OAK EXPANSION IN THE CHAUTAUQUA HILLS KANSAS: A REGIONAL
ASSESMENT OF HISTORIC CHANGE
A Thesis by
Thomas Rogers
Bachelor’s of Arts, Wichita State University, 2010
Submitted to the Department of Biological Sciences
and the faculty of the Graduate School of
Wichita State University
in partial fulfillment of
the requirements for the degree of
Master of Science
May 2012
© copyright 2012 by Thomas Rogers
All Rights Reserved
iv
OAK EXPANSION IN THE CHAUTAUQUA HILLS KANSAS: A REGIONAL
ASSESMENT OF HISTORIC CHANGE
The following faculty members have examined the final copy of this thesis for form and content
and recommend that it be accepted in partial fulfillment of the requirements for the degree of
Master with a major of Biological Sciences.
Leland Russell, Committee Chair
Karen Brown, Committee Member
Greg Houseman, Committee Member
Michael Hall, Committee Member
iii
DEDICATION
To my parents, grandmother, and brothers
iv
“Conservation is getting nowhere because it is incompatible with our Abrahamic concept
of land. We abuse land because we regard it as a commodity belonging to us. When we see land
as a community to which we belong, we may begin to use it with love and respect.”
-Aldo Leopold
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ACKNOWLEDGEMENTS
I would like to thank my academic adviser, Leland Russell, for sharing his wealth of
knowledge, invaluable guidance, support, and patience during my entire academic career. I
would also like to thank Randall Rogers for his consistent help with field work. Several others
deserve credit for helping me along the way, and influencing my academic interests: Dr. Karen
Brown and Dr. Greg Houseman. Finally, I am grateful to the Kansas Academy of Science and
the High Plains Regional Climate Center for funding this project.
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ABSTRACT
Woody plant expansion into grasslands and savannas is a globally occurring process
which can cause loss of biodiversity and alter biogeochemical cycles. The Chautauqua Hills, in
southeast Kansas, is the northernmost extent of the Cross Timbers vegetation type, Quercus
stellata and Quercus marilandica are the dominant tree species. Government Land Office
records from the 1860’s indicate sparse tree cover in much of this region, which is now
characterized by dense oak woodlands. I use a multi-site, dendrochronological approach to
address four research questions: 1) when did oak expansion occur? 2) from what landscape
position did oaks expand?, 3) how have physiological differences between members of the
Erythrobalanus (Q. marilandica) and leucobalanus (Q. stellata) subgenera influenced
recruitment patterns?, and 4) which drivers of woody plant encroachment coincide with oak
expansion in the Chautauqua Hills? Quercus stellata comprised a greater proportion of ancient
(>100 years) trees than Q. marilandica at all sites. Quercus stellata age structures differed from
both the normal and negative exponential distributions at all sites, while Quercus marilandica
did not differ significantly from the normal distribution at three sites, and did not differ from the
negative exponential distribution at two site. Three of the four study sites likely were savanna
prior to Euro-American settlement, indicated by the over-representation of older age classes
compared to the negative exponential distribution.
Drought during the 1930’s, favorable
attitudes towards trees following the dustbowl, livestock grazing, and changes in fire frequency
all likely contributed to oak expansion in the Chautauqua Hills.
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TABLE OF CONTENTS
Chapter
Page
1.
1
INTRODUCTION
Tree-grass interactions
External drivers of woody plant expansion
Topographic influence
The Cross Timbers Ecosystem
Scope of this study
2.
2
3
5
7
9
METHODS
10
Study species
Study sites
Field methods
Statistical analysis
3.
10
11
13
16
RESULTS
19
Tree species composition at the study sites
Size-age relationships
Stage structures of oak populations
Landscape position effects
Analysis of climate data
4.
DISCUSSION
`
19
20
20
21
21
22
Temporal patterns of oak regeneration
Historic vegetation physiognomy of the Chautauqua Hills
External drivers of expansion
Future studies
Implications for land managers
22
23
25
29
31
BIBLIOGRAPHY
32
APPENDIX
40
viii
LIST OF FIGURES
Figure
Page
1. Tree species composition of study sites
41
2. Proportion of single and multi-stemmed trees
42
3. Diameter-age relationships of Post oak
43
4. Diameter-age relationships of Blackjack oak
44
5. Size structures of Post oak
45
6. Size structures of Blackjack oak
46
7. Comparison of Post oak age structures to the normal distribution
47
8. Comparison of Post oak age structures to the negative exponential distribution
48
9. Comparison of Blackjack oak age structures to the normal distribution
49
10. Comparison of Blackjack oak age structures to the negative exponential distribution 50
11. Association of ancient trees and slope position
51
12. Coincidence of oak regeneration at Cross Timbers State Park with temperature
52
13. Coincidence of oak regeneration at Fall River State Lake with temperature
53
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LIST OF TABLES
Table
Page
1. Description of tree community compositions
54
2. Size structures of oak populations
55
3. Oak demographics
56
4. Patterns of oak recruitment
57
5. Coincidence of oak regeneration with climatic variables
58
6. External influences of oak expansion in the Chautauqua Hills, KS
59
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CHAPTER 1
INTRODUCTION
Woody plant expansion is a globally occurring phenomenon, found in a diverse array of
grassland and savanna ecosystems (Crawford and Kennedy 2009). The rate at which trees and
shrubs are encroaching into grasslands and savanna ecosystems has increased substantially
within the last 50-300 years (Archer et al. 1988) and can have profound economic and ecological
impacts including decline in forage production for livestock (Bokdam et al. 2000; Dube et al.
2009), reduced biodiversity (Meik et al. 2002) and potential changes in hydrological and
biogeochemical processes (Archer et al. 2001; Huxman et al. 2005). Human population growth,
migration and, associated land-use changes in the eighteenth and nineteenth centuries have had
profound and lasting influences on tree-grass systems worldwide (Scholes and Archer 1997)
particularly in North America (Abrams 2003). Virtually every region in the United States has
been affected by woody plant expansion including the Pacific Northwest (Rochefort and
Peterson 1996), the mountainous regions of the Southwest (Archer and Brown 1987; Moore and
Huffman 2004), eastern North America through the southern Appalachians (Crawford and
Kennedy2009), and the Great Plains ( Bragg and Hulbert 1976; Abrams 1986).
At the eastern edge of the Great Plains, lies the Forest-Prairie Transition-Region which, as the
name implies, is transitional between the relatively mesic eastern forests and prairies, and the
drier prairies and woodlands in the west (Johnson et al. 2009). Historically, savanna formed an
extensive transition zone between the eastern deciduous forest and the tallgrass prairie that
extended from Texas to Minnesota (Nuzzo 1986). The Chautauqua Hills in southeastern Kansas
are the northernmost extent of the cross timbers ecosystem, which extends through eastern
1
Oklahoma and into east-central Texas. The potential vegetation of the cross timbers has been
described as a “complex mosaic of oak woodlands, tall grass prairie, and oak savanna”
(Dyksterhuis 1939). Like the majority of the Great Plains region, the ecosystems of the
Chautauqua Hills have experienced profound changes in land-use and large climatic fluctuations
over the past 150 years and these drivers have likely influenced broad scale change in the overall
physiognomy of cross timbers vegetation in the region.
Tree-grass interactions
Much effort has focused on studying the causes of woody plant expansion and, although they
remain difficult to identify, outcomes suggest that contributing factors include interacting natural
and anthropogenic activities such as changes in human land use (McPherson 1997; Hessl and
Graumlich2002; Abrams 2003) and climate change (Archer and Boutton 2001; Rheumtulla et al.
2002), which alter the balance of interactions between trees and grasses. Further, patterns of
topographic and edaphic gradients contribute to the spatial variation and densities of woody
plants and, as such, these gradients are often cited as contributing to woody plant encroachment
(Bragg and Hulbert 1976; Nowacki et al 1990; Moore and Huffman 2004). It is well known that
woody plants can have considerable effects on understory vegetation; however this is not a onesided interaction. Herbaceous, understory plants have the ability to affect every life-history stage
of woody plants from germination and emergence, to growth and survival, to, finally,
reproduction (Borchert et al. 1989). Nevertheless, the most profound effects that grasses have on
woody plants appear to be concentrated in the germination/emergence life-stages.
Soil moisture, which can be influenced by precipitation, evaporation and edaphic factors, is
considered the environmental factor that most constrains woody plant establishment in
grasslands and savannas worldwide (McPherson 1997; Weltzin et al. 2003). Walters (1954,
2
1972) two layer hypothesis of tree grass coexistence in savannas maintains that shallow fibrous
roots systems of grasses will compete with woody plant seedlings for water in the upper soil
profile. Wilson (1993) experimentally demonstrated that belowground competition was the
dominant form of plant competition between tree seedlings of Eucalyptus pauciflora and the
grasses Poa constiniana, and Celmisia longifolia, suggesting that competition for soil moisture
between grasses and woody plants is most intense during the seedling/sapling life stages, but can
also limit future growth and ultimately survival.
In addition to competition for soil moisture, competition for light with grasses affects woody
plant germination and emergence. Emergence rates, decrease with increasing understory cover
as a result of light attenuation by herbaceous plants. Bush and Van Auken (1990) experimentally
demonstrated that shade and herbaceous competition reduced the germination and growth of
Prosopis glandulosa (honey mesquite). These interactions between woody and herbaceous
plants are highly dependent upon the biomass of each, and relationships may be affected by
combinations of external climatic and anthropogenic influences.
External drivers of woody plant expansion
Herbivory has the potential to influence the abundances of both woody plants and grasses.
Cattle and sheep grazing are often associated with increases in woody plant abundances (Archer
1988; McPherson 1997) because livestock are effective dispersers of woody plant seeds through
their feces (Brown and Archer 1989). Further, grazers may remove enough herbaceous biomass
to increase the amount of light reaching the soil surface thus increasing germination and
emergence of woody plant seedlings. Grazing is often associated with smaller plant root systems
and reduced grass root biomass in upper soil layers may allow water to percolate deeper in the
soil profile further benefiting established woody plants with deeper root systems (McPherson
3
1997; Cahill 2002). Finally, livestock grazing reduces the amount of available fuel, leading to
reduced frequency and intensity of fires which, over prolonged periods of time, leads to
accelerated shifts from grasslands to woodlands (Johnson and Riser 1975; Abrams 1985; Archer
1997).
Fire has long been thought to be an important process structuring plant communities in North
America (Abrams 2003). Following a period of debate after the great fires of 1910 the U.S.
government, with full support of the citizenry, implemented aggressive fire suppression policies
(Rieman et al. 2010). Fire suppression combined with geographic fragmentation of habitats that
inhibits fire spread has virtually eliminated wildfires in the Great Plains Region.
Modern
prescribed prairie fires, used for rangeland management purposes, are typically controlled spring
burns which may not have enough biomass (fuel) to create fires with enough intensity to
suppress woody plant recruitment (Knapp 2009).
Fire behavior (intensity, size, and rate of spread) is influenced by physical and biological
factors including: fuel conditions, weather, topography, and plant community composition
(Knapp et al. 2009). High-intensity and frequent fires are thought to favor herbaceous vegetation
and exclude woody plants because most woody plants are highly susceptible to fire in the
sapling/seedling stages, becoming more resistant to surface fires as they mature. Adult oaks, in
particular, have thick, insulating bark (Preston and Braham 2002) and are able to re-sprout from
belowground buds if the aboveground portion is killed or severely damaged by fire (Johnson et
al. 2009).
The combined variability of precipitation and temperature exerts considerable control over
the structure of most plant communities, in this case grasslands and savannas. Changes in global
and regional precipitation regimes are expected to significantly influence the distribution,
4
structure, composition, and diversity of plant and animal populations and communities. Recall
that Walter’s two-layer hypothesis (1954, 1979) states that soil moisture is often the
environmental factor that most limits woody plant recruitment, and that grasses and juvenile
woody plants will compete for the water in the upper soil profile. Further, periods of heavy
precipitation create periods of high soil moisture which, in turn, may facilitate woody plant
recruitment (Archer et al. 1988; Wright 1990).
The increase in woody plant recruitment
associated with high precipitation may be especially large if a period of substantial precipitation
follows a period of drought. During dry periods, plants with deeper roots (i.e. woody plants) are
able to access water from deep in the soil profile while grasses may have more limited supplies,
and hence die back (Christensen 1988). The seasonality of precipitation is also known to affect
the ratio of woody vs. herbaceous plants. In winter, evaporation and transpiration are both
limited so water can accumulate and infiltrate deeper into the soil profile, providing an
immediate water and nutrient supply at the onset of the growing season (McPherson 1997).
Changes in external drivers that allow initial encroachment of trees and shrubs into
herbaceous communities could initiate positive feedback processes that will influence further
change. In the case of woody plants, it is well known that they often act as “nurse plants”
creating fertile islands which facilitate further woody establishment, while excluding herbaceous
competitors. This sort of autogenic succession has been shown to be capable of significantly
increasing the area occupied by woody plants over time (Archer 1986).
Topographic Influence
While climatic factors may explain vegetation patterns at a regional scale, local landscape
topography may influence the distribution of water and nutrients, thereby influencing the
distribution of species across that landscape. Grime and Lloyd (1973) found that plant species
5
occupied a variety of topographic locations depending on their physiologies. Nagamatsu et al.
(2002) found that tree seedling germination and emergence differed between species, with more
drought tolerant species being most successful on ridge-tops and drought prone species restricted
to lower slopes and drainages. Abrams (1986) concludes that woody plant expansion in the Flint
Hills, Kansas is due primarily to the changes in natural fire regimes which allowed oak savannas
to expand from drainages to form woodlands, and undergo succession.
Typically, three
topographic features (elevation, slope steepness, and aspect) interact to influence resource
availability and disturbance regime, thereby influencing species compositions and structures of
communities (Perry et al. 2008).
Aspect, the direction that a slope faces, determines the amount of solar radiation that will
reach it. Slopes that face south and west have direct sunlight for longer durations of the day in
the northern hemisphere. The exposure to more solar radiation makes soils warmer and drier,
making competition for soil resources more intense for highly delicate juvenile woody plants
(Grime and Lloyd 1973). Conversely, slopes facing north and east receive less solar radiation,
have higher soil moisture, and less competitive conditions for soil resources allowing woody
seedlings to grow more rapidly. One consequence of these more rapid growth rates may be that
juvenile woody plants escape the flame zone of surface fires in a shorter time period (Perry et al.
2008).
However, in the case of oaks, Johnson et al. (2009) asserts that the cool, moist
microclimate of northeastern facing slopes may favor their initial establishment, but that neutral
or more xeric aspects may be more favorable for long-term survival, root development, and
reproduction.
Slope steepness also influences water and nutrient availability. The steeper the slope, the
more run-off there will be, taking with it water and nutrients. Vegetation on steep slopes will
6
typically have deeper root systems, be adapted to drier conditions, or be small seeded winddispersed weeds which establish in openings of unstable soils (Grime and Loyd 1973).
Slope
steepness, combined with fuel loads, also influences fire behavior (Bradstock et al 2009; Linn et
al. 2010).
Bradstock et al. (2009) showed that fire intensity was lower on steeper slopes due to
discontinuity of fuel loads. Further, steepness affects the probability of crown fires which are
most common on ridge tops where fuel is driest and there is more exposure to wind (Bradstock et
al. 2009).
The Cross Timbers ecosystem
South of the Flint Hills in Kansas are the Chautauqua Hills, home to the northernmost extent
of the Cross-Timbers ecosystem.
The main difference between the Flint Hills and the
Chautauqua Hills is their geology, the latter having a sandstone cap, part of the Douglas
Formation formed during the Pennsylvanian epoch (Kansas Geological Survey 2009). Further,
although the vegetation physiognomies of the Flint Hills and Chautauqua Hills are similar
(tallgrass prairie and oak woodlands), the tree species composition differs, with Quercus
macrocarpa common in the Flint Hills and Quercus marilandica, and Quercus stellata common
in the Chautauqua Hills.
The Cross Timbers lies in a band 10-180 km wide from the southern edge of the bluestem
prairie in Kansas southward across east central Oklahoma to the Trinity River in east Texas
(Barbour and Billings 2000). Kuchler (1964) defined the potential natural vegetation of the
Cross Timbers as xeric oak woodlands and “savanna-like patches, characterized by tallgrass
prairie with low broadleaf deciduous trees scattered singly or in groves of varying size.” Portions
of the cross timbers that occupy steep, rocky landscapes, unsuitable for agriculture are thought to
7
contain some of the largest tracts of old growth, oak-dominated stands in North America (Stahle
and Cheney 1994, Therrel and Cheney 1998).
Due to the variety of potential vegetation types in the cross timbers, and the lack of detailed
historical information, identifying past vegetation physiognomy at individual sites within the
Cross Timbers is difficult. Government Land Office surveys from the 1860’s indicate the
absence of large wooded tracts in the Chautauqua Hills, but occasionally mention
Q.
marilandica and Q. stellata (the dominant trees in present oak woodlands) as present on the
landscape. Studies from Stahle (1980 ITRDB) and Guyette et al. (2011) found that some post
oaks in the Chautauqua Hills are older than 250 years. Presence of these ancient trees has
sparked some debate concerning the possible historic physiognomy of the region. One argument
is that these very old trees are remnants of former savannas which, over time, expanded forming
the large tracts of oak woodlands present today. Others suggest that these ancient trees are really
remnants of large logging events following European settlement of the region. Disentangling the
complexities of ecosystem change through time requires an understanding of the species that
currently occupy landscapes and how their physiologies allowed them to respond to past and
current ecological conditions.
Quercus marilandica and Q. stellata belong to two different oak sub-genera (Erythrobalanus
and Leucobalanus) which differ physiologically in their abilities to tolerate fire, shade, and
drought. These physiological differences may influence both their demography and their spatial
distributions across the landscape. Abrams (2003) found that post oaks were older and larger at
sites with recurring, low intensity understory fires than at sites without fire, because of their
ability to form tyloses, which encapsulate, and compartmentalize fire scar wounds. Arevalo
8
(2002) found that both species exhibited clear spatial preferences, Q. marilandica dominating
forest edges, while Q. stellata dominated forest interiors.
Further, oaks reproduce both sexually by seed, and asexually by sprouting, and oak species
differ in their dependence on either mode of regeneration (Clark and Hallgren 2003, Johnson
2009). Sprouts originate from dormant buds at or near the base of the root crown and can
originate from the bases of trees killed by disturbances. As long as the crown of the parent tree is
alive, buds usually remain dormant due to growth suppression from the parent (Vogt and Cox
1970 in Johnson 2009 ch2).
Clark and Hallgren (2003) reported that 99 percent of oak
reproduction originated from sprouting in three cross timbers stands in north central Oklahoma.
While there is some evidence that xerophytic oaks (e.g. Q. marilandica and Q. stellata) may rely
more on sprouting than their mesophytic counterparts, the presence of multi-stemmed oaks
suggests a history of disturbances that has killed parent trees.
Scope of this study
This study seeks to further understand the historical timing, patterns and causes of oak
expansion in the Chautauqua Hills Kansas by addressing four main questions: 1) When did oak
expansion occur in the region? 2) Which topographic features influenced the historic landscape
position of oaks, and hence the points from which oak populations have expanded? 3) Do Q.
marilandica and Q. stellata exhibit differential timing and spatial patterns of expansion, and 4)
Do periods of oak expansion coincide with changes in land use, climate, and fire regime? To
address these questions, I collected increment cores at four sites in the Chautauqua Hills to
describe the age structures of Q. marilandica and Q. stellata populations.
9
CHAPTER 2
METHODS
Study Species
Quercus marilandica
Quercus marilandica (Blackjack oak) belongs to the Erythrobalanus subgenus sometimes
referred to as “red” oaks. Blackjack oaks are a long lived, small tree growing 6 – 9 m in height.
They are easily distinguishable by their scrubby appearance and low branches due to their
inability to self prune. They have deciduous, coriaceous leaves that average 6-13 cm in length
and 5-10 cm in width (Harlow et al 1996). Flowering occurs in April and acorns develop in
October in the second year. Their range includes New Jersey, Long Island and central
Pennsylvania, south to northwest Florida, west to southeastern and central Texas, north to
southern Iowa, parts of Oklahoma, and southeast Kansas (Harlow et al. 1996). Blackjack oaks
are the most drought tolerant of all oaks (Johnson et al. 2009).
They are common on poor
sterile soils, where they can be found in mixtures with other species such as mockernut hickory
(Carya tomentosa), post oak, black oak (Quercus kelloggii), and southern red oak (Quercus
falcata) (Preston and Braham 2002; Harlow et al. 1996). They have been known to hybridize
with a number of other oak species; pin oak (Quercus palustris) and shingle oak (Quercus
imbricaria) are among those in Kansas (Preston and Braham 2002). Blackjack oak is known to
have been used for lumber, but is not a good fuel source (Johnson 2009).
Quercus stellata
Quercus stellata (Post oak) belongs to the Leucobalanus sub-genus, commonly referred to as
“white” oaks. Post oaks are a long lived, small to medium sized tree ranging in height from 15
m to 21 m. They have deciduous; oblong –obovate leaves that are cruciform in appearance and
10
range from 9-20 cm length. Flowers develop in April along with the leaves and acorns are
developed by October of the first year (Stephens 1969). The range of post oaks extends from
southeastern Massachusetts, Rhode Island, southern Connecticut, southeastern New York, west
to southeastern Pennsylvania and West Virginia, central Ohio, southern Indiana, central Illinois,
southeastern Iowa and Missouri, south to eastern Kansas, western Oklahoma, northwest and
central Texas, and east to central Florida (Little 1979). They are a shade intolerant species, but
highly drought tolerant with seedlings having large taproots. Post oak is typically found on dry,
sandy, or gravelly soil and rocky ridges and often occurs in association with black oak, blackjack
oak, hickories, and eastern red cedar (Juniperus virginiana) (Harlow et al. 1996). Post oak has
been reported as being largely dependent on sprouting as a mode of regeneration (Johnson et al.
2009). It is known to be a good source of lumber (especially fence posts) and a good fuel source.
Study Sites
The criteria used to select study sites were 1) the presence of both Q. marilandica and Q.
stellata and 2) the site did not appear to have heavy tree cover in General Land Office (GLO)
records from the 1860’s. Four sites were selected; Cross Timbers State Park (37” 44’ N, 95” 56’
W), Fall River State Lake (37” 39’ N, 96” 02’ W), Woodson State Fishing Lake (37” 47 N, 95”
50’ W) and Stotts’ Ranch (37” 30’ N, 96” 01’ W). Cross Timbers State Park, Fall River State
Lake and Woodson State Fishing Lake are managed by Kansas Department of Wildlife and
Parks. Stotts’ Ranch is under private management. The maximum distance between sites (Stotts’
Ranch and Woodson State Lake) is 66.57 km. Soil types were similar across sites, containing
sandstone derived soils typical of the cross timbers (Dyksterhuis 1948).
Cross Timbers State Park
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Cross Timbers State Park is located within the Toronto Township in Woodson County,
Kansas. Formerly known as Toronto State Lake, this site was purchased by the State of Kansas
in 1960 for flood control. Managed by the Kansas Department of Wildlife and Parks, this site is
burned following a three year rotational strategy which includes a spot burning component to
control future invasion of woody plants and non-native species. Agricultural census from 18851925, reveal that the majority of agricultural income was generated by the sale of poultry and
dairy products, and that the majority of the cattle present during this period were used for dairy
production (KS Board of Agriculture 1885-1925).
Fall River Lake
Fall River Lake is found within the Fall River Township in Greenwood County, Kansas.
Agricultural censuses from 1885-1925 reveal that the majority of income generated in this region
was from row crop agriculture. Production of dairy products was the second largest source of
income and as such, cattle present were for this purpose or for personal use. Part of the 1944
Flood Control Act, dam construction was completed in 1949 by the Army Corps of Engineers.
The resulting lake has since been managed by the Corps of Engineers. This site follows a similar
rotational burn strategy to that used at Cross Timbers SP, which also includes spot burning to
eliminate woody expansion and invasion of non-native species.
Stotts’ Ranch
The Stotts’ family ranch is located in the Painterhood Township in Elk County Kansas.
According to U.S. agricultural census’ (1885-1925) the majority of revenue generated from
agriculture was from the sale of livestock (i.e. cattle). The ranch was purchased by W.D. Pratt in
the 1930’s. Shortly thereafter he hired men to build fences and began intensively grazing the
land.
During the 1950’s to 70’s, there were 2 different land managers, the latter of which is
12
reported to have grazed heavily. “I think it’s fair to say that through most of this time period the
land was being overgrazed” (Caleb Stotts personal communication). Since 2000, stocking rates
have been monitored and have been at or below recommended National Resources Conservation
Service levels. Fire strategies have differed among land managers of the ranch. The current
management strategy (2007-2011) has had a goal of burning 2-4 out of five years depending on
circumstances (Caleb Stotts personal communication).
Woodson State Lake
Woodson State Lake, formerly known as Fegan Lake, lies within the Belmont Township in
Woodson County Kansas. In 1933, J.C. Fegan donated 320 acres to the Kansas Forestry, Fish
and Game commission in order to create a state lake. Dam construction was completed by the
civilian conservation corps in 1937, and the lake has since been under the management of Kansas
Department of Wildlife and Parks. Prior to 1933, cattle ranching was prominent at this site and
was the primary source of revenue for citizens in the township according to U.S. agricultural
census’. The current burn strategy is similar to the other two state parks.
Field Methods
Within each site, discrete woodland patches were defined by natural or man-made boundaries
(e.g. drainages, roads, fences). Each site had multiple patches that could be sampled, and
accessibility and patch size were considered in selecting one woodland patch to sample per site.
Next, using google earth, I determined the distance between the northern and southern, and
eastern and western most points of each selected woodland patch. These maximum distances
along north-south, east-west axes were used to establish an X-Y coordinate system. A point on
this X-Y coordinate system to begin sampling trees to quantify age structures was selected using
13
a random number table. Once I found this point, I ran 100m transects in the four compass
directions.
The point-quarter method of sampling was used at 20m increments along each of the four
transects. At each 20m increment along a transect, the nearest Q. marilandica and Q. stellata
tree was sampled within each of four quadrants. This yielded a sample of 80 trees per species
(20 trees per species for each transect) per site. If a transect exited the woodland patch before 20
samples were collected, sampling on the next transect was extended to compensate for the
difference. Upon encountering a cluster of stems, possibly the same genetic individual, only one
of the stems was sampled. Stems were considered to be the same genetic individual if it was
obvious that one tree sprouted from the base of another, they were ≤ 30cm apart, they were
arranged in a circular pattern, and they leaned away from each other. To select one stem to
sample in multi-stemmed genetic individuals, the stem closest to the transect was numbered 1,
and numbering continued to the right. A coin was then flipped excluding one stem at a time.
For each sampled tree, I measured the diameter of the tree at its base and at breast height
(dbh); extracted an increment core from the base of trees ≥10cm dbh; recorded GPS coordinates,
slope aspect and slope position category (ridge-top, mid-slope, or drainage); and measured slope
steepness at the base of each tree using a clinometer. In addition, to quantify tree species
composition at the sites where I sampled, all trees within 5m of each sampled oak in the
northeast quadrant at each sampling point were identified to species and their diameters at breast
height were recorded.
Since oaks are known to regenerate from sprouts, and sprout growth is suppressed until the
death of the parent plant, quantifying the number of multi-stemmed vs. individual stemmed oaks
could provide insight into whether study sites had once been logged. To quantify the proportions
14
of single and multi-stemmed individuals at each site, I walked the length of the north and south
transect (200m) counting whether trees within 5m to either side of the transect were single
stemmed or in clusters. Sprouts <10cm (dbh) were not included in these counts because they
were not considered in other portions of this study.
Additional sampling was necessary to increase the sample size for comparing tree age among
topographic positions. Since the primary objective of this study was to quantify representative
age-structures of oak woodlands, sampling was random and, as a result, trees within drainages
were under represented in the overall sample (<10%). At each of the four sites I continued upon
a preexisting transect toward the nearest drainage. Upon reaching the drainage I flipped a coin to
determine which direction (upstream vs. downstream) I would sample, and which side of the
drainage I would sample. Upon selecting a side and direction, a 100 m transect was placed 10m
from the edge of the stream bed, and sample trees were randomly selected by drawing a piece of
paper (labeled NE, SE, NW & SW) corresponding to a respective quadrant. Two cores (one per
species) were extracted at each 20m increment along transects until a total of five cores were
collected for each species at a site. Across all four sites, forty additional cores were sampled
using this method and were included in the landscape position analysis.
Sampling occurred June-August 2010 at the Stotts’ Ranch, March-April 2011 at Cross
Timbers State Park, April-May 2011 at Fall River State Lake, and May-July 2011 at Woodson
State Lake. Increment cores were allowed to dry for ≥96 hrs whereupon they were mounted, and
sanded with progressively finer (160-400 grit) paper. Ring widths were measured using Coo
Recorder (Lars Arke Arkeson 2010) and were analyzed using visual cross-dating techniques.
Visual cross-dating of cores was confirmed using the computer programs CDendro (Lars Arke
Arkeson) and COFECHA (Holmes 1994). These programs are used to eliminate variation in
15
radial growth rates related to tree age, and to detect missing or false rings. Although deciduous
oaks rarely produce missing or false rings (Schweingruber 1993), cross-dating cores ensured that
age structures were as accurate as possible.
Statistical Analysis
Temporal patterns in oak recruitment
Mean, median, and modal ages were identified for both Q. marilandica and Q. stellata at each
site. Age structures also allowed meto determine when continuous episodes of recruitment for Q.
stellata and Q. marilandica began at each site. To determine if there was a significant difference
in size (dbh) between the two Quercus species, I used a 2-way ANOVA with site as a random
effect. For each site, I evaluated the extent to which stem age could be predicted from diameter
at breast height using least squares linear regression. Upon examining patterns in the residuals,
some instances revealed that ln(size) was a better predictor of age than size, and ln(size) was
used as the independent variable in those cases.
To assess historical patterns of oak expansion and possible factors influencing current oak
recruitment rates, age structures were compared to normal and negative exponential frequency
distributions. Since oaks are shade intolerant, the mode of their age distributions should reflect
the point at which light became a limiting factor for regeneration and exponential population
growth ceased (Veblen and Lorenz 1987, Russell and Fowler 2002). In this context, the modal
age class represents the point at which the forest canopy has developed to the point that it begins
to limit germination and survival of oak seedlings. To compare age structures of the oak
populations that I sampled with normal distributions, I used a Komolgorov-smirnov goodness of
fit test to compare observed and predicted frequencies in age classes.
16
Age structures were also compared to negative exponential distributions to determine whether
observed age structures conformed to expectations for an exponentially growing population, such
as a tree population expanding into an open grassland. Specifically, I was interested in whether
older age classes were over represented as compared to the negative exponential distribution,
potentially indicating the presence of savanna trees that pre-dated oak expansion. In this case I
am comparing to the negative exponential distribution because it could describe the age structure
of an exponentially growing population if there is no mortality in older age classes (Ross et al.
1982, Russell and Fowler 2002). To determine whether observed age structures fit the negative
exponential model, I used a chi-square goodness of fit test to compare observed frequencies of
trees in age classes with frequencies predicted by the negative exponential distributions.
Interpreting Topographic Influence on Tree Age
One objective of this study was to determine the historic landscape position of oaks, and
hence the points from which oak populations have expanded. Literature suggested the
hypotheses that older oaks might be associated with 1) steeper slopes, 2) mesic slope aspects or
3) drainages along streams. The relationship between slope steepness and tree age was tested
with an ANCOVA using steepness as a continuous covariate, species as an independent variable,
and site as a random effect.
To analyze the relationship between slope aspect and tree age, I
used Fisher’s exact test to determine whether ancient trees (≥100 yrs.old) were associated with
particular slope aspects Trees on north and east facing aspects were pooled into a “mesic”
category, and trees on south and west facing aspects were pooled into a “xeric” category. To
examine the relationship between landscape position (drainage, mid-slope or ridge) and tree age
(ancient vs. recent) I used a 2x3 contingency table with a chi-square analysis.
17
Climate Analysis
Historical records of daily temperature and precipitation over a period of 112 yrs. (1896-2008)
were obtained for three weather stations in the Chautauqua Hills, Chanute MJ Airport (37’67” N
95’48” W) Fredonia (37’53” N 95’56” W) and Fall River Lake (37’65” N 96’08” W) courtesy of
the High Plains Regional Climate Center (University of Nebraska, Lincoln). In addition to
calculating total annual precipitation, total precipitation was divided into dormant season (Nov.
1st-March 31st) and growing season (Apr. 1st-Oct. 31st) categories. Five-year averages were
calculated for mean annual temperature, total annual precipitation, dormant season precipitation
and growing season precipitation. Deviations of these 5-year averages from the long term means
(long-term means were calculated over the entire 112 year climate record) were used to visually
identify major climatic events, or patterns that coincide with oak recruitment in the region. I
compared the timing of Q. marilandica and Q. stellata recruitment with each climate variable.
For each site, I used a one-way ANOVA to compare these climate variables during continuous
recruitment episodes of both species with the same variables during intervals when no
recruitment occurred. Continuous recruitment waves were defined as a series of 5-year intervals
in which both species were simultaneously recruiting with no interruption.
18
CHAPTER 3
RESULTS
Tree species composition at the study sites
Stands sampled at Cross Timbers State Park and Fall River Lake occupied north and east
(mesic) facing aspects, while stands at Stotts’ Ranch and Woodson State Lake occupied south
and west (xeric) facing aspects. Tree species richness differed between all four sites with the
two mesic sites, Fall River Lake and Cross Timbers State Park having the greatest tree species
richness (seven and six tree species), followed by the more xeric sites, Woodson State Lake and
Stotts’ Ranch (three and two tree species) (Table 1). The relative densities of tree species also
differed between sites, but at all sites the majority of the total stand basal area was comprised of
the two oak study species. Q. stellata had higher densities than all other tree species at every site
except for Stotts’ ranch, where Q. marilandica was slightly more abundant (Fig. 1). At Fall
River Lake and Woodson State Lake, Q. marilandica was the second most abundant tree.
Interestingly, at Cross Timbers State Park, where 6 tree species were encountered, Q.
marilandica had the second lowest density of all tree species while, Prunus serotina, Juniperus
virginiana, and Cercis canadensis occurred at higher densities (Table 1).
The occurrence of multi-stemmed oaks differed across sites (Fig. 2). However, with the
exception of Woodson State Lake, the majority of Q. marilandica and Q. stellata individuals
were single stemmed. Clusters of Q. marilandica ranged from 2-4 stems, while 2-6 stem clusters
were recorded for Q. stellata. At Woodson, approximately 1:1 ratios of single and multistemmed trees were observed for both Q. stellata and Q. marilandica.
19
Size-age relationships
Size, specifically diameter at breast height, significantly predicted post oak age at two sites
(Cross Timbers State Park and Fall River Lake), and accounted for 7 and 28 percent of the
variability respectively (Fig 3). At Woodson, ln(size) was a better predictor of age (Fig 3).
Significant diameter-age relationships were not detected using either size or ln(size) at Stotts’
Ranch (Fig.3). Linear regressions reveal that black jack oak size was a better predictor of age
than (ln)size at Stotts’ Ranch and Woodson State Lake but was only statistically significant at
Woodson State Lake (Fig. 4). At Cross Timbers and Fall River, ln(size) was a better predictor of
age but was only statistically significant at Fall River Lake (Fig 4).
However, significant
diameter-age predictions for both species accounted for only a small amount of the total variation
(Fig 3 & 4).
Stage Structures of Oak Populations
Size Structures
Size structures did not differ significantly from normal distributions at all four sites (Table 2;
Fig 5 & 6). Diameter at breast height ranged from 15-175 cm for both species across all four
sites, except at Woodson State Lake (Table 2). At this site, there were post oaks up to 235 cm
dbh. At every site except Woodson State Lake, the largest trees (measured by dbh) were
blackjack oaks.
However, there was no significant difference in size between species
(F1,593 =0.17; p=0.68).
Age Structures
Age structures of post oak did not conform to normal distributions at all four sites (Tables 3, 4
& Fig. 7). Post oak age structures also did not conform to negative exponential distributions at
three out of four sites, Stotts’ Ranch being the exception (Table 3, Fig. 8). At Cross Timbers
20
State Park, Fall River Lake and Woodson State Lake, ancient (>100 years old) post oaks
represented between 6.45%-30.77% of the total population (Table 3) and were overrepresented
relative to age structures predicted by the negative exponential model (Fig. 8).
Age structures of blackjack oak were normally distributed at every site except for Woodson
State Lake (Table 4; Fig. 9). Blackjack oak population age structures did not fit the negative
exponential model, with the exception of Stotts’ Ranch (Table 4; Fig.10).
Ancient blackjack
oaks were found only at Fall River Lake and Woodson State Lake where they comprised 2.04%
and 5.41% of the total population respectively (Table 3). Ancient blackjack oaks appeared to be
over represented as compared to the negative exponential model at Fall River and Woodson State
Lake (Fig. 10).
Landscape Position Effects
Slope steepness did not significantly influence tree age (F6,436=11.60; p=0.75). Further,
ancient trees were not strongly associated with any particular topographic position (Fig 11;
χ22=1.05; p = 0.18). The relationship between tree age and slope aspect also was not statistically
significant (χ21 = 0.1156 ; p=0.73).
Analysis of climate data
Total annual precipitation did not differ significantly between periods of continuous
recruitment and periods preceding recruitment (Table 5). Similarly for both dormant season
precipitation and growing season precipitation, there was no significant difference during periods
of continuous recruitment and the preceding time periods (Table 5). Annual temperatures were
significantly warmer during periods of continuous recruitment than during the interval preceding
recruitment for both species at Cross Timbers State Park and Fall River Lake (Table 5; Fig. 12 &
13).
21
CHAPTER 4
DISCUSSION
Temporal patterns of oak regeneration
All four study sites are uneven aged stands and by definition; auto-accumulating oak
woodlands, representing more than four cohorts of reproduction over more than twenty years
(Johnson 1993; Clark and Hallgren 2003). It appears that continuous waves of oak recruitment in
the Chautauqua Hills began between 1929 and 1943, and the majority of the trees in this region
are between fifty and sixty years old. There are two distinct time periods that continuous
recruitment began; 1929-33 (Cross Timbers State Park and Fall River Lake) and 1939-43 (Stotts’
Ranch and Woodson State Fishing Lake).
This is interesting given that Abrams (1986)
concluded that oak recruitment in the Flint Hills ceased around 1930-40.
Since land
management practices have been similar between these regions (livestock grazing and prescribed
fire) it is reasonable to expect that patterns of oak recruitment might also be similar.
Recruitment of both Quercus species appears to have occurred continuously since the 1920’s1930’s in the Chautauqua Hills, but has declined in recent decades likely due light limitation
following the formation of woodland canopies.
However, an abundance of seedlings and
saplings were observed at all four sites, and appear to be concentrated near woodland edges and
canopy gaps. Although waves of regeneration were similar among sites, recruitment patterns
were quite different between the two study species.
At three out of four sites, Q. stellata was significantly older than Q. marilandica, and
comprised a greater proportion of ancient trees (Table 3). I believe these differences reflect the
alternate life histories, specifically life spans, of the two species, Q. stellata being much longer
lived than Q. marilandica. In general, species in the oak sub-genus Leucobalanus may be longer
22
lived than species in the sub-genus Erthyrobalanus. This is consistent with Abrams (2003)
which indicates a similar pattern between Quercus alba (sub-genus Leucobalanus) and Quercus
rubra (sub-genus Erythrobalanus), the former living up to 400 yrs while the latter lives around
175 yrs.
At three of the four sites, Q. stellata recruitment appears to peak before recruitment of Q.
marilandica peaks. This is interesting given that Arevalo (2002) asserted that post oak is more
common in forest interiors while blackjack oak is more common at forest edges. Newly
recruited post oaks may have facilitated blackjack recruitment by creating edge like conditions
which might explain the observed lag between peaks in recruitment. It is also possible that these
lags could be explained by the difference in acorn production and dispersal; post oak producing
acorns annually, blackjack oak producing them biannually. Multiple studies suggest that small
mammals will preferentially consume white oak acorns and cache red oak acorns (Steele and
Smallwood 2002; Steele et al 2005a).
Since caching increases the probability of acorn
germination (Johnson 2009), it seems counterintuitive that species of the white oak subgenus
would recruit at a faster rate than species of the red oak subgenus. However, acorns from the
two subgenra germinate at different times of the year; white oak acorns germinating in the fall
and red oak acorns germinating in the spring. Since small mammals dislike radicles from
developing acorns and other food sources are scarce during winter, red oak acorns become a
more important component of animal diets which may lead to a greater proportion of them being
consumed (Johnson 2009).
Historical vegetation physiognomy in the Chautauqua Hills
Post oak age structures did not fit a normal distribution or a negative exponential model at
most sites (due to the overrepresentation of older age classes) while blackjack oak age structures
23
fit a normal distribution at three sites and a negative exponential at two.
At Woodson State
Lake and Fall River Lake (the only sites containing ancient blackjacks) blackjack oak did not fit
the negative exponential model because older age classes were over represented compared to
predicted values. It is possible that historically blackjack oak was not present on the landscape,
but it is also possible that blackjack oak was not represented in the older age classes because of
its shorter life span.
Further, we know that both species were historically present on the
landscape because they were periodically identified by government land office surveyors in the
1800’s, but it is the extent to which both of these species were historically distributed that was a
focus of this study.
It seems likely that these very old oaks, particularly post oaks, are remnants of former
savannas. If these older trees were remnants of historic woodlands that underwent logging, one
would expect to find a disproportionate number of multi-stemmed individuals (especially of post
oak) and evidence of stumps, which I did not. While it is possible that these stumps may have
been destroyed by fire or have decomposed, stumps are present in eastern deciduous forests that
were logged during early settlement periods (Bellemare et al. 2002) and it seems likely that with
drier climate in the Kansas Cross Timbers region stump decomposition might be slower here
than in eastern forests. Further, savannas are typically characterized as having 3-30% cover
(Lauver et al. 1999; Faber-Langendeon 2001; Stotts 2007) and at every site except Stotts’ Ranch
ancient post oaks comprised a proportion of trees within this range. Ancient blackjack oaks were
within this range at Fall River Lake and Woodson State Lake, but their current representation
may differ from their historic demography considering their shorter life span.
An additional line of evidence supporting the “savanna” hypothesis is that ancient oaks were
not associated with any particular landscape position. Given the various advantages associated
24
with different landscape positions (steepness, aspect, and slope position), and that Abrams
(1986) identified oak expansion in the Flint Hills as originating from drainages, it is reasonable
to hypothesize that oak expansion in the Chautauqua Hills might have followed a similar pattern.
However, Frangaviglia (2000) reports that Q. stellata has been observed to grow poorly in wellwatered riparian areas, and reports that they should be found on rugged uplands. The lack of
topographic correlation with tree age may imply that historically, oaks were sparsely scattered
across the landscape. Such a spatial distribution would be consistent with a savanna in which the
locations of trees were likely influenced by the balance of tree-grass interactions and random
processes (e.g. seed dispersal and microsite conditions) affecting tree propagation and long-term
survival.
External Drivers of Expansion
It appears that oak expansion in the Chautauqua Hills Kansas can best be explained by
external influences that have altered the natural balance of tree-grass interactions, specifically,
land management and the alteration of natural fire regimes. For example, the Prairie States
Forestry Project (1935-42), implemented by Franklin Roosevelt as a measure of protection from
the dustbowl and continued soil degradation, led to the planting of over 217 million trees and
more than 18,000 miles of shelterbelt (Sauer 2007). This program may have altered landowners’
attitudes towards woody plant establishment during the period in which oak expansion began in
the Chautauqua Hills (1929-43). Further, all three state-owned study sites have distinct
boundaries, on at least one patch edge, comprised of commonly used hedgerow species (e.g. M.
pomifera and J. virginiana) suggesting an intent to buffer areas on one side of the boundary from
erosion. It is further reasonable to expect that if landowners planted hedgerows to protect
pastures on one side of the boundary, they were not likely concerned with woody plants
25
expanding into the steep rocky slopes on the opposite side of the boundary. In this context, oaks
would have been allowed to expand to create additional erosion control and protection for
pastures.
All three state-owned study sites were converted to lakes for flood control and/or to
provide recreational opportunities to Kansas residents. It is reasonable to hypothesize that oak
expansion coincides with the change in land ownership (i.e. management) but this coincidence
was only detected at Woodson State Lake, which was donated to the State of Kansas in 1937 and
continuous oak recruitment began in 1939.
Oak expansion at Cross Timbers and Fall River
Lake began in 1929-33 and may have been influenced by the Prairie State Forest Project, and the
drought and depression of the 1930’s.
Temperatures were significantly greater during the period of oak expansion at two of my
study sites than during the preceding years (Fig 12 & 13). Warmer air temperature increase the
rate of soil moisture evaporation, and combined with the drought of the 1930’s, may have led to
increased transpiration and increased mortality of herbaceous plants, leaving the two drought
tolerant oak species at a competitive advantage. Finally, prolonged periods of drought limit
herbaceous plant growth resulting in less productive grasslands that produce less continuous fuel
loads for fires. Less continuous fuel loads, in turn, could result in a reduction of fire intensity. If
fires were less intense during this period, they might not be hot enough to kill juvenile oaks.
Fire has been shown to significantly influence the balance of tree-grass interactions and,
as such, alteration of historic fire regimes may contribute to altered landscape physiognomies.
Prescribed fire is an important land management tool in the Chautauqua Hills and typically
occurs during the dormant season (Guyette et al. 2011). Multiple tree-ring studies indicate that
fire was more frequent in the 20th century than in recent preceding centuries based on pre-
26
settlement fire records from other Cross Timbers sites (Desantis etal. 2010; Allen and Palmer
2011; Guyette et al.2011). Further, Guyette et al. (2011) states that the fire scars at the Stotts’
Ranch were more frequent than any other comparable site in the Great Plains. It appears that
oaks in the Chautauqua Hills have expanded in spite of this increased fire frequency, which may
be explained by their physiological abilities to tolerate fire, or perhaps by the decreased intensity
of the more frequent fires.
Increases in fire frequency have likely caused the fires to be less intense because there is
less accumulation of fuel, and these less intense fires may not be hot enough to exclude woody
juveniles. Knapp et al. (2009) suggest that fire prior to Euro-American settlement of the Great
Plains occurred during both the growing and dormant seasons. Native Americans are known to
have used fire for hunting purposes and were not trying to mimic dormant season lightning
induced fires. As such, modern prescribed burning practices are likely much different than the
fires that shaped plant communities for centuries before Euro-American settlement. Similarly,
Allen and Palmer (2011) assert that the seasonality and spatial scale of fires in northern
Oklahoma cross timbers stands have also been altered by anthropogenic activities and do not
reflect fire regimes before settlement of the region. In summation, the combined alteration of the
seasonality, frequency, intensity, and spatial scale of fire, have likely been influential in
facilitating oak expansion in the Chautauqua Hills.
In addition to drought, economic depression was rampant in the thirties and forced many
Kansas residents to sell their estates or forfeit them to banks, leaving land unmanaged for
prolonged periods (Hornbeck 2009). This phenomenon was common throughout the Great
Plains, exemplified in the Chautauqua Hills by the purchase of the land now owned by the Stotts’
family in Elk County Kansas by W.D. Pratt in the 1930’s.
27
Therefore, woody plant expansion
may have been facilitated by a period in which historic disturbance regimes had been altered, and
land managers were not actively controlling woody plants.
While it is difficult to determine exactly which factors led to the expansion of oaks in this
region, it is clear that oak woodlands began to form in the late 1920’s and are currently
expanding where they are not limited by fragmented landscapes.
The fact that all four
woodlands began recruiting during the period of 1929-1943 suggests causes that are regional in
spatial scale and not site-specific. Alteration of natural fire regimes, the Prairie States Forestry
Project, periods of drought and conversion of land to state parks are all possible causes of oak
expansion in the Chautuaqua Hills. Although it remains difficult to define a single cause of
expansion, due to the interaction of all possible causes and site specific differences (Table 6), it
seems that the alteration of historic fire regimes has disproportionately contributed to oak
expansion in the Chautauqua Hills.
It is likely that oak expansion at Cross Timbers State Park and Fall River Lake occurred
through the autogenic succession of savanna trees. Examination of age structures at these sites
reveals recruitment rates that are far greater than predicted by the negative exponential model
which may indicate a competitive release and/or facilitation by initial colonizers. In addition to
the alteration of historic fire regimes, I believe the favorable attitude towards woody plants to
reduce erosion and the droughty conditions of the 1930’s contributed to the existing oak
woodlands at these two sites.
I believe oak expansion at Stotts’ Ranch has been influenced by the alteration of fire
regimes, but also was highly influenced by a long history of intensive livestock grazing. Oak
expansion, in the patch sampled at Stotts’ ranch appears to have undergone one continuous wave
of recruitment, and this patch was probably tall grass prairie with a few scattered oaks prior to
28
1954. Since the oldest tree in this patch was only 82 years old, it does not appear that savanna
characterized this portion of the landscape prior to Euro-American settlement.
However,
Guyette et al. (2011) identified trees >200 years old in other portions of this 6,000 acre ranch,
which leads me to believe that other woodland patches in the ranch may have different histories
than the one I sampled.
Finally, Woodson State Lake is the most curious of all four sites and oak expansion is
probably best explained by a combination of the alteration of historical fire regime and drought,
a history of livestock grazing, the Prairie States Forestry program, and its conversion to a state
lake in the 1930’s. Since post oak recruitment has been continuous for the last 150 years, it
remains unclear whether oak woodlands at this site formed by way of autogenic succession from
former savanna trees, or if recruitment beginning in 1939 is actually representative of a second
wave of growth as may be supported by the almost equal proportion of single and multi-stemmed
trees
Future Studies
It would be interesting to determine the mechanisms by which oak expansion occurred at
these four sites.
Dating the trees surrounding ancient oaks may provide insight into the
importance of these trees as foci of oak expansion. It seems likely that older trees at Cross
Timbers and Fall River facilitated oak recruitment through a process of nucleation. This process,
described by Archer et.al (1988), refers to a process in which individual trees or clusters of trees,
alter microsite conditions creating a “fertile island” which facilitates further woody plant
recruitment.
This does not seem to be the case at Woodson Lake or Stotts’ Ranch, because
recruitment has been somewhat continuous at Woodson over the last century, and because age
29
structures at Stotts’ Ranch did not significantly differ from the negative exponential distribution.
If nucleation had occurred at Stotts’ Ranch, recruitment rates would have been greater than those
predicted by the negative exponential model, in other words, I expect that nucleation would
produce accelerating oak population growth rates. Recruitment at these two sites suggests other
possible mechanisms like sprouting, rodent dispersal, or masting events. The evolved strategies
hypothesis (Norton and Kelley 1988) predicts that a trade-off should occur between incremental
growth
and
reproduction
efforts.
Speer
(2001)
developed
a
technique
coined
“dendromastecology” which allows for the historic reconstruction of masting events based on
tree rings. This technique could provide further insight into the ecological conditions and
mechanisms that have influenced oak expansion in the Chautauqua Hills.
Finally, the higher tree species richness found on north- and east-facing woodlands, at
Cross Timbers State Park and Fall River Lake, suggests that woodlands on these aspects are
undergoing successional changes which, as discussed in DeSantis (2009), may lead to
mesophytic species like J. virginiana, P. serotina and C. canadensis, eventually excluding and
replacing existing oaks. Although these two sites are periodically burned, canopy closure from
previous oak expansion, has greatly reduced the amount of herbaceous understory plants, and as
such fires are less intense. It is likely that the combination of a closed canopy, and reduced fire
intensity has set the stage for mesophytic species to establish in these oak woodlands What is
curious is that we do not see mesophytic species at all four sites, which have similar soils,
topography, and canopy cover. It would be interesting to know what ecological differences have
allowed the establishment of these mesophytic species at two sites and not at the others.
30
Implications for Land Managers
Land managers in southeast Kansas should strive to use fire in a way that reflects the
natural fire history of the Great Plains as identified by Guyette et al. (2011). Frequent, dormant
season fires may not be hot enough, especially after low-productivity, droughty growing seasons,
to effectively kill woody plant juveniles. A rotational grazing and burning strategy would likely
be the most effective strategy to exclude future woody plant expansion in the region. Rotational
grazing would allow more intense competition for resources between woody and herbaceous
plants in ungrazed plots. Further, rotational grazing would increase the amount of fine fuel loads
in ungrazed plots, which should potentially increase fire intensity and therefore the likelihood of
fire excluding woody plant juveniles.
Special attention should be paid to excluding J.
virginiana, which is encroaching into virtually every habitat type in the Chautauqua Hills.
Further, mechanical thinning of dense oak woodlands to mimic savanna, a proven component of
the historic Cross Timbers physiognomy in the Chautauqua Hills, will increase landscape
heterogeneity, wildlife diversity, and forage production for livestock.
31
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39
APPENDIX
40
20
40
60
Cross Timbers
Fall River Lake
Stotts' Ranch
Woodson State Lake
tal
is
ra
oc
ltis
Ce
Ma
c lu
ra
p
cid
om
en
ife
sr
ub
Qu
e
rcu
ero
us
s
Pr
un
na
ca
rci
s
Ce
ra
a
tin
is
de
ns
ca
eri
am
us
Ulm
eru
sv
nip
na
na
inia
irg
rila
Ju
erc
us
Qu
Qu
e
rcu
ma
ss
tel
nd
lat
a
ic a
0
Proportion of Stems per Site
80
Tree Species Composition of Study Sites
Species
Fig. 1. Proportional representation of tree species at the four cross timbers woodlands.
41
Post Oak
1.0
Cross Timbers
Fall River
Stotts'
Woodson
Proportion of trees
0.8
0.6
0.4
0.2
0.0
0
1
2
3
4
5
6
7
Blackjack Oak
1.0
Proportion of trees
0.8
0.6
0.4
0.2
0.0
0
1
2
3
4
5
Number of stems per genetic individual
Fig. 2. Proportion of single and multi-stemmed blackjack and post oak trees at four cross timbers
woodlands in the Chautauqua Hills, KS.
42
Post Oak
Cross Timbers State Park
Fall River Lake
160
140
140
120
Tree Age
120
p=0.03;R2=0.07
100
100
80
80
60
60
40
40
20
20
0
20
40
60
80
100
120
140
160
p<0.01;R2=0.28
p<0.01;R2=0.15
0
180
20
40
Stotts' Ranch
60
80
100
120
140
160
180
200
Woodson State Lake
140
80
120
100
60
Tree Age
80
60
40
40
20
20
0
-20
0
p=0.10;R2=0.04
P<0.01; R2=0.29
-40
-60
0
20
40
60
80
100
120
140
160
180
0
50
100
150
200
dbh (cm)
Fig. 3. Diameter-age relationships for Q. stellata at the four study sites. Regression equations
are: Cross Timbers: y=0.26(x)+45.53; Fall River: y=70.43-0.17(x); Stotts’ Ranch:
y=0.10(x)+25.06; Woodson Lake: y=43.46(ln(x)) -107.23.
43
Blackjack Oak
Cross Timbers
Fall River Lake
100
120
100
80
Tree Age
80
60
60
40
40
20
0
20
-20
P=0.41;R2=0.02
0
p=0.04;R2=0.10
-40
-60
0
50
100
150
200
0
250
20
40
Stotts Ranch
60
80
100
120
140
160
180
Woodson State Lake
100
140
120
Tree Age
80
100
80
60
60
40
40
20
20
p=0.06; R2=0.07
p<0.01;R2=0.11
0
0
0
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
140
160
180
200
dbh (cm)
Fig. 4. Diameter-age relationships of Q. marilandica at the four study sites. Regression
equations are: Cross Timbers: y=3.41(ln(x)) +41.51; Fall River:y=14.57(ln(x)) -7.25; Stotts’
Ranch:y=0.14(x)+31.73; Woodson Lake: y=0.33(x)+28.64.
44
Post Oak
Fall River State Lake
Number of Trees
Cross Timbers State Park
14
30
12
25
10
20
8
15
6
10
4
5
2
0
Number of Trees
0
50
100
150
200
0
0
20
Stotts' Ranch
40
60
80
100
120
140
Woodson State Lake
10
16
14
8
12
10
6
8
4
6
4
2
2
0
0
0
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
140
160
diameter at breast height (cm)
Fig. 5 Size structures of Post oak at four cross timbers stands in the Chautauqua Hills, KS
45
Number of Trees
Blackjack oak
Cross Timbers State Park
Fall River State Lake
30
18
16
25
14
20
12
10
15
8
10
6
4
5
2
0
0
0
50
100
150
200
0
50
100
150
Woodson State Lake
Stotts' Ranch
14
30
12
25
Number of Trees
10
20
8
15
6
10
4
5
2
0
0
0
20
40
60
80
100
120
140
160
0
50
100
150
dbh (cm)
Fig. 6 Size structures of blackjack oak at four cross timbers stands in the Chautauqua Hills, KS
46
Post oak
Cross Timbers
Fall River Lake
16
14
14
Number of Trees
12
12
10
10
8
8
6
6
4
4
2
2
0
0
20
40
60
80
100
120
140
160
0
0
20
Number of Trees
Stotts' Ranch
40
60
80
100
120
140
Woodson State Lake
16
10
14
8
12
10
6
8
4
6
4
2
2
0
0
0
20
40
60
80
0
50
100
150
200
250
Tree age (yrs.)
Fig. 7. Post oak age structures at the four cross timbers woodlands. Dots represent expected
frequencies in 5-year age classes based upon normal distributions.
47
Post oak
Cross Timbers
Fall River Lake
30
16
Number of Trees
14
25
12
20
10
15
8
6
10
4
5
2
0
0
60
80
100
120
140
60
160
80
Number of Trees
Stotts' Ranch
100
120
140
160
Woodson State Lake
16
18
14
16
12
14
12
10
10
8
8
6
6
4
4
2
2
0
20
30
40
50
60
70
0
80
20
40
60
80
100
120
140
160
180
200
220
Tree age (yrs.)
Fig. 8 Comparison of post oak age structures to the negative exponential frequency distribution.
Frequency distributions depict recruitment beginning with the oldest age class in the population
up to the modal age class. Dots represent the expected frequencies in 5-year age classes based
upon negative exponential distributions.
48
Blackjack oak
Fall River Lake
Cross Timbers
Number of Trees
12
10
10
8
8
6
6
4
4
2
2
0
0
0
20
40
60
80
100
0
20
40
60
80
100
120
140
100
120
140
Woodson State Lake
Stotts' Ranch
14
10
Number of Trees
12
8
10
6
8
6
4
4
2
2
0
0
0
20
40
60
80
0
100
20
40
60
80
Tree age (yrs.)
Fig. 9 Blackjack oak age structures at the four cross timbers woodlands. Dots represent the
expected frequencies in 5-year age classes based upon normal distributions.
49
Blackjack oak
Fall River Lake
Cross Timbers
25
Number of Trees
12
10
20
8
15
6
10
4
5
2
0
0
40
50
60
70
80
90
40
100
50
Stotts' Ranch
70
80
90
100
110
120
100
110
120
Woodson State Lake
10
Number of Trees
60
14
12
8
10
6
8
6
4
4
2
2
0
0
30
40
50
60
70
80
90
40
50
60
70
80
90
Tree age (yrs.)
Fig. 10 Comparison of blackjack oak age structures to the negative exponential frequency
distribution. Frequency distributions depict recruitment beginning with the oldest age class in
the population up to the modal age class. Dots represent the expected frequencies in 5-year age
classes based upon negative exponential distributions.
50
Topographic Position
0.25
Proportion of Ancient Trees
Post
Blackjack
0.20
0.15
0.10
0.05
0.00
Ridge-top
Mid-Slope
Drainage
Slope Position Categories
Fig 11. Proportion of all trees sampled that are ancient (≥100 yrs. old) on three topographically
distinct slope positions.
51
1869
1874
1879
1884
1889
1894
1899
1904
1909
1919
1924
1929
1934
1939
1944
1949
1954
1959
1964
1969
1974
1979
1984
1989
1994
1999
2004
0
2
4
6
8
Number of Trees
10
12
14
Fig 12.
structures of both Quercus species at Cross Timbers State Park.
52
1994
1999
2004
1979
1984
1989
1964
1969
1974
1949
1954
1959
pre-recruitment
1934
1939
1944
1919
1924
1929
1904
1909
1914
1894
1899
Five Year Deviations From The Long Term Mean
Average Temperature (C)
10
during
5
0
-5
-10
-15
-20
Cross Timbers State Park
Blackjack Oak
Post Oak
Year
Five-year deviations from the long term mean in annual temperature compared with age
10
pre-recruitment
during
5
0
-5
-10
-15
1994
1999
2004
1979
1984
1989
1964
1969
1974
1949
1954
1959
1934
1939
1944
1919
1924
1929
1904
1909
1914
-20
1894
1899
Five Year Deviations From The Long Term Mean
Average Temperature (C)
Fall River State Lake
10
Blackjack Oak
Post Oak
Number of Trees
8
6
4
2
1869
1874
1879
1884
1889
1894
1899
1904
1909
1914
1919
1924
1929
1934
1939
1944
1949
1954
1959
1964
1969
1974
1979
1984
1989
1994
1999
2004
0
Year
Fig 13. Five year deviations from the long term mean in annual temperature compared with age structures
of both Quercus species at Fall River State Lake.
53
Table 1. Description of tree community composition in cross timbers woodlands at the four
study sites. Relative stand proportions refers to the proportion of the total density of trees
contributed by each species.
Site
Species
Richness
Species
Composition
Mean(±SE)
DBH (cm)
Cross
Timbers
6
Q. stellata
Fall
River
Lake
7
Stotts’
Ranch
2
Woodson 3
Lake
Tree Density
(stems/ha)
55.23 ± 2.70
Basal
Area
(m2/ha)
3.16
133.96
Relative
Stand
Proportion
0.33
Q. marilandica
C. canadensis
J. virginiana
P. serotina
U. americana
66.85 ±4.51
11.06±1.44
13.91±2.10
12.12±2.02
34.97±5.18
1.01
0.06
0.26
0.50
0.16
28.72
36.36
70.8
120.56
9.56
0.07
0.09
0.17
0.30
0.02
Q. stellata
56.56± 3.06
4.38
280.84
0.70
Q. marilandica
C. canadensis
C. laevigata
J. virginana
M. pomifera
P. serotina
67.71±3.88
15.63±4.81
11 (n=1)
11.16±1.20
14 (n=1)
26.08±6.45
1.52
0.06
0.002
0.02
0.003
0.10
51.08
29.8
4.24
34.04
4.24
21.28
0.13
0.07
0.01
0.09
0.01
0.05
Q. stellata
56.66 ± 3.05
3.05
197.48
0.49
Q. marilandica
63.71 ± 2.94
3.72
202.52
0.51
Q. stellata
68.63± 4.75
3.94
262.08
0.66
Q. marilandica
Q. rubra
39.10± 2.30
33.51±8.91
0.59
0.78
101.16
36.8
0.25
0.09
54
Table 2. Description of size structures of oak populations and p-values from KomolgorovSmirnov goodness of fit tests to determine if size structures correspond to normal distributions.
Site
Diameter Range (cm)
Normality
Q.marilandica
12.2-133.4
Yes (p=0.15)
Q. stellata
10.1-142.5
Yes (p=0.15)
Q.marilandica
12-163.5
Yes (p=0.15)
Q. stellata
12-164.4
Yes (p=0.15)
Q.marilandica
19-152
Yes (p=0.15)
Q. stellata
12-115
Yes (p=0.15)
Q.marilandica
12-152.6
Yes (0.10)
Q. stellata
11-137.5
Yes (0.10)
Cross Timbers
Fall River Lake
Stotts’ Ranch
Woodson Lake
55
Table 3.
Description of oak demography at four cross timbers woodlands. p-values were
generated by comparing mean ages of the two oak species.
Site
Age
Range
Mean
(±SE) Age
Cross
Timbers
Q.marilandica
17-86
57.03±2.17
Q. stellata
26-138
66.73±2.87
11-115
55.61±2.92
5-148
57.89±3.21
11-83
41.07±2.28
4-68
33.71±1.65
4-112
41.42±3.21
6-193
80.51±6.71
Fall
Lake
p-values
p=0.002*
Modal
Age
Continuous
recruitment
waves
Percent
Trees
≥100
yrs.
55
1929-1974
0
64
1923-1978
9.26
56
1939-1993
5.41
58
1939-1998
6.45
41
1954-2004
0
32
1954-1998
0
46
1939-current
2.04
62
1939-2003
30.77
River
Q.
marilandica
Q. stellata
p=0.344
Stotts’ Ranch
Q.
marilandica
Q. stellata
Woodson
Lake
Q.
marilandica
Q. stellata
p=0.007*
p<0.001*
56
Table 4. Results of comparing oak age structures to the normal and the negative exponential
models. “Ancient trees χ2 contribution” refers to the proportion of the total chi-square value
contributed by ancient trees.
Site
Species
Q. marilandica
Q.stellata
Normal Age
Distribution
Yes (p=0.15)
No (p=0.01)
Negative
Exponential
No (p<0.01)
No (p<0.01)
Ancient Trees
X2 Contribution
0
0.20
Cross Timbers
Fall River Lake
Q. marilandica
Q. stellata
Yes (p=0.11)
No (p=0.02)
No (p<0.01)
No (p<0.01)
0.38
0.55
Stotts’ Ranch
Q. marilandica
Q. stellata
Yes (p=0.15)
No (p=0.01)
Yes (p=0.27)
Yes (p=0.39)
0
0
Woodson Lake
Q. marilandica
Q. stellata
No (p=0.01)
No (p=0.01)
No (p<0.01)
No (p<0.01)
0.25
0.75
57
Table 5. Differences in climatic variables from the beginning of a continuous wave of
recruitment to the modal age class of each species vs. the years preceding recruitment. P-values
are from 1-way ANOVA. Bonferroni correction was used to adjust α of 0.05 resulting in an α of
0.006.
Site
Species
Dormant
Precipitation
0.0475
Growing
Precipitation
0.9523
MeanTemperature
Blackjack
Total
Precipitation
0.4760
Cross
Timbers
Post
0.4919
0.6053
0.3512
0.0001 *
Fall River
Lake
Blackjack
0.6790
0.2925
0.6744
0.0002 *
Post
0.7536
0.2789
0.4933
0.0002 *
Stotts’ Ranch
Blackjack
Post
0.5455
0.5455
0.1200
0.1200
0.9759
0.9759
0.2623
0.2623
Woodson
Lake
Blackjack
0.8455
0.7628
0.9457
0.0855
Post
0.0969
0.4584
0.1128
0.0957
58
0.0015 *
Table 6. Summary of the influences of fire, grazing, climate and logging on oak expansion in the
Chautauqua Hills, KS based on quantitative analysis of tree ring and climate data, and qualitative
assessments of site histories.
Site
Fire
Grazing
Climate
Logging
Cross Timbers
Yes
Unlikely
Possible
No
Fall River
Yes
Unlikely
Possible
No
Stotts’ Ranch
Yes
Yes
No
Possible
Woodson State Lake
Yes
Yes
No
No
59
60