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><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&amp;nbsp;</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.

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>

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

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.

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 ...

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3.5K Views.  University of Washington. Ons onderzoek is gericht op het begrijpen van de relatie tussen planteigenschappen en klimaat en ecologie in moderne planten. Vervolgens kunnen we met behulp van deze relaties proxies of modellen ontwikkelen die we kunnen gebruiken om het klimaat en de ecologie in oude plantengemeenschappen te reconstrueren. Recent onderzoek heeft zich gericht op het begrijpen van de biologische, fysiologische, evolutionaire onderbouwing van de relatie tussen planteigenschapp...

Video: Auteur in de schijnwerpers: analyse van bladeigenschappen voor reconstructie van klimaat en ecologie in moderne en oude plantengemeenschappen

Measuring Rates of Herbicide Metabolism in Dicot Weeds with an Excised Leaf Assay

In order to isolate and accurately determine rates of herbicide metabolism in an obligate-outcrossing dicot weed, waterhemp (Amaranthus tuberculatus), we developed an excised leaf assay combined with a vegetative cloning strategy to normalize herbicide uptake and remove translocation as contributing factors in herbicide-resistant (R) and &#8211;sensitive (S) waterhemp populations. Biokinetic analyses of organic pesticides in plants typically include the determination of uptake, translocation (delivery to the target site), metabolic fate, and interactions with the target site. Herbicide metabolism is an important parameter to measure in herbicide-resistant weeds and herbicide-tolerant crops, and is typically accomplished with whole-plant tests using radiolabeled herbicides. However, one difficulty with interpreting biokinetic parameters derived from whole-plant methods is that translocation is often affected by rates of herbicide metabolism, since polar metabolites are usually not mobile within the plant following herbicide detoxification reactions. Advantages of the protocol described in this manuscript include reproducible, accurate, and rapid determination of herbicide degradation rates in R and S populations, a substantial decrease in the amount of radiolabeled herbicide consumed, a large reduction in radiolabeled plant materials requiring further handling and disposal, and the ability to perform radiolabeled herbicide experiments in the lab or growth chamber instead of a greenhouse. As herbicide resistance continues to develop and spread in dicot weed populations worldwide, the excised leaf assay method developed and described herein will provide an invaluable technique for investigating non-target site-based resistance due to enhanced rates of herbicide metabolism and detoxification.

This manuscript describes how herbicide metabolism rates can be effectively quantified with excised leaves from a dicot weed, thereby reducing variability and removing any possible confounding effects of herbicide uptake or translocation typically observed in whole-plant assays.

In order to isolate and accurately determine rates of herbicide metabolism in an obligate-outcrossing dicot weed, waterhemp (Amaranthus tuberculatus), we ...

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

This manuscript describes a duplex digital PCR assay (EntHF183 dPCR) for simultaneous quantification of Enterococcus spp. and the human fecal-associated HF183 marker. The EntHF183 duplex dPCR (referred as EntHF183 dPCR hereon) assay uses the same primer and probe sequences as its published individual quantitative PCR (qPCR) counterparts. Likewise, the same water filtration and DNA extraction procedures as performed prior to qPCR are followed prior to running dPCR. However, the duplex dPCR assay has several advantages over the qPCR assays. Most important, the dPCR assay eliminates the need for running a standard curve and hence, the associated bias and variability, by direct quantification of its targets. In addition, while duplexing (i.e. simultaneous quantification) Enterococcus and HF183 in qPCR often leads to severe underestimation of the less abundant target in a sample, dPCR provides consistent quantification of both targets, whether quantified individually or simultaneously in the same reaction. The dPCR assay is also able to tolerate PCR inhibitor concentrations that are one to two orders of magnitude higher than those tolerated by qPCR. These advantages make the EntHF183 dPCR assay particularly attractive because it simultaneously provides accurate and repeatable information on both general and human-associated fecal contamination in environmental waters without the need to run two separate qPCR assays. Despite its advantages over qPCR, the upper quantification limit of the dPCR assay with currently available instrumentation is approximately four orders of magnitude lower than that achievable by qPCR. Consequently, dilution is needed for measurement of high concentrations of target organisms such as those typically observed following sewage spills.

A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters

This manuscript describes a duplex digital PCR assay that can be used to simultaneously quantify Enterococcus spp. and the HF183 genetic markers as indicators of general and human-associated fecal contamination in recreational waters.

This manuscript describes a duplex digital PCR assay (EntHF183 dPCR) for simultaneous quantification of Enterococcus spp. and the human fecal-associated ...

Modification and Application of a Leaf Blower-vac for Field Sampling of Arthropods

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropod populations in rice. When used in combination with an enclosure, application of this sampling device provides absolute estimates of the populations of arthropods as numbers per standardized sampling area. The sampling efficiency depends critically on the sampling duration. In a mature rice crop, a two-minute sampling in an enclosure of 0.13 m2 yields more than 90% of the arthropod population. The device also allows sampling of arthropods dwelling on the water surface or the soil in rice paddies, but it is not suitable for sampling fast flying insects, such as predatory Odonata or larger hymenopterous parasitoids. The modified blower-vac is simple to construct, and cheaper and easier to handle than traditional suction sampling devices, such as D-vac. The low cost makes the modified blower-vac also accessible to researchers in developing countries.

Assessment of arthropod abundance in crops is critical for investigating population dynamics and species interactions. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropods in rice.

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the ...

Specific and Accurate Detection of the Citrus Greening Pathogen Candidatus liberibacter spp. Using Conventional PCR on Citrus Leaf Tissue Samples

Citrus greening, also known as huanglongbing, is a destructive citrus disease ravaging citrus farms globally. This disease causes asymmetrical yellow leaf mottling, vein yellowing, defoliation, root decay, and ultimately, the death of the citrus plant. When infected, the citrus plants have stunted growth and produce flowers out of season. These flowers rarely yield fruit, and those that do yield small, bitter, irregularly shaped citrus fruit that are not desirable. This disease is spread by the Asian citrus psyllid, Diaphorina citri, and by the grafting of infected citrus tissue. The pathogen has a long and variable incubation period within the citrus plant&#8212;sometimes years, before symptoms appear. Attempts to culture this pathogen in vitro have been unsuccessful, possibly due to the low and uneven concentration of the pathogen within infected citrus tissue, or because it is difficult to replicate the environmental conditions conducive to growth of the pathogen. It is very difficult to identify the disease before it has spread, due to its long incubation period and researchers&#39; inability to culture the pathogen. As a result, the disease only becomes apparent after suddenly destroying a citrus farmer&#39;s entire yield. Presented here is a method for the accurate and specific detection of the citrus greening pathogen, Candidatus liberibacter spp. using a genomic DNA extraction kit and PCR. This method is simple, efficient, cost effective, and adaptable for quantitative analysis. This method can be adapted for use on any citrus tissue; however, it is potentially limited by the amount of pathogen present in the tissue. Nevertheless, this method will allow citrus farmers to identify infected citrus plants earlier, and curb the spread of this destructive disease before it can further spread.

Specific and Accurate Detection of the Citrus Greening Pathogen Candidatus liberibacter  spp. Using Conventional PCR on Citrus Leaf Tissue Samples

Citrus Greening is a particularly destructive disease affecting citrus crops globally. Presented here is a simple method using PCR and genomic DNA extraction of citrus leaf tissue for the accurate and precise identification of the citrus greening pathogen, Candidatus liberibacter spp.

Citrus greening, also known as huanglongbing, is a destructive citrus disease ravaging citrus farms globally. This disease causes asymmetrical yellow leaf ...

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Accurate estimations of leaf area index (LAI), defined as half of the total leaf surface area per unit of horizontal ground surface area, are crucial for describing the vegetation structure in the fields of ecology, forestry, and agriculture. Therefore, procedures of three commercially used methods (litter traps, needle technique, and a plant canopy analyzer) for performing LAI estimation were presented step-by-step. Specific methodological approaches were compared, and their current advantages, controversies, challenges, and future perspectives were discussed in this protocol. Litter traps are usually deemed as the reference level. Both the needle technique and the plant canopy analyzer (e.g., LAI-2000) frequently underestimate LAI values in comparison with the reference. The needle technique is easy to use in deciduous stands where the litter completely decomposes each year (e.g., oak and beech stands). However, calibration based on litter traps or direct destructive methods is necessary. The plant canopy analyzer is a commonly used device for performing LAI estimation in ecology, forestry, and agriculture, but is subject to potential error due to foliage clumping and the contribution of woody elements in the field of view (FOV) of the sensor. Eliminating these potential error sources was discussed. The plant canopy analyzer is a very suitable device for performing LAI estimations at the high spatial level, observing a seasonal LAI dynamic, and for long-term monitoring of LAI.

An accurate estimation of leaf area index (LAI) is crucial for many models of material and energy fluxes within plant ecosystems and between an ecosystem and the atmospheric boundary layer. Therefore, three methods (litter traps, needle technique, and PCA) for taking precise LAI measurements were in the presented protocol.

Accurate estimations of leaf area index (LAI), defined as half of the total leaf surface area per unit of horizontal ground surface area, are crucial for ...

A Method for Quantifying Foliage-Dwelling Arthropods

Terrestrial arthropods play an important role in our environment. Quantifying arthropods in a way that allows for a precise index or estimates of density requires a method with high detection probability and a known sampling area. While most described methods provide a qualitative or semi-quantitative estimate adequate for describing species presence, richness, and diversity, few provide an adequately consistent detection probability and known or consistent sampling areas to provide an index or estimate with adequate precision to detect differences in abundance across environmental, spatial, or temporal variables. We describe how to quantify leaf-dwelling arthropods by sealing the leaves and end of branches in a bag, clipping and freezing the bagged material, and rinsing the previously frozen material in water to separate arthropods from the substrate and quantify them. As we demonstrate, this method can be used at a landscape scale to quantify leaf-dwelling arthropods with adequate precision to test for and describe how spatial, temporal, environmental, and ecological variables influence arthropod richness and abundance. This method allowed us to detect differences in density, richness, and diversity of leaf-dwelling arthropods among 5 genera of trees commonly found in southeastern deciduous forests.

We describe how to quantify leaf dwelling arthropods by sealing the leaves and end of branches in a bag, clipping and freezing the bagged material, and rinsing the previously frozen material in water to separate arthropods from the substrate for quantification.

Terrestrial arthropods play an important role in our environment. Quantifying arthropods in a way that allows for a precise index or estimates of density ...

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Leaf area index (LAI) is an essential canopy variable describing the amount of foliage in an ecosystem. The parameter serves as the interface between green components of plants and the atmosphere, and many physiological processes occur there, primarily photosynthetic uptake, respiration, and transpiration. LAI is also an input parameter for many models involving carbon, water, and the energy cycle. Moreover, ground-based in situ measurements serve as the calibration method for LAI obtained from remote sensing products. Therefore, straightforward indirect optical methods are necessary for making precise and rapid LAI estimates. The methodological approach, advantages, controversies, and future perspectives of the newly developed LP 110 optical device based on the relation between radiation transmitted through the vegetation canopy and canopy gaps were discussed in the protocol. Furthermore, the instrument was compared to the world standard LAI-2200 Plant Canopy Analyzer. The LP 110 enables more rapid and more straightforward processing of data acquired in the field, and it is more affordable than the Plant Canopy Analyzer. The new instrument is characterized by its ease of use for both above- and below-canopy readings due to its greater sensor sensitivity, in-built digital inclinometer, and automatic logging of readings at the correct position. Therefore, the hand-held LP 110 device is a suitable gadget for performing LAI estimation in forestry, ecology, horticulture, and agriculture based on the representative results. Moreover, the same device also enables the user to take accurate measurements of incident photosynthetically active radiation (PAR) intensity.

Fast and precise leaf area index (LAI) estimation in terrestrial ecosystems is crucial for a wide range of ecological studies and calibrating remote sensing products. Presented here is the protocol for using the new LP 110 optical device for taking ground-based in situ LAI measurements.

Leaf area index (LAI) is an essential canopy variable describing the amount of foliage in an ecosystem. The parameter serves as the interface between ...

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the primary photosynthetic organ, and their size and shape vary developmentally and environmentally within a plant. For these reasons studies on leaf morphology require measurement of multiple parameters from numerous leaves, which is best done by semi-automated phenomics tools5,6. Canopy shade is an important environmental cue that affects plant architecture and life history; the suite of responses is collectively called the shade avoidance syndrome (SAS)7. Among SAS responses, shade induced leaf petiole elongation and changes in blade area are particularly useful as indices8. To date, leaf shape programs (e.g. SHAPE9, LAMINA10, LeafAnalyzer11, LEAFPROCESSOR12) can measure leaf outlines and categorize leaf shapes, but can not output petiole length. Lack of large-scale measurement systems of leaf petioles has inhibited phenomics approaches to SAS research. In this paper, we describe a newly developed ImageJ plugin, called LeafJ, which can rapidly measure petiole length and leaf blade parameters of the model plant Arabidopsis thaliana. For the occasional leaf that required manual correction of the petiole/leaf blade boundary we used a touch-screen tablet. Further, leaf cell shape and leaf cell numbers are important determinants of leaf size13. Separate from LeafJ we also present a protocol for using a touch-screen tablet for measuring cell shape, area, and size. Our leaf trait measurement system is not limited to shade-avoidance research and will accelerate leaf phenotyping of many mutants and screening plants by leaf phenotyping.

Demonstration of key methods for high throughput leaf measurements. These methods can be used to accelerate leaf phenotyping when studying many plant mutants or otherwise screening plants by leaf phenotype.

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the ...

Biology

Relating Stomatal Conductance to Leaf Functional Traits

Leaf functional traits are important because they reflect physiological functions, such as transpiration and carbon assimilation. In particular, morphological leaf traits have the potential to summarize plants strategies in terms of water use efficiency, growth pattern and nutrient use. The leaf economics spectrum (LES) is a recognized framework in functional plant ecology and reflects a gradient of increasing specific leaf area (SLA), leaf nitrogen, phosphorus and cation content, and decreasing leaf dry matter content (LDMC) and carbon nitrogen ratio (CN). The LES describes different strategies ranging from that of short-lived leaves with high photosynthetic capacity per leaf mass to long-lived leaves with low mass-based carbon assimilation rates. However, traits that are not included in the LES might provide additional information on the species&#39; physiology, such as those related to stomatal control. Protocols are presented for a wide range of leaf functional traits, including traits of the LES, but also traits that are independent of the LES. In particular, a new method is introduced that relates the plants&#8217; regulatory behavior in stomatal conductance to vapor pressure deficit. The resulting parameters of stomatal regulation can then be compared to the LES and other plant functional traits. The results show that functional leaf traits of the LES were also valid predictors for the parameters of stomatal regulation. For example, leaf carbon concentration was positively related to the vapor pressure deficit (vpd) at the point of inflection and the maximum of the conductance-vpd curve. However, traits that are not included in the LES added information in explaining parameters of stomatal control: the vpd at the point of inflection of the conductance-vpd curve was lower for species with higher stomatal density and higher stomatal index. Overall, stomata and vein traits were more powerful predictors for explaining stomatal regulation than traits used in the LES.

Unraveling how physiology and morphology are linked allows a deeper understanding of mechanistic functioning of plant leaves. We present both a procedure to derive parameters of stomatal regulation from stomatal conductance measurements and correlations with traditional functional leaf traits.

Leaf functional traits are important because they reflect physiological functions, such as transpiration and carbon assimilation. In particular, ...

An Overview of bGDGT Biomarker Analysis for Paleoclimatology

Source: Laboratory of Jeff Salacup&#160;- University of Massachusetts Amherst
Throughout this series of videos, natural samples were extracted and purified in search of organic compounds, called biomarkers, that can relate information on climates and environments of the past. One of the samples analyzed was sediment. Sediments accumulate over geologic time in basins, depressions in the Earth into which sediment flows through the action of fluid (water or air), movement, and gravity. Two main types of basins exist, marine (oceans and seas) and lacustrine (lakes). As one might guess, very different types of life live in these settings, driven in large part by the difference in salinity between them. Over the last few decades, organic geochemists discovered a toolbox of biomarker proxies, or compounds that can be used to describe climate or environment, some of which work in marine environments and some of which work in lacustrine. We turn our attention here to the lacustrine realm and branched glycerol dialkyl glycerol tetraethers (Figure 1).
In this section we focus on analysis of terrestrial paleotemperature using branched glycerol dialkyl glycerol tetrathers (Figure 1; brGDGTs) and the MBT/CBT proxy. This proxy was initially described by Weijers et al.1&#160;and is based on the distribution of ring and branch structures in brGDGTs. They found that the cyclization of branched tetraethers (CBT) was directly related to soil pH.
CBT = -log ((Ib + IIb) / (I + II))
And that the methylation of branched tetraethers (MBT) was determined by mean annual air temperature (MAAT) and, to a lesser extent, soil pH.
MBT = (I + Ib + Ic) / (I + Ib + Ic) + (II + IIb + IIc) + (III + IIIb + IIIc)
Thus, taken together and calibrated, MBT/CBT relates the distribution of brGDGTs to both soil temperature and pH.
MBT = 0.122 + (0.187 x CBT) + (0.020 x MAAT)
Branched GDGTs are thought to be membrane spanning lipids and their production was initially attributed to anaerobic Acidobacteria bacteria living in soils and peat2-5, but subsequent work suggested they could also be produced in oxic and anoxic lake and marine water columns and sediments6-9. The hypothesis holds that Acidobacteria transform sites of methylation into cyclizations in response to lowering temperature in order to increase unsaturation (cyclization effectively removes two hydrogen atoms) and maintain membrane fluidity (by analogy, saturated fat (butter) is a solid a room temperature while unsaturated fat (olive oil) is a liquid), but branched GDGTs have not yet been identified as the main membrane lipids in Acidobacteria cultures. Thus their exact provenance is unknown.
Calibration of branched GDGTs to environmental variables (temperature, pH, salinity, precipitation, etc.) is a topic of widespread research. Organic geochemistry laboratories all over the world are involved in the task of developing both global1,10&#160;and regional11-13&#160;calibrations between branched GDGTs and (primarily) temperature. Thus, the equations given above are regularly being refined and perfected.
Branched GDGTs are usually extracted from lacustrine sediments, although coastal marine sediments have also been investigated. The extracts undergo a silica gel column to purify the GDGTs from other compounds which may not be LC amenable or that may co-elute with GDGTs chromatographically. The GDGTs come out in the polar fraction that elutes in methanol.
Once the total lipid extract is purified, the extracted and purified sample is run on a high performance liquid chromatograph coupled to a chemical ionization mass spectrometer. The relative concentration of the GDGTs is determined by obtaining the area under the curve for the selected mass ion (m/z; Figure 1) for each of the compounds on computer software designed for just this purpose (such as Agilent Chemstation). These areas are then put into the selected calibration equation in order to arrive at a paleotemperature determination.
<img alt="Figure 1" src="/files/ftp_upload/10256/10256fig1.jpg" /> 
Figure 1. Structures of the branched GDGTs used for calculating temperature via MBT/CBT proxy (used with permission of Dr. Isla Casta&#241;eda, who produced the image). <a href="https://www.jove.com/files/ftp_upload/10256/10256fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<div class="chapter_text" id="script_0">
Organic compounds called biomarkers can be used in Earth Science as paleothermometers to relate information on climates and environments of the past.
Living organisms produce these biomarkers, which provide us with information about the environment in which they lived. They can act as a proxy to tell us information about past events, like the Earth&#8217;s temperature millions of years ago.
Terrestrial paleotemperature can be analyzed using biomarkers found in sediment from fresh-water basins. One key class of these biomarkers are branched glycerol dialkyl glycerol tetraethers, or branched GDGTs.
This video will introduce the area of study, called paleoclimatology, which investigates past changes in fresh-water environments over hundreds of millions of year. This helps elucidate current and future climate and environmental changes.
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<div class="chapter_text" id="script_1">
Sediments accumulate over geologic time, due to fluid movement and gravity, in sedimentary basins, or low areas in the Earth&#8217;s crust. Sedimentary basins include oceans, which collect marine sediment, or lakes, which collect lacustrine sediment. Marine and lacustrine basins contain different types of organisms, driven in large part by the difference in salinity between them. Thus, marine and lacustrine basins contain different biomarkers.
Branched GDGTs are thought to be membrane-spanning lipids of anaerobic acidobacteria. Research suggests that the producing organisms change membrane properties in response to changing temperature.
This change is caused by the transformation of methylated sites on the branched GDGT&#8217;s to cyclized sites at colder temperatures, thereby enhancing membrane fluidity. This change in structure can then be correlated to temperature through a proxy. Proxies are measureable physical phenomena that are correlated to immeasurable variable.
This proxy relates the number of methylations or MBT, and cyclizations, or CBT, in the biomarker to temperature. An experimentally-derived equation can relate MBT and CBT to the past Mean Annual Air Temperature.
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<div class="chapter_text" id="script_2">
To study the relationship between branched GDGT biomarkers and soil temperature, lacustrine sediment must be collected, extracted by one of three techniques, purified, and analyzed.
To begin studying the relationship between branched GDGT biomarkers and soil temperature, the lipid molecules are first extracted from lacustrine sediments, using a variety of techniques. Extraction via sonication is the simplest and least expensive method of obtaining the total lipid extract, or TLE, from a sediment sample. For this, an ultrasonic bath is used to agitate the sample in a vial containing organic solvent. A mixture of methanol and dichloromethane is used to extract biomarkers with a wide range of polarities. Another extraction technique utilizes Soxhlet extraction. A Soxhlet extractor enables the reflux, or continuous cycling, of organic solvent from a round-bottom flask upward into a condenser, which is cooled by cold water and returned. The condensed solvent falls into a glass fiber thimble containing the sample. Once full, the chamber siphons the organic solvent back into the round-bottom flask, enabling continuous extraction over time.
This technique is helpful in the extraction of large sediment masses, and the preparation of large volumes of standards for instrument calibration. Finally, accelerated solvent extraction, or ASE, is a trademarked method of extraction that utilizes high temperature and pressure to increase the kinetics of the extraction process. The ASE instrument holds up to 24 individual samples, and allows for precise control of all parameters in the extraction process. Due to its speed and simplicity of use, ASE is commonly used as the standard method of solvent extraction.
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<div class="chapter_text" id="script_3">
Once the lipid sample is extracted using one of these techniques, it is purified in preparation for analysis. Typically, silica gel column chromatography is used to purify the lipid sample based on its polarity. For this, a small glass column is loaded with a fine powder of silica, called a gel. The column is then saturated with an apolar solvent, typically hexane, and then the sample loaded on the top. The separation of the extract is based on the affinity of the target compound for either the solid phase or the solvent phase.
Polar compounds, in this case branched GDGT&#39;s, are more attracted to the polar silica than the apolar hexane. Thus, the apolar compounds, such as hydrocarbons, the mid-polar compounds, such as ketones and alcohols, and the highly polar compounds, will travel the column at different rates and in response to solvents of increasing polarity.
The eluents are then collected in separate fractions.
The purified GDGT&#39;s are then analyzed using high performance liquid chromatography coupled to a mass spectrometer, or LC-MS. LC-MS first separates the compounds, and then analyzes them based on their mass-to-charge ratio.
This enables the determination of the relative concentration of each type of GDGT using the area under the curve for the selected mass ion. MBT is calculated as the fraction of the group 1 molecules to the total.
CBT is then calculated as a negative log using molecules in groups 1 and 2. MBT and CBT are then plugged into an experimentally-derived equation, in order to arrive at a paleotemperature determination.
</div>
<div class="chapter_text" id="script_4">
The determination of paleotemperature using biomarker proxies is useful in a range of applications in earth science.
First, paleothermometry enables the determination of the Earth&#39;s temperature over long periods of time. Using various techniques, the temperature of Earth has been estimated as far back as 500 million years. This tells us the envelope of temperature within which different forms of life evolved and informs investigations of the effects of temperature on Earth&#39;s biosphere, hydrosphere, lithosphere, and atmosphere in the past, and by extension, the future.
More recent trends in the Earths temperature can also be quantified against records constructed using paleothermometry. The Earths surface temperature has increased by nearly 1 degree from 1850 to the present with an accentuated warming trend in the last two decades. To understand the anthropogenic impact on global climate, accurate paleoclimate records must be developed and used as context.
</div>
<div class="chapter_text" id="script_5">
You&#39;ve just watched JoVE&#39;s Overview of Branched Glycerol Dialkyl Glycerol Tetraether Paleothermometry. You should now understand how the branched GDGT biomarkers are used, and the overall technique of extracting and purifying them. The following videos in this series will go into more detail about this complex process.
Thanks for watching!
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Reconstructing Terrestrial Paleoclimate and Paleoecology with Fossil Leaves Using Digital Leaf Physiognomy and Leaf Mass Per Area

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