Sugar maple (Acer saccharum Marsh.) decline during 1979–1989 in northern Pennsylvania

Forest Ecology and Management 170 (2002) 1±17

Sugar maple (Acer saccharum Marsh.) decline during 1979±1989 in northern Pennsylvania P.J. Drohana,*, S.L. Stoutb, G.W. Petersenc a

Institute for Environmental Studies, Shepherdstown, WV 25443, USA USDA Forest Service Northeastern Research Station, Irvine, PA, USA c Department of Crop and Soil Sciences, Pennsylvania State University, University Park, PA 16802, USA b

Received 9 April 2001; accepted 14 July 2001

Abstract Sugar maple decline has been observed in northern Pennsylvania since the early 1980s. We investigated the interactions between soil moisture stresses in sugar maple and other factors, such as soil chemistry, insect defoliation, geology, aspect, slope, topography, and atmospheric deposition. In the summer of 1998, we sampled 28 sugar maple (Acer saccharum Marsh.) plots drawn from the USFS Forest Inventory and Analysis (FIA) plots, containing declining and non-declining sugar maple trees across northern Pennsylvania for a variety of soil physical and chemical parameters, site characteristics, and tree health. Foliage from declining plots was found to have signi®cantly lower base cations and higher Mn as compared to that from nondeclining plots. Soils in declining plots had lower base cations and pH, a Ca:Al  1, lower percent clay and higher percent sand and rock fragments than soils on non-declining plots, suggesting that trees on declining plots are at risk of nutritional and drought stress. Regression relationships between foliar and soil chemistry indicated that foliar nutrition was highly correlated with soil chemistry in the upper 50 cm of the soil. Declining sugar maple plots in this study occurred at higher elevations on sandstone dominated geologies. Soils were found to be base poor-sandy soils that contained high percentages of rock fragments. Soils below 50 cm on declining plots had lower soil pH and foliar chemistry indicated lower foliar base cations. A trend, while not signi®cant was found with declining plots experiencing a greater number of and more severe insect defoliations. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Sugar maple; Forest decline; Soil chemistry; Atmospheric deposition; Defoliation

1. Introduction Over the last 20 years in northern Pennsylvania, sugar maple populations have experienced severe dieback and mortality, suggesting a forest species decline (Drohan et al., 1999; Horsley et al., 2000; Kolb and McCormick, 1993; McWilliams et al., 1996). Sugar maple decline in Pennsylvania appears most severe in the northwestern and north central unglaciated portions * Corresponding author. E-mail address: [email protected] (P.J. Drohan).

of the state (Horsley et al., 2000). McNab and Avers (1994) describe these ecoregions as the Northern Unglaciated Allegheny Plateau Section (212G) and Northern Glaciated Allegheny Plateau Section (212F) of the Laurentian Mixed Forest Province. The hypothesized causes of the decline are extensive and include: insect defoliation, disease, drought, root freezing with shallow snow packs, atmospheric deposition contributing to soil acidi®cation, speci®c site characteristics, and historic forest management (Houston, 1999). Recently, Horsley et al. (2000) linked Pennsylvania's decline to stands on unglaciated soils with low

0378-1127/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 1 1 2 7 ( 0 1 ) 0 0 6 8 8 - 0

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P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

foliar Mg and high foliar Mn chemistry, and greater than threshold amounts of insect defoliation. Horsley et al. (2000) studied 43 sugar maple stands at 19 locations, and classi®ed six as declining. Drohan et al. (1999) used 1979±1989 data from the FIA database in the Pennsylvania portion of these ecoregions and classi®ed 25 of 248 plots as declining. Several other authors suggested that soil and foliar chemistry are important variables in sugar maple decline in Pennsylvania (Kolb and McCormick, 1993; Long et al., 1997; Sharpe et al., 1999). Soils in declining stands often are characterized by low base cations, especially Ca and Mg (Kolb and McCormick, 1993; Long et al., 1997; Sharpe et al., 1999). Heisey (1995) showed that low soil Ca limited sugar maple growth in north central Pennsylvania and southern New York and that soil Al may have inhibited Ca uptake in trees. Foliar chemistry in declining stands seems to parallel soil chemistry. Pennsylvania sugar maple foliage in declining stands is often characterized by a low base cation content, Ca:Al ratio, and high Mn (Kolb and McCormick, 1993; Long et al., 1997; Sharpe et al., 1999). Several Canadian studies have showed similar relationships among soil and foliar chemistry and decline: Cote and Camire (1995) in southern Quebec, Ouimet and Camire (1995) in the Quebec Appalachians, Bernier and Brazeau (1988b) in the Lower Laurentians, and Mohamed et al. (1997) in Ontario. In addition, declining stands often experience repeated stresses, such as drought and insect defoliation, along with poor soil chemistry (Horsley et al., 2000; Kolb and McCormick, 1993; Long et al., 1997). Kolb and McCormick (1993) noted that insect defoliation was linked to growth constraints and eventual tree decline in sugar maple in north central Pennsylvania in the 1970s and 1980s. Extensive insect defoliation has occurred in Pennsylvania during the last 20 years and has involved both exotic and native insect species (Stout et al., 1995). Sugar maple in the region of our study have been defoliated by pear thrips, elm spanworm, and forest tent caterpillar one to many times between 1980 and the present (Stout et al., 1995, Rhoads, 1993). Horsley et al. (2000) showed that decline developed on plots that had experienced two or more moderate to severe defoliations within a decade. Skilling (1964) showed that arti®cial defoliation of sugar maple on sites with no nutrient limitations and desirable soil texture induced symptoms

similar to those observed in `natural' declines. Induced drought exacerbated the symptoms (Skilling, 1964). Extreme weather events in Pennsylvania have also occurred over this period. Several droughts occurred in northern Pennsylvania during the 1960s and in 1971 (Kolb and McCormick, 1993). In the area of our study, the 1980s brought warmer than usual temperatures (Kolb and McCormick, 1993) with infrequent and shallow snow packs that could have left tree roots susceptible to freezing. In May 1992, a late spring frost in the same area damaged buds. Sharpe et al. (1999) suggested atmospheric deposition as another potential cause for sugar maple decline. Pennsylvania, especially north western and north central, receives some of the highest deposition loadings in the United States (Lynch et al., 1995). Likens et al. (1994, 1996, 1998) showed that soil acidi®cation due to acidic deposition inputs could cause accelerated leaching of base cations from forest soils; the resulting low soil base cation status may be a cause of sugar maple decline (Kolb and McCormick, 1993; Long et al., 1997; Sharpe et al., 1999). Levine and Ciolkosz (1988), who classi®ed the sensitivity of Pennsylvania soils to acid deposition (non, slightly, and very sensitive), estimated that 37% of the state's soil series are slightly or very sensitive to acidic atmospheric deposition. Finally, a variety of site factors in¯uence the relationships between soil chemistry and tree health. These include site geology, hydrologic ¯ow paths, rockiness, particle size distribution, weathering rates, and landscape position (Marschner, 1995; Bailey et al., 1999). 2. Research questions and hypotheses This paper reports results from a larger study that explored the utility of using geographic information systems (GIS) and existing databases to explore landscape relationships between sugar maple decline and hypothesized causal factors. Using a GIS (Drohan et al., 1999; Drohan, 2000) sugar maple decline was detected in the USFS Forest Inventory and Analysis (FIA) database for our study area for the inventory period 1979±1989 (Hansen et al., 1992). We found no differences in the distributions of slope and aspect between declining and non-declining plots across the study region as a whole or within either the glaciated

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

or unglaciated subregion. In the unglaciated region and the study area as a whole, we found small but signi®cant differences between declining and non-declining plots with respect to mean annual deposition of ammonium, calcium, hydrogen, nitrate, and sulfate, but not magnesium. For the portion of the study reported here, we used the health clusters established through K-means cluster analysis and a GIS as the basis for selecting plots during the summer of 1998 for an intensive on-plot study of potential causal factors. These clusters consisted of declining plots with 26% dead sugar maple basal area and non declining plots with <26% dead sugar maple basal area (Drohan et al., 1999). The objectives of the intensive plot work presented in this paper were to investigate the interactions between factors such as soil and foliar chemistry, soil moisture stresses, insect defoliation, geology, aspect, slope, topography, and atmospheric deposition in declining and non-declining sugar maple plots. We wanted to determine whether relationships that were suggested by the GIS approach (Drohan et al., 1999) would be veri®ed with intensive measurements, and whether intensive site measurements would show new relationships. We were particularly interested in the potential role of soil moisture status as a mediator for nutritional stress in sugar maple. We were also interested in whether a study of sugar maple decline with plots selected on the basis of conditions measured in 1989 on plots in a systematic monitoring system would con®rm the relationships reported (Kolb and McCormick, 1993; Long et al., 1997; Sharpe et al., 1999; Horsley et al., 2000) with plots selected speci®cally to study sugar maple decline. In particular, we hypothesized that sites in Pennsylvania experiencing sugar maple decline have characteristics that can exacerbate soil moisture de®cits (high sand and/or rock fragments, S to SW aspects, topographic positions that do not allow hydrologic ¯ow paths to contribute to the site's nutrient or moisture capital or higher elevation sites) and may have low base cation status. We measured potential for soil moisture stress through a series of surrogate variables. These were soil particle size and percent rock fragments; site slope, aspect, topography, geology and elevation; and drought. In addition, we examined soil and foliar chemistry, number and severity of insect defoliations, number of droughts, and atmospheric deposition.

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3. Methods 3.1. Study sites The study area (Fig. 1) is the Pennsylvania portion of the Northern Unglaciated Allegheny Plateau Section (212G) and Northern Glaciated Allegheny Plateau Section (212F) of the Laurentian Mixed Forest Province (McNab and Avers, 1994). The area is characterized by V-shaped valleys that are narrow and winding with relief of 150 to >300 m (Hough and Forbes, 1943). The geology of the region is varied, with areas that are glaciated and unglaciated and derived from Devonian-, Mississippian-, and Pennsylvanian-aged rocks. Soils in unglaciated areas are derived from sandstones, siltstones, and shales and have various ages and degrees of weathering; soils typically consist of Inceptisols, and Ultisols derived from materials that have undergone periglacial erosion and cryoturbation. Truncated eroded materials are commonly found in lower slope positions. Truncation has resulted in a mantle of colluvium of varied thickness over steep and slightly sloping areas of the unglaciated region (Ciolkosz et al., 1999; Mader and Ciolkosz, 1997; Waltman et al., 1990). Glaciated areas are covered with tills of the Wisconsan, Illinoian, and perhaps earlier glaciations. Soils typically are Inceptisols (Ciolkosz et al., 1999). Soil temperature regimes in the glaciated and unglaciated areas are dominantly frigid cool phase mesic and the soil moisture regimes are dominantly udic or perudic (Waltman et al., 1997). To select study plots from the FIA database, we conducted a K-means cluster analysis (Minitab Inc., 1999) based on 248 plots that had total basal area of at least 9.2 m2 ha 1 (representing continuous forest cover) and a sugar maple basal are of at least 2.3 m2 ha 1 (to ensure enough sugar maple for analysis) and found two clusters (Drohan et al., 1999). The cluster of declining plots had 25 members, all with at least 27% dead sugar maple basal area (PDSMBA) and a median PDSMBA of 33%. The non-declining plots all had 26% dead sugar maple basal area with a median of zero PDSMBA (Drohan et al., 1999). From these 248 plots, 60 were chosen for sampling in the summer of 1998. About 32 plots were eliminated due to harvesting, road-construction, excessive ¯ooding or landslides leaving 28 plots (19 non-declining and 9 declining) (Fig. 1) that were ®eld sampled.

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P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

Fig. 1. The map shows the 28 sample plots locations ((*) declining; (~) non-declining) in northern Pennsylvania, political county boundaries (Ð) and ecoregional boundaries ( ).

3.2. Plot characteristics Plots were visited during July and the ®rst 2 weeks of August 1998. Data recorded on each plot included percent slope (%slope), aspect and topographic position. Aspect was converted for statistical analysis using the Beers transformation with a weighting on the 458 aspect (converted aspect ˆ cos…Amax A† ‡ 1, where Amax is the aspect assigned the highest value or weight) (Beers et al., 1966). Due to FIA requirements protecting precise plot locations (Hansen et al., 1992), the 28 plots sampled could only be located using 7.5 min, 1:24,000 USGS topographic quadrangles. Locations were determined and entered into a GIS. From the USGS 7.5 min quadrangle maps and ®eld observations, topographic position (ridge-top, nose-slope, side-slope, foot-slope, and toe-slope) (Boul et al., 1989) was determined. Because of insuf®cient sample size in some topographic position classes, the ridge-top and nose-slope positions were grouped into a up-slope class and the foot-slope and toe-slope positions were grouped into a bottom-slope class. Elevation was obtained from USGS 1:24,000 digital terrain models and crosschecked with the USGS 7.5 min quadrangles. Plot geology was determined by overlaying plot locations in a GIS with a surface geology map of

Pennsylvania. This map is a statewide composite of county sur®cial geologic contacts (1:250,000) (Berg, 1980). Plot formation and dominant formation lithology (sandstone or shale) were determined from map data tables. Whether a plot had been glaciated was determined from ®eld data and from Bailey's Ecoregion map (Bailey et al., 1994). 3.3. Soil sampling On each plot, four excavations (satellites) approximately 50 cm deep were made to characterize the soil variability on the plot. These excavations were made at approximately 35±43 m from plot center. From the four 50 cm excavations, the excavation most representative of the plot was chosen and was further dug to a depth of 120 cm, where possible (the pit). The most representative excavation was considered to be the 50 cm hole that was the modal hole for a speci®c plot based on horizon ®eld texture, structure, color, consistence, and rock fragments. Therefore, on each plot, there were three holes 50 cm deep and one that went to 120 cm where possible. Only one hole per plot was sampled to 120 cm due to several studies that show a decrease in variability in soil characteristics with soil depth (Harradine, 1949; Petersen and Calvin, 1986; Swistock et al., 1990).

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

A brief morphological description was made on the three satellite holes and a more detailed description was made on the pit. Standards of the Natural Resources Conservation Service (NRCS) were followed in describing and sampling the soils (USDA, 1993). The percentage rock fragments (%rock) was determined in situ via a visual cross section of the horizon faces using pattern diagrams and linear transects (USDA, 1993). Soils were sampled for laboratory analysis by horizon, and kept refrigerated (4 8C) until analysis. Soils were then air-dried, passed through a 2 mm sieve, and stored at 4 8C until all chemical analyses were completed. Soils were analyzed for pH and exchangeable Ca, Mg, K, Na, Al, Mn, and Fe. The effective cation exchange capacity (ECEC), percent base saturation (%BS), and particle size were also determined. Soil chemical analysis was conducted on each mineral and Oa horizon. Particle size analysis was done by the pipette method (mineral horizons only) (Gee and Bauder, 1986). Soil pH was determined using a Fisher Scienti®c Accumet 1002 pH meter in a 0.01 M CaCl2 solution (1:2 mineral; 1:10 Oa) (Blume et al., 1990). Exchangeable cations (Ca, Mg, K, Na, Al, Mn, and Fe) were determined in a 6 h 1 M NH4Cl extraction (Blume et al., 1990), and the analysis was done on an Instrumentation Laboratory Video 22 AA/AE spectrophotometer. Aluminum in the ECEC measure was extracted with a 1 M KCl solution (Robarge and Fernandez, 1987), and the analysis was done by AA/AE Spectrophotometer. Exchangeable acidity was estimated using a 1 M KCl extraction that was titrated with a 0.02 M NaOH solution to a pH 7 endpoint (Robarge and Fernandez, 1987). Percent base saturation (%BS) was the sum of the base cations divided by the ECEC (Ca, Mg, K, Na, Al,), multiplied by 100. Along with laboratory standards, 10%of the samples in every analysis were duplicated and differences compared for quality assurance and control. 3.4. Foliage sampling During the last 12 days of August 1998, ®ve healthy dominant or codominant trees within each nondeclining and declining plot were chosen for foliage sampling (Horsley et al., 2000). Health on declining plots was determined by using North American Maple Project ratings protocol. Trees not exhibiting decline

5

on declining plots were sampled instead of declining trees to minimize foliar chemistry variability and to derive a base line foliar chemistry signature from the healthiest of possible trees on the plot. Sugar maple branches were shot from mid-outer canopy positions with a shotgun. Twenty-®ve leaves per tree were collected, and ®ve leaves per tree were randomly chosen for analysis. The ®ve leaves per tree were dried to a constant mass at 65 8C and the mean leaf mass for each ®ve leaf sample was determined before analysis. The ®ve leaves per tree were ground in a Spex Mill and then analyzed as a composite. A 20% HCl digestion was performed (AOAC, 1990) and foliage samples were analyzed for: P, K, Ca, Mg, Mn, Fe, B, Al, Zn, and Na (all ug g 1) using atomic adsorption spectrophotometry. 3.5. Plot disturbance: insect defoliation, atmospheric deposition, and drought From over-¯ights conducted within each state forest district, digital insect defoliation data were obtained from the Pennsylvania Bureau of Forestry, Division of Pest Management in Harrisburg, Pennsylvania through Mr. John Quimby. An annual survey of defoliation within Pennsylvania has been conducted since 1969. Digital defoliation data for the Allegheny National Forest were obtained from Mr. John Omer of the USFS in Morgantown, West Virginia. The defoliation database de®nes defoliation using three categories of intensity: heavily defoliated, >60% of trees affected ˆ 3; moderately defoliated, 31±60% of trees affected ˆ 2; lightly defoliated, 6±30% of trees affected ˆ 1, and none ˆ 0±5%. Using a GIS, defoliation history for each plot was determined for the FIA plot period and for 1969±1989. The number of times a plot was defoliated over the plot period (NDE10) and 1969±1989 (NDE20), as well as the sum of the severity indices over the plot period (DEI10), and 1969±1989 (DEI20) was used in analysis. Wet deposition data (NO3, NH4, SO4, H, Ca, and Mg (kg ha 1 per year)) were based on a spatially explicit model for Pennsylvania (Lynch et al., 1995). Deposition for the state is modeled using a regression routine with the variables precipitation, slope, aspect, elevation, and latitude and longitude (Grimm and Lynch, 1997). These data were obtained in digital format for use in a GIS for the period 1987±1997;

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P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

data could not be acquired for sample plots for the 1979±1989 measurement period. To approximate missing years, the average 11-year deposition value was multiplied by the number of missing years to derive a total loading value for those missing years. Therefore, data used in analysis were a summed total loading for the 18-year period, 1979±1997. Monthly Palmer drought severity index (PDSI) data were used to determine the effect of drought during the period of the study. For each year (1979±1989), an average June±July index value was used as an indicator of a drought year. Each plot's Palmer drought index region was ®rst determined and then a plot value based on the Palmer region value was derived by summing the number of years with an average June±July PDSI value  1. A similar method has been used before by Kolb and McCormick (1993). In their study, the months of June and July are believed to be the best months for effectively relating climatic drought to plot health because of the occurrence of the drought during the main period of photosynthetic activity in the yearly life cycle of the tree (Thomas Kolb, personal communication). 3.6. Statistical analysis Within a plot, three soil depth categories based on mean horizon values were evaluated: Oa horizons, mineral soil horizons  50 cm, and mineral soil horizons  50 cm. To derive a plot mean for the Oa horizon, the mean of three satellites and pit Oa horizon was used. Mineral horizons were differentiated into two categories, horizons  50 cm and horizons > 50 cm. The 50 cm depth was chosen based on the literature-reported sugar maple feeder root depth of 40±50 cm (Biswell, 1935; Harlow and Harrar, 1950; Jarvis, 1956; Stout, 1956; Fayle, 1965; Bauce and Allen, 1991). To derive a plot mean for the 50 cm depth class, the average of each holes horizons  50 cm was taken and then the average of the four holes in the plot in this depth category (three satellites and pit) was taken. In 11 of the pits, the horizon found at 50 cm was shallower or deeper than 50 cm. In this case, the horizon was left in the depth category is was more dominantly occurring in. To discern if this would bias analysis, the horizons in question were ¯ipped into the opposite depth classÐno statistical difference was found in doing so, so the former procedure was

used. For the >50 cm category, only the pit was used. Foliar chemistry means for a plot were derived by taking the mean of the ®ve trees per plot to generate an average value for the plot. All data (soil physical and soil and foliar chemical) were screened for normality and homogeneity of variance and data not meeting assumptions of normality underwent log±normal transformations (log10). %Rock fragments could not be adequately transformed using any transformation, so the nonparametric Mann±Whitney test was used (Minitab Inc., 1999). A categorical health variable (declining and non-declining) derived from the K-means cluster analysis and the percent dead sugar maple basal area (PDSMBA) (Drohan et al., 1999) was used to examine relationships between sugar maple health and other variables. Pearson correlations were conducted between all continuous variables and PDSMBA. PDSMBA was also used as a dependent variable in analysis of variance (Tukey Multiple comparisons) (Minitab Inc., 1999) with topographic position and aspect. Soil physical properties and soil and foliar chemistry variables were used in analysis of variance (Tukey Multiple comparisons) with topographic position. Regression analysis was used to examine relationships between PDSMBA and foliar, soil, and atmospheric deposition variables. Regression analysis was also used to examine relationships between foliar and soil chemistry. Univariate t-tests were used to examine differences between declining and nondeclining plots with regard to soil physical and chemical properties, foliar chemical properties, atmospheric deposition and drought index data. Univariate t-tests were also used to examine differences between plots with different dominant lithologies and between plots in different glacial history classes. An alpha of 0.05 was used to indicate signi®cance, but P  0:10 were also noted for further investigation. Results are presented using untransformed data. 4. Results 4.1. Soil chemistry, foliar chemistry, and physical properties by sugar maple decline class Oa horizons in non-declining plots had a higher pH (Table 1). The 50 cm depth soils had signi®cantly

Table 1 Untransformed mean foliar chemistry; soil chemistry and physical properties by health classa n

%BS

%Rocks %Clay

%Sand

Oa horizons Declining Non-declining P-value

9 19

54 60 0.520

36 21 0.140

Horizons  50 cmb Declining Non-declining P-value

9 19

10 23 0.037

38 29 0.028

Horizons > 50 cmb Declining Non-declining P-value

9 19

16 27 0.730

49 55 0.600

n

B

Cu

Fe

9 19

57.1 54 0.550

7.48 19.2 0.008

116.3 31.0 121.1 33.0 0.980 0.610

%Silt

Al

Ca

Ca:Al

ECEC

K

Mg

Mg:Mn

Mn

pH

b

Foliarc Declining Non-declining P-value a

10 6.2 0.240

9.3 6.7 0.680

2.0 20.5 4.0 12.7 0.650 0.330

1 0.51 0.360

1.6 1 0.800

0.5 0.7 0.590

4.4 1.8 0.280

3.1 3.4 0.023

18 37 21 31 0.050 0.016

50 5.6 54 5.5 0.037 0.880

0.53 1.63 0.016

0.1 7.8 0.031

6.2 7.4 0.039

0.11 0.17 0.013

0.08 0.34 0.007

3 4.7 0.037

0.32 0.29 0.810

3.7 3.8 0.010

19 39 19 37 0.820 0.650

42 4.5 43 3.5 0.710 0.045

0.56 1.2 0.960

0.2 0.9 0.058

5.6 5.5 0.074

0.09 0.11 0.960

0.23 0.38 0.900

21.1 16.9 0.530

0.21 0.07 0.490

3.8 4.1 0.033

Zn

P

Al

1213 24.1 1355 47 0.270 0.930

Ca

Ca:Al

4978 163 7528 322 0.046 0.140

%BS: percent base saturation; %rocks: percent rock fragments. Blank columns indicate no data measurement made. Units for ions, ECEC: mmol C kg 1. c Units for ions, ECEC: ug g 1. b

K

Mg

8444 921 9412 1260 0.040 0.050

Mg:Mn 0.7 2.2 0.002

Mn

Na

3169 2036 0.005

12.68 11.1 0.780

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

Health class

7

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P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

(P < 0:05) higher K, Mg, Ca, Ca:Al , Mg:Mn , pH, %BS and higher ECEC. Soil variables in the >50 cm depth class showed similar trends, but not as many variables showed signi®cance. Soils >50 cm on nondeclining plots had signi®cantly (P < 0:05) lower Al and higher pH. They also had signi®cantly (P < 0:10) higher Ca:Al and ECEC. Non-declining plot soil horizons  50 cm had signi®cantly (P < 0:05) higher percent clay (%clay) and silt (%silt) and lower percent sand (%sand) and %rocks. Non-declining plots had signi®cantly (P < 0:05) higher foliar Ca, Mg, K, Mg:Mn and lower Mn than declining plots (Table 1). 4.2. Regression relationships between soil and foliar chemistry Several regression equations were developed (Table 2) in an attempt to predict foliar chemistry based on soil chemistry. We found a strong relationship between soil base cation status, especially Ca and Mg, and foliar Ca and Mg (Table 2). Between 60 and 77% of the variation in the foliar base cation chemistry was explained by the soil base cation chemistry. Soil chemistry also successfully predicted foliar molar ratios of Mg:Mn and Ca:Al (59±74% of variation

explained), but not foliar Ca and Mg. Soil chemistry variables were poorer predictors of the variation in foliar Mn and P (42±62% variation explained). 4.3. Plot characteristics and decline Sixty-one percent of the 28 plots in this study were found most often on side-slope topographic positions (Tables 3 and 4). There were no differences in the distribution of Beers aspect values between declining and non-declining plots (P ˆ 0:240) (Table 4). Declining plots occurred at signi®cantly (P < 0:05) higher elevations than non-declining plots (Table 4). PDSMBA increased from bottom-slope to side-slope to up-slope topographic positions (Table 5a) but the trend was not signi®cance. Plots were distributed across most aspects (Tables 3 and 5b). While not signi®cant difference was found, high PDSMBA was found on S, SW, W, and NW although the east aspect had a PDSMBA of 16.3 (Table 5b). Plots fell upon six different geologic formations (Table 6). Only one plot was found on the Shenango thru Riceville formation. The formations were divided into sandstone dominated or shale dominated lithologies. Sandstone-dominated formations included

Table 2 Regression relationships for predicting foliar chemistry (ug g 1) with all 28 plotsa Foliar chemistry

Regression equation (transformed variables)

r2

P-value

Ca

3.95 ‡ 0.413 (50 cm soil Ca) 4.22 ‡ 0.442 (50 cm soil Mg) 3.17 ‡ 0.596 (50 cm %BS) 0.000 ‡ 0.992 (50 cm soil pH)

76.7 75.3 75.0 70.4

<0.001 <0.001 <0.001 <0.000

Mg

3.34 ‡ 0.320 (50 cm soil Mg) 3.14 ‡ 0.287 (50 cm soil Ca) 0.399 ‡ 0.690 (50 cm soil pH)

63.2 59.3 53.5

<0.000 <0.000 <0.000

Mn

4.16 2.94 2.97 2.89 3.16

60.1 51.7 47.7 46.5 42.4

<0.000 <0.000 <0.000 <0.000 <0.000

P

‡ 0.0720 (50 cm soil Al) 1.55 (50 cm soil ECEC) 0.368 (50 cm soil Mg) ‡ 0.658 (>50 cm soil Al) ‡ 0.210 (50 cm soil %BS) ‡ 0.150 (50 cm soil Ca)

Mg:Mn

0.812 ‡ 0.729 (50 cm soil Mg) 0.328 ‡ 0.615 (50 cm soil Ca) 0.113 ‡ 0.695 (50 cm soil Mg:Mn )

74.3 65.3 59.3

<0.000 <0.000 <0.000

Ca:Al

2.47 ‡ 0.542 (50 cm soil Ca) 2.83 ‡ 0.578 (50 cm soil Mg) 2.69 ‡ 0.408 (50 cm soil Ca:Al )

65.2 60.7 62.3

<0.000 <0.000 <0.000

a

All variables in model equations signi®cant at a ˆ 0:05.

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

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Table 3 Plot identi®cation (plot), %slope (slope), aspect, elevation (Elev.), topographic position (Topo. Pos.), glacial status, %dead sugar maple basal area (PDSMBA), K-means cluster derived population grouping (decline status), and NRCS soil series Plot

Slope

Aspect

Elev. (m)

Topo. Pos.

Glacial status

PDSMBA

Decline status

Soil series

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

15 50 16 15 35 0 14 37 38 52 30 24 18 19 28 39 14 31 6 5 8 0 30 0 28 24 13 20

50 105 201 110 40 0 289 84 192 85 110 180 123 320 59 85 55 27 330 160 35 0 117 0 73 303 148 17

605 354 561 536 610 597 655 558 585 481 661 424 582 524 512 518 588 530 567 521 561 600 582 613 579 546 497 506

Side-slope Fide-slope Side-slope Ridge-top Ridge-top Side-slope Side-slope Side-slope Fide-slope Fide-slope Fide-slope Valley bottom Side-slope Side-slope Ridge-top Side-slope Side-slope Side-slope Side-slope Ridge-top Ridge-top Nose-slope Side-slope Side-slope Side-slope Side-slope Side-slope Side-slope

Glaciated Glaciated Glaciated Unglaciated Unglaciated Unglaciated Glaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Glaciated Glaciated Glaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated Unglaciated

28 0 0 0 33 0 33 0 0 0 42 0 55 30 2 0 12 18 0 0 62 27 14 64 0 0 16 0

Declining Non-declining Non-declining Non-declining Declining Non-declining Declining Non-declining Non-declining Non-declining Declining Non-declining Declining Declining Non-declining Non-declining Non-declining Non-declining Non-declining Non-declining Declining Declining Non-declining Declining Non-declining Non-declining Non-declining Non-declining

Wellsboro Udifluvents Wellsboro Leck Kill Cookport Buchanon Lackawanna Hartleton/Buchanon Cattaraugus Hazleton Oquaga Cattaraugus Cattaraugus Wellsboro Lordstown and Oquaga Oquaga and Lordstown Oquaga and Lordstown Hartleton Hartleton Hazleton Cookport Hartleton Hartleton/Buchanon Cookport Cookport Ernest Hazleton Gilpin

Burgoon (sandstone), Huntley Mountain (sandstone), and Pottsville (dominantly sandstone, some shale, siltstone, limestone in places); shale-dominated formations included Catskill (shale, siltstone, conglomerate sandstone); and Shenango thru Oswayo (dominantly shale and siltstone and some ®ne sandstone) and Shenango thru Riceville (dominantly shale). Plots on sandstone-dominated formations had a signi®cantly (P < 0:05) higher PDSMBA than those on dominantly shale formations (Table 6). No differences were seen in PDSMBA due to glaciation status (Table 4).The only signi®cant differences found in soil or foliar chemistry with glaciation occurred in the >50 cm depth class on glaciated plots. These plots had signi®cantly higher %sand (P ˆ 0:030), lower %clay (P ˆ 0:004), higher %rocks (P ˆ 0:009), and lower Al (P ˆ 0:028) (chemistry and physical property differences due to glacial history for

soil Oa and 50 cm horizons were not signi®cant at all alphas and data are not reported). Differences in foliar chemistry were found with plot dominant lithology (sandstone or shale) (Table 7). Foliar chemistry on sandstone formations had signi®cantly (P < 0:05) lower Ca, Mg, and Mg:Mn and near signi®cant differences (P < 0:10) in Ca:Al as compared to shale formations. These foliar chemistry differences paralled trends of interest in the chemistry of soils in the 50 cm depth class. Signi®cant differences in the 50 cm depth class soil chemistry and foliar chemistry were found with plot topographic position (Table 8). Soil Oa horizons had signi®cantly (P < 0:05) lower Al, a higher, Ca:Al , pH, and %BS (P < 0:10) in bottom-slope versus up-slope positions. Soil horizons in the 50 cm depth class had higher signi®cantly (P < 0:05) Mg and %BS in bottomland versus ridge-top positions

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P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

Table 4 Mean values (standard deviation in parenthesis) by health class of percent dead sugar maple basal area (PDSMBA); plot slope, Beer's aspect, elevation; number of defoliation events in the last 10 years (NDE10) and 20 years (NDE20); defoliation severity index for the last 10 years (DEI10) and 20 years (DEI20)a Variable n plots

Declining (n ˆ 9)

Non-declining (n ˆ 19)

P-value

Stand health PDSMBA

41 (15)

3 (6)

<0.001

Plot topography % Slope Beer's aspect Elevation (m)

18 (9) 0.92 (0.58) 601 (43)

25 (14) 0.62 (0.64) 529 (61)

0.120 0.240 0.002

Stress history NDE10 NDE20 DEI10 DEI20 Palmer index

1 (1) 3 (2) 1.6 (1.8) 5.6 (5.8) 2 (1)

1 (1) 2 (1) 1.4 (1.6) 4.2 (3.0) 2 (1)

0.960 0.620 0.800 0.510 0.240

Deposition (kg ha Sulfate Ammonium Nitrate Hydrogen ion Calcium Magnesium Potassium

Stand health PDSMBA >50 cm %sand >50 cm %clay >50 cm %rocks >50 cm Al

1

per year) 328 (34) 35 (3) 225 (19) 9 (1) 14 (1) 2 (1) 3 (1)

Table 5 Mean percent dead sugar maple basal area (PDSMBA) Topographic position

1979 SMBA

1989 SMBA

(a) By topographic position Bottom-slopea 5 Side-slope 17 6 Up-slopeb P-value

4.46 a 6.62 a 5.47 a 0.498

7.63 a 6.51 a 6.1 a 0.799

8a 16 a 21 a 0.625

(b) By aspect N NE E SE S SW W NW P-value

2.53 a 7.01 a 8.85 a 4.6 a 7.13 a 5.75 a 5.38 a 7.08 a 0.786

4.14 a 9.08 a 10.92 a 5.52 a 6.78 a 5.75 a 7.03 a 5.79 a 0.706

0a 9a 16 a 9a 21 a 16 a 16 a 27 a 0.843

a b

320 (40) 34 (3) 217 (22) 8 (1) 13 (2) 2 (1) 3 (1)

Glaciated (n ˆ 7)

Unglaciated (n ˆ 21)

13 (16) 46 (9) 15 (3) 72 (16) 3 (1)

16 (22) 35 (10) 20 (5) 46 (27) 4 (2)

0.340 0.230 0.270 0.330 0.370 0.150 0.480

0.700 0.030 0.004 0.009 0.028

a Mean June±July Palmer drought index by health class. Mean summed atmospheric deposition by health class. Mean PDSMBA, >50 cm soil Al, %rocks, %clay and %sand by glacial status.

(Table 8). Foliar Ca and Mg was signi®cantly (P < 0:10) higher in bottom-slope versus up-slope positions, while foliar B tended to be lower. 4.4. Plot disturbance: insect defoliation, atmospheric deposition, and drought The NDE and DEI variables used to asses the effects of defoliation varied considerably; the NDE10 ranged from 0 to 3 years and the NDE20 ranged from 0 to 7

n

2 2 2 3 2 4 7 6

PDSMBA

Includes ®de-slope and valley bottom positions. Includes nose and ridge-top positions.

years across all plots. Non-declining and declining plots were not signi®cantly different from one another in the number of times defoliated (NDE10, NDE20) or in the severity (DEI10 and DEI20) of defoliation (Table 4). We found no signi®cant difference between any of the deposition variables by decline class, nor was there any relationship between the PDSMBA and deposition. No relationship was found between the PDSMBA or health class and the Palmer index (Table 4). Table 6 Mean PDSMBA, Mean percent dead sugar maple basal area (PDSMBA), plot geologic formation. The lithology in parenthesis under Geologic formation in the upper part of the table indicates the dominant lithology of the formation n Geologic formation P ˆ 0.202 (ANOVA) Burgoon (sandstone) Huntley Mountain (sandstone) Pottsville group (sandstone) Catskill Fm (shale) Shenango Fm thru Riceville Fm (shale)a Shenango Fm thru Oswayo Fm (shale) Dominant lithology P ˆ 0.032 (t-test) Sandstone Shale

2 6 6 7 1 6 14 14

PDSMBA 29 a 16.6 a 29.4 a 6.3 a 0a 9.5 a 24 7

a Grouped with `Shenango Fm thru Oswayo Fm' for ANOVA analysis.

Table 7 Mean percent dead sugar maple basal area (PDSMBA), dominant lithologya n

%BS

%Rocks %Clay

Oa horizons Sandstone Shale P-value

14 14

53.4 62.5 0.270

30 22 0.410

Horizons ˆ 50 cm Sandstone Shale P-value

14 14

13.9 23.5 0.130

19 21 0.270

31 34 0.710

35 31 0.230

Horizons > 50 cm Sandstone Shale P-value

14 14

17 29.4 0.230

18 20 0.170

51 54 0.770

n

B

Cu

14 14

57.3 52.5 0.250

11.3 19.6 0.370

Foliar Sandstone Shale P-value a

%Sand

%Silt

Al

Ca

Ca:Al

ECEC

K

Mg

Mg:Mn

Mn

pH

9.3 5.6 0.430

8.3 6.7a 0.860

4a 2.8 0.620

18.3 12.1 0.240

0.9 0.5 0.180

1.4 0.9 0.820

0.6 0.8 0.510

3.4 1.9 0.640

3.2 3.4 0.210

49 56 0.300

5.6 5.5 0.750

0.8 1.8 0.095

0.4 10.4 0.130

6.5 7.6 0.180

0.13 0.18 0.084

0.14 0.37 0.080

3.3 5.1 0.200

0.29 0.3 0.460

3.7 3.8 0.120

40 35 0.170

42 44 0.390

3.9 3.7 0.670

0.56 1.44 0.250

0.2 1.1 0.300

5.1 6 0.190

0.08 0.12 0.110

0.15 0.52 0.066

12.2 24.3 0.490

0.14 0.08 0.730

3.9 4.1 0.430

Fe

Zn

P

Al

Ca

Ca:Al

K

Mg

Mg:Mn

Mn

Na

116.3 121.1 0.150

32.2 33 0.210

1256 1362 0.230

28.2 51.3 0.550

5510 7907 0.050

179 363 0.100

8969 9232 0.450

957 1346 0.014

1.1 2.4 0.038

2654 2146 0.170

11.6 11.6 0.950

Mean soil and foliar chemical properties by dominant lithology. Blank columns indicate no data measurement made.

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

Dominant lithology

11

12

Table 8 Soil and foliar chemical properties by topographic positiona Topographic position n

%BS

%Rocks %Clay

%Sand

%Silt

Al

Ca

Ca:Al

ECEC

K

Mg

Mg:Mn Mn

pH

3.6 a 8.5 b 7.5 ab 0.032

11.1 a 7.4 a 4.7 a 0.165

10.3 a 2.2 b 1.0 b 0.007

15.5 a 16.5 a 11.4 a 0.733

0.51 a 0.77 a 0.50 a 0.734

1.5 a 1.3 a 0.8 a 0.316

0.7 a 0.7 a 0.6 a 0.542

2.4 a 2.9 a 1.9 a 0.642

3.9 a 3.2 b 3.2 b 0.002

b

5 17 6

78.4 a 53.5 b 53.3 ab 0.053

44 a 23 a 19 a 0.159

Horizons  50 cmb Bottom-slope Side-slope Up-slope P-value

5 17 6

33.2 a 17.0 b 11.5 b 0.035

46 a 31 a 23 a 0.149

21 a 20 a 20 a 0.709

32 a 34 a 30 a 0.551

52 a 53 a 53 a 0.467

4.2 a 5.7 ab 6.2 b 0.063

1.9 a 1.3 b 0.6 b 0.100

1.2 a 8.4 ab 0.2 b 0.094

6.6 a 7.3 a 6.7 a 0.7

0.21 a 0.14 a 0.14 a 0.223

0.43 a 0.25 b 0.13 b 0.044

7.2 a 3.7 a 2.9 a 0.417

0.41 a 0.31 a 0.18 a 0.64

4.0 a 3.8 ab 3.7 b 0.095

Horizons > 50 cmb Bottom-slope Side-slope Up-slope P-value

5 17 6

36.0 a 23.0 a 13.9 a 0.402

73 a 52 ab 39 b 0.100

18 a 19 a 21 a 0.494

37 a 40 a 31 a 0.161

44 a 41 a 48 a 0.13

3.3 a 3.7 a 4.6 a 0.211

1.4 a 1.1 a 0.4 a 0.442

1.7 a 0.6 a 0.1 a 0.318

5.3 a 5.6 a 5.6 a 0.927

0.11 a 0.10 a 0.10 a 0.712

0.45 a 0.31 a 0.32 a 0.406

28.1 a 14.6 a 20.4 a 0.425

0.08 a 0.14 a 0.07 a 0.678

4.1 a 4.0 a 3.9 a 0.343

n

B

Cu

Fe

Zn

P

Al

Ca

Ca:Al

K

Mg

Mg:Mn Mn

Na

5 17 6

48.7 a 56.4 b 55.6 ab 0.094

22.5 a 16.0 a 8.1 a 0.342

111.1 a 126.4 a 107.4 a 0.341

38.4 a 30.5 a 33.7 a 0.216

1428 a 1280 a 1291 a 0.632

42.0 a 45.4 a 21.9 a 0.414

9738 a 6095 b 5921 b 0.071

368 a 220 a 335 a 0.187

9316 a 9008 a 9183 a 0.918

1611 a 1037 b 1093 b 0.063

2.8 a 1.6 a 1.1 a 0.153

10.5 a 12.4 a 10.0 a 0.465

c

Foliar Bottom-slope Side-slope Up-slope P-value a

2066 a 2424 a 2611 a 0.516

Different letters following means in a column indicate topographic positions that were signi®cantly different at a ˆ 0:05. Blank columns indicate no data measurement made. Units for ions, ECEC: mmol C kg 1. c Units for ions, ECEC: ug g 1. b

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

Oa horizons Bottom-slope Side-slope Up-slope P-value

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

13

5. Discussion

5.2. Health and soil physical properties

5.1. Health and soil and foliar chemistry

Soil horizons 50 cm on declining plots had signi®cantly higher sand and rock fragments and lower clay. Sandy soils hold less moisture than clay soils (Petersen et al., 1968) and in general have a lower cation exchange capacity (Boul et al., 1989). Soils high in rock fragments would hold less water and would have a lower CEC. Relationships found in this study with higher %sand, %rock fragments and lower base cations on declining plots suggest future research is needed to ascertain the role soil moisture stress has in sugar maple decline as compounded by soil chemistry relationships.

Soil and foliar chemical analysis in this study produced results consistent with those found in the literature (Bernier and Brazeau, 1988a,b; Burton et al., 1993; Horsley et al., 2000; Kolb and McCormick, 1993; Mader and Thompson, 1969; Sharpe et al., 1999). Both foliar and soil chemistry are key differences between non-declining and declining sugar maple populations. In particular, differences in both foliar and soil Ca and Mg have been found in many studies of sugar maple decline (Bernier and Brazeau, 1988a; Cote and Camire, 1995; Long et al., 1997; Horsley et al., 2000; Kolb and McCormick, 1993, Mader and Thompson, 1969; Ouimet and Camire, 1995). In this study, differences in soil and foliar K were also important; this result has been reported, but less often in the past (Cote and Camire, 1995; Bernier and Brazeau, 1988a; Oiumet et al., 1995; Ouimet and Camire, 1995) and in Pennsylvania has not been considered an ion as important to sugar maple health as Ca, Mg, or Mn (Horsley et al., 2000; Long et al., 1997). In soil horizons 50 and >50 cm on the 28 plots in this study, non-declining plots had an average Ca:Al higher than the health threshold of 1 cited by Cronan and Grigal (1995). Declining plots had an average Ca:Al < 1, indicating a greater risk to sugar maple health. The ranges of the foliar ions on all 28 plots generally fell within the `healthy' literature range published by Kolb and McCormick (1993) and for the limed plots in Long et al. (1997). Foliar Mn on the non-declining plots in this study is higher than in Kolb and McCormick (1993), and the Ca:Al ratio in this study is higher than reported in Long et al. (1997). Foliar chemistry was strongly related to 50 cm soil chemistry (Table 1), supporting observations by previous researchers of non-declining sugar maple's dependence on high base cation soils (Bernier and Brazeau, 1988a; Cote and Camire, 1995; Kolb and McCormick, 1993; Long et al., 1997; Horsley et al., 1999; Ouimet and Camire, 1995). For example, where 50 and >50 cm soil Ca is high in non-declining plots foliar Ca is also high. The regression relationships between foliar nutrition and soil chemistry in Table 2 further con®rm this relationship.

5.3. Plot characteristics and decline status Declining plots were found more often at higher elevations, and there was a trend towards increased PDSMBA in higher topographic positions. In Horsley et al. (2000), elevation was not correlated with decline status, although topographic position was. Higher elevation plots could be susceptible to air and soil temperature extremes. Temperatures in north central Pennsylvania decrease with increasing elevation (Waltman et al., 1997), although slope and aspect can modify this relationship (Auchmoody, 1986). With increased elevation, the number of growing degree days also decreases and wind and ice damage increases (Auchmoody, 1986). Soils on ridge-top positions in this study were in general more acidic. Plots at higher elevations, with soil base cation levels in the declining range of this study, and on up-slope positions may therefore be characterized as `high risk' landscape positions for sugar maple. 5.4. Geology and sugar maple health Plots with dominantly sandstone lithologies (Burgoon, Huntley Mountain, and Pottsville) were found to have a higher mean PDSMBA in contrast to the more easily weathered shale dominated geologies, the Catskill formation (Briggs, 1999) and Shenango thru Oswayo formations. Dominantly sandstone formations have a high quartz content, are resistant to weathering (Way, 1999) and would contribute less to base cation replenishment as compared to plots with shales. Shales in this study hold more K, Ca, and

14

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

Mg. Similar results were also found with foliar nutrition data suggesting that soil chemistry relationships on our plots are directly related to the plot geology. However, the relationship between plot geology and soil chemistry is complicated by two main factors and should be interpreted with caution. First, several of the geologic formations in our study have important inclusions of other lithologies (Catskill and Huntley Mountain, for example) (Briggs, 1999; Way, 1999). These other lithologies contribute additional mineralogy via weathering and therefore affect soil chemistry. For example, Ciolkosz et al. (1993) in a report on the total elemental content of Pennsylvania parent materials shows that pro®les of the sandstone dominated Clymer series have a C horizon composition of 74% SiO2, 0.03% CaO, 3% K2O and 0.5% MgO. Soils of the sandstone dominated Cookport series have a C horizon composition of 78% SiO2, 0.05% CaO, 2% K2O, and 0.4% MgO. While these soils may have a potential to contribute base cations, pro®les of sandstone-derived soils of the Cookport series (unglaciated) (four plots) in this study also have the heaviest mortality. Secondly, the transport of materials on landscapes in Pennsylvania over time has complicated the interpretation of parent material contributions. Pennsylvania's periglacial environment and present colluvial transport (Ciolkosz et al., 1999; Mader and Ciolkosz, 1997; Waltman et al., 1990) are examples. Thus, inclusions and mineral transport should be seriously considered along with dominant lithology in making local predictions of the soil and foliar chemistry of sugar maple in northern Pennsylvania. Unlike Horsley et al. (2000), we did not in large ®nd differences in soil base cation status between glaciated and unglaciated soils, although we did ®nd differences in deeper soil physical properties and Al status. Soils >50 cm in glaciated areas in this study had higher %sand and %rock fragments and lower %clay and Al. This may re¯ect the fact that we included a wider glaciated area, extending both further east and further west than the plots assessed by Horsley et al. (2000). Glaciated soils across the region of our study are not uniform due to soils on these landscapes forming from parent materials that were distributed by various glacial events and then re-worked in a periglacial environment (Ciolkosz et al., 1999). This leads to conditions where glaciated soils may have either

`good' or `poor' soil chemistry conditions for sugar maple. For example, three plots found in this study on glaciated soils were experiencing decline (1, 7, and 14). All three of these plots had foliar Mg (683, 531, and 670 ug g 1, respectively) well below that reported by Horsley et al. (2000) for glaciated sites, all occurred on sandstone lithologies and two of the plots occurred at some of the highest elevations (>600 m) which may suggest additional stresses that lead to decline. 5.5. Plot disturbance: atmospheric deposition, insect defoliation and drought Drohan et al. (1999) found a relationship between atmospheric deposition and sugar maple decline status in Pennsylvania based on the 248 plots from which the 28 plots intensively measured for this study were chosen. Sharpe et al. (1999) also suggested this relationship. Burton et al. (1993) developed regression equations using many similar variables in a regional atmospheric deposition±climate gradient study across northeastern Minnesota and central lower Michigan. However, results from the present study did not show any strong relationships between foliar chemistry and atmospheric sulfate deposition as Burton et al. (1993) found. We also found no signi®cant differences between decline status classes based on 18-year sum loadings for the deposition sulfate nor any other deposition variable. This may re¯ect the effect of a smaller sample size on relationships detected in the larger data set used by Drohan et al. (1999), or may re¯ect the use of modeled deposition inputs. In our study, soil chemistry is much more strongly correlated with foliar chemistry than is atmospheric deposition, suggesting a primary role for site chemistry based on geology and soil development rather than for atmospheric deposition. Only limited data exist about soil chemistry change over recent time in Pennsylvania (Drohan and Sharpe, 1997), but these data suggest decreases over the past 14±35 years in nutrients shown to be important to sugar maple health. If these trends continue or accelerate, they could become important for long-term sugar maple health in our study region. In this study, we attempted to relate drought to decline but found this produced inconclusive results. Drought and defoliation have been linked in inciting further decline (Skilling, 1964). This can occur via an

P.J. Drohan et al. / Forest Ecology and Management 170 (2002) 1±17

alteration of tree biochemistry that results in a weakening of the tree (Bauce and Allen, 1991, Wargo and Harrington, 1991). Our results may be due to two factors. First, the PDSI is a region wide estimator for drought (Palmer, 1965). Therefore, plot level drought occurrences may have been missed unless severe enough for a whole region to experience a similar climatic condition. In addition, the PDSI is an index designed for agricultural lands and is less indicative of soil moisture conditions in forested, mountainous areas (Palmer, 1965). Second, as indicated by the PDSI, the period of study was relatively normal as far as rainfall is concerned. An objective of our study was to test the use of the regional FIA database for detecting sugar maple decline, and to determine whether existing large-scale data bases could be used to detect important correlations between the decline and hypothesized causal factors. We did detect the decline and found that decline was correlated to conditions observed in the ®eld a decade later, but the results for using large-scale databases to test correlations were mixed; ®eld sampling provided better data and led to more conclusive results. Defoliation stresses were an important part of the history of our plots, but we did not ®nd signi®cant relationships between defoliation and decline status using the existing large-scale databases. Atmospheric deposition loading is also an important environmental variable in our study region, and we detected relationships between decline status and deposition loading for several ions in the 248 plots data base. These relationships were not important on the 28 plot subset for which we conducted intensive measurements. Glaciation history, found to be important in other studies of sugar maple decline, was not important in either our 248 or 28 plot database; both databases include a wider (west to east) range of glaciated soils than those studied by Horsley et al. (2000). The results from this study largely af®rm relationships found previously between sugar maple decline and foliar and soil chemistry. Ca and Mg, shown to be important for sugar maple in most previous studies, were highly correlated with sugar maple decline status in this study as well. In addition, our study shows a high correlation between sugar maple decline status and K in both foliage and soil. We also found foliar Mn toxicity to be a factor in sugar maple decline. As suggested by Horsley et al. (2000), the soil and foliar

15

characteristics in Pennsylvania associated with nondeclining sugar maple are highly correlated with several site variables. In our study, these were dominant lithology and macro-topography. Our study suggests new relationships between geology and site chemistry and soil physical properties and moisture stress may be important in Pennsylvania's decline and should be investigated further. Our hypothesis that factors associated with moisture stress might play an important role in mediating nutritional stress is only weakly af®rmed. There were few direct relationships between moisture stress-related variables (%sand and %rock fragments) and sugar maple decline status. Acknowledgements The authors gratefully acknowledge the ®nancial support of the US Forest Service Northeastern Research Station. Thanks are also due to the following individuals and organizations: USDA Forest Service, Northeastern Research Station, Steve Horsley, Irvine, PA; Scott Bailey, Durham, NH; Bob Long, Delaware, OH; Tom Frieswyk and Will McWilliams, New Town Square, PA; John Omer, Northeastern State and Private Forestry, Morgantown, WV; Pennsylvania Game Commission; Thomas Hall and John Quimby, Pennsylvania Department of Conservation and Natural Resources, Division of Forest Pest Management; Edward Ciolkosz, Rick Day, Lee Syme, Mark Reider, Katy Sheridan, Karrie Brown, Ray Crew, Jake Reynolds, Frank Von Willert, Mary Kay Amistadi, Jon Chorover and Rick Stehouwer, Penn State University, Department of Crop and Soil Sciences of Agronomy Department; David DeWalle, School of Forest Resources, Penn State; Richard Royer, Environmental Engineering Department, Penn State; and George Baumer and Joy Drohan, Penn State Environmental Resources Research Institute. References AOAC, 1990. Of®cial Methods of Analysis, 15th Edition. Metals in Plants. Method No. 985.01. C, p. 42. Auchmoody, L.R., 1986. Soil-site relations for northern hardwoods. In: Nyland, R.D. (Ed.), Proceedings of a Silvicultural Symposium on Managing Northern Hardwoods. SUNY College of Environmental Science and Forestry, Syracuse, NY, pp. 14±24.

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