Spring.wmf (18300 bytes) Plant Physiology (Biology 327)  - Dr. Stephen G. Saupe;  College of St. Benedict/ St. John's University;  Biology Department; Collegeville, MN  56321; (320) 363 - 2782; (320) 363 - 3202, fax;    ssaupe@csbsju.edu

Leaf Margin Analysis, Or, Are Leaves Good Predictors of Climate?

ObjectivesThe purpose of this lab is to:

  1. test models for estimating mean annual temperature (MAT) that are derived from leaf margins
  2. read an analyze a scientific paper
  3. perform chi square tests (2 x 2 contingency table and goodness-of-fit)

    Since plants are stationary they must respond developmentally, and ultimately evolutionarily, to their environment.  As a result, it's not surprising that leaf morphology (shape) has been shown to be related to climate.  For example, some the following correlations have been reported (Wiemann et al, 1998):  (a) leaf length is directly related to the mean annual temperature (MAT); (b) leaf area is directly correlated to both mean annual precipitation (MAP) and MAT; and (c) leaf width is directly correlated with MAP.  Thus, leaves are longer and larger in climates with warmer temperatures and higher rainfall. 

    Another interesting observation that was first reported about 100 year ago is that woody deciduous plants having leaves with toothed margins (termed serrate) predominate in temperate climates while species with smooth (termed entire) leaf margins predominate in frigid (arctic, montane), dry (or saline), and tropical climates.  This relationship has been used to derive a mathematical model for predicting climate from leaf margins.  One application of this model is to determine MAT in the geological past by analyzing the leaf margins of fossil plants.

    It is not clear why there should be such a strong correlation between leaf margin and temperature.  A recent analysis suggests that serrated margins provide regions of quicker photosynthesis in cooler conditions (Royer & Wilf, 2006).   

    Wiemann et al. (1998) and Wilf (1997) report that the following equations have been derived to predict MAT (in degrees C) or MAP (in cm) from leaf margin structure (% is expressed as a whole number, not a decimal fraction):

    The purpose of today's lab is to test the accuracy of these models for our area. 

Pre-Lab Study:

  1. Print, read, and bring to class a copy of this exercise. 

  2. Complete Table 1 by locating the data for our area for mean annual temperature (MAT) and mean annual precipitation (MAP).  These data can be obtained from a variety of web-based sources such as the Midwest Regional Climate Center (click on: Climate of the Midwest/Climate Summaries).  If you need to convert unit, there are many web sites that will help.


    Table 1.  Climate Data for Central Minnesota
    MAT deg C deg F
    MAP inches cm
    if web site, date accessed:  
  3. Complete Table 2 by calculating the predicted percentage of leaves of deciduous woody trees in our area with serrate leaves using all five temperature models.  Then, calculate the mean predicted percentage of leaves with serrate margins.
    Table 2.  Predicted % of woody species in central Minnesota with serrate leaf margins
    Model Predicted % with serrate leaves
    Equation 1  
    Equation 2  
    Equation 3  
    Equation 4  
    Equation 5  


  4. Complete Table 3 by calculating the predicted percentage of woody species with large leaves.
    Table 3.  Predicted % of woody species in central Minnesota with large leaves
    Model Predicted % with large leaves
    Equation 6  


  5. Before lab begins, send to me an email that includes Table 1, 2 & 3.   In addition, be sure to record these data on the lab handout and bring them to lab:

  6. For each of your assigned species, bring to class:  (a) an image (8.5 x 11) of the plant clearly showing the leaf margins; (b) write on the image whether the plant is native to Minnesota or not; and (c) write on the image the length and width of a typical leaf (in cm) or mark the scale on your diagram so that we can calculate the length/width from the image.  Most of this information can be obtained from sources such as the USDA Plants Database, Flora of North America project site, or books of images such as the Illustrated Guide to Accompany Gleason & Cronquist's Manual of Plants of NE United States and Canada.  Images are available in this site or found through a Google "Image" search or other.

    Once in lab, examine the herbarium specimens provided and/or images & data obtained by your lab mates and complete Table 4.  For a leaf to be considered serrate, the tooth must be an extension of a vein (vascular extension).  In other words, veins should run into the teeth.  Do not count "spines," as in holly, as teeth.  By definition (Wiemann et al., 1998), a leaf that has an area greater than 33 cm2 is considered "large."  A quick method to approximate the area of a leaf is to multiply leaf length x leaf width x 2/3 (Manual of Leaf Architecture).  Alternately, compare an "average" leaf of each species to a model that you cut out of paper that is 33 cm2 (or 5.8 x 5.8 cm).  Once you have collected your data, complete the summary data tables (5, 6 & 7). 

Table 4.  Characteristics of leaves of deciduous woody plants in Central Minnesota
Species Native (N) or non-native (X) Entire (E) or Serrate (S) Leaf Length (cm) Leaf Width (cm) ca. Leaf Area (cm2) Leaf Size (L = large > 33 cm2; S = small, < 33 cm2)

Acer  ginnala � Amur maple


Acer platanoides � Norway maple

Acer rubrum � Red maple            
 Acer saccharum � Sugar maple            

Acer saccharinum � Silver maple

Acer negundo � Box elder             
Rhus glabra � Smooth sumac            
Rhus typhina � Staghorn sumac            
Ilex verticillata � Winterberry            

Berberis thunbergii � Japanese barberry

Berberis vulgaris � Common or European barberry            
Alnus incana � Speckled alder            

B. alleghaniensis (=B. lutea) � Yellow birch

Betula papyrifera � White or paper birch            
Betula nigra � River birch            
Carpinus caroliniana � Blue beech            

Corylus americana � American hazelnut

Corylus cornuta � Beaked hazelnut            

Ostrya virginiana �  Ironwood, Hophornbeam

Catalpa speciosa �  Common catalpa            
Diervilla lonicera � Bush honeysuckle            

Lonicera tartarica � Honeysuckle

Sambucus canadensis � Common elderberry            
Sambucus pubens � Red elder            

Symphoricarpos albus - Snowberry

Symphoricarpos occidentalis � Wolfberry            
Viburnum lentago � Nannyberry            

Viburnum rafinesquianum - Arrowwood

Viburnum trilobum � High-bush cranberry            
Celastrus scandens � Bittersweet            

Euonymus alatus � Winged euonymus

Cornus alternifolia  � Pagoda dogwood            
Cornus foemina � Gray dogwood            

Cornus rugosa � Round-leaved dogwood

Cornus stolonifera � Red osier dogwood            
Eleagnus angustifolia � Russian olive            

Amorpha canescens � Lead plant

Gleditsia triacanthos � Honey locust            
Gymnocladus dioica � Kentucky coffee tree            

Quercus alba � White oak

Quercus bicolor � Swamp white oak            
Quercus macrocarpa � Bur oak            

Quercus rubra (= Q. borealis) � Northern red oak

Quercus ellipsoidalis � Northern pin oak            
Ribes cynobasti  � Prickly gooseberry            

Aesculus glabra � Buckeye

Juglans nigra � Black walnut            
Juglans cinerea � Butternut            

Ribes lacustre � Swamp currant 

Syringa reticulata � Japanese tree lilac            
Syringa vulgaris � Common lilac            

Fraxinus americana � White ash

Fraxinus pennsylvanica � Green ash            
Fraxinus nigra � Black ash            

Rhamnus cathartica � European Buckthorn

Amalanchier canadensis � Serviceberry            
Aronia melanocarpa � Black chokeberry            

Crataegus sp. � Hawthorne

Pyrus malus � Apple            
Physocarpus opulifolius � Ninebark            

Potentilla fruticosa � Cinquefoil

Prunus americana � Wild plum            
Prunus pensylvanica � Pin cherry            

Prunus serotina � Black cherry

Prunus virginiana � Chokecherry            
Sorbaria sorbifolia � False spiraea            

Sorbus aucuparia  � Mountain ash

Spiraea alba � Meadowsweet            
Phellodendron amurense � Amur cork tree, Cork tree            
Zanthoxylum americanum � Prickly ash            

Salix discolor � Pussy willow

Salix exigua � Sandbar willow            
Salix nigra � Black willow            

Populus alba � White or silver poplar

Populus deltoides � Cottonwood            
Populus grandidentata � Large toothed aspen            

Populus  nigra cv. italica � Lombardy poplar

Populus tremuloides � Quaking aspen            
Populus balsamifera � Balsam popular            

Dirca palustris � Leatherwood;

Tilia americana � Basswood, Linden            
Celtis occidentalis � Hackberry            

Ulmus americana � American elm

Ulmus pumila � Chinese elm            
Ulmus rubra � Slippery elm            


Table 5.  Data Summary
  Native Introduced Total
Species number      
Percent of total species      
Number of species with serrate leaves      
Number of species with entire leaves      
Percent species with serrate leaves      
Percent species with entire leaves      
Number of species with large leaves      
Percent of native species with large leaves      


Table 6.  Predicted MAT  for central Minnesota based on leaf morphology of all woody species, native woody species and non-native species
Model MAT (C) - data from all species MAT (C) - native species data MAT (C) -  introduced species data
Equation 1      
Equation 2      
Equation 3      
Equation 4      
Equation 5      




Table 7.  Predicted MAP  for central Minnesota based on leaf morphology of all woody species, native woody species and non-native species
Model MAP (cm) - data from all species MAT (cm) - native species data MAT (cm) -  introduced species data
Equation 6      


Data & Analysis:  Once you have collected your data:


  1. Complete the summary data tables (5, 6 & 7). 

  2. Perform a chi square test to determine if the there is a statistically significant difference between the number of native species with serrate leaf margins and entire margins. Perform a chi square test by completing the table below.  (Click here for the Concepts web site statistical tests).

Table 8.  Chi square goodness-of-fit test comparing native species with serrate and entire margins

 null hypothesis:  


Observed  values: serrate:                          entire:
Expected values: serrate:                           entire:
p value =   
Conclusion:  The null hypothesis should be:      rejected     accepted


  1. Perform a chi square 2 x 2 contingency test to determine if there a statistically significant difference in distribution of leaf margins (serrate vs. entire) between native and non-native species.  Complete the table below.  (Click here for the Concepts web site statistical tests)
Table 9.  Chi square 2 x 2 contingency table, comparing leaf margins on native and non-native species
  native species non-native species


Table 10.  Results of chi square 2 x 2 contingency table, comparing leaf margins on native and non-native species

 null hypothesis:  
p value =   
Conclusion:  The null hypothesis should be:      rejected     accepted


Post-Lab Assignment:  Write an abstract of this lab.  Append to your abstract completed copies of tables 5 - 10.  In you abstract address questions such as:

  1. Which species should we use in our analyses - native, introduced or all woody species?
  2. Which model(s) is most accurate for our area? How much error exists in the model(s)?  [calculate percent difference = (observed - expected)/expected x 100]
  3. If you are a horticulturalist, which species would most likely be suited for introduction to our area - those with serrate or entire margins?  Explain.
  4. Offer an explanation why plants with serrated leaf margins predominant in our area and are correlated to temperature.  (see article by Royer & Wilf, 2006)
  5. How might global warming impact woody species in our area? 



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Last updated:  01/07/2009     � Copyright  by SG Saupe