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Paleobotany |
2Department of Geosciences, Pennsylvania State University, University Park, Pennsylvania, 16802 USA; 3Florida Museum of Natural History, University of Florida, Gainesville, Florida 32611 USA
Received for publication September 29, 2004. Accepted for publication March 28, 2005.
ABSTRACT
The sizes and shapes (physiognomy) of fossil leaves are widely applied as proxies for paleoclimatic and paleoecological variables. However, significant improvements to leaf-margin analysis, used for nearly a century to reconstruct mean annual temperature (MAT), have been elusive; also, relationships between physiognomy and many leaf ecological variables have not been quantified. Using the recently developed technique of digital leaf physiognomy, correlations of leaf physiognomy to MAT, leaf mass per area, and nitrogen content are quantified for a set of test sites from North and Central America. Many physiognomic variables correlate significantly with MAT, indicating a coordinated, convergent evolutionary response of fewer teeth, smaller tooth area, and lower degree of blade dissection in warmer environments. In addition, tooth area correlates negatively with leaf mass per area and positively with nitrogen content. Multiple linear regressions based on a subset of variables produce more accurate MAT estimates than leaf-margin analysis (standard errors of ±2 vs. ±3°C); improvements are greatest at sites with shallow water tables that are analogous to many fossil sites. The multivariate regressions remain robust even when based on one leaf per species, and the model most applicable to fossils shows no more signal degradation from leaf fragmentation than leaf-margin analysis.
Key Words: climate proxies • leaf economics • leaf mass per area • leaf physiognomy • paleobotany • paleoclimate • paleoecology • paleotemperature
Paleontologists have long used the environmental sensitivity of plants to reconstruct paleoclimate from fossilized plant remains (Seward, 1892
; see Parrish, 1998
for summaries). In particular, the size and shape (physiognomy) of leaves have been widely used as proxies for temperature and moisture variables (e.g., Bailey and Sinnott, 1915
; Dilcher, 1973
; Wolfe and Upchurch, 1987
; Parrish and Spicer, 1988
; Greenwood and Wing, 1995
; Wolfe, 1995
; Wing et al., 2000
; Wilf et al., 2003
). Leaf-margin analysis, the oldest and most reliable physiognomic technique, is based on the observation in present-day forests that the percentage of woody dicotyledonous species in a flora whose leaf margins are untoothed (here termed "margin percentage") correlates significantly with mean annual temperature (MAT) (Bailey and Sinnott, 1915
, 1916
; Wolfe, 1979
; Wilf, 1997
). Because leaf physiognomy reflects convergent responses to climate in different lineages (Wolfe, 1993
; Greenwood et al., 2004
), leaf-margin analysis can be used even when precise systematic placement of fossil leaf species is not possible (Spicer and Parrish, 1986
; Wolfe and Upchurch, 1987
; Parrish et al., 1998
). The technique continues to be important in recent literature (e.g., Utescher et al., 2000
; Wing et al., 2000
; Wilf et al., 2003
).
Despite its wide use, leaf-margin analysis is based on only one character state, the presence or absence of teeth. If leaf size and shape were more fully described, significant improvements should be possible. To this end, Wolfe (1993
, 1995
) developed a method involving 31 leaf character states, including margin percentage, called the Climate-Leaf Analysis Multivariate Program (CLAMP). This approach uses ordination techniques, such as canonical correspondence analysis, to correlate leaf character states with temperature and moisture variables. In theory, because leaf physiognomy is described more fully by CLAMP, more accurate predictions of MAT should result. However, in practice CLAMP, for considerably more effort, generally yields MAT predictions that are no more accurate than leaf-margin analysis (Jacobs and Deino, 1996
; Wilf, 1997
; Wiemann et al., 1998
; Gregory-Wodzicki, 2000
; Kowalski and Dilcher, 2001
). This is because, at least in part, some of the CLAMP character states are not defined with sufficient precision, leading to variable scoring of the same leaves by different investigators (Wilf, 1997
; Wiemann et al., 1998
; Wilf et al., 1999
).
In an attempt to improve upon leaf-margin analysis without the scoring imprecision of CLAMP, a new procedure for quantifying leaf physiognomy primarily based on computerized image analysis was introduced in a preliminary paper (Huff et al., 2003
). This technique, known as digital leaf physiognomy, offers two important advantages over leaf-margin analysis and CLAMP. First, subjectivity and irreproducibility in the data collection process are largely removed because fixed algorithms process most of the measurements. Second, digital leaf physiognomy uses continuous variables, such as number of teeth and tooth area, in contrast to the discrete, usually binary character states native to leaf-margin analysis and CLAMP (Huff et al., 2003
).
Huff et al. (2003)
investigated two temperate floras and one humid tropical flora. Significant differences were apparent between the tropical and temperate sites for several character states, including the ratio of tooth area to blade area, shape factor (a modified area to perimeter ratio; see Table 1 for definition), and tooth count (which does not require computerized measurement), suggesting that the paleoclimatic potential of their new approach should be tested further. Because only three sites were investigated, Huff et al. did not quantify correlations among sites between physiognomy and climate.
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; Richards, 1996
; Wright and Westoby, 2002
), thick fossil leaves, in combination with other characters, are associated with long leaf lifespan (e.g., Wilf et al., 2001
). Also, a high frequency of drip tips in fossil floras has been used as evidence for tropical rainforest vegetation (e.g., Spicer, 1989
; Johnson and Ellis, 2002
). However, these ecological proxies are difficult to quantify accurately in fossils.
In living floras, there is growing recognition that leaf ecological traits such as lifespan, mass per area, and nitrogen content correlate with one another worldwide (Field and Mooney, 1986
; Reich, 1992
; Reich et al., 1992
, 1997
, 1999
; Wright et al., 2004
); like the leaf-climate correlations, these do not appear to be strongly influenced by phylogeny (Ackerly and Reich, 1999
). Leaves with long lifespans tend to have a high mass per area, low nitrogen and phosphorus content, and low photosynthetic and dark respiration rate (e.g., Wright et al., 2004
). Insect herbivory also appears to be related to this trait array: feeding is more intense on leaves with short lifespans and high nitrogen contents, which are generally associated with low concentrations of qualitative defensive compounds and low leaf toughness (Coley, 1983
, 1988
; Coley et al., 1985
; Lowman, 1992
; Basset, 1994
). The convergent relationships among these leaf ecological variables form what has been termed a "leaf economics spectrum" (Wright et al., 2004
), running from "quick" to "slow" returns on nutrient investments.
An improved understanding of leaf economics in the geologic past would add an important dimension for interpreting ancient terrestrial ecosystems by comparison to modern analogs (Falcon-Lang, 2000
; Wilf et al., 2001
). However, proxies are lacking for leaf economic variables that cannot be directly measured in fossils, such as mass per area and nitrogen content. Leaf physiognomy represents a potential proxy for leaf economics because many of the selective filters that determine leaf economic traits, namely the optimization of carbon, water, and mineral nutrient fluxes (e.g., Wright et al., 2004
), also influence leaf physiognomy (Webb, 1968
; Vogel, 1970
; Lewis, 1972
; Parkhurst and Loucks, 1972
; Givnish, 1979
; Richards, 1996
).
Here, we test the potential of digital leaf physiognomy as a proxy for climate and leaf economics at a statistically significant number of localities by expanding the Huff et al. (2003)
pilot study from three to 17 sites. The primary objectives are to (1) develop regression models for predicting MAT using variables derived from digital leaf physiognomy, (2) investigate preliminary correlations between leaf physiognomy and leaf economics, and (3) assess the potential of these correlations as paleoclimatic and paleoecological proxies to be used on fossil floras. This assessment includes testing how many leaves per species and species per site are required to achieve reliable predictions, as well as evaluating the physiognomic variables that show the most potential for application to fragmentary fossil leaves. Finally, because both the climatic and ecological proxies are based on correlations with leaf physiognomy, we test their statistical independence.
MATERIALS AND METHODS
Seventeen living floras were sampled, resulting in 572 species-site pairs and 1423 photographed leaves (Fig. 1; Table 2). The sampled floras were derived from two sources, the first being 14 sites from eastern North America (Fig. 1). The MAT of these sites ranges from 5.6–25.8°C (Table 2). Between 15 and 31 native species of dicotyledonous trees and shrubs were sampled from each site by E. A. K. and D. L. D. between September and December of 1998, 2000, and 2001. Fifteen to 50 representative leaves per species were collected, pressed, and dried; the vouchers are housed at the Florida Museum of Natural History (see Kowalski and Dilcher, 2003
for details). A subsample of 3–6 leaves (or leaflets, in the case of compound leaves) from each species per site with completely or nearly completely intact margins was photographed digitally at 2048 x 1536 pixels resolution (Nikon Coolpix 995 camera, Nikon, Melville, New York, USA).
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(italicized in Table 2), for which one digital image per species was available. Repository information is given in Huff et al. (2003)
. Two of the sites, Allegheny National Forest and York County, Pennsylvania, were derived from field transects as detailed in Wilf (1997)
(see Fig. 1). Leaves from the third site, on Barro Colorado Island, Republic of Panama, were collected from a one hectare plot by R. Burnham (University of Michigan) and S. Wing (National Museum of Natural History, Smithsonian Institution), as described in Wilf (1997)
.
The digitized leaves were manipulated using Adobe Photoshop 8.0 (Adobe Systems, San Jose, California, USA) as described in Huff et al. (2003)
and outlined here briefly. First, damaged margins were restored and shadows removed. The petiole was then removed so that it would not interfere with the subsequent measurements. Next, leaf teeth were selected; because no computer algorithm can reliably detect leaf teeth at the required resolution, teeth were selected manually before being measured by a computer. Tooth selection follows the protocols of Huff et al. (2003)
, except in a few cases when the protocols were found to be imprecise. New rules were developed to increase the reproducibility of tooth selection and are summarized in Appendix S1 (see Supplemental Data accompanying the online version of this article). Due to the minor revisions in tooth selection methods, all of the images from the Huff et al. (2003)
study were reanalyzed. All leaf images studied here are available at servers linked from P. W.'s web site (www.geosc.psu.edu/
pwilf) or can be requested from D. L. R. or P. W. Authorities for binomial nomenclature are given by Croat (1978)
for Barro Colorado Island and by USDA (2004)
for all other sites.
For each photographed leaf, separate images were prepared of the petiole, leaf blade, leaf teeth, and leaf blade minus the leaf teeth. Image sizes were calibrated using the photographed scale. Image detection algorithms native to Sigma Scan Pro 5.0 (SPSS Science, Chicago, Illinois, USA) were then used to calculate the following variables: blade area, perimeter, internal perimeter, feret diameter, compactness, shape factor, major axis length, minor axis length, and tooth area (see Table 1 for definitions). These are the same variables measured by Huff et al. (2003)
except for internal perimeter, which is new here. The number of teeth was determined visually. All other physiognomic variables (Table 1) were derived from these primary data.
Species means for each physiognomic variable were calculated based on the 1–6 images captured per species. Site means were then derived from the species means. For variables involving teeth, untoothed species were removed to retain normal distributions of data (Huff et al., 2003
). Site medians, minima, maxima, means with the 5% tails removed (±2 SD), and natural logs of means were also computed.
The resulting physiognomic data were correlated with climate variables (Table 2) using single and multiple linear regression (SPSS 12.0; SPSS Science) and canonical correspondence analysis (Canoco 4.5; Microcomputer Power, Ithaca, New York, USA; see Ter Braak, 1987
). Two criteria were used to select multivariate models: all predictor variables were required to be significant at the 
= 0.05 level and not to show a high degree of collinearity with the other predictor variables (variance inflation factor <10; Sokal and Rohlf, 1995
). Correlations with rainfall variables were not pursued because of their limited range at the test sites (Table 2).
Computer code was developed in Mathematica 5.0 (Wolfram Media, Champaign, Illinois, USA) to subsample the physiognomic data randomly many times (N = 10 000 adopted here) at a designated number of leaves per species or of species per site. This program was used to test the sensitivity of MAT predictions to sample size.
All leaf economic data were derived from the eastern North American transect discussed earlier (14 sites; 338 species-site pairs; 1185 leaves). Leaf mass per area was calculated from the dry mass and area of the leaf blade, including the petiole (Cornelissen et al., 2003
). Nitrogen content was determined for one leaf per species per site. Approximately 3.5 mg of dry leaf tissue were cored from the leaf center and measured for % nitrogen on a Carlo Erba 1108 elemental analyzer (Thermo Electron, Milan, Italy). The nitrogen content analyses should be considered preliminary because they are based on leaves collected near the end of their growing seasons (September–December), when foliar nutrient concentrations typically decline (Chapin and Kedrowski, 1983
; Reich et al., 1992
).
RESULTS
Physiognomic correlations with climate
Strong univariate correlations are evident between the site means of many physiognomic character states and MAT (Fig. 2; see also Appendix; for raw data and correlation matrix of climatic and physiognomic variables, see Appendices S2–3 in Supplemental Data with online version of this article). Most notably, significant relationships exist for number of teeth (r2 = 0.79; P < 0.001), perimeter ratio (r2 = 0.74; P < 0.001), and shape factor (r2 = 0.73; P < 0.001), as well as the traditional variable, margin percentage (r2 = 0.80; P < 0.001) (see Table 1 for definitions of variables). Some of these relationships are significant even within individual species (e.g., Fig. 3). Correlations with growing season length, growing degree days, and growing season degree days are comparable to the correlations with MAT: r2-values for these climate variables vs. physiognomy are within 0.05 units of one another.
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One potential shortcoming of digital leaf physiognomy is that some variables, such as number of teeth and tooth area, require leaves with wholly intact margins. The fossil leaf record, however, is dominated by incomplete specimens (see discussion in Huff et al., 2003
). Importantly, many of the variables that can be measured on portions of leaf margins, and therefore do not require a complete outline, also correlate well with MAT, such as the ratio of the number of teeth to internal perimeter, the ratio of tooth area to internal perimeter, and perimeter ratio (Fig. 2; see also Appendix). In general, the correlations with MAT for these derived variables are not appreciably different from their primary counterparts (e.g., the ratio of tooth area to internal perimeter vs. tooth area; see Appendix).
Multiple linear regressions result in more significant models for MAT prediction relative to the univariate regressions (Table 3, Fig. 4). Two classes of multivariate models were investigated, based either on the inclusion of all physiognomic variables or on the inclusion of only those variables that are potentially applicable to leaf fragments. The standard error of the most significant multiple linear regression based on all available variables is ±1.8°C ("overall"; r2 = 0.95; P = 10–7), and the standard error of the most significant model based on variables that can be applied to fragments is ±2.0°C ("fossil"; r2 = 0.93; P = 10–7; Table 3, Fig. 4; in the fossil regression, untoothed leaves are represented by the margin percentage character state). In contrast, the most significant univariate regression, based only on margin percentage (i.e., leaf-margin analysis), has a standard error of ±3.0°C (r2 = 0.80; P = 10–6; Table 3, Fig. 4). One attractive alternative to multiple linear regression for leaf-climate correlations is canonical correspondence analysis (CCA) (e.g., Wolfe, 1993
, 1995
). However, the accuracy of MAT predictions using CCA here was considerably poorer in all models relative to the linear regressions, probably because the second axis of environmental variation in the ordination, rainfall, is of minor importance in this data set (Table 2).
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). Leaf-margin analysis underestimates MAT at these sites (mean error = –7.0°C), whereas errors are minimized in the multiple linear regressions (mean errors = –2.4°C and –4.1°C for the overall and fossil regressions, respectively). Although CLAMP is a multivariate technique, it also underestimates MAT greatly at these sites (mean error = –8.3°C using CCA; Kowalski and Dilcher, 2003
).
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Physiognomic correlations with leaf economic variables
The site means of several physiognomic character states correlate significantly with leaf mass per area and nitrogen content. Specifically, the ratio of tooth area to perimeter gives the most robust correlations (Fig. 6; for raw data, see Supplemental Appendix S2). Most other significant correlations are also related to tooth area, such as the natural log of tooth area (r2 = 0.69, P < 0.001 for leaf mass per area; r2 = 0.33, P = 0.03 for nitrogen content) and the ratio of tooth area to blade area (r2 = 0.30, P = 0.04; and r2 = 0.43, P = 0.01). In contrast to the site means, no significant correlations between physiognomy and leaf economic variables are evident at the species level (N = 335 species-site pairs; r2 
0.13 for all comparisons).
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DISCUSSION
Digital leaf physiognomy as a paleoclimate proxy
This study marks the first application of digital leaf physiognomy at a statistically significant number of sites, and the results are consistent with the preliminary assessment of Huff et al. (2003)
. Not only are plant species that grow in colder environments more likely to have teeth (Fig. 2A), as long known, but they are also likely to have more teeth (Fig. 2B), larger tooth areas (Fig. 2D), and more dissected blades (Fig. 2E, F). This information about climatic selection of leaf shape cannot be recovered with other methods, highlighting a major strength of digital leaf physiognomy. Two new multiple linear regressions based on a suite of continuous, reproducible physiognomic character states show significant improvements over leaf-margin analysis for predicting MAT (Table 3, Fig. 4).
A weakness of leaf-margin analysis and CLAMP is their underestimation of MAT in riparian and wet soil environments, which typically host a disproportionate number of species with teeth compared to adjacent forests with the same climate (MacGinitie, 1953
; Burnham et al., 2001
; Kowalski and Dilcher, 2003
). This effect is important because many fossil floras represent similarly wet, disturbed habitats (e.g., Wing and DiMichele et al., 1992
). Digital leaf physiognomy appears to be less sensitive to this "wet soil" bias because temperature errors at two sites with shallow water tables are reduced on average by over 3°C compared to leaf-margin analysis (Table 4; see also Fig. 2), and over 5°C compared to CLAMP (Kowalski and Dilcher, 2003
). This suggests that digital leaf physiognomy could yield significantly more accurate paleotemperature estimates for fossil floras derived from riparian or wet soil habitats.
The resampling results indicate that one leaf per species is sufficient for precise MAT predictions (Fig. 4). This is good news for paleobotanists: although a higher number of replicates is always desirable, sometimes only one fossil specimen of a species is available, for example with rare species or small museum collections. The overall and fossil regressions show a similar sensitivity to leaf-margin analysis for number of species used (Fig. 5; but see discussion later). Typically, at least 20, and preferably many more woody dicotyledonous species are required for robust MAT predictions with leaf-margin analysis (Wolfe, 1993
; Wilf, 1997
; Burnham et al., 2001
); a similar minimum is suggested here for digital leaf physiognomy. However, in colder environments more species may be required to achieve comparable precision to leaf-margin analysis, particularly for the overall regression (Fig. 5). This degradation in colder climates is probably due to the greater variability of character states such as tooth area (Fig. 2D) and perimeter ratio (Fig. 2E) at cold sites relative to warm sites (see also Appendix).
Fragmented leaves are typical in the fossil record and inevitably lead to less precise climate estimates. Leaf-margin analysis and the fossil model are associated with similar signal losses for leaves that are 75% intact (0.8 vs. 1.0°C, respectively; t16 = –0.71; P = 0.25), whereas leaf-margin analysis produces greater errors for leaves that are 50% intact (2.2 vs. 1.7°C; t16 = 1.9; P = 0.04). The loss of precision in leaf-margin analysis is due to leaves that had teeth only in the distal quarter or half that consequently were scored as being untoothed. Failed tooth preservation in incomplete fossil leaves is probably a common, conventionally overlooked problem that can seriously affect paleotemperature estimates, especially in low-diversity samples with singleton species. Digital leaf physiognomy may help to mitigate this by increasing the number of reproducible character states.
One potential weakness of all MAT proxies based on leaf physiognomy is that the computation of MAT is equally weighted by all days in a year, including those when plant growth is negligible. Given this confounding factor, it is probable that climate variables other than MAT are more closely linked with plant growth, and by extension leaf physiognomy. Growing season length (GSL), growing degree days (GDD), and growing season degree days (GSDD) are three climate variables often invoked as having a close association with plant growth (e.g., Johnson et al., 2000
; see Table 1 for definitions). However, these variables also correlate strongly with MAT (Fig. 7; Supplemental Appendix S3; see also Wolfe et al., 1995
), indicating that even if a variable such as GDD is more causally linked to leaf physiognomy than MAT, in practice MAT can be reliably predicted from fossils, providing the relationship between MAT and GDD has not changed. Furthermore, both univariate and multivariate correlations between physiognomy and climate are no stronger (difference in r2 values < 0.05) when based on MAT vs. the alternative climate variables GSL, GDD, GSDD, and MAT.
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; Mosbrugger and Roth, 1996
), and fast growth correlates with high nitrogen content, low leaf mass per area, and other variables on the "quick" end of the leaf economics spectrum (Wright et al., 2004
). This mechanistic link may also explain why correlations with character states not related to tooth area (e.g., number of teeth) are not as strong.
Recommendations
This study establishes correlations in living plants that link quantitative leaf physiognomy to climate and leaf economics, and it lays a foundation for using digital leaf physiognomy to quantify both climatic and leaf economic variables from fossil plants. There is potential for refinements in these correlations; most critically, increased sampling of modern test sites is needed to increase species counts (see Fig. 5), geographic and climatic coverage (see Fig. 8), and the number of leaf economic variables (e.g., leaf lifespan). The test sites used here do not differ appreciably in precipitation, except for Barro Colorado Island (Table 2). An expansion to sites with larger differences in rainfall would potentially allow for calibrations with precipitation variables and further increase the usefulness of the approach.
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FOOTNOTES
1 The authors thank B. Cariglino and E. Currano for help with processing leaf images, E. Currano for preparing and analyzing leaf tissues for nitrogen content, Y. Watanabe for assistance with the elemental analyzer, T. Lott and K. Skroski for help with collecting leaves in the field, A. Devanzo for assistance with leaves, and J. Royer for comments on the manuscript. This work was supported in part by the Petroleum Research Fund of the American Chemical Society, Grants 35229-G2 (to P. W.) and 40546-AC8 (to P. W. and D. L. R.), and National Science Foundation Grants EAR-9905668 (to D. L. D.) and DEB-0345750 (to P. W. and others). Some of this work partially fulfilled the B.S. requirements of a Senior Thesis in Geosciences for D. A. J. 
4 Author for correspondence (e-mail: droyer{at}wesleyan.edu
), present address: Department of Earth and Environmental Sciences, Wesleyan University, Middletown, CT 06459 USA
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) and standard errors of the means (SE) of physiognomic variables, where means are based on species averages (1–6 leaves per species). r2 values of variables vs. mean annual temperature (MAT) are given in the row labeled ‘Mean’. r2 values are also given for various transformations of the raw data vs. MAT (see text for definitions); values in boldface represent improved correlations relative to the standard ‘Mean’ correlation. Physiognomic character states and site abbreviations are defined in Tables 
