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)

Introduction
    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 MAT.  This model has been used to predict past climate 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 F deg C
    source:  
    if web site, date accessed:  
  3. Complete Table 2 by calculating the predicted percentage of leaves of deciduous woody trees in our area with entire leaves using the five temperature models.  Then, calculate the mean predicted percentage of leaves with entire margins.  Finally, calculate the predicted percentage of serrate margins.     
     
    Table 2.  Predicted % of woody species in central Minnesota with entire leaf margins
    Model Predicted % with entire leaves
    Equation 1  
    Equation 2  
    Equation 3  
    Equation 4  
    Equation 5  
    Mean % entire margins  
    Predicted % serrate margins  

     

  4. Before lab begins, send to me an email that includes Table 1 & 2.   In addition, be sure to record these data on the lab handout.
     

  5. For each of your assigned species, bring to class:  (a) an image of a characteristic leaf of each species that clearly shows the leaf margin; (b) label each image with the species; and (c) indicate if the leaf is serrate or entire (see methods.  note - you may have to find a high resolution image for some).  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.

Methods:
    Once in lab, we will examine herbarium specimens and the images obtained by your lab mates to complete Table 4.  For a leaf to be considered serrate, the tooth must be an extension of a vein (vascular extension).  Lobed leaves, without teeth, are considered entire.  In other words, veins should run into the teeth.  Do not count "spines," as in holly, as teeth.  Once you have collected your data, complete the summary data tables (5, 6 & 7).  It might be easiest to paste your data into an Excel spreadsheet to complete the necessary calculations.

Table 4.  Characteristics of leaves of deciduous woody plants in Central Minnesota
  Species Native to central Minnesota (1 = yes; 0 = no) Margins (toothed = 0; entire = 1)
1

Acer negundo � Box elder 

   
2

Acer platanoides � Norway maple

   
3

Acer rubrum � Red maple

   
4

Acer saccharinum � Silver maple

   
5

Acer saccharum � Sugar maple

   
6

Acer  ginnala � Amur maple

   
7

Aesculus glabra � Buckeye

   
8

Alnus incana � Speckled alder

   
9

Amalanchier canadensis � Serviceberry

   
10

Amorpha canescens � Lead plant

   
11

Aronia melanocarpa � Black chokeberry

   
12

Berberis thunbergii � Japanese barberry

   
13

Berberis vulgaris � Common barberry

   
14 Betula alleghaniensis (=B. lutea) � Yellow birch    
15

Betula nigra � River birch

   
16

Betula papyrifera � White or paper birch

   
17

Carpinus caroliniana � Blue beech

   
18

Catalpa speciosa �  Common catalpa

   
19

Celastrus scandens � Bittersweet

   
20

Celtis occidentalis � Hackberry

   
21

Cornus alternifolia  � Pagoda dogwood

   
22

Cornus foemina � Gray dogwood

   
23

Cornus rugosa � Round-leaved dogwood

   
24

Cornus stolonifera � Red osier dogwood

   
25

Corylus americana � American hazelnut

   
26

Corylus cornuta � Beaked hazelnut

   
27

Crataegus sp. � Hawthorne

   
28

Diervilla lonicera � Bush honeysuckle

   
29

Dirca palustris � Leatherwood;

   
30

Eleagnus angustifolia � Russian olive

   
31

Euonymus alatus � Winged euonymus

   
32

Fraxinus americana � White ash

   
33

Fraxinus nigra � Black ash

   
34

Fraxinus pennsylvanica � Green ash

   
35

Gleditsia triacanthos � Honey locust

   
36

Gymnocladus dioica � Kentucky coffee tree

   
37

Ilex verticillata � Winterberry

   
38

Juglans cinerea � Butternut

   
39

Juglans nigra � Black walnut

   
40

Lonicera tartarica � Honeysuckle

   
41

Ostrya virginiana �  Ironwood, Hophornbeam

   
42

Phellodendron amurense � Amur cork tree, Cork tree

   
43

Physocarpus opulifolius � Ninebark

   
44

Populus  nigra cv. italica � Lombardy poplar

   
45

Populus alba � White or silver poplar

   
46

Populus balsamifera � Balsam popular

   
47

Populus deltoides � Cottonwood

   
48

Populus grandidentata � Large toothed aspen

   
49

Populus tremuloides � Quaking aspen

   
50

Potentilla fruticosa � Cinquefoil

   
51

Prunus americana � Wild plum

   
52

Prunus pensylvanica � Pin cherry

   
53

Prunus serotina � Black cherry

   
54

Prunus virginiana � Chokecherry

   
55

Pyrus malus � Apple

   
56

Quercus alba � White oak

   
57

Quercus bicolor � Swamp white oak

   
58

Quercus ellipsoidalis � Northern pin oak

   
59

Quercus macrocarpa � Bur oak

   
60

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

   
61

Rhamnus cathartica � European Buckthorn

   
62

Rhus glabra � Smooth sumac

   
63

Rhus typhina � Staghorn sumac

   
64

Ribes cynobasti  � Prickly gooseberry

   
65

Ribes lacustre � Swamp currant 

   
66

Salix discolor � Pussy willow

   
67

Salix exigua � Sandbar willow

   
68

Salix nigra � Black willow

   
69

Sambucus canadensis � Common elderberry

   
70

Sambucus pubens � Red elder

   
71

Sorbaria sorbifolia � False spiraea

   
72

Sorbus aucuparia  � Mountain ash

   
73

Spiraea alba � Meadowsweet

   
74

Symphoricarpos albus - Snowberry

   
75

Symphoricarpos occidentalis � Wolfberry

   
76

Syringa reticulata � Japanese tree lilac

   
77

Syringa vulgaris � Common lilac

   
78

Tilia americana � Basswood, Linden

   
79

Ulmus americana � American elm

   
80

Ulmus pumila � Chinese elm

   
81

Ulmus rubra � Slippery elm

   
82

Viburnum lentago � Nannyberry

   
83

Viburnum rafinesquianum - Arrowwood

   
84

Viburnum trilobum � High-bush cranberry

   
85

Zanthoxylum americanum � Prickly ash

   

 

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

Mean

     

 

 

Data & Analysis:  Once you have collected your data:

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

  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 those with entire margins. Perform a chi square test by completing the table below.  (Click here for the Concepts web site statistical tests).

Table 7.  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 tables 8 & 9.  (Click here for the Concepts web site statistical tests)
     
Table 8.  Chi square 2 x 2 contingency table, comparing leaf margins on native and non-native species
  native species non-native species
serrate    
entire    

 

Table 9.  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, typed on a separate sheet of paper not photocopies of this handout,  completed copies of tables 5 - 9.  In your 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 climate change impact woody species in our area? 

 

References:

Web Sites:

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