Botany
Distinguishing Impatiens capensis from Impatiens pallida (Balsaminaceae) using leaf traits
Journal: Botany
Manuscript ID cjb-2020-0022.R1
Manuscript Type: Article
Date Submitted by the 20-Mar-2020 Author:
Complete List of Authors: Whitfield, Heather; University of Wisconsin Madison, Botany Toczydlowski, Rachel; Michigan State University, Integrative Biology
jewelweed, leaf shape, linear discriminate analysis, morphometric Keyword: analysis, speciesDraft identification Is the invited manuscript for consideration in a Special Not applicable (regular submission) Issue? :
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Distinguishing Impatiens capensis from Impatiens pallida (Balsaminaceae) using leaf traits
Running head: Impatiens leaf traits
Heather L. Whitfield
Rachel H. Toczydlowski1
Authors’ Affiliation:
Department of Botany, University of Wisconsin-Madison, 430 Lincoln Drive, Madison, WI
53706 Draft
For: Botany
1Corresponding author: Rachel H. Toczydlowski, [email protected]
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Abstract
Impatiens capensis (orange jewelweed) and Impatiens pallida (yellow jewelweed) are annual species with similar phenotypes that grow in similar environments throughout the eastern United
States. This makes them extremely difficult to distinguish when (chasmogamous) flowers are absent. We use morphometric analyses to identify leaf characters that distinguish these species.
After collecting and scanning 342 leaves from plants of each species growing in co-occurring populations in Madison, WI, we quantified: leaf size, shape (using elliptical Fourier analysis), serratedness, and color. Using leaf size and shape traits, a linear discriminate analysis assigned up to 100% of leaves to the correct species. The uppermost fully expanded leaf yielded the most accurate species assignments based on size and shape traits. This leaf was on average, smaller, less deeply serrated, with a more acute base,Draft apex, and elliptical shape in I. capensis as compared to I. pallida. Impatiens pallida leaves had more color contrast (lighter veins and margins) than I. capensis, which were solid green throughout. Morphometric analysis is a promising technique to identify species-distinguishing characters in the absence of binary traits or molecular genetic analyses. Leaves from across these species’ ranges should be analyzed to test the robustness of the species-distinguishing characters we present.
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Keywords: jewelweed, leaf shape, linear discriminate analysis, morphometric analysis, species
identification
Introduction
Both Impatiens capensis (Meerb.) and Impatiens pallida (Nutt.), commonly referred to as orange
and pale touch-me-not, or jewelweed, respectively, are challenging to distinguish annual dicot
species. These species share many characteristics in both physical appearance and growing
environment (Voss and Reznicek 2012). Both species are native to North America, and the range
of I. pallida is completely nested within the range of I. capensis (USDA 2007). Although I. capensis tends to grow more frequently Draftin wetter and shadier areas whereas I. pallida prefers a more mesic environment, there is considerable overlap in the environmental niches of these
species (Lechowicz 1988). In fact, the two species sometimes grow in completely intermixed
stands (Lechowicz 1988). It is therefore difficult to identify the species based solely on habitat
requirements.
The species can be reliably distinguished from one another using (chasmogamous) flower color
and shape – I. pallida flowers are light yellow, usually with limited spotting, and about as long as
they are wide (Fernald 1950; Gleason and Cronquist 1991). In contrast, I. capensis flowers are
orange, commonly with varying degrees of red spotting, and longer than they are wide (Fernald
1950; Schoen and Latta 1989). Chasmogamous flowers are typically only present from late July
through mid-September or the first frost, however (Rust 1977). Aside from genetic analysis,
which is costly and time intensive, there remains no concrete way to distinguish these species in
the absence of chasmogamous flowers. Although some argue that petiole color can be used as a
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distinguishing character – red on I. capensis and green on I. pallida – petiole color is sensitive to light levels (among other environmental factors) and is thus not a reliable trait for species identification (personal observations).
Experienced botanists can often distinguish morphologically similar species from one another based on “gestalt” or their “overall impression” of the plant (Harris et al. 2015). These perceptual skills require considerable time and effort to acquire, and when experts are able to detect species distinguishing patterns, they often find it difficult to convey this knowledge to others (Harris et al. 2015; Daston 2008).
Recent advances in digital imaging and Draftcontemporary computing power now allow us to quantify and analyze subtle, complex, and/or continuous variation in shape and size (Rohlf and
Marcus 1993; Adams et al. 2004). These modern or geometric morphometric outline analyses may allow us to identify, quantify, and describe the “gestalt” characters used by experienced botanists to distinguish highly similar species (Cope et al. 2011). Complex outlines (e.g. of a leaf) are decomposed into a set of mathematical coefficients using Elliptical Fourier analysis
(EFA) that quantitatively describe the (size and) shape of the outline (McLellan and Endler 1998; see Bonhomme et al. 2014 for an accessible in-depth explanation). These mathematical coefficients from EFA can be used to visualize average shapes. They can also be summarized using principal components analysis (PCA) and fed into a discriminant analysis to test how accurately these continuous shape traits can assign individuals to species. For example, EFA has been used to distinguish agricultural crops from weeds (Neto et al. 2006), species of grapes
(Klein et al. 2017), soft corals (Carlos et al. 2011), mosquitos (Rohlf and Archie 1984), and
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hominids (Gonzalez-Jose et al. 2008). We applied modern morphometric techniques to
investigate the extent to which I. capensis and I. pallida can be distinguished based on leaf
shape, size, and color.
Methods
Leaf collections
We identified three nature reserves (sites) on the University of Wisconsin – Madison campus in
Madison, Wisconsin, USA: Muir Woods, Bill’s Woods, and Picnic Point Marsh (Fig. 1) that
contained co-occurring populations of I. capensis and I. pallida. These sites varied in light, soil
moisture, and likely genetic composition. Within each site, we collected 3 leaves from each of 5-
10 randomly selected plants of each speciesDraft within each of 3 sub-locations (areas). We sampled
until we reached 25 plants of each species in each site. We collected from areas where both
species were growing in intermixed stands to standardize the range of environmental variability
sampled across species. We could only find two areas of intermixed Impatiens in Bill’s Woods
so we sampled two additional areas where only one of the species was present. We could only
find two areas of I. capensis to sample in Muir Woods.
We collected leaves from three standardized locations within each plant (Supp. Fig. 1) to test for
differences in leaf shape or size depending on where they were growing on the plant (essentially
leaf height, age, and susceptibility to herbivory and disease). We defined leaf position 1 as the
lowest leaf on the plant borne on the main stem. The second leaf came from the middle of a
branch that diverged from the main stem in the leafiest region of the plant. The third leaf was the
fully expanded leaf closest to the top of the plant on the main stem. We collected leaves into
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envelopes labeled with the leaf position, species, site, and area and pressed them in a plant press.
All leaves were collected between August and September of 2017.
Leaf scanning and image processing
We scanned pressed leaves one at a time using a CanoScan 8800F desktop scanner at a resolution of 300dpi in color photo mode with auto exposure settings and saved the scans in
TIFF format. All leaves were scanned with the leaf tip positioned at twelve o’clock on the scanner bed.
Using the program FIJI (Schindelin et al. 2012), we converted leaf scans into binary black and white images. We removed any leaves thatDraft did not have entire margins and filled in any interior holes using the black paint tool. We removed leaf petioles in the scans by painting over them with the white paint tool (petioles were torn at variable lengths when collected). We retained a total of 342 leaf blade silhouettes for morphometric analysis.
Morphometric analysis – quantifying leaf shape, size, and color
To characterize leaf shape, we performed elliptical Fourier analysis using the Momocs package
V1.2.9.3 (Bonhomme et al. 2014) in R (R Core Team 2017). Elliptical Fourier analysis mathematically characterizes and quantifies complex shapes by decomposing them into a series of ellipses or harmonics that vary in shape, size, and orientation. We interpolated 500 points around each leaf margin and defined 4 landmarks at the maximum and minimum points in the horizontal and vertical directions to maintain a consistent orientation across leaf outlines. Using the landmarks, we scaled, centered, and aligned all leaf outlines with the fgProcrustes() function
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and performed elliptical Fourier analysis with 20 harmonics and no normalization, as suggested
by Bonhomme et al. 2014. We used principal components analysis to summarize the output from
the elliptical Fourier analysis. We retained the first 10 PC’s, which cumulatively explained
93.7% of the variance. We used the following Momocs functions to calculate leaf blade size:
Out() for length and width, coo_area() for area, and coo_perim() for perimeter. We note that our
pressed leaves dried out before they were scanned, so the sizes here are slightly smaller than
expected for fresh leaves. However, because all samples were treated the same, this is unlikely to
affect our species comparisons. We also calculated leaf solidity using the coo_solidity() function.
Solidity generally characterizes the degree of serration in the leaf margin and ranges between 0-
1, where 1 represents a perfectly entire margin, and smaller values represent more deeply
serrated margins. Draft
The exposure settings on our original scans were not standardized to allow for meaningful
comparisons of color, so we re-scanned “leaf 2” from 2 randomly selected individuals from each
area and species (N = 33) to investigate differences in leaf color. We used leaf 2 for the color
analysis because it was most representative of leaf color on the plant as a whole based on our
field observations. Often, leaf 1 was partially senesced and leaf 3 was too young to have
developed full color. We used a color card to ensure the color parameters of all leaves were
standardized across scans. Using FIJI, we adjusted color threshold values to exclude the ink
markings on each leaf (denoting leaf number) from the analysis. We then generated a distribution
of the RGB values present in each leaf (FIJI: Analyze Color Histogram) and calculated the
mode and variance of this leaf color distribution. We chose the mode (as opposed to the mean)
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because it is not influenced by minor blemishes and imperfections on the leaf surface and is likely closer to the color we perceive than the mean value.
Statistical analysis
We first constructed general linear models of form: leaf trait ~ species + leaf position + site + area[site] + individual[area,site] for each leaf shape (PC’s 1-10) and size trait in JMP Pro V
14.0.0. Leaf position is a categorical variable describing which of three standardized locations on the plant a leaf was collected from (see Methods – Leaf collections). We log transformed leaf blade length, width, perimeter, and area to meet the model assumption of normality. We used these models to gauge the extent to which each leaf trait varied between the two species while controlling for variation in sampling locationDraft and individual leaves. We also used these models to order the traits from most promising to distinguish the species (highest F-values for “species”) to least promising (lowest F-values for “species”).
To test the ability of leaf size and shape traits to distinguish among I. capensis and I. pallida, we performed linear discriminant function analysis (LDA) with species as the response using the lda() function in the R package MASS (Venables and Ripley 2002). Given the amount of variability in leaf size and shape that we discovered among the different leaf collection positions
(using GLMs), we used only leaf 3 (N = 132) for the LDA analyses. We chose leaf 3 (the uppermost fully expanded leaf on the main stem of each plant) because it was consistently present on the plants, undamaged, and easily identifiable. Our goal was to distinguish the two species, so we focused on finding the set of traits with the highest discriminatory power, not building the most parsimonious model.
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We used a sequential model-building approach where we added predictors one at a time based on
the highest F-values for “species” in the aforementioned general linear model continuing to
lowest significant trait (P < 0.05). We then constructed one final model with all traits included,
regardless of if they were significantly different between species. We applied this sequential
approach to “common” leaf traits (the non-color, non-elliptical Fourier shape traits), elliptical
Fourier shape traits (PC’s), and all traits pooled (excluding color due to small sample sizes). For
each set of predictors, we ran 5 iterations of the model, splitting the data randomly each time to
use 80% of the leaves as a training dataset to build the model and the remaining 20% to validate
the model. Draft
To further explore site to site variation in leaf traits and test the generalizability of our model, we
used all of the leaves collected for this study (referred to as 2017 leaves) to assign species labels
to 167 leaves collected in previous years and at more geographically distant locations (referred to
as 2014 leaves; see Supp. Fig. 2 for a map of collection locations). We included 2017 leaves
from all three positions on the plant in these models as the 2014 leaves were collected from
random locations on the plants. We used all 2017 leaves (N = 342) as the training dataset and all
2014 leaves (N = 167) as the validation dataset. We ran 3 models: all “common” traits, all
elliptical Fourier shape traits, and all traits (excluding color).
Finally, we built a general linear model to test for differences in leaf color between I. capensis
and I. pallida: leaf trait ~ species + site + area[site]. Leaf color traits included the mode and
standard deviation for the distribution of red, green, and blue in each leaf (RGB values).
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Results
Leaf size and shape
Leaf size and shape traits varied significantly within individual plants for both I. capensis and I. pallida. Leaf position, a categorical variable describing which of three standardized locations on the plant a leaf was collected from, was highly significant (P < 0.0001) for 9 of the 16 leaf size and shape traits (Supp. Table. 1). Furthermore, for 7 of the 16 traits analyzed, the relationship between the two species varied depending on the leaf position (judged by visually comparing mean trait values for each species/leaf position combination). For example, I. capensis had a more acute leaf apex and base than I. pallida at leaf position 1, but a less acute leaf apex and based than I. pallida at leaf position 3. TheseDraft results suggest that it is important to develop species distinguishing leaf characters for a specific, standardized leaf on the plant.
We identified leaf 3, the uppermost fully expanded leaf on the main stem of each plant, as a leaf that is reliably present on the plants, easy to locate, and less likely to be damaged than older leaves. Leaf 1 was missing from 53 of 144 plants that we collected from, and from many other plants that we avoided. Leaf 2 was present on 140 of 144, but our definition (a leaf from the middle of a branch off the main stem) encompassed many leaves, not one leaf specifically, like leaf 3. For these reasons, we present size and shape results solely for leaf 3.
Impatiens pallida leaves were larger than I. capensis as measured by length (P < 0.0001), width
(P < 0.0001), area (P < 0.0001), and perimeter (P = 0.004; Table 1). Impatiens pallida were also narrower (length/width ratio; P < 0.0001) and more deeply serrated (solidity score; P < 0.0001)
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than I. capensis (Table 1; Fig. 2). Three of the ten principal components (PC’s) that we retained
to quantify variation in leaf shape differed significantly between species. The type of shape
variation described by each PC is shown visually in Supp. Fig. 3. Impatiens pallida had
significantly greater values along PC 1 (P < 0.0254; 40.4% of leaf shape variation) and PC 9 (P
< 0.0001; 1.2% of leaf shape variation) and significantly smaller values along PC 4 (P < 0.0001;
7.2% of leaf shape variation) as compared to I. capensis (Table 1; Fig. 2). Overall, these analyses
suggest that I. capensis leaves are, on average, smaller and more elliptical with a more acute base
and apex as compared to I. pallida, which are larger and more ovate with a more rounded base
and apex (Fig. 3).
Distinguishing species Draft
Using leaf 3, linear discriminant analysis assigned up to 100% of the leaves in the validation set
to the correct species for eight of the thirteen combinations of leaf traits tested (Fig. 4). Using
both shape and size traits together yielded the highest overall predictive power with a range of
75%-100% correctly predicted for I. pallida and 92%-100% correct for I. capensis (across 5
replicates and 5 combinations of traits; Fig. 4). The combined traits model with solidity,
perimeter, PC 4, PC 9, L:W ratio, and PC 1 produced the most correct predictions of all thirteen
trait combinations tested (83%-100% correct assignments for I. pallida and consistent 100%
correct assignment for I. capensis across 5 model iterations). The combined traits models were
the only models that achieved 100% correct predictions for I. pallida.
Using the leaves from all positions decreased the species prediction accuracy for both species
across all models fit compared to the models using only the leaves from position 3 (Supp. Fig. 4).
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The average prediction accuracy remained lower when we used a random subset of leaves from positions 1, 2, and 3 equal in sample size to just leaf 3 (results not shown). Similarly, fewer leaves were assigned to the correct species when using just leaf 2 (results not shown). We did not have enough leaves from position 1 to test the predictive power of only leaf 1. We concluded that leaf 3 has the highest predictive power to distinguish species based on leaf size and shape.
Correct species assignments declined when using 2017 leaves as a training data set to predict
2014 leaves but were still above 75% correctly assigned for both species depending on the traits used (Supp. Fig. 5). Using the combination of all size and shape traits, 43% of I. pallida and 92% of I. capensis leaves were correctly predicted. When we applied the same LDA procedure to just the 2014 leaves, correct species assignmentsDraft remained similar (Supp. Fig. 6).
Leaf color
Mode leaf color (as measured by RGB values) did not differ significantly between species, but the standard deviation of each color did (Table 2). Impatiens pallida leaves were significantly more variable than I. capensis in the amount of red (P = 0.01), green (P = 0.001), and blue (P =
0.003) within a leaf. These results match our observations in the field. Impatiens pallida leaves were lighter in color around the leaf veins and margins and darker green further away from veins and margins. In contrast, I. capensis leaves exhibited a more consistent color throughout the leaf and had less visually defined veins.
Discussion
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We determined that I. pallida and I. capensis leaves can be distinguished from one another when
multiple morphometric variables are used and leaves from a standardized location on the plant
are compared. We suggest focusing on the size and shape of the uppermost fully expanded leaf
on the main stem of a plant (our leaf 3) to distinguish between species for the following reasons:
it is the easiest to locate and identify on the plant; unlike leaves 1 and 2, leaf 3 is present on
nearly every plant; it is one of the youngest leaves on the plant making it less likely to be
diseased, chewed, or senesced; and finally, it was the only leaf position where 100% of leaves
were correctly assigned to species in our linear discriminant analyses.
The high predictive power of leaf 3 is likely the result of smaller leaf-to-leaf variation present
among leaves at this position on the plant.Draft Position 3 referred to only one specific leaf on each
plant, unlike leaf position 2, which encompassed any/all leaves on branches in the middle region
of the plant. Leaves at position 1 were often ragged or absent. The leaf position × species
interaction that we found also likely reduced the predictive power of linear discriminant analysis
to distinguish the two species using leaves from all positions in the same model. That is, it is
difficult to define a set of species distinguishing rules that apply uniformly to all leaves when the
rules change depending on the position on the plant that the leaf was collected from.
We are unsure why the identity of Impatiens pallida leaves was generally more difficult to
predict than I. capensis leaves. It may reflect higher variability in both shape and size traits in I.
pallida as compared to I. capensis; the standard deviation around all trait means was slightly
larger for I. pallida than I. capensis. We expected I. pallida to show less within-species
variability than I. capensis given that the species range of I. pallida is completely nested within
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the range of I. capensis in North America (USDA 2007). Even though I. capensis has a broader geographic distribution than I. pallida, higher plasticity and/or reduced genetic variation may reduce within-species variation in I. capensis as compared to I. pallida. Numerous studies have measured a high degree of (adaptive) plasticity in I. capensis (e.g. Schmitt 1993; Dudley and
Schmitt 1996; Donohue et al. 2000; Schmitt et al. 2003). This plasticity is not surprising given that I. capensis depends on a high degree of spatial and temporal disturbance (often flooding) to thrive (Menges and Waller 1983; Johnson et al. 2014) and populations are subject to non- adaptive genetic drift (Toczydlowski and Waller 2019). The more mesic upland sites that I. pallida prefers may favor genetically determined adaptation and less plasticity if the environment is relatively predictable from year to year (Schemske 1984; Stewart and Schoen 1987;
Bennington and McGraw 1995). We areDraft not aware of any studies comparing the genetic diversity of these two species. Increased extinctions and colonizations in the high disturbance environments that I. capensis frequents may reduce genetic diversity compared I. pallida, but this has yet to be tested. Regardless, it appears more likely to misidentify I. pallida as I. capensis than to misidentify I. capensis as I. pallida.
It is important to recognize that morphological differences between leaves of these two species likely reflect the environment as well as species differences. Sampling location commonly accounted for about twice as much variation as species in our models. Reassuringly, leaf shape described by the first and fourth principal component axes and the amount of variation in the red and green color ranges varied significantly between species but not among sampling sites or sub- sites (areas). Although leaf solidity (a proxy for leaf margin serration depth) varied significantly among sampling sites, only about 10% of leaf trait variation was attributable to site verses almost
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80% of the variation attributable to interspecies differences. We suggest focusing on these traits,
as they are more likely to reflect interspecies differences as opposed to environmental variation
according to our analyses. Leaves in the vertical middle region of the plant (leaf 2 in this study)
provide the most representative leaf color for the species because they are developed enough to
exhibit the color patterns and pigments of a typical mature leaf but are unlikely to be diseased or
senesced, which often alter leaf color.
Not surprisingly, using leaves collected in 2017 for this study to predict species of leaves
collected in 2014 for a different study resulted in fewer correct species assignments than models
using only 2017 leaves. However, even though the 2014 leaves were collected from random
locations on the plants, non-paired populations,Draft and a much broader geographical extent (for I.
capensis – see Supp. Fig. 2), our model was still able to correctly assign over 75% of the leaves
for both species depending on the traits used. The leaves collected in 2014 were not collected
from specified locations on each plant as were leaves collected in 2017, so we were not able to
standardize our analysis to solely the uppermost fully expanded leaf, as we discovered is
necessary to achieve high correct species assignments. We expect our predictions would improve
if we used only the uppermost fully expanded leaf.
A useful next step would be to test how many leaves volunteers can correctly identify as I.
capensis or I. pallida using Fig. 3 and a written description of the interspecies differences in leaf
traits that we described here – essentially, a human linear discriminate analysis. Direct
quantification of variation in leaf margin serrations (e.g. with Winfolia, which efficiently
measures multiple detailed leaf serration traits like depth, number, and angle of serrations) would
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also be useful. We were limited to freely available software and struggled to find an efficient way to quantify specific variation in leaf serration. Finally, our study is limited in that we only sampled within Wisconsin, USA, and primarily near the University of Wisconsin-Madison. It would be valuable to collect leaves from a greater extent of the geographic ranges for these species to test how broadly these species distinguishing rules can be applied. Growing plants from a variety of populations and environments in a common garden would also help to further quantify inter- and intra-species leaf trait variation. Going one step further, reciprocal transplants could shed light on inter- and intra-species leaf trait variation as well as the habitat preferences of these species, both of which could help to define species distinguishing characteristics.
We provided an example of how morphologicalDraft variation can be quantified and used to aid in species identification. Such variation is more challenging to quantify but is especially important when obvious binary characters do not distinguish species. We were encouraged by how closely our morphological quantifications and statistical analyses matched our “field-developed gestalt” that inspired this study. We demonstrate how morphometric analysis can be used to quantify and translate the gestalt of experienced taxonomists and field biologists into characters and keying rules. Applying these methods to flora generally has the potential to broaden the scope of people that can distinguish morphologically similar species, especially based on sterile characters alone
(Cope et al. 2011). While there has been a push towards genetic-based taxonomy, traditional taxonomy based on morphology remains central to identifying and conserving biodiversity
(Wheeler 2004).
Acknowledgements
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We thank the University of Wisconsin-Madison Lakeshore Nature Preserve for collection
permits; S. Friedrich (UW-Madison, Botany Media Studio) for scanner support; and R. Kriebel
for morphometric expertise. R. H. Toczydlowski was supported by the NSF GRFP (DGE-
1256259), the Wisconsin Alumni Research Foundation, and the UW-Madison Graduate School
and Botany Department. D. Waller, J. Richards, J. Randall, and an anonymous reviewer provided
helpful comments. Data and scripts associated with this article are archived online at
https://doi.org/10.5061/dryad.79cnp5hrz.
Draft
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Tables
Table 1. F-ratios from a general linear model constructed to test for differences in leaf size and shape between Impatiens capensis and I. pallida. Leaves were the uppermost fully expanded leaf on the main stem of plants (leaf 3) and were collected from 3 areas nested within each of 3 sites in Madison, WI, USA. N = 132 leaves.
Speciesa Siteb Area[Site]c ln(Length) 23.58*** 29.78*** 2.90* ln(Width) 17.43*** 26.35*** 2.74* L:W 9.05* 5.17* 1.89+ ln(Perimeter) 47.01*** 25.93*** 2.59* ln(Area) 23.62*** 27.95*** 2.89* Solidity score 100.76*** 7.41** 2.42* PC1 5.13* 0.48 1.35 PC2 1.65 10.39*** 1.92+ PC3 0.03 5.72*Draft2.09* PC4 38.29*** 1.07 1.92+ PC5 0.35 2.19 0.53 PC6 0.26 0.03 0.70 PC7 0.61 2.49+ 1.28 PC8 0.08 1.96 1.08 PC9 27.65*** 4.07* 0.24 PC10 0.99 8.04** 2.11* + P < 0.1; * P < 0.05; ** P < 0.001; *** P < 0.0001 a Numerator df = 1; b Numerator df = 2; c Numerator df = 7 Denominator df = 121
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Table 2. F-ratios from a general linear model constructed to test for differences in leaf color
between Impatiens capensis and I. pallida. Leaves were collected from the middle of a branch
(leaf 2) and were collected from 3 areas nested within each of 3 sites in Madison, Wisconsin,
USA. N = 33 leaves.
Speciesa Siteb Area[Site]c Mode Red 0.21 3.96* 0.67 Green 0.49 4.36* 0.84 Blue 0.03 1.40 0.89 Standard Deviation Red 7.82* 0.97 0.21 Green 15.80** 0.47 0.29 Blue 11.17* 5.40* 0.30 * P < 0.05; ** P < 0.001 a Numerator df = 1; b Numerator df = 2; c Numerator df = 7 Denominator df = 22 Draft
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Figure captions
Figure 1. Locations where Impatiens capensis (orange triangles) and I. pallida (yellow circles) leaves were collected for morphometric analysis. N = 2 species × 3 sites × ~ 25 plants/site × 1-3 leaves/plant = 375 total leaves collected. Figure created using R V3.4.4 and base map data from
US Census Bureau (2016, inset) and City of Madison Open Data (2018, main).
Figure 2. Distributions of leaf size and shape traits that varied significantly between Impatiens capensis (orange/left) and I. pallida (yellow/right). All traits were measured on the leaf blade of the uppermost fully expanded leaf on the main stem. N = 132. PC1, PC4, PC9 are principal component scores from an elliptical Fourier shape analysis (see Supp. Fig. 3). Higher solidity values reflect less deeply serrated leaf margins.Draft
Figure 3. Average leaves for Impatiens capensis (black outline) and I. pallida (green fill) collected from three standardized locations on the plant: (1) the lowest leaf on the plant born on the main stem, (2) a leaf from the middle of a branch, and (3) the uppermost full-expanded leaf on the main stem. Outlines were constructed by calculating and plotting mean values of each of
500 points around the outline of 342 leaf scans.
Figure 4. Average percent of leaves correctly distinguished as Impatiens capensis (circles) or I. pallida (triangles) based on shape and size traits. Eighty percent of the leaves were used as a training dataset (open shapes) to predict the remaining 20% (filled shapes) using linear discriminate analysis (N = 132 leaves). All traits were measured on the leaf blade of the uppermost full-expanded leaf on the main stem (leaf 3) and are abbreviated as: perimeter
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(perim); solidity of leaf margin to capture degree of serration (solidity); length to width ratio
(LWratio); 10 principal component axes capturing shape variation (PC1-PC10). Traits were
added in order of decreasing F-values for the species term in a general linear model (GLM; see
Table 1) and then a final model with all terms. * denotes all terms in GLM were significant at P
< 0.05. Error bars represent minimum and maximum percent correctly predicted from 5 replicate
models.
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Species Draft I. capensis I. pallida
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