experimental species had significantly lower above-ground fresh biomass compared to control

species (Fig. 9).

Dependent Variable: Biomass

Source

Type III Sum of



Intercept 898.068 1 898.068 528.855 .000

Species .122 1 .122 .072 .789

Treatment 177.950 1 177.950 104.791 .000

Species * Treatment 1.374 1 1.374 .809 .370

Error 278.495 164 1.698

Total 1409.858 168



16

0

0.5

1

1.5

2

2.5

3

3.5

4Series1

Mea

n A

bove

-Gro

und

Fres

h B

iom

ass

(g)


Species p=0.789Treatment p<0.001Interaction p=0.370

Experimental species had significantly lower average above-ground fresh biomass (p<0.001 ). Error

Table 4 Generalized linear model revealed the significant effect of CO2 treatment on above-ground fresh biomass

None of the experimental subjects managed to flower in the 53 days that the experiment ran (Fig.

10). By the end of the experiment on August 10, 2015, 19 control M. cupreus and 36 M. l. luteus

subjects flowered.

Number of Leaves

Average number of leaves varied significantly by species (p<0.001, Fig. 11, Table 5) and by

treatment (p<0.001, Fig. 11, Table 5), but not by species-treatment interaction (p=0.634, Fig. 11,

Table 5). Both experimental species had significantly fewer leaves (Fig. 11). In both control and

experimental treatments, M. cupreus had a slightly higher average number of leaves than M. l.

luteus.

Dependent Variable: Leaf Number

17

Control CO2

Figure 10 Average number of days to first flowering. Experimental subjects failed to flower. Error bars represent 95% confidence intervals.

Figure 11 Both species and treatment had a significant effect on average number of leaves at time of death (p<0.001 and p<0.001, respectively). Experimental conditions significantly lowered average leaf number, and under both control and experimental conditions M. l. luteus had a lower average leaf number than M. cupreus. Error bars represent 95% confidence intervals.

051015202530354045505560

Series1

Aver

age

Num

ber o

f Lea

ves

at T

ime

of D

eath


Species p<0.001Treatment p<.0001Interaction p=.0634

05

101520253035404550

Mean Days to Flowering

Ave

rage

Num

ber o

f Day

s to

Firs

t Fl

ower

ing

Experimental CO2

Source

Type III Sum of



Intercept 298602.525 1 298602.525 1862.472 .000

Species 17791.434 1 17791.434 110.970 .000

Treatment 2399.162 1 2399.162 14.964 .000

Species * Treatment 36.390 1 36.390 .227 .634

Error 26293.446 164 160.326

Total 355054.000 168



Leaf Image Analysis

The results of our principal component analyses of greyscale images had three separate parts: a

mean image (Fig. 12A and 13A); a visual collection of the first four principal components (Fig.

12B and 13B); and a graphic representation of the second and third principal components (Fig.

12C and 13C). The mean image is, as its name suggests, a visual compilation of the “average

image” within an analyzed group. The mean greyscale image from both control and experimental

M. cupreus (Fig. 12A) and control and experimental M. l. luteus (Fig. 13A) were used as a

representative, or “base image,” on which to project morphological factors. The first four

principal components (“factors”) identified in control and experimental M. cupreus species (Fig.

12B), as well as control and experimental M. l. luteus species (Fig. 13B), did not confirm the

presence of any distinct morphological differences between treatments. Rather, our analysis

registered highly localized contrasts at leaf edges, which were caused by minor variations in light

levels between images. In order to confirm this observation, we graphed the second and third

factors (Fig. 12C and 13C), which gave us a 1-D representation of the second and third 2-D

factors within our images. We omitted the first factor because it consisted largely of image noise,

18

Table 5 Generalized linear model revealed the significant impact of species and CO2 treatment on average leaf number at time of death.

plants. Figure 13B (center) Projection of the first four factors . Our analysis failed to uncover any clear phenotypic factors between control and

plants, and instead highlighted variations in light between individual pictures. Figure 13C (right) A graphical representation of the second (x-axis) and third (y-axis) factors between control and experimental M. l. luteus plants. This graph proves that there are differences between control and experimental images; however, we cannot determine from our data whether these differences are due to variations in morphology or in image lighting. Black dots represent control M. l. luteus, while blue dots represent experimental M. l.

and the fourth because it was such a minor factor. Due to the fact that the points did not

completely overlap, our graphs confirmed the presence of variation between images. However,

we cannot determine whether this variation is due to leaf morphological variations or variation in

image lighting. It is possible that the third principle component in M. cupreus was the result of

morphological variations along the edges of the lower half of each leaf; again, though, we cannot

confirm this observation from our existing data.

The results of our principal component analyses of masked color images were projected onto a

total of four factor grids: two for control and experimental M. cupreus (Fig. 14A and Fig. 14B)

19

Figure 12A (left) Mean image of control and experimental M. cupreus plants. Figure 12B (center) Projection of the first four factors found between greyscale images of M. cupreus. Our analysis failed to uncover any clear phenotypic factors between control and experimental M. cupreus species, and instead highlighted variations in light between individual pictures. Figure 12C (right) A 1-D representation of the second (x-axis) and third (y-axis) factors between control and experimental M. cupreus plants proves that there are differences between control and experimental images. While it is possible that the third principle component might reflect morphological variations at the base of each leaf; we cannot confirm from our data whether these differences are due to variations in morphology or in image lighting. Black dots represent control M. cupreus, while blue dots represent experimental M. cupreus.

and two for control and experimental M. l. luteus (Fig. 15A and 15B). The x-axis was scaled to

correspond to each individual leaf image as they were imported into Matlab, and the y-axis was

scaled to correspond to the first three principal components within each respective color space

(sRGB—red, green, and blue). The color of each individual quadrate corresponds to the presence

of the color in each color space. A blue quadrant corresponds to a low presence of color; a green

quadrant corresponds to a neutral presence of color; and yellow, orange and red colors

correspond to a high presence in color (in increasing order). The depth of coloration of a

quadrate corresponds to the strength of the negative, neutral, or positive presence of a color. Our

analysis confirmed that there were in fact qualitative differences in sRGB color spaces between

control and experimental M. cupreus and M. l. luteus plants. M. cupreus plants showed an overall

decrease in the presence of color in each of the three sRGB color spaces, with less overall color

in experimental plants. In addition, the leaves of experimental M. cupreus plants were more

similar to one another in their lack of color across the sRGB color space than the leaves of

control M. cupreus plants. Both control and experimental M. l. luteus had similar levels of

variation in color presence across the sRGB spectrum, and did not show any notable trends in

color presence between one another. Principal component analysis does not assign significance

to observed variation, and as such it is impossible to quantify the intensity of the variation we

observed between the two species. However, it is worth noting that casual visual observation of

experimental plants did confirm these findings. Both experimental M. cupreus and M. l. luteus

plants appeared sickly and stunted.

Control Experimental

Red

20

Green

Blue

Control Experimental

Red

Green

Blue

DISCUSSION

21

Figure 14A (left) Projection of the first three sRGB color factors of control M. cupreus plant leaves. X-axis scale corresponds to individual leaf images as they were imported into Matlab; y-axis corresponds to the first three principal components in each respective sRGB color space. Figure 14B (right) Projection of first three sRGB color factors in experimental M. cupreus plant leaves. Experimental M. cupreus plant leaves had less variation in color presence than control M. cupreus plant leaves, and had an overall decreased presence of color in the sRGB color space.

Figure 15A (left) Projection of the first three sRGB color factors in control M. l. luteus plant leaves. Control M. l. luteus plant leaves had more variations in color presence across the full sRGB spectrum. Figure 15B (right) Projection of the first three sRGB color factors in experimental M. l. luteus species. Both control and experimental M. l. luteus plant leaves had similar levels of variation in color presence across the sRGB color space.

The purpose of this experiment was to investigate the physiological and phenotypic responses of

M. cupreus and M. l. luteus to elevated concentrations of atmospheric CO2. It sought to address

the following hypotheses: first, that elevated CO2 concentrations would result in higher rates of

physiological activity in both M. cupreus and M. l. luteus; second, that M. l. luteus would be less

affected by the increased CO2 concentrations than M. cupreus; and third, that experimental CO2

treatment would result in phenotypic variations in both species. Before elaborating upon our

specific findings, it is important to note that experimental conditions were associated with

significant quantitative and qualitative signs of plant stress. These observations strongly suggest

that our experimental conditions were severe enough to negatively impact the physiology and

fitness of M. cupreus and M. l. luteus. Further discussion of our results and drawing comparisons

with other high CO2 physiological studies may help us determine the underlying causes of the

stress responses we observed, ameliorate some of the difficulties we encountered, and improve

the execution and analysis of similar future experiments.

Physiological Responses to Experimental Treatment

Our study revealed that the mean rates of photosynthesis, mean rate of transpiration, and mean

stomatal conductance were all significantly higher in control plants than in experimental plants.

These findings did not support our first hypothesis that elevated CO2 concentrations would result

in higher rates of physiological activity in both M. cupreus and M. l. luteus. Several observations

from our physiological results merit further discussion and comparison to findings from other

high CO2 studies. Doing so may not only help explain our own findings, but also improve future

experiments.

22

The first noteworthy observation pertains to our findings on mean photosynthetic rates under

experimental CO2 treatment. Our study found that experimental CO2 treatment was associated

with significantly lower rates of photosynthesis in both M. cupreus and M. l. luteus. This finding

does not reflect the well-supported understanding that CO2-enrichment increases photosynthetic

rates (Alexandre et al., 2012, Delucia et al., 2000). One reason why our results differ may be due

to the nature of our CO2 treatment. By exposing experimental plants to twice-daily CO2

concentrations of 1260ppm and 1803ppm, and then allowing the CO2 to dissipate to yield an

average daily exposure of 850ppm, our treatment may have inadvertently restricted cellular

respiration, or may have oversaturated the enzyme rubisco with CO2. While a reduction in

cellular respiration would explain the significant signs of physiological and phenotypic stress

that we observed, further research would have to be performed to support this claim; currently, a

wide range of studies both support and refute the theory that CO2 enrichment decreases rates of

respiration in plants (Amothor, 1991). Another reason why our results differ may be due to other

variations between control and experimental treatment. Light levels in the CO2 chamber were

26% lower than light levels in the greenhouse. Additionally, temperature in the CO2 chamber

remained constant at 27°C, whereas temperature in the greenhouse varied between 20°C-34°C.

While we made provisions to try and ameliorate this difference in light levels by installing new

lights in the chamber, we cannot discount the impact that this variation, and the lack of variation

in temperature, may have had on experimental plant growth.

The second noteworthy observation again pertains to our photosynthesis results, which do not

reflect the well-supported understanding that CO2 enrichment increases the efficiency of

photosynthesis in C3 plants by decreasing the rate of photorespiration (Cousins et al., 2001). One

reason why our experimental species had lower mean rates of photosynthesis may be due to a

23

common “acclimation phenomenon” (Bowes, 1991) facing rubisco. This phenomenon suggests

that long periods of time growing in a CO2-enriched environment can cause an overall decline in

rubisco protein production and activity, and a parallel increase in carbohydrate production.

Increased carbohydrate production has been linked to subsequent decreases rates of

photosynthesis, inhibition of rubisco regeneration, and disruption to chloroplasts (Bowes, 1991).

While our physiological results appear to be supported by this acclimation phenomenon, our

phenotypic results are not. We did not see an increase in biomass accumulation, as would be

expected with a parallel increase in carbohydrate production. Further research will have to be

performed to determine the exact point at which increased carbohydrate production begins to

decrease photosynthesis, inhibit rubisco regeneration, and disrupt chloroplasts.

The final noteworthy observation from our physiological results pertains to our findings on

stomatal conductance and transpiration. Under high-CO2 conditions, both mean stomatal

conductance and mean rate of transpiration decreased by a statistically significant amount in M.

cupreus and M. l. luteus. This decrease supports the well-documented relationship between

stomatal conductance and transpiration (Martin et al., 1999). In a CO2-enriched environment like

that of our experimental treatment, stomatal conductance should decrease because stomata can

be open for shorter periods of time and still take in the necessary quantity of CO 2 to perform

photosynthesis. This decrease in stomatal conductance should also correspond to a decrease in

rates of transpiration, because less water is lost to transpiration when the stomata are open for

shorter periods of time. This decrease in stomatal opening, though, could also explain why rates

of photosynthesis were lower in experimental plants. It is possible that plants were unable to take

in the necessary amount of CO2, or that their ability to absorb CO2 was oversaturated, while

stomata were open.

24

Our study also revealed that both control and experimental M. l. luteus had significantly higher

mean rates of transpiration and stomatal conductance than control and experimental M. cupreus.

However, in all three physiological traits that we examined, experimental treatment negatively

affected M. l. luteus to nearly the same degree as it affected M. cupreus. As such, our data does

not support the part of our second hypothesis that suggests that M. l. luteus would be less

affected physiologically by increased CO2 concentrations than M. cupreus, despite inhabiting a

larger geographic range being exposed to more environmental variation. This finding is contrary

to studies that have found that plant and animal generalists are less impacted by a variety of

environmental changes than plant and animal specialists (Berger et al., 2014 and Wilson et al.,

2008). However, the impact of environmental changes on specialists and generalists is still a

highly researched area that is continually undergoing reanalysis and reinterpretation (Colles et

al., 2009).

Several changes in experimental methods, or improvements on current experimental methods,

could improve the future execution of this study. First, control plants could be grown in an

identical incubation chamber under normal CO2 conditions, which would eliminate variabilities

in light and temperature between control and experimental treatments. If a second chamber is not

available, then a staggered experiment could also eliminate these variabilities. Control plants

could be grown in the modified chamber at normal CO2 levels, tested, and disposed; then,

experimental plants could be grown in the modified chamber at elevated CO2 levels, tested, and

disposed. However, staggering the experiment in this manner would effectively double the length

of the experiment. Second, the CO2 chamber could receive a constant stream of high CO2 at the

desired concentration, rather than two elevated applications. This would more accurately

replicate the conditions of the aforementioned FACE experiments, but would require additional

25

equipment that was not available for this experiment. Lastly, physiological measurements of

experimental plants could be taken while plants are still under high CO2 conditions (rather than

under control conditions, as they were in this experiment). This could give us a more meaningful

understanding of the high CO2 physiology of Mimulus.

Several other future studies could also increase our understanding of how and why elevated CO2

affects the physiology of M. l. luteus and M. cupreus. One such study could examine the ideal

CO2 concentration at which photosynthetic rate is maximized and transpiration is minimized in

M. l. luteus and M. cupreus. Another such study could more specifically examine how cellular

respiration and photorespiration are impacted from high-CO2 concentrations in M. cupreus and

M. l. luteus. Such a study could find the ideal CO2 concentration that minimizes photorespiration

and maximizes photosynthesis and cellular respiration. Lastly, further research into the long-term

impacts of CO2 enrichment on rubisco production, activity, regeneration, and inhibition in M.

cupreus, M. luteus, and other model species could increase our understanding of photosynthesis

and CO2 enrichment across a wide range of plants.

Phenotypic Responses to Experimental Treatment

Our study revealed that control species consistently outperformed experimental species in the

three physiological traits we examined. Mean above-ground fresh biomass, leaf number, and

days to flowering were all significantly lower in the experimental species than the control

species. Our results thus support our third hypothesis that experimental CO2 treatment would

result in phenotypic variations in both species.

As was the case with our physiological results, restricted rates of respiration, increases in

photorespiration, and or variations between our control and experimental treatments could be

26

responsible for our phenotypic results. Future analysis of plant respiration and photorespiration

under high CO2 conditions could thus help clarify both our physiological and phenotypic

findings. Additionally, further refinement of our experimental procedures could eliminate the

possibly that variations between our control and experimental procedures caused/influenced our

results.

Leaf Image Analysis

Our principal component analyses of greyscale images did not reveal any meaningful

morphological variations between control and experimental treatments within the first four

factors. This was due to the fact that our images had minor variations in light levels around the

edges of the leaves, which were then registered as the only principal components. While it is

possible that the third factor in M. cupreus may reflect variations along the edge of the lower half

of leaves, we cannot confirm from our PCA results whether this was the result of morphological

variations or variation in lighting between control and experimental images. Casual visual

observation did not uncover any morphological outliers, other than a trend in an overall decrease

in leaf size.

Our principal component analyses of masked images gave us slightly more meaningful, if not

entirely quantifiable, information about color presence and variations between control and

experimental M. cupreus and control and experimental M. luteus. Our analysis found that there

was more variation between control and experimental M. cupreus, regarding the presence and/or

lack of color, than there was between control and experimental M. l. luteus. Our analysis also

found that M. luteus had more overall variation in color presence across the sRGB spectrum in

both control and experimental conditions. As a whole, experimental leaves in both species had

27

less variation in, and presence of, color than their control counterparts. These variations in color

were not easy to see with the naked eye. Our PCA of leaf color thus reflects the capacity of

sRGB color analysis to make more meaningful qualitative discernments between minor, easy-to-

miss differences in color variation and presence.

Variations in leaf color may be the result of chemical responses to CO 2 enrichment, and can be

broken down by specific color space. Variation in green color space would most likely indicate a

change in the presence and/or density of chlorophyll in chloroplasts. Further chemical or photo

analysis would have to be performed to determine what extent chlorophyll was increased or

decreased. Variation in the red and blue spectrum would most likely indicate a change in

anthocyanin production, which could serve as an indicator of plant stress (Glover et al., 2012).

Again, further chemical and photographic analyses would have to be performed to determine the

extent and type of variation occurring.

In the future, one way that we could obtain more meaningful morphological data from our

greyscale images could be by overlaying points on each image (Fig.

16A) that corresponded to key, predetermined

phenotypic features such as serrations, extreme

points, and corners. These points could then be exported as a

new image (Fig. 16B) and imported into Matlab.

Doing so would quantify leaf morphological

variations and simultaneously eliminate the

qualitative variations that impacted our morphological analysis in this study.

CONCLUSION

28

Figure 16A An example overlay of key phenotypic points to consider on an experimental M. cupreus leaf.

Figure 16B Key phenotypic points from Fig. 15A exported as a separate image to import into Matlab.

Our study revealed that elevated concentrations of CO2 were associated with significant signs of

physiological and phenotypic stress in M. cupreus and M. l. luteus, including: decreases in above

ground fresh biomass, flower and leaf production, and photosynthetic rate; and increases in

transpiration and stomatal conductance. Our PCA of leaf color found that experimental M.

cupreus leaves were more similar in color to one another than control M. cupreus leaves, and

lacked more color across the sRGB colorspace. While our findings may be the result of our

unique twice-daily CO2 regimen or other variables between our control and experimental

treatments that were not accounted for, they still broadly illustrate how shifts in environmental

conditions may have diverse physiological, phenotypic, and ecological effects for Mimulus.

ACKNOWLEDGEMENTS

I would like to thank the following individuals for their assistance with this project: Professor

Arielle Cooley, for her guidance and support in designing, completing, and analyzing this study;

Professor Douglas Hundley, for his computational and mathematical knowledge and support; and

Larry North, for his help in designing our CO2 chamber. Additionally, I would like to thank the

Whitman College Abshire Grant for funding this study.

LITERATURE CITED

Alexandre, Ana, Joao Silva, Pimchanok Buapet, Mats Bjork, and Rui Santos. "Effects of CO2 Enrichment on Photosynthesis, Growth, and Nitrogen Metabolism of the Seagrass Zostera Noltii." Ecology and Evolution 2.10 (2012): 2620-630. Web.

Amthor, Js. "Respiration in a Future, Higher-CO2 World." Plant Cell and Environment 14.1 (1991): 13-20. Web.

Angert, Amy. "Growth and Leaf Physiology of Monkeyflowers with Different Altitude Ranges." Oecologia 148.2 (2006): 183-94. Web.

29

Beardsley, Paul M., and Richard G. Olmstead. "Redefining Phrymaceae: The Placement of Mimulus, Tribe Mimuleae, and Phryma." American Journal of Botany 89.7 (2002): 1093-1102. Web.

Berger, D., R. J. Walters, and W. U. Blanckenhorn. "Experimental Evolution for Generalists and Specialists Reveals Multivariate Genetic Constraints on Thermal Reaction Norms." Journal of Evolutionary Biology 27.9 (2014): 1975-989. Web.

Boback, Scott M., and Lynn M. Siefferman. "Variation in Color and Color Change in Island and Mainland Boas (Boa Constrictor)." Journal of Herpetology 44.4 (2010): 506-15. Web.

Bowes, G. "Growth at Elevated CO2: Photosynthetic Responses Mediated through Rubisco." Plant, Cell & Environment 14.8 (1991): 795-806. Web.

Bunce, Ja. "Direct and Acclimatory Responses of Stomatal Conductance to Elevated Carbon Dioxide in Four Herbaceous Crop Species in the Field." Global Change Biology7.3 (2001): 323-31. Web.

Colles, Audrey, Lee Hsiang Liow, and Andreas Prinzing. "Are Specialists at Risk under Environmental Change? Neoecological, Paleoecological and Phylogenetic Approaches." Ecology Letters 12.8 (2009): 849-63. Web.

Cousins, Asaph B., Neal R. Adam, Gerard W. Wall, Bruce A. Kimball, Paul J. Pinter, Steven W. Leavitt, Robert L. LaMorte, Allan D. Matthias, Michael J. Ottman, Thomas L. Thompson, and Andrew N. Webber. "Reduced Photorespiration and Increased Energy‐use Efficiency in Young CO2‐enriched Sorghum Leaves." New Phytologist 150.2 (2001): 275-84. Web.

Decker, John P. "Some Effects of Temperature and Carbon Dioxide Concentration on Photosynthesis of Mimulus." Plant Physiology 34.2 (1959): 103-06. Web.

DeLucia, E., and R. Thomas. "Photosynthetic Responses to CO2 Enrichment of Four Hardwood Species in a Forest Understory." Oecologia 122.1 (2000): 11-19. Web.

Donohue, Randall J., Michael L. Roderick, Tim R. McVicar, and Graham D. Farquhar. "Impact of CO2 Fertilization on Maximum Foliage Cover across the Globe's Warm, Arid Environments." Geophysical Research Letters 40.12 (2013): 3031-035. Web.

Glover, and Martin. "Anthocyanins." Current Biology 22.5 (2012): R147-150. Web.

30

Holliday, Trenton W., and Lukáš Friedl. "Hominoid Humeral Morphology: 3D Morphometric Analysis." American Journal of Physical Anthropology 152.4 (2013): 506-15. Web.

Idso, S.B., Kimball, B.A., Anderson, M.G., and Mauney, J.R. "Effects of Atmospheric CO2 Enrichment on Plant Growth: The Interactive Role of Air Temperature." Agriculture, Ecosystems and Environment 20.1 (1987): 1-10. Web.

Intergovernmental Panel on Climate Change Ipcc. "Intergovernmental Panel on Climate Change: Fourth Assessment Report: Climate Change 2007: Synthesis Report." IDEAS Working Paper Series from RePEc (2008): IDEAS Working Paper Series from RePEc, 2008. Web.

Jin, Jian, Caixian Tang, Roger Armstrong, Clayton Butterly, and Peter Sale. "Elevated CO2 Temporally Enhances Phosphorus Immobilization in the Rhizosphere of Wheat and Chickpea." Plant and Soil 368.1-2 (2013): 315-28. Web.

Kloda, J.M., Dean, P.G.D, Maddren, C., Macdonald, D.W., and Mayes, S.. "Using Principle Component Analysis to Compare Genetic Diversity across Polyploidy Levels within Plant Complexes: An Example from British Restharrows (Ononis Spinosa and Ononis Repens)." Heredity 100.3 (2007): 253. Web.

Lau, Jennifer, and A. Tiffin. "Elevated Carbon Dioxide Concentrations Indirectly Affect Plant Fitness by Altering Plant Tolerance to Herbivory." Oecologia 161.2 (2009): 401-10. Web.

Martin, Timothy A., Thomas M. Hinckley, Frederick C. Meinzer, and Douglas G. Sprugel. "Boundary Layer Conductance, Leaf Temperature and Transpiration of Abies Amabilis Branches." Tree Physiology 19.7 (1999): 435-43. Web.

Murren, Courtney J, Larry Douglass, Amanda Gibson, and Michele R Dudash. "Individual and Combined Effects of Ca/Mg Ratio and Water on Trait Expression in Mimulus Guttatus." Ecology 87.10 (2006): 2591-602. Web.

Nakamura, Ito, et al. "Phenotypic and Genetic Differences in a Perennial Herb Across a Natural Gradient of CO2 Concentration." Oecologia 165.3 (2011): 809-18. Web.

Romero, Irene, Teresa Sanchez-Ballesta, M., Maldonado, Roberto, Isabel Escribano, M., and Merodio, Carmen. "Anthocyanin, Antioxidant Activity and Stress-induced Gene Expression in High CO2-treated Table Grapes Stored at Low Temperature." Journal of Plant Physiology 165.5 (2008): 522-30. Web.

31

Sage, Rf. "Variation in the K(cat) of Rubisco in C-3 and C-4 Plants and Some Implications for Photosynthetic Performance at High and Low Temperature." Journal of Experimental Botany 53.369 (2002): 609-20. Web.

Sheppard, Christine S., and Margaret C. Stanley. "Does Elevated Temperature and Doubled CO2 Increase Growth of Three Potentially Invasive Plants?" Invasive Plant Science and Management 7.2 (2014): 237-46. Web.

Shuib, Nor Hassifi, Shaari, Khozirah, Khatib, Alfi, Maulidiani, Ralf, Kneer, Seema, Zareen, Salahudin Mohd., Raof, Nordin, Hj. Lajis, Victor, and Neto, Victor. "Discrimination of Young and Mature Leaves of Melicope Ptelefolia Using 1H NMR and Multivariate Data Analysis." Food Chemistry 126.2 (2011): 640-45. Web.

Taiz, Lincoln, and Eduardo Zeiger. Plant Physiology. Sunderland, MA: Sinauer, 2010. Print.

Valentine, Celine. "Physiological Responses to Environmental Stresses in Mimulus Luteus and Mimulus Cupreus." Thesis. Whitman College, 2014. Print.

Von Bohlen C. 1995a. “El género Mimulus l. (Scrophulariaceae) en Chile.” Gayana Botanica 52: 7-28. Web.

Wang, Shiow Y, James A Bunce, and J L Maas. "Elevated Carbon Dioxide Increases Contents of Antioxidant Compounds in Field-grown Strawberries." Journal of Agricultural and Food Chemistry 51.15 (2003): 4315-20. Web.

Wang, Xianzhong, Daniel Taub, and R. Jablonski. "Reproductive Allocation in Plants as Affected by Elevated Carbon Dioxide and Other Environmental Changes: A Synthesis Using Meta-analysis and Graphical Vector Analysis." Oecologia 177.4 (2015): 1075-087. Web.

Wijnen, Marijke. "Heat and Drought Stress Responses and the Adaptive Significance of Petal Anthocyanins in Chilean Mimulus." Thesis. Whitman College, 2015. Print.

Wilson, Shaun, Scott Burgess, Alistair Cheal, Mike Emslie, Rebecca Fisher, Ian Miller, Nicholas Polunin, and Hugh Sweatman. "Habitat Utilization by Coral Reef Fish: Implications for Specialists vs. Generalists in a Changing Environment." The Journal of Animal Ecology 77.2 (2008): 220-28. Web.

Wu, C.A., Lowry, D.B., Cooley, A.M., Wright, K.M., Lee, Y.W., and J.H. Willis. "Mimulus Is an Emerging Model System for the Integration of Ecological and Genomic Studies." Heredity 100.2 (2007): 220. Web.

32