Regional carbon storage, land ownership, and land-ownership changes in the southeastern United States

Michael W. Binford1,4, Grenville Barnes2, and Henry L. Gholz3

1Department of Geography, University of Florida, Gainesville, FL 32611, USA.

2School of Forest Resources and Conservation (Geomatics Program), University of Florida, Gainesville, FL 32611, USA.

3Division of Environmental Biology, National Science Foundation, 4201 Wilson Blvd., Arlington, VA 22230, USA.

4Corresponding author (mbinford@ufl.edu)

Plantation forests dominate the carbon dynamics of the southeastern United States coastal

plain, where most land is owned and managed by private individuals or firms. Primary land

owners include timber companies, private individuals, governments, mining companies, and

other commercial entities. We asked how ownership and changes in ownership affect regional

carbon storage, hypothesizing that (a) private land would contain the most carbon but that public

land would have the highest carbon density, (b) changing land ownership would trigger timber

harvest and decrease overall carbon storage, and (c) that timber companies would cut more as

pulpwood prices rise also decreasing overall carbon density (Mg C ha-1), but that other ownership

types would be indifferent to prices. We determined trajectories of ownership and associated

carbon storage from 1975-2001 for each parcel of land > 4 ha in three study areas in north-

central Florida. Parcel owners and ownership type were identified at five-year intervals from

property tax rolls. Parcel and ownership trajectory maps were overlain on time-series carbon

density surfaces derived from multi-temporal Landsat data. Timber companies owned the largest

share of the land (34.9% in 2000), followed by private individuals (31.6%), government entities

(16.3%), mining companies (9.8%) and commercial firms (7.4%). From 1975 to 2000, total land

1

2

3456789

10111213141516

17

18

19

20

21

22

23

24

25

26

27

28

29

30

area owned by private individuals and timber companies each decreased 14%, while mining

company and government land increased. Carbon density and total landscape carbon were

greater on timber company land than any other ownership type, with a consistent cycling pattern

around a long-term average except when large wildfires occurred. Changes in ownership

sometimes decreased landscape carbon density, and neither total C nor carbon density on timber

company land responded to pulpwood prices. No ownership type showed systematic responses

of total carbon or density to either land sales or pulpwood prices, although commercial-timber

and private-government exchanges were occasionally associated with carbon losses. These

results indicate that much of the understanding of forest ownership and carbon dynamics derived

from work in western United States does not apply to forests in the eastern US, and suggests that

more research is needed where forest ownership is not predominantly federal.

Key words: ownership, Landsat, carbon, Florida, timber company, forests, ownership trajectory, pine plantations

Introduction

Global land-use and consequent land-cover changes (LUCC), including deforestation, forest

degradation, timber harvest, conversion to agriculture, and urbanization, were responsible for as

much as 33% of the increase in globally averaged atmospheric CO2 concentration from 1850 to

2006 (Canadell et al. 2007). LUCC and subsequent carbon cycling and storage patterns are the

aggregate sums of processes on individual parcels of land and are determined by the

management practices of individual landowners (Alig 2003, Kittredge et al. 2003). Management

practices and objectives of individual land owners, whether governments or a variety of private

organizations, are difficult to determine without extensive surveys of owners who may be

31

32

33

34

35

36

37

38

39

40

41

42

4344454647

48

49

50

51

52

53

54

55

reluctant to respond because of privacy or business concerns. Land ownership, which is defined

in public records at the county level throughout the United States, may be useful as a proxy for

management practices when ownership categories can be defined so that the members have

similar objectives and land-use practices.

We ask in this study how land ownership and land-ownership changes affect carbon storage

in the southeastern United States coastal plain, a large area of predominantly private ownership

and plantation forests. More than half (56%) of all forested land in the United States is held by

private owners, including individuals and corporations including timber companies (Smith et al.

2009). Private owners hold about 85% of the forested land in the southeastern United States with

more than half (54%) of that in corporate hands. Plantation forests, all of which are on private

land, comprise 22% of all forested land in the southeastern United States (182,000 km2, Smith et

al. 2010). Timber harvesting of plantations dominates regional carbon dynamics in most years,

although other disturbances (e.g., insect outbreaks or wildfire) and climate variation may also be

important locally in space and time (Binford et al. 2006, Haynes 2003, Bracho et al. in press).

Plantations of southern pines contain > 6 Pg C and averaged close to 0.4 Tg C yr-1 in net

accumulation between 1990 and 2000 (Conner and Hartsell 2002). Binford et al. (2006)

estimated that ecosystems of the southeastern United States coastal plain provided a net sink of

about 1-2 Mg C ha-1 yr-1 from 1975-2000, which they attributed to the broad-scale accumulation

of detrital carbon because of the reduced use of fire as a management tool in recent years, even

after accounting for wildfire occurrence and conversion of some forest land to mining.

Land ownership in the region is spatially complex and dynamic, and includes federal, state

and local government land, as well as land owned by private timber companies, individuals,

mining companies, investment organizations, and other commercial entities. All ownership

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

parcels contain some mix of land uses and different owners can have very different management

objectives. Land owners determine how their land is managed, with management of pine

plantations one of several alternatives available to private landowners. Land owner dynamics

also include transfers from one owner to another through sales, mergers, inheritances and other

transactions. The economic objectives and management practices associated with parcels can

change with such transfers, potentially affecting carbon uptake and storage (Alig and Butler

2004).

We know very little about how land ownership affects carbon storage as a consequence

of the application of different management practices. Previous ecological studies of land

ownership have mainly examined effects of differences in ownership on timber harvest rates or

patterns (Cohen et al. 1998, Kettridge et al. 2003, Jin and Sader 2006), spatial heterogeneity of

forests (Cohen et al. 1995, Crow et al. 1999), forest fragmentation (Spies et al. 1994, Ko et al.

2006), tree species composition and forest structure (Ohmann et al. 2007), forest cover

“structure” (diversity, patch size, connectivity; Stanfield et al. 2002), land-cover distribution and

change (Turner et al. 1995, Wimberly and Ohmann 2004, Medley et al. 2003), and landscape

composition and configuration (Croissant 2004).

Few studies have gone beyond comparing carbon storage (considering dry biomass to be

50% carbon) on public vs. private land. These studies are difficult to compare in drawing general

conclusions, mainly because no two use the same ownership classifications. Land tenure and

land use can vary considerably across the United States, so different classifications are

inevitable. For example, northern Maine has large areas of cutover private land that are

designated “regulated private” lands and regulated by a Land Use Regulation Commission that

has no counterpart in other states. These areas have lower carbon per hectare (carbon density)

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

than any other ownership types in Maine (Zheng et al. 2010). In a separate study in the same part

of Maine, Jin and Sader (2006) showed that investment-focused management companies (i.e.,

Timberland Investment Management Organizations (TIMO), Real-Estate Investment Trusts

(REITs)), logging companies and non-governmental organizations all shortened the rotation

time for natural forests, probably decreasing total forest carbon. In some other areas, there is

little difference between the carbon content of land under different ownership. Public lands may

contain more carbon than private lands, if stand ages are younger on private lands as a

consequence of more frequent harvests. But in other areas, public lands are the dominant sources

of timber, which may reduce carbon densities. For example, generally older stands on National

Forest lands in western Oregon and generally younger stands on non-federal lands resulted in

lower carbon density in the non-federal lands (161 + 128 Mg ha-1 vs. 89 + 72 Mg ha-1,

respectively); but when age was controlled, there was no difference in carbon trends over time

(Van Tuyl et al. 2005). A follow-up study found that, while there was great variability among six

different sub-regions, forests of the Pacific Northwest in private ownership had a slightly greater

carbon density (in dead plus living organic matter) than public lands (derived from Hudiburg et

al. 2009). In New England, aboveground carbon content of forests on public lands was greater

than on “other private” lands, or on “regulated private” lands (Zheng et al. 2010). In central

Massachusetts, the public authority responsible for managing the watershed of the Quabbin

Reservoir, the water supply for metropolitan Boston, had the highest proportion of its land area

harvested annually (1.4%) and nearly the highest intensity of cutting (69.3 m3 wood ha-1 yr-1)

compared to all other landowner classes (McDonald et al. 2006, Kittredge et al. 2003). In

Colorado, private lands contain the bulk of the state’s carbon stocks, leading all federal, state,

municipal, and Native American landowners (Failey and Dilling 2010).

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

Based on these prior observations, we hypothesized that, in the southeastern United States

coastal plain, most of the carbon in forests is found on private land, the majority of which

belongs to timber companies and private individuals. We further propose that carbon density will

be higher on public land (owned by local, state and federal governments), because more frequent

harvesting on private lands keeps forests in a younger state with less biomass (as in Oregon, but

not Colorado, in the studies cited above).

None of the previous studies addressed the carbon consequences of changing timber prices.

Although there have been a number of studies that model the potential economics of carbon

storage (Boyland 2006, Lorenz and Lal 2009), there is no existing monetary incentive in the

United States to store carbon in forest biomass. Consequently, our perspective is that carbon

storage is not an important aspect of land management in the United States and that decisions to

harvest are made solely in terms of immediate and medium-term economic returns on the sale of

the timber. This theory goes back to Faustman (1849) who first estimated the optimum time to

harvest a forest. The relationship between timber price and harvest magnitude, and consequently

carbon storage, is a complex problem in forest economics and includes calculation of

management costs and forecasts of future timber prices (Adams et al. 1991, Prestemon and Wear

2000). Various models explain the interaction of inventory, owner objectives, and prices that

lead to a supply of harvestable timber to meet demand for pulp and lumber (Adams and Haynes

1996, Adams 2002). The fundamental economic logic underlying these models is that if demand

for timber products increases, for example with an upturn in housing starts which is a leading

driver of timber prices (Adams and Haynes 2007), then the price of the products will increase

and harvest intensity will then increase to meet the demand. An increase in harvest intensity

would then reduce the standing biomass (or carbon density) of the forests. Some empirical

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

evidence supports this idea (Adams and Haynes 2007). The fundamental demand-supply process

may hold for industrial forest managers, but owners of non-industrial private forests (NIPF)

generally have a more complex decision making context and may or may not exhibit the same

responses (Amacher et al. 2003, Beach et al. 2005, Ní Dhubháin et al. 2007). On the one hand,

NIPF owners in central Massachusetts consistently removed the most “…commercially valuable

tree sizes, grades, and species…” (Kittredge et al. 2003). In a counter-example, a study of

changing forest tenure and land use in three townships in the Kickapoo Valley in Wisconsin

found that timber harvesting by NIPF owners occurs on an impromptu basis with little

consideration of timber market prices (Heasley and Guries 1998). Nonetheless, we hypothesize

that because a large proportion of land in our study area is owned or managed by industrial

forestry companies, increases in timber prices will drive increases in harvesting frequency and

thereby decrease carbon storage.

Nothing we could find considered the consequences for carbon storage of land transfers from

one ownership class to another, or for that matter, from one owner to another within an

ownership class. To address this gap in understanding, we focused on the role of changing land

ownership in triggering management responses by either a current owner in anticipation of a

sale, or by a buyer (new owner) with different management objectives. We developed

trajectories of land ownership from 1975-2001 and associated carbon storage for each parcel of

land in three 15 km x 15 km study areas in north-central Florida. Ownership parcels are the

smallest unit of land ownership readily available in map form at the county level in the United

States and were the basis for our analysis. Ownership changes might lower overall parcel carbon

storage because trees are either the most valuable and liquid commodity, or because other forms

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

of land use (e.g., agriculture/pasture or mining) have lower inherent carbon stocks because

existing trees must first be cleared in any case.

In summary, we hypothesize that:

(i) In the southeastern United States coastal plain, most of the carbon in forests (total

carbon content Mg C region-1) is found on private land, the majority of which belongs

to timber companies and private individuals.

(ii) Carbon density (Mg C ha-1), however, will be higher on public land (government

owned: municipal, county, state or federal) because harvest rotations on timber

company land are shorter, keeping forests in a younger state and therefore with less

carbon.

(iii) Carbon density and content are negatively related to changes in ownership type,

because such changes in this region usually signal a shift in land use/management

strategy accompanied by a liquidation of the main asset, trees.

(iv) Carbon density and landscape content decrease with frequency of owner/ownership

type change because the average time for carbon recovery declines.

(v) For land continually under timber company ownership, carbon density and content

are not affected by changes in owner because the overall management objectives

remain the same.

(vi) Deviations from long-term average carbon density and content are mainly related to

timber market prices, because timber companies cut more during years of higher

timber prices.

(vii) Effects of exogenous disturbances, such as wildfires, may be locally important in

space and time, but will not significantly alter the results; effects of climate change

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

over the 25-year study period are assumed to be random and minor enough to ignore

(Bracho et al. in press).

Materials and Methods

Study areas

We selected three of the four 15 x 15 km areas in north Florida previously used by Binford et

al. (2006) to represent the southeastern United States coastal plain (Figure 1). The largest land

use in the region is plantations of pine, which covered about a third of the area in 2000. Other

land uses include, in order of areal importance, cultivated agriculture and pasture, unmanaged

upland forest (mostly pine but including some mesic hardwoods), unmanaged wetland (including

riparian) forests, phosphate mines, and urban/suburban/exurban areas (Myers and Ewel 1990).

The selected study areas were all in a single Landsat footprint (WRS II path 17, row 39) to

simplify image processing, and each of the study areas was completely within one county to

simplify the retrieval of land ownership data. Developed (urban/suburban) areas, characterized

by high concentrations of small parcels, and areas with large federal government ownerships

(e.g., National Forests or military bases) were avoided in the original selection of the four areas.

Even-aged plantation forests have a well-known pattern of carbon exchange and storage

(Bracho et al. in press). Harvested management units become large sources of CO2 to the

atmosphere for several years, after which regrowing planted trees and detritus accumulate carbon

at high rates for about two decades, when harvesting occurs again. Unmanaged forests are

uneven-aged and are smaller annual carbon sinks than growing plantation stands, although they

maintain positive carbon storage over much longer periods of time (Powell et al. 2008, Clark et

193

194

195

196

197

198199200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

al. 2004). Conversion of forests to agriculture (Woodbury et al. 2006), or clearing for mining or

urbanization, results in large and long-lasting net releases of carbon.

Characterizing ownership parcels

Parcels and their owners were identified at five-year intervals from 1975 through 2000 from

publicly available county property tax rolls and maps that included owner names and parcel

identification numbers. Ownership category (private individual, commercial entity, timber

company, mining company, government institution) was inferred from the owner names. Timber

companies, mining companies and government institutions are well known and easily identified

by their names on the tax rolls. Commercial ownership consisted of law firms, investment firms,

or other institutions that were not categorized as government or timber companies. Private

ownership included owners with individual or joint-ownership names.

While parcel data are available in digital form as polygon GIS layers for recent years in

Florida counties, past records are not. We reconstructed parcel data by inferring prior sales and

parcel subdivisions from tax roll data with a method that we developed previously (Barnes et al.

2003), producing a time series of GIS layers consisting of parcel maps at five-year intervals from

1975-2000 . We eliminated parcels that were < 4 ha in 2000 to remove homesteads and other

smallholder properties that were not likely to manage their land resources (as distinguished from

houses) for financial reasons. These same parcels were then also eliminated in all the earlier

parcel maps to provide a consistent geographic area for our analysis. All Landsat and GIS data

storage and calculations were conducted with ArcGIS 9.1 to 9.3 or ERDAS Imagine 9.3.

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

The parcel polygon data layers were rasterized to the same spatial resolution as Landsat TM

or ETM+ reflective band data (30-m grid cells), yielding several raster data layers with each grid

cell in each layer having one of the attributes from the original polygons. The time series of

parcels and ownership categories were then aggregated into “ownership trajectories” in two

ways. First the sequence of ownership categories from 1975-2000 was assigned to each grid cell.

For example, a parcel owned by a timber company in 1975, 1980 and 1985 that was sold to a

government entity between 1985 and 1990 and remained in government ownership through 2000

was designated TTTGGG. Second, in the case of “constant timber” (TTTTTT) ownership

trajectories, we analyzed owner names to determine the trajectory of transfers from one timber

company to another. For example, if Rayonier LLC held the land for the first three time periods,

then sold to Smurfit-Stone, who held it for two periods and then sold it to Georgia-Pacific, each

pixel in the parcel would be assigned the attribute RaRaRaSmSmGe. For efficiency, we

determined when a property was sold (and in the case of timber companies also the buyers and

the sellers) for only the most important parcel trajectories, which we defined as those each

covering >1% of the landscape area.

Landsat imagery was available from a previous study for almost every year from 1975 to

2001 between December and March, the period of minimum leaf area index. We analyzed it

using the methods of Binford et al. (2006) to create annual maps of stand age for plantation

forests and land-cover classification for other major land cover categories (wetland, agriculture,

unmanaged forest). Urban areas were excluded from all the years, based on their extent in the

study areas in 2000, mainly because their parcel sizes were generally under our minimum size of

4 ha.

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

Annual maps of cell-level carbon content (Mg C cell-1) and carbon density (Mg C ha-1) were

calculated for each grid cell (0.09 ha) using a look-up table that relates measurements made in

various studies from north-central Florida to stand age for plantations or to constant values for

wetland and unmanaged forests (Table 1). The gridded parcel, ownership trajectory, timber

company trajectory, and carbon density maps were then overlaid and the carbon content and

density of each parcel and of each ownership category calculated using a zonal analysis.

Average annual pulpwood prices from 1975-2000 were obtained from TimberMart-South

reports for Florida Region 1 (http://www.timbermart-south.com/index.html, last accessed 15

April 2011), normalized to constant 2000 dollars, and used to test Hypothesis 4.

Results

Parcels and ownership

In 2000 the three study areas had in aggregate 1299 parcels (> 4 ha) and 483 different

owners, including 19 timber companies, 38 commercial firms, 18 government agencies, 403

private owners, and 1 mining company. The total number of parcels and number of parcels

belonging to different ownership categories were not constant through time, because land was

bought and sold, subdivided and aggregated, or otherwise altered (Table 2). However, the total

area that we studied did not change over time since we considered only those parcels that were >

4 ha in 2000; if there were subdivisions of large parcels earlier in the study that led to small (<4

ha) parcels later, the land was not included at any time in the analysis. Also, ownership changes

are zero-sum; transactions between owners in different categories simply transferred the entire

land area and its associated carbon from one ownership category to another.

Each of the study areas had very different patterns of ownership and changes in ownership

over time. Figure 2 shows maps of ownership classes for each time period for the three study

261

262

263

264

265

266

267

268

269

270271272273274275

276

277

278

279

280

281

282

283

284

285

286

areas. One area, within the Clay County study area, experienced a very large change over time to

government ownership from various other ownership categories that did not happen in the other

two areas. Figure 3 shows the spatially explicit time series of carbon for the three areas, with

ownership boundaries shown as color-coded polygons. Although Figure 3 displays carbon data at

five year intervals, annual carbon raster data sets, equivalent maps and GIS data are available in

the ESA data archive { data.esa.org/xxxxxxxxxxx}.

Ownership and Carbon

The time series of aggregate area, aggregate carbon density, and total landscape carbon

content by ownership category is shown in Figure 4. These data are for parcels that were under a

particular ownership in each year; as parcels changed hands, their carbon stocks were transferred

to the purchaser’s category. Private owners and timber companies held most of the land. Land

owned by timber companies and private individuals decreased over the study period, while

government ownership increased (Figure 4a). The bulk of conversion to government ownership

occurred in the Clay County area, where a single family sold 6,390 ha (11.5% of the total study

area) over 10 years to various government agencies to eventually form the Jennings State Forest

(owned by the State of Florida). In the Hamilton County area, the largest transfer of ownership

was from land previously owned mainly by timber companies to a phosphate mining company.

There were no similarly large-scale transfers among ownership categories in the Alachua County

study area.

Privately owned land and timber company land had the highest aggregate carbon densities,

averaging about 37 Mg C ha-1 and 30 Mg C ha-1, respectively (Figure 4b). Carbon density

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

gradually increased over time on privately held land, while it fluctuated with about a 10-year

cycle on timber land. Carbon density on government-owned land fluctuated more than land

under any other ownership, as government agencies bought a diverse array of land with disparate

original vegetation and different original carbon densities. Carbon density on land owned by

mining companies increased over time as they bought more forested land with plans to mine in

the future. Although some of the acquired land was eventually mined and all carbon essentially

removed, much more was simply transferred and left un-mined with no further timber

management, and thus continued to slowly accumulate carbon over time.

As with carbon density, regional carbon storage (i.e., for all three study areas combined) was

by far the highest on private land and timber company land (Figure 4b), largely because these

two ownership categories held the most land. In contrast, the other three ownership types held a

very small proportion of the total carbon stocks, regardless of its density at the pixel scale.

Land Ownership Trajectories

A total of 52 different ownership category trajectories were found across the three study

areas over the 25-year period (Table 3). ‘No-change’ trajectories (TTTTTT, PPPPPP,

MMMMMM, GGGGGG, and CCCCCC) dominate, covering 40,328 ha (73%) of the 55,109 ha

landscape (Table 2). One-change trajectories covered an additional 13,882 ha (25%). The rest of

the trajectories covered less than 2% of the study areas. Eleven individual trajectories each

covered at least 1% of the landscape and 36 of the trajectories together covered 99% of the total

landscape. Of the top six trajectories where ownership category changed, the dominant change

was from private to government, followed by private or timber to mining company, and timber to

commercial. Again, most of the area that went into government ownership over time was in the

Clay County area.

310

311

312

313

314

315

316

317

318

319

320

321

322323324325

326

327

328

329

330

331

332

333

334

The largest number of changes within any one trajectory was three, and that happened in only

one case of a parcel of 63 ha (Tables 3 and 4). The vast majority of the landscape did not change

hands or did so only once. Only 899 ha (<2%) out of the entire 55,109 ha in the aggregate study

area were sold to a member of a different category more than once during the study period

Important Trajectories, Total Landscape Carbon, and Carbon Density

The vast majority of carbon was contained in areas that remained under the constant

ownership of timber companies (TTTTTT) and private owners (PPPPPP) (Figure 5). Carbon on

land in constant private ownership steadily increased over the study period, while the carbon on

timber company land declined slightly from the early 1980’s, but then stabilized. The small drop

in 1998 for the TTTTTT trajectory was caused by a large wildfire in Alachua County that burned

across mostly timber company land (red arrow). Total carbon contents of all other trajectories

were stable over time.

Carbon density was also highest for the constant timber and private trajectories, with density

on private land gradually increasing over the study period and that on timber company land

varying around a long-term mean of about 42 Mg C ha-1 (Figure 5). Carbon density of

government land and of land owned by commercial firms fluctuated for most of the period

around an average of 33 Mg C ha-1, while lands owned consistently by mining companies

showed a gradual increase. Carbon density of lands in the other six important trajectories

behaved variously. With one exception, there were only weak relationships between changes in

carbon content and changes in ownership category. In that one case, the sale of land in the

PPPPPG trajectory from a private owner to a government agency between 1995 and 2000 was

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

associated with a drop from around 60 to 47 Mg C ha-1, due to large-scale timber harvesting that

occurred in the years prior to the sale. There was also a small decline for the PPPGGG trajectory

that may have been associated with a land sale between 1985 and 1990. But the vast majority of

land ownership changes in the “to-government” trajectory were part of a series of transactions in

the Clay County area between one family and several government agencies to create the Jennings

State Forest. Other changes in carbon were not associated with land sales.

Timber Company Ownership Transfers

Carbon storage dynamics for land transfers within the continual timber company ownership

category (TTTTTT) for the Clay and Alachua County areas are shown in Figure 6 and Table 3

(Hamilton County had no such transfers). While there is some evidence of reductions in carbon

either just before or just after an ownership change (e.g., red ellipses in Figure 6), there are also

cases where transfers were not related to changes in carbon stocks (e.g., green ellipses in Fig. 6),

and cases where no change in carbon density occurred around a transfer (e.g., black circles in

Figure 6). The JS-GI-GI-JS-JS-JS trajectory, involving Jefferson-Smurfit (JS) and Gilman Paper

Company (GI), shows a significant reduction in carbon for the first transfer between JS and GI

between 1975 and 1980, but no change associated with the transfer back to JS between 1985 and

1990. The GI-GI-GI-GI-GI-FF trajectory (where FF = Fulghum Fibers) also supports our

hypothesis, with significant carbon decreases in the period 1995-2000 when Gilman sold to

Fulghum Fibers. But there is no further support for this hypothesis in the Clay County study area.

In fact, our data show that the other two transfers that occurred in the Clay County area did not

lead to changes in carbon, other than increases over time through tree growth. Furthermore, the

steep decline in carbon in the 1975-1980 period for the GI-GI-GI-GI-GI-JS trajectory mirrors

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

the JS-GI-GI-JS-JS-JS trajectory, suggesting that harvests occurred in both cases for reasons

other than an ownership transition (since no transfer occurred in the first trajectory for this

period).

Our analysis of the timber ownership trajectories for the Alachua County area shows similar

results (Table 5). The colored cells in the table show where a change in carbon content occurred

during the ownership trajectory associated with ownership change. If our hypothesis that such

ownership transitions trigger timber harvesting is valid, then we would expect to see negative

values in these colored cells. Even though some of the larger losses in carbon coincide with a

transfer (e.g., 47.6% loss when Jefferson Smurfit sold to Timberlands), it is clear from the data

that this is not the general case. In fact, the largest loss (-75%) does not coincide with a change in

ownership, and the mean change in carbon over all trajectories is positive both when transfers

occurred and when they were absent.

Carbon Dynamics and Timber Prices

Not a single landowner category or important (in area) ownership trajectory showed a

relationship between either aggregate total carbon or carbon density and constant-dollar timber

prices during the study period (Figure 7, 8, and 9).

Discussion and conclusions

Hypothesis tests

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

Our first hypothesis that the majority of carbon would be found on private, especially timber-

company, land was supported. The vast majority of the study areas were owned by either timber

companies or private individuals, so they of course owned most of the carbon. The second

hypothesis that public lands would have the highest carbon density was rejected. “Natural

regrowth” and natural forests in the south-eastern United States have less biomass than the

managed plantation forests, both because the spatial density of trees is so much less (individual

trees are farther apart), and because land may have been acquired from previous owners who

harvested the forests before selling the land. Non-plantation forests in the south-eastern United

States coastal plain do not sequester more carbon than plantations, even when accounting for

longer harvest rotations (Clark et al. 1999, Binford et al. 2006, Powell et al. 2008).

The third hypothesis, that both carbon density and total carbon content on the landscape

would be negatively related to changes in ownership category, because such changes would

trigger a change in land-use or management practices, was rejected. We saw no pattern in carbon

storage related to changes in ownership category. Sometimes there was a reduction associated

with a sale, but more often there was not. There were also many instances of reductions in

carbon in the absence of sales. Some of these were caused by large wildfires, but most were

caused by normally-scheduled tree plantation harvesting.

The fourth hypothesis predicted that carbon density and content would decrease as parcels

were sold more often, because if the first hypothesis were true, then the time for regrowth would

be shortened. The main result from this part of the study was that the hypothesis is rejected

primarily because land sales were far less frequent over the 25 years than we had anticipated.

The dominant ownership category trajectories were either no-change or one-sale, with only a tiny

fraction of the aggregate landscape being sold more than once. In addition, land sales and

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

changes in carbon totals and density were not related. Landowners cut when the harvest rotation

time comes (or, anecdotally, sometimes a few years prematurely to pay extraordinary bills), but

seldom because land ownership is about to, or has just, changed hands.

The fifth hypothesis predicted that the carbon density and content of land always belonging

to timber companies would be independent of changes in specific company ownership because

the overall management objectives of all timber companies are similar. This hypothesis was

supported by our results. Furthermore, changes in carbon density and content occurred during

intervals when land was both sold and not sold, and in other cases there were no changes in

carbon when sales did occur.

Our sixth hypothesis was that timber market prices would influence carbon density and

content because timber companies would respond to higher prices by cutting more forests on

land that they owned. This hypothesis was rejected. No relationship between carbon storage and

timber price was seen. Instead, fluctuations in carbon storage and content in these study areas

was more related to exogenous disturbances such as wildfires. This was surprising, but probably

reflects the larger scale economic conditions to which timber companies in the region, some of

which are large and multi-national corporations, respond. Timber companies own vast areas of

land, some fraction of which must be harvested each year to maintain a revenue stream

regardless of prices. Timber prices are set more or less globally (with some regional variation).

Empirical studies of the relationship between harvesting and prices often show only some price

elasticity (Adams et al. 1991), or complete price inelasticity. Some studies suggest that steadily

increasing prices would tend to increase the rotation period, while decreasing prices would drive

owners to harvest earlier, as the Net Present Value (NPV) of standing timber steadily increases in

the former case but decreases in the latter case (Thomson 1992). Finally, leasing of timber rights

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

by private owners to timber companies is common in the south-eastern United States coastal

plain. If timber companies cut timber on their own land in response to price fluctuations, then

they would probably also cut on their leased land, and the response should be clear in the data.

Rejecting four of the six hypotheses may seem to be to be an overwhelmingly negative result

of this study. Furthermore, concluding that most of the carbon in the region and the highest

carbon densities are found on private and timber company land is counter to most previous

studies. Instead, we find these results remarkable because they indicate that much of our theory

and understanding about how land ownership influences carbon storage is at best incomplete and

at worst wrong. Much of the theory and most previous results, as described in the introduction

section of this paper, are based on case studies in the western United States where the

government owns a very large proportion of the forested landscape. Private individuals and

organizations own the majority of land in the eastern United States and the management

practices of the different landowners are much more diverse. The generalized understanding that

more carbon is found on public land is not correct for eastern forests, based both on our results

and those from other areas. More research is required across these forests to develop a reliable

understanding of the complex relationship between land ownership, LUCC and carbon

dynamics.

Is ownership a good proxy for management? The land ownership categories that we used are

only partially a reasonable proxy for management approaches. Unlike Massachusetts, which

since 1983 has required forest harvest plans to be filed for even small parcels so the data can be

mined to determine trends in resource management (Kittredge et al. 2003), no states in the south-

eastern United States coastal plain keep official records of individual landowner management

practices. Our data show differences in carbon behavior among all the landowner categories. The

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

time series of total carbon and carbon density for private and timber company owner categories

were quite different, despite a considerable amount of plantation-style harvesting on private land,

while the other three were more similar to one another but still distinguishable. Carbon on timber

company lands varied around an average, with several peaks and valleys, and even declined

slightly over the study period. Plantation forests existed on both private and timber company

land, but not on government properties (except for a state-owned experimental forest in the

Alachua study area) and commercial properties. Private landowners with large forest holdings

tend to manage as timber companies, often by leasing timber rights or timber contracts to forest-

product companies. Because the aggregate carbon density and total carbon mostly increased over

the study period, we infer that the very large number of private landowners with smaller forest

holdings tend not to mimic conventional timber company harvest patterns.

The number of sales over time in our study region was surprisingly low, especially compared

with other areas in the eastern United States. Jin and Sader (2006) report that 80% of the timber

lands in northern Maine changed hands in the six years between 1994 and 2000, 74% of which

were sold to TIMOs and the remainder to other industrial forest owners. Nationwide, around

12.6 million ha (31 million acres) were sold to TIMOs and REITs in the decade 1996-2006, as

vertically integrated timber products companies sold out to, or transformed into, publicly traded

investment companies (Froese et al. 2007). These studies point out how the national forest-

products industry has undergone significant changes since the 1980’s, especially through the

creation of TIMO and REIT ownerships that may or may not have the same management

objectives as traditional timber companies (Smith et al. 2009, Jin and Sader 2006). Most of the

conversion of vertically integrated timber companies in the south-eastern United States occurred

since 2000, the last year considered in our study. We did not investigate restructuring within

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

timber companies, as these data are not readily available and would involve additional interviews

and more labor-intensive data mining. As only about 2% of the timber land in the southeast was

owned by TIMOs or REITs in 1999 (Conner and Hartsell 2002), most of the sales occurred after

the end of our study. These changes in the industry probably did not confound our results, but

any analyses of subsequent ownership changes at this level clearly must take this into account.

Our finding that carbon densities are greater on private plantation forests than forests under

other forms of ownership implies that carbon sequestration strategies, at least in the eastern

United States, must focus more centrally on forests that are managed as plantations. Our

ownership analysis does not distinguish between owner-managed forests and private forests that

are leased and managed by a third party. The complex relationship between ownership and

management has been further complicated in recent years by the restructuring of vertically

integrated timber companies and the emergence of investment-oriented REITs or TIMOs,

broadening the suite of management goals that may have a much greater impact on carbon

sequestration than the property transfers examined in this paper. Although we found relatively

few land transfers across ownership categories (73% remained in the same ownership category

across our 25 year study period), a recent U.S. Forest Service survey indicates that almost one

quarter of private timberland owners intend to “sell or transfer the land in the near future.”

(Smith et al. 2009: 20). Since owners are the ultimate decision-makers as to how a forest is

managed, or whether or not to sell or lease forest land, the monitoring of our nation’s forests

must include information on ownership. Finally, forest policy, particularly with regard to carbon

sequestration, must recognize the fundamental differences between forests and forest ownership

in the western United States and those elsewhere.

Acknowledgements

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

The project was originally funded by the NASA Earth Science Enterprise Land Cover–Land Use

Change Program, grant NAG5- 9331. Levent Genc, Scot Smith, Anurag Agrawal, Balaji

Ramachandran, Vijay Sivaraman, and Bharath Pudi, and Elizabeth Binford all contributed to the

original data preparation and analysis. This paper was partially based on work supported by the

National Science Foundation, while H.L.G. was working at the Foundation. Any opinion,

findings and conclusions expressed here are those of the author and do not necessarily reflect the

views of the Foundation.

519

520

521

522

523

524

525

526

References

Adams, D.M. 2002. Harvest, inventory, and stumpage prices: Consumption outpaces harvest,

prices rise slowly. Journal of Forestry 2: 26-31.

Adams, D.M., C.S. Binkley, and P.A. Cardellichio. 1991. Is the sevel of National Forest timber

harvest sensitive to price? Land Economics67:74-84

Adams, D. M., and R.W. Haynes. 2007. Resource and market projections for forest policy

development: twenty-five years of experience with the U.S. RPA timber assessment.

Springer, New York.

Adams, D.M., and R.W. Haynes. 1996. The 1993 Timber Assessment market model: Structure,

projections, and policy simulations. USDA Forest Service, General Technical Report

PNWGTR- 358. Portland, OR.,

Alig, R.J. 2003. U.S. landowner behavior, land use and land cover changes, and climate change

mitigation. Silva Fennica 37: 511–527.

Alig, R.J., and B.J. Butler. 2004. Projecting large-scale area changes in land use and land cover

for terrestrial carbon analysis. Environmental Management 33: 443-456.

Amacher, G., S., M.C. Conway, and J. Sullivan. 2003. Econometric analyses of nonindustrial

forest landowners: Is there anything left to study? Journal of Forest Economics 9: 137-164.

Barnes, G., A. Agrawal, L. Genc, B. Ramachandran, V. Sivaraman, B. Pudi, M.W. Binford, and

S. Smith. 2003. Developing a spatio-temporal cadastral database using county appraisal data

from northern Florida. Surveying and Land Information Science 63: 243-251.

Beach, R.H., S. K. Pattanayak, J.C. Yang, B.C. Murray, and R.C. Abt. 2005. Econometric

studies of non-industrial private forest management: a review and synthesis. Forest Policy

and Economics 7: 261-281.

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

Binford, M. W., H. L. Gholz, G. Starr, and T. A. Martin. 2006. Regional carbon dynamics in the

southeastern US coastal plain: Balancing land cover type, timber harvesting, fire, and

environmental variation. Journal of Geophysical Research 111: D24S92,

doi:10.1029/2005JD006820.

Boyland, M. 2006. The economics of using forests to increase carbon storage. Canadian Journal

of Forest Research 36: 2223–2234.

Bracho, R., G. Starr, H.L. Gholz, T.A. Martin, W.P. Cropper, Jr., and H.W. Loescher. 2011.

Controls on carbon dynamics by ecosystem structure and climate for southeastern U.S. slash

pine plantations. Ecological Monographs (ms in press).

Brown, S. 1981. A comparison of the structure, primary productivity, and transpiration of

cypress ecosystems in Florida. Ecological Monographs 51:403–427.

Brown, S.L., P. Schroeder, and J.S. Kern. 1999. Spatial distribution of biomass in forests of the

eastern USA. Forest Ecology and Management123 : 81-90.

Canadell, J., C. Le Quere, M.R. Raupacha, C.B. Field, E.T. Buitenhuis, P. Ciais, T.J. Conway,

N.P. Gillett, R.A. Houghton, and G. Marland. 2007. Contributions to accelerating

atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural

sinks. Proceedings of the U.S. National Academy of Sciences 104: 18866–18870.

Clark, K.L., H.L. Gholz, J.B. Moncrieff, F. Cropley, and H.W. Loescher. 1999. Environmental

controls over net exchanges of carbon dioxide from contrasting ecosystems in north Florida,

Ecological Applications 9: 936–948.

Clark, K.L., H.L. Gholz, and M. Castro. 2004. Carbon dynamics along a chronosequence of slash

pine plantations in north Florida. Ecological Applications 14: 1154–1171.

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

Cohen, W.B., T.A. Spies, and M. Fiorella. 1995. Estimating the age and structure of forests in a

multi-ownership landscape of western Oregon, USA. International Journal of Remote

Sensing 16 : 721–746.

Cohen, W.B, M. Fiorella, J. Gray, E. Helmer, and K. Anderson. 1998. An efficient and accurate

method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery.

Photogrammetric Engineering and Remote Sensing 64: 293–299.

Conner, R.C., and A.J. Hartsell. 2002. Forest area and conditions. Ch. 16 in D.N. Wear and J.G.

Greis, editors. Southern Forest Resource Assessment - Technical Report. General Technical

Report SRS-53. U.S. Department of Agriculture, Forest Service, Southern Research Station.

Asheville, NC.Croissant, C. 2004. Landscape patterns and parcel boundaries: an analysis of

composition and configuration of land use and land cover in south-central Indiana.

Agriculture, Ecosystems and Environment 101: 219–232.

Crow, T. R, G. E Host, and D. J Mladenoff. 1999. Ownership and ecosystem as sources of

spatial heterogeneity in a forested landscape, Wisconsin, USA. Landscape ecology 14 : 449–

463.

Ní Dhubháin, Á., R. Cobanova, H. Karppinen, D. Mizaraite, E. Ritter, B. Slee, and S. Wall.

2007. The values and objectives of private forest owners and their influence on forestry

behaviour: the implications for entrepreneurship. Small-scale Forestry 6 : 347-357.

Failey, E.L, and L. Dilling. 2010. Carbon stewardship: land management decisions and the

potential for carbon sequestration in Colorado, USA. Environmental Research Letters 5:

024005. doi:10.1088/1748-9326/5/2/024005.

Faustman, M. 1849. Gerechnug des wertes welchen waalkbaden sowie nach haubare

Holzbestunce fur die waldwirtschaft besitze, Allgemeine Forst und Sogd-Zeitug 25: 441-45.

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

Froese, R., M. Hyslop, C. Miller, B. Garmon, H. McDiarmid, A. Shaw, L. Leefers, M. Lorenzo,

S. Brown, and M. Shy 2007. Large-tract forestland ownership change: land use,

conservation, and prosperity in Michigan’s Upper Peninsula. Report, Michigan

Technological University, Houghton, MI (available from forestlands.mtu.edu).

Gholz, H.L., and R.F. Fisher. 1982. Organic matter production and distribution in slash pine

(Pinus elliottii) plantations. Ecology 63: 1827-1839.

Haynes, R. W. 2003. An analysis of the timber situation in the United States: 1952–2050.

General Technical Report PNW-560. USDA Forest Service, Portland, Oregon.

Heasley, L., and R. Guries 1998. Forest Tenure and Cultural Landscapes: Environmental

Histories in the Kickapoo Valleypp. 182-207 in Who Owns America? Social Conflict Over

Property Rights. University of Wisconsin Press, Madison, WI..

Hudiburg, T., B.E. Law, D.P. Turner, J. Campbell, D. Donato, and M. Duane. 2009. Carbon

dynamics of Oregon and Northern California forests and potential land-based carbon storage.

Ecological Appliations 19: 163-180.

Jin, S., and S.A. Sader. 2006. Effects of forest ownership and change on forest harvest rates,

types and trends in northern Maine. Forest Ecology and Management 228: 177-186..

Kittredge, D.B., A.O. Finley, and D.R. Foster. 2003. Timber harvesting as ongoing disturbance

in a landscape of diverse ownership. Forest Ecology and Management 180: 425-442.

Ko, D. W, H. S He, and D. R Larsen. 2006. Simulating private land ownership fragmentation in

the Missouri Ozarks, USA. Landscape Ecology 21: 671–686.

Lorenz, K., and R. Lal. 2009. Carbon Sequestration in Forest Ecosystems. Springer, New York.

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

McDonald, R.I., G. Motzkin, M.S. Bank, D.B. Kittredge, J. Burk, and D.R. Foster. 2006. Forest

harvesting and land-use conversion over two decades in Massachusetts. Forest Ecology and

Management 227: 31-41..

Medley, K.E, C.M. Pobocik, and B.W. Okey. 2003. Historical changes in forest cover and land

ownership in a midwestern U.S. landscape. Annals of the Association of American

Geography 93: 104-120.

Myers, R. L.,and J. J Ewel. 1990. Ecosystems of Florida. University Presses of Florida, Orlando,

FL.

Ohmann, J.L., M.J. Gregory, and T.A. Spies. 2007. Influence of environment, disturbance and

ownership on forest vegetation of coastal Oregon. Ecolical Applications 17:18-33.

Powell, T.L ., H.L. Gholz, K.L . Clark, G. Starr, W.P. Cropper, Jr., and T.A. Martin. 2008.

Carbon exchange of a mature, naturally regenerated pine forest in north Florida. Global

Change Biology 14: 1–16. Prestemon, J.P., and D.N. Wear. 2000. Linking harvest choices to

timber supply. Forest Sciience 46: 377-389.

Smith, W.B, P.D. Miles, C.H. Perry, and S.A. Pugh. 2009. Forest Resources of the United States,

2007. General Technical Report WO-78. U.S. Department of Agriculture, Forest Service,

Washington, D.C. .

Spies, T. A., W. J. Ripple, and G. A. Bradshaw. 1994. Dynamics and pattern of a managed

coniferous forest landscape in Oregon. Ecolical Applications 4:555-568.

Stanfield, B. J, J. C Bliss, and T. A Spies. 2002. Land ownership and landscape structure: a

spatial analysis of sixty-six Oregon (USA) Coast Range watersheds. Landscape Ecology 17:

685–697.

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

Thomson, T.A. 1992. Optimal forest rotation when stumpage prices follow a diffusion process.

Land Economics 68: 329-342..

Turner, D.P., G.J. Koerper, M.E. Harmon, and J.J. Lee. 1995. A carbon budget for forests of the

conterminous United States. Ecolical Applications 5 421-436.. doi:10.2307/1942033.

Van Tuyl, S., B.E. Law, D.P. Turner, and A.I. Gitelman. 2005. Variability in net primary

production and carbon storage in biomass across Oregon forests-an assessment integrating

data from forest inventories, intensive sites, and remote sensing. Forest Ecology and

Management 209: 273-291.

Wimberly, M. C,, and J. L Ohmann. 2004. A multi-scale assessment of human and

environmental constraints on forest land cover change on the Oregon (USA) coast range.

Landscape Ecology 19: 631–646.Woodbury, P., L. Heath, and J. Smith 2006. Land use

change effects on forest carbon cycling throughout the southern United States. Journal of

Environmental Quality 35: 1348-1363.

Zheng, D., L. Heath, M. Ducey, and B. Butler. 2010. Relationships between major ownerships,

forest aboveground biomass distributions, and landscape dynamics in the New England

region of USA. Environmental Management 45: 377-386.

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

Table 1. Look-up table for Pinus elliottii plantation biomass, biomass of natural regrowth pine

forests, and biomass of forested wetland (cypress) wetlands for assigning values to grid cells

based on stand stand age or vegetation type (based on Brown 1981, Gholz and Fisher 1982, ,

Brown et al. 1999, Clark et al. 1999, ,, Powell et al. 2008, and Bracho et al. 2011).

Age (yr)

Tree Biomass

(Mg C ha-1) s.d.Understory (Mg C ha-1)

Forest Floor (Mg C ha-1)

CoarseWoodyDebris

(Mg C ha-1)Total

(Mg C ha-1)

0 0.0 0.0 1.5 0.0 35.0 36.51 0.1 0.0 1.5 0.7 32.7 34.92 0.1 0.0 1.5 1.4 30.3 33.43 1.2 1.5 2.2 28.0 32.94 2.3 1.5 2.9 25.7 32.35 3.4 1.8 1.5 3.6 23.3 31.86 6.8 1.5 4.3 21.0 33.67 10.3 6.0 1.5 5.0 18.7 35.58 13.7 1.9 1.5 5.8 16.3 37.39 19.1 1.5 6.5 14.0 41.110 24.5 1.5 7.2 11.7 44.911 29.9 1.5 7.9 9.3 48.712 35.3 1.5 8.7 7.0 52.513 40.7 1.5 9.4 4.7 56.214 46.1 20.4 1.5 10.1 2.3 60.015 49.0 1.5 10.8 0.0 61.316 51.8 1.5 11.5 0.0 64.917 54.7 1.5 12.3 0.0 68.518 57.6 0.5 1.5 13.0 0.0 72.019 61.2 1.5 13.7 0.0 76.420 64.8 1.5 14.4 0.0 80.821 68.5 1.5 15.1 0.0 85.122 72.1 1.5 15.9 0.0 89.523 75.7 1.5 16.6 0.0 93.824 79.4 1.5 17.3 0.0 98.225 83.0 1.5 18.0 0.0 102.526 86.6 23.5 1.5 18.8 0.0 106.927 85.7 1.5 19.5 0.0 106.7

655

656

657

658

659

28 84.8 1.5 20.2 0.0 106.529 83.8 1.5 20.9 0.0 106.330 82.9 1.5 21.6 0.0 106.031 82.0 1.5 22.4 0.0 105.832 81.0 1.5 23.1 0.0 105.633 80.1 1.5 23.8 0.0 105.434 79.2 37.2 1.5 24.5 0.0 105.2

Natural pine

regrowth 66.5 9.1 0.0 0.0 0.0 0.0Cypress wetlands 107.5 0.0 0.0 0.0 0.0

660661

Table 2. Time series of numbers of parcels and ownership classes in the three study areas, 1975-2000.

1975 1980 1985 1990 1995 2000

ALACHUA Total 300 316 329 340 367 374Timber 68 70 71 76 81 90Commercial 13 14 13 13 30 33Private 198 209 220 228 227 214Government 21 23 25 23 29 37

CLAY Total 627 627 627 627 627 622Timber 90 108 98 97 96 96Commercial 111 89 104 114 111 107Private 394 406 423 396 301 300Government 32 24 2 20 119 119

HAMILTON Total 221 240 251 267 292 303Timber 51 51 51 49 39 38Commercial 1 3 3 4 10 10Private 130 128 139 150 160 169Government 2 2 2 2 2 2Mining 37 56 56 62 81 84

AGGREGATE Total 1148 1183 1207 1234 1286 1299Timber 209 229 220 222 216 224Commercial 125 106 120 131 151 150Private 722 743 782 774 688 683Government 55 49 29 45 150 158Mining 37 56 56 62 81 84

662

Table 3. Aggregated ownership category trajectories. Trajectories are designated by the first

letter of the ownership category at each time period. T = timber company, P = private owner, C =

commercial firm, G = government agency. Trajectories above the first line (under PPPPPG) each

cover >1% of the total study area. Trajectories in gray together cover 99% of the total study area.

Study Areas indicate in which county/ies each trajectory occurred (A = Alachua, C = Clay and H

= Hamilton).

TrajectoryArea (ha)

Cumulative Area (ha)

Area (%)

Cumulative Area (%)

Ownership Category Changes Study Area

TTTTTT 18009 18009 32.7 32.7 0 A,C,H

PPPPPP 16109 34118 29.2 61.9 0 A,C,H

PPPPGG 4928 39046 8.9 70.9 1 A,C

MMMMMM 2525 41571 4.6 75.4 0 H

CCCCCC 2208 43779 4.0 79.4 0 A,C,H

GGGGGG 1477 45256 2.7 82.1 0 A,C,H

PPPGGG 1428 46685 2.6 84.7 1 C

PMMMMM 988 47673 1.8 86.5 1 H

TTTTMM 980 48652 1.8 88.3 1 H

TTTTCC 753 49406 1.4 89.7 1 A,H

PPPPPG 683 50089 1.2 90.9 1 A,H

CCTTTT 382 50471 0.7 91.6 1 C

CCCTTT 355 50826 0.6 92.2 1 A

TMMMMM 335 51161 0.6 92.8 1 H

TTTMMM 327 51488 0.6 93.4 1 H

PPPPCC 287 51775 0.5 93.9 1 A,C

TTTPPP 284 52058 0.5 94.5 1 H

PCCCGG 262 52320 0.5 94.9 2 A

TTTTPP 242 52562 0.4 95.4 1 H

PPPPPT 226 52788 0.4 95.8 1 A

PPCCCC 199 52987 0.4 96.1 1 A

663

664

665

666

667

668

669

CCCCGG 153 53140 0.3 96.4 1 C

TPPPPP 145 53285 0.3 96.7 1 C

TTPPPP 142 53427 0.3 96.9 1 C

PTTTTT 141 53568 0.3 97.2 1 A,C

TTTTCM 138 53707 0.3 97.5 2 H

PPPPPC 98 53805 0.2 97.6 1 A

PCCCCC 94 53899 0.2 97.8 1 C

CCCPPC 85 53984 0.2 98.0 2 C

CPPPPP 82 54066 0.1 98.1 1 A,H

CCPPPP 81 54148 0.1 98.3 1 A,H

TTTTTC 76 54224 0.1 98.4 1 H

PPPPTT 75 54299 0.1 98.5 1 A

GGCCCC 69 54368 0.1 98.7 1 C

PPTTTT 69 54438 0.1 98.8 1 C

PPPPCP 64 54501 0.1 98.9 2 A

TPCCPP 63 54564 0.1 99.0 3 A

PPPGPP 62 54626 0.1 99.1 2 C

TTPPPP 55 54681 0.1 99.2 1 H

PPPCCP 52 54733 0.1 99.3 2 C

PGPPPP 50 54783 0.1 99.4 2 C

TCCCCC 44 54827 0.1 99.5 1 C

PCPPPP 44 54870 0.1 99.6 2 A

PPPMMM 43 54913 0.1 99.6 1 H

PGGGCC 42 54955 0.1 99.7 2 A

TTTCCC 38 54993 0.1 99.8 1 H

CCCGGG 37 55030 0.1 99.9 1 C

TTPPPC 34 55064 0.1 99.9 2 C

MMMMCC 18 55082 0.0 100.0 1 H

GGGGGP 15 55096 0.0 100.0 1 H

CMMMMM 10 55106 0.0 100.0 1 H

TTPCCC 3 55109 0.0 100.0 2 H670

Table 4. Frequency distribution of trajectory changes.

Number of Changes in Trajectory

Number of Trajectories

Land area of changes trajectory (ha)

0 5 403281 35 138832 12 8363 1 63

Total 54 55109

671

672

Table 5. Percentage change in carbon over five-year intervals for timber company ownership

trajectories in Alachua County. The grayed cells in the table show where a change in carbon

content occurred during the ownership trajectory associated with ownership change.

Year/trajectory

COCOCOCOCORA

COCOCOCOCOTI

COCOCOCORARA

HUHUHUGEGENO

COCOCOCOJSTI

COCOCOCOJSRA

ITITITRARATI

ITHUHUHUHUHU

ITITITRARARA

ITHUHUGEGENO

COCOCOCOJSJS

1975                      1981 3.4 24.3 37.2 15.7 -75.0 14.5 66.4 32.9 8.5 7.6 -25.91985 -12.7 -9.8 -51.5 19.2 42.2 -1.4 21.9 23.9 1.7 6.4 1.11990 -0.1 6.1 -1.4 -13.1 83.3 7.1 3.1 -24.4 -30.4 -9.7 -14.61995 10.8 -3.4 57.7 17.2 59.8 -3.7 -55.0 25.5 25.1 6.7 32.02000 2.9 -7.1 29.6 -24.5 -47.6 -12.1 37.6 -5.4 -12.6 -8.3 22.2

Mean % change in Carbon

COCOCOCOCORA

COCOCOCOCOTI

COCOCOCORARA

HUHUHUGEGENO

COCOCOCOJSTI

COCOCOCOJSRA

ITITITRARATA

ITHUHUHUHUHU

ITITITRARARA

ITHUHUGEGENO

COCOCOCOJSJS

No Transfers 0.3 4.3 3.5 17.4 16.8 6.7 4.8 7.2 1.6 -0.9 -4.3

Transfers 2.9 -7.1 57.7 -18.8 6.1 -7.9 29.7 23.9 1.7 -1.1 32.0All Trajectories

No Transfer

s 5.2Transfer

s 10.8

Note: CO – Container Corp; GE – Georgia-Pacific; HU – Hudson Pulp and Paper; JS – Jefferson Smurfit;

NO – North American Timber; RA – Rayonier Timberlands; TA – Tatum Brothers; TI – Timberlands; IT

- ITT Rayonier

673

674

675

676

677678

679

680

681

682

683

FIGURE LEGENDS

Figure 1. Study region with individual study areas outlined. Study areas are named by the county

in which they occur. The background is a Landsat ETM+, band 5-4-3 composite image

from 04 January 2001.

Figure 2. Maps of ownership classes for each time period for the three study areas.

Figure 3. Time series of carbon density (Mg C pixel-1) maps from the study area, with the

ownership classes shown as color-coded polygon boundaries. The carbon density maps show

the data only for 1975, 1980, 1985, 1990, 1995, and 2000. All the annual C density raster

data sets, equivalent maps and GIS data are deposited in the ESA data archive {URL

HERE}.

Figure 4. Aggregate area, carbon density, and aggregate total carbon by ownership category.

Figure 5. Time series of (a) aggregate total carbon, and (b) carbon density for ownership type

trajectories that comprise >1% of the study area. Color-coded arrows indicate the time of

sales for each trajectory.

Figure 6. Timber company owner trajectories and carbon densities for 1975-2000. Red ellipses

indicate times of major change in carbon density associated with sale from one company to

another, green ellipses indicate changes in carbon density not associated with a sale.

Figure 7. Relationships between pulpwood prices and aggregate total carbon (top) and carbon

density (bottom) for all ownership categories. Note that parcels that change ownership

category are counted in the new category.

Figure 8. Relationships between pulpwood prices and aggregate total carbon on land with

constant ownership trajectories (top) and other important trajectories (bottom). Note that,

684685686

687

688

689

690

691

692

693

694

695

696

697

698

699

700

701

702

703

704

705

706

unlike in Figure 7, constant trajectories mean that the land ownership category never

changed.

Figure 9. Relationships between pulpwood prices and overall carbon density on land with

ownership trajectories (top) and other important trajectories (bottom).

707

708

709

710

711

Figure 1712

713

Figure 2A – Alachua714

715

Figure 2B: Clay 716

717

718

Figure 2C: Hamilton Study area719

720

Figure 3A-C721

722

Figure 2C. 723

724

725

726

Figure 5.727

728

Figure 6.

1970 1975 1980 1985 1990 1995 20000.000

2.000

4.000

6.000

8.000

10.000

12.000

JS-GI-GI-JS-JS-JSGI-GI-GI-GI-GI-JSGI-GI-GI-GI-GI-FF

Carb

on (M

g/ha

)

729

730731732

Figure 7.733

734735

Figure 8.736

737738

Figure 9.739

740741