Name: Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities
Uploaded: 2024-10-25T13:40:00
Duration: 1 min 59 s
Description: Climate and environment strongly influence the size, shape, and toothiness (physiognomy) of plants&#39; leaves. These relationships, particularly in woody ...

AJL thanks the 2020-2022 undergraduate Team Leaf at the University of Washington for the motivation and suggestions for making effective training materials for DiLP. AGF, AB, DJP, and DLR thank the many undergraduates at Wesleyan University and Baylor University who measured modern and fossil leaves and whose input was invaluable in modifying and updating this protocol. The authors acknowledge the PBot Quantitative Traits Working Group and the PBOT team for encouraging the work to formalize this protocol to make it more accessible to broader communities. This work was supported by the National Science Foundation (grant EAR-0742363 to DLR, grant EAR-132552 to DJP) and Baylor University (Young Investigator Development Program to DJP). We thank two anonymous reviewers and the Review Editor for feedback that helped improve the clarity and comprehensiveness of this protocol.

Digital Leaf Physiognomy for Paleoclimate and Paleoecological Reconstruction

This article presents how continuous traits of leaf physiognomy can be measured on woody dicot angiosperm fossil leaves and subsequently applied to proxies developed from modern calibration data to reconstruct paleoclimate and paleoecology. This requires that care be taken to align methodological steps with those represented in the proxy calibration datasets5,6,10. This consideration starts before the application of this protocol during fossil leaf collection, particularly with respect to sample size. Pooling fossil leaf assemblages across as narrow a range of stratigraphy as possible to obtain a suitable number of measurable specimens and morphotypes to minimize time averaging is recommended. Limiting paleoclimate reconstruction to sites with at least 350 identifiable specimens and at least 15-20 woody dicot angiosperm morphotypes is also recommended19,51,52. Further, when choosing leaves for analyses, measuring as many leaves per morphotype as possible and, at minimum, choosing specimens that represent the variability of leaf physiognomy within a morphotype is recommended.
Further care must be taken while implementing the preparation and measuring sections to remain consistent with the calibration dataset. Steps carried out during the preparation stages have the greatest potential for subjectivity and varied results between users. However, if the protocol is followed deliberately and the additional considerations tables (Table 2, Table 3) and rules document (Supplementary File 3) are referenced often, this method results in objective and reproducible measurements of leaf physiognomy. For users new to the method, confirming that the leaves have been prepared correctly with someone who has more experience is suggested. Special care needs to be taken when measuring petiole width for MA reconstructions. Because these values are squared, inaccuracy in the measurements will become exaggerated. Incomplete preservation and damage can alter petiole dimensions and should be carefully avoided.
There are some limitations to these methods that are worth noting. Most importantly, the proxy reconstructions included in the dilp R package are for woody dicot angiosperms only and, thus, may exclude other plant groups that were prominent components of ancient communities. However, additional leaf petiole-based proxies for species-level MA have been published for petiolate and broadleaf gymnosperms5,8, herbaceous angiosperms8, and ferns9, which a user could incorporate separately if desired. The exclusion of prominent plant groups in communities beyond woody dicot angiosperms is likely most impactful to reconstructions of site-level MA mean and variance, as they will provide an incomplete perspective of economic strategies within the entire community. Phylogenetic history influences the occurrence of leaf teeth23, introducing the potential that analyzing fossil communities with novel taxonomic composition may impart uncertainty in resulting estimates, though the realization of this potential influence has not yet been tested and demonstrated.
Fossil leaves also need to be adequately preserved to incorporate quantitative measurements of leaf physiognomy beyond the margin state. For DiLP, this is especially true for entire-margined leaves, as they can only contribute information beyond the margin state if the whole leaf, or half leaf, is preserved or can be reconstructed. Similarly, leaves can only be incorporated into MA reconstructions if (1) both their petiole at its insertion to the leaf blade is preserved or, in specific cases, if the base of the leaf and the basal most portion of the midvein is preserved (see Note in step 3.6), and (2) if the size of the leaf can be estimated, either through whole leaf measurement or half leaf reconstruction. This means some morphotypes may be excluded altogether from site-level MA analyses. Lastly, time is a limitation with this protocol, as univariate alternatives for paleoclimate reconstructions take comparatively less time to produce.
Despite these limitations, the use of DiLP and MA reconstruction methods still has several advantages over other methods. MA reconstructions are one of the only ways to reconstruct leaf economic strategies in the fossil record, and the use of two-dimensional petiole width and leaf area measurements allows reconstructions to be done using common impression/compression leaf fossils. For DiLP, the incorporation of multiple continuous measurements that are functionally linked with climate improves the reproducibility of measurements and the accuracy of resulting climate reconstructions6,13. This protocol is designed to accommodate the incomplete nature of the fossil record by permitting leaf toothiness measurements to be made using leaf fragments. Although continuous measurements of leaf area provide more information about leaf size, DiLP MAP estimates can be complemented by those using leaf size classes in an effort to increase sample size16,53 or through the incorporation of vein scaling estimates of leaf area42,54,55. As with most involved methods, the time efficiency of this protocol will improve as a user becomes more experienced and confident, particularly in the preparation steps. The fact that the site-level DiLP measurements have been made following this protocol for &#62;150 modern6,10,56 and at least 22 fossil assemblages to date attest to its feasibility6,38,39,40,41,42. Lastly, comprehensive measurements of leaf physiognomy have applications beyond those discussed here and may be useful in describing other aspects of plant ecology, physiology, evolution, and development, with application to both modern56 and paleo studies40.
In summary, the implementation of the methods detailed in this article allows a user to reconstruct paleoclimate and paleoecology using robust and reproducible methods. These methods provide an important opportunity to showcase past examples of climate and ecosystem responses to environmental perturbations and to provide further insight into the complex interactions of Earth&#39;s natural systems.

Leaves are fundamental production units that facilitate the exchange of energy (e.g., light, heat) and matter (e.g., carbon dioxide, water vapor) between the plant and its surrounding environment1,2. To perform these functions, leaves must mechanically support their own weight against gravity in still and windy air 3,4. Because of these intrinsic links, several aspects of the size, shape, and toothiness of leaves (physiognomy) reflect the details of their function and biomechanics and provide insight into their environment and ecology. Prior work has quantified relationships between leaf physiognomy, climate, and ecology across the modern world to establish proxies that can be applied to fossil leaf assemblages5,6. These proxies provide important opportunities to reconstruct paleoclimate and paleoecology and contribute to a greater understanding of the complex interplay between various systems of the planet throughout its history. This article details the methods necessary for the use of two proxies: 1) the leaf mass per area reconstruction method to elucidate paleoecology, and 2) digital leaf physiognomy to reconstruct paleoclimate.
Leaf dry mass per area (MA) is a frequently measured plant trait in both neo- and paleobotany. The primary value of MA, especially for fossil reconstructions, is that it is part of the leaf economics spectrum, a coordinated axis of well-correlated leaf traits that includes leaf photosynthetic rate, leaf longevity, and leaf nutrient content by mass7. The ability to reconstruct MA from fossils provides a window into these otherwise inaccessible metabolic and chemical processes and ultimately can reveal useful information about plant ecological strategy and ecosystem function.
Royer et al.5 developed a method to estimate the MA of woody non-monocotyledonous (dicot) angiosperm fossil leaves based on the area of the leaf blade and the width of the petiole. Theoretically, the leaf petiole acts as a cantilever, holding the weight of the leaf in the optimal position3,4. The cross-sectional area of the petiole, which makes up the most significant component of beam strength, should, therefore, be strongly correlated with the mass of the leaf. By simplifying the shape of the petiole into a cylindrical tube, the cross-sectional area of the petiole can be represented with the petiole width squared, allowing leaf mass to be estimated from a two-dimensional fossil (for more detail, see Royer et al.5). Leaf area can be measured directly. Together, petiole width squared divided by leaf area (i.e., the petiole metric; Table 1) provides a good proxy for fossil MA and allows paleobotanists to step into modern trait-based ecology. MA reconstruction methods have also been expanded to broadleaf and petiolate gymnosperms5,8, herbaceous angiosperms8, and ferns9, which produced relationships that differ from the relationships observed for woody dicot angiosperms and from each other. An expanded woody dicot dataset and new regressions equations for reconstructing the variance and mean of MA at the site level allow the inference of the diversity of leaf economic strategies and what strategies are most prevalent, among woody dicot angiosperms in fossil floras10.
The relationship between physiognomic leaf traits and their climate has been noted for over a century11,12. Specifically, the physiognomy of woody dicot angiosperm leaves is strongly correlated with temperature and moisture13. This relationship has formed the basis for numerous univariate14,15,16,17 and multivariate6,18,19,20,21,22 leaf physiognomic proxies for terrestrial paleoclimate. Both univariate and multivariate leaf physiognomic paleoclimate methods have been widely applied to angiosperm-dominated fossil floras across all continents spanning the last ~120 million years of Earth&#39;s history (Cretaceous to modern)23.
Two fundamental observations utilized in leaf physiognomic paleoclimate proxies are 1) the relationship between leaf size and mean annual precipitation (MAP) and 2) the relationship between leaf teeth (i.e., outward projections of the leaf margin) and mean annual temperature (MAT). Specifically, the average leaf size of all woody dicot angiosperm species at a locality is positively correlated with MAP, and the proportion of woody dicot angiosperm species at a locality with toothed leaves, in addition to the size and number of teeth negatively correlate with MAT6,12,13,14,15,16,24.
A functional link between these leaf physiognomy-climate relationships is strongly supported by both theory and observation1,2,25. For example, although larger leaves provide greater photosynthetic surface area, they require greater support, lose more water through transpiration, and retain more sensible heat due to a thicker boundary layer 1,26,27. Thus, larger leaves are more common in wetter, hotter environments because water loss through increased transpiration effectively cools leaves and is less problematic. In contrast, smaller leaves in drier hot climates reduce water loss and avoid overheating instead by increasing sensible heat loss28,29. Details of what factors, or combination of factors, contribute most strongly to explaining functional links remain enigmatic for other leaf traits. For example, there have been several proposed hypotheses to explain the leaf teeth-MAT relationship, including leaf cooling, efficient bud packing, enhanced support and supply of thin leaves, guttation through hydathodes, and enhanced early season productivity30,31,32,33.
Most leaf physiognomic paleoclimate proxies rely on categorical division of leaf traits rather than quantitative measurements of continuous variables, leading to several potential shortcomings. The categorical approach excludes the incorporation of more detailed information captured by continuous measurements that are strongly correlated with climate (e.g., number of teeth, leaf linearity), which can reduce the accuracy of paleoclimate estimates6,20,34. Additionally, in some of the leaf trait scoring methods, the traits being categorically scored can be ambiguous, leading to issues in reproducibility, and some traits have limited empirical evidence to support their functional link to climate6,15,16,35,36.
To address these shortcomings, Huff et al.20 proposed digitally measuring continuous leaf traits in a method known as digital leaf physiognomy (DiLP). A key advantage of DiLP over previous methods is its reliance on traits that 1) can be measured reliably across users, 2) are continuous in nature, 3) are functionally linked to climate, and 4) display phenotypic plasticity between growing seasons6,37. This has led to more accurate estimates of MAT and MAP than previous leaf physiognomic paleoclimate methods6. In addition, the method accommodates the imperfect nature of the fossil record by providing steps to account for damaged and incomplete leaves. The DiLP method has been successfully applied to a range of fossil floras from multiple continents spanning a large range of geologic time6,38,39,40,41,42.
The following protocol is an expansion of that described in earlier work5,6,20,34. It will explain the procedures necessary to reconstruct paleoclimate and paleoecology from woody dicot angiosperms fossil leaves using the DiLP and MA reconstruction methods (see Table 1 for an explanation of the variables measured and calculated through the use of this protocol). In addition, this protocol provides steps to record and calculate leaf traits not included in DiLP or MA analysis but that are easy to implement and provide useful characterizations of leaf physiognomy (Table 1). The protocol follows the following format: 1) Imaging fossil leaves; 2) leaf digital preparation, organized into five possible preparation scenarios; 3) leaf digital measurement, organized into the same five possible preparation scenarios; and 4) DiLP and MA analyses, using the R package dilp10.
The protocol for MA reconstructions is embedded within the DiLP protocol because both are convenient to prepare for and measure alongside each other. If a user is interested in MA analyses only, they should follow the preparation steps described in DiLP preparation scenario 2, whether or not the leaf margin is toothed, and the measuring steps describing petiole width, petiole area, and leaf area measurements only. A user can then run the appropriate functions in the dilp R package that performs the MA reconstructions.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Copy stand or tripod</td>
			<td></td>
			<td></td>
			<td>For fossil photography</td>
		</tr>
		<tr>
			<td>Digital camera</td>
			<td></td>
			<td></td>
			<td>For fossil photography, high resolution camera preferred</td>
		</tr>
		<tr>
			<td>Image editing software&#160;</td>
			<td></td>
			<td></td>
			<td>For digital preperation. Examples include Adobe Photoshop and GIMP, the latter of which is free (https://www.gimp.org/)</td>
		</tr>
		<tr>
			<td>ImageJ software</td>
			<td>IJ1.46pr</td>
			<td></td>
			<td>For making digital measurments, free software (https://imagej.net/ij/index.html)</td>
		</tr>
		<tr>
			<td>Microsoft Excel</td>
			<td>Microsoft</td>
			<td></td>
			<td>Or similar software for data entry</td>
		</tr>
		<tr>
			<td>R software</td>
			<td>The R foundation</td>
			<td></td>
			<td>For running provided R script (https://www.r-project.org/). R studio offers a user friendly R enviornment (https://posit.co/download/rstudio-desktop/). Both are free.</td>
		</tr>
		<tr>
			<td>dilp R Package</td>
			<td></td>
			<td></td>
			<td>Can be installed following instructions here: https://github.com/mjbutrim/dilp</td>
		</tr>
	</tbody>
</table>

reconstructing terrestrial paleoclimate and paleoecology with fossil leaves using digital leaf physiognomy and leaf mass per area

Climate and environment strongly influence the size, shape, and toothiness (physiognomy) of plants&#39; leaves. These relationships, particularly in woody non-monocotyledonous angiosperms, have been used to develop leaf-based proxies for paleoclimate and paleoecology that have been applied to reconstruct ancient terrestrial ecosystems for the last ~120 million years of Earth&#39;s history. Additionally, given that these relationships have been documented in living plants, they are important for understanding aspects of plant evolution and how plants respond to climatic and environmental changes. To conduct these types of analyses on modern and fossil plants, leaf physiognomy must be measured accurately using a reproducible methodology. This protocol describes a computer-based method for measuring and analyzing a variety of leaf physiognomic variables in modern and fossil leaves. This method allows for the measurement of leaf physiognomic traits, in particular variables related to leaf serrations, leaf area, leaf dissection, and linearity that are used in the digital leaf physiognomy proxy for reconstructing paleoclimate, as well as petiole width and leaf area, which are used for reconstructing leaf mass per area, a paleoecological proxy. Because this digital leaf trait measurement method can be applied to fossil and living plants, it is not limited to applications related to reconstructing paleoclimate and paleoecology. It can also be used to explore leaf traits that may be informative for understanding the function of leaf morphology, leaf development, phylogenetic relationships of leaf traits, and plant evolution.

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities

Reconstructing Terrestrial Paleoclimate and Paleoecology with Fossil Leaves Using Digital Leaf Physiognomy and Leaf Mass Per Area

Our research is focused on understanding the relationship between plant traits and climate and ecology in modern plants. Then using these relationships, we can develop proxies or models that we can use to reconstruct climate and ecology in ancient plant communities. Recent research has focused on understanding the biological, physiological, evolutionary underpinnings of the relationship between plant traits and climate and ecology, today and in the past.This research has shown that plant traits respond to climate and ecology, both in the lifetime of a single plant and in evolutionary time scales. Our research has demonstrated that despite the complex factors that influence plant traits, there are very strong empirical relationships between leaf traits and climate and ecology. We've used these relationships to develop models for reconstructing climate and ecology that have been applied in a variety of settings through Earth history.These methods refine the reconstruction of paleoclimate and paleoecology to provide a much more comprehensive understanding of terrestrial ecosystems and climate than is available by just studying our modern Earth. Such a perspective is critical in understanding what our future Earth holds as climates and ecosystems respond to anthropogenic impact. This is a leaf-physionomy-based approach, meaning that it does not require an accurate taxonomic identification.In addition, DiLP uses digital measurements of continuous leaf characters as opposed to qualitative and categorical, which allows for greater reproducibility, and these leaf characters have a functional relationship to climate.

A previously published dataset of leaf physiognomy measurements from the early Eocene McAbee fossil site in south-central British Columbia was used to&#160;provide an example of representative results using both the digital leaf physiognomy (DiLP) and leaf mass per area (MA) reconstruction methods (Lowe et al.38; data provided in Supplementary File 2). The site provides an opportunity to reconstruct paleoclimate and paleoecology during the warmest interval of the Cenozoic (the Early&#160;Eocene Climatic Optimum) in an upland and volcanic landscape38,45,46,47. Fossil assemblages were sampled from two separate&#160;horizons in a lacustrine sequence, named H1 (28 cm thick) and H2 (27 cm thick), pooled over a narrow range of stratigraphy using a census technique, whereby all specimens able to be assigned a morphotype were collected or counted38,48.
The McAbee leaf physiognomic data passed the error checks flagged by dilp_errors(), and seven outliers flagged by dilp_outliers() were double-checked to ensure the values represent true variation in data and not a methodological mistake. The data was subsequently run through the dilp() function to produce paleoclimate and the lma() function for leaf mass per area reconstructions.
MA reconstructions and the lower and upper bounds of their 95% prediction intervals are reported in Table 4 at both the species- and site level, using equations presented in Royer et al.5 and Butrim et al.10. Reconstructed values are within the range of MA typical for modern terrestrial species (30-330 g/m2)49. Using thresholds discussed in Royer et al.5, most species have a reconstructed MA that aligns with leaf life spans of &#60;1 year (&#8804;87 g/m2), some ~1 year (88-128 g/m2), while none are typical of &#62;1 year (&#8805;129 g/m2). Reconstructions of site MA mean, and variance at McAbee reflect the prevalence and diversity of leaf economic strategies at a site10,50. There are no prominent differences between site mean and variance between H1 and H2, and thus, there is no evidence that the composition and diversity of leaf economic strategies varied between the two points in time. Additionally, the site-mean reconstructions made using the equations of Royer et al.5&#160;and Butrim et al.10&#160;were very similar.
Reconstructions of mean annual temperature (MAT) and mean annual precipitation (MAP) using multiple linear regression (DiLP) and single linear regression (leaf margin and leaf area analyses) equations presented in Peppe et al.6 are shown in Table 5. Paleoclimate estimates are most reliably inferred if the leaf physiognomy of the fossil leaf assemblages occurs within the physiognomic space of the calibration dataset. This is assessed through the canonical correspondence analysis (CCA) analysis step carried out by the function dilp_cca(). Both McAbee H1 and H2 fall within the range of leaf physiognomy observed in the calibration dataset (Figure 7A). If sites had reconstructed values that fell outside calibration space, paleoclimate reconstructions should be interpreted cautiously (e.g., through comparison to independent lines of evidence; see Peppe et al.6 for further discussion). Reconstructed MAT and MAP for both H1 and H2 are consistent with a temperate seasonal biome (Figure 7B,C), which agrees well with independent lines of evidence, including nearest living relative based inferences of both the fossil floral and insect communities at McAbee45.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66838/66838fig01.jpg" /> 
Figure 1: Leaf physiognomy and architectural terminology throughout this article. (A) A pinnately veined, unlobed, and entire-margined leaf, (B) a palmately veined, unlobed, and toothed leaf, (C) a pinnately veined, lobed, and entire-margined leaf, (D) a palmately veined, lobed, and toothed leaf. <a href="https://www.jove.com/files/ftp_upload/66838/66838fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66838/66838fig02.jpg" /> 
Figure 2: Flow chart of the method. A flowchart demonstrating how different leaf preservation conditions and leaf types determine what general type of leaf traits can be measured reliably (yellow box). This determines which preparation scenario will be followed in the protocol, and in which columns data will be entered in the data entry spreadsheet (bullet points). <a href="https://www.jove.com/files/ftp_upload/66838/66838fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66838/66838fig03.jpg" /> 
Figure 3: Different preparation scenarios. Different preparation scenarios demonstrating examples of completed digitally prepared images ready for the measurement phase. (A) Scenario 1, entire margined leaf whose area, or half area, cannot be reconstructed, (B) Scenario 5, toothed leaf whose area, or half area, cannot be reconstructed and does not have &#8805;2 consecutive teeth and/or &#8805;25% of the leaf preserved, (C) Scenario 2, entire margined leaf whose area, or half area, is preserved or can be reconstructed, (D) Scenario 3, a toothed leaf whose area, or half area, cannot be reconstructed but has &#8805;2 consecutive teeth and &#8805;25% of the leaf preserved, (E) Scenario 4, a toothed leaf whose area, or half area, is preserved or can be reconstructed. <a href="https://www.jove.com/files/ftp_upload/66838/66838fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66838/66838fig04.jpg" /> 
Figure 4: Illustration of damage removal. Illustrating how to cut out damaged margin, and the leaf area adjacent to that damaged margin. Dashed red lines demonstrate how selections are made with the lasso tool. Note that the bounds of damage were intentionally started at the sinuses of primary teeth (see Supplementary Figure 2 for help differentiating primary from subsidiary teeth). (A) A pinnately veined leaf where the selection is extended to the mid-vein. (B) A palmately veined leaf where the selection is extended to the nearest primary vein. <a href="https://www.jove.com/files/ftp_upload/66838/66838fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66838/66838fig05.jpg" /> 
Figure 5: Illustrating an example of how to cut out teeth. (A) Dashed red lines demonstrate how selections are made with the lasso tool. Note that in this case, the teeth are compound, so selections were made between primary sinuses only (see Supplementary Figure 2 for help differentiating primary from subsidiary teeth), (B) a zoomed-in perspective of how teeth selection was made, with red dots representing where the mouse was clicked during selection, (C) the copy of the leaf when the teeth are removed. <a href="https://www.jove.com/files/ftp_upload/66838/66838fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/66838/66838fig06v2.jpg" /> 
Figure 6: Illustration of preparation scenario 4. Illustration of preparation decisions and measuring steps for an example leaf prepared in scenario 4. (A) A preparation scenario where it was decided that a half leaf provided the most reliable leaf shape and area measurements, and preserved margins on both medial halves were included for tooth measurements. (B) An example demonstrating which variables are measured on various components of the prepared leaf. Bolded text highlights measurements needed for DiLP and MA analyses, while non-bolded text (blade perimeter, minimum Feret, and artificial middle perimeter) highlights measurements that are not required but useful for additional physiognomic characterizations (e.g., shape factor and compactness; Table 1). <a href="https://www.jove.com/files/ftp_upload/66838/66838fig06largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/66838/66838fig07.jpg" /> 
Figure 7: Representative results. Results from two fossils horizons (H1 and H2) sampled at the early Eocene McAbee Fossil Beds from Lowe et al.38. (A) Canonical correspondence analysis showing the representation of multivariate leaf physiognomy in the calibration dataset. Calibration data is from Peppe et al.6. The leaf physiognomy of the two McAbee horizons are overlain and occur within the calibration space. (B and C) Temperature and precipitation estimates, and their associated uncertainty (standard errors of the models), using equations presented in Peppe et al.6 of the two McAbee horizons overlain on a Whittaker Biome diagram. (B) Estimates reconstructed using the Digital Leaf Physiognomy (DiLP) multiple linear regressions models (MLR), (C) Estimates reconstructed using the leaf area analysis (LAA) and leaf margin analysis (LMA) single linear regressions (SLR) equations of the two McAbee horizons overlain on a Whittaker Biome diagram. <a href="https://www.jove.com/files/ftp_upload/66838/66838fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Leaf physiognomic variables. Variables that are measured and/or calculated and applied in predictive models using this protocol to reconstruct leaf dry mass per area (MA), mean annual temperature (MAT), and mean annual precipitation (MAP). MAT and MAP are reconstructed with equations presented in Peppe et al.6 using a multivariate approach for Digital Leaf Physiognomy (DiLP) and univariate approaches for leaf margin analysis (LMA) and leaf area analysis (LAA). Variables listed as Other are not used in MA, DiLP, LMA, and LAA analyses but are still measured and calculated using this protocol because they are easy to implement and provide useful characterizations of leaf physiognomy. <a href="https://www.jove.com/files/ftp_upload/66838/Table 1_RE.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Additional considerations and explanations for preparation steps. <a href="https://www.jove.com/files/ftp_upload/66838/Table 2_RE.xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: Additional considerations and explanations for measuring steps. <a href="https://www.jove.com/files/ftp_upload/66838/Table 3_RE.xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Reconstructions of leaf dry mass per area (MA) and associated upper and lower bounds of the 95% prediction intervals for McAbee Fossil Beds from Lowe et al.38. Reconstructions are made for morphotype mean5, site mean5,10, and site variance10. <a href="https://www.jove.com/files/ftp_upload/66838/Table 4_RE.xlsx" target="_blank">Please click here to download this Table.</a>
Table 5: Reconstructions of mean annual temperate (MAT) and mean annual precipitation (MAP) for Horizon 1 (H1) and 2 (H2) at the early Eocene McAbee Fossil Beds using the multiple linear regressions (MLR) of Digital Leaf Physiognomy (DiLP) and the single linear regressions (SLR) of leaf margin analysis (LMA) and leaf area analysis (LAA) presented in Peppe et al.6. <a href="https://www.jove.com/files/ftp_upload/66838/Table 5_RE.xlsx" target="_blank">Please click here to download this Table.</a>
Supplementary Figure 1: Quercus rubra leaf from Harvard Forest illustrating the lobe vs. tooth rule. Line segments p and d are defined in text. Scale bars = 1 cm. <a href="https://www.jove.com/files/ftp_upload/66838/Supplementary Figure 1_RE.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: Betula lutea leaf from Harvard Forest illustrating the rules for differentiating subsidiary teeth from primary teeth. The isolated leaf segment has been magnified 2X. The blue line connects sinuses with the greatest degree of incision (i.e., primary sinuses), and teeth associated with these sinuses are considered primary (blue arrows). Red dots mark teeth that can be differentiated as a subsidiary because their apical sinuses are incised to a lesser degree. Teeth denoted by the red arrows have a similar degree of incision compared to the primary teeth but can be identified as subsidiary by a relatively thinner gauged principal vein compared to the primary teeth. Scale bars = 1 cm. <a href="https://www.jove.com/files/ftp_upload/66838/Supplementary Figure 2_RE.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: Illustration of tooth selection, the pinnate lobe rule, and the lobe priority rule. (A) Tooth selection for a Hamamelis virginiana leaf from Huyck Preserve. The darkened areas correspond to leaf tissue that is included in total tooth selection because subsidiary teeth are differentiated from primary teeth. (B) Quercus alba leaf from IES illustrates the lobe priority rule. The darkened areas are measured as lobes, and the undarkened are measured as teeth, but all projections are considered lobes via the lobe priority rule. Scale bars = 1 cm. <a href="https://www.jove.com/files/ftp_upload/66838/Supplementary Figure 3_RE.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 4: Acer saccharum leaf from Allegheny National Forest illustrating the extension and solitary tooth rules. Dashed lines depict tooth selections. The solid line depicts the axis of symmetry for the associated tooth. The black area is a weight used to flatten the leaves for photography. Scale bars = 1 cm. <a href="https://www.jove.com/files/ftp_upload/66838/Supplementary Figure 4_RE.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 5: Illustrating the ideal way to cut a petiole out that is positioned on top of a cordate base. <a href="https://www.jove.com/files/ftp_upload/66838/Supplementary Figure 5_RE.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 1: Data entry template for all measured digital leaf physiognomy variables. This file should not be modified, as it will be used as the input file for the R package. <a href="https://www.jove.com/files/ftp_upload/66838/Supplementary File 1 (data entry template)_RE.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 2: Example data from McAbee fossil beds from Lowe et al.38. This data was used to generate Figure 7 and for the discussion of representative results. <a href="https://www.jove.com/files/ftp_upload/66838/Supplementary File 2 (McAbee example data)_RE.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 3: Rules document for fossil digital leaf physiognomy. <a href="https://www.jove.com/files/ftp_upload/66838/Supplementary File 3 (Rules)_RE.zip" target="_blank">Please click here to download this File.</a>

The protocol presented shows digital measurement and analysis of continuous leaf physiognomic traits on fossil leaves to reconstruct paleoclimate and paleoecology using the digital leaf physiognomy and leaf mass per area reconstruction methods.

Climate and environment strongly influence the size, shape, and toothiness (physiognomy) of plants&#39; leaves. These relationships, particularly in woody ...