New Forests15: 139–159, 1998.c 1998Kluwer Academic Publishers. Printed in the Netherlands.

Predicting survival of planted Douglas-fir andponderosa pine seedlings on dry, low-elevation sites insouthwestern Oregon

WILLIAM G. SCHNEIDER1, STEVEN A. KNOWE1 andTIMOTHY B. HARRINGTON21Department of Forest Science, College of Forestry, Oregon State University, Corvallis, OR97331, USA;2D.B. Warnell School of Forest Resources, University of Georgia, Athens, GA30602-2152, USA

Accepted 20 September 1997

Key words: artificial shading, competing vegetation, logistic regression, mulching, reforesta-tion, tree size

Application. Equations were developed for predicting the probability of survival during thefirst and first to third growing seasons after implementing vegetation management treatmentsin Douglas-fir [Pseudotsuga menziesii(Mirb.) Franco] and ponderosa pine (Pinus ponderosaDougl. ex Laws.) plantations less than 6 years old. A quantitative approach for predictingsurvival allows the integration of many factors that may influence survival – tree size and age,competition from associated vegetation, silvicultural maintenance treatments, topography, andclimate. Survival predictions from these equations could be used by silviculturalists to evaluatethe success of regeneration and to decide whether applying silvicultural treatments will helpmaintain desired stocking levels.

Abstract. Four equations were developed for predicting the probability of Douglas-fir[Pseudotsuga menziesii(Mirb.) Franco] and ponderosa pine (Pinus ponderosaDougl. exLaws.) survival for the first (0–1) and first to third (1–3) growing seasons after applyingmulching, scalping, or artificial shading (shade cards) treatments in plantations in southwest-ern Oregon, U.S.A. Variables describing conifer size, levels of competing vegetation, presenceof silvicultural treatments, site factors, and climate factors were collected from 13 sites rangingfrom 0 to 6 years after planting and examined as potential predictors of survival. Age, stemdiameter, a competition index for shrubs, severity of growing season at time of treatment,average annual precipitation, aspect, and slope angle were predictors of Douglas-fir survivalduring 0–1 and 1–3 growing seasons after treatment; the presence of silvicultural treatmentswas also a predictor only during the first growing season after treatment. Age, aspect, andslope angle were predictors of ponderosa pine survival over both 0–1 and 1–3 growing seasonsafter treatment; height-diameter ratio, competition indices for herbs, shrubs, and hardwoods,silvicultural treatment, severity of growing season at time of treatment, and average annualprecipitation were also predictors only during the first growing season after treatment; crownwidth was a predictor of survival only during 1–3 growing seasons after treatment. When sig-nificant in the models, predicted probability of survival increases with treatments, less severeweather conditions, diameter, crown width, age, and precipitation; probability decreases withincreasing height-diameter ratio and competition indices for herbs, shrubs, and hardwoods.

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Introduction

Soil moisture appears to be a primary factor limiting survival and growth ofconifer seedlings on dry, low-elevation sites in southwestern Oregon. Annualprecipitation on these sites typically ranges from 50 to 100 cm, often lessthan 20% of which falls during the May through September growing season(Froehlich and others 1982, McNabb and others 1982). Vegetation can depletemost of the available water for conifer seedlings by June (White 1988). Twoor more silvicultural treatments, such as mulch installation and vegetationcontrol, are frequently performed to minimize mortality of planted Douglas-fir[Pseudotsuga menziesii(Mirb.) Franco] and ponderosa pine (Pinus ponderosaDougl. ex Laws.) seedlings on these sites. Decisions about whether or whento apply silvicultural treatments usually are based on expected risk of conifermortality. Risk assessment is difficult, however, because it requires integrativeanalyses of current conifer vigor, levels of competing vegetation, and sitefactors, along with assumptions about future weather conditions.

Seedling characteristics that are commonly or easily measured and thatmay indicate tree vigor include age, stem diameter, height, and crown width.The number of growing seasons since planting is generally known and indi-cates the length of time a seedling has been successful in the environmentin which it was planted. Less vigorous seedlings and those in less suitablelocations may die earlier, leaving other seedlings with better chances of sur-vival. First-year survival can be critical because seedlings must contend withhandling and environmental stresses. In some cases, near or total failureof a plantation may occur in the first year for a variety of reasons, includingdrought and animal damage (Hermann 1965). If conditions are favorable, littlemortality may occur during the first year and even less may occur thereafter(Helgerson and others 1989, 1991); in less favorable circumstances, addi-tional mortality may continue into later years (Helgerson and others 1992).

Size variables such as stem diameter and height are among the mostcommon measures of seedling quality (Shiver and others 1990, Rose andothers 1991). Zaerr and Lavender (1976) found that Douglas-fir survivalwas positively related to fresh weight, which was highly correlated withstem diameter. Above a fresh weight of 4 g (diameter = 2.6 mm), survivalwas unaffected by size; below 4 g, survival decreased with size. Shiver andothers (1990) found a similar threshold relationship between diameter andsurvival for loblolly pine (Pinus taedaL.). South and Mexal (1984) alsofound a positive relationship between root-collar diameter and survival ofnewly planted loblolly pine and slash pine (Pinus elliottii Engelm.). Largeheight-diameter ratios have also been suggested as possible indicators of lowtree vigor and of competition from overtopping and encroaching vegetation(Cole and Newton 1987).

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Vegetation can influence survival in dry climates. Tesch and Hobbs (1989)found a negative correlation between shrub cover and 3-year survival ofDouglas-fir (r = –0.70,p = 0.12). Survival of ponderosa pine decreased withincreasing shrub biomass in a study by Wagner and others (1989). Brand(1986) developed a brush competition index for predicting relative growth ofDouglas-fir using cover and relative height along with a variable indicatingproximity of the brush to the tree. Cover indicates how much biomass maybe removing soil moisture and nutrients. Relative height might indicate therelative competitive ability of the vegetation and the tree for light. Thisapproach also may be useful for predicting survival.

Silvicultural treatments applied to individual trees, including mulching,artificial shading, and radial scalping, can effectively promote survival(Bradley 1962, Hermann 1964, 1965, Helgerson 1990), although they mayhave no effect during mild growing seasons when mortality is very lowanyway (Flint and Childs 1987). These treatments likely increase soil wateravailable to the seedlings by lowering soil surface temperature, reducing soilsurface evaporation, or reducing vegetative competition for soil water (Flintand Childs 1987).

Because heat and moisture can affect seedling survival (Cleary 1971, Atzet1982), climate differences across sites and between growing seasons mightbe good indicators of survival probability. Without adequate soil moisture inlate summer, seedling moisture stress can reach lethal levels (Cleary 1971).Cleary (1971) planted ponderosa pine on the same site in consecutive yearswith drastically different results in survival. During the first year, survivalwas only 10%, but during the second year, which had a wet August, survivalwas 100%.

Site factors affecting heat and soil moisture, such as slope and aspect, mightalso be good indicators of survival probability. Stage (1976) proposed usingtwo interaction terms that include the slope and the sine or cosine of aspectas a means of incorporating topographic effects. In this way, the optimumaspect for survival depends on the sign and magnitude of the coefficientsdetermined through regression (Stage 1976, Wagner and Radosevich 1991). Aregeneration establishment model developed for conifer forests in the InlandNorthwest (Stage 1973) predicts higher probabilities of stocking on north-facing aspects than south-facing aspects (Stage and Boyd 1987).

A quantitative technique for predicting conifer survival that includes someof the above factors would be beneficial. This paper discusses the develop-ment of logistic regression models for predicting Douglas-fir and ponderosapine survival through one and three growing seasons in plantations less than6 years old. Models like these could be used in regeneration monitoring pro-grams in southwestern Oregon; survey data could thus be used to decide if

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Table 1. Characteristics1 of sites used to develop survival models for Douglas-fir andponderosa pine, southwestern Oregon, U.S.A.

Plantation % CoverSite Elev Aspect Slope Prec Trmts age(s) Herbs Shrubs Hdwds

(m) (�) (cm) (yrs)

1 823 S 24 89 M,Sh 1 14 42 72 762 SSW 19 89 G 4 54 24 63 914 E 19 76 M3 1 16 15 142 457 SSE 27 95 M 0,2 87 18 15 762 S 24 76 B 4 0 68 116 640 SW 35 102 B,M,Sh 0,1 3 65 1872 457 N 27 114 B 1 26 83 382 671 SSW 5 102 M 1 36 14 69 762 W 24 95 M 0,3 20 7 5

10 610 W 31 95 M,Sh 0,3 32 4 0112 914 S 14 76 M 0 4 2 212 914 SSW 19 76 Sc 0 12 0 1132 518 N 33 89 Sc4 5 3 41 33

1Elev = Elevation; Prec = annual precipitation; Trmts = silvicultural treatments: B = brushcutting, G = grubbing, M = mulching, Sc= radial scalping, Sh = artificial shading; Age =plantation age (two ages indicate interplanting); Hdwds = hardwoods.2No ponderosa pine present.3Mulches displaced, apparently from grazing cattle.4Few trees treated.

silvicultural treatments to promote survival are warranted and to assess therelative importance of different silvicultural options.

Methods

Data collection

Data were collected on 13 sites in the vicinity of Medford and Roseburg,Oregon (Douglas, Jackson, and Josephine Counties). Sites were selected fromplantations less than 6 years old in which competition-associated mortalitywas expected to be a problem, based on previous experience by the DistrictForesters, and silvicultural treatments were planned. Sites had been plantedeither with a mixture of Douglas-fir and ponderosa pine or with Douglas-firalone. Characteristics such as elevation, aspect, slope, amount and type ofvegetation, and plantation age varied among the sites; therefore, the resultsmight be applicable over a wide range of conditions (Table 1). Most of thesites had been burned by either wildfire or prescribed fire prior to planting.

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Most of the trees monitored were from initial plantings, but some were frominterplantings or replantings on low-stocked sites.

At each site, two 0.2-ha to 0.3-ha plots were established. One received asilvicultural treatment and the other did not. Treatments performed shortlyafter planting included paper or plastic mulching, radial scalping (removingall vegetation and a thin layer of soil in a 1-m radius around each tree), andartificial shading (cardboard or mesh shade cards to the south of seedlings orpeat pots covering the lower 10 cm of stem). Treatments carried out in olderplantations included brush cutting and grubbing (removal of shrub seedlings)(Table 1). Within each plot, 2.37-m radius circular subplots (0.0018-ha) weresystematically established on a grid until 80 conifers had been sampled orall subplot locations were exhausted. Data from sites 1–7 are from the 1992growing season, which was warmer and drier than normal; data from sites8–13 are from the 1993 growing season, which was more typical with therespect to temperature and precipitation (Oregon Climate Service 1994). Thestatus of each seedling (dead or alive) was recorded one growing season aftertreatment (1 GSAT) and three growing seasons after treatment (3 GSAT).

Data included measures of seedling size and morphology, treatments,vegetation composition and abundance, and site (topographic) and climaticfactors. Seedling size variables included height, diameter 15 cm above thegroundline, crown widths in two perpendicular directions, and age (sinceplanting). On trees less than 15 cm tall, diameter was measured near the topof the stem. Seedling morphology variables included height to crown base,number of buds on the terminal shoot, number of branches formed sinceplanting, needle length, number of needles per stem length on the terminalshoot, and a subjective assessment of foliage color. In addition, the presenceand condition of mulch, the presence of artificial shading, and evidence ofradial scalping were noted. In each subplot, percent vegetative cover up to100% and average height were determined visually for each species. For thevegetation cover and height assessments, each subplot was divided into fourquadrants, and results were averaged for the subplot. Each species, except forconifers, was then assigned to a lifeform class (herb, shrub, hardwood); coverwas totaled and height was averaged for each lifeform class. Climate and sitevariables included average annual precipitation from 1961 to 1990 (Taylor1994), average site aspect and slope, and an index of annual solar radiationbased on aspect, slope, and latitude (Frank and Lee 1966).

Model development

The modeling approach used logistic regression with a logit linking functionand is described by Hann and Wang (1990). The resulting equations predictthe probabilities (ranging from 0 to 1) that individual trees will live. Trees

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with probabilities near 1 are more likely to live than those with probabilitiesnear 0. The probabilities for all trees in a sample can be summed and dividedby the sample size to estimate the proportion of sample trees that will live.The equations take the following form:

P= 1=(1+ EXP(X)) (1)

where P is the probability of a seedling surviving; EXP(X) = ex, where e is thebase of the natural logarithm; and X is a function of the predictor variablesincluded in the models. For this approach, all trees (1358 Douglas-fir and621 ponderosa pine) were pooled across sites and treated as independentobservations.

Preliminary models were developed by fitting stepwise logistic regres-sions to 1 GSAT survival with subsets of the following variables (Schneiderand others 1995): 11 for seedling size and morphology, 2 for silviculturaltreatments, 8 for vegetation abundance and composition, 3 for climate, and 2for topography. Excluding categories of predictor variables revealed associa-tions among several variables. Vegetation abundance appeared to compensatefor foliage morphology, climate, and topography variables. Cover of herbsand shrubs were negatively correlated, as were climate and topography, andseedling morphology and silvicultural treatment. Many of the associationsinvolved easily measured or readily available variables. Therefore, we limitedthe variables considered for the final models to those that either have beenuseful in previous studies or are readily available to forest managers.

Growth and yield models for southwestern Oregon reflect short-term detri-mental effects of release treatments during the first growing season after treat-ment (Knowe and others 1992, Knowe 1994). This “thinning lag” (Harringtonand Reukema 1983) may be a consequence of reduced photosynthesis whenshade leaves are suddenly exposed to full sunlight (Donner and Running1986). Furthermore, the warmer and drier weather during the 1992 growingseason moderated and became more normal during the 1993–1995 grow-ing seasons. These factors likely contributed to the inconsistent trends inobserved survival rate ([periodic proportion survival]1=t – 1, where t = lengthof period). For ponderosa pine, the survival rate increased from –0.1683 dur-ing the period between plot installation until 1 growing season after treatment(0–1 GSAT) to –0.1094 during the period between 1 to 3 growing seasonsafter treatment (1–3 GSAT). However, the survival rate during the periodbetween plot installation until 3 growing seasons after treatment (0–3 GSAT)was –0.1698, which was less than for 0–1 GSAT. Survival rates for Douglas-fir were lower than for ponderosa pine: –0.1796 for 0–1 GSAT; –0.1390 for1–3 GSAT; and –0.1787 for 0–3 GSAT.

These considerations, coupled with the need to make decisions aboutapplying silvicultural maintenance treatments or replanting a stand at an

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early age (Hitch and others 1996), led us to develop separate models forthe nonoverlapping growth periods 0–1 GSAT and 1–3 GSAT. To ensureconsistency and to facilitate application, models for both growth intervalswere developed by using values of the predictor variables at 0 GSAT. Becausemodels for the 1–3 GSAT period were based only on trees that were alive afterthe 0–1 GSAT period, the predicted value may be considered a conditionalprobability of survival. Thus, cumulative probability of survival 0–3 GSATmay be obtained by multiplying periodic survival probabilities. This approachprevents illogical crossover of predicted survival for the two overlappinggrowth periods, especially for survival of ponderosa pine.

Thirteen predictor variables (Table 2) were screened with a stepwise logis-tic regression procedure for inclusion in each of four final models for periodicsurvival: 0–1 and 1–3 GSAT for Douglas-fir, and 0–1 and 1–3 GSAT for pon-derosa pine. The significance level for entering and staying in the models wasset at 0.05. The variables represent characteristics of seedlings, vegetation,silvicultural treatments, climate, and sites (Table 2). Seedling characteristicsincluded the number of growing seasons since planting (AGE), stem diameter(D), height (H), crown width (CW), and height-diameter ratio (HDRATIO).Vegetation characteristics included competition indices that combine vegeta-tive cover and height of the vegetation relative to seedling height (CIHERB,CISHRUB, CIHDWD).

Specific silvicultural treatments were not included in the equations becausescalping was only used on two sites and mulching was used both with andwithout shading. Instead, a general term, TRMT, was used to represent thepresence or absence of any of these treatments. Whole plot treatments suchas brushing and grubbing were only accounted for indirectly, through theireffects on vegetation as indicated by the competition indices.

Climate characteristics included average annual precipitation (PREC) anda variable indicating the growing season during which a site was initiallymonitored (YEAR). YEAR was included because sites 1–7 were initiallymonitored during the 1992 growing season, which was 10% warmer and had50% less precipitation than normal, while sites 8–13 were initially moni-tored during the 1993 growing season, which was more normal in terms oftemperature and precipitation. In Medford, Oregon, which is in the vicinityof several of the plots, total precipitation during July and August of 1992(YEAR = 1) and 1993 (YEAR = 0) was 1.5 cm and 4.7 cm, respectively;average maximum daily temperatures were 33.9�C and 29.4�C, respectively(Oregon Climate Service 1994).

Site characteristics included aspect (ASP) and slope angle (SLOPE) intwo interaction terms [cos(ASP)�SLOPE and sin(ASP)�SLOPE]. (Hereafterin this paper, these interaction terms are referred to as the cosine term and

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Table 2. Variables tested as predictors of Douglas-fir and ponderosa pine survival after 1and 3 growing seasons after application of silvicultural maintenance treatments, southwesternOregon, U.S.A.

Category Variable Definition

Seedling AGE Number of growing seasons since planting

H Seedling height (cm)

D Seedling stem diameter at 15 cm above groundline (mm)

CW Seedling crown width from needle tip to needle tip averaged fortwo perpendicular measurements (cm)

HDRATIO Height-diameter ratio [(H/D)�10] (cm/cm)

Vegetation CIHERB Competition index for herbs (%)=% herb cover� (mean height of herbs/seedling height)

CISHRUB Competition index for shrubs (%)=% shrub cover� (mean height of shrubs/seedling height)

CIHDWD Competition index for hardwoods (%)=% hardwood cover�(mean height of hardwoods/seedling height)

Treatment TRMT Presence of silvicultural treatment= 1 if mulch present in fair condition, or mulch and shade cardpresent, or scalp evident; = 0 otherwise

Climate YEAR Year site initially monitored= 1 if 1992 (10% hotter and 50% as much precipitation asnormal); = 0 if 1993 (normal temperature and precipitation)

PREC Average annual precipitation (cm)

Site ASP Slope aspect (radians1)

SLOPE Slope angle (degrees2)

1Radians = degrees�0.017543.2Slope degrees = tan�1(slope%/100).

sine term, respectively.) These terms differ slightly from Stage’s (1976) inthat slope is expressed as an angle in degrees rather than as a percent [slopeangle = tan�1(% slope/100)].

The effects of important predictor variables were demonstrated graph-ically. The fit of equations was assessed by computing the proportion ofvariability explained: [(corrected total sum of squares) – (residual sum of

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Table 3. Observed survival of Douglas-fir and ponderosa pine by age class and yearmonitored, southwestern Oregon, U.S.A.

1-year survival 3-year survival

# of % survival # of % survivalSpecies Age1 trees2 Total Range3 trees2 Total Range

Douglas-fir 0 507 73 13–100 498 49 0–821 455 87 53–99 453 73 25–982 70 99 —4 70 93 —4

3 37 95 94–100 37 89 88–1004 139 96 95–98 139 94 94–955 150 100 —4 150 99 —4

1358 85 71

Ponderosa 0 335 84 26–99 329 57 10–82Pine 1 171 89 87–90 171 86 —4

4 115 97 90–100 115 96 90–98621 88 72

Total 1979

1Number of growing seasons since planting.2Number of trees may be less for 3-year survival, because trees killed in landslide were notincluded.3Among sites.4Either age class only represented by one site or no difference in survival among sites.

squares)]/(corrected total sum of squares). Graphs of observed and predictedsurvival were also prepared for species, survival periods, and treatments.

Results

Observed survival among the sampled Douglas-fir and ponderosa pineseedlings is presented in Table 3. Screening the predictor variables resultedin a generalized model with the following form:

P = 1 /f1 + EXP[�0 + �1�AGE + �2�D + �3�CW +

�4�HDRATIO + �5�CIHERB +�6�CISHRUB +

�7�CIHDWD + �8�TRMT + �9�YEAR + �10�PREC +

�11�cos(ASP)�SLOPE +�12�sin(ASP)�SLOPE]g (2)

where�0–�12 represent the regression coefficients (Table 4). Note that not allof the variables in equation (2) are present in each of four equations developed[i.e., regression coefficients for some variables are 0 (Table 4)].

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Table 4. Estimated regression coefficients and significance levels for predicting periodicsurvival for 0–1 and 1–3 growing seasons after application of silvicultural maintenancetreatments (GSAT) to Douglas-fir and ponderosa pine in southwestern Oregon, U.S.A.

0–1 GSAT survival 1–3 GSAT survival

Variable1 �i Coefficient Prob.> �2 Coefficient Prob.> �2

Douglas-fir

Intercept �0 3.9852 0.0001 2.8001 0.0032AGE �1 –0.6059 0.0001 –0.4131 0.0001D �2 –0.2241 0.0001 –0.2244 0.0001CISHRUB �6 0.0031 0.0075 –0.0190 0.0001TRMT �8 –1.0402 0.0001 0 —YEAR �9 3.0668 0.0001 1.4416 0.0001PREC �10 –0.0641 0.0001 –0.0320 0.0021Cos(ASP)�SLOPE �11 –0.0071 0.3461 0.0234 0.0029Sin(ASP)�SLOPE �12 –0.0449 0.0001 –0.0505 0.0001

Ponderosa pine

Intercept �0 13.4699 0.0020 –2.8940 0.0004AGE �1 –1.0729 0.0001 0.7203 0.0279CW �3 0 — –0.0956 0.0006HDRATIO �4 0.0369 0.0278 0 —CIHERB �5 0.0124 0.0050 0 —CISHRUB �6 0.0063 0.0028 0 —CIHDWD �7 0.0023 0.0202 0 —TRMT �8 –1.0413 0.0052 0 —YEAR �9 5.5590 0.0001 0 —PREC �10 –0.2419 0.0001 0 —Cos(ASP)�SLOPE �11 0.0473 0.0396 –0.0430 0.0632Sin(ASP)�SLOPE �12 –0.1550 0.0001 –0.1465 0.0001

1Variables are defined in Table 2.

Means and ranges for variables in equation (2) are presented in Table 5.Variables with negative coefficients in the equations show a positive rela-tionship with survival and vice versa (Table 4). With few notable exceptionsand when not equal to 0, the coefficients indicate that probability of survivalincreases as AGE, D, CW, and PREC increase and if TRMT = 1; probabilitydecreases as CIHERB, CISHRUB, CIHDWD, and HDRATIO increase andif YEAR = 1 (more severe growing season). Exceptions are CISHRUB inthe model for 1–3 GSAT survival of Douglas-fir, which indicates increasingprobability of survival, and AGE in the model for 1–3 GSAT survival ofponderosa pine, which indicates decreasing probability of survival.

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Table 5. Summary of variables in survival equations for Douglas-fir and ponderosapine 1 and 3 growing seasons after application of silvicultural maintenance treatments,southwestern Oregon, U.S.A.

Douglas-fir Ponderosa pine

Variable1 (units) Mean Range Mean Range

AGE (years) 1.5 0.0–5.0 1.0 0.0–4.0D (mm) 7.4 1.7–38.0 9.8 2.0–41.0Height2 (cm) 47.4 9.0–179.0 34.4 4.0–144.0CW (cm) — — 27.0 3.0–138.0HDRATIO (mm/mm) — — 34.7 7.5–76.4CIHERB — — 26.8 0.0–219.3Herb cover2 (%) — — 18.1 0.0–100.0Herb height2 (cm) — — 26.6 0.0–110.2CISHRUB 44.9 0.0–926.8 75.2 0.0–1862.5Shrub cover2 (%) 26.1 0.0–153.8 22.2 0.0–103.8Shrub height2 (cm) 46.5 0.0–183.8 41.8 0.0–277.5CIHDWD — — 41.4 0.0–1526.8Hdwd cover2 (%) — — 4.6 0.0–77.5Hdwd height2 (cm) — — 50.8 0.0–346.7TRMT 0.3 0.0–1.0 0.4 0.0–1.0YEAR 0.5 0.0–1.0 0.6 0.0–1.0PREC (mm) 93.0 76.2–114.3 86.2 76.2–101.6ASP (degrees) n/a 20.0–345.0 n/a 90.0–285.0SLOPE (degrees) 23.2 5.0–35.0 24.4 20.0–35.0

1Variables are defined in Table 2.2Used in calculating CIHERB, CISHRUB, CIHDWD, or HDRATIO.

Because they work in combination, the cosine and sine terms were includedin all four equations even though the cosine term was not significant in the 0–1GSAT Douglas-fir model or in the 1–3 GSAT ponderosa pine model (Table 4).Maximum 0–1 GSAT and 1–3 GSAT survival probabilities for Douglas-firoccurred at eastern aspects of 81� and 115�, respectively. Maximum 0–1GSAT and 1–3 GSAT survival probabilities for ponderosa pine occurred ateastern aspects of 107� and 74�, respectively. The effect of slope in thecosine and sine terms is to increase survival probability as slope increaseson favorable aspects, while decreasing survival probability as slope increaseson unfavorable aspects. When an additional slope term was introduced asStage (1976) suggested, slope effects were inconsistent among the models;therefore, this additional term was not included. Also, when percent slope wasused in the interaction terms instead of slope angle, aspects with maximumsurvival probabilities varied from 11� to 253�; therefore, slope angle wasused because it provided more consistent results.

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All of the seedling variables (AGE, D, H, CW) except HDRATIO werehighly correlated (r > 0.73); therefore, the absence of some in the final modelsdoes not necessarily indicate that they are poor predictors of survival. Rather,it implies that they do not contribute significant statistical information in thepresence of other variables included in the models. Also, multicollinearitymay exist between some of the other variables screened, in part due to oursampling design. For example, 8 of 13 sites were located between 135� and225� aspect. Shrubs were the dominant vegetation on steep north- or south-facing slopes, while herbs were the dominant vegetation on sites with aspectsranging from 200� to 270�. Also, shrubs tended to exclude herbs, resulting ina negative correlation, but were positively associated with increasing hard-wood cover. Though these relationships do not preclude the development ofpredictive models, they do restrict our ability to draw inferences regardingrelative importance from the significance of variables in the models.

The 0–1 and 1–3 GSAT survival models for Douglas-fir are virtually thesame; differences include the magnitude of the coefficients, the absence ofTRMT in the 1–3 GSAT model, and a slight difference in the optimum aspect.TRMT may be absent in the 1–3 GSAT Douglas-fir model because the directeffects are temporary. CISHRUB may have detrimental effects at young agesdue to increased soil moisture depletion, but then have beneficial effects inlater years due to root collar shading without the negative impacts of thinningshock following release treatments.

The two ponderosa pine models differ to a greater extent. This may reflectdifferent factors involved in immediate and longer term survival, althoughthis is not well supported by the similar Douglas-fir models. For instance, thevariable YEAR indicates the severity of the weather during the first growingseason after treatment, but neither YEAR nor PREC was included in the 1–3GSAT model. Also, treatments such as mulches break down over time, andtheir initial effectiveness may be short-lived. HDRATIO was included in the0–1 GSAT model for ponderosa pine as a negative effect on survival, but wasreplaced by CW in the 1–3 GSAT model as a positive effect that appears tocompensate for the negative effects of AGE. Competition indices for herbs,shrubs, and hardwoods (CIHERB, CISHRUB, CIHDWD) were included asnegative effects on survival in the 0–1 GSAT model, but were not included inthe 1–3 GSAT model. In addition, unexpected events such as animal damagecan affect the ability to predict survival. For instance, at least 31 of the 78living ponderosa pines on site 9 were partially or completely girdled priorto the second growing season. This animal damage killed at least 17 treesand accounted for at least 17% of the ponderosa pine mortality followingthe first growing season. A term for girdling was screened in the 1–3 GSATponderosa pine model to account for this source of mortality, but it was not

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Figure 1. Predicted versus observed proportion survival for (A) Douglas-fir and (B) ponderosapine for 0–1, 1–3, and 0–3 growing seasons after treatment, southwestern Oregon, U.S.A. Eachdata point represents survival for treated and untreated plots on one site. A reference line withslope = 1 is also presented.

significant. Perhaps this unexplained variation affected the significance ofother variables.

Fit of equations

Survival predicted by the four equations is reasonably well correlated withobserved survival, but there is variability for which the equations do not

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account. The models for Douglas-fir accounted for 77.9%, 59.3%, and 63.9%of the variation in survival for the 0–1 GSAT, 1–3 GSAT, and 0–3 GSATperiods, respectively. For ponderosa pine, the models accounted for 80.0%,84.1%, and 76.8% of the variation in survival during the 0–1 GSAT, 1–3 GSAT, and 0–3 GSAT periods, respectively (Figure 1). Several factorsthat likely influence survival but were not included, such as physiologicalcondition of the trees, condition of roots, soil properties, and unpredictableevents such as animal damage, were not considered as predictor variables.The higher proportion of explained variation for 0–1 GSAT survival forDouglas-fir probably resulted from having most observations at the extremes(observed percent survival< 30% or> 80%), whereas observed 1–3 GSATsurvival included more intermediate values (Figure 1). For ponderosa pine,survival during the 1–3 GSAT period was greater than during the 0–1 GSATperiod.

Model predictions

Examples in this section illustrate the model predictions under different sets ofconditions. For instance, the 0–1 GSAT Douglas-fir equation predicts that theconditions during the initial growing season strongly influence probabilitiesof survival. During a hot, dry growing season (YEAR = 1) under set conditions(ASP = 180�; SLOPE = 25�; PREC = 93 cm; and CISHRUB = 44.9), Douglas-fir at AGE = 0 (i.e., newly planted) would have probabilities of survival as lowas 0.28, whereas Douglas-fir at AGE = 4 would have probabilities of survivalof 0.89 or higher; probabilities generally increase with seedling diameter(D) (Figure 2A). Under the same set of conditions during a relatively cool,wet growing season (YEAR = 0), in contrast, all ages would have predictedprobabilities of survival of 0.89 or higher (Figure 2B).

The 0–1 GSAT Douglas-fir equation also predicts that treatment and ageaffect survival probability. For Douglas-fir at AGE = 0 and AGE = 1, with andwithout treatment (TRMT), higher probabilities of survival are predicted withtreatment and older AGE; probablities increase with D (Figure 3). TRMT alsohas a greater predicted effect on newly planted Douglas-fir during a severegrowing season (YEAR = 1) (Figure 4). Once probabilities of survival aredetermined for each tree in a sample, they can be added together, divided bythe sample size, and multiplied by 100 to determine the percentage of treeslikely to survive. YEAR and CISHRUB interact to produce dramaticallydifferent survival trends. During cool and moist years (Figure 5A), such asobserved during 1993–1995, the relationship between survival and coverwas concave downward, with decreasing survival as shrub height increased.During hot and dry years, such as the 1992 growing season, the relationshipbetween survival and cover was approximately negative exponential and the

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Figure 2. Predicted probabilities of survival for Douglas-fir 1 growing season after treatmentfor AGE = 0, AGE = 2, and AGE = 4 with (A) YEAR = 1 (hot, dry) and (B) YEAR = 0 (cool,moist) (CISHRUB = 44.9; SLOPE = 25�; ASP = 180�; PREC = 93 cm). Variables are definedin Table 2.

impacts of shrub height were more pronounced. Similar relationships wereobserved for ponderosa pine.

Discussion and conclusions

Equations for predicting Douglas-fir and ponderosa pine survival were devel-oped successfully with the logistic model form, and they incorporated severalfactors found by other researchers to be related to survival. This quantitative

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Figure 3. Predicted probabilities of survival for Douglas-fir 1 growing season after treatmentfor AGE = 0 and AGE = 1, with and without TRMT (YEAR = 1; CISHRUB = 44.9; SLOPE= 25�; ASP = 180�; PREC = 93 cm). Variables are defined in Table 2.

Figure 4. Predicted probabilities of survival for Douglas-fir 1 growing season after treatmentfor YEAR = 0 (cool, moist) and YEAR = 1 (hot, dry), with and without TRMT (AGE = 0;CISHRUB = 44.9; SLOPE = 25�; ASP = 180�; PREC = 93 cm). Variables are defined inTable 2.

approach for predicting survival integrated many factors that may influencesurvival, including silvicultural treatments, the age and size of trees, the size

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Figure 5. Predicted probabilities of survival for newly planted Douglas-fir 1 growing seasonafter treatment with varying cover and relative height of shrubs for (A) YEAR = 0 and (B)YEAR = 1 (Age = 0; D = 4.4; TRMT = O; slope = 25�; aspect = 180�; and PREC = 89).Variables are defined in Table 2.

and cover of vegetation, and site and weather conditions. The signs of coef-ficients for all variables were consistent with the results of previous studies(Bradley 1962, Hermann 1964, 1965, Cleary 1971, Zaerr and Lavender 1976,South and Mexal 1984, Tesch and Hobbs 1985, Wagner and others 1989,Helgerson 1990, Shiver and others 1990) and field observations.

As in previous studies, seedling size and quality variables were importantpredictors of survival on dry sites in southwestern Oregon. Plantation age

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reflected the length of time seedlings had been successful in the plantingenvironment. Less vigorous seedlings and those planted on poor micrositesmay have died earlier, and additional seedlings may die over several yearsfollowing an unfavorable planting season (Helgerson et al. 1992). In our study,age was important for both Douglas-fir and ponderosa pine survival duringthe 0–1 and 1–3 GSAT periods. However, age was associated with reducedsurvival of ponderosa pine during the 1–3 GSAT period. Stem diameter, acommon measure of seedling quality (Shiver and others 1990, Rose and others1991), was associated with increased survival only for Douglas-fir during the0–1 and 1–3 GSAT periods. Height-diameter ratio, an expression of vigor(Cole and Newton 1987), was associated with reduced survival of ponderosapine during the 0–1 GSAT; crown width, which was positively correlated withstem diameter, was associated with increased survival during the 1–3 GSATperiod and appeared to compensate for the negative effects of plantation age.

We observed negative effects of the shrub competition index on survivalof Douglas-fir and negative effects of the herb, shrub, and hardwood com-petition indices on survival of ponderosa pine during the 0–1 GSAT period.However, during the 1–3 GSAT period, the shrub competition index waspositively associated with survival of Douglas-fir and no vegetation variableswere related to ponderosa pine survival. This 1-year lag in apparent facilita-tion may be similar to the response observed in young stand growth and yieldmodels (Knowe and others 1992, Knowe 1994), or it may be a manifestationof thinning shock (Harrington and Reukema 1983). Both negative and pos-itive effects of vegetation abundance have been previously reported. Teschand Hobbs (1989) reported the lack of significant differences in Douglas-firsurvival among shrub control treatments on a dry, west-facing site in south-western Oregon. They attributed low sensitivity to competition of survival tothe combined effects of good stomatal control and lower soil temperaturesproduced by shading. Similarly, Wagner and others (1989) reported that heightand diameter growth were reduced at much lower levels of competitive stressthan was survival. Mitchell and others (1993) reported a positive relationshipbetween surface soil moisture and hardwood and grass density throughoutthe growing season. Positive effects on soil moisture due to decreased tem-perature under plant canopies may be more likely to occur during warm, dryyears (Goldberg 1990). However, any positive effects of vegetation on soilmoisture would be short-lived because surface soil moisture was depleted ata greater rate as the density of vegetation increased.

Silvicultural maintenance treatments such as mulching, artificial shad-ing, and radial scalping were associated with increased surivival of bothDouglas-fir and ponderosa pine only during the 0–1 GSAT period. This resultis consistent with the report that maintenance treatments had no effect on

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survival during mild growing seasons (Flint and Childs 1987), such as dur-ing 1993–1995. The temporary response to treatments may be the result ofrapid resprouting of shrubs (Tesch and Hobbs 1989) or exposure followingrelease from overtopping shrubs (Harrington and Reukema 1983, Donner andRunning 1986).

Climatic variables were consistently related to survival of Douglas-fir andponderosa pine in both survival periods. Precipitation was associated withincreased survival, probably due to maintaining adequate soil moisture intolate summer (Cleary 1971). Year of initial measurement was associated withdecreased survival during the 1992 growing season, which was 10% warmerand 50% drier than the subsequent “average” growing seasons. Cleary (1971)reported poor ponderosa pine survival during a severe growing season, but100% survival during the next growing season.

Because incident solar radiation was greatest on south-facing slopes andwest-facing slopes received maximum radiation during the heat of the after-noon, northeast-facing slopes were generally considered coolest and moistestand, therefore, might be most favorable for survival. Stage and Boyd (1987)categorized sites into north- and south-facing aspects and found probabilityof stocking to be greatest on northern exposures. However, eastern aspectscombined cooler conditions with more sunlight to promote growth, so it isreasonable that they were optimum for survival in our study.

The equations appeared to fit the data reasonably well, as indicated by59%–78% explained variation in observed stand-level survival for Douglas-fir and 80%–84% explained variation in observed stand-level survival forponderosa pine. Hitch and others (1996) reported 19%–48% explained vari-ation in observed stand-level survival obtained by using a similar logisticregression approach. How well our models predict survival on other sitesdepends on how closely those site and weather conditions resemble the con-ditions during this study. A more balanced and comprehensive samplingdesign would have included sites with many more combinations of ages andsite conditions (e.g., aspect, precipitation, and vegetation cover) that weremonitored during both mild and harsh growing seasons, but that was not fea-sible in this study. Ultimately, survival predictions from equations like thosepresented in this paper could be used by silviculturists to evaluate the likelysuccess of regeneration and the usefulness of applying silvicultural treatmentsto maintain desired stocking levels.

Acknowledgement

The authors wish to thank the USDI Bureau of Land Management for assis-tance with and financial support for this project.

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