ELSEVIER Forest Ecology and Management 118 (1999) 107-115 Pores;i;ology Management The effects of population growth on timber management and inventories in Virginia David N. Wear”>*, Rei Liub, J. Michael Foremanc, Raymond M. Sheffieldd “USDA Forest Service, Economics of Forest Protection and Management, PO. Box 12254, Research Triangle Park NC 27709, USA bSenior GIS Analyst, Commonwealth of Virginia, Department of Forestry, Charlottesville VA, USA ‘Chiej Forest Resources Utilization, Commonwealth Virginia, Department of Forestry, Charlottesville VA, USA ‘USDA Forest Service, Forest Inventory and Analysis, Asheville NC, USA Received 25 February 1998; accepted 5 October 1998 Abstract Expanding human populations may have important effects on the availability of timber from private lands in the South. To examine the effects of development on timber supply, we compared the density of populations and various site variables with expert opinions on the future location of commercial timberland for a study site in Virginia. Population density is a significant predictor of commercial timberland and resulting probability equations provide a method for adjusting timber inventories. Findings indicate that the transition between rural and urban land use occurs where population density is between 20 and 70 people per square mile. Population effects reduce commercial inventories between 30 and 49% in the study area. 0 1999 Elsevier Science B.V. All rights reserved. 1. Introduction An expanding human population may have impor- tant implications for forest resources in the United States. As domestic and global populations grow, so grows demands for resource products and natural s e t t i n g s . I n c r e a s i n g p r o d u c t i o n , i n t u r n , m a y a d v e r s e l y affect the environmental and aesthetic quality of for- e s t s . A t t h e s a m e t i m e , t h e e x p a n s i o n o f r e s i d e n t i a l a n d u r b a n a r e a s w i l l l i k e l y r e d u c e t h e a m o u n t o f r e s o u r c e s available for the production of goods and services (Marcin, 1993; Harris and DeForest, 1993). Over time, *Corresponding author. Tel.: +l-919-549-4011; fax: +1-919- 549-4047. these concurrent impacts on both timber demand and timber supply could result in increasing market scar- c i t y a n d c o n t i n u e d u p w a r d p r e s s u r e o n t i m b e r p r i c e s . Additionally, expanding ‘urban-rural interfaces,’ as they are sometimes called, may hold implications for other resource values (Shands, 1991). For example, wildlife habitat may become more fragmented and otherwise less effective as an area becomes more populated. Managing forest fuel loads may also become increasingly problematic and forest fires are likely to be more difficult to fight and more costly as p o p u l a t i o n d e n s i t y i n c r e a s e s . T h i s s t u d y e x a m i n e s t h e p o t e n t i a l e f f e c t s o f p o p u l a - tion growth on timber supply. In particular, we exam- ine how expanding populations in a part of western 037%1127/99/$ - see front matter 0 1999 Elsevier Science B.V. All rights reserved. PII: SO378-1127(98)00491-5 1 0 8 D.N. Wear et al/Forest Ecology and Management I18 (1999) 107-115 V i r g i n i a m a y i n f l u e n c e t h e m a n a g e m e n t o f f o r e s t s a n d the eventual supply of timber from forest lands. We p o s i t t h a t i n c r e a s i n g p o p u l a t i o n d e n s i t y a f f e c t s t i m b e r s u p p l y i n t w o d i f f e r e n t w a y s . O n e i s t h e c o n v e r s i o n o f forests from a timber-growing use to a residential or urban use. When this occurs, land will no longer be a v a i l a b l e f o r t i m b e r h a r v e s t o r t i m b e r g r o w i n g , t h o u g h the transition may be coincident with some timber h a r v e s t i n g - s o c a l l e d ‘ r e a l e s t a t e c u t s ’ . A m o r e s u b t l e effect may be the reduced investment in timber pro- duction in areas of moderate population density as l a n d o w n e r s a n t i c i p a t e c o n t i n u e d p o p u l a t i o n g r o w t h a n d c h a n g e s i n l a n d u s e s . T h i s p e r c e i v e d i m p e r m a n e n c e o f land use may discourage active investment in timber production, thereby reducing future timber supply. In this study, we examine the potential effects of population growth on timber production using a two- step approach. First, we compare expert opinions on where forests will not be managed as commercial timberland with population density in these areas, and test for a relationship between the two. Then, we use the relationship between population density and likely timber management to adjust timber inven- tories for population effects. The study provides i n s i g h t s i n t o t h e s e i m p o r t a n t i s s u e s b u t , p e r h a p s m o r e importantly, offers some practical methods for asses- sing the effects of population growth on estimates of timberland area and timber inventories. 2. Study site O u r s t u d y s i t e i s V i r g i n i a ’ s T h o m a s J e f f e r s o n P l a n - ning District shown in Fig. 1. This area is defined by five counties in the general vicinity of the city of Charlottesville: Albemarle, Fluvanna, Louisa, Greene, and Nelson. The area has the hilly topography char- acteristic of the Piedmont of the Appalachian Blue Ridge and a variety of forest types. The Oak-Hickory type predominates but significant areas of Loblolly- Shortleaf Pine and Oak-Pine types are also present (Thompson, 1992). Accordingly, hard hardwoods c o m p r i s e t h e l a r g e s t s h a r e o f g r o w i n g s t o c k v o l u m e s , b u t s o f t h a r d w o o d s a n d p i n e a l s o r e p r e s e n t s i g n i f i c a n t c o m p o n e n t s o f i n v e n t o r y . 3. Methods 3 . 1 . R e l a t i o n s h i p b e t w e e n p o p u l a t i o n d e n s i t y a n d c o m m e r c i a l f o r e s t r y Physical measures of timberland may provide lim- ited insights into whether or not forest land will actually be used for timber production. The first step of our analysis was therefore to define where lands were indeed likely to be managed for timber produc- t i o n . W e a s k e d c o u n t y f o r e s t e r s f a m i l i a r w i t h t h e s t u d y Fig. 1. Map of the study area D . N . W e a r e t al./Forest E c o l o g y a n d M a n a g e m e n t 1 1 8 ( 1 9 9 9 ) 1 0 7 - 1 1 5 1 0 9 s i t e t o m a p t h e i r o p i n i o n o f w h e r e c o m m e r c i a l f o r e s t r y w o u l d a n d w h e r e i t w o u l d n o t l i k e l y b e p r a c t i c e d i n t h e future. They further identified those areas that would not be managed due to some special site feature (not necessarily related to population and land use pres- sures) such as critical species habitats, proximity to water, or buffers. These maps were drawn at a 1 : 24000 scale and were digitized and entered as a GIS map layer. W e t h e n t e s t e d t h e r e l a t i o n s h i p b e t w e e n t h i s e x p e r t classification of potential commercial timberland (PCT) and several factors describing the accessibility and operability of the site and the population density of the area. That is, we posited that the probability of land (L) being labeled as PCT is given as: Pr(L = PCT) =f(Xi) = 1 - F(g[X]) (1) where F is a cumulative distribution function which depends on a function (g) of a vector of explanatory variables (X). We assumed that F had a logistic form which is a close approximation to the normal distribu- tion. The resulting ‘Logit’ model has the following form: Pr(L = PCTIX) = 1 - F(g[X]) = 1 eITcxj (2) The vector X includes the following variables: the p o p u l a t i o n d e n s i t y o f t h e a r e a ( P O P , p e o p l e p e r s q u a r e mile), site index (SI, height. at age 50), slope ( S L O P E % ) , a n d t w o d u m m y v a r i a b l e s t h a t d e f i n e e a s e of access to the site (AC-EASY and AC-HARD). AC- E A S Y i s e q u a l t o 1 w h e r e s u r v e y c r e w s i n d i c a t e d t h a t , while roads did not exist, they could be easily built; equal to zero otherwise. AC-HARD is equal to 1 if roads were deemed difficult or very difficult to build. T h e n u l l c a s e i s d e f i n e d w h e r e r o a d s t o t h e s i t e a l r e a d y exist. Taken together, these variables describe the comparative advantage of each site for various land uses. To estimate the model, we define the functional form of g as follows: g(X) = a + bl I’!? +b2 (s-: +b3 SLYPE + b4 x4-7 -EASY + b5 i4+7 -HARD (3) where the signs in parentheses indicate our expecta- t i o n s r e g a r d i n g t h e e f f e c t o f t h e r e f e r e n c e d v a r i a b l e o n t h e p r o b a b i l i t y o f f o r e s t c o v e r . W e e x p e c t t h a t increas- ing population density increases demand for non- forest land uses, that ease of operability (i.e., low s l o p e s ) a l s o r e d u c e s t h e l i k e l i h o o d o f f o r e s t c o v e r , a n d t h a t l e s s a c c e s s i b l e s i t e s a r e m o r e l i k e l y t o b e f o r e s t e d . W e e x p e c t t h e e f f e c t o f s i t e i n d e x t o b e n e g a t i v e g i v e n t h a t h i g h e r q u a l i t y l a n d m a y h a v e c o m p a r a t i v e a d v a n - tage for use in agriculture. P r e v i o u s s t u d i e s h a v e u s e d s i m i l a r m o d e l s t o e x a m - i n e t h e h a r v e s t c h o i c e s o f i n d i v i d u a l l a n d o w n e r s . T h e earliest application (Binkley, 1981) addresses the effects of income, price, education, and costs on the decision to harvest timber. Subsequent studies (e.g., D e n n i s , 1 9 9 0 ; K u u l u v a i n e n a n d S a l o , 1 9 9 1 ) h a v e u s e d d i s c r e t e c h o i c e m e t h o d s t o s i m u l t a n e o u s l y a d d r e s s t h e d e c i s i o n t o h a r v e s t a n d t h e q u a n t i t y o f t h e h a r v e s t . T h e p r e s e n t s t u d y i s p e r h a p s m o s t c l o s e l y r e l a t e d t o W e a r and Flamm’s (1993) cross-sectional model of harvest choice in a single watershed. Their analysis uses site f e a t u r e s ( a s s u m i n g c o n s t a n t d e l i v e r e d p r i c e s ) t o p r o x y f o r t h e c o s t s o f h a r v e s t i n g . T h e p r e s e n t s t u d y i s d i s t i n c t by virtue of its independent variable. The use of an expected land-use is an attempt to address long-run resource allocation. To test this relationship, we examined the land use classification for US Forest Service permanent inven- t o r y p l o t s l o c a t e d i n t h e s t u d y a r e a . U s i n g t h e s e p l o t s gave us access to several other descriptive variables and allowed us to subsequently estimate the implica- tions of population density on standard measures of timberland area and timber inventories. We overlaid t h e p l o t l o c a t i o n s t h r o u g h t h e G I S t o a s s i g n a p o p u l a - t i o n d e n s i t y t o e a c h p l o t . W e t h e n s c r e e n e d t h e p l o t s t o define the subset of forest plots in private ownership without the aforementioned special features defined b y l o c a l e x p e r t s . W e t h e n d e f i n e d L a s a b i n a r y v a r i a b l e where those plots that were classified as PCT were a s s i g n e d L=l; o t h e r w i s e t h e y w e r e a s s i g n e d L=O. Plot observations were then used to estimate the Logit model defined by Eq. (2) using standard max- imum likelihood estimation applied to individual sur- vey plots.’ To test for the effect of the independent ‘This involves constructing the likelihood function based on the probabilities defined by Eqs. (2) and (3) and solving for coefficients that yield the highest likelihood that the model generated the data. We used the statistical package LIMDEP (Greene, 1992). Techniques are described in detail in Maddala (1983). 1 1 0 D.N. Wear et al./Forest Ecology and Management 118 (1999) 107-115 variables on probability that land would be commer- c i a l t i m b e r l a n d w e t e s t t h e s i g n i f i c a n c e o f t h e m o d e l a s a whole using log likelihood ratio tests and for the s i g n i f i c a n c e o f c o e f f i c i e n t s u s i n g t - s t a t i s t i c s . 3.2. Estimating the effects of population on forest a r e a a n d t i m b e r i n v e n t o r i e s E q . (2), i n a d d i t i o n t o b e i n g u s e d t o t e s t o u r h y p o t h - e s i s a b o u t t h e e f f e c t o f p o p u l a t i o n o n t h e p o t e n t i a l f o r c o m m e r c i a l f o r e s t r y , a l s o p r o v i d e s a w a y t o p r e d i c t t h e probability of commercial forestry as a function of p o p u l a t i o n d e n s i t y a n d o t h e r v a r i a b l e s a t a n y l o c a t i o n within the study area. Given that the fit of Eq. (2) is s i g n i f i c a n t , t h e n c o e f f i c i e n t e s t i m a t e s a n d m e a s u r e s o f t h e i n d e p e n d e n t v a r i a b l e s c a n b e u s e d t o e s t i m a t e t h e probability that land will be commercial timberland. This estimated probability also defines the share of t h e f o r e s t a r e a w i t h t h i s p o p u l a t i o n d e n s i t y t h a t w o u l d be expected to be commercial forest. To estimate the total effect of population density on timberland area then, we apply the probability of commercial forestry to the area represented by each permanent inventory p l o t i n t h e s t u d y a r e a u s i n g m e t h o d s d e f i n e d b y H a r d i e and Parks (1991). In equation form: E(CF) = &&f(x) (4) i=l where E(CF) is the expected total area of commercial f o r e s t i n t h e s t u d y a r e a , Ai t h e a r e a e x p a n s i o n f a c t o r f o r plot i, xi the population density at plot i, and n the n u m b e r o f p l o t s i n t h e a r e a . W e a p p l y t h e s e e s t i m a t e s to all plots in the five-county study area to estimate total effects and calculate effects on a county by c o u n t y b a s i s a s w e l l . W e u s e d t h e s a m e a p p r o a c h w i t h inventory expansion factors to estimate the effect of p o p u l a t i o n d e n s i t y o n g r o w i n g s t o c k i n v e n t o r i e s i n t h e area. 4. Data T h e m e t h o d s d e f i n e d a b o v e r e q u i r e o v e r l a y i n g f o u r sets of spatially referenced data: (1) expert opinion maps of where forests will and will not likely be managed as commercial forests; (2) US Census data on population density recorded at the block level; (3) U S F o r e s t S e r v i c e i n v e n t o r y p l o t s a n d t h e i r a s s o c i a t e d data; and (4) USGS land use categories. 4.1. Expert opinion maps Expert opinions were developed by the field fores- t e r s a t t h e c o u n t y l e v e l w h o h a v e c o n s i d e r a b l e k n o w l - edge of forest ownership and production. Experts mapped their opinions directly onto maps at a 1 : 24000 scale for Albemarle, Greene and Nelson c o u n t i e s . B o u n d a r i e s w e r e t h e n d i g i t i z e d a n d s t o r e d a s a map layer in the Arc-Info geographic information s y s t e m . 4.2. US Census population Census Tiger files were used to map population d e n s i t y ( p e o p l e p e r s q u a r e m i l e ) . D e n s i t y i s d e f i n e d f o r U S C e n s u s b l o c k s . B l o c k s a r e t h e s m a l l e s t g e o g r a p h i c u n i t s t h a t t h e C e n s u s c a l c u l a t e s s t a t i s t i c s f o r . 4 . 3 . F o r e s t i n v e n t o r y d a t a Forest inventory plots measured in 1991 (Thomp- son, 1992; U.S.D.A. Forest Service, 1985) were then overlaid within the GIS. We recorded volume esti- m a t e s a n d a r e a a n d v o l u m e e x p a n s i o n f a c t o r s f r o m t h e i n v e n t o r y p l o t d a t a b a s e . W e a l s o r e c o r d e d s l o p e , s i t e index, and access categories for each plot. Expert opinion observations and population density could t h e n b e a s s i g n e d t o e a c h i n v e n t o r y p l o t b y o v e r l a y i n g map layers. 4.4. Land use W e a l s o u s e d l a n d u s e c l a s s i f i c a t i o n m a p s f r o m t h e U S G S t o i d e n t i f y t h o s e p l o t s t h a t a r e i n b u i l t - u p u r b a n a r e a s . 5. Results The logit model defined by Eqs. (2) and (3) was estimated using 94 forest survey plots. Of these 94 plots, 30 were identified as potential commercial forest. Coefficient estimates and standard error ( T a b l e 1 ) i n d i c a t e t h a t p o p u l a t i o n d e n s i t y i s n e g a t i v e l y related to the probability of commercial forest T a b l e 1 Coefficient estimates of the logit model defined by Eqs. (2) and (3), using data from Albemarle, Greene, and Nelson counties. An asterisk indicates significance at the 5% level. The log likelihood ratio (LLR) for testing the overall significance of the model is also reported Coefficient Intercept pop Slope Site AC-EASY AC-HARD LLR n=94 Estimate 0.0617 - 0 . 0 4 2 4 0 . 0 1 6 1 0 . 2 0 7 6 -1.0528 2 . 0 2 3 7 30.96 * S E 1 . 5 8 0 3 0 . 0 1 3 3 * 0.0158 0 . 2 0 8 5 0 . 6 7 6 5 2 . 0 6 2 3 (throughout this paper significance was tested at p=O.O5). However, all other variables (slope, site index, and access categories) have insignificant coef- f i c i e n t s . W e a l s o t e s t e d t h e s i g n i f i c a n c e o f p o p u l a t i o n density by estimating the logit model without the variable and constructing the log likelihood ratio statistic for the constrained model (chi-squared dis- tribution with one degree of freedom). The calculated statistic (23.362) is greater than the critical value (3.841), so we again reject that the variable has no e f f e c t . A s a l l o t h e r v a r i a b l e s a r e i n s i g n i f i c a n t , w e e s t i m a t e d a c o n d e n s e d m o d e l w i t h o n l y p o p u l a t i o n d e n s i t y a s a n e x p l a n a t o r y v a r i a b l e t o a p p l y t h e p r o b a b i l i t y m o d e l t o area and volume expansion factors using Eq. (4). For this model, the intercept was 1.9065, the population density coefficient was -0.0421, and both coefficients were significant. We tested the overall significance of both the original model and the condensed model using a log likelihood ratio test (chi-squared distribu- tion, with degrees of freedom equal to the number of explanatory variables). For both models we reject no explanatory power (see Table 1). T o f u r t h e r e x a m i n e t h e e f f e c t s o f p o p u l a t i o n d e n s i t y on timber production, we plotted the probability of forest being commercial timberland as a function of p o p u l a t i o n d e n s i t y . F i g . 2 s h o w s t h e e x p e c t e d i n v e r s e r e l a t i o n s h i p b e t w e e n p o p u l a t i o n d e n s i t y a n d P C T . A t a p o p u l a t i o n d e n s i t y o f 0 , t h e p r o b a b i l i t y o f P C T i s 0 . 8 2 . The probability declines as population density increases and approaches zero as density reaches ca. 150 people per square mile (psm). The odds of D . N . W e a r e t al./Forest E c o l o g y a n d M a n a g e m e n t 1 1 8 ( 1 9 9 9 ) 1 0 7 - 1 1 5 11 1 50 1 0 0 1 5 0 200 250 300 350 400 Population Density Fig. 2. The predicted probability that forest is commercial timberland as a function of population density. being commercial forest land are roughly 50 : 50 at a population density of 45 people psm and the prob- ability of commercial forestry is >0.75 at ca. 20 people p s m . The next step in the analysis was to estimate the predicted probability of commercial forestry for all s u r v e y p l o t s i n t h e P l a n n i n g D i s t r i c t a s a w h o l e . F i g . 3 s h o w s t h e d i s t r i b u t i o n o f p l o t s b y p r o b a b i l i t y v a l u e s . Thirty percent of the plots have probability values of 0.8 or greater and 57% have probability values of 0.7 o r g r e a t e r . H o w e v e r , 2 5 % o f t h e p l o t s h a v e p r o b a b i l i t y values that are ~0.5, indicating a ~50 : 50 chance of commercial forestry. Area and volume expansion factors for all plots were then used to calculate the expected commercial g 1 0 0 - e C.27) ge 60- 9 In 5 60- a ij5 ‘lo- (.13) E 610) 2 20. (0.7) O- I I 2 2 % 2 2 2 2 2 B B P 0 !! 2 2 B 0 5 2 a x a 2 2 Prob (Commercial Forest) 1 630) ~ o! .- 0 a .? 2 2 Fig. 3. Number (proportion) of inventory plots by the predicted probability that forest is commercial timberland. 1 1 2 D . N . W e a r e t &./Forest E c o l o g y a n d M a n a g e m e n t 1 1 8 ( 1 9 9 9 ) 1 0 7 - 1 1 5 Table 2 Area of timberland in the study area for the 1991 survey. Subsequent columns show the effects of (1) removing lands in public ownership, (2) removing lands classified as urban, and (3) reducing availability related to increasing population density Area Land area 1992 survey Acres Minus public Minus urban Minus pop. effect T o t a l 929 557 907 015 851358 548 985 Albemarle 278 205 215 169 267 596 171717 Fluvanna 137 348 136358 107 064 65 167 Greene 53 599 52472 52 472 26 173 Louisa 228 537 227 742 208 952 125 421 Nelson 231868 215 274 215 274 160 508 forest area and associated growing stock inventories f o r e a c h c o u n t y i n t h e s t u d y a r e a . P r o j e c t i o n s o f f o r e s t area are shown in Table 2. The first column in Table 2 lists the total forest area estimated by forest survey p l o t s i n t h e P l a n n i n g D i s t r i c t ( 9 2 9 5 . 5 7 a c r e s ) . W e n e x t subtract the public lands from the area. This reduces total acreage by ca. 2.4% to a total of 907 015 acres. N e l s o n C o u n t y h a s a d i s p r o p o r t i o n a t e l y l a r g e s h a r e o f the public forest land (ca. 7.2%) while Fluvanna has very little public forest land (ca. 0.7%). The area of land in urban land uses (USGS codes 10-17) is then excluded, removing another 5% of the forest area and leaving 851358 acres. We then applied Eq. (4) to t h e s e r e m a i n i n g a c r e s t o c a l c u l a t e t h e e f f e c t s o f p o p u - lation density on the availability of rural private timberland. Comparing columns three and four in Table 2 s h o w s t h e t o t a l e f f e c t o f p o p u l a t i o n d e n s i t y o n f o r e s t land availability. These are also charted in Fig. 4. Population effects reduce by an additional 32% the estimate of available forest land in the Thomas Jef- ferson Planning District . Removing urban and public lands and adjusting forest area for population effects results in a total reduction of available forest area by 41%. The effects are highest in percentage terms for Fluvanna and Greene counties (-52.6 and -51.2%, respectively) and least for Nelson County (-30.8%). T h e e f f e c t s o n g r o w i n g s t o c k i n v e n t o r i e s a r e s h o w n i n Table 3 and are similar to effects found for timberland area. Pine volume is reduced by the greatest amount (49%). The growing stock inventory of other soft- woods is reduced by 38% from forest inventory v a l u e s . ,000 900 800 2 700 ; ua u c 500 I 2 400 f 300 200 100 0 Minus Public Minus Urban Minus Pop. Effect Scenario 4 b . L a n d A r e a b y C o u n t y 300 250 z; 200 u 7 150 !! OE 100 50 0 Albemarle FllWaillla GV.?elle Louisa NdSOll county Fig. 4. Total timberland in the Thomas Jefferson Planning District: (1) defined by the 1992 inventory; (2) after screening public lands; (3) additional screening of urban lands; and (4) additional screening based on the predicted probability of commercial timberland. D . N . W e a r e t al/Forest E c o l o g y a n d M a n a g e m e n t 1 1 8 ( 1 9 9 9 ) 107-115 1 1 3 Table 3 Growing stock volumes in the study area for the 1991 survey. Subsequent columns show the effects of (1) removing lands in public ownership, (2) removing lands classified as urban, and (3) reducing availability related to increasing population density (a) Pine volume 1992 Survey Minus public Minus urban Minus pop. effect Thousand cubic feet T o t a l 300149 289571 251904 154439 Albemarle 76035 76035 61394 42211 Fluvanna 64580 64580 44468 22116 Greene 28301 28301 28301 19395 Louisa 96042 96042 93122 51902 Nelson 35191 24619 24619 18209 (b) Other softwood volume T o t a l 42038 41570 40748 25994 Albemarle 16416 16416 15 955 1372 Pluvanna 461 467 467 260 Greene 1806 1806 1806 1365 Louisa 4928 4928 4567 3016 Nelson 18421 17 953 17 953 13981 (c) Soft hardwood volume T o t a l 431076 411474 396438 259772 Albemarle 119 620 119620 119620 81411 Fluvanna 40866 40866 28456 20287 Greene 41097 41097 41097 17 836 Louisa 80684 80684 78058 44484 Nelson 148 809 129 207 129 207 95155 (d) Hard hardwood volume Total 864177 830395 113794 511489 Albemarle 282528 281634 276830 190825 Fluvanna 87864 85143 67772 39780 Greene 51151 57151 57151 23968 Louisa 189445 189445 155 019 94039 Nelson 241189 217022 217022 162877 6. Future population growth density areas. We did not attempt to develop and apply a sophisticated model of urban and suburban Populations will likely continue to expand in the expansion for this exercise; rather, we examined a Thomas Jefferson Planning District. To examine the simple model that expanded populations by an equal p o t e n t i a l e f f e c t s o n f o r e s t s w e e s t i m a t e d t h e n e t e f f e c t p r o p o r t i o n a c r o s s t h e e n t i r e s t u d y a r e a . T h e s e projec- t h a t v a r i o u s l e v e l s o f p o p u l a t i o n g r o w t h m i g h t h a v e o n t i o n s t h e r e f o r e d o n o t r e p r e s e n t f o r e c a s t s , b u t t h e y d o commercial forest area using the methods developed allow for a qualitative examination of the conse- here. We increased the population density for indivi- quences of population growth. dual plots and recalculated the probability of com- R e s u l t s o f t h e p o p u l a t i o n s i m u l a t i o n s a r e c h a r t e d i n mercial timberland using Eq. (2). These values were Fig. 5, with timberland plotted against population then used to screen the survey data using Eq. (4). (both are charted in terms of percentage change from Population growth is a spatially defined process their present values). The results show an approxi- with growth concentrated at the periphery of high m a t e l y l i n e a r r e l a t i o n s h i p b e t w e e n p o p u l a t i o n g r o w t h 114 D.N. Wear et al/Forest Ecology and Management 118 (1999) 107-115 1 0 0 140 180 220 260 300 Percent of 1990 Population Fig. 5. Percent change in timberland as a function of change in population density. and timberland declines in timberland area. For each 20% increment in population, timberland area drops by roughly 4%. As Fig. 3 indicates, a large share of timberland is in areas with a very low population density (the two right-most bars in Fig. 3). There is relatively little timberland area in the transition popu- lation densities of 20-70 psm. As a result, the existing estimate of timberland area may be fairly robust to moderate expansion in population density. 7. Conclusion Population growth may influence forests and for- estry in several direct and indirect ways. We have examined the net effect that population density may have on the availability of forests for timber produc- t i o n . W h i l e p e r h a p s o n l y a f i r s t a p p r o x i m a t i o n o f t h e s e e f f e c t s , o u r r e s u l t s i n d i c a t e t h a t c h a n g e s a t t h e urban- rural interface may have important influence on the future supply of timber. Because population data are so readily available in spatially referenced form (i.e. t h r o u g h t h e U S C e n s u s T i g e r / L i n e f i l e s ) , t h i s a p p r o a c h may prove especially useful for examining the effects o f s u b u r b a n i z a t i o n o n t i m b e r p r o d u c t i o n o v e r b r o a d e r a r e a s . We have tested for and estimated the relationship between population density and the potential for c o m m e r c i a l f o r e s t r y . T h e r e s u l t s i n d i c a t e a c o n t i n u o u s r e l a t i o n s h i p , b u t a l s o s u g g e s t s s o m e i m p o r t a n t t h r e s h - o l d s . O n e i s t h a t t h e p r o b a b i l i t y o f f o r e s t m a n a g e m e n t approaches zero at ca. 150 people psm. At 70 psm there is a 25% chance of commercial forestry. At ca. 45 psm the odds are 50 : 50 that commercial forestry w i l l b e p r a c t i c e d a n d a t 2 0 p s m t h e r e i s a 7 5 % c h a n c e . The implication is that a transition between rural and urban use of forests occurs between 20 and 70 psm, s u g g e s t i n g t h a t f u t u r e r e s e a r c h s h o u l d f o c u s o n u n d e r - s t a n d i n g l a n d u s e d y n a m i c s a n d r e s o u r c e m a n a g e m e n t in this zone. T h e r e s u l t s o f t h i s s t u d y i n d i c a t e t h a t r a w e s t i m a t e s of timberland -based on physical criteria alone - may substantially overstate the availability of timber. We estimated that population effects reduced timberland a r e a a n d g r o w i n g s t o c k v o l u m e s b y r o u g h l y 4 0 % f r o m their measured values. While only a first approxima- t i o n o f t h e e f f e c t s o f p o p u l a t i o n g r o w t h o n f o r e s t l a n d s , t h e s e r e s u l t s i n d i c a t e t h a t t h e e f f e c t s c a n b e s u b s t a n - tial. Of course these results are developed for only a small area and would therefore benefit from replica- t i o n i n o t h e r a r e a s . I t w o u l d b e u s e f u l t o k n o w w h e t h e r t h e s e r e l a t i o n s h i p s h o l d g e n e r a l l y . D o t h e y , f o r exam- D.N. Wear et al./Forest Ecology and Management 118 (1999) 107-115 1 1 5 ple, differ in areas with different topography, land- ownership pattern, or relative resource values? The study also illustrates the value of linking bio- physical forest inventories with social data. This l i n k a g e c o u l d b e i m p r o v e d b y r e c o r d i n g c e n s u s b l o c k i d e n t i f i e r s f o r e a c h p l o t i n a f o r e s t s u r v e y . T h i s w o u l d b o t h i m p r o v e t h e p r e c i s i o n o f s u b s e q u e n t a n a l y s e s a n d allow for direct screening of inventories without link- age to a GIS. More extensive study in this area could l e a d t o s i g n i f i c a n t i m p r o v e m e n t s i n o u r u n d e r s t a n d i n g of timber supply from private lands and the general expression of social phenomena on forested land- s c a p e s . References Binkley, C.S., 1981. Timber supply from nonindustrial forests. Bulletin No. 92, Yale University, School of Forestry and Environmental Studies, New Haven, Connecticut. Dennis, D.F., 1990. A profit analysis of the harvest discussion using pooled time series and cross-sectional data. Journal of Environmental Economics and Management 18, 176-187. Greene, W.H., 1992. LIMDEP User’s Manual and Reference Guide (Version 6.0). Econometric Software, Inc., Bellport, NY. Hardie, I.W., Parks, P.J., 1991. Individual choice and regional acreage response to cost-sharing in the South, 1971-1981. Forest Science 37(l), 175-190. Harris, T., DeForest, C., 1993. Policy implications of timberland loss, fragmentation, and urbanization in Georgia and the Southeast. In: Wear, D.N., Talmon, J. (Ed.), Policy and forestry: Design, evaluation and spillovers. Proceedings of the 1993 Southern Forest Economics Workshop. Duke University, Durham, NC, pp. 70-83. Kuuluvainen, J., Salo, J., 1991. Timber supply and life cycle harvest of nonindustrial private forest owners: an empirical analysis of the Finnish case. Forest Science 37(4), 101 l-1029. Maddala, G.S., 1983. Limited Dependent and Qualitative Variables in Econometrics. Cambridge University Press, New York. Marcin, T.C., 1993. Demographic change: implications for forest management. Journal of Forestry 91(1 l), 944. Shands, W.E., 1991. Problems and prospects at the urban-forest interface. Journal of Forestry 89(6), 23-26. Thompson, M.T., 1992. Forest statistics for the northern Piedmont of Virginia. Resource Bulletin SE-127, USDA Forest Service, Southeastern Forest Experiment Station, Asheville, NC, 1992. Wear, D.N., Flamm, R.O., 1993. Public and private forest disturbance regimes in the Southern Appalachians. Natural Resource Modeling 7(4), 379-397. U.S.D.A. Forest Service, Field Instructions for the Southeast. USDA Forest Service, Southeastern Forest Experiment Station, Forest Inventory and Analysis Work Unit, Asheville, NC, 1985.