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Predicting white oak masting potential:

Implications for forest wildlife management

Jessie L. Birckhead

M.S. Candidate

University of Tennessee Knoxville

Forestry, Wildlife, and Fisheries

Wednesday, November 16, 2011Wednesday, November 16, 2011 12:20pm Room 160 Plant Biotech Building12:20pm Room 160 Plant Biotech Building

IntroductionAcorns are a staple of winter diets of many wildlife

species

Acorn crop influences wildlife populations

• white-tailed deer

• black bear

• ruffed grouse

• gray squirrels

Oaks are economically and ecologically important

• 7th largest standing timber volume (3.2% of all volume)

• 10th most abundant tree species (1.9% of all trees)Wentworth et al. 1992, Nixon et al. 1975, Norman and Kirkpatrick 1984, Eiler et al. 1989, USFS FIA 2010

IntroductionOak masting is variable

• Among species (Erythrobalanus vs. Leucobalanus)

Y t ( ti l d th )• Year to year (masting cycles and weather)

• Site to site (topography and stand conditions)

• Tree to tree (genetics, size, and age)

Greenberg and Parresol 2002, Sharp and Sprague 1967, Goodrum et al. 1971

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IntroductionPrevious literature has focused on red oaks

Thinning may increase mast production in red oaks

Fertilizing oaks is often suggested in popular e t g oa s s o te suggested popu aliterature, but has not been tested

White oaks are the most common oak in the eastern US

Commonly believed that white oak acorns are preferred by some species of wildlife

Healy 1997, Guariguata and Saenz 2002, Perry and Thrill 2003, Lombardo and McCarthy 2008

Objectives

Determine baseline acorn production potential of individual white oaks

Determine the effects of fertilization and thinning on white oak acorn production

Study site

Chuck Swan SF and WMA

30 km N of Knoxville

Elevation: 310 – 520 m

24 444 acres; 92% forested24,444 acres; 92% forested

Mixed hardwoods

130 cm annual rainfall

Well drained, acidic soils

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Data collection

120 white oaks

• dominant / codominant

• DBH 12.7 – 32.2 inches

Three 1 m² baskets per treeThree, 1 m² baskets per tree

Acorns collected biweekly

• Sept – Nov

DBH and crowns measured

Data collection

Acorn soundness estimated by float-testing

Marked acorns returned to monitor depredation in basketsbaskets

Up to 50 acorns from each tree dried and weighed to estimate biomass in 2008

Data analysis

Production classes (modified from Healy et al. 1999)Based on mean acorn production / m²

• Excellent: ≥ 2x the mean acorns / m² • Good: < 2x but > mean acorns / m² • Moderate: < mean but ≥60% mean acorns / m² • Poor: < 60% mean acorns / m²

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Annual variability

70%

80%

90%

100%

70

80

90

100

ng

White oak mast crop variability,Chuck Swan SF, 2006-2010

acorns / m²

% producing

0%

10%

20%

30%

40%

50%

60%

70%

0

10

20

30

40

50

60

70

2006 2007 2008 2009 2010

% t

rees

pro

du

cin

Aco

rns

/ m2

Annual variability

0.7

0.8

0.9

1

70

80

90

100

on

White oak mast crop depredation,Chuck Swan SF, 2006-2010

acorns / m²

% depredation

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

10

20

30

40

50

60

70

2006 2007 2008 2009 2010

% d

epre

dat

io

Aco

rns

/ m2

Production classes

12%

Proportion of white oaks by production class,Chuck Swan SF, 2006-2010

excellent

good

moderate

29%

18%

41%poor

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Production classes

11%

Proportion of acorns produced by production class,

Chuck Swan SF, 2006-2010

excellent

good

moderate

35%

40%

14%

11% moderate

poor

Production classes

2006 2007 2008 2009 2010

Poor Trees 78% 69% 50% 67% 47%

Production Characteristics, Chuck Swan SF, 2006 – 2010

Poor Trees:% of acorns

20% 6% 18% 17% 11%

Excellent Trees 11% 13% 14% 14% 18%

Excellent Trees:% of acorns

62% 81% 36% 53% 55%

Identifying strong producers

250

300

350

400

m2

Relationship between DBH and Acorns / m2,Chuck Swan SF, 2008 & 2010

R2 = 0.016115

0

50

100

150

200

250

10 15 20 25 30 35

Aco

rns

/ m

DBH (cm)

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Identifying strong producers

Trees per production

class

% Correct strong years

% Correct poor years

When should we monitor trees to predict production class?

Excellent 14 75% 14%*

Good 35 36% 11%

Moderate 22 50% 14%

Poor 49 87% 59%

Overall 64% 32%

*7 out of 14 excellent producers misidentified as poor producers

Discussion

Acorn production is highly variable

In good years, more trees set acorns, and more acorns are produced

A l i i fl d b t l f tAnnual crop is influenced by external factors, but production potential is inherent

Physical traits are poor predictors of masting potential

Discussion

Few trees produce most of the acorns

Poor producers may constitute most of a stand

In order to identify strong producers, mast production should be monitored during good mast years

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Management ImplicationsTo increase mast

production monitor individuals first

When thinning for wildlife:g- remove spp. with low wildlife value first - then poor producers

Retain strong producers and a diversity of spp.

Management Implications

Monitoring Options:

- fall surveys for- fall surveys for acorns

- spring surveys for seedlings

Acknowledgements

Project Team: Dr. Craig Harper, Michael McCord, Seth Basinger, Ashley Unger

Logistics: Neal White, Brook g ,Smith, Theresa Harper

Our many wonderful volunteers!

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References• Eiler, J.H., W.G. Wathen, and M.R. Pelton. 1989. Reproduction in black bears in the southern Appalachian mountains. Journal of Wildlife Management

53 (2):353–360.

• Goodrum, P.D., V.H. Reid, C.E. Boyd. 1971. Acorns yields, characteristics, and management criteria of oaks for wildlife. Journal of Wildlife Management 35:520–532.

• Greenberg, C.H., and B.R. Parresol. 2002. Dynamics of acorn production by five species of southern Appalachian oaks. Pages 149-172 in W.J. McSheaand W.M. Healy, (eds.). Oak Forest Ecosystems: Ecology and Management for Wildlife. Johns Hopkins University Press, 432pp.

• ______, and G.S. Warburton. 2007. A rapid hard-mast index from acorn presence-absence tallies. Journal of Wildlife Management 71(5):1654-1661.

• Guariguata, M.R., and G.P. Sáenz. 2002. Post-logging acorn production and oak regeneration in a tropical montane forest, Costa Rica. Forest Ecology and Management 167:285–293.

• Healy, W.M. 1997. Thinning New England oak stands to enhance acorn production. Northern Journal of Applied Forestry 14(3):152–156.

• ______, A.M. Lewis, and E.F. Boose. 1999. Variation of red oak acorn production. Forest Ecology and Management 116:1-11.

• Perry, R.W. and P.A. Thill. 2003. Initial effects of reproduction cutting treatments on residual hard mast production in the Ouachita Mountains. Southern Journal of Applied Forestry 27(4):253–258.

• ______, ______, P.A. Tappe, and D.G. Peitz. 2004. The relationship between basal area and hard mast production in the Ouachita Mountains. Pages 55-59 in Ouachita and Ozark Mountains symposium: ecosystem management research. Gudlin, J.M. ed. USDA Forest Service GTR SRS-74:55–59.

• Nixon, C.M., McClain, M.W., and R.W., Donohoe. 1975. Effects of hunting and mast crops on a squirrel population. Journal of Wildlife Management 39:l–25.

• Norman, G.W. and R.L. Kirkpatrick. 1984. Foods, nutrition, and condition of ruffed grouse in southwestern Virginia. Journal of Wildlife Management 48:183–187.

• Sharp, W.M. 1958. Evaluating mast yields in the oaks. The Pennsylvania State University Agricultural Experiment Station Bulletin 635.22 p.

• ______, and V.G. Sprague. 1967. Flowering and fruiting in the white oaks. I. Staminate flowering through pollen dispersal. Ecology 42:365–372.

• Wentworth, J.M., Johnson, A.S., and P.E Hale. 1992. Relationships of acorn abundance and deer herd characteristics in the southern Appalachians. Southern Journal of Applied Forestry 16:5–8.

Questions?