United States 

Department of 

Agriculture 

Forest Service 

Southern 

Research Station 

Nondestructive Estimation of 

Leaf Area for Pondberry 

Brian Roy Lockhart, Emile S. Gardiner,Theran P. Stautz, 

Theodor D. Leininger, Paul 6. Hamel, Kristina F. Connor, 

Nathan M Schiff, A. Dan Wilson, and Margaret S. Devall 

Research Note 

SRS-14 

February 2007 

Abstract 

Pondberry (Lindera rnelirsifol~ia Walt.] Blume) is a federally listed 

endangered shrub found as isolated populations in seasonally flooded 

forests across the Southeastern United States. Because this shrub is rare, it 

has received little research attention, and basic knowledge of its ecology 

and physiology is lacking. To facilitate future ecological and physiological 

studies on pondberry, we developed and tested a model to predict area 

of individual leaf blades from simple dimensions that are obtained 

nondestmctively. A linear function, using the product of blade length 

and width as the independent variable, was found to be the most suitable 

predictor of pondberry leaf blade area based on correlation coefficients 

(r2 = 0.99561, plots of actual versus predicted values, and predicted versus 

residual values. We demonstrate that simple dimensions that are obtained 

nondestructively, such as blade length and width, can be used to reliably 

predict leaf blade area of pondberry, but model coefficients should be 

calibrated for local colonies to improve estimates. Development of this 

model allows for leaf blade area determination at the plant level without 

the need to destructively harvest foliage. 

tntroduction 

Pondberry (Lindera melissifolia Walt.] Blume), also 

known as hairy spicebush, southern spicebush, or swamp 

spicebush, is a niember of the Lauraceae. This rhizomatous 

shrub grows up to 2 m tall in seasonally flooded forests 

across the Southeastern United States (Devall and others 

2001, Radford and others 1968, U.S. Fish and Wildlife 

Service 1993). Pondberry has been described as one of 

the rarest shrubs in the United States (Steyermark 1949), 

and is a federally listed endangered species (U.S. Fish 

and Wildlife Service 1986). A subsequent recovery plan 

Brian Roy Lockhart, Research Forester; Emile S. Gardiner, Research 

Forester; Theran I? Stautz, Biological Science Technician; Theodor D. 

Leininger, Project Leader; Paul B. Hamel, Research Wildlife Biologist; 

Nathan M Schiff, Research Entomologist; A. Dan Wilson, Research Plant 

Pathologist; and Margaret S. Devall. Research Ecologist, USDA Forest 

Service, Southern Research Station, Stoneville, MS 38776; and Kristina 

E Connor, USDA Forest Service, Southern Research Station, Auburn, AL 

36849. 

published in 1993 (U.S. Fish and Wildlife Service 1993) 

documents the need to acquire additional knowledge about 

the biology and ecology of this species. The lack of biological and ecological knowledge of pondbeny has hampered 

development of strategies for recovery of pondbeny and 

removal of the plant from the endangered species list. 

A key to increasing knowledge of pondbeny growth and 

development is data on cause-and-effect relationships 

between species physiology and various environmental 

factors, such as light availability, soil moisture availability, and response to competition. Ledig (1976) stated 

that information about the partitioning of growth among 

plant tissues is necessary to understand how environmental 

conditions and cultural practices affect growth. Leaf area is 

widely considered an important index of plani growth and 

development (Larsen and Kershaw 1991, Rarnkhelawan 

and Brathwaite 1990) because it is directly related to light 

interception, carbon assimilation, and gas exchange (Lu 

and others 2004). Methods for determining leaf area vary, 

but a common technique involves building regression equations that predict leaf blade area from measurements of 

blade or stem variables, e.g., blade length, blade width, 

petiole length, and internode length (Larsen and Kershaw 

1991). These area models can then be used to compute total 

leaf area of a plant without destructively sampling leaves 

(Bange and others 2000, Guo and others 1995). Since the 

availability of a nondestructive technique for estimating 

leaf area would advance future research on the species, the 

objective of this study was to develop a model to predict 

pondbeny leaf blade area based on simple, nondestructive 

measurements of leaf or stem dimensions. 

Materials and Methods 

Pondberry leaf material used in this experiment was 

harvested from 28 seedlings raised in a growth chamber. 
These seedlings were established from seed collected on 

the Delta National Forest (latitude 32" 58' N., longitude 

90" 44' W.), near Rolling Fork, MS. In May 2004, seed that 

had been stratified for 3 months at 2 "C was potted in 7.5-L 

containers with a soil mix consisting of peat moss, perlite, 

046-0 and 10-10-10 (nitrogen-phosphow-potassium) 

fertilizer, and Milorganite (manufactured by 'Milwaukee 

Metropolitan Sewage District, Milwaukee, WI). Seed were 

germinated and seedlings were grown for 3 months under 

one of two environmental conditions -high-light availability or low-light availability. These treatment levels were 

selected to produce a range in pondbeny leaf sizes for 

development of a robust model for leaf blade area prediction. The 14 seedlings receiving high light were exposed 

to a maximum of 600 pmol m2 per second photosynthetically active radiation (PAR) while those receiving low light 

were exposed to a maximum of 120 pmol m2 per second 

PAR. The low-light regime was implemented in the growth 

chamber by placing 63 percent neutral density shade 

cloth above the pots assigned to this treatment. Both light 

regimes were applied for 16 hours each day and included 

a daily 2-hour step up and 2-hour step down period during 

which light levels were increased or decreased by one 

quarter each half hour. Daytime temperature was maintained at 26 "C, nighttime temperature was maintained at 

20 "C, and all seedlings were watered three to four times 

per week to maintain adequate soil moisture. 

Following 3 months of growth, all leaves were excised 

from each seedling. Total leaf blade length (mm) along 

the midrib, width (mm) at the widest point perpendicular 

to the midrib, and the subtending intemode length (mm) 

were measured with a ruler. Area (cm2) of each leaf blade 

was measured twice with a LiCor L13 100 leaf area meter 

and the two measurements for each leaf blade were averaged. We evaluated the suitability of eight linear regression models developed to predict blade area. Models 

included various combinations of blade length, width, and 

subtending internode length used as independent variables, 

and the averaged blade area used as the dependent variable. 

We also tested for differences between leaves grown under 

high and low light availability. These tests for differences in 

leaf blade length, width, lengthiwidth ratio, and leaf blade 

area were conducted with t-tests at an alpha level of 0.05. 

To further assess the usefulnesb of the selected model to 

predict leaf area of wild plants, we sampled an additional 

90 leaves (30 from each of 3 wild pondberry colonies) 

growing at a field site about 10 km north of Cleveland, MS 

(latitude 32" 45' N., longitude 88" 50' W.). Dimensional 

measurements and dimensions of leaf blade area were 

conducted on these leaves as described above. Regression 

analyses with dummy variables were used to compare 

coefficients of leaf blade area models developed for plants 

grown in the growth chamber with those of plants grown in 

the field. All data analyses and modeling were conducted 

with PC-SAS (SAS 1985). 

Results and Discussion 

Pondberry leaves are simple, have a smooth margin, and 

are generally oblong with a pointed apex and a round base. 

Table 1 presents basic leaf characteristics measured on 

pondbeny seedlings examined in this study. Leaf blade 

length, width, and area of plants studied in this research 

differed according to light regime. Leaves from seedlings 

grown under low light developed blades 35 percent longer 

and 40 percent wider than leaves of seedlings grown under 

high light (table 1). Shade leaves are commonly larger 

than sun leaves (Gardiner and others 2004, Givnish 1988, 

Hamerlynck and Knapp 1994, Lichtenthaler and others 

1981). Also, blade area of leaves that developed under 

low light was 64 percent greater than that of leaves that 

developed under high light. Greater pondbeny total leaf 

area under low- and intermediate-light regimes has been 

Table 1 -Leaf blade characteristics of pondberry seedlings grown under high and 

low light regimes in a growth chamber 

n Length Width Ratio (lengh, width) Blade area 

- - - - - - mm * - - - - - cm2 

High light" 320 57 * 2b 24* 1 2.32 * 0.02 10.8 + 0.6 

Rangec 11 - 130 5-53 1.20 - 3.40 0.1 - 40.8 

Low light 47 1 88 * 2 40* 1 2.28 & 0.02 30.0 + 1.3 

Range 11- 184 5- 106 1.21 - 3.60 0.1 - 136.0 

p-value 0.0001 0.0001 0.1516 0.0001 

" Seedlings under the high light regime received up to 600 pmol m2 per second photosynll~etically active 

radiation (PAR), while those under the low ligl~t regime received lip to 120 plnol m2 per second PAR. 

bLfean * 1 standard error of the mean. 

'Range is minimum md maximum values. 
reported previously (Aleric and Kirkman 2005). In our 

study, the ratio of leaf blade length to leaf blade width 

was the same for plants that were grown under high and 

low light conditions. This finding meant that the high light 

leaves and the low light leaves had the same shape and 

allowed us to pool mekurements from both treatments for 

the regression analyses discussed below. 

The eight linear regression models tested for leaf blade area 

prediction differed in effectiveness (table 2). Plots of leaf 

blade length and width indicated that simple linear regression without &ta transformation would provide adequate 

prediction of pondbeny leaf blade area. Internode length 

was not as strong a predictor of blade area as were blade 

length and width (table 2). muation 3 was selected as the 

most suitable predictor of pondberry leaf blade area based 

on ease of measurement, time to conduct measurements 

(length and width versus length or width), high correlation coefficients, the plot of actual versus predicted values 

(fig. 1). and the plot of predicted versus residual values 

Table 2-Linear equations tested to predict leaf blade area of pondberry raised under two light regimes in 

a growth chamber 

Equation Mean square Standard error of 

number Modela r * error regression coeff. 

1 A = - 19.0700 + 0.5413 (lengthlb 0.8588 9.29 0.0078 

2 A=- 17.2138 + 1.1558 (width) 0.9380 6.16 0.0106 

3 A=- 0.88340 + 0.0067 (length x width) 0.9956 1.64 0.0007 

4 A=- 15.0243 + 2.191 5 (internode) 0.5726 16.16 0.0674 

5 A=- 2.0004 + 0.0032 (length x length) 0.9475 5.66 0.00003 

6 A = 1.617472 + 0.012850 (width x width) 0.9695 4.32 0.0001 

7 A = - 17.351268 + 0.014248 (length) 0.9381 6.16 0.0174 

+ 1.1-28022 (width) 0.0356 

8 A = - 0.754028 + 0.001414 (length x length) 0.9966 1.45 0.00002 

+ 0.007617 (width x width) 0.00007 

" P-value for regression was 0.0001 for all equations. 

A= predrcted leaf blade area in cm2; length = leaf blade length (mm) along the midrib; width = leaf blade width (mm) at the widest point 

perpendicular to the midrib; internode = subtending internode length in mm. 

140 1 0 Leaf Made area 

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 

Length (mm) x width (mm) 

Figure 1 -Leaf Made area vetsus pmhcted leaf Made area of podberry using blade lenglh times blade width as 

the independent variable. 

3 
(fig. 2). The estimated gain (r2) of the product of blade 

length and width versus length was 0.14 and that of the 

product of blade length and width versus width was ,0.06. 

This improvement justifies the additional time necessary to measure both leaf blade length and width rather 

than measwing only a single dimension. The 0.001 gain 

observed for Equation 8 was not considered high enough to 

warrant use of multiple linear regression to predict pondberry leaf blade area. 

The cross product of leaf length and width has been highly 

correlated with leaf blade area in several other plant species. 

Applications of such models to broadleaf tree species are 

discussed in Guo and others (1995) for chenybark oak 

(Quercz4.s pagoda Raf., rZ = 0.98) and in Kubicek (1971) 

for sessile oak (Q. petraea Ih.lattuschka] Liebl., r2= 0.W). 

Applications to herbaceous dicots are discussed in Bange and 

others (2000) for sunflower (Helianthus annuus L., r2= 

0.94) and in Hughes and others (1979) for pigeon pea 

(Cajanus cajan [L.] Millsp., where r2 ranged from 0.91 

to 0.98 depending on cul tivar). Chanda and others (1985) 

and Krisbnamurthy and others ( 1974) presented examples 

with monocots including pearl millet (Pennisetum americanum [L.] Leeke, r2 = 0.998) and grain sorghum (Sorghum 

bicolor [L.] Moench, where r2 ranged from 0.956 to 0.987 

depending on variety). Thus, models of this type have been 

used to predict leaf blade area over a range of plant types. 

As described in the Materials and -Methods section, we 

collected pondbeny leaves from a nearby field site and 

used them to test the capability of the model to predict leaf 

blade area of wild plants. Leaves harvested in the field were 

generally larger in length, width, and area than those grown 

in the growth chamber (table 3). The slope coefficient for 

Predicted leaf blade area (cm2) 

Wgure 2-Plot of residual versus predicted leaf blade area for pondbeny raised under two light regimes in a 

growth chamber. 

Table 3-Characteristics of leaf blades harvested from wild pondberry colonies, 

Boliver County, MS 

Colonv Gender n Length Width Ratio (len~thlwidth) Blade area 

1 Male 30 102 * 6"a 48 rt 3 a 2.17 * 0.03 a 35.3 + 3.9 ab 

2 Male 30 120*7a 52+4a 2.36 + 0.05 b 48.3 k 5.7 a 

3 Female 30 100k7a 43k3a 2.37 + 0.06 b 34.0 * 4.5 b 

p-value 0.0756 0.1719 0.0059 0.0706 

"Mean i 1 standard error of the mean. 

Memu within a cvl~umn followed by different letters are signiEmutly different @ 5 0.05). 
growth chamber plants differed from that for field-grown 

plants (p < 0.0001). Two of the three wild colonies had 

slope coefficients different from that for growth chamber 

plants @ = 0.5435,0.0232, and 0.0041 for colonies 1,2, 

and 3, respectively). These results indicate that calibrating 

the model for pondberry colonies of interest may improve 

prediction of leaf blade area for field-grown plants. 

In summary, we developed a simple, nondestructive method 

for predicting leaf blade area of pondberry from basic leaf 

dimensions. A linear function with the product of blade 

length and width as the independent variable was found to 

be the most suitable predictor of pondberry leaf blade area 

(table 2). This function and easily obtained leaf measurements can be used to accurately estimate pondberry leaf 

blade area, though the function may need to be cqibrated 

for local colonies. The availability of this function will 

enable researchers to develop comprehensive ecological and 

physiological studies that involve leaf area dynamics over 

multiple scales. Such research will lead to a more complete 

understanding of the environmental requirements for pondbeny growth and development, which is critical to the 

development and implementation of management strategies 

that will hasten the recovery of this endangered species. 

Acknowledgments 

Support for this pondbemy research was provided by the 

U.S. Army Corp of Engineers, Vicksburg Disrrict; U.S. Fish 

and Wildlife Service; and U.S. Department of A,giculture 

Forest Service, Southern Research Station. We thank Tracy 

Hawkins, Tom Dell, and Alan Salmon for constructive 

comments on earlier drafts of this manuscript. Seed mate- 

.rial used in this research was collected under U.S. Fish and 

Wildlife Service permit number Endangered-Threatened 

Species Subpermit SA-0142-Amendment 3. 

Literature Cited 

Aleric, K.M.; Kirkman, L.K. 2005. Growth and photosynthetic responses 

of the federally endangered shrub, Lindera melissifolia (Lauraceae), to 

varied light environments. American Journal of Botany. 92. 682-689. 

Bange, M.P.; Hammer, G.L.; Milroy, S.P.; Rickerl, K.G. 2000. Improving 

estimates of individual leaf area of sunflower. Agronomy Journal. 92: 

761-765. 

Chanda, S.V.; Joshi, A.K.; Vaishnav, P.P.; Singh, Y.D. 1985. Leal area 

determidon in pearl millet wing linear measurements-area and 

matter-area relationships. Photosynthetica 19: 424-427. 

Devall, M.; Schiff, N.; Boyette, D. 2001. Ecology and reproductive 

biology of the endangered pondberry, Lindera melissijolia (Walt) 

Blume. Natural .beas Journal. 21: 250-258. 

Gardiner, E.S.; Stanturf, J.A.; Schweitzer, C.J. 2004. An aorestation 

system for restoring bottomland hardwood forests: biomass 

accumulation of Nuttall oak seedlings interplanted beneath eastern 

cottonwood. Restoration Ecology. 12: 525-532. 

Givnish, T.J. 1988. Adaptation to sun and shade: a whole plant perspective. 

Australian Journal of Plant Physiology. 15: 63-92. 

Guo, Y.; Lockhart, B.; Hodges, J. 1995. Prediction of leaf area in 

individual leaves of cherrybark oak seedlings (Quercuspagoda Raf.). 

Proceedings of the Arkansas Academy of Science. 49: 52-55. 

Hamerlyuck. E.P.; bpp, A.K. 1994. Leaf-level responses to light 

and temperature in two co-occurring Querclrs (Fagaceae) species: 

implications for tree distribution patterns. Forest Ecology and 

Management. 68: 149- 159. 

Hughes. G.; Keatinge, J.D.H.; Swtt. S.P. 1979. Leaf area estimation by 

uon-destructive methods in pigeon pea (Cajanus cajan (L.) Millsp.). 

Tropical Agriculture. 56: 371-374. 

Krishnamurthy, K.; Jagaunath, M.K. ; Rajashekara, B.G.; Raghunatha, 

G. 1974. Estimation of leaf area in grain sorghum from single leaf 

measurements. Agronomy Journal. 66: 544-545. 

Kubicek. F. 1971. Leaf area estimation in Querclrs petraea using linear 

regression equations. Photosynthetica. 5: 426428. 

Larsen, D.R.; Kershaw, J.A., Jr. 1991. Leaf area measurements. In: 

Lassoie. J.P.; Hickley, T.M.. eds. Techniques and appmaches in forest 

tree ecophysiology. Boca Raton, FL: CRC Press: 465-475. 

Ledig, F.T. 1976. Physiological genetics, photosynthesis and growth 

models. In: Cannell, M.G.R.; Last. F.T., eds. Tree physiology and yield 

improvement. New York: Academic Ress: 21-54. 

Lichtenthala. H.K.; Buschmann, C.; Doll. M. [and others]. 1981. 

Photosynthetic activity, chloroplast ultrastructure, and leaf 

characteristics of high-light and low-light plants and of sun and shade 

leaves. Photosynthesis Research. 2: 115- 141. 

Lu, H.; Lu, C.; Wei, M.; Chan. L. 2004. Comparison of different models 

for nondestructive leaf area estimation in taro. Agronomy Jourqal. 96: 

448-453. 

Radford, A.E.; Ahles, H.E.; Bell. C.R. 1968. Manual of the vascular flora 

of the Carolinas. Chapel Hill, NC: University of North Cardina. 

Ramkhelawan, E.; Brathwaite. R.A.I. 1990. Leaf area estimation by 

non-destructive methods in sour orange (Citrus aurantim L.). Tropical 

Agriculture. 67: 203-206. 

SAS Institute Inc. 1985. SAS!ST.\T guide for personal computers. Version 

6. edition. Cary, NC: SAS Institute Inc. 

Steyermark. J.A. 1949. Lindera melissaefolia. Rhodora. 51: 153-162. 

U.S. Fish and Wildlife Service. 1986. Endangered and threatened wildlife 

and plants: determination of endangered status for Lindera melissifolia. 

Federal Register. 51: 27.495-27.499. 

U.S. Fish and Wildlife Service. 1993. Recovery plan for pondberry 

(Lindera melirsifolia). Atlanta. 

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