T12035 Rogers Seq.Pdf (547.7Kb)

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

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

“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

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.

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.

vii

TABLE OF CONTENTS

Chapter Page

1. INTRODUCTION 1

Tree-grass interactions 2 External drivers of woody plant expansion 3 Topographic influence 5 The Cross Timbers Ecosystem 7 Scope of this study 9

2. METHODS 10

Study species 10 Study sites 11 Field methods 13 Statistical analysis 16

3. RESULTS 19

Tree species composition at the study sites 19 Size-age relationships ` 20 Stage structures of oak populations 20 Landscape position effects 21 Analysis of climate data 21

4. DISCUSSION 22

Temporal patterns of oak regeneration 22 Historic vegetation physiognomy of the Chautauqua Hills 23 External drivers of expansion 25 Future studies 29 Implications for land managers 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

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

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

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 one- sided 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,

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

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 wind- dispersed 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

(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.

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

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 1885-

1925, 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.

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.

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.

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 multi- stemmed trees were observed for both Q. stellata and Q. marilandica.

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

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;

2 χ 2=1.05; p = 0.18). The relationship between tree age and slope aspect also was not statistically

2 significant (χ 1 = 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).

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’s-

1930’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 well- watered 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 th is 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. Therefore, woody plant expansion

27 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

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.

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.

BIBLIOGRAPHY

BIBLIOGRPAHY

Abrams, M. 1986. Historical Development of Gallery Forests in Northeast Kansas. Vegetatio. 65:29-37

Abrams,M. 1990. Adaptations and responses to drought in Quercus species of North America. Tree Physiology. 227-238

Abrams, M. Fire and development of Oak forests. 1992. Bioscience. 42: 346-353

Abrams,M. 2003. Where Has All the White Oak Gone? Bioscience. 53: 927- 939.

Allen, M.S. and Palmer, M.W. 2011. Fire history of a prairie/forest boundary: more than 250 years of frequent fire in a North American tallgrass prairie. Journal of Vegetation Science.22:436-444.

Archer, S. Scrifes, C. Bassam C.R., and Maggio, R. 1988. Autogenic succession in a subtropical savanna: conversion of grassland to thorn woodland. Ecological Monographs. 58:111-127.

Archer, S. 1989. Have Southern Texas Savannas Been Converted To Woodlands In Recent History? The American Naturalist. 134: 545-561.

Archer, S. 1990. Development and stability of grass/woody mosaics in a subtropical savanna parkland, Texas,U.S.A. Journal of Biogeography. 17:453-462.

Archer, S. Schimel,D. S., and Holland E.A. 1995. Mechanisms of Shrubland Expansion: Land use, Climate, or CO2? Climate Change. 29:91-99.

Archer, S., Boutton, T.W., and Hibbard,K.A. 2001. Trees in grasslands: biogeochemical consequences of woody plant expansion. In: Global Biogeochemical Cycles in the Climate. (ed. By E.-D. Schulze, M. Heimann, S. Harrison, E. Holland, J. Lloyd, I. Prentice and D. Schimel) pp. 115-138. Academic Press, San Diego,CA.

Arévalo, J.R.2002. Distribution of trees and saplings at the edge of cross timbers forests, Oklahoma, USA. Natural Areas Journal. 22:99-107.

Asner, G., Elmore, A., Olander, A., Martin, R. and Harris, T. 2004. Grazing Systems, Ecosystem Responses, and Global Change. Annu. Rev. of Env. And Resources. 29:261-299.

Barnes, P.W. and Archer, S. 1999. Tree-shrub interactions in a subtropical savanna parkland: competition or facilitation? Journal of Vegetation Science. 10:525-536.

Black, B. and Abrams, M. 2001. Influences on Native American and Surveyor Biases on Metes and Bounds Witness-Tree Distribution. Ecology. 82: 2574-2586.

Borchert, M., Davis,F.W., Michaelsen, J. and Oyler, L.D. 1989. Interactions of Factors Affecting Seedling Recruitment of Blue Oak ( Quercus Douglasii) in California. Ecology. 70. 389-404.

Boyd, M. Identification of Anthropgenic Burning in the Paleoecological Record of the Northern Prairies: A New Approach. 2002. Annals of the Association of American Geographers. 92. 471-487.

Bradstock, R. A., Hammilll, K.A., Collins,L., Price, O. 2010. Effects of weather, fuel, and terrain on fire severity in topographically diverse landscapes of South Eastern Australia. Landscape Ecology. 25:607-619.

Bragg, T.B., and Hulbert, L.C. 1976. Woody Plant Invasion of Unburned Kansas Bluestem Prairie. Journal of Range Management. 29:19-24.

Briggs, J.M., Knapp, A.L., Brock, B.L., Expansion of Woody Plants in Tallgrass Prairie: A Fifteen Year Study of Fire and Fire-Grazing Interactions. The American Midland Naturalist. 147:287-294. z

Brown, J.R., and S. Archer. 1987. Woody plant Invasion of grasslands: establishment of honey mesquite ( Prosopis gandulosa var. glandulosa) on sites differing in herbaceous biomass and grazing history. Oecologia. (Berl.) 80:19-26.

Bush, J.K. and Van Auken , O.W. 1990. Growth and Survival of Prosopis Glandulosa Sedlings Associated With Shade and Herbaceous Competition. 1990. Bot. Gaz. 151:234-239.

Cahill, J.F. 2002 Interactions between root and shoot competition vary among species. Oikos.99:101-112.

Callaway,R.M., Nadkarni, N.M., Mahall,B.E. 1991. Facilitation and Interference of Quercus douglasii on Understory Productivity in Central California. Ecology. 72:1484-1499.

Christensen, N.L. 1988. Vegetation of the southeastern coastal plain. Pp. 317-363. Barbour and Billings 1988.

Clark, J.S. 1990. Landscape Interactions among Nitrogen Mineralization, Species Composition, and Long-Term Fire Frequency. Biogeochemistry. 11:1-22.

Coppedge,B.R., Engle, D.M., Masters,R.E., and Gregory, M.S., 2001. Avian Resonse to Landscape Change in Fragmented Southern Great Plains Grasslands.Ecological Applications11:47-59.

Cottam,G. 1949. The Phytosociology of an Oak Woods in Southwestern Wisconsin. Ecology. 30: 271-287.

Crawford, C.J., Kennedy, L.M. 2009. Spatial and Temporal Patterns of Tree Encroachment Into a Southern Appalachian Grass/Heath Bald Using Tree Rings. Natural Areas Journal. 29:367-375

DeSantis, R.D., Hallgen, S.W., Lynch, T.B., Burton, J.A. and Palmer, M.W. 2010. Long-term directional changes in upland Quercus forests throughout Oklahoma, USA. Journal of Vegetation Science. 21:606-615.

Dyksterhuis, E.J. 1949. The Vegetation of the Western Cross Timbers. Ecological Monographs. 18:325-376.

Grime, J.P. and Lloyd, P.S. 1973. An Ecological Atlas of Grassland Plants. Edward Arnold (Publishers) Limited, London.

Guyette, R.P., Stambaugh, M.C., and Marschall, J.M. 2011. A quantitative analysis of fire history at national parks in the Great Plains. A report prepared for the Great Plains cooperative Ecosystem Studies Unit and National Park Service. 78 pp.

Hamilton,KGA. 2005. Bugs reveal an extensive, long-lost northern tall-grass prairie. Bioscience. 55:49-59

Harlow,W.M., Harrar,E.S., Hardin, J.W., White,F.M. 1996. Textbook of Dendrochronology. McGraw-Hill Inc.

Henderson, R. 1995. Oak savanna communities. In Wisconsin’s biodiversity as a management issue: A report to Department of Natural Resource Managers. Madison:Wisconsin Department of Natural Resources. P88-96.

Hessl, A.E., Graumlich,L.J., 2002. Interactive effects of human activities, herbivory and fire on quaking aspen (Populus tremuloides) age structure in western Wyoming. Journal of Biogeography. 29:889-902.

House,J.I., Archer, S., Breshears, D.D., Scholes,R.J., and NCEAS Tree-Grass Interactions Participants. 2003. Conundrums in mixed woody-herbaceous plant systems. Journal of Biogeography. 30:1763-1777.

Huxman, ,T., Bradford, W.P., Breshears,D.D., Scott, R.L., Snyder,K.A., Small, E.E., Pockman,W.T., and Jackson, R.B. 2005. Ecohydrological Implications of Woody Plant Encroachment. Ecology. 86:308-319.

Johnsen,T.N.1962. One-seed juniper invasion of Northern Arizona grasslands. Ecological Monographs 32:187-207.

Johnson,F.L., and Riser,P.G. 1975. A quantitative comparison between an oak forest and an oak savannah in central Oklahoma. The Southwestern Naturalist. 20: 75-84.

Johnson,P.S., ShifleyS.R., and Rogers,R. 2009. The Ecology and Siviculture of Oaks. 2nd Ed. CABI International.

Kittleson,P.M., Pinahs,C., Dwyer,J., Ingersoll,A., Mans, E., Rieke,J., Rutman, B., and Volnec,M. 2009. Age Structure and Genetic Diversity of Four Quercus macrocarpa (Michx.) Populations in Fragmented Oak Savanna along the Central Minnesota River Valley. Am. Midl. Nat. 161:301-312.

Knapp, Eric E.; Estes, Becky L.; Skinner, Carl N. 2009. Ecological effects of prescribed fire season: a literature review and synthesis for managers. Knapp, Eric E.; Estes, Becky L.; Skinner, Carl N. 2009. Ecological effects of prescribed fire season: a literature review and synthesis for managers. Gen. Tech. Rep. PSW- GTR-224. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station. 80 p.

Kreuter, H. Diaz-Solis, Dube, S., Kothmann, M.M., Pinchak, W.E., AND Ansley, R.J. 2008. An ecologic economic simulation model for assessing fire and grazing Management effects on Mesquite rangelands in Texas. Ecological Economics. 64:611-624.

KS. Board of Agriculture.1885-1925 Census. Elk County, KS. Painterhood Township.

KS. Board of Agriculture.1885-1925 Census. Greenwood County, KS.Fall River Township.

KS. Board of Agriculture.1885-1925 Census. Woodson County, KS. Belmont Township.

KS. Board of Agriculture.1885-1925 Census. Woodson County, KS. Toronto Township.

Lauver,C.L. Kindscher,K., Langendoen,D.F., and Schneider,R., 1999. A Classification Of the Natural Vegetation of Kansas. The Southwestern Naturalist. 44:421-443.

Linn, R.R., Winterkamp,J.L., Weise,D.R., and Edminster, C., 2010. A numerical study of slope and fuel structure effects on coupled wildfire beaviour. International Journal of Wildland Fire. 19:179-201.

McPherson, G.R. 1997. Ecology and Management of North American Savannas. The University of Arizona Press. Tuscon.

Milchunas,D.G., Lauenroth, W.K., Chapman, P.L., and Kazempour, M.K. 1989.Effects of Grazing, Topography, and Precipitation on the Structure of a Semi-Arid Grassland. Vegetatio.89:11-20.

Miller, R.F., and Wigand P.E. 1994. Holocene changes in semiarid pinyon- juniper Woodlands. Bioscience 44:465-473.

Moore, M.M., and Huffman,D.W. 2004. Tree Encroachment on Meadows of the North Rim, Grand Canyon National Park, Arizona U.S.A. Artic, Antartic, and Alpine Research.. 36:474-483. Nagamatsu, D., Seiwa, K., and Sakai, A. Seedling establishment of deciduous trees in various topographic positions. Journal of Vegetation Science. 13:35-44.

Norton, D.A., and Kelly, D. 1988. Mast seeding over 33 years by Dacrydium cupressinum Lamb. (rimu)(Podocarpacaea) in New Zealand: the importance of economies of scale. Functional Ecology 2: 399–408

Nowacki, G.J., Abrams, M.D., and Lorimer, C.G. 1990. Composition, Structure, and Historical Development of Northern Red Oak Stands along an Edaphic Gradient in North-Central Wisconsin. Forest Science. 36:276-292.

Nuzzo, V.A., 1986. Extent and status of Midwest oak savanna:Presettlement and 1985. Natural Areas Journal. 6:6-36.

Perry, D.A., Oren, R., and Hart, S.C. 2008. Forest Ecosystems. The Johns Hopkins Press 2008.

Preston,R.J. Jr., and Braham, R.R. 2002. North American Trees 5th Ed.. Iowa State Press. Blackwell Publishing Company.

Rice, K.J. 1987. Interaction and Disturbance patch size and herbivory in Erodium Colonization. Ecology. 68:1113-1115.

Rieman,B.E., Hessburg,P.F., Luce,C. and Dare,M.R. 2010. Wildfire and Management of Forests and Native Fishes: Conflict of Opportunity for Convergent Solutions? Bioscience. 60:460-468

Russell, F.L., and Fowler, N.L. 2002. Failure of adult recruitment in Quercus buckleyi populations on the eastern Edwards Plateu, Texas. American Midland Naturalist. 148:201-217.

Samson,F., and Knopf, F. 1994. Prairie Conservation in North America. Bioscience. 44: 418-421.

Scholes,R.J., and Archer,S.R. 1997. Tree-Grass Interactions in Savannas. Annu. Rev. of Eco. and Syst. 28:517-544.

Sims, P.L. and Risser,P.G. 2000. Grasslands. In: North American Terrestrial Vegetation, 2nd Ed. Barbour, M.G., and Billings, D.B. Cambridge University Press.

Speer, J.H. 2001. Oak mast history from dendrochronology: A new technique demonstrated in the southern Appalachain Region. Ph.D. Dissertation.University of Tennessee, Knoxville.

Stambaugh,M.C., Guyette, R.P, Godfrey, R.McMurry, E.R. and Marshall, J.M. 2009. Fire, drought, and human history near the western terminus of the Cross Timbers, Wichita Mountains, Oklahoma. Fire Ecology 5 (2): 51-56.

Steele, M.A. and Smallwood, P.D. 2002. Acorn dispersal by birds and mammals. In: McShea,W. and Healy, W.M. (eds) Oak Forest Ecosystems: Ecology and Management for Wildlife. Johns Hopkins University Press, Baltimore Maryland,pp182-195.

Steele, M.A., Wauters, L., and Larsen, K. 2005a. Selection, predation, and dispersal of seeds by tree squirrels in temperate and boreal forests: are tree squirrels keystone granivores? In: Forget,P.M., Vander Wall, S., Lambert, J., and Hulme, P. (eds) Seed fate: Predation, Dispersal and Seedling Establishment. CAB International, Wallingford, Oxon, UK, pp 205-220.

Stephens,H.A. 1969. Trees, Shrubs, and Woody Vines in Kansas. University Press of Kansas.

Stotts, C., Palmer,M.W., and Kindscher, K. 2007. The Need for Savanna Restoration in the Cross Timbers. Oklahoma Native Plant Journal. 7:78-90. Veblen, T.T., and Lorenz, D.C. 1987. Post-fire stand development of Austocedrus-Nothofagus forests in northern Patagonia. Vegetatio. 71:113-126.

Walter,H. 1954. Die verbuschung eine erscheinung der subtropischen savannengebiete, und ihre oklogischen urscachen. Vegeatatio.6: 6-10

Walter,H. 1979. Vegetation of the Earth and Ecological Systems of the Geo-Biosphere. Springer-Verlag, New York.

Warren, S.D., Nevil,M.B., Blackburn, W.H., and Garza, N.E., 1986. Soil response to trampling under intensive rotation grazing. Soil Science Society of America Journal. 50:1336-1341.

Wilson,S.D., 1993. Competition and Resource Availability in Heath and Grassland in the Snowy Mountains of Australia. Journal of Ecology. 81:445-451.

Wilson,S.D., 2007. Competition, Resources, and Vegetation During 10 Years in a Native Grassland. Ecology. 88. 2951-2958.

APPENDIX

Tree Species Composition of Study Sites

80 Cross Timbers Fall River Lake Stotts' Ranch Woodson State Lake

20 Proportion of Stems per Site

Quercus rubra Quercus stellata Prunus serotina Ulmus americana Maclura pomifera Cercis canadensis Celtis occidentalis Quercus marilandicaJuniperus virginiana

Species

Fig. 1. Proportional representation of tree species at the four cross timbers woodlands.

Post Oak

1.0 Cross Timbers Fall River Stotts' 0.8 Woodson

0.6

0.4 Proportion of trees Proportionof

0.2

0.0 0 1 2 3 4 5 6 7

Blackjack Oak

1.0

0.8

0.6

0.4 Proportiontrees of

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.

Post Oak Cross Timbers State Park Fall River Lake 160 140

140 120 p=0.03;R2=0.07 120 100

100 80

80 60

60 40

40 20 p<0.01;R2=0.28 Tree Age Tree 20 0 p<0.01;R2=0.15 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 140 160 180 200

Stotts' Ranch Woodson State Lake 80 140

120

60 100

40 60

20 20

0 -20 Tree Age Tree p=0.10;R2=0.04 -40 P<0.01; R2=0.29 -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.

Blackjack Oak

Cross Timbers Fall River Lake 100 120

100 80 80

60 60

40 40 20

0 20

Tree Age Tree -20 P=0.41;R2=0.02 0 -40 p=0.04;R2=0.10

-60 0 20 40 60 80 100 120 140 160 180 0 50 100 150 200 250

Stotts Ranch Woodson State Lake 100 140

120 80 100

60 80

60 40 40 Tree Age Tree 20 20 p=0.06; R2=0.07 0 p<0.01;R2=0.11 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.

Post Oak

Cross Timbers State Park Fall River State Lake

14 30

12 25

10 20

8 15 6

10 4

Number of Trees 5 2

0 0 0 50 100 150 200 0 20 40 60 80 100 120 140

Stotts' Ranch Woodson State Lake

16 10

14 8 12

10 6

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

Blackjack oak

Cross Timbers State Park Fall River State Lake

30 18

16 25 14

20 12

10 15 8

10 6 Number Number Trees of 4 5 2

0 0 0 50 100 150 200 0 50 100 150

Stotts' Ranch Woodson State Lake

14 30

12 25

10 20

15 6

10 4

2 5

0 0 Number Number Trees of 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

Post oak

Cross Timbers Fall River Lake 16 14

14 12

12 10 10

8 8

6 6

4 4

2 2 NumberTrees of 0 0 20 40 60 80 100 120 140 160 0 0 20 40 60 80 100 120 140

Stotts' Ranch Woodson State Lake 16 10

14 8 12

10 6

4 6

NumberTrees of 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.

Post oak

Cross Timbers Fall River Lake 16 30

14 25

20 10

8 15

6 10

5 2 Number of of NumberTrees

0 0 60 80 100 120 140 160 60 80 100 120 140 160

Stotts' Ranch Woodson State Lake 16 18

14 16

12 14

12 10

10 8 8 6 6 4 4 2 Number of of NumberTrees 2 0 0 20 30 40 50 60 70 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.

Blackjack oak

Cross Timbers Fall River Lake 12 10

10 8

8 6

4 4

2 2 Number Trees of Number

0 0 0 20 40 60 80 100 0 20 40 60 80 100 120 140

Stotts' Ranch Woodson State Lake 14 10

8 10

6 8

6 4

2 2 Number Trees of Number 0 0 0 20 40 60 80 100 0 20 40 60 80 100 120 140

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.

Blackjack oak

Cross Timbers Fall River Lake 12 25

10 20

8 15

10 4

5 2 Number Number of Trees

0 0 40 50 60 70 80 90 100 40 50 60 70 80 90 100 110 120

Stotts' Ranch Woodson State Lake 10 14

12 8

6 8

6 4

4 Number Number of Trees 2 2

0 0 30 40 50 60 70 80 90 40 50 60 70 80 90 100 110 120

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.

Topographic Position

0.25

Post Blackjack 0.20

0.15

0.10

Proportion of Ancient Trees Ancient of Proportion 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.

Average Temperature (C)

10 pre-recruitment during

-5

-10

-15

-20 Year Deviations The LongMean Five From Term

1894 1899 1904 1909 1914 1919 1924 1929 1934 1939 1944 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 2004

Cross Timbers State Park

Blackjack Oak Post Oak 10 12 14 Number of Trees 0 2 4 6 8 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 Year

Fig 12. Five-year deviations from the long term mean in annual temperature compared with age structures of both Quercus species at Cross Timbers State Park.

Average Temperature (C)

10 pre-recruitment during

-5

-10

-15

-20 Year Deviations The LongMean Five From Term 1894 1899 1904 1909 1914 1919 1924 1929 1934 1939 1944 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 2004

Fall River State Lake

10 Blackjack Oak Post Oak 8

4 Number of Trees

0 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 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.

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 Species Mean(±SE) Basal Tree Density Relative Richness Composition DBH (cm) Area (stems/ha) Stand 2 (m /ha) Proportion Cross 6 Q. stellata 55.23 ± 2.70 3.16 133.96 0.33 Timbers Q. marilandica 66.85 ±4.51 1.01 28.72 0.07 C. canadensis 11.06±1.44 0.06 36.36 0.09 J. virginiana 13.91±2.10 0.26 70.8 0.17 P. serotina 12.12±2.02 0.50 120.56 0.30 U. americana 34.97±5.18 0.16 9.56 0.02

Fall 7 Q. stellata 56.56± 3.06 4.38 280.84 0.70 River Lake Q. marilandica 67.71±3.88 1.52 51.08 0.13 C. canadensis 15.63±4.81 0.06 29.8 0.07 C. laevigata 11 (n=1) 0.002 4.24 0.01 J. virginana 11.16±1.20 0.02 34.04 0.09 M. pomifera 14 (n=1) 0.003 4.24 0.01 P. serotina 26.08±6.45 0.10 21.28 0.05

Stotts’ 2 Q. stellata 56.66 ± 3.05 3.05 197.48 0.49 Ranch Q. marilandica 63.71 ± 2.94 3.72 202.52 0.51

Woodson 3 Q. stellata 68.63± 4.75 3.94 262.08 0.66 Lake Q. marilandica 39.10± 2.30 0.59 101.16 0.25 Q. rubra 33.51±8.91 0.78 36.8 0.09

Table 2. Description of size structures of oak populations and p-values from Komolgorov- Smirnov goodness of fit tests to determine if size structures correspond to normal distributions. Site Diameter Range (cm) Normality Cross Timbers Q.marilandica 12.2-133.4 Yes (p=0.15) Q. stellata 10.1-142.5 Yes (p=0.15)

Fall River Lake Q.marilandica 12-163.5 Yes (p=0.15) Q. stellata 12-164.4 Yes (p=0.15)

Stotts’ Ranch Q.marilandica 19-152 Yes (p=0.15) Q. stellata 12-115 Yes (p=0.15)

Woodson Lake Q.marilandica 12-152.6 Yes (0.10) Q. stellata 11-137.5 Yes (0.10)

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 Mean p-values Modal Continuous Percent Range (±SE) Age Age recruitment Trees waves ≥100 yrs. Cross Timbers Q.marilandica 17-86 57.03±2.17 55 1929-1974 0 p=0.002* Q. stellata 26-138 66.73±2.87 64 1923-1978 9.26

Fall River Lake

Q. 11-115 55.61±2.92 56 1939-1993 5.41 marilandica p=0.344 Q. stellata 5-148 57.89±3.21 58 1939-1998 6.45

Stotts’ Ranch

Q. 11-83 41.07±2.28 41 1954-2004 0 marilandica p=0.007* Q. stellata 4-68 33.71±1.65 32 1954-1998 0

Woodson Lake Q. 4-112 41.42±3.21 46 1939-current 2.04 marilandica p<0.001* Q. stellata 6-193 80.51±6.71 62 1939-2003 30.77

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 Normal Age Negative Ancient Trees Distribution Exponential X2 Contribution Cross Timbers Q. marilandica Yes (p=0.15) No (p<0.01) 0 Q.stellata No (p=0.01) No (p<0.01) 0.20

Fall River Lake Q. marilandica Yes (p=0.11) No (p<0.01) 0.38 Q. stellata No (p=0.02) No (p<0.01) 0.55

Stotts’ Ranch Q. marilandica Yes (p=0.15) Yes (p=0.27) 0 Q. stellata No (p=0.01) Yes (p=0.39) 0

Woodson Lake Q. marilandica No (p=0.01) No (p<0.01) 0.25 Q. stellata No (p=0.01) No (p<0.01) 0.75

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 Total Dormant Growing MeanTemperature Precipitation Precipitation Precipitation Cross Blackjack 0.4760 0.0475 0.9523 0.0015 * Timbers Post 0.4919 0.6053 0.3512 0.0001 *

Fall River Blackjack 0.6790 0.2925 0.6744 0.0002 * Lake Post 0.7536 0.2789 0.4933 0.0002 *

Stotts’ Ranch Blackjack 0.5455 0.1200 0.9759 0.2623 Post 0.5455 0.1200 0.9759 0.2623

Woodson Blackjack 0.8455 0.7628 0.9457 0.0855 Lake Post 0.0969 0.4584 0.1128 0.0957

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