Wesleyan University
The Honors College
Digital leaf physiognomy: correlating leaf size and shape
to climate in the Fox Hills, Fort Union, and Hanna Basin
Formations
by
Sofia Oliver
Class of 2010
A thesis submitted to the
faculty of Wesleyan University
in partial fulfillment of the requirements for the
Degree of Bachelor of Arts
with Departmental Honors in Earth and Environmental Science
Middletown, Connecticut
April, 2010
TABLE OF CONTENTS
LIST OF FIGURES
i
LIST OF TABLES
ii
LIST OF APPENDICES
ii
ACKNOWLEDGEMENTS
iii
ABSTRACT
1
INTRODUCTION
Leaf traits and function
Multivariate approaches
Leaf economics
Application to the fossil record
Research questions and hypotheses
How robust are climate estimates from digital leaf
physiognomy?
Are evergreen taxa selected against across the K/T
boundary?
Salt stress hypotheses
2
4
7
11
12
15
METHODOLOGY
Digital leaf physiognomy
Leaf mass per area
Field areas
The Fox Hills
Fort Union Formation
Hanna Basin
19
19
22
23
23
27
30
RESULTS
Climate estimates
Leaf mass per area
37
37
40
DISCUSSION
Climate
Mean annual temperature
Mean annual precipitation
Comparison of digital leaf physiognomy to independent
assessments of climate
Deciduous diversification across the K/T boundary
Salt stress
Implications for leaf physiognomy climate models
42
42
42
45
15
16
17
48
50
51
53
LITERATURE CITED
54
APPENDIX
68
LIST OF FIGURES
Figure 1
Locality map of study areas with geologic timescale
14
Figure 2
Representative toothed specimen prepped for analysis
20
Figure 3
Representative untoothed specimen prepped for
analysis
21
Paleomap of North America (~65 Ma) with field area
locations
24
Figure 5
Stratigraphy of the Fox Hills Formation
25
Figure 6
Stratigraphy of the Fort Union Formation in the
Williston Basin
28
Stratigraphy of the Ferris Formation in the Hanna
Basin
32
Stratigraphy of the Hanna Formation in the Hanna
Basin
35
Digital leaf physiognomy and leaf-margin analysis
mean annual temperature estimates
37
Digital leaf physiognomy and leaf-area analysis
mean annual precipitation estimates
38
Figure 11
Site means for angiosperm leaf mass per area
41
Figure 12
Digital leaf physiognomy temperature variables
plotted against mean annual temperature using the 95
site calibration of Peppe et al. (in preparation)
43
Digital leaf physiognomy precipitation variables
plotted against mean annual precipitation using the
95 site calibration of Peppe et al. (in preparation)
46
Figure 4
Figure 7
Figure 8
Figure 9
Figure 10
Figure 13
i
LIST OF TABLES
Table 1
Physiognomic variables and definitions
9
Table 2
Mean annual temperature and mean annual
precipitation equations for digital leaf physiognomy,
leaf-margin analysis, and leaf-area analysis
22
Mean annual temperature and mean annual
precipitation estimates for digital leaf physiognomy,
leaf-margin analysis, and leaf-area analysis
39
Site mean values for physiognomic variables used in
the digital leaf physiognomy temperature and
precipitation models
40
Table 3
Table 4
LIST OF APPENDICES
Appendix 1
Specimen values for measured physiognomic variables
68
ii
ACKNOWLEDGEMENTS
I would like to thank the Wesleyan McNair Fellows Program for funding my research.
Without the McNair Program, many countless hours would have been spent distributing and
shelving books in the library instead of processing images. Many thanks are given to Laurel
Appel and Santos Cayetano, the director and associate director of the McNair Program, for
exposing me to an academic realm outside of Wesleyan. Special thanks also go to my McNair
summer cohort for listening to and giving feedback on my presentations on digital leaf
physiognomy over and again. I would also like to thank Dr. Suzanne O’Connell for encouraging
me to join the McNair program, without which I may have never begun this project.
I owe much to Dan Peppe for his instruction on how to process a digital image of a leaf
fossil, looking over the hundreds of images – blurry and focused – I processed over the summer,
and answering my never-ending emails with questions. A special thanks to Dan and his wife for
welcoming me into their home so that Dan and I could take additional pictures of leaf fossils.
Even after all of this Dan agreed to sit on my thesis committee.
This project could not have been completed without the permission of Regan Dunn to use
the Hanna Basin images and her assistance with information on the fossil specimen.
I would also like to thank the Oliver Family and Pamela Quezada for their support and
for listening to my leaf rants, even though they didn’t have a clue as to what I was talking about.
Most of all I would like to thank Dr. Dana Royer for allowing me to join his lab over the
summer and encouraging me to take on this final project. Without his patience and willingness to
answer questions and read through the many drafts of my thesis, this project would be nothing
iii
more than a few hundred images. His help has been invaluable, and I will carry all that I have
learned long after leaving Wesleyan.
iv
ABSTRACT
Leaf size and shape (physiognomy) is widely used by paleobotanists as a
proxy for paleotemperature and paleoprecipitation. The most common physiognomic
method for reconstructing temperature is based on the correlation with the percentage
of untoothed species at a single site. Similarly, the correlation between rainfall and
site-mean leaf-area is extensively used. A new multivariate approach for
reconstructing paleoclimate, called digital leaf physiognomy, shows considerable
promise for being significantly more accurate than existing approaches. However, the
approach has yet to be applied to fossils. Here, using a 95 site calibration, I
reconstruct paleotemperature and paleoprecipitation for six leaf-fossil sites from the
latest Cretaceous and early Paleocene of the US Western Interior. At all sites, climate
estimates are significantly warmer and drier than that from the more simple
physiognomic methods. Critically, these revised climate estimates are more in line
with independent estimates of climate at the same sites. Thus, my field areas provide
an important “proof-of-concept” for digital leaf physiognomy, boosting confidence
for its application to less-studied floras where independent climate estimates may not
exist.
1
INTRODUCTION
It has long been recognized that the size and shape (physiognomy) of leaves
correlate strongly to temperature and precipitation (Bailey and Sinnot, 1915, 1916;
Wolfe, 1979, 1993; Wilf, 1997; Huff et al., 2003; Royer et al., 2005). For example,
floras growing under cool temperature regimes exhibit higher percentages of species
with toothed leaves while floras inhabiting warmer environments yield a higher
percentage of untoothed (entire) species. A higher proportion of toothed species is
also typically found in wetter environments, including riparian habitats, relative to
nearby, drier environments (Bailey and Sinnot, 1916; Wolfe, 1993; Burnham et al.,
2001; Kowalski and Dilcher, 2003; Royer et al., 2009a). Abundant moisture is also
linked to leaf size: plants in moist climates tend to have larger leaves than plants in
drier climates (Givnish, 1984; Wilf et al., 1998). These leaf-climate relationships
have been observed on most continents, reflecting a wide range of climates and
phylogenetic histories (Greenwood 2005; Green 2006; Peppe et al., in preparation).
Several techniques exist which draw on these relationships to estimate mean
annual temperature (MAT) and mean annual precipitation (MAP) and are extensively
used by paleobotanists (Wolfe, 1979, 1993; Hickey, 1980; Wing and Greenwood,
1993; Wilf, 1997, 2000; Wilf et al., 1998, 2003a, 2003b; Wing et al., 2000, 2005;
Burnham et al., 2001; Dunn, 2003; Huff et al., 2003; Kowalski and Dilcher, 2003;
Uhl et al., 2003, 2007; Wilf and Johnson, 2004; Royer et al., 2005, 2008; Peppe et al.,
in preparation). The most widely used MAT method, leaf-margin analysis (LMA), is
based on the percentage of woody dicot species whose margins are entire (margin
2
percent) (Wolfe, 1979; Wilf, 1997). A number of independent climate estimates
support the LMA approach. LMA climate estimates based on Eocene fossil leaf
assemblages from Patagonia have been corroborated by independent estimates based
on nearby, coeval sea surface temperatures (Wilf et al., 2003a). Oxygen isotopes of
bone, shell, and hematite have also corroborated temperatures derived from LMA of
multiple Cretaceous and Paleogene sites within the US (Wing et al., 2000; Wilf et al.,
2003b; Fricke and Wing, 2004). Overall, isotopic studies have shown temperature
estimates to be in general agreement with other independent estimates (Wing and
Greenwood, 1993; Wing et al., 2000; Carpenter et al., 2003; Wilf et al., 2003b).
Leaf-area analysis uses site-mean leaf area to reconstruct MAP (Wilf et al.,
1998). Errors associated with leaf-area analysis, however, are relatively large and
MAP estimates should be interpreted with caution (Wilf et al., 1998). Despite lacking
precision, MAP estimates using leaf-area analysis are in general agreement with other
methods for estimating precipitation from the fossil record. For example, leaf-area
analysis of the Paleocene Cerrejon flora in Colombia yielded MAP estimates of 324
cm (Wing et al., 2009); comparison of leaves from the fossil site to extant Neotropical
forests suggest a MAP that exceeds 300 cm (Wing et al., 2009). In another example,
Wing et al. (2005) analyzed late Paleocene-early Eocene fossil floras from the
Bighorn Basin in Wyoming. These fossils coincide with the Paleocene-Eocene
Thermal Maximum (PETM), a period of rapid global warming. While MAP of the
late Paleocene averages 138 cm, leaf sizes discern a transient dry period (estimated
MAP = 41 cm) that corresponds in time to peak PETM warmth (Wing et al., 2005).
Kraus and Riggins (2007) developed a parallel MAP record for the Bighorn Basin
3
using paleosols of the same age as the leaf specimens used by Wing et al. (2005).
This paleosol record of MAP also picks up the transient dry period, boosting
confidence in both approaches.
There are other methods for approximating paleoclimates from fossil plants.
The nearest living relative (NLR) approach and coexistence approach (CA) are based
on the phylogenetically-closest extant relatives of fossils. Once identified, the
climatic tolerances of NLRs are extrapolated to their fossil counterparts. Comparisons
have been made between leaf-margin analysis and NLR and CA methods (Uhl et al.,
2003, 2007). Leaf-margin analysis yields results that are sometimes in agreement
with CA temperature estimates for individual stratigraphic layers (Uhl et al., 2003,
2007). However, leaf-margin analysis temperatures are more variable than CA
temperatures. Nonetheless, LMA and NLR estimates are broadly similar despite the
scatter.
Leaf traits and function
While the correlative strength of leaf teeth to climate is well documented, the
function of leaf teeth is not entirely understood. Leaf teeth are defined as vascularized
extensions of the margins of leaves incised less than ¼ of the distance to the midvein
of the leaf (Ellis et al., 2009). Brown and Lawton (1991) hypothesized toothed and
lobed leaves serve as a defense mechanism against insect herbivory. However, when
tested several years later, Rivero-Lynch et al. (1996) found that more damage
occurred on medium to deeply lobed, toothed leaves rather than entire margin leaves.
4
Baker-Brosh and Peet (1997) exposed young, emerging leaves to 14CO2 and
took autoradiographic images to record photosynthetically active areas. The regions
of leaves with 14C corresponded to assimilation of 14C due to photosynthetic activity.
High concentrations of photosynthetic activity occurred in teeth and tips of lobes. In
addition to carbon uptake, it is important to note that teeth and leaf margins are areas
of high water potential gradients and, as such, are likely sites of high evaporation
rates (Canny, 1990). Also, major veins extending to teeth are present in most toothed
species (Canny, 1990). The tips of teeth have high concentrations of hydathodes
(pores) that are thought to increase sap flow and water loss (Canny, 1990; Feild et al.,
2005). Teeth and lobe tips have also been found to reach early maturation while the
rest of the leaf was still expanding (Feild et al., 2005). This enhancement in gas
exchange activity early in the growing season may be advantageous to plants in areas
where the growing season is brief, as teeth ultimately allow the plant to get started
earlier in the growing season, effectively maximizing the season of potential growth
(Wing et al., 2000; Royer and Wilf, 2006). Such observations could provide a
functional basis for the prevalence of toothed species in colder climates (Royer and
Wilf, 2006).
Royer and Wilf (2006) quantitatively tested the hypothesis of Baker-Brosh
and Peet (1997) and found that the margins of toothed species exhibit higher rates of
both photosynthesis and transpiration, and that the interval of enhanced gas-exchange
was limited to the first month after bud break. This pattern of enhanced, early-season
gas exchange was most prominent in species native to colder climates (Royer and
Wilf, 2006). Thus, quantitative gas-exchange observations provide a functional basis
5
for the relationship between the presence of teeth and temperature. Moreover,
because teeth are centers of water loss, the gas-exchange hypothesis can also provide
a functional basis for why species are more likely to be toothed in wet environments.
In short, the gas-exchange hypothesis explains why cold, mesic floras contain the
highest fraction of toothed plants while hot, dry floras contain the smallest.
Hydathodal pores in leaf teeth are thought to prevent the flooding of
intercellular spaces in plants (Feild et al., 2005). In wet climates, positive root
pressure can cause flooding in intercellular spaces if transpiration in not sufficiently
high. Hydathodes at the apices of teeth have been found to play a significant role in
guttation (drops of water discharged at leaf tips and margins due to root pressure) and
may serve as a „release valve‟ for excess root pressure. Feild et al. (2005) found that
when hydathodes were experimentally blocked, intercellular spaces became flooded
and photosynthesis rates dropped by as much as 40%. These observations provide an
alternative explanation for why species native to mesic environments are often
toothed. Also, this „release-valve‟ behavior of hydathodes may help to minimize
freeze-thaw embolisms, which in turn provides another functional explanation for the
prevalence of toothed species in cold climates (Feild et al., 2005). Interestingly,
hydathodes are commonly filled with wax plugs by the time leaves have fully
matured (Stevens, 1956; Takeda et al., 1991), suggesting that the function of
hydathodes is likely restricted to the first month of leaf growth. This behavior thus
complements well the study of Royer and Wilf (2006).
Leaf dissection is also correlated to temperature (Royer et al., 2008): plants
native to colder climates are more highly dissected than those native to warmer
6
climates (Royer et al., 2005, 2008). Highly dissected leaves are commonly associated
with high transpiration rates owing to their high perimeter-area ratios (Canny, 1990),
and thus may be functionally adaptive in cold (see above).
Multivariate approaches
While univariate techniques such as LMA and leaf-area analysis have been
corroborated by isotopic records and other independent estimates, there may be room
for improvement. Univariate approaches may be too simple and may not be as
sensitive to climatic signals as multivariate approaches that more fully describe leaf
size and shape. For example, leaf teeth are sensitive primarily to temperature but also
to moisture; thus, a MAP model incorporating information about teeth and leaf size
will likely be more robust than one based on leaf size alone.
The Climate-Leaf Analysis Multivariate Program (CLAMP) attempts to
improve on LMA and leaf-area analysis by categorizing and incorporating 31 leaf
characters in relation to climate (Wolfe, 1993; Spicer, 2010). The fuller description of
leaf shape should, in principle, create more robust climate models. However,
comparisons of CLAMP to univariate methods show CLAMP to be no more accurate
than LMA for estimating MAT (Jacobs and Deino, 1996; Wilf, 1997; Wiemann et al.,
1998; Gregory-Wodzicki, 2000; Kowalski and Dilcher, 2003) while precipitation was
consistently overestimated (Wilf et al., 1998).
Several problems prevent CLAMP‟s multivariate approach from improving on
LMA and leaf-area analysis. First, variables used in CLAMP are categorical; for
7
example, leaf size is defined by nine categories. This ultimately limits CLAMP, as
well as any model based on categorical variables (including LMA and leaf-area
analysis), because natural variation is most precisely described with continuous
variables. Second, many of the CLAMP variables for scoring leaves are ambiguous,
causing the same leaf to be scored differently by different workers (Wilf, 1997;
Wiemann et al., 1998; Wilf et al., 1998). Third, many of the characters scored in
CLAMP yield little-to-no information about climate; thus, these „excess characters‟
do nothing to improve the precision or accuracy of CLAMP.
Clearly, there is a need for a new multivariate approach that is reproducible and is
based on continuous, climatically-informative variables. Recently, a new technique
known as digital leaf physiognomy has been developed by Huff et al. (2003) and
Royer et al. (2005). Digital leaf physiognomy uses digital images of leaves to
measure variables such as leaf area, leaf perimeter (with and without teeth), major
length, number of teeth, and tooth area, as well as margin percent (Table 1 and Fig.
2). Multiple physiognomic variables are then correlated to climate using multiple
linear regression. There are now 95 geographically diverse sites in the calibration,
including 42 sites from the CLAMP data set (Peppe et al., in preparation).
Expansion of the number of calibration sites has shown that the standard error
of LMA is larger than previously reported. Typical error associated with LMA is
reported to be ± 2 °C (e.g. Wilf, 1997; Wilf et al., 2003b), but such errors are based
on provincial calibration data sets (e.g., east Asian calibration of Wolfe, 1979). A
more global 95 site calibration, however, has shown LMA error to be larger than
typically reported (± 4.9 °C vs. classical ± 2 °C) (Peppe et al., in preparation). In a
8
Table 1. Physiognomic variables and definitions.
Variables
Definition
Margin percent
Petiole width
Percentage of untoothed species
Width of the preserved petiole (cm)
Petiole area
Area of the preserved petiole (cm2)
Leaf mass per area (MA)
Number of primary teeth
Inverse of specific leaf area, a measure of
leaf thickness and/or density (g/m2)
Area of the reconstructed leaf with the
petiole removed (cm2)
Petiole area plus inferred blade area
(cm2)
Area of the reconstructed leaf after the
damaged portions of the margin are
removed (cm2)
Perimeter of reconstructed lead after the
damaged portions of the margin are
removed (cm)
Perimeter of the reconstructed leaf after
leaf teeth and damaged portions of
the margin and teeth are removed
(cm)
Area of the reconstructed leaf after leaf
teeth and damaged portions of the
margin are removed (cm2)
Total length of the „cut‟ portions of the
reconstructed leaf (cm)
Raw perimeter minus the length of the
cut perimeter (cm)
Raw internal perimeter minus the length
of the cut perimeter (cm)
Perimeter (total preserved)/internal
perimeter (total preserved)
(dimensionless)
Longest measured line across the leaf
blade (cm)
Diameter of a circle with the same area
as the leaf (cm)
Feret diameter/major length
(dimensionless)
Count of primary teeth
Number of secondary teeth
Count of secondary teeth
Total number of teeth
Number of teeth : perimeter (total preserved)
Total number of primary and secondary
teeth
(cm-1)
Number of teeth : internal perimeter (total preserved)
(cm-1)
Number of teeth : raw blade area
(cm-2)
Tooth area
Area of teeth (cm2)
Average tooth area
(cm2)
Tooth area : perimeter (total preserved)
(cm)
Tooth area : internal perimeter (total preserved)
(cm)
Tooth area : raw blade area
(dimensionless)
Inferred blade area
Inferred leaf area
Raw blade area
Raw perimeter
Raw internal perimeter
Raw internal blade area
Length of cut perimeter
Perimeter (total preserved)
Internal perimeter (total preserved)
Perimeter ratio
Major length
Feret diameter
Feret diameter ratio
9
direct comparison using the same 95 site calibration, the standard error of digital leaf
physiognomy is ± 3.9 °C (Peppe et al., in preparation). The standard error for leafarea analysis MAP estimates is 0.61 (in natural log space) while digital leaf
physiognomy MAP standard error is 0.56 (Peppe et al., in preparation). For example,
a site receiving 100 cm of precipitation will have a positive error of 75.3 cm and a
negative error of 43 cm using digital leaf physiognomy, but a positive error of 84.2
cm and a negative error of 45.7 cm using leaf-area analysis. Thus, the precipitation
ranges using digital leaf physiognomy are smaller (compare a MAP range of 57-175.3
cm using digital leaf physiognomy and a MAP range of 54.3-184.2 cm using leaf-area
analysis). Digital leaf physiognomy has therefore made a substantial improvement
upon the precision of leaf-climate models (Peppe et al., in preparation).
There are several reasons why digital leaf physiognomy produces more
accurate climate estimates than LMA, leaf-area analysis, and CLAMP. First, the
reproducibility of scoring and analyzing leaf specimens has improved because much
of the processing is done by computer algorithms, with the exception of tooth
selection (see “methods” section below). Second, the measured variables are more
biologically significant and their inclusion serves to enhance the sensitivity of digital
leaf physiognomy to climate (see “leaf traits and function” section above). In
accordance with functionally-based expectations, leaves have larger, more abundant
teeth and are more highly dissected in colder climates (Royer et al., 2005). Critically,
digital leaf physiognomy should remain accurate even in times of rapid climate
change. This becomes important when estimating temperatures as plants may have
acclimated to a prior climate regime and may not capture the true climate. In order to
10
assess whether morphological changes occur rapidly enough in leaves to reliably
predict climate, Acer rubrum was grown for two years in differing climates (Royer et
al., 2009b). Seeds were collected in Ontario, Canada, Pennsylvania, South Carolina,
and Florida and representatives from each location were grown in gardens in Rhode
Island and Florida. At the end of a two year period, clear differences were observed
among seed sources and between growth sites. Plants grown in Rhode Island had a
greater number of teeth and were more highly dissected than those grown in Florida,
regardless of seed source. Thus, leaf shape in Acer rubrum can respond plastically to
climate change, implying that fossil leaf deposits record a true, temporally-resolved
snapshot of paleoclimate (Royer et al., 2009b). Finally, by using continuous variables,
more information can be gained from leaf traits that are sensitive to temperature and
precipitation. Digital leaf physiognomy takes into account the variation observed in
leaf traits rather than treating all toothed/dissected specimens as identical. For
example, two leaves with one and 100 teeth would be scored identically with leafmargin analysis but would be readily distinguished with digital leaf physiognomy.
Leaf economics
In addition to inferring climate from fossil leaves, one can also interpret
resource acquisition strategies and leaf habit (deciduous vs. evergreen). Leaf mass per
area (MA), photosynthetic rate, leaf nutrient concentration, dark respiration rate, and
leaf lifespan are all intercorrelated (Wright et al., 2004). Collectively, these leaf traits
comprise the leaf economics spectrum (Wright et al., 2004). High M A species exhibit
11
lower photosynthetic rates, dark respiration rates, and concentrations of nutrients
while having longer leaf lifespans (> 12 months). Such “slow-return” species have
leaves that maximize resource retention, are more expensive to construct, and exhibit
a greater thickness which acts against insect herbivory (Grime, 1974; Grubb, 1998).
In contrast, low MA species have higher photosynthetic rates, dark respiration rates,
and nutrient concentration while having shorter lifespans. These “fast-return” species
specialize in rapid resource acquisition (Grime, 1974; Grubb, 1998).
Although none of the leaf economic variables can be measured directly from
fossils, at least one of the variables, MA, can be inferred via proxy. In most plants,
leaf mass scales with petiole width because petioles play an important role in the
mechanical support of leaves (Royer et al., 2007). This provides a means to
reconstruct paleo-leaf mass in fossil leaves, which can then be divided by measured
leaf area, yielding MA. Further, because all of the leaf economic variables are
intercorrelated, evergreen and deciduous leaf habits can also be distinguished through
leaf mass per area: species with high MA (> ~120 g/m2) are typically evergreen while
species with low MA (< ~100 g/m2) are typically deciduous (Wright et al., 2004,
2005; Poorter et al., 2009).
Application to the fossil record
Digital leaf physiognomy is becoming an established method for
reconstructing paleoclimate. Its calibration is the most climatically, geographically,
and phylogenetically diverse (Peppe et al., in preparation). Living floras have
12
repeatedly shown strong correlations between climate and physiognomic variables
related to tooth count, tooth number, and leaf dissection (Huff et al., 2003; Royer et
al., 2005, 2008, 2009a, b). Further, these leaf-climate correlations have been found
within species (Royer et al., 2008, 2009b). Critically, errors in reconstructing MAT
and MAP are improved relative to LMA and leaf-area analysis (see “multivariate
approaches” section above). However, a serious gap in the digital leaf physiognomy
approach is that it has yet to be applied to the fossil record.
Here, I analyze six fossil plant sites from the Western Interior of the US using
digital leaf physiognomy. These include the Fox Hills flora from the Fox Hills
Formation in central North Dakota, Fort Union floras from the Fort Union Formation
in southwestern North Dakota, and Hanna Basin floras from the Hanna Basin in
south-central Wyoming (Fig. 1) (see “field areas” section below for details). The Fort
Union floras are divided into three floral zones, WBI, WBII, and WBIII, while the
Hanna Basin floras are divided into two floral zones, HBI and HBII.
The sites span in age from the latest Cretaceous to late Paleocene, and thus
provide the opportunity to study the climatic impact of the Cretaceous-Tertiary (K/T)
mass extinction event 65.51 Ma (million years ago). More generally, comparison of
results to other, independent estimates of climate will provide an important “proof of
concept” for the new digital leaf physiognomy approach. The reconstruction of leaf
mass per area will also provide insight into the paleoecology of these sites.
Specifically, because some of the sites are interpreted as marginal marine (see
“research questions and hypotheses” below), I will test whether salt stress impacts
leaf economic strategy. In addition, I will track the relative abundance of deciduous
13
vs. evergreen taxa, with an emphasis on potential changes across the K-T boundary
(see “research questions and hypotheses” below).
Figure 1. A. Locality map of the study area with B. the field areas indicated by stars and other major
basins. The blue star represents the Fox Hills, the red star represents Fort Union, and the green stars
indicate the Hanna Basin. WB = Williston Basin, PRB = Powder River Basin, BM = Bull Mountain,
CM = Crazy Mountains, CFB = Clark‟s Fork Basin, BB = Bighorn Basin, BH = Black Hills, WRB =
Wind River Basin, GRB = Green River Basin, HB = Hanna Basin, CB = Carbon Basin, UB = Uinta
Basin, DB = Denver Basin, SJB = San Juan Basin, RB = Raton Basin. Map from Peppe (2009). C.
Timescale showing the relative dates of the six floral sites. FHI = Fox Hills, WBI-WBII = Fort Union
floral zones, HBI-HBII = Hanna Basin floral zones. Putative salt-stressed zones are indicated with a
cross.
14
Research questions and hypotheses
How robust are climate estimates from digital leaf physiognomy?
The K/T boundary is marked by a mass extinction event popularly known for
the extinction of non-avian dinosaurs. Plant species may have experienced an
extinction rate as high as 57% in western North America (Wilf and Johnson, 2004).
Popularly cited climate estimates for the K/T boundary have been made using LMA.
Such estimates may be called into question for reasons discussed above (see
“multivariate approaches” section above). For example, leaf-margin analysis of fossil
sites from the Hell Creek Formation in southwestern North Dakota indicate cooler
temperatures than that of the coeval Fox Hills to the east (12-13 °C vs. 15.8-16.4 °C)
(Wilf et al., 2003b; Peppe et al., unpublished manuscript). The warmer estimates at
Fox Hills have been attributed to the moderating influences of the nearby Western
Interior Seaway (Peppe, 2003; see also Fig. 4). In support of this interpretation,
oxygen isotope values of shell material taken from the Western Interior Seaway also
suggest warmer temperatures, with values of 16-25 °C for nearshore environments
and 19-29 °C in brackish environments (Cochran et al., 2003). Alternatively, it is
possible that at Fox Hills LMA is being biased by non-temperature factors, for
example salt stress (see “salt stress hypotheses” section below). The application of
digital leaf physiognomy to the Fox Hills site will provide another means of
examining MAT for late Cretaceous Western Interior environments.
Previously published MAT estimates using LMA for the three Fort Union
sites (WBI-WBIII) are considerably cooler than similarly dated sites from the Hanna
15
Basin (HBI-HBII); HBI is equivalent in time to zone WBI, and averages 19.2 °C
compared to ~10 °C for the WBI. Similarly, HBII temperatures average at 20.5 °C
while the coeval WBIII is estimated to be ~8 °C. The Williston Basin is further north
than the Hanna Basin (~47 vs. 44 °N paleolatitude), but a 10 °C difference in MAT is
larger than what latitudinal temperature gradients would predict (~1 °C per degree
latitude for mid-latitudes; Fricke and Wing, 2004; Greenwood and Wing, 1995). Wilf
et al. (2003b) estimate temperatures for other earliest Paleocene Fort Union localities
in the Williston Basin that are also in agreement with WBI-WBIII estimates (~9-10
°C). Such low temperatures indicate winter months below freezing, conditions that
certain plant species (see “field areas” section below) found within the Fort Union
Formation could not tolerate. This suggests that LMA temperatures are too cool.
Critically, while many species in the WB floras are toothed, in many cases teeth are
not abundant and tooth size is commonly small. Because digital leaf physiognomy
incorporates information about tooth size and number, I hypothesize that estimates of
MAT using this method will be warmer than with LMA and more in line with the
other, independent data.
Are evergreen taxa selected against across the K/T boundary?
The K/T boundary mass extinction event is believed to be an important causal
factor behind the diversification and expansion of deciduous plants in the Northern
Hemisphere (Wolfe, 1987). The overall warmth of the late Cretaceous allowed for
evergreens to dominate. Fossil records may document a sharp shift to deciduousness
following the K/T boundary and this observation is classically attributed to the
16
cooling and short-lived darkness following the bolide impact (impact winter). Wolfe
(1987) used qualitative measures to determine leaf habit, principally observation of
the fossils‟ nearest living relatives and the propensity of the fossils to form leaf mats
(a trait most common in deciduous taxa). As described above (“leaf economics”
section), evergreens generally exhibit high MA while deciduous plants generally have
low MA. This can serve as an additional check on the cause of diversification of
deciduous plants. The reconstruction of MA for the latest Cretaceous Fox Hills flora
and the earliest Paleocene sites from the Fort Union and Hanna Basin Formations will
provide the first quantitative check, albeit limited in scope, on the hypothesis of
Wolfe (1987).
Salt stress hypotheses
Lastly, at least one site in each of the three field areas has been potentially
affected by salt stress. Salt stress can affect paleoclimate estimations in at least two
ways: through leaf size and margin type. Plants exposed to high amounts of salt spray
experience water stress (Larcher, 1995). Symptoms of water stress can include
impairment of photosynthetic rates, growth inhibition, delayed bud opening,
reduction in mineral uptake, the production of smaller leaves, and leaves that wilt
prematurely (Larcher, 1995). Plants growing in salt-stressed environments also tend
to exhibit higher values of MA (Poorter et al., 2009); such a “slow-return” leafeconomic strategy is common in stressed environments. Salt stress also selects against
the occurrence of leaf teeth because teeth are expensive users of water (see “leaf traits
and function” section above) (Royer et al., 2009a). Therefore, I hypothesize that sites
17
experiencing salt stress will contain plants with smaller leaves and a higher
percentage of untoothed species relative to climate-equivalent sites with no salt stress.
This bias could lead to falsely-warm and dry climate estimates. Also, if the salt stress
bias is present, I will test which approach is more affected by the bias: digital leaf
physiognomy or the univariate methods (LMA and leaf-area analysis).
18
METHODOLOGY
Digital leaf physiognomy
Digital images of woody dicot fossil leaves were taken from the Fox Hills,
Fort Union, and Hanna Basin Formations (Fig. 1 and 4), with a total of 356
photographs used in the final analysis. Photographs of leaf specimens were taken by
Daniel J. Peppe (Baylor University) for the Fox Hills, Daniel J. Peppe and Sofia
Oliver (Wesleyan University) for the Fort Union, and Regan E. Dunn (University of
Washington) for the Hanna Basin. All photographs incorporated a scale for sizing.
The Fort Union and Hanna Basin data were grouped into floral zones due to an
insufficient number of species at individual sites (see “field areas” section below).
Typically, a minimum of ~20 species are required to produce meaningful climate
estimates (Wolfe, 1993; Wilf, 1997; Burnham et al., 2001).
All digital images were processed using Adobe Photoshop CS3. (Adobe
Systems, San Jose, California, USA) as described by Huff et al. (2003) and Royer et
al. (2005) (Fig. 2 and 3). First, the leaf outline was traced and copied onto a new layer
in Photoshop. Any portion of the leaf margin that was damaged was restored by
reconstructing those segments as straight lines, allowing for the reconstruction of the
entire leaf (Fig. 2 and 3). Damaged leaves whose complete areas could not be reliably
reconstructed were excluded from this step. Next, the petiole, if present, was
separated from the rest of the leaf blade. This reconstructed leaf blade was used to
calculate leaf area, feret diameter (diameter of a leaf if its area was contained within a
19
circle), and major length (the longest line across a leaf) (Table 1). Owing to the
fragmentary nature of most fossils, leaf perimeter was not calculated (determining
leaf perimeter for fragmentary leaves with many fine teeth is particularly
problematic).
Figure 2. A. Representative toothed specimen from the Fort Union Formation (Fagopsiphyllum
groenlandica, WBII) with mm ruler for scale. B. Traced outline of the fossil specimen with restored
margins used for measuring leaf area, feret diameter, and major length (indicated by the red line).
Sinuses of the teeth are shown by the black line. C. Image of the fossil with the damaged portions of
the margin removed. Gray lines indicate the portions of the leaf that are measured and used for
calculating the length of the cut perimeter. Note: gray lines are not used in analysis, but are used here
to indicate which portions of the leaf are measured. D. Image of the fossil with teeth separated from the
leaf margin .
Next, for toothed specimens, the leaf is copied onto a new layer and the
damaged portions of the leaf are removed by „cutting‟ (digitally removing)
perpendicular to the nearest primary vein (Fig. 2). This cutting is done to allow
calculation of tooth variables that include leaf area, such as number of teeth / blade
20
area and tooth area / blade area. The blade area that remains after the cutting is
arguably linked physiologically to the companion teeth, and thus most likely to show
Figure 3. Representative untoothed specimen (DMNH 23605, HBII) with mm ruler for scale. Black line
on the petiole is used for petiole width measurement. DMNH = DMNS = Denver Museum of Nature
and Science specimen number.
a functional relationship to climate. In a final layer, teeth are separated from the
margins of the leaves (Fig. 2). This tooth selection is typically made from sinus to
sinus, but with important exceptions as outlined in Huff et al. (2003) and Royer et al.
(2005). A sinus refers to a marginal embayment, incision, or indentation between
marginal projections of any sort, typically lobes, teeth, or the base of leaves (Ellis et
21
al., 2009; see also Fig. 2). All processed images were imported into Image-J
(http://rsb.imfo.nih.gov/ij/) in order to derive measurements for leaf physiognomic
variables (Table 1). Tooth number was determined visually. Site means were
calculated from species means. Mean annual temperature (in °C) was calculated using
digital leaf physiognomy and LMA methodologies with the 95 site calibration (see
“multivariate approaches” section in the Introduction and Table 2). Mean annual
precipitation (cm) was also calculated using digital leaf physiognomy and leaf-area
analysis for comparison (Table 2).
Table 2. Mean annual temperature (MAT) and mean annual precipitation (MAP) equations and
standard errors for digital leaf physiognomy (DiLP), leaf-margin analysis (LMA), and leaf-area
analysis (LAA). P = margin percent, FDR = feret-diameter ratio, T:BA = number of teeth : blade area,
LA = leaf area, PR = perimeter ratio. Equations are based on a 95 site calibration (Peppe et al., in
preparation).
MAT
Models
Equation
SE
DiLP
MAT = (P × 0.255) + (FDR × 34.128) + (T:BA × -0.491) + -17.308
3.97
LMA
MAT = (P × 0.197) + 4.694
4.91
MAP
Models
Equation
SE
DiLP
ln (MAP) = (LA × 0.449) + (T:BA × 0.088) + (PR × -2.34) + 1.655
0.56
LAA
ln (MAP) = (LA × 0.22) + 3.381
0.61
Leaf mass per area
The widths of clearly preserved petioles were measured using Photoshop (Fig.
3): a line was drawn perpendicularly across the petiole from the basal-most insertion
point between the petiole and leaf blade. Petiole width was squared to better represent
22
petiole cross-sectional area. Values were averaged for each woody dicot species,
which were then averaged for site means. Site-mean leaf mass per area was then
calculated using the equation:
where MA = leaf mass per area (g/m2), PW = petiole width (cm) and A = leaf area
(cm2) (Royer et al., 2007). Additionally, M A for two gymnosperm species from the
Fox Hills site was reconstructed using the gymnosperm equation (Royer et al., 2010):
Ninety-five percent prediction intervals were calculated following Royer et al. (2007,
2010).
Field areas
The Fox Hills
Bordered by the North American craton to the east and the emerging
American Cordillera to the west, a large seaway – the Western Interior Seaway –
reaching from the Gulf of Mexico in the south to the Arctic Ocean in the north
occupied the Western Interior during most of the mid- and Late Cretaceous (Fig. 1
and 4). By the late Maastrichtian the seaway had retreated, forming the Dakota
Isthmus and dividing the seaway into northern and southern counterparts. Estuaries,
lagoons, streams, and tidal channels dotted the Dakota Isthmus with brackish water.
Seas again transgressed North Dakota and South Dakota for a final time during the
23
early Paleocene (Cochran et al., 2003; Peppe, 2003; Peppe et al., unpublished
manuscript).
A megafloral zone, Fox Hills I (FHI), was recently described within the
Linton Member of the Fox Hills Formation (Fig.5) (Peppe, 2003; Peppe et al.,
unpublished manuscript). FHI is based on two fossil sites in Emmons and Morton
Counties, North Dakota. This study focuses on the Emmons County site. The Fox
Hills I flora is 85% distinct from the most similar overlying Hell Creek floral zone
Figure 4. Map of North America ~65 Ma with locations of the three field areas indicated by yellow
stars. Light blue indicates the area covered by the Western Interior Seaway. Map created by Ron
Blakey, Northern Arizona University Geology (http://jan.ucc.nau.edu/~rcb7/nam.html).
and is defined by the presence of Nilssoniocladus yukonensis, Rhamnus salicifolious,
Nilssoniocladus comtula, and Mesocyparis borealis. The Hell Creek Formation
overlies the Fox Hills Formation in Montana, North Dakota, and South Dakota. The
Fox Hills-Hell Creek contact is regionally conformable, however, an erosional
contact has been suggested (Johnson et al., 2001; Peppe, 2003). The Hell Creek
24
Formation is composed of unconsolidated clays, silts, and fine to medium sands that
are typically lignitic, and contains some marine deposits (Frye, 1969). The two
formations interfinger with one another to the east, as a result of seaway regression
and eastward progradation of the Sheridan Delta during the late Maastrichtian (Gill
and Cobban, 1973; Peppe and Erickson, 2002; Peppe, 2003). The Fox Hills and Hell
Creek are thus, in part, laterally equivalent, and the Hell Creek floras in southwestern
North Dakota were terrestrial to deltaic and coexisted with the marginal marine to
estuarine Fox Hills floras in south central North Dakota (Peppe et al., unpublished
manuscript; Peppe 2003).
Figure 5. Stratigraphy of the Fox Hills Formation. Gray box indicates the Linton Member of the Fox
Hills Formation. Bones indicates the presence of excavated animal fossils. Leaf indicates the Fox Hills
floral zone. Cross indicates putative salt-stressed conditions.
25
There is strong evidence that the paleoenvironment of the Fox Hills flora was
marginal marine (estuarine to deltaic plain). Casterolimulus kletti, a shallow marine
limulid xiphosuran or marine horseshoe crab, as well as the pelvis and leg bones of a
juvenile champsosaur were found during excavation (Holland et al., 1975; Peppe,
2003; Hoganson et al., 2007). Two freshwater plant species, Pistia corrugata and
Paranymphaea hastata, are also present in the leaf bed (Peppe, 2003). The marine
oyster Crassotrea subtrigonalis has also been found in the upper sediments of Fox
Hills (Peppe, 2003). If the marginal marine interpretation is correct, the Fox Hills
flora would have been affected by salt spray. A transgressing and regressing Western
Interior Seaway may have also caused salt accumulation in soils that were then
colonized by the Fox Hills plants.
Dating of the Fox Hills Formation remains uncertain due to its poor exposure
and lack of radiometric ages (Peppe et al., unpublished manuscript). Nonetheless,
presence of the palynomorph Wodehouseia spinata places the Fox Hills within the
middle to late Maastrichtian (Peppe, 2003). Importantly, palynomorphs of the Hell
Creek Formation also places Hell Creek floras within the Wodehouseia spinata zone,
and plant morphotypes from each of the Hell Creek floral zones occur within Fox
Hills.
Stratigraphically, the Fox Hills flora is ~70 m below the K/T boundary
(Peppe, 2003; Peppe et al., unpublished manuscript). Using a sedimentation rate of
73.3 m/my from Hicks et al. (2002) for the Hell Creek Formation and a CretaceousTertiary boundary age of 65.51 Ma, Peppe et al. (unpublished manuscript) estimate an
age of ~66.5 Ma for Fox Hills flora.
26
Fort Union Formation
The Paleocene Fort Union Formation is exposed in the Williston, stretching
from western North Dakota, southeastern Saskatchewan, to eastern and southeastern
Montana. The two basins are separated by the Miles City Arch, which extends
northwest to the Yellowstone River (Belt et al., 2002, 2004; Brown, 1993; Dickinson
et al., 1988). All leaf fossils used here are from southwestern North Dakota.
Within the Williston Basin in the Little Missouri River Valley of North
Dakota, the Fort Union Formation conformably overlies the Cretaceous Hell Creek
Formation and is composed of the Ludlow, Tongue River, and Sentinel Butte
members (Fig. 6) (Fox and Olsson, 1969; Fox and Ross, 1942; Kroeger and Hartman,
1997). The Ludlow Member, the stratigraphically lowest of the three members, is
190-210 m thick and is characterized by alternating beds of yellow to brown
sandstone, siltstone, and mudstone beds (Peppe et al., 2009). Carbonaceous shales
and lignite deposits are also abundant. Lower and upper Ludlow deposits are
distinctly different, with thin lignite beds and siltstone and sandstone layers in the
lower Ludlow and extensive lignite beds and thick medium to coarse grained
sandstone layers in the upper Ludlow (Belt et al., 1984; Peppe et al., 2009).
During the early Paleocene, the Cannonball Seaway transgressed the western
interior of the North American craton, depositing brackish units adjacent to the
Ludlow Member (Peppe, 2003). This marine to marginal-marine Cannonball Member
is laterally equivalent to the Ludlow Member and in southwestern North Dakota is
exposed at two different stratigraphic intervals named the Boyce Tongue and the
Three V Tongue (Hartman, 1993; see Peppe, 2009 and Peppe et al., 2009 for detailed
27
Figure 6. Stratigraphy of the Fort Union Formation in the Williston Basin. The open star indicates
dated ash bed KJ0731 and the closed star indicates dated ash bed KJ0728. Cross indicates a putative
salt-stressed floral zone.
discussion). The Boyce Tongue is distinguished from the Three V Tongue by the
presence of bivalves while the latter is abundant in oyster fossils (Peppe et al., 2009).
The Tongue River Member, ~185 m thick, overlies and is separated from the
Ludlow Member by thick sand deposits (Hickey, 1977; Royse, 1972). Lemon yellow
to buff tan sandstone, and siltstone and shale interbedded with abundant thick lignite
28
deposits characterize the Tongue River Member (Hares, 1928; Hickey, 1977; Royse,
1972; Warwick and Luck, 1995). Two ash beds, KJ0731 and KJ0728, have been
dated within the Tongue River Member. The KJ0731 ash bed is found 223 m above
the base of the Fort Union Formation and is 60.5 Ma and the KJ0728 ash bed is found
303 m above the base of the Fort Union Formation and is 59 Ma (Peppe et al., 2009).
The Sentinel Butte Member, 120-150 m thick, contains drab grey to brown sandstone
and mudstone beds, an abundance of petrified wood, siderite concretions, and a thick
basal sand unit (Hares, 1928; Brown, 1948; Royse and Holland, 1969; Royse, 1972;
Hickey, 1977).
Three floral zones have been described within the Fort Union Formation of
the Williston and Powder River Basins (Peppe, 2009). These are, in stratigraphic
order, Williston Basin I (WBI), Williston Basin II, (WBII), and Williston Basin III
(WBIII). Only the Williston Basin contains all three floral zones and the following
descriptions correspond to their occurrence in the Williston Basin.
WBI occurs within the Ludlow Member of the Williston Basin from 0-130 m
(~65.5-64.04 Ma) and is recognized by the presence of Paranymphaea crassifolia,
Cornophyllum nebrascensis, “Populus” nebrascensis, Quereuxia angulata, and
Nyssidium eckmanii. Two palm species, Amesoneuron sp. and Sabilites sp., also occur
within the WBI zone. WBII also occurs within the Ludlow Member from 165-205 m
(~63.5-63.1 Ma) and is recognized by the presence of Macginitiea nascens,
“Populus” cordata, Averhoites affinus, Ternstromites paucimissouriensis,
Fagopsiphyllum groenlandica, and “Planera” crenata. WBIII occurs within the
Tongue River Member, from 206-325 m (~60.7-58.4 Ma), and is recognized by
29
Nyssidium arcticum, “Populus” acerifolia, Meliosma vandaeleium, Aesculus hickeyi,
Macginitiea nobilis, and Davidia antiqua (Peppe, 2009).
Floral change at the WBI-WBII transition in the Fort Union Formation is
coincident with the final regression of the Cannonball Seaway ~64 Ma; a marine unit
exists between the WBI and WBII floral zones (Peppe, 2009). While no direct
evidence for an estuarine environment exists within the WBII zone, proximity to the
regressing Cannonball Seaway may have created unique environmental conditions
leading to the observed floral changes. Additionally, proximity to the Cannonball
Seaway may have resulted in salt-stressed floras.
In the geographically adjacent Powder River Basin (Fig. 1), only WBI and
WBIII are present. The absence of WBII has been attributed to the environmental
effects related to the regression of the Cannonball Seaway at 64 Ma (Peppe, 2009).
The WBI-WBII transition in the Williston Basin occurs at the same time as final
deposits of the Cannonball Seaway (~64 Ma) while no marine deposits exist during
this interval in the Powder River Basin (Peppe, 2009). This suggests environmental
conditions unique to the Williston Basin as a cause in the change from WBI to WBII
flora (Peppe, 2009).
Hanna Basin
The Hanna Basin is located in south-central Wyoming and contains >11 km of
marine and non-marine strata dating from the Late Cretaceous to Early Eocene
(Dunn, 2003). Beginning in the Late Cretaceous and continuing through the
Paleocene, subduction of the Farallon Plate beneath the North American plate
30
resulted in the uplift of a broad belt of mountains in the western regions of North
America (Gill et al., 1970). The Hanna Basin was formed by these tectonic processes
and as a result experienced high rates of sediment accumulation. The Hanna Basin is
today bound on all sides by such Laramide uplifts, with the Seminoe and Shirley
Mountains to the north, the Rawlins Uplift to the west, the Simpson Ridge to the east,
and the Sierra Madre and Medicine Bow Mountains to the south (Blackstone, 1993).
Strata in the northeast corner of Hanna Basin are highly deformed due to stresses
caused by uplifting events (Dunn, 2003). This study focuses on the Ferris and Hanna
Formations within the Hanna Basin.
Due to an insufficient number of plant species to use with digital leaf
physiognomy, the Hanna Basin floras are divided into two floral zones based on the
ages of each locality. Hanna Basin I (HBI) includes the Smurfs Rebel, Miller‟s
Forest, Tip of Pats Bottom, and Este Lado localities and is the older of the two zones
(~64.9-64.2 Ma). Hanna Basin II (HBII, ~61.4-58.6 Ma) includes Moby‟s Meal, Beer
Mug Vista, Emily‟s First, Jingo Quarry, and Wing Ding localities.
The Ferris Formation is located in the west-central area of the Hanna Basin
and encompasses upper Cretaceous and Paleocene strata. Ages are based on the
presence of land mammal fossils and plant microfossils. The lower section of the
Ferris Formation, The Miller Estates, has been described as mostly sandstone with a
small pebbly sandstone component ~1200 m thick (Fig. 7) (Eberle and Lillegraven,
1998; Dunn, 2003). Above is the Seminoe Reservior section that is unexposed but
has been estimated to be ~155 m thick (Dunn, 2003). The third section, Pats Bottom,
is characterized by fine-grained siltstone and sandstone deposits with coal beds
31
Figure 7. Stratigraphy of the Ferris Formation in the Hanna Basin. Bones indicate excavated animal
fossils. Leaves indicate floral sites which make up the Hanna Basin I floral zone. Crosses indicate saltstressed sites. North American Land Mammal Ages (NALMA) are determined by the presence of
fossil mammals. The Pats Bottom and Seminoe River sections are placed in the Torrejonian based on
their estimated ages calculated from sedimentation rates.
(Dunn, 2003). Strata are relatively undeformed within the Ferris Formation. Marine
ichnofossils can be found within the Ferris Formation and trace fossils of burrows
consistent with Rhizocorallium, a U-shaped burrow, are also present (Dunn, 2003).
Such fossils are indicative of shallow marine conditions and it may have been likely
that leaf assemblages occurred at or near sea level (Dunn, 2003). Ages for Ferris
32
Formation localities were calculated assuming a sedimentation rate of 575 m/my
(Dunn, 2003) and a one million year duration for the Puercan North American Land
Mammal Age (NALMA), a system of dating based on the first/last appearances of
mammalian fauna.
The first and oldest locality, Smurfs Rebel, is dated to ~64.8 Ma. Floras are
characterized by Platanus raynoldsii and “Populus” nebrascensis. The depositional
environment is described as a crevasse splay due to sandstone layers coarsening
upward with rooting on the upper surfaces (Dunn, 2003).
Miller‟s Forest is located 16 m above Smurfs Rebel and is dated to ~64.5 Ma.
The environment has been described as a freshwater pond. The plants at this locality
are dominated by Platanus raynoldsii (Dunn, 2003).
The Tip of Pats Bottom site is interpreted to be an abandoned channel based
on laminated mudstones and siltstones with sandstones on either side. This locality
has an age of ~64.1 Ma. The Tip of Pats Bottom floras are dominated by
Glyptostrobus cf G. europaeus as well as Browniea serrata, Platanus raynoldsii, and
“Populus” nebrascensis (Dunn, 2003).
The last of the Ferris Formation localities, Este Lado, occurs just above the
Tip of Pats Bottom. Due to the presence of fine-grained sandstone deposits with
cross-beds and current ripples, the depositional environment is interpreted to be that
of a channel margin. Characteristic floras include Platanus raynoldsii, and Browniea
serrata (Dunn, 2003).
The Tip of Pats Bottom and Este Lado localities within the HBI floral zone
contain trace fossil forms consistent with Rhizocorallium. Other reports have been
33
made on the occurrence of marine ichnofossils near these localities (Wroblewski,
2002). These fossils are indicative of shallow marine conditions and suggest a
potentially salt-stressed environment for the HBI floral zone.
The Hanna Formation overlies the Ferris Formation and is Paleocene in age
based on palynomorphs and vertebrate fossils (Blackstone, 1993; Dunn, 2003).
Thickness may exceed 3048 m and lithology is characterized by a conglomeratic
sandstone base with sandstone, shale, and coals beds alternating to the top of the
formation (Fig. 8) (Blackstone, 1993; Dunn, 2003). In the northeastern corner of the
basin, in an area known as The Breaks, the Hanna Formation is entirely non-marine
with carbonaceous shales and mudstones interspersed with conglomeratic and
sandstone layers (Dunn, 2003). Freshwater crayfish burrows, freshwater molluscs and
fish, and dull-colored paleosols all suggest fluvial channel and floodplain deposits.
All five sites considered in this study have been dated using sedimentation
rates constrained by the land mammal Hyracotherium granger, an indicator species of
55 Ma according to NALMA, found near the top of The Breaks section at 3400 m,
and the NALMA Torrejonian-Tiffanian boundary (~61.4 Ma) at 1350 m within the
section (Lillegraven, 1994; Dunn, 2003).
Moby‟s Meal is stratigraphically the lowest locality in the Hanna Formation.
Horizontally-bedded laminated mudstones and siltstones with oxidized surfaces and
concretionary structures are indicative of a pond or small lake. The most common
floras are Fagopsiphyllum groenlandica and Platanus raynoldsii. Beer Mug Vista is
160 m above Moby‟s Meal and its depositional environment is a pond or small lake
34
Figure 8. Stratigraphy of the Hanna Formation in the Hanna Basin. Bones indicate excavated animal
fossils. Leaves indicate floral sites which make up the Hanna Basin II floral zone. North American
Land Mammal Ages (NALMA) are determined by the presence of fossil mammals.
similar to Moby‟s Meal. Fagopsiphyllum groenlandica is the dominant species. The
stratigraphic position of Moby‟s Meal and Beer Mug Vista suggest an age within the
early Paleocene. However, the presence of certain palynomorphs (e.g.
Caryapollenites wodehousei, Momipites wyomingensis) suggests a younger age
(Dunn, 2003). Dunn (2003) suggests a previously unmapped fault occurring in this
35
section has placed older strata over younger strata, including the Moby‟s Meal and
Beer Mug Vista sites. Importantly, the floral composition and diversity of the Moby‟s
Meal and Beer Mug Vista localities are more similar to the younger Jingo Quarry and
Wing Ding localities (see below).
Emily‟s First, a flood basin environment, is the only locality within a
vertebrate fossil bearing zone. Emily‟s First has been dated to ~61.5 Ma and
dominant floras include Platanus raynoldsii and Aesculus hickeyi.
Jingo Quarry, dated to 59 Ma, is a lacustrine environment characterized by
laminated siltstone to very fine grained siltstone with horizontal laminations,
oscillation ripples, and the presence of fossil fish scales.
Wing Ding, also dated to 59 Ma, is separated from Jingo Quarry by 10 m of
strata. The depositional environment is also lacustrine, similar to Jingo Quarry, and is
dominated by Averrhoites affinis.
36
RESULTS
Climate estimates
All digital leaf physiognomy MAT and MAP estimates are considerably
warmer and drier than MAT estimates using LMA and MAP estimates using leaf-area
analysis (Fig. 9 and 10 and Table 3; values for all physiognomic variables are
provided in Appendix 1). On average, digital leaf physiognomy estimates are ~3.3 °C
warmer and 17 cm drier. Though there is a slight increase in MAT in the WBIII floral
zone, overall, digital leaf physiognomy temperatures decrease through the Paleocene,
while LMA temperature estimates show no strong temporal pattern (Fig. 9).
Similarly, digital leaf physiognomy MAP estimates trend towards drier values while
there is little variation in leaf-area analysis MAP estimates (Fig. 10).
Figure 9. Mean annual temperature estimates for the Fox Hills, Fort Union, and Hanna Basin floral
sites. Ages were determined by taking the mean of the age range. Closed symbols correspond to digital
leaf physiognomy estimates and open symbols correspond to leaf-margin analysis estimates.
Horizontal bars are temperature errors and gray bars represent the age ranges for each floral site.
37
The Fox Hills has the second-highest temperature estimate and is the driest of
all the sites (Fig. 9 and 10). Digital leaf physiognomy temperatures for the Fort Union
floral zones decrease through time and then increase again slightly in the WBIII floral
zone. Digital leaf physiognomy shows a drop in temperatures by ~1.5 °C for WBII
while LMA temperatures decrease throughout each of the Fort Union floral zones.
MAP estimates parallel temperatures of WBI-WBIII using digital leaf physiognomy;
there is a drop in MAP in WBII by ~19 cm and increases again in WBIII. In contrast,
leaf-area analysis MAP estimates remain unchanged throughout all three Fort Union
floral zones.
Figure 10. Mean annual precipitation estimates for the Fox Hills, Fort Union, and Hanna Basin floral
sites. Ages were determined by taking the mean of the age range. Closed symbols correspond to digital
leaf physiognomy estimates and open symbols correspond to leaf-area analysis estimates. Horizontal
bars are precipitation errors and gray bars represent the age ranges for each floral site.
Both floral zones from the Hanna Basin are warmer than the Fox Hills and
Fort Union floral zones with the exception of the Fox Hills flora, which is warmer
than HBII. The Hanna Basin digital leaf physiognomy temperatures indicate a drop in
38
temperatures in the HBII floral zone while LMA temperatures show a slight increase
through the HBI-HBII zones (Fig. 9). Both digital leaf physiognomy and leaf-area
analysis MAP estimates drop in the HBII floral zone, though digital leaf
physiognomy yields drier estimates for both Hanna Basin zones (Fig. 10).
Table 3. Mean annual temperature (MAT, °C) and mean annual precipitation (MAP, cm) estimates
using digital leaf physiognomy, leaf-margin analysis (LMA), and leaf-area analysis (LAA) with
corresponding errors.
Digital leaf physiognomy
LMA
LAA
MAT
+
error
error
MAP
+
error
error
MAT
+
error
error
MAP
+
error
error
FHI
19.6
23.6
15.6
128.3
224.9
73.2
14.5
19.5
9.6
150.6
277.5
81.8
WBI
13.5
17.5
9.5
157.9
276.8
90.1
10.8
15.7
5.9
155.5
286.4
84.4
WBII
12.1
16.1
8.1
137.3
240.7
78.3
10.1
15.0
5.2
154.3
284.2
83.8
WBIII
13.0
17.0
9.1
138.0
241.9
78.7
9.4
14.3
4.5
154.4
284.4
83.8
HBI
20.9
24.9
16.9
146.8
257.4
83.8
16.3
21.2
11.4
164.6
303.3
89.4
HBII
18.6
22.5
14.6
136.9
239.9
78.1
16.9
21.8
12.0
149.3
275.1
81.1
Zones WBI and HBI overlap in time (age ranges: 65.5-64.4 Ma and 64.9-64.2
Ma), but the HBI floral zone is much warmer than WBI using both climate models.
Similarly, WBIII and HBII overlap in time (age ranges: 60.7-58.4 Ma and 61.4-58.6
Ma), and the HBII floral zone again has a much higher temperature estimate than
WBIII. MAP values for each of these floral zones are comparable using both models,
though the Hanna Basin yields slightly drier estimates using digital leaf
physiognomy.
39
Table 4. Site mean values for physiognomic variables used in digital leaf physiognomy climate
models. FDR = feret diameter ratio. Also shown are the site-mean estimates of leaf mass per area and
their corresponding errors.
Floral
Zone
Margin
%
FDR
# Teeth
to Blade
Area
FHI
50.0
0.747
2.66
27.21
1.19
Leaf
mass per
area
(g/m2)
81.2
WBI
30.8
0.705
2.27
24.27
1.09
WBII
27.4
0.676
1.36
23.27
WBIII
23.7
0.732
1.40
HBI
58.7
0.696
HBII
61.9
0.613
Leaf
Area
(cm2)
Perimeter
Ratio
+
error
error
89.4
73.7
85.3
94.4
77.2
1.11
73.3
81.2
66.1
25.73
1.11
65.6
73.7
58.4
1.06
34.96
1.13
67.5
74.9
60.9
1.69
23.31
1.09
93.2
102.1
85.1
Leaf mass per area
Overall, site means for leaf mass per area decrease through time, with the
exception of a large increase observed in the HBII floral zone (Fig. 11 and Table 4).
Leaf mass per area values decrease through the Fort Union floral zones. The HBII
floral zone has the highest MA values of the six sites.
Species means ranges from 32-219 g/m2 across all sites. Of these, 8.4% have
leaf mass per area values ≥ 100 g/m2 and 2.1% have values ≥ 120 g/m2. Only one
species within the Fox Hills averages ≥ 100 g/m2. The two gymnosperm species
within the Fox Hills include Nilssoniocladus comtula and Nilssoniocladus yukonensis
have an estimated leaf mass per area of 142.8 and 123.9 g/m2, respectively. The
estimates for the gymnosperms are higher than 99% of the species means for
angiosperm species from all sites. The two gymnosperm species have been
interpreted as deciduous due to the frequent occurrence of mats of Nilssoniocladus
40
leaves and cutoff petiole bases with features representing abscission zones (see Peppe
et al., 2007 for discussion).
Figure 11. Site means for angiosperm leaf mass per area. Ages were determined by taking the mean of
the age range. Horizontal bars are leaf mass per area errors and gray bars represent
41
DISCUSSION
Climate
Mean annual temperature
The digital leaf physiognomy approach yields warmer and drier climates at all
sites relative to the univariate approaches leaf-margin analysis and leaf-area analysis
(Fig. 9 and Table 3). An analysis of the variables that compose the digital leaf
physiognomy models reveals the reasons for the differences.
The three leaf traits used to derive MAT in digital leaf physiognomy are
margin percent, which is the single variable used in leaf-margin analysis, feret
diameter ratio, and the ratio of the number of teeth to leaf blade area (Table 2). Feret
diameter ratio is a proxy for leaf shape: leaves whose shapes resemble lines will have
ratios equal to zero while round leaves will haves ratios closer to one. Highly lobed
leaves tend to have high feret diameter ratios (Royer et al., 2008). Relative to the 95
site calibration, the six fossil sites are on the high end of the feret spectrum (Fig. 12).
Feret diameter ratios are negatively correlated with temperature (Fig.12). Leaves in
warm climates are more line-like while rounder leaves are most common in cool
climates. This leaf-shape gradient may be related to leaf temperature: larger, rounder
leaves will have a thicker boundary layer, a layer of static air adjacent to the leaf,
which inhibits heat and water vapor transfer from the leaf more effectively than
linear-shaped leaves. The Fox Hills and HBI-HBII have the highest temperature
estimates and also have lowest site-mean feret diameter ratios. For example, 15% of
42
species from the Fox Hills have feret-diameter ratios of ≤ 0.4 while 3% of the species
in zones WBII and WBIII have values of ≤ 0.4 and WBI has none. This trend is also
observed at the high end of feret diameter ratios: zones WBI-III, which have the
coolest reconstructed temperatures, have the highest percentage of species with
values of 0.8-0.9. The coolest sites correspond to the roundest leaves, while the
warmer sites correspond to obvate-linear leaves.
In the context of feret diameter ratio only, digital leaf physiognomy
temperature estimates should be cooler than the leaf-margin analysis estimates (Fig.
12). Therefore, tooth number to blade area is the variable that is driving the warmer
estimates of digital leaf physiognomy.
Tooth number to blade area ratios tend to decrease with increasing
temperatures as the average number of teeth drops with higher temperatures (Fig. 12).
This relates to the function of leaf teeth as areas of high gas exchange and water loss
via evaporation (see “leaf traits and function” section above). While there is a
difference in the number of teeth across sites, when compared to the 95 site
calibration, tooth numbers are consistently low (Fig. 12); thus, it would be expected
that the fossil sites would have warmer temperatures than LMA.
Figure 12. Next page: Variables used in the digital leaf physiognomy mean annual temperature (MAT)
model plotted against MAT using the 95 site calibration of Peppe et al. (in preparation). DiLP = digital
leaf physiognomy. The Fox Hills, Fort Union, and Hanna Basin floral zones are also plotted for
comparison.
43
100
90
Percent untoothed
80
70
DiLP Calibration
60
Fox Hills
50
Fort Union
40
Hanna Basin
30
20
10
0
0
5
10
15
20
25
30
Mean annual temperature ( C)
0.75
Feret diameter ratio
0.70
0.65
0.60
DiLP Calibration
0.55
Fox Hills
0.50
Fort Union
0.45
Hanna Basin
0.40
0.35
0.30
0
5
10
15
20
25
30
Mean annual temperature ( C)
35
#Teeth/Blade Area
30
25
DiLP Calibration
Fox Hills
Fort Union
Hanna Basin
20
15
10
5
0
0
5
10
15
20
25
30
Mean annual temperature ( C)
44
Mean annual precipitation
There is also a large difference in MAP estimates between digital leaf
physiognomy and leaf-area analysis (Fig. 10 and Table 3). Again, it is the additional
physiognomic variables used in digital leaf physiognomy that are driving
precipitation estimates toward drier values.
Leaf area is the only variable used in leaf-area analysis to estimate MAP. Leaf
area can be used as a proxy for paleoprecipitation because larger leaves are typically
found in moist environments (see Introduction). The sole use of leaf area, however,
would put the six fossil sites at higher precipitation values using the 95 site
calibration, as is seen in the leaf-area analysis estimations (Fig. 13). For example, Fox
Hills has the second largest average leaf area, but it is the driest of the six sites
according to digital leaf physiognomy.
In addition to leaf area, tooth number to blade area and perimeter ratios are
used to model MAP using digital leaf physiognomy (Table 2). Values for the number
of teeth to blade area are quite similar at all six sites (Table 4). Globally, the number
of teeth to blade area is negatively correlated to precipitation. Given the function of
leaf teeth, a positive correlation would be expected as species with more teeth are
typically found in moist environments (see “leaf traits and function” section).
However, Royer et al. (2008) found the same negative correlation between tooth
number to blade area and MAP. There may be two explanations for this paradox.
First, plant water availability (and thus the abundance of teeth) need not correlate
with MAP; for example, plants in ridge-top environments often do not have much
available water, even when MAP is high. Second, number of teeth to blade area is
45
also influenced by MAT, and MAT is loosely correlated with MAP. Thus, in warm
and wet environments, it may make sense that tooth numbers are low. This potential
confounding influence warrants further study.
Perimeter ratio can also be thought of as a function of the number of teeth and
tooth area; a species with a high number of teeth, or large teeth, will have a higher
perimeter ratio, as teeth add to the total perimeter of a given leaf. Though the Fox
Hills has the least total number of teeth, it has the highest perimeter ratio of the six
sites because average tooth area at Fox Hills is among the highest of the six sites.
Even so, perimeter ratios for the Fort Union and Hanna Basin are quite similar to the
Fox Hills, as these sites also have the greatest number of teeth. Globally, perimeter
ratio is inversely related to MAP (Fig. 13). This may also be related to the effect of
temperature on the number of teeth as discussed above. On a global scale, perimeter
ratios for the six sites are low, as is tooth number and area (Appendix 1). Therefore, it
may be the number of teeth and tooth area that are driving precipitation estimates
towards lower values.
Figure 13. Next page: Variables used in the digital leaf physiognomy mean annual precipitation (MAP)
model plotted against MAP using the 95 site calibration of Peppe et al. (in preparation). DiLP = digital
leaf physiognomy. The Fox Hills, Fort Union, and Hanna Basin floral zones are also plotted for
comparison.
46
300
Leaf area (cm2)
250
200
DiLP Calibration
150
Fox Hills
Fort Union
100
Hanna Basin
50
0
0
100
200
300
400
Mean annual precipitation (cm)
500
35
# Teeth/Blade area
30
25
DiLP Calibration
20
Fox Hills
15
Fort Union
Hanna Basin
10
5
0
0
100
200
300
400
Mean annual precipitation (cm)
500
1.35
Perimeter ratio
1.30
1.25
1.20
DiLP Calibration
Fox Hills
1.15
Fort Union
1.10
Hanna Basin
1.05
1.00
0
100
200
300
400
Mean annual precipitation (cm)
500
47
Comparison of digital leaf physiognomy to independent assessments of climate
The digital leaf physiognomy MAT estimate for the Fox Hills shows a warmer
temperature (19.6 °C) than LMA (14.5 °C) and LMA estimates from the coeval Hell
Creek (see “research questions and hypotheses” section above). Importantly, this new
temperature is in agreement with the warm temperatures estimates of Cochran et al.
(2003) (19-29 °C for brackish environments) and Carpenter et al. (2003) (18 °C)
using oxygen isotopes. Though the digital leaf physiognomy estimate is drier than
leaf-area analysis, in general MAT and MAP for the Fox Hills indicates a warm-wet
environment. Further, digital leaf physiognomy MAP and MAT estimates are in
agreement with the paleoenvironmental interpretation of the Fox Hills as a warmtemperate estuarine to marginal marine forest: the presence of marine ichnofossils, a
marine clam and oyster, and a non-marine champsosaur all suggest an estuarinemarginal marine Fox Hills environment (Peppe et al. unpublished manuscript).
Nilssoniocladus yukonensis leaves are also indicative of such an environment, as this
species increases in abundance in coastal environments and delta plains with warm,
temperate climates (Krassilov, 1973, 1978). Additionally, the presence of the
freshwater species Pistia corrugata and Paranymphaea hastata suggest a wet
environment (Peppe et al., unpublished manuscript; see also “field areas” section).
That the Fox Hills remains significantly warmer using digital leaf physiognomy than
the coeval Hell Creek supports the interpretation of a moderating Western Interior
Seaway near the Fox Hills during the late Maastrichtian.
As with the Fox Hills, the Fort Union is warmer and drier, though MAP for
the WBI is nearly identical to the corresponding leaf-area analysis estimate (Fig. 9
48
and 10 and Table 3). The low numbers of teeth that characterize Fort Union leaves
explain the warm digital leaf physiognomy temperatures (see “leaf traits and
function” section and “mean annual temperature” section above). The Fort Union is
the strongest “proof of concept” for digital leaf physiognomy: coeval bottom water
temperatures are ~10 °C (Zachos et al., 2001) and thus land temperatures are unlikely
to have been 10 °C or cooler as leaf-margin analysis suggests. Similarly, palm species
present in the Fort Union are indicative of warmer temperatures. Palm species are
highly sensitive to frost and, as such, are restricted to climates with MAT >10 °C
(Greenwood and Wing, 1995). Digital leaf physiognomy temperature estimates for
the Fort Union are therefore more reasonable than leaf-margin analysis estimates.
There are currently no other precipitation estimates from the Fort Union Formation or
the Hanna Basin and therefore the only comparisons that can be made are between
digital leaf physiognomy and leaf-area analysis estimates.
The time equivalent Hanna Basin floral zones are considerably warmer than
the Fort Union floral zones (Fig. 9 and Table 3). The Hanna Basin floral zones have a
greater percentage of untoothed species, and fewer teeth with smaller tooth areas
(Appendix 1), all factors that contribute to warmer temperatures. The Fort Union and
the Hanna Basin have similar leaf areas, number of teeth to blade area ratios, and
perimeter ratios, and hence precipitation estimates for these zones are also similar
(Fig. 10 and Table 3). Latitudinal temperature gradients suggest a ~1 °C decrease in
temperature per 1° increase in latitude (Fricke and Wing, 2004; Greenwood and
Wing, 1995). The Fort Union and the Hanna Basin were separated by ~3°
(paleolatitude), and therefore a 3 °C difference in temperature would be expected.
49
Though temperature estimates are still larger than latitudinal gradients would predict,
these new temperature estimates have narrowed the gap between the sites (compare a
9 °C difference between the WBI and HBI and a 12 °C difference between the WBIII
and HBII with older LMA estimates to a 7.4 °C and 5.5 °C difference using digital
leaf physiognomy).
Deciduous diversification across the K/T boundary
The end Cretaceous events have been suggested as a cause for the selection
against evergreens and the consequent diversification of deciduous plants in the
Northern Hemisphere (Wolfe, 1987; see also “research questions and hypotheses”
section above). Warm temperatures of the late Cretaceous selected for evergreens;
deciduous plants were only advantageous in places with periods of unfavorable
growth (low levels of winter light, low temperatures) and in disturbed or successional
environments, where rapid growth is favored (Givnish, 1979; Wolfe, 1987). An
impact winter caused by the bolide impact at the end Cretaceous is thought to have
caused a brief cooling excursion that resulted in the selection against evergreen plants
during the Paleocene (Wolfe, 1987). Evergreen and deciduous taxa can be
distinguished by their values of leaf mass per area (see “leaf economics” section
above). Briefly, species with low MA (< 100 g/m2) are typically deciduous while
species with high MA (> 120 g/m2) are typically evergreen (Wright et al., 2004, 2005;
Poorter et al., 2009).
50
The six sites studied here offer a unique opportunity to assess the K/T
deciduous hypothesis: the Fox Hills should exhibit the highest MA values while the
Fort Union and Hanna Basin should indicate a trend toward lower values. The Fort
Union shows a clear trend toward lower values (Fig. 11). Though there is an increase
from FHI to WBI, it is a non-significant 4 g/m2 increase. Even with this trend in M A,
there is no clear shift from typical evergreen to deciduous values. The Fox Hills sitemean values are representative of deciduous plants. Further, there is a large increase
in MA from HBI to HBII (a 26 g/m2 shift) where MA values are the highest (Fig. 11
and Table 4). Therefore, while there is a clear trend within the Fort Union, strong
evidence for the selection against evergreen plants across the K/T boundary is not
seen.
Salt stress
Leaf mass per area can also be used as an indicator of a salt-stressed
environment (see “salt stress hypotheses” section in the Introduction). As there is
evidence for salt stress in the Fox Hills, WBII, and HBI floral zones, these sites
should also exhibit smaller leaves with a greater proportion of entire-margined
species and higher MA values relative to sites that are not salt-stressed.
Leaf areas in the Fox Hills and HBI zones are opposite to what would be
expected of sites subject to salt stress: their leaf areas are larger than sites with no
evidence of salt stress (Table 4). Though the WBII does have a smaller average leaf
area than WBI and WBIII, there is only a difference of 1-2 cm2 between them. Leaf
51
margin percent can also be used to determine whether the zones experienced salt
stress because teeth are associated with high water loss: plants in salt-stressed
environments should have a higher percentage of entire margin species. The Fox Hills
and HBI floral zones do have some of the highest margin percentages of the six sites.
However, WBII and HBI do not have the highest margin percentages of their
respective field areas (Table 4). Leaf mass per area values are also not consistent with
expected effects of salt stress on plants (Table 4). Rather, the highest MA values are
found at sites with no evidence for salt stress. Therefore, either the salt stress
indicators are incorrect, or the Fox Hills, WBII, and HBI floral zones were not
affected by salt stress. Based on these data, it is not clear which interpretation is
correct.
If we assume that these three sites were salt-stressed, the sensitivity of digital
leaf physiognomy to salt stress relative to the univariate LMA and leaf-area analysis
can be tested. Salt stress, through the selection against toothed species and large
leaves, should cause anomalously high MAT and anomalously low MAP estimates if
the climate model is sensitive to salt stress. Digital leaf physiognomy MAT and MAP
estimates for the Fox Hills, WBII, and HBI zones are all warmer and drier than LMA
and leaf-area analysis (Fig. 9 and 10 and Table 3). This may suggest that digital leaf
physiognomy is more sensitive to salt stress. However, the pattern of warmer and
drier estimates characterizes all digital leaf physiognomy estimates and is not
restricted to the salt-stressed zones. Critically, the climate estimates from digital leaf
physiognomy are more in line with independent assessments of paleoclimate,
especially for MAT (see “mean annual temperature” section above).
52
Additional sites that are salt-stressed and have time-equivalent sites
experiencing no salt stress are needed to accurately assess the sensitivity of leaf
climate models to salt stress.
Implications for leaf physiognomy climate models
Digital leaf physiognomy consistently produces estimates that are warmer and
drier than univariate climate models. Though a strong correlation exists between leaf
margin types and temperature, the sole use of margin percent in temperature
reconstruction results in temperature estimates that are too cold. Similarly, using leaf
area as a proxy for paleoprecipitation yields estimates that are much wetter than other
leaf physiognomic variables would predict. The inclusion of functionally-significant
leaf traits has been shown to be the mechanism driving the warmer and drier
estimates of digital leaf physiognomy.
This new multivariate method for reconstructing paleoclimates is in
agreement with other independent methods for estimating climate. Digital leaf
physiognomy can be easily used with fossil leaves – even when specimens are
fragmented – and is highly reproducible. A large, 95-site calibration has shown an
improvement in standard errors for MAT and MAP relative to leaf-margin analysis
and leaf-area analysis. Digital leaf physiognomy is a promising new method for
reconstructing paleotemperature and precipitation that is both more accurate and
functionally significant than univariate leaf climate models.
53
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67
68
Morphotype
FH2
FH2
FH2
FH2
FH2
FH2
FH3
FH3
FH3
FH3
FH3
FH3
FH3
FH3
FH10
FH10
FH10
FH10
FH10
FH10
FH10
FH10
FH11
FH11
FH12
FH12
FH12
FH15
FH15
FH20
FH20
FH26
FH26
FH26
FH26
FH26
FH26
FH26
FH26
Floral
zone
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
ND03-1
ND03-1
ND03-1
ND03-1
ND03-1
ND03-1
ND03-1
Locality
number
ND03-1.84
ND03-1.32
DSCN0635
DSCN0684
DSCN0686
ND03-1.41
M7
DSCN0516
DSCN0590
DSCN0591
DSCN0609
DSCN0755
DSCN0766
ND03-1.75
M3
DSCN0401
DSCN0406
DSCN0482
DSCN0601
ND03-1.86b
M10
rhamnus-1
rhamnus.go
DSCN0414
ND03-1.83
DSCN0699
M12
M12_go2
ND03-1.48
DSCN0773
DSCN0761
DSCN0763
DSCN0546
DSCN0661
DSCN0666
DSCN0732
DSCN0733
DSCN0782
DSCN0544
T
T
T
T
T
T
T
T
E
E
E
E
E
E
E
E
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
T
T
E
E
E
E
E
T
T
T
T
T
T
T
T
Specimen number Margin
0.038
0.052
0.106
0.13
0.10
0.08
0.036
0.002
0.017
0.168
0.112
0.17
0.06
0.07
0.15
0.10
0.63
0.044
0.309
0.639
0.607
0.13
0.19
0.19
0.15
0.20
0.21
0.12
0.076
13.92
39.07
39.99
53.30
48.95
21.95
53.57
15.62
6.51
7.85
29.29
37.11
66.20
61.91
46.15
80.12
74.08
73.19
24.46
67.31
71.72
25.57
16.24
13.42
17.22
24.83
8.27
11.35
7.83
8.17
27.23
51.07
28.94
32.56
48.36
Inferred
Petiole
blade area
2
area (cm )
(cm2)
0.35
0.11
Petiole
width
(cm)
1.57
1.72
13.89
12.03
7.89
6.78
9.77
9.21
19.22
6.46
10.37
13.78
4.65
1.57
3.63
11.07
4.43
Raw
blade
area
(cm2)
11.50
6.03
31.15
23.63
13.25
22.64
32.53
17.69
23.22
20.79
23.19
26.39
19.87
9.08
12.21
21.17
13.62
Raw
perimeter
(cm)
6.23
9.23
9.57
9.51
8.80
6.99
11.45
4.77
3.38
3.28
9.17
10.31
10.26
9.77
9.78
12.83
12.48
10.06
7.00
11.60
12.56
7.74
7.44
9.60
9.57
9.99
6.90
8.00
6.45
7.67
8.92
9.91
7.28
7.60
8.62
Major
length
(cm)
8.57
3.74
25.00
19.35
6.39
8.90
22.66
10.66
11.79
13.82
16.55
15.73
17.12
7.25
8.28
11.65
7.28
Length of
cut
perimeter
(cm)
11.28
5.83
30.11
22.92
12.62
21.70
31.35
17.32
22.30
20.23
22.76
26.03
19.63
8.97
12.05
20.45
13.52
Raw
internal
perimeter
(cm)
1.54
1.68
13.59
11.84
7.63
6.68
9.62
9.04
18.95
6.39
10.35
13.61
4.55
1.55
3.60
10.91
4.39
Raw
internal
blade area
(cm2)
3
3
7
4
4
11
13
7
20
8
0
0
0
0
0
0
0
0
7
2
4
3
3
14
3
1
0.035
0.04
0.304
0.186
0.268
0.106
0.147
0.175
0.271
0.072
0
0
0
0
0
0
0
0
0.025
0.165
0
0.1
0.02
0.037
0.167
0.043
Tooth
# 1o # 2o
teeth teeth area (cm2)
Appendix 1. Specimen values for measured physiognomic variables. Floral zones: FHI = Fox Hills I, WBI = Williston Basin I, WBII = Williston Basin II, WBIII = Williston Basin III, HBI = Hanna
Basin I, HBII = Hanna Basin II. Morphotypes represent unique leaf types based on leaf characters. Locality/specimen numbers: ND03-1 = North Dakota Geological Society location/specimen number
of specimens collected in 2002 from the Linton, North Dakota site, A140 = St. Lawrence University accession number for specim ens collected in 1973 from the Linton, North Dakota site, DMNH =
DMNS = Denver Museum of Nature and Science, YPM = Yale Peabody Museum. Note that some specimen from the Fox Hills and Fort Un ion Formations have no museum numbers and instead their
file names are listed. Margin: E = entire, T = toothed, E/T = species includes both toothed and entire specimens. To retain normal data distributions, toothed characters were excluded from untoothed
specimens (blank in table), with the exception of species that include both toothed and untoothed specimens. In addition, too thed specimens with teeth too small to discern in images were assigned a
tooth area value of zero and a perimeter ratio value of one, and the number of teeth was left blank.See Table 1 for definitions of physiognomic variables.
69
Morphotype
FH27
FH28
FH30
FH30
FH35
FH36
FH36
FH40
FH41
FH49
FH49
FH50
FH51
FH51
FH58
FH61
FH61
FH63
FH64
FH64
FH70
FH71
FH72
LM4
LM4
LM4
LM4
LM4
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
LM5
Floral
zone
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
FHI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
ND03-1
ND03-1
A140
ND03-1
DP0517
DP0517
DP0517
DP0517
DP0517
DP0525
DP0553
DP0553
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
DP0716
ND03-1
ND03-1
ND03-1
ND03-1
A140
A140
A140
ND03-1
ND03-1
ND03-1
Locality
number
ND03-1.40
DSCN0551
DSCN0567
DSCN0568
DSCN0613
DSCN0649
DSCN0651
ND03-1.52
ND03-1.58
DSCN0789
DSCN0804
ND03-1.69
ND03-1.65
ND03-1.35
ND03-1.105
A140 11-12
A140 11-12
A140.10
bigleaf1
ND03-1.49
ND03-1.1
A140.3
ND03-1.62
YPM80063
YPM80086
YPM80092
YPM80098
YPM80101
70
42
YPM175086
49
53
54
55
56
57
58
YPM8218
YPM53467
YPM80235
YPM175008a-2
YPM175009-2
YPM175010-2
YPM175011-1
YPM175013-1
YPM80219-2
T
E
T
T
T
T
T
T
T
T
T
T
T
T
E/T
E
E
T
E
E
E
E
E
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
Specimen number Margin
0.213
0.211
0.02
0.07
0.15
0.25
0.043
0.05
0.11
0.22
0.26
0.506
0.029
0.17
0.23
0.029
0.14
0.041
0.062
0.12
1.416
0.004
1.008
43.93
43.66
3.69
9.79
9.06
34.75
22.88
11.31
30.43
46.39
9.41
46.08
11.44
44.01
22.84
15.54
38.60
21.71
29.71
35.63
12.66
20.46
21.35
16.22
41.06
22.75
22.17
35.56
27.16
50.29
45.19
1.27
0.95
18.66
17.80
46.81
3.65
6.67
6.66
12.03
33.99
40.25
3.18
Inferred
Petiole
blade area
2
area (cm )
2
(cm )
0.28
0.14
0.06
0.04
0.13
0.15
0.09
0.07
0.03
0.1
0.13
0.05
0.26
0.07
Petiole
width
(cm)
21.84
7.72
18.97
19.46
11.78
3.05
16.82
8.23
6.11
22.13
14.36
10.18
18.70
24.45
15.10
24.45
20.53
26.24
15.85
18.11
25.33
11.50
16.03
26.72
12.55
22.97
11.67
9.81
4.31
4.53
14.74
17.22
5.82
20.59
7.67
16.07
9.93
5.84
12.36
5.15
7.21
18.00
5.99
8.16
5.77
9.49
11.90
13.45
19.97
20.42
3.94
4.28
15.13
21.22
5.56
4.99
21.77
16.40
1.57
3.11
2.77
15.35
17.67
0.34
0.79
3.10
3.30
0.66
1.03
4.46
9.23
1.71
33.38
Raw
perimeter
(cm)
Raw
blade
area
2
(cm )
16.43
12.36
12.26
3.92
5.43
4.25
9.04
6.09
5.14
6.97
9.06
3.98
8.24
4.21
8.58
6.17
5.68
8.31
5.80
7.19
7.36
5.69
6.59
5.27
5.63
8.05
5.83
6.19
7.34
6.83
10.72
8.55
2.59
2.36
8.53
7.07
10.26
3.04
3.50
3.62
4.82
7.65
7.92
2.02
Major
length
(cm)
4.69
9.05
8.82
5.71
8.03
14.22
9.80
4.41
11.96
16.46
6.36
4.40
8.82
17.84
5.88
7.80
17.01
4.77
12.02
13.31
4.90
11.88
3.69
5.65
6.44
9.71
11.86
12.13
2.44
2.21
11.87
17.63
2.59
2.67
18.87
9.19
Length of
cut
perimeter
(cm)
23.70
7.34
17.10
18.78
11.24
19.65
21.52
14.08
10.03
18.10
23.71
14.19
22.74
19.72
24.84
14.68
17.04
23.11
10.92
15.34
25.45
11.95
22.20
5.28
9.43
11.58
13.17
18.41
18.21
3.20
4.01
14.83
20.91
5.08
4.68
21.53
15.22
Raw
internal
perimeter
(cm)
32.21
2.82
16.04
7.81
5.91
10.98
9.43
4.18
4.48
14.33
16.71
5.48
19.78
7.49
15.49
9.27
5.28
11.47
4.57
6.74
17.53
5.67
7.85
1.61
1.56
3.04
2.70
14.04
14.98
0.32
0.76
3.04
3.26
0.55
0.95
4.42
7.81
Raw
internal
blade area
2
(cm )
15.95
3
12
4
8
12
6
5
3
7
7
5
23
16
6
5
4
5
4
2
3
9
9
1
3
12
7
5
5
5
3
9
13
5
3
4
3
16
1
1
1
1
1
1
1
1
4
4
1
1
0.388
0.136
0.047
0.411
0.514
0.339
0.808
0.183
0.581
0.66
0.552
0.889
0.58
0.472
0.477
0.321
0.308
0
0.69
0
0.239
0.782
0.42
0.202
0.099
0.014
0.066
0.065
1.309
2.69
0.021
0.022
0.054
0.041
0.104
0.082
0.04
1.42
0.478
o
o
Tooth
#1
#2
2
teeth teeth area (cm )
70
Morphotype
LM5
LM5
LM5
LM6
LM6
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM10
LM17
LM17
LM17
LM17
LM17
LM17
LM17
LM22
LM22
LM26
LM26
LM26
LM29
LM29
LM29
LM32
LM35
LM39
LM39
LM39
LM39
LM39
LM39
LM39
LM39
LM39
LM39
Floral
zone
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
DP0716
DP0717
DP0717
DP0527
DP0527
DP0517
DP0525
DP0527
DP0553
DP0553
DP0717
DP0717
DP0739
DP0739
DP0739
DP0739
DP0739
DP0739
DP0739
DP0748
DP0517
DP0527
DP0527
DP0527
DP0527
DP0711
DP0711
DP0739
DP0739
DP0428
DP0525
DP0525
DP0716
DP0716
DP0716
DP0504
DP0525
DP0520
DP0520
DP0525
DP0525
DP0525
DP0553
DP0553
DP0553
DP0715
DP0716
Locality
number
YPM80248a-4
60
61
65
66
YPM80010
71
YPM53487-2
42
51
59
YPM175038-2
39
40
41
YPM17593a
YPM175090a
YPM175093a
YPM175095
YPM175156a
YPM80079
67
YPM175030
YPM175031
YPM53487
68
69
YPM175091
YPM175094
44
74
YPM175145
52
YPM175005a-1
YPM175016a-2
YPM80214
YPM78681-1
YPM175021-2
YPM8655-3
72
73
YPM78659
43
YPM175087a
YPM175087b
45
50
T
T
T
E/T
E/T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
E
E
E
E
E
E
E
T
T
T
T
T
E
E
E
T
T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
Specimen number Margin
0.238
0.024
0.188
0.088
0.12
0.19
0.13
0.09
0.14
0.082
0.068
0.028
0.16
0.13
0.21
0.15
0.14
0.1
0.15
0.08
0.14
0.014
0.101
0.122
0.1
0.13
0.11
0.122
0.065
0.089
0.19
33.65
4.69
14.64
19.61
9.63
76.28
52.87
17.17
45.42
25.34
23.50
16.60
15.59
24.28
26.60
31.31
28.20
29.43
41.41
23.55
8.77
5.15
12.20
5.57
13.85
17.82
14.38
19.21
8.12
15.27
45.78
59.97
45.68
18.07
40.59
36.53
35.49
22.92
19.20
11.75
35.54
5.36
14.26
44.33
18.23
26.33
37.89
Inferred
Petiole
blade area
2
area (cm )
2
(cm )
0.29
0.16
0.18
0.08
0.15
0.19
Petiole
width
(cm)
6.84
14.67
35.56
6.27
2.08
26.62
22.01
15.98
25.33
13.26
14.41
28.37
18.69
9.11
13.53
12.20
16.89
43.06
18.36
16.34
24.42
20.72
5.79
10.75
13.10
15.92
18.46
14.05
21.05
16.10
25.54
19.89
Raw
perimeter
(cm)
17.43
9.39
11.00
3.26
7.28
18.09
Raw
blade
area
2
(cm )
16.36
1.59
5.09
6.38
4.26
17.15
10.48
2.78
15.28
7.67
1.79
5.94
2.53
5.48
6.80
5.29
12.48
4.26
10.44
7.00
7.31
3.21
5.00
5.52
3.82
11.65
12.87
6.79
10.99
8.54
7.78
5.94
6.61
7.53
8.97
8.92
6.78
8.37
9.85
7.26
6.33
4.82
6.94
5.17
6.81
8.04
6.90
7.70
5.68
6.60
11.25
11.32
9.44
7.00
8.85
10.01
9.26
8.07
7.58
6.20
9.37
3.63
5.91
12.33
8.59
8.21
12.35
Major
length
(cm)
9.37
10.52
3.50
11.03
5.35
14.46
6.90
6.15
15.55
Length of
cut
perimeter
(cm)
8.55
4.69
8.30
4.90
10.17
29.45
11.67
12.12
15.89
13.56
4.16
5.26
9.10
10.03
13.51
9.43
9.12
12.69
17.48
12.94
13.89
31.68
6.23
21.59
14.14
23.60
12.67
13.89
28.08
Raw
internal
perimeter
(cm)
17.47
8.60
13.07
11.77
16.70
40.63
17.05
15.56
23.18
19.68
5.77
10.46
12.95
15.71
17.81
13.25
17.61
15.46
22.95
17.50
6.53
24.99
2.05
16.76
8.82
10.72
3.12
7.15
17.97
Raw
internal
blade area
2
(cm )
15.59
1.46
4.90
6.13
4.18
16.79
10.09
2.72
15.04
7.48
1.77
5.78
2.51
5.44
6.63
5.18
11.72
4.17
10.17
6.77
7
4
0
3
0
0
0
7
0
4
0
24
20
11
3
10
4
4
4
3
14
9
14
23
18
2
5
3
2
9
9
17
8
16
20
1
1
3
5
4
1
8
1
1
3
4
1
3
0
0.67
0.563
0
0.026
0
0
0
1.621
0
0.305
0
0.283
0.132
0.134
0.116
0
0.773
0.13
0.196
0.25
0.08
0.361
0.396
0.06
0.245
0.189
0.016
0.157
0.021
0.037
0.175
0.112
0.764
0.086
0.272
0.224
o
o
Tooth
#1
#2
2
teeth teeth area (cm )
71
Morphotype
LM39
LM39
LM39
LM39
LM39
LM39
LM47
LM47
LM92
LM92
LM93
LM99
LM100
LM104
LM117
LM121
LM122
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM5
LM5
LM5
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM6
Floral
zone
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBI
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
DP0739
DP0553
DP0513
DP0513
DP0513
DP0513
DP0513
DP0513
DP0513
DP0515
DP0518
DP0518
DP0518
DP0728
DP0728
DP0734
DP0513
DP0513
DP0515
DP0513
DP0513
DP0513
DP0513
DP0513
DP0513
DP0513
DP0518
DP0518
DP0518
DP0518
DP0518
DP0722
DP0722
DP0722
DP0722
DP0722
DP0722
DP0503
DP0527
DP0527
DP0527
DP0526
DP0504
DP0520
DP0717
DP0517
Locality
number
YPM175103 holo
YPM175088
4
YPM80047
YPM80053b
YPM80054
YPM80069
YPM80140
YPM80128
YPM78645
YPM80176
YPM80181
YPM80103b
YPM175042-1
YPM175049
62
8
9
YPM78650
3
5
YPM80046
YPM80056
YPM80119a
YPM8012b
YPM80133
YPM8017
YPM80165
YPM80166
YPM80169
YPM80182
46
47
48
YPM175088
YPM175083a
YPM175084
YPM52360a
YPM78685-1
YPM175028-1
YPM78687-1
YPM175027-2
YPM175189
YPM175022a-2
YPM175081-2
YPM80039b
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
T
T
T
T
T
T
T
E
E
Specimen number Margin
0.114
0.1
0.249
0.006
0.3
0.047
0.033
0.162
0.103
0.14
0.1
0.2
0.13
0.13
0.12
0.1
0.334
0.53
0.22
0.15
0.209
0.15
0.318
0.034
0.05
0.08
0.27
0.198
52.90
32.38
47.24
23.57
28.90
108.78
148.12
235.63
29.75
45.75
22.47
101.99
6.73
12.80
35.91
10.90
19.20
9.16
15.02
5.49
11.44
19.98
42.24
16.93
23.57
16.19
16.04
10.55
47.00
61.47
5.64
22.97
13.68
43.99
26.68
27.01
7.40
28.05
31.38
38.53
12.41
5.18
10.01
Inferred
Petiole
blade area
2
area (cm )
2
(cm )
0.14
0.17
0.06
0.15
0.18
Petiole
width
(cm)
18.70
4.38
12.80
26.18
13.80
15.44
18.28
14.83
11.21
9.38
10.94
15.97
31.22
15.17
13.87
11.38
14.13
19.45
19.06
36.78
21.21
30.55
34.56
23.37
21.44
43.80
22.85
33.16
17.93
27.86
19.60
14.78
9.12
8.06
11.94
7.93
14.24
10.45
28.86
Raw
perimeter
(cm)
2.34
9.92
13.68
10.51
9.88
4.58
3.04
12.85
18.85
78.74
11.72
21.10
40.33
13.72
12.29
38.12
24.02
40.78
9.25
16.67
8.94
11.49
1.99
1.49
1.37
1.46
6.23
3.03
14.82
Raw
blade
area
2
(cm )
10.86
9.41
9.47
12.40
9.92
14.52
15.70
25.03
7.82
8.72
5.58
12.85
4.35
5.30
8.14
4.60
5.22
4.81
5.20
4.33
5.15
6.32
8.79
4.96
6.34
5.20
5.26
6.88
12.60
10.16
3.97
6.40
6.62
10.98
8.85
6.67
4.77
8.11
8.66
8.09
4.43
4.45
6.01
Major
length
(cm)
9.98
6.31
4.35
11.16
1.49
7.17
4.37
21.11
2.27
5.02
4.80
8.79
7.79
9.43
24.15
13.90
12.47
9.78
7.28
11.91
23.60
15.57
17.96
10.44
16.47
9.92
6.83
5.20
4.53
8.58
5.90
6.86
4.18
11.82
Length of
cut
perimeter
(cm)
23.72
13.68
14.89
16.78
14.65
10.59
14.61
30.40
15.14
13.47
11.18
13.78
17.81
18.45
35.45
21.03
29.15
31.67
23.02
20.79
42.19
21.80
30.62
15.99
27.16
19.02
14.18
8.40
7.65
11.78
7.84
14.02
9.76
27.63
Raw
internal
perimeter
(cm)
17.34
4.30
12.47
8.77
11.08
2.26
9.41
13.26
10.48
9.65
4.47
3.00
12.54
18.11
77.80
11.57
20.56
39.22
13.58
12.03
37.39
23.16
38.51
8.64
16.46
8.77
11.17
1.94
1.45
1.34
1.44
6.09
2.83
14.42
Raw
internal
blade area
2
(cm )
5
9
6
1
3
2
0
0
4
0
8
0
6
1
5
3
22
6
10
3
16
26
4
10
18
3
7
9
5
9
0
0
0
0
0
1
17
9
10
7
9
9
21
3
2
1
2
5
3
1
1
0.037
0.31
0.742
0.935
0.145
0.541
1.109
0.145
0.26
0.736
0.8662
2.267
0.607
0.203
0.173
0
0.077
0.516
0.423
0.031
0.234
0.11
0
0
0.131
0
0.612
0
1.358
0.08
0.329
0
0
0
0
0
0.316
0.05
0.037
0.026
0.025
0.138
0.201
0.405
o
o
Tooth
#1
#2
2
teeth teeth area (cm )
72
Morphotype
LM6
LM6
LM6
LM6
LM7
LM7
LM7
LM7
LM7
LM7
LM7
LM10
LM12
LM13
LM13
LM13
LM14
LM14
LM14
LM15
LM15
LM15
LM15
LM15
LM15
LM15
LM36
LM37
LM38
LM38
LM38
LM38
LM41
LM44
LM48
LM94
LM106
LM106
LM106
LM106
LM107
LM109
LM109
Floral
zone
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
WBII
DP0513
DP0513
DP0535
DP0515
DP0728
DP0728
DP0513
DP0513
DP0515
DP0515
DP0513
DP0513
DP0515
DP0515
DP0515
DP0515
DP0513
DP0728
DP0728
DP0728
DP0728
DP0513
DP0518
DP0728
DP0728
DP0728
DP0728
DP0728
DP0513
DP0535
DP0515
DP0515
DP0515
DP0535
DP0535
DP0535
DP0515
DP0515
DP0515
DP0515
DP0515
DP0515
DP0515
Locality
number
YPM1750446-3
YPM175048
YPM175051
YPM175053-3
7
YPM80159
63
64
YPM175051
YPM1750447
YPM175053-4
6
YPM78691
YPM80145c
YPM80264a
YPM175188
38
YPM175110
YPM78615
YPM78649
YPM80023
YPM80262
YPM80266
YPM80145b
YPM175098
YPM53466 holo
YPM80257
exemplar
YPM80261
exemplar
2
YPM80145a
YPM175159
YPM78654
YPM78672a
exemplar
YPM78660 holo
YPM175052
YPM175043-1
YPM175129
YPM80136
YPM175097
YPM80022
YPM175123
YPM175124a
exemplar
YPM175128
T
T
E
T
E
T
T
T
T
T
E
T
T
T
T
T
T
E/T
E/T
E/T
E/T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
Specimen number Margin
0.147
0.049
0.037
0.047
0.053
0.09
0.1
0.06
0.07
0.07
0.1
0.09
0.19
0.16
0.14
0.11
0.08
0.05
0.152
0.047
0.256
0.037
0.021
0.572
0.331
0.14
0.13
0.1
0.17
0.107
0.206
0.256
30.53
23.75
30.99
34.48
22.30
17.01
8.76
36.22
17.34
32.91
18.99
50.49
35.93
53.64
55.32
4.20
29.15
25.23
21.86
27.16
52.12
19.61
35.86
17.64
10.63
5.51
16.75
14.66
14.94
7.67
5.29
12.36
36.63
15.77
9.32
39.72
10.67
13.10
Inferred
Petiole
blade area
2
area (cm )
2
(cm )
0.12
0.18
Petiole
width
(cm)
26.41
15.57
20.05
28.97
16.91
20.82
20.53
6.85
22.87
14.16
9.90
5.85
8.94
5.74
14.38
10.84
35.79
25.20
35.03
36.63
9.72
5.23
10.37
13.45
23.22
19.10
11.80
22.85
9.13
12.49
9.62
24.80
16.28
16.56
12.92
21.85
13.00
7.42
6.72
16.88
8.98
15.31
19.59
25.84
18.57
33.65
34.40
Raw
perimeter
(cm)
11.02
5.10
26.88
21.84
31.27
22.48
2.31
Raw
blade
area
2
(cm )
1.16
3.94
2.11
18.68
8.80
8.44
16.14
4.73
4.69
4.84
23.95
5.14
6.03
4.18
12.99
5.13
2.42
2.35
3.64
2.21
8.36
7.73
7.08
10.02
21.18
24.13
10.22
9.54
6.79
8.10
6.44
5.16
6.70
12.01
7.19
11.60
7.09
12.24
10.88
13.00
15.74
2.82
9.70
9.85
8.86
11.37
13.26
8.20
7.56
7.35
4.56
4.10
7.08
6.40
7.38
3.58
3.48
4.19
7.50
4.98
3.62
7.89
4.01
4.36
Major
length
(cm)
19.91
9.48
12.89
10.70
6.57
10.04
12.29
3.75
5.57
17.68
12.83
15.83
24.31
5.59
Length of
cut
perimeter
(cm)
2.61
4.21
6.97
10.12
8.60
2.63
15.75
4.21
6.52
5.26
8.95
10.09
7.89
2.96
3.77
6.16
2.60
4.54
6.98
5.11
9.61
10.27
16.07
7.98
11.55
10.35
26.18
15.35
19.77
25.89
14.78
19.36
18.34
14.21
10.05
34.85
24.82
34.81
36.22
9.58
Raw
internal
perimeter
(cm)
5.12
9.65
13.26
22.26
18.17
11.40
22.06
8.93
12.27
9.43
23.66
15.01
15.97
11.35
18.22
11.26
7.03
6.36
16.22
8.35
14.25
17.43
24.45
16.49
26.43
29.10
8.87
5.65
6.62
22.22
13.68
9.76
5.39
10.92
4.96
26.38
21.52
30.98
22.37
2.26
Raw
internal
blade area
2
(cm )
1.11
3.54
1.94
17.64
8.61
8.22
15.79
4.61
4.31
4.75
22.69
4.99
5.90
3.57
10.96
4.26
2.37
2.32
3.58
2.01
7.81
7.01
6.76
8.98
18.84
22.28
19
5
4
21
13
5
12
6
3
17
13
12
7
3
1
6
2
6
31
4
4
3
2
9
9
15
5
8
11
3
14
11
17
7
5
11
8
7
25
22
1
2
6
8
1
2
4
1
0.064
0.093
0.232
0.651
0.473
0.144
0.455
0.097
0.135
0.5
0.314
0.288
0.108
0.05
0.05
0.396
0.175
1.033
0.187
0.225
0.345
0.122
0.386
0.091
1.256
0.143
0.133
0.614
2.023
0.868
0.056
0.032
0.064
0.206
0.544
0.721
0.318
1.037
2.344
1.851
o
o
Tooth
#1
#2
2
teeth teeth area (cm )
73
Morphotype
LM109
LM110
LM118
LM119
LM120
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM4
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM6
LM7
LM7
LM7
LM7
LM7
LM7
LM7
LM7
LM7
LM7
LM7
LM7
LM7
LM13
LM17
LM21
LM21
LM21
LM21
LM22
LM22
Floral
zone
WBII
WBII
WBII
WBII
WBII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
DP0732
DP0754
DP0754
DP0725
DP0732
DP0732
DP0732
DP0754
DP0724
DP0724
DP0725
DP0746
DP0746
DP0754
DP0754
DP0754
DP0754
DP0754
DP0724
DP0724
DP0724
DP0725
DP0725
DP0725
DP0725
DP0754
DP0754
DP0754
DP0754
DP0754
DP0754
DP0732
DP0725
DP0724
DP0724
DP0725
DP0513
DP0515
DP0515
DP0502
DP0725
DP0725
DP0725
DP0725
DP0513
Locality
number
YPM175060a,b-1
31
32
33
30
16
YPM175067
12
28
YPM175171
23
YPM175173
YPM175175a
YPM175177
YPM175179
18
F2
YPM175068b
13
15
YPM175056a-1
YPM175064-1
21
22
25
26
27
29
34
YPM78668-2
YPM78673a
YPM78673b
11
YPM175114a
exemplar
20
24
YPM175131a
YPM175130
exemplar
YPM175100a
YPM80024a
YPM52359
14
YPM175060a,b
YPM175054a
YPM175055
T
T
T
T
T
T
T
T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
E/T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
E
T
T
T
T
T
E
E
T
T
T
T
T
Specimen number Margin
0.592
0.031
0.017
0.02
0.133
0.103
0.18
0.16
0.07
0.06
0.07
0.07
0.06
0.018
0.181
0.057
0.16
0.11
0.09
0.16
0.15
0.015
0.008
0.379
0.072
0.083
0.1
0.11
0.14
0.11
0.08
0.24
0.12
0.063
0.736
0.17
0.511
11.64
5.58
12.98
29.81
21.98
164.61
50.76
36.48
4.47
4.19
3.71
8.90
7.47
7.78
13.19
8.93
11.95
27.07
11.91
9.88
5.57
24.50
10.64
10.69
25.52
16.04
19.24
19.62
28.27
40.83
19.32
12.08
16.02
30.27
30.48
31.96
12.07
16.76
17.54
26.31
38.67
30.18
22.34
39.77
Inferred
Petiole
blade area
2
area (cm )
2
(cm )
0.11
0.07
0.12
0.12
0.11
0.14
0.14
0.11
Petiole
width
(cm)
9.17
2.42
7.04
24.96
6.86
7.78
16.09
13.58
21.19
29.74
26.64
18.62
20.94
18.39
7.97
19.22
12.34
13.42
13.86
14.28
19.85
21.46
36.63
46.88
20.81
16.09
12.33
10.88
13.22
18.87
12.12
14.42
8.90
7.47
6.98
8.02
8.01
9.96
9.39
4.02
2.87
4.89
3.98
6.52
4.75
11.39
10.69
11.82
20.31
29.05
17.53
7.48
28.94
25.97
28.24
24.87
30.70
9.00
8.37
35.48
22.49
9.16
21.49
23.86
10.29
1.96
18.78
10.69
8.12
25.72
15.38
11.62
4.30
1.56
11.64
22.01
6.17
Raw
perimeter
(cm)
5.82
8.79
Raw
blade
area
2
(cm )
2.06
4.42
4.01
5.74
7.05
6.95
19.55
8.66
9.30
3.16
3.81
2.78
4.04
3.40
3.59
4.44
4.00
4.28
6.72
4.53
3.96
3.04
6.03
4.13
4.14
6.26
5.13
5.51
5.44
6.72
8.33
5.88
6.50
8.86
7.01
7.18
7.00
5.83
8.78
9.65
8.03
7.82
7.15
5.54
8.64
Major
length
(cm)
5.21
7.97
10.89
12.28
15.85
9.97
7.30
8.91
2.74
10.12
7.57
3.92
8.26
3.96
7.34
9.40
19.70
27.31
4.77
7.81
3.02
9.24
2.31
3.44
17.93
18.57
15.84
19.10
21.78
0.99
4.26
15.46
13.03
6.81
8.94
5.00
9.48
Length of
cut
perimeter
(cm)
4.02
14.97
11.78
18.62
28.52
25.83
17.84
19.65
17.79
7.47
19.05
12.01
12.54
13.47
14.02
19.23
20.32
35.42
45.72
20.34
15.96
11.63
10.44
13.08
18.63
12.06
13.99
28.50
25.61
27.73
24.60
30.06
8.35
8.24
34.45
22.42
9.02
20.18
11.51
21.35
Raw
internal
perimeter
(cm)
6.04
8.98
2.22
6.76
24.57
6.67
7.45
8.97
3.81
2.75
4.81
3.90
6.03
4.54
11.17
10.29
11.36
19.40
28.28
16.88
7.35
8.64
7.21
6.93
7.72
7.91
9.72
10.49
8.01
25.10
15.13
11.31
4.05
1.44
23.24
10.27
1.89
18.08
5.80
8.50
Raw
internal
blade area
2
(cm )
1.99
27
14
24
25
15
13
5
5
6
4
12
12
5
0
11
12
3
4
2
6
0
21
13
11
3
7
8
6
4
8
9
13
16
5
3
13
1
2
7
8
9
6
1
2
2
3
3
1
0.191
0.199
0.281
0.389
0.187
0.335
0.209
0.109
0.625
0.246
0.306
0.257
0.125
0
0.261
0.252
0.047
0.298
0.092
0.239
0
0.42
0.202
0.124
0.077
0.083
0.49
0.201
0.225
0.405
0.452
0.908
0.771
0.649
0.126
0.618
0.023
0.069
0.703
0.019
0.288
0.068
o
o
Tooth
#1
#2
2
teeth teeth area (cm )
74
Morphotype
LM22
LM22
LM22
LM22
LM24
LM24
LM24
LM24
LM25
LM25
LM25
LM25
LM25
LM25
LM31
LM31
LM31
LM33
LM48
LM48
LM97
LM97
LM103
LM105
LM113
LM113
LM113
LM113
LM113
LM114
LM114
LM124
HB1
HB2
HB5
HB7
HB8
HB9
HB10
HB11
HB12
HB13
Floral
zone
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
WBIII
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
DP0732
DP0741
DMNH1.2603
DMNH1.2603
DMNH1.2632
DMNH1.2629
DMNH1.2603
DMNH1.2603
DMNH1.2603
DMNH1.2603
DMNH1.2629
DMNH1.2629
DP0732
DP0732
DP0732
DP0732
DP0732
DP0754
DP0754
DP0754
DP0754
DP0724
DP0724
DP0724
DP0741
DP0724
DP0724
DP0724
DP0724
DP0724
DP0754
DP0536
DP0724
DP0724
DP0724
DP0724
DP0725
DP0725
DP0725
DP0724
DP0724
DP0732
Locality
number
YPM175176
YPM175178
YPM175180a
YPM175181a
YPM175073-1
YPM175076-2
YPM78675a-1
35
17
F1
YPM175069
YPM175071a
YPM175075
YPM78678
37
YPM175065
YPM175066a-1
19
YPM48689
YPM175062-2
YPM175057a-2
YPM175058-1
YPM175080a-1
YPM175078
YPM175116-1
YPM175118
paratype
YPM175118
paratype_bw
YPM175121a
holotype
YPM175121b
holotype
36
YPM175113
exemplar
YPM175162
DMNH23594
DMNH23595
DMNH23598
DMNH23600
DMNH23601
DMNH23602
DMNH23603
DMNH23604
DMNH23605
DMNH23606
E
T
T
T
E
T
E
E
E
E
E
E
T
E
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
E
E
T
T
T
E
T
Specimen number Margin
0.014
0.014
0.148
0.12
0.04
0.19
0.16
0.2
0.14
0.07
0.1
0.13
0.226
0.036
0.084
0.131
0.286
0.205
0.015
0.127
0.259
0.017
0.08
0.094
25.45
58.35
31.73
65.18
19.68
34.51
56.69
21.76
26.22
44.90
44.67
10.70
5.29
37.74
18.89
31.06
8.55
6.32
20.02
21.03
8.81
38.26
93.57
18.45
18.86
28.83
51.85
14.13
38.44
47.48
20.11
23.57
22.37
28.24
21.01
13.41
8.31
9.61
65.85
8.53
20.65
Inferred
Petiole
blade area
2
area (cm )
2
(cm )
0.11
0.11
0.1
0.06
0.06
0.1
0.06
0.08
0.08
0.17
0.1
0.1
0.05
0.09
Petiole
width
(cm)
14.48
20.01
18.61
27.69
19.90
21.76
8.20
12.61
7.58
3.23
1.39
13.96
29.03
7.81
12.81
18.13
3.49
5.04
20.08
16.08
6.12
9.35
6.90
23.21
Raw
blade
area
2
(cm )
5.41
3.97
8.64
11.90
4.29
41.08
21.21
28.54
40.85
21.49
28.44
21.32
24.92
18.71
7.55
5.94
21.90
30.67
12.05
23.17
42.50
16.62
9.82
24.74
30.53
15.68
21.82
22.53
28.52
23.07
22.65
23.36
28.82
14.31
Raw
perimeter
(cm)
8.45
12.36
7.48
13.39
6.71
11.05
10.23
6.15
8.59
11.45
10.29
6.07
4.10
12.81
9.11
14.53
5.33
4.70
7.86
6.30
4.40
8.92
15.11
4.86
5.38
6.88
9.77
4.41
8.71
9.14
5.91
5.77
5.71
6.66
7.71
6.99
3.90
4.28
12.70
8.20
10.22
Major
length
(cm)
22.48
11.37
18.51
25.44
5.10
10.72
9.78
12.32
9.85
4.78
2.34
16.05
12.69
6.88
8.49
24.16
9.42
5.31
8.72
19.74
9.19
13.88
15.40
9.66
Length of
cut
perimeter
(cm)
10.99
11.78
13.42
15.12
6.60
34.63
20.43
26.85
40.20
20.66
27.95
20.96
24.81
18.16
7.43
5.60
21.19
27.48
11.39
21.79
38.95
16.00
8.75
21.84
29.18
14.57
20.86
21.96
27.46
Raw
internal
perimeter
(cm)
17.82
18.94
20.86
26.34
13.34
13.94
19.38
18.12
27.40
19.66
21.62
8.06
12.54
7.47
3.20
1.36
13.76
26.20
7.51
12.42
17.39
3.25
4.73
19.34
15.46
5.67
9.07
6.75
22.42
Raw
internal
blade area
2
(cm )
5.18
3.73
8.25
11.38
4.14
41
11
10
12
18
12
8
3
10
8
5
7
8
7
18
32
5
7
22
19
5
10
6
9
37
35
20
23
16
1
2
2
1
1
1
2
3
2
1
2
2
0.538
0.629
0.484
0.289
0.244
0.137
0.139
0.067
0.11
0.029
0.036
0.194
0.231
0.235
0.383
0.522
0.152
0
2.824
0.302
0.392
0.738
0.244
0.306
0.746
0.628
0.446
0.278
0.148
0.792
o
o
Tooth
#1
#2
2
teeth teeth area (cm )
75
Morphotype
HB14
HB18
HB22
HB36
HB40
HB41
HB43
HB56
HB111
HB114
HB115
HB117
HB120
HB125
HB128
HB132
HB154
HB155
HB170
HB26
HB27
HB29
HB32
HB35
HB39
HB42
HB45
HB46
HB46
HB60
HB65
HB71
HB76
HB77
HB105
HB106
HB109
HB121
HB122
HB134
HB138
HB139
HB144
HB148
HB161
HB167
HB168
Floral
zone
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBI
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
HBII
DMNH1.2629
DMNH1.2603
DMNH1.2603
DMNH1.2632
DMNH1.2632
DMNH1.2632
DMNH1.2632
DMNH1.2632
DMNH1.2603
DMNH1.2603
DMNH1.2603
DMNH1.2632
DMNH1.2629
DMNH1.2632
DMNH1.2632
DMNH1.2629
DMNH1.589
DMNH1.589
DMNH1.2629
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2634
DMNH1.2634
DMNH1.2634
DMNH1.2634
DMNH1.2634
DMNH1.2634
DMNH1.2634
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2634
DMNH1.2634
DMNH1.2634
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2630
DMNH1.2725
DMNH1.2725
DMNH1.2724
Locality
number
DMNH23653
DMNH23658
DMNH23702
DMNH23669
DMNH23670
DMNH23698
DMNH23699
DMNH23702
DMNH22495
DMNH22496
DMNH24281
DMNH24284
DMNH24285
DMNH24287
DMNH24293
DMNH24344
DMNH24350
DMNH24351
DMNH23607
DMNH23611
DMNH23615
DMNH23629
DMNH23633
DMNH23634
DMNH23636
DMNH23649
DMNH23704
DMNH23707
DMNH23708
DMNH23710
DMNH23713
DMNH22499
DMNH24275
DMNH24279
DMNH24299
DMNH24338
DMNH24353
DMNH23619
DMNH23620
DMNH23622
DMNH23625
DMNH23628
DMNH23632
DMNH23635
DMNH23638
DMNH23639
T
E
T
T
T
E
T
T
T
E
E
E
T
T
E
T
E
E
E
T
T
E
E
T
E
E
T
T
T
E
T
E
E
T
E
T
E
E
E
T
T
T
T
E
T
E
T
Specimen number Margin
0.05
0.158
0.14
0.031
0.112
0.126
0.074
0.018
0.02
0.47
0.11
0.05
0.08
0.06
0.15
0.13
0.07
0.13
0.09
0.1
0.06
0.08
0.19
0.11
0.2
0.04
0.07
0.026
0.102
0.014
0.018
0.04
0.09
0.08
0.04
0.07
0.14
0.08
0.011
0.053
10.15
18.09
44.67
23.59
17.02
18.74
6.87
0.52
5.83
56.43
23.88
14.87
6.61
22.88
84.01
8.59
22.34
18.15
14.98
16.20
83.70
9.58
58.84
6.15
13.45
19.66
24.99
37.02
19.54
53.01
14.42
53.91
11.64
4.28
1.68
42.92
69.23
100.96
13.15
26.06
46.90
Inferred
Petiole
blade area
2
area (cm )
2
(cm )
0.19
0.09
0.09
0.07
0.18
Petiole
width
(cm)
25.95
7.14
2.08
10.73
16.87
8.22
14.64
28.16
31.22
10.42
30.56
21.54
5.32
4.87
3.63
4.73
10.69
23.32
2.96
21.04
7.23
1.10
27.32
25.01
7.97
11.69
14.96
26.15
5.42
7.82
6.31
15.50
7.52
12.41
40.45
2.58
4.29
15.23
1.14
2.63
18.82
20.50
25.65
34.43
Raw
perimeter
(cm)
8.76
9.61
22.15
Raw
blade
area
2
(cm )
29.67
6.02
7.43
11.72
11.03
7.10
9.03
4.09
1.41
6.62
13.57
7.72
6.92
6.24
9.40
12.68
5.13
8.69
7.98
4.82
7.99
17.37
5.27
9.43
5.16
6.58
8.05
8.84
11.37
9.90
10.37
7.43
13.78
5.71
4.46
2.09
9.72
17.91
14.76
5.21
9.31
10.28
Major
length
(cm)
13.26
3.46
7.42
5.78
9.85
14.99
13.61
6.07
2.60
23.12
16.69
4.48
10.90
15.63
7.87
15.83
3.58
6.04
27.61
9.93
9.78
7.38
Length of
cut
perimeter
(cm)
15.90
23.68
6.83
16.66
7.90
14.44
26.29
30.52
10.20
29.50
21.05
4.98
26.43
6.07
15.44
24.19
14.49
25.73
6.99
12.05
39.60
17.49
18.74
25.28
Raw
internal
perimeter
(cm)
33.65
10.34
1.99
4.79
3.60
4.70
10.36
23.04
2.92
20.51
6.95
1.02
11.58
1.11
2.62
7.73
5.23
7.78
2.47
4.21
14.95
8.28
9.29
22.00
Raw
internal
blade area
2
(cm )
29.30
14
5
3
2
2
17
8
5
3
8
3
17
9
2
10
7
8
2
5
6
4
20
13
14
1
1
0.391
0.093
0.077
0.032
0.027
0.338
0
0.278
0.043
0.53
0.28
0.078
0.108
0.025
0.006
0.238
0.188
0.046
0.109
0.081
0.278
0.484
0.314
0.153
0.371
o
o
Tooth
#1
#2
2
teeth teeth area (cm )