Developmental Stability of Grapevine Leaf Morphometrics: Allometry, Heteroblasty, and Interannual Variability of Vitis spp. Grape Species

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Imagine, if you willIts autumn, and the leaves have changed to a brilliant spectrum of colours, from scarlet red to goldenrod yellow. One by one, like snowflakes, they gracefully flutter to the ground, covering the ground like a rich, polychromatic patchwork quilt. Your parents have raked a leafy replica of the Appalachian Mountains, and like an Olympic diver(?), you leap into the air, and descend into the salad.We see leaves wherever we go (pictures of St. Louis streets), eat them for our greens, press and put them into scrapbooks, but rarely do we consider why a leaf is the shape it is. Hi, Im [person doing the intro]Im [2nd person]Im [3rd person]Im [4th person]((Simultaneously: And together, we are Team Vitis.) L10ShootbaseShoottip

To account for heteroblasty, we assign each leaf a leaf number, which is the node position of the leaf when counting from the shoot base to the shoot tip. The assumption here is that the more basal leaves on the shoot are fully mature--therefore, differences in shape are not the result of the leaves being different ages, but rather being produced at different nodes.

Species, developmental stage,& leaf number effects

S1S2S9S10S3S4S5S6S7S8L1L2L1L3L4L5L6L7L8L9L10DevelopmentalStageS1 -> S10LeafNumberL1 -> L10ShootbaseShoottip

To account for allometry, by contrast, we also assign each leaf a leaf stage, counting from the first visible leaf at the tip of the shoot in the direction of the base. The assumption here is that differences between the leaves at the tip of the shoot are the result of these leaves being different ages, and therefore at different stages of leaf development.

Species, developmental stage,& leaf number effects

ShootbaseShoottipLeaf numberDevelopmental stage

From there, it is an easy step to account for evolutionary differences--we can just analyze many species and compare them to each other, across leaf number (heteroblasty) and leaf stage (allometry).

Year 1Year 2Species, developmental stage,leaf number, & plasticity effects

The final ingredient, as youll recall, is phenotypic plasticity due to environmental variation. To build this into our study, we simply took advantage of natural variability in environmental conditions through interannual variability. 27

Collection of LeavesUSDA Vitis germplasm collection in Geneva, NY In 2013 and in 2015Single shoots were collected per vineThe abaxial side of the leaf was imaged

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----- Meeting Notes (7/30/15 13:22) -----Number these? ----- Meeting Notes (7/31/15 15:04) -----NO NUMBERS. Sinuses with black outline. Define lobes and sinuses.

The AnalysisA Generalized Procrustes Analysis(GPA) was used with the R shape packagesDifferent species, developmental stage, and leaf number were all analyzed independently The visualization was performed in ggplot2

Generalized Procrustes Analysis

http://www.mythweb.com/today/media/procrustes07.gifhttp://openi.nlm.nih.gov/imgs/512/37/3119416/3119416_IJEB2011-290245.002.png

----- Meeting Notes (7/31/15 15:04) -----Go through each step. Translation, scaling, rotation. Mention superimpose and compare. 36

Why Principal Component Analysis?Useful for multidimensional analysis: new axes that better explain variation in the data are chosenWhich independent variables matter the most?

Understanding Principal Component AnalysisxxC1yyC2

C1

C2

Principal Components

----- Meeting Notes (7/31/15 15:04) -----Don't use word circularity--vein thickness, lobed unlobed, things like "more prominent base" or "more prominent skinny tip"----SPELLING "principal" not "princple"

Principal Components

Here is a movie showing illustrating the first three principal components, moving from from minus three standard deviations to plus three standard deviations. Here is PC1PC2.and PC3. 40

PC1

Leaf Stage (Allometry)PC1

Here weve plotted the first principle component on the y-axis, and leaf stage on the x-axis. PC1 is illustrated in the top left corner, and again we can see how it primarily influences vein thickness and the angles between veins. Leaf stage, as youll recall, is a measure of allometryunequal expansion of different parts of the leaf relative to one another. Weve shown the three most abundant species to demonstrate interspecific variation, represented by the different colors, and separated them by year to account for interannual variation (represented by the dashed and solid lines). Each plot is shown with a 95% confidence interval.

And from this graph, we can see that the driving force behind variation in PC1 is allometryvein size decreases dramatically and the angle between veins increases as the leaf grows older. At the same time, there is some interspecific variation in PC1, while interannual variation is almost negligible. 41

Leaf Number (Heteroblasty)

PC1

PC1

And we can see this same type of pattern when we look at PC1 from the perspective of heteroblasty, represented by node position or leaf number on the x-axis. Again, though we see some separation by species, the overall trend of the curve appears remarkably conserved as PC1 increases with successive nodes. From this graph and the one before it, we can see that vein thickness and the angle size between veins correlate most strongly with developmentboth allometric and heteroblastic. 42

PC2PC2Leaf Stage (Allometry)

PC2, however, tells a slightly different story when we plot it against allometry. From this movie, we can see that PC2 corresponds to the length/width ratio of the leaf. While PC2 generally does increase as the leaf expands, interspecific variation becomes a much more important determinant of leaf shape for this principal component. At the same time, there is no significant difference between years, suggesting a low degree of plasticity. 43

PC2PC2Leaf Number (Heteroblasty)

This is mirrored when we plot PC2 against leaf number. Again we see unambiguous differences between species, and evolution appears to be more important in controlling the length to width ratio of the leaf than developmenteither in terms of leaf stage or leaf number. And again, environmental variation appears negligible. 44

PC3

PC3Leaf Stage (Allometry)

In PC3, by contrast, we do begin to see clear signs of inter-annual variability. Recall that PC3 is largely tied to the degree of leaf dissection. Especially in the later leaf stages, we see that the 2015 leaves are consistently more deeply lobed than their 2013 counterparts. 45

PC3

Inter-annual Variability: PC3Leaf Stage (Allometry)

To see this more clearly, we can look at all vines sampled in both years, regardless of species. Here, we appear to find a very neat shift between the two years, seeming to indicate both precocious development and deeper lobing in the 2015 leaves. Moreover, it tells us that this developmental pattern isnt just noise, but is in fact a real phenomenon that is reproducible between years.46

PC3

PC3Leaf Number (Heteroblasty)

And if anything, that reproducibility becomes even more striking when examining PC3 in the context of heteroblasty. Once again, we clearly see differences emerging between species and between years, yet all plots retain this same sinusoidal shape whichif we were to observe only in a single species or timepointwe might wrongfully dismiss as noise.

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PC3

Inter-annual Variability: PC3Leaf Number (Heteroblasty)

And again, when we only compare years, we observe a clear shift in the plotthe shape of curve remains almost totally unchanged between the years. What this graph is telling us is that there is a shocking degree of stability in the patterns of development both inter-annually and inter-specifically, but that despite this developmental stability, PC3 character does appear to be plastic.

----- Meeting Notes (7/31/15 15:04) -----Say things like, "PC3 predominantly correlates to lobing". Visualizing weather data. Making specific hypothesis. 48

Determinants of Leaf Shape: PCs

PC1

PC2

PC3

In summary, PC1 is primarily determined by development, both allometric and heteroblastic. PC2 is primarily determined by species, and PC3 by inter-annual variability. 49

Determinants of Leaf Shape: PCs

PC3

Now lets take a closer look at lobing, the one aspect of leaf shape that appears to display the greatest degree of plasticity. 50

http://nmnh.typepad.com/100years/2013/09/when-is-a-leaf-a-thermometer.html

OR?

Remember the importance of lobing as a thermometer of past climates. We know that more dissected leaves are representative of colder, drier climates, while entire leaves are the hallmarks of warm, wet climates. What remains unresolved is the degree to which this relationship is set by plasticity versus evolution. If there is interannual variability in lobing, that would have important implications for both our understanding of the paleorecord, and for our predictions regarding the effects of modern climate change on leaf morphology. 51

Leaf Stage (Allometry)

Sqrt(Sinus Area/Leaf Area)Inter-annual Variability: Sinus Area

We know that PC3 can change between yearsbut remember, PC3 is only correlated with lobing. For a more rigorous test of plasticity in this trait, we calculated the ratio of the totaled area of the proximal and distal sinuses over the area of the whole leaf. The square root of this ratio is shown on the y-axis, whereby a higher value indicates deeper sinuses. On the x-axis we show leaf stage to account for allometric effects. What this graph shows us is a highly significant difference in sinus depth between years, with the 2015 leaves more deeply lobed than those from 2013 across all stages of leaf development, confirming our expectations from PC3. 52

Inter-annual Variability: Sinus Area

Sqrt(Sinus Area/Leaf Area)

Leaf Number (Heteroblasty)

And the same can be said for the heteroblastic series: the 2015 leaves are significantly more lobed than their 2013 counterparts, regardless of leaf number. 53

Day in Growing Season Temperature (F)

Inter-annual Variability: WeatherMarchMayJulyNov.Jan.MarchMayMid-June: CollectionSept.

So we confirmed the existence of inter-annual variability in sinus depth. But does this variability correspond to environmental variability in the same way that paleoclimate models would have us predict? To answer this question, we plotted the minimum, maximum, and average daily temperatures throughout the two growing seasons. Each growing season began in March of the year before vines were collected, when leaf primordia are typically first observed, and lasted until June the following year. The red plot therefore represents daily temperatures March 2012 to June 2013, while the blue plot represents March 2014 to June 2015. Weve shown the plots divided by monthrepresented by the dotted vertical linesand year, with the switch from 2012 to 2013 and 2014 to 2015 shown by the solid vertical line. And true to our expectations, the 2013 leaves did experience a hotter growing season than the 2015 leaves.

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Cumulative Leaf Wetness Hours

Day in Growing Season MarchMayJulyNov.Jan.MarchMayMid-June: CollectionSept.Inter-annual Variability: Weather

But what about precipiation? We also plotted cumulative leaf wetness hoursshown here on the y-axisthroughout the growing season. Here we see that the 2012-2013 growing season was not only hotter than 2014-2015, but wetter. This is a really interesting, and potentially very important, resultjust as we observe more dissected leaves in colder, drier climates throughout the paleorecord and around the world today, leaves from the exact same plants sampled in two different years were more dissected when they experienced a colder, drier growing season. Though two timepoints arent really enough to make any claims with certainty, this would seem to indicate that the climate-lobing correlation that is so fundamental to the way we look at leaf morphology is at least partially the result of phenotypic plasticity, in line with the conclusions drawn by Royer et al. in 2009.

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https://en.wikipedia.org/wiki/Neoteny#/media/File:Human_development_neoteny_body_and_head_proportions_pedomorphy_maturation_aging_growth.png

But despite the fact that there is great plasticity in this one aspect of shape, we found there are some attributes of shape that are startlingly predictable from allometry alone. Just as there are stringent rules of allometry in the human body, we can look for patterns among relative areas of the different parts of leaves.

Differential Growth

For our analysis, we divided leaf area into two categories: the veinsdivided into six individual veins as we described earlierand the bladedivided into seven subparts, shown here. 57

Veins: Isometric

ln(Leaf Area)ln(Vein Areas)

Here weve shown the log-transformed areas of the different veins against the log-transformed area of the whole leaf. Not only are these relationships linear, but they are all virtually parallel with each other, indicating that their growth is isometric. The other really interesting thing about this graph is that there is almost no variation in any of these relationshipsthey always look like this, regardless of species, year, leaf stage, or leaf number. 58

Blade Areas: Isometric

ln(Leaf Area)ln(Blade Areas)

And the same can be said for the different subparts of the blade; once again, we have incredibly narrow confidence intervals. And once again, the lines appear nearly parallel, suggesting isometric growth. 59

Blade and Veins: Allometricln(Leaf Area)ln(Sub-Areas)

But when we plot them together, we can see that the veins and the blade are allometric to one anotherthat is, growing at unequal rates. Altogether like this, it becomes truly striking just how perfectly linear these relationships areand how little variation there is. Which begs the question: how is this possible?

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?FUNCTIONAL CONSTRAINT?

How is it that we go from this enormous variety of morphologieswith more than 15 species, over two years of collection, and across dozens of developmental stages at the level of both the vine and the leafto this incredibly robust linear relationship?

The answer is unclear, but we what think we might be seeing here is a biological constraintsomething that neither evolution or development can get around. Functionally, maybe this is just the way the leaf has to be built, perhaps for nutrient flow, structural stability, or some other physiological necessity that we dont fully understand yet. 61

Conclusions Interannual variability in some traits, but not othersLobing appears fairly plastic: increased confidence in paleoclimate reconstruction? Flexibility in the face of climate change? Leaf development is complex but stableNot only from year to year, but over evolutionary timescales Future directions: expand study to more lineages

In any case, we have come away from this study with several conclusions. Our finding that lobing is plastic between years has important implications for predicting plant response to climate change, perhaps giving us a reason to hope that leaves will be able to change over the lifetime of individual plants in response to changing environmental conditions. In terms of paleoclimate reconstruction, it may mean that leaf lobing is a more reliable thermometer of past temperatures than would have been the case if dissection evolved slowly in response to climate change.

But while we know that leaf development is complex, what truly surprised us was the degree to which this development was stable, not only from year to year, but over evolutionary timescalescaptured by the interannual and interspecific comparisons, respectively. At most, these factors seem to modulate the patterns set by allometry, heteroblasty, and the biological constraints of physiology. But, to really test this hypothesis, studies of this nature will have to expand to other lineages outside of this single genus. We are really excited about what these future studies might have to tell us about the underlying causes of leaf morphology. 62

AcknowledgementsDan ChitwoodViktoriya ConevaMargarent FrankJesse AngaranoNSFDonald Danforth Plant Science Center Geneva Germplasm Collection, NY

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http://www.danforthcenter.org/

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