Forest Service Northern Research Station General Technical Report NRS-172

How Climatic Conditions, Site, and 

Soil Characteristics Affect Tree Growth 

and Critical Loads of Nitrogen for 

Northeastern Tree Species

Molly J. Robin-Abbott 

Linda H. Pardo

United States Department of Agriculture

September 2017
Manuscript received for publication 15 July 2015

Abstract

Forest health is affected by multiple factors, including topography, climate, and soil 

characteristics, as well as pests, pathogens, competitive interactions, and anthropogenic 

deposition. Species within a stand may respond differently to site factors depending on 

their physiological requirements for growth, survival, and regeneration. We determined 

optimal ranges of topographic (elevation, aspect, slope gradient), climatic (average 

temperature for January, July, and May to September; annual and May to September 

precipitation), and soil (pH, percent clay, percent coarse sand, permeability, depth to 

bedrock) parameters for 23 tree species of the northeastern United States. We primarily 

used importance values (a measure of how dominant a species is in a given forest area 

under existing site conditions) from a published analysis of more than 100,000 U.S. Forest 

Service Forest Inventory and Analysis plots to set optimal ranges for the abiotic factors. The 

region included in this assessment is defined by level 2 ecoregions: mixed wood plains in 

the Eastern Temperate Forest Ecoregion; Atlantic highlands and mixed wood shield in the 

Northern Forest Ecoregion. In addition to summarizing ranges for abiotic modifying factors, 

we also determined the critical load of nitrogen—the deposition below which no harmful 

ecological effects occur—for each species. The information can be used in forest health 

assessments to determine whether species growth at a site is expected to be optimal or 

suboptimal, and can also be used to modify critical load ranges for each species based on 

site conditions.

Quality Assurance

This publication conforms to the Northern Research Station’s Quality Assurance 

Implementation Plan which requires technical and policy review for all scientific 

publications produced or funded by the Station. The process included a blind technical 

review by at least two reviewers, who were selected by the Assistant Director for Research 

and unknown to the author. This review policy promotes the Forest Service guiding 

principles of using the best scientific knowledge, striving for quality and excellence, 

maintaining high ethical and professional standards, and being responsible and 

accountable for what we do.

Cover Art

Leaves of northeastern forest trees. Image by Molly Robin-Abbott, used with permission.

Published by

U.S. FOREST SERVICE

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September 2017

For additional copies:

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Graphs for importance values versus climate and soil parameters for Acer rubrum, Acer 

saccharum, and Thuja occidentalis were added to Appendix 1 on November 28, 2018. 

Graphs for these species were inadvertently omitted from the original.
How Climatic Conditions, Site, and Soil 

Characteristics Affect Tree Growth and Critical 

Loads of Nitrogen for Northeastern Tree Species

General Technical Report NRS-172

The Authors

MOLLY J. ROBIN-ABBOTT is a contractor with the U.S. Forest Service, Northern Research 

Station, 705 Spear St., South Burlington, VT 05403; mjrobina@gmail.com

LINDA H. PARDO is an environmental engineer with the U.S. Forest Service, Northern 

Research Station, 81 Carrigan Dr., UVM Aiken Center, Room 204C, Burlington, VT 05405; 

lpardo@fs.fed.us
Acknowledgments

This project was funded, in part, by the National Park Service Air Resource 

Division and was supported by the Northeastern States Research 

Cooperative (NSRC) grant program. The conclusions and opinions in this 

paper are those of the authors and not of the NSRC, the Forest Service, or the 

USDA. We thank Jennifer Pontius for her general guidance and her geospatial 

expertise and Paul Schaberg for his generous advice and plant physiology 

expertise. Anantha Prasad and Matthew Peters provided data and insight 

from the Climate Change Tree Atlas. Jason Coombs developed the GIS tool 

based on information in this report. We appreciate the input of our reviewers: 

John Battles, Natalie Cleavitt, Benoit Côté, Anthony D’Amato, Michael Day, 

Martin Dovčiak, Timothy J. Fahey, Ivan Fernandez, Kendra Gurney, Richard 

Hallet, Joshua Halman, Kevin Horn, Daniel Houle, Laura Kenefic, Alexandra 

M. Kosiba, William B. Leak, Erik Lilleskov, Robert Long, Rock Ouimet, Michael 

Ostry, Alejandro Royo, R. Quinn Thomas, and Alan White.
Contents

 1 Introduction..........................................................................................................................................1

 2 Methods .................................................................................................................................................5

 3 Abies balsamea (balsam fir)..........................................................................................................15

 4 Acer rubrum (red maple) ................................................................................................................18

 5 Acer saccharum (sugar maple).....................................................................................................21

 6 Betula alleghaniensis (yellow birch) ..........................................................................................25

 7 Betula papyrifera (paper birch)....................................................................................................28

 8 Castanea dentata (American chestnut)....................................................................................31

 9 Fagus grandifolia (American beech) .........................................................................................34

10 Fraxinus americana (white ash)...................................................................................................37

11 Fraxinus pennsylvanica (green ash).......................................................................................... 40

12 Juglans cinerea (butternut)...........................................................................................................43

13 Picea mariana (black spruce)....................................................................................................... 46

14 Picea rubens (red spruce)...............................................................................................................49

15 Pinus resinosa (red pine) ................................................................................................................52

16 Pinus rigida (pitch pine)..................................................................................................................55

17 Pinus strobus(eastern white pine) .............................................................................................58

18 Populus grandidentata (bigtooth aspen) ................................................................................61

19 Populus tremuloides(quaking aspen)...................................................................................... 64

20 Quercus alba (white oak) ...............................................................................................................67

21 Quercus prinus(chestnut oak) .....................................................................................................70

22 Quercus rubra (northern red oak) ..............................................................................................73

23 Thuja occidentalis(northern white-cedar)..............................................................................76

24 Tsuga canadensis(eastern hemlock).........................................................................................79

25 Ulmus americana (American elm) ..............................................................................................82

Appendix 1.................................................................................................................................................95

Importance Values Versus Climate and Soil Characteristics for Each Species

Appendix 2...............................................................................................................................................137

Ranges for Optimal and Suboptimal Growth for Site, Climate, and Soil Variables
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1

1 INTRODUCTION

species characteristics—i.e., climate, soil composition, 

topographic position, competition, and other biotic 

interactions—should affect the critical load. At sites 

where growth conditions are optimal, we expect that 

plant nutrient demands will be higher, and more of the 

increased plant-available N will be incorporated into 

terrestrial biomass (Schimel et al. 1997). At sites with 

suboptimal growth conditions, plant nutrient demands 

should be lower, and less of the plant-available N 

should be incorporated into the terrestrial biomass. 

Therefore, in these suboptimal sites, more of the excess 

N will cycle through soil and aquatic systems with 

potentially detrimental ecosystem effects. The critical 

load for a species with higher potential nutrient N 

utilization growing at a site with optimal conditions for 

growth will thus be higher than for the same species 

growing at a suboptimal site. The critical load for a site 

where growth conditions are optimal for all species 

present would be higher than if growth conditions were 

suboptimal for most species present at that site.

The primary objective of this publication is to describe 

how site characteristics, climatic conditions, and soil 

characteristics influence the response of individual tree 

species to N deposition in the northeastern United States 

and how they affect the critical load. As a first step, we 

determined critical loads of N for individual species 

based on a comprehensive literature review. Previously 

reported critical loads were presented as a single range 

for each ecoregion for all tree species combined based 

on all types of detrimental responses observed for 

the Northern Forest ecoregion and for the Eastern 

Temperate Forest ecoregion (Pardo et al. 2011a, 2011c). 

In this project, we estimated critical loads for individual 

tree species. Next, to address our primary objective, we 

present a simple framework for determining the effects 

of abiotic site conditions (topographic, climate, and 

soil) on critical loads in order to determine critical loads 

at a finer scale than the ecoregion scale. It is important to 

characterize how species respond to climatic conditions 

in order to better understand how their response is likely 

to change with a changing climate. Forest managers and 

policy makers can use this framework to assess critical 

loads across the landscape as a function of site conditions 

and interactions between N deposition and climate. To 

facilitate critical load assessments, critical load values 

and abiotic modifying factors from tables presented in 

this report have been incorporated into a geographic 

BACKGROUND AND OBJECTIVES

The most significant threats that impact ecosystem 

health and sustainability over the long term are climate 

change, anthropogenic nitrogen (N) deposition, pest 

outbreaks, and land use change and fragmentation 

(Rockström et al. 2009). Recent work has improved 

our understanding of plant response to N deposition 

(Bobbink and Hettelingh 2011, Pardo et al. 2011c, 

Simkin et al. 2016) and climate change (Campbell et al. 

2009, Groffman et al. 2012, Iverson et al. 2008, Ollinger 

et al. 2008). However, less is known about the impact of 

interactions between N deposition and climate change 

on plant communities (Porter et al. 2013). In spite of 

the lack of information about interactions among the 

most significant stressors, resource managers and policy 

makers must make decisions to manage and maintain 

forest health over the long term.

Nitrogen is an element essential for growth and 

development of all organisms; increased N inputs can 

have a fertilizing effect on plant growth. However, 

over the past century human activity has resulted in 

large increases in N deposition over preindustrial levels 

(Galloway et al. 2003), which may lead to detrimental 

ecological effects. Detrimental effects of increased N 

deposition include changes in ecosystem structure 

and function such as acidification of soil and surface 

waters, changes in species composition, and increased 

susceptibility to secondary stresses (Galloway et al. 

2003). The critical load concept is a useful technique 

for conveying the expected effects of a pollutant of 

concern, in this case N, on an ecosystem. A critical 

load is the estimate of exposure to a pollutant below 

which harmful effects on sensitive elements of the 

environment do not occur over the long term according 

to present knowledge (UBA 2004).

The previously reported critical load for northeastern 

forests (Pardo et al. 2011b) provided a single range 

of values, which encompasses all observed types of 

detrimental effects for forests across an entire ecoregion. 

However, the response to N deposition, and thus the 

critical load, may vary based on site conditions as well 

as species present; trees will respond to increasing N 

based on inherent nutrient requirements, functional 

traits, and abiotic site conditions (Leyton 1957). 

Thus, the same factors that affect forest growth and 
2

information system (GIS)-based tool, Nitrogen Critical 

Loads Assessment by Site (N-CLAS), that can be 

accessed from the National Atmospheric Deposition 

Program (NADP) Web site at http://nadp.sws.uiuc.

edu/committees/clad/links.aspx. N-CLAS can be used 

to calculate critical load and exceedance values for 

numerous geographic areas based on species present and 

site environmental conditions. The figures and tables that 

are generated summarize impacts by area and facilitate 

evaluation of the severity and extent of risk from N 

deposition across the landscape.

NITROGEN DEPOSITION EFFECTS 

AND CRITICAL LOADS

Elevated anthropogenic N inputs to terrestrial 

ecosystems may first alter N cycling (Aber et al. 1989, 

1998) and then ecosystem structure and function (Fenn 

et al. 2010, Pardo et al. 2011a, 2011c). Ecosystem 

responses to elevated N deposition include changes in 

species composition through altered growth, survival, 

and regeneration dynamics. Responses in herbaceous 

dominated ecosystems and by epiphytic lichens have 

been particularly dramatic because changes in species 

composition can occur at low levels of N deposition 

and may occur rapidly. Statistically significant declines 

in grassland species abundance occurred at the lowest 

level of N deposition on a European gradient (Payne 

et al. 2013). Plant species richness was reduced with 

increasing N deposition in grasslands in Great Britain, 

with the number of N-sensitive species reduced at high 

depositions (Stevens et al. 2004). Changes in species 

composition in alpine environments in the Rocky 

Mountains occurred as deposition increased from 4 to 

10 kg N ha-1 yr-1 (Bowman et al. 2006), while shifts in 

lichens from oligotrophs to eutrophs occurred along an 

N deposition gradient of 2.7 to 9.2 kg N ha-1 yr-1 in the 

Pacific Northwest (Geiser et al. 2010). The critical load 

of inorganic N in throughfall is estimated to be 1.54 

kg N ha-1 yr-1 for Pacific Northwest lichen communities 

(Root et al. 2015). Increased N deposition in temperate 

forests can result in decreased mycorrhizal biomass, 

diversity, and activity ( Janssens et al. 2010).

Responses to elevated N deposition in forest 

ecosystems include increased soil N content, increased 

rates of N cycling and leaching, as well as elevated plant 

tissue N concentrations. Further responses include 

plant nutrient imbalances, increased susceptibility to 

secondary stressors (freezing, drought, and pests), and 

changes in tree growth and survival (Galloway et al. 

2003). Increased N and sulfur (S) deposition can result 

in soil acidification and soil base cation depletion. 

Another outcome of increased N deposition can be N 

saturation, the series of ecosystem changes that occur as 

available N exceeds plant and microbial demand (Aber 

et al. 1989, 1998). Chronically elevated nitrate leaching 

is often considered an indication of N saturation at the 

catchment level. In Europe, a survey across 65 forested 

plots found little N leaching with deposition <10 kg N 

ha-1 yr-1, moderate leaching with deposition from 10 

to 25 kg N ha-1 yr-1, and leaching from all sites with 

deposition >25 kg N ha-1 yr-1 (Dise and Wright 1995). 

In the northeastern United States, nitrate leaching 

generally increased above 1 μmol L-1 when watershed 

N deposition was above 8 kg ha-1 yr-1 (Aber et al. 

2003). Factors that can affect soil N content, cycling, 

and leaching include tree species present (Lovett et 

al. 2004), growing degree days (Bedison and Johnson 

2009), and soil texture.

In the northeastern and north central United States, 

responses to increasing N deposition vary by species. 

Thomas et al. (2010) reported decreased growth for 

three tree species and decreased survival for eight tree 

species along an N deposition gradient from 3 to 11 kg 

ha-1 yr-1; growth and survival increased for eleven and 

three species, respectively. McNulty et al. (2005) found 

decreased basal area growth for spruce-fir plots in 

Vermont with annual fertilization of 15.7 kg N ha-1 yr-1

in addition to 5.4 kg ha-1 yr-1 bulk N deposition. Pardo 

et al. (2011b) set the critical load for northern forest 

ecosystems at >3 to <26 kg ha-1 yr-1 N deposition based 

on decreased growth and survival of multiple species 

in published research, including Thomas et al. (2010) 

and McNulty et al. (2005). An analysis of global carbon 

flux found that canopy photosynthesis for evergreen 

needleleaf forests in temperate climates related 

positively to N deposition up to about 8 kg ha-1 yr-1. In 

that study, N deposition effects in deciduous broadleaf 

forests in temperate climates were small or absent 

(Fleischer et al. 2013).

INTERACTING EFFECTS OF N 

DEPOSITION AND CLIMATE

Climate Predictions for the Northeast

Current research indicates that climate change 

in the northeastern United States has caused and 
3

will continue to cause increasing temperatures and 

precipitation, with longer, more drought-prone growing 

seasons and altered species distribution and ecosystem 

function (Allen et al. 2010, Hayhoe et al. 2007, Iverson 

and Prasad 2001, Iverson et al. 2008, Rustad et al. 

2012). Extreme climate events are expected to have a 

larger effect on growth than smaller climatic variations 

(Graumlich 1993). Although productivity is initially 

expected to increase with warmer and longer growing 

seasons, competition, invasive species, drought, and 

pollution may confound expected effects. Insect pest 

ranges may expand, and forest pathogens may increase 

(Paradis et al. 2008, Rustad et al. 2012). Nutrient cycling, 

N leaching, mineral weathering, and base cation leaching 

are all expected to change with changing climate 

(Campbell et al. 2009, Rustad et al. 2012). Extension 

of the growing season coupled with a decreasing snow 

pack may lead to an asynchrony between the timing of 

when soil nutrients become available and when plant 

uptake is initiated (Groffman et al. 2012).

Carbon and Tree Growth

Climate change can initiate rapid change and stress 

in forest ecosystems; however, less is known about its 

interaction with N deposition. Climate change and N 

deposition may act synergistically or antagonistically, 

depending on ecosystem-specific and taxa-specific 

dynamics (Porter et al. 2013). Increased temperature, 

carbon dioxide (CO2), and N can result in increased 

net primary productivity (NPP) (Hyvönen et al. 

2007). Mathematical modeling in Scotland (Cannell 

et al. 1998) showed that increases in N, CO2, and 

temperature might account for 50 percent of forest 

growth enhancement in the 20th century. An analysis 

of four decades of forest growth in British Columbia, 

Canada, attributed 70 percent of growth enhancement 

to changes in climate (primarily temperature but 

also precipitation, solar radiation, and water vapor 

pressure), with the rest of the growth enhancement due 

to increased CO2 and N (Wu et al. 2014). European 

modelling generally predicts increasing NPP with 

increasing N, temperature, CO2, or the interactions 

of these factors, assuming precipitation is sufficient 

(Laubhann et al. 2009, Mäkipää et al. 1999, Wamelink 

et al. 2009). Increases in CO2 without concomitant 

increases in N could limit growth increases (Reich et 

al. 2006), although the complexity of biogeochemical 

cycling makes future predictions difficult (Lukac 

et al. 2010). Other factors might complicate these 

interactions. For example, ozone-induced declines in 

photosynthesis can offset potential growth resulting 

from increased CO2 and N (Ollinger et al. 2002).

Differences by Species and Ecosystem

Increased productivity does not occur equally across 

species or ecosystem types. Across a region with small 

climatic contrasts, differences between species were 

greater than differences between sites (Graumlich 

1993). Deciduous species in the Northeast are expected 

to have increased growth with increased temperatures, 

while coniferous species are expected have a smaller 

increase or decreased growth in response to increased 

temperatures (Ollinger et al. 2008, Way and Oren 

2010). Actual response by species may depend on 

timing of precipitation and temperature increases. 

For example, in the Great Lakes region, cooler April 

temperatures favor growth of mesic hardwoods over 

growth of drought tolerant hardwoods and conifers, 

while warmer April temperatures favor conifers 

(Graumlich 1993). Abiotic site conditions also 

influence growth responses differentially by species. In 

the Canadian boreal forest, this can be seen with soil 

organic layer thickness. Black spruce (Picea mariana) 

basal area increment, a measure of growth, was not 

affected by soil organic layer thickness, but increasing 

soil organic layer thickness did have a negative effect 

on basal area increment for quaking aspen (Populus 

tremuloides Michx.) (Gewehr et al. 2014). This is related 

to temperature and moisture preferences. Growth of 

black spruce is favored by cooler temperatures and 

wetter conditions, while quaking aspen growth is 

favored by higher temperatures and drier conditions 

(Drobyshev et al. 2012). Responses to changing 

temperature and precipitation vary by region. In the 

western United States where conditions are already 

warm and dry, increasing temperatures and decreasing 

growing season precipitation resulted in a decrease 

in suitable habitat for quaking aspen, especially in 

marginal areas (Worrall et al. 2013).

Variable responses of tree species to elevated N inputs, 

climate change, pests, and pathogens may ultimately 

lead to alterations in species composition. Over a 40-

year period in the Green Mountains of Vermont, basal 

area of northern hardwoods, primarily sugar maple 

(Acer saccharum Marsh.), increased while basal area 

decreased in most boreal species, especially red spruce 

(Picea rubens Sarg.) and paper birch (Betula papyrifera

Marsh.), in the lower half of the northern hardwoodsboreal forest ecotone (Beckage et al. 2008). In the upper 
4

part of the ecotone, balsam fir (Abies balsamea [L.] 

Mill.) and paper birch had the greatest increase in basal 

area. Over this time there was a 1.1 degree increase in 

temperature and a 34 percent increase in precipitation. 

It is not clear if increased red spruce mortality was due 

to acid precipitation, climate change, or an interaction 

of both factors (Beckage et al. 2008). In the White 

Mountains of New Hampshire, Landsat aerial surveys 

from 1987 to 2010 show increased greenness in early 

spring at lower elevations, indicative of increased 

conifer composition at lower elevations, possibly a result 

of increased balsam fir growth and decreased paper 

birch growth (Vogelmann et al. 2012). At the Hubbard 

Brook Experimental Forest in New Hampshire, several 

species, including yellow birch (Betula alleghaniensis

Britton), beech (Fagus grandifolia Ehrh.), and paper 

birch, experienced a decline in biomass over a 10-year 

period ending in 2006 (van Doorn 2011). Biomass 

increased in other species including sugar maple, 

balsam fir, red spruce, red maple (Acer rubrum L.), 

hemlock (Tsuga canadensis [L.] Carrière), and white ash 

(Fraxinus americana L.) (van Doorn 2011).

Soil characteristics can also influence the response of 

tree species to climate and deposition. At Hubbard 

Brook, declines in sugar maple growth, especially at high 

elevation sites, have been attributed to soil acidification 

and low calcium (Ca) availability ( Juice et al. 2006). 

Similarly, increased mortality of sugar maple in the 

Appalachian Plateau of Pennsylvania was attributed 

to low levels of soil Ca and magnesium (Mg) in 

combination with insect defoliation events (Bailey et al. 

2004). In Vermont, paper birch growing on sites already 

impacted by elevated N deposition with consequent low 

soil calcium availability were not able to recover from 

ice storm damage (Halman et al. 2011); such storms are 

projected to increase with climate change.

ABIOTIC MODIFYING FACTORS, 

CRITICAL LOADS OF N, AND FOREST 

HEALTH

The interaction of multiple factors can make it difficult 

for forest managers and policy makers to accurately 

assess the effect of changing environmental conditions 

on forest health. Our previous report provided a broad 

range of critical loads for northern and eastern forests 

but was neither species nor site specific and did not 

differentiate between growth and survival responses 

(Pardo et al. 2011c). In this report, we used previously 

reported data for tree species growth ranges and optimal 

growing conditions to determine potential effects of 

N deposition on tree species response under different 

site, climate, and soil conditions. Land managers and 

policy makers can use this information via N-CLAS, 

our online GIS-based tool, to determine critical loads 

of N for forested sites across the northeastern United 

States. N-CLAS, when combined with climate change 

scenarios, pest and pathogen abundances, and land 

management, could be used to predict forest health 

in the face of changing environmental conditions. 

Additional receptors, including lichens and herbaceous 

plants, would increase the scope of this analysis and may 

be included in future iterations of N-CLAS.

REPORT ORGANIZATION

In this report we assess critical loads for 23 tree species 

of concern in northeastern U.S. forests and delimit 

topographic, climatic, and soil variables that affect 

growth and critical loads. In Chapter 2, we describe 

the approach we used to determine species specific 

critical loads for trees and the general approach we 

used to determine how the abiotic modifying factors 

of topography, climate, and soil affect tree growth. 

Subsequent chapters in this report present critical loads 

and threshold values for abiotic modifying factors by 

species, organized alphabetically by scientific name. 

Each chapter includes details of the methods that apply 

specifically to that species, and any deviations from the 

general methods are explained. Appendix 1 contains 

scatter plots of importance values versus climate and 

soil characteristics for each species; the data from these 

plots were used to establish threshold values for abiotic 

modifying factors. Appendix 2 contains bar charts 

summarizing ranges for optimal and suboptimal growth 

for each site, climate, and soil variable for all species. A 

detailed description of N-CLAS, the GIS based tool 

that utilizes these tables, is not included in this report. 

N-CLAS can be accessed via the NADP Web site at 

http://nadp.sws.uiuc.edu/committees/clad/links.aspx.
5

2 METHODS

for a specific species will be in the lower half or upper 

half of the species’ critical load range. The critical load 

for a given site can be determined in a variety of ways 

depending on management objectives and conservation 

concerns. For example, critical loads for all species at 

the site can be combined so the range spans the critical 

load for all species of management concern at that site. 

Alternatively, the critical load for a site can be set more 

conservatively to protect the most sensitive species.

CRITICAL LOADS OF N

We determined critical loads of N for 23 tree species 

in the northeastern United States (Table 2.1). The 

tree “species of concern” were selected with input from 

resource managers in Vermont, New Hampshire, and 

Maine and include species of management concern, 

species of commercial interest, or dominant species in 

the northern forest.

As part of the development of N-CLAS, we evaluated 

how abiotic site conditions affect individual species’ 

response to nitrogen (N) deposition and the critical 

load for N deposition. N-CLAS is a geographic 

information system (GIS)-based tool for resource 

managers and policy makers used to assess the 

combined impacts of nitrogen deposition and climate 

change on forest ecosystems in the northeastern United 

States. The tool uses geospatial data layers for key 

topographic, climatic, and soil abiotic factors to predict 

whether growth at a site will be optimal or suboptimal 

for each species. In this chapter, we first describe the 

approach we used to determine the critical load of N 

for 23 tree species based on previously reported growth, 

survival, and regeneration responses to N deposition. 

Next, we describe the approach we used to determine 

the range of values for each abiotic parameter that are 

expected to result in optimal growth for each species. 

At each site, the combined effect of all modifying 

factors on growth determines whether the critical load 

Table 2.1—Tree species of concern in the forests of the northeastern United States 

Scientific name Common name Reason for concern

Abies balsamea Balsam fir Wildlife habitat; commercial

Acer rubrum Red maple Wildlife; commercial

Acer saccharum Sugar maple Commercial–sap, lumber

Betula alleghaniensis Yellow birch Wildlife; commercial–lumber

Betula papyrifera Paper birch Wildlife; commercial–lumber

Castanea dentata American chestnut Rare species

Fagus grandifolia American beech Wildlife habitat

Fraxinus americana White ash Wildlife; commercial–lumber

Fraxinus pennsylvanica Green ash Wildlife; commercial–lumber

Juglans cinerea Butternut Rare species in the northeast

Picea mariana Black spruce Wildlife habitat

Picea rubens Red spruce Commercial–lumber

Pinus resinosa Red pine Commercial–lumber

Pinus rigida Pitch pine Rare species in the northeast

Pinus strobus Eastern white pine Commercial–lumber

Populus grandidentata Bigtooth aspen Wildlife habitat; commercial–pulp

Populus tremuloides Quaking aspen Wildlife habitat; commercial–pulp

Quercus alba White oak Wildlife; commercial–lumber

Quercus prinus Chestnut oak Rare species in the northeast

Quercus rubra Northern red oak Wildlife; commercial–lumber

Thuja occidentalis Northern white-cedar Wildlife; commercial–lumber

Tsuga canadensis Eastern hemlock Commercial; wildlife habitat

Ulmus americana American elm Wildlife habitat; commercial
6

The critical load is based on the deposition range over 

which detrimental responses, including decreased 

growth rate, survival, and regeneration, are observed. 

Many factors, including genetic variation, stand 

structure, competition, and site conditions, affect 

the response of trees to N deposition. Critical loads 

are expressed as a range to account for some of this 

variability. Specific descriptions of data sources and 

the decisionmaking process for setting the critical load 

range are described in each species chapter. Growth 

and survival data from the U.S. Forest Service, Forest 

Inventory and Analysis (FIA) program (http://www.

fia.fs.fed.us/) analyzed along an N deposition gradient 

(Thomas et al. 2010) were the primary source of 

information for most species. The spatially extensive 

correlation analysis spanned a deposition range of 3 

to 11 kg ha-1 yr-1 total (wet + dry) inorganic N and 

included more than 2,000 trees for each species. 

Nitrogen deposition was calculated using data from the 

National Atmospheric Deposition Program (wet; see 

http://nadp.sws.uiuc.edu) and CASTNET (dry; see 

http://www.epa.gov/castnet/).

The responses of growth and survival across a 

deposition gradient were used to determine species 

specific critical loads for nitrogen. If growth or survival 

rate in an analysis showed an inflection point (an 

increase in response to N deposition followed by a 

decrease), the critical load based on that response was 

set as a range of 2 kg N ha-1 yr-1 on either side of the 

inflection point (Fig. 2.1a). The range accounts for 

variation across sites. If both growth and survival rates 

in a study decreased across the deposition range, the 

critical load based on that response was set below the 

deposition range (Fig. 2.1b). If growth or survival rate 

increased and the other factor increased or remained 

static, the critical load was set above the high end 

of the N deposition range for that study (Fig. 2.1c). 

Whenever possible, multiple studies were used to set 

the critical load ranges. Study scale and strength of 

response were considered when setting the critical 

load ranges based on multiple studies. When species 

specific responses to N deposition were not available 

in the literature, responses from species in the same 

genus or family were used to set the critical load. 

Figure 2.1—Hypothetical examples of critical loads (CL) derived from Thomas et al. (2010) using growth and survival responses.
7

Critical load values for these species will be reevaluated 

when relevant information becomes available. If a 

species specific upper estimate for the critical load 

range was not available, the upper critical load was 

set at 26 kg N ha-1 yr-1 based on Pardo et al. (2011b), 

where an upper critical load value of 26 kg N ha-1 yr-1

was set for northern forests in the United States. In a 

European study, leaching occurred at all forested sites 

with deposition >25 kg N ha-1 yr-1 (Dise and Wright 

1995), indicating a similar potential upper limit for N 

deposition.

Empirical critical loads for N in the United States 

have been calculated by ecoregion (Pardo et al. 

2011a). However, land managers typically need to 

evaluate susceptibility to air pollution and assess 

potential impacts on forest health at smaller spatial 

scales. Because factors such as growth respond to 

site conditions, response to N deposition (and thus 

critical loads) can also vary with site conditions. An 

approach for modifying critical loads based on site 

factors was proposed in Europe in the early 2000s 

(e.g., Achermann and Bobbink 2003). However, it 

was not implemented at that time because of the 

challenges in determining the direction of impact and 

considering interactions of multiple factors across the 

diverse climatic and site conditions in Europe. The 

approach was later used on a smaller scale in the United 

Kingdom (Hall and Wadsworth 2010). In our study, we 

evaluated how abiotic site conditions are likely to affect 

species response to N deposition. Data presented can 

be used to determine site-level critical loads based on 

an approach that determines conditions favorable for 

optimal growth.

Abiotic factors that may influence critical loads at 

the site level include topographic, climate, and soil 

variables. Based on a fundamental concept in ecology 

and plant nutrition ( Jenny 1994), we expect that for 

each parameter, a range of values for optimal growth 

exists; for values above or below that range, growth 

is not optimal (Fig. 2.2). In any given location, the 

observed values for the abiotic factor may not span 

the entire theoretical range. In that case the actual 

growth response curve only represents a portion of the 

theoretical curve, resulting in a curve that, for example, 

increases or decreases across the entire observed range.

Optimal growth conditions should result in increased 

N use (Hyvönen et al. 2007), pushing the critical load 

to the upper half of the range, as will conditions that 

mitigate the detrimental impact of increased levels of 

N deposition. Suboptimal growth conditions should 

result in decreased N use, pushing the critical load 

to the lower half of the range, as will conditions that 

exacerbate the negative effects of increased levels of 

N deposition. In N-CLAS, our GIS tool, the impact 

of individual modifying factors on the site critical 

load is determined by the weight of evidence for each 

factor; the weight of evidence is a measure of the 

certainty of the effect of the modifying factor on the 

critical load. In addition, N-CLAS has the ability to 

incorporate the weight of influence of each factor on 

tree growth for each species. For a given species, one 

factor (e.g., temperature) may be far more significant 

than all others; for another species, several factors 

may be important. The combined impact of all abiotic 

modifying factors will determine whether the critical 

load is in the lower or upper half of the range.

Figure 2.2—A range of values exists for 

optimal growth (green shaded region) for 

each abiotic parameter; above and below 

Abiotic Parameter that range of values, growth is not optimal.

Growth

Optimal 

Growth
8

Assessment of Uncertainty: 

Weight of Evidence

A key part of this analysis was to clearly define the 

basis of the relationships we report, the inherent 

assumptions, and the level of certainty of our 

evaluation. Thus, for each abiotic modifying factor, we 

also assessed the weight of evidence (sensu Hall and 

Wadsworth 2010), which is a measure of our certainty 

of the modifying factor thresholds. The weight of 

evidence is rated from 1 to 5, with 1 being weak and 5 

being strong evidence. Abiotic modifying factor tables 

in each species chapter provide the weight of evidence 

for each factor.

We determined the weight of evidence for abiotic 

modifying factors using two matrices (Tables 2.2 and 

2.3). The matrix in Table 2.2 was used to assign the 

weight of evidence for abiotic modifying factor 

thresholds set by using FIA-based importance value 

data from the Climate Change Atlas (U.S Forest 

Service, n.d.). The overall uncertainty rating was 

determined through the arithmetic mean of three 

weighting criteria: number of samples, shape of 

response curve, and accuracy of parameter 

measurements. Graphs of each modifying factor were 

visually inspected to determine the weight for the 

shape of the response curve. The matrix in Table 2.3 

was used to assign the weight of evidence for abiotic 

modifying thresholds set by using data from the Forest 

Service silvics handbook, “Silvics of North America” 

(Burns and Honkala 1990a, b), the PLANTS database 

(NRCS 2014), or other literature sources.

Weight of Influence

The importance of an abiotic factor in critical loads 

calculations is determined by the weight of influence. 

Simply put, some factors have a greater impact on 

site suitability than others. For example, a tree will 

not grow at a site with optimal soil conditions if the 

climate conditions are unsuitable.

ABIOTIC MODIFYING FACTORS

For each tree species in this study, we identified the 

optimal range for growth for specified topographic, 

climatic, and soil abiotic modifying factors. Ranges 

were based on importance values and distribution 

data from the Climate Change Atlas (U.S. Forest 

Service, n.d.) and information from other sources, 

including the Forest Service silvics handbook, “Silvics 

of North America” (Burns and Honkala 1990a, b); 

the PLANTS database (NRCS 2014); additional 

Table 2.2—Weight of evidence assessment for abiotic modifying factor thresholds based on FIA importance values (IV)

Weight of evidence

Weighting criteria Weak – 1 Moderately weak – 2 Moderate – 3 Moderately strong – 4 Strong – 5

Number of samples <50 50-149 150-299 300-449 ≥450

Shape of response 

curve

Weak response, 

no obvious peak, 

points dispersed 

over a wide range

Some clustering 

of points, points 

dispersed over wide 

range

Definite peak 

but numerous 

points outside of 

the main peak, 

thresholds unclear

Definite peak, several 

points outside of 

the main peak make 

determining precise 

thresholds difficult

Clear peak, clear 

thresholds, no or 

single outliers

Accuracy of 

parameter 

measurements

Inaccurate 

measurement 

techniques; 

geospatial values 

represent highly 

generalized data

Inaccurate 

measurement 

techniques; 

geospatial values 

represent primarily 

modeled data

Accurate 

measurement 

techniques; 

geospatial values 

represent primarily 

modeled data

Accurate 

measurement 

techniques; geospatial 

values represent 

mix of real data and 

modeled data

Accurate 

measurement 

techniques; 

geospatial values 

represent real data or 

very accurate models

Table 2.3—Weight of evidence assessment for literature-based abiotic modifying factor thresholds

Weight of evidence

Weak – 1 Moderately weak - 2 Moderate – 3 Moderately strong – 4 Strong - 5

General value, 

unreliable source

General value, 

reliable source

Species value, 

reliable source

Species value, multiple 

studies, reliable source

Species value, 

thresholds well 

established
9

literature sources; and input from experts on individual 

tree species. It is important to note that data were 

evaluated in an ecosystem context, so the optimal 

ranges are for tree growth with competition from other 

trees. For many species, optimal growth conditions 

without competition from other species may differ from 

the optimal growth ranges provided in this report. In 

addition, the optimal ranges we defined are based on 

the available data. For species with low numbers of 

samples, e.g., butternut (Juglans cinerea) and American 

chestnut (Castanea dentata), the threshold may not 

accurately represent the optimal ranges (see discussion 

of uncertainty in this chapter). We believe the 

thresholds established for modifying factors will help 

delineate optimal growing conditions for most forested 

sites. However, as with any system that uses a threshold 

approach, sites with values close to the threshold 

might not fit well into either optimal or suboptimal 

growth categories. Finally, other modifying factors not 

considered in our analysis may positively or negatively 

affect site growing conditions.

Tree species importance values based on FIA data from 

the Climate Change Atlas (U.S Forest Service, n.d.) 

are our primary source for determining optimal 

growth ranges. Importance values are used to indicate 

the relative importance of a species within a plot. 

Importance Values(X) = (50 * basal area(X) / basal 

area(all species)) + (50 * number of stems(X) / number 

of stems(all species)), where X is a single species. We 

compared importance values with abiotic modifying 

factor values (U.S Forest Service, n.d.) using a threshold 

importance value of 10, which is above the median 

importance for all species in this analysis, to select 

plots with good growth conditions for each species. 

We then determined the range for each modifying 

factor above the importance value threshold. In order 

to exclude outliers, we used the 5th to 95th percentile 

of the modifying factor values. We refer to this as 

the high importance value range throughout this 

document. Figure 2.3 demonstrates the optimal range 

set using this technique. For those species that were not 

sufficiently represented by importance values greater 

Figure 2.3—Abiotic modifying factor range determined using importance value threshold. The optimal range is shaded green; 

suboptimal range is shaded purple.

0

5

10

15

20

25

30

0 200 400 600 800 1000 1200

Importance Value

May to September Precipitation (mm)

Betula alleghaniensis

437 695

Importance value threshold
10

than 10, we used a lower importance value threshold. 

Scatter plots of importance values versus climate and 

soil data used to establish threshold values for abiotic 

modifying factors for each species are available in 

Appendix 1, and bar charts summarizing ranges for 

optimal and suboptimal growth for each site, climate, 

and soil variable for all species are in Appendix 2. 

When sources other than the Climate Change Atlas 

(U.S. Forest Service, n.d.) were used to set optimum 

growth thresholds, values were generally taken directly 

as reported in the source material.

In the following sections, we describe the abiotic 

modifying factors and provide an overview of methods 

used to determine thresholds for topographic, climatic, 

and soil modifying factors. We initially considered a 

large number of site-based factors that could potentially 

modify vegetation responses to N deposition, including 

temperature, precipitation, growing degree days, 

elevation, aspect, soil wetness, depth to bedrock, soil 

orders, Ca:Al (calcium: aluminum), base saturation, 

nitrification, N mineralization, leaching potential, 

and management intensity. Some factors were 

eliminated because they provided similar information 

(e.g., growing degree days are related to temperature 

parameters), and other factors were eliminated because 

the effect of the factor on growth and critical loads 

was not clearly defined (e.g., soil C:N, nitrification, 

mineralization, and leaching potential). We included 

factors for which GIS data are available, as well as 

factors that can be used to assess critical loads if more 

geospatial information becomes available. Data from 

the Climate Change Atlas (U.S. Forest Service, n.d.) 

were used to set thresholds for many modifying factors. 

Abiotic modifying factors included in this report are 

shown in Table 2.4.

Topography

Elevation

The effects of elevation on growth are due to multiple 

interacting factors, including temperature, soil depth, 

precipitation, and exposure. For many species in the 

northeastern United States, colder temperatures at high 

elevations limit growth relative to milder temperatures 

at low elevations. Thus high elevations would result 

in suboptimal growth, while low elevations would 

result in optimal growth. In the southeastern United 

States, growth responses might be reversed at high 

and low elevations for some species. We determined 

the effect of elevation on growth based on species 

abundance by elevation; elevations at which the species 

occurred infrequently or not at all were associated with 

suboptimal growth, while elevations where species 

were abundant were associated with optimal growth. 

We used information on forest vegetation related to 

elevation in New Hampshire (Leak and Graber 1974), 

elevation ranges from “Silvics of North America” 

(Burns and Honkala 1990a, b), basal area dominance 

at various elevations in the Green Mountains of 

Table 2.4—Abiotic modifying factors and data sources

Abiotic modifying factor Units Primary Data source

Elevation, northeastern U.S. m Beckage et al 2008, Burns and Honkala 1990, 

Leak and Graber 1974

Aspect NRCS 2014

Slope Gradient % Soil Conservation Service 1991a

January temperature average °C Hayhoe et al. 2007 a

July temperature average °C Hayhoe et al. 2007 a

May to September temperature average °C Hayhoe et al. 2007 a

Precipitation, annual mm Hayhoe et al. 2007 a

Precipitation, May to September mm Hayhoe et al. 2007 a

Soil pH Soil Conservation Service 1991 a

Soil clay % Soil Conservation Service 1991 a

Soil coarse sand % Soil Conservation Service 1991 a

Soil permeability cm hr-1 Soil Conservation Service 1991 a

Soil depth to bedrock (minimum rooting 

depth)

m NRCS 2014

a As cited in Climate Change Atlas (U.S. Forest Service, n.d.).
11

Vermont (Beckage et al. 2008), and other sources to 

set approximate elevation ranges for each species for 

the northeastern United States. Data from the Climate 

Change Atlas (U.S. Forest Service n.d.) were not used 

for elevation because of the potential influence of 

latitude on the importance value analysis. The weight of 

evidence for elevation ranges from 2 to 4.

Aspect

Aspect can affect species composition and soil 

properties, including soil moisture, pH, organic matter 

content, and nutrient cycling (Gilliam et al. 2014). 

Southwestern slopes are generally warmer and more 

exposed than northeastern slopes at the same elevation 

and latitude, which suggests that growth of drought 

tolerant and shade intolerant species would be better 

on southwestern aspects, while growth of drought 

intolerant and shade tolerant species would be better on 

northeastern aspects. Effects of aspect on growth were 

based on species’ shade and drought tolerances reported 

in the PLANTS database (NRCS 2014), aspect 

preferences reported in the “Silvics of North America” 

(Burns and Honkala 1990a, b), and general assumptions 

about moisture and temperature characteristics of 

different aspects. The weight of evidence for aspect is 2 

for all species.

Slope Gradient

Slope gradient can affect soil thickness, water 

retention, and nutrient retention, among other factors. 

In the Catskill Mountains of New York, slope was 

significantly correlated with organic horizon thickness 

and exchangeable cations ( Johnson et al. 2000). In 

the Rocky Mountains of Wyoming and Colorado, 

catchment slope and terrain roughness were positively 

correlated with stream nitrate concentrations and 

negatively correlated with dissolved inorganic nitrogen 

retention (Sickman et al. 2002). Tree mortality 

probability generally increases with increasing slope 

gradient for the pines and hardwoods species groups, 

while other conifer groups are unaffected or have 

slightly decreased mortality probability with increased 

slope gradient (Dietz and Moorcroft 2011). We 

determined the optimum range for slope gradient based 

on the range above a threshold importance value of 10 

using data from the Climate Change Atlas (U.S. Forest 

Service, n.d.) unless otherwise indicated. Values outside 

of this range were considered suboptimal. The weight of 

evidence for slope gradient ranges from 2 to 4.

Climate

Temperature

Empirical and experimental evidence supports optimal 

temperatures for growth and nutrient uptake (i.e., a 

response curve as in Figure 2.2), as well as increased 

nutrient demand with increased growth. Globally, 

maximum growth for mature forests (expressed as 

biomass carbon density) occurs around 10 °C mean 

annual temperature (Liu et al. 2014). The optimal 

temperature for activity of Rubisco, the enzyme 

responsible for catalyzing a major step in carbon 

fixation during photosynthesis, is around 25 °C (Lukac 

et al. 2010). Optimal winter and summer temperature 

ranges for growth and survival vary by species 

(Drobyshev et al. 2012, Ollinger et al. 2008, Way 

and Oren 2010). Multivariate analyses of red spruce 

across the eastern United States indicate that annual 

growth is negatively correlated with anomalously 

warm late summers and anomalously cold early 

winters (McLaughlin et al. 1987). Warm late summer 

months are negatively correlated with growth for 

multiple species (Kipfmueller et al. 2010, Leonelli et 

al. 2008, Tardif et al. 2001). If a tree is growing below 

optimal temperatures, increasing temperatures may 

promote growth. An analysis of changing growth on 

experimental plots in Europe indicates that increased 

temperatures and extended growing seasons can result 

in increased growth rates for forest stands; growth 

increases are strongest on fertile sites (Pretzsch et al. 

2014). Rising temperatures can result in increasing 

nutrient uptake up to a maximum threshold. For 

example, Gessler et al. (1998) found maximum 

ammonium uptake in spruce occurred at 20 °C.

Average January, July, and May to September 

Temperatures

For most species, we determined the optimum range 

for average January, July, and May to September 

temperatures based on the range above a threshold 

importance value of 10 using data from the Climate 

Change Atlas (U.S. Forest Service, n.d.) unless 

otherwise indicated. Values outside of this range were 

considered suboptimal. When possible, we confirmed 

temperature range values using information from the 

Forest Service silvics handbook, “Silvics of North 

America” (Burns and Honkala 1990a, b). The weight 

of evidence for January, July, and May to September 

temperature ranges from 3 to 5.
12

Annual Precipitation and May to September 

Precipitation

Liu et al. (2014) found that globally, biomass carbon 

density in mature forests reaches a maximum when 

precipitation ranges from 1000 to 2500 mm annual 

precipitation. Annual precipitation in most of the 

northeastern United States falls within this range 

(http://nadp.sws.uiuc.edu/NADP). Because the 

optimal precipitation range for growth varies by species, 

some Northeastern tree species will have optimal 

growth outside the reported global range for maximum 

biomass.

In addition to affecting growth, suboptimal 

precipitation may affect plant response to increasing 

nitrogen. Davis et al. (1999) found decreased bur 

and pin oak seedling survival with increasing N at 

dry sites compared to wet sites. It is also likely that 

growing season precipitation affects tree growth more 

than annual precipitation. Leonelli et al. (2008) found 

a positive correlation of quaking aspen growth in 

British Columbia, Canada, with prior year summer 

precipitation, while Kipfmueller et al. (2010) found 

a positive relationship of red pine, white pine, and 

northern white-cedar growth with current year June 

to July precipitation in northern Minnesota. Radial 

growth of eastern hemlock, sugar maple, and American 

beech in southwestern Quebec was positively correlated 

with summer precipitation (Tardif et al. 2001).

Precipitation below optimal ranges may be more 

detrimental than precipitation above optimal ranges. 

In Tennessee, Hanson et al. (2001) found increased 

growth rates for multiple species, including chestnut 

oak, white oak, and red maple, growing in years with 

annual and May to September precipitation above our 

high importance value range compared to years with 

precipitation within the range. Years with relatively 

lower growth rates experienced May to September 

precipitation below our high importance value range. 

Extreme precipitation events such as floods and 

droughts may have a bigger impact on tree growth, 

survival, and forest health in general than average 

annual or May to September precipitation in the 

northeastern United States.

We determined the optimum range for mean annual 

precipitation and May to September precipitation 

based on the range above a threshold importance value 

of 10 using data from the Climate Change Atlas (U.S. 

Forest Service, n.d.) unless otherwise indicated. Values 

outside of this range were considered suboptimal. The 

range was verified using species specific data in “Silvics 

of North America” (Burns and Honkala 1990a, b). The 

weight of evidence for annual and May to September 

precipitation ranges from 3 to 5.

Soil Characteristics

Soil pH

Although most forest species grow well over a large 

range of pH (Williston and Lafayette 1978), individual 

species have a preferred pH range. Nutrients can 

become either toxic or unavailable at pH extremes 

(<4.5 and >8.5; Londo et al. 2006), while soils with a 

pH of 6.0-7.0 often have the highest concentration 

of available nutrients (Williston and Lafayette 1978). 

High rates of N and sulfur (S) deposition can result 

in increasing soil acidity, leaching of base cations, and 

increased mobilization of aluminum (Al) (Driscoll et 

al. 2001). When determining the optimal pH range, we 

considered both the optimal range for growth as well 

as the potential of the soil to buffer against increasing 

acidity. The optimal range for pH was based on the 

range above a threshold importance value of 10 using 

data from the Climate Change Atlas (U.S. Forest 

Service, n.d.) unless otherwise indicated. Values outside 

of this range were considered suboptimal. Information 

from the PLANTS database (NRCS 2014) and “Silvics 

of North America” (Burns and Honkala 1990a, b) were 

also considered when setting the range. The weight of 

evidence for soil pH ranges from 2.3 to 4.3.

Soil Texture and Permeability

Soil physical characteristics affect many factors that 

influence tree growth and establishment, including 

nutrient and water availability, retention, movement, 

and aeration. Although trees can grow over a wide 

range of soil textures, they typically grow best on moist, 

well-drained soils (e.g., sandy loams to clay loams). 

These soils generally have sufficient water, nutrients, 

and air, as well as adequate drainage (Osman 2013). 

Clay soils are often fertile but heavy and poorly 

drained. Sandy soils are lighter but lack water and 

nutrients.

We determined optimal ranges for percent clay, 

percent coarse sand, and permeability for mineral soil. 

Permeability is generally determined from soil texture 

(Table 2.5) and can be used as a modifying factor 

when soil texture information is not available. Clay 

soils with very slow permeability and poor drainage 
13

higher base saturation have increased growth, decreased 

mortality, and decreased susceptibility to nutrient 

imbalance associated with N saturation (Demchik and 

Sharpe 2000, Duchesne et al. 2002, Horsley et al. 2000, 

Sullivan et al. 2013). Sites with low base saturation 

experience decreased foliar and forest floor Ca, as 

well as the potential for increased dieback, reduced 

sugar maple regeneration, and reduced basal area with 

increased N deposition or N and S inputs (Moore and 

Houle 2013, Sullivan et al. 2013). Sullivan et al. (2013) 

found little sugar maple regeneration when B horizon 

base saturation was below 12 percent; the greatest 

amount of regeneration occurred when base saturation 

was greater than 20 percent. Unless species specific 

information is available, mineral soil base saturation 

greater than 15 percent is associated with upper critical 

loads while base saturation less than 15 percent is 

associated with lower critical loads. The weight of 

evidence for base saturation is 2 for all species except 

sugar maple, for which the weight of evidence for base 

saturation is 3.

Ca:Al

The ratio of calcium to aluminum in the soil solution is 

another metric used to assess susceptibility to elevated 

N (and S) deposition. Aluminum, which is toxic to 

plants, is mobilized by acid deposition inputs and 

subsequent soil acidification. General soil solution 

Ca:Al thresholds were set based on the work of Cronan 

and Grigal (1995), who found a 50 percent chance of 

adverse impacts on tree growth and nutrition with a 

molar ratio of 1, a 75 percent chance of adverse impacts 

with a Ca:Al ratio of 0.5, and a 95 percent or greater 

chance of adverse impacts with a molar ratio of 0.2. 

With a Ca:Al molar ratio of 2, the chance of adverse 

impacts is approximately 25 percent. A Ca:Al ratio 

of 2 is associated with optimal growth. The weight of 

evidence for the Ca:Al ratio is 2 for all species.

are considered suboptimal for growth, as are sandy 

soils with rapid and very rapid permeability. Growth 

is expected to be optimal with moderately slow to 

moderately rapid permeability. We determined the 

optimal range for percent clay, percent coarse sand, 

and permeability based on the range above a threshold 

importance value of 10 using data from the Climate 

Change Atlas (U.S. Forest Service, n.d.) unless 

otherwise indicated. Values outside of this range were 

considered suboptimal. Soil texture preferences were 

confirmed using “Silvics of North America” (Burns 

and Honkala 1990a, b). The weight of evidence for 

clay ranges from 2.3 to 3.7, the weight of evidence 

for coarse sand ranges from 2 to 4, and the weight of 

evidence for permeability ranges from 1.3 to 3.3.

Depth to Bedrock (minimum rooting depth)

Depth to bedrock, which indicates potential soil 

thickness, can be used to assess whether minimum 

rooting depth requirements are met. Trees growing 

in soils of adequate depth are assumed to have better 

growing conditions, including access to a larger pool of 

nutrients and base cations, and thus a higher tolerance 

for increased N deposition. Optimal growth conditions 

are assumed to occur when depth to bedrock is greater 

than the minimum rooting depth. Values for minimum 

rooting depth were obtained from the PLANTS 

database (NRCS 2014) and were used to set depth to 

bedrock values. The weight of evidence for depth to 

bedrock is 3 for all species.

Base Saturation

Research indicates that low base saturation can result 

in forest stress. Cronan and Grigal (1995) reported 

that forest stress from increased Al availability occurs 

when soil base saturation is less than 15 percent of 

effective cation exchange capacity. In research focusing 

on sugar maples and red oaks, trees on soils with a 

Table 2.5—Permeability and Textural Class from O’Geen (2012)

Permeability class

Permeability

(cm hr-1) Textural class

Very slow <0.13 clay

Slow 0.13–0.5 sandy clay, silty clay

Moderately slow 0.5–2.0 clay loam, sandy clay loam, silty clay loam

Moderate 2.0–6.3 very fine sandy loam, loam, silt loam, silty clay loam, silt

Moderately rapid 6.3–12.7 sandy loam, fine sandy loam

Rapid 12.7–25.4 sand, loamy sand

Very Rapid >25.4 coarse sand
14

External Influences

Biomass removal

The effect of biomass removal on critical loads is 

considered from a nutrient budget perspective as 

opposed to an optimal growth perspective. As biomass 

removal increases, N and other nutrients are removed 

from the site. Critical loads on sites with high biomass 

removal are expected to be higher than sites with low 

biomass removal as long as base cations and other 

nutrients are sufficient.

Pests

Increased foliar N can make forest trees more palatable 

to insect pests (McClure 1991). A meta-analysis by 

Furong et al. (2016) indicates that elevated N inputs 

increase food sources and palatability for insects and 

decrease tree resistance to herbivores, especially for 

broadleaved species. Defoliation can decrease tree 

growth and increase mortality (Lovett et al. 2002). We 

expect that having an abundance of insect pests would 

result in suboptimal growth, while having low levels 

or an absence of insect pests would result in optimal 

growth.

Pathogens

Increased N can make forest trees more susceptible to 

pathogens (Latty et al. 2003). Colonization with fungal 

pathogens can result in reduced growth and, in some 

cases, decreased survival. Trees weakened by fungal 

pathogens are expected to have suboptimal growth; 

trees with minimal or no fungal pathogens are expected 

to have optimal growth.
15

3 ABIES BALSAMEA (BALSAM FIR)

Elevation

We set optimal elevation ranges for the northeastern 

United States based on basal area dominance of trees at 

various elevations in the Green Mountains of Vermont 

(Beckage et al. 2008) and elevation ranges from the 

White Mountains of New Hampshire (Leak and 

Graber 1974). Balsam fir was present in significant 

numbers on plots in the Green Mountains between 762 

and 1158 m, with greatest abundance around 1000 m 

(Beckage et al. 2008). In the White Mountains, balsam 

fir was present between 610 and 1373 m, with an 

increasing percentage of total basal area at the highest 

elevations (Leak and Graber 1974). We associated 

elevations from 900 to 1400 m with optimal growth and 

elevations outside of this range with suboptimal growth.

Aspect

Because balsam fir is drought intolerant, shade tolerant 

(NRCS 2014), and generally more cold tolerant, we 

associated southwestern aspects with suboptimal growth 

and northeastern aspects with optimal growth.

Slope Gradient

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

thresholds for slope. Growth for balsam fir is expected 

to be optimal from 0 to 6.4 percent.

Average January, July, and May to 

September Temperatures

Reported optimal average January temperatures range 

from -18 to -12 °C, and average July temperatures range 

from 16 to 18 °C (Frank 1990). These values are slightly 

cooler than temperature ranges determined using high 

importance values from the Climate Change Atlas 

(U.S. Forest Service, n.d.) and may reflect the northern 

range of balsam fir that is not included in the Climate 

Change Atlas data. For the purposes of this report, high 

importance values were used to set optimal temperature 

ranges. Growth may also be optimal at cooler 

temperatures. We set the optimal January temperature 

range as -16.3 to -7.7 °C, the optimal July temperature 

range as 17.3 to 20.1 °C, and the optimal May to 

September temperature range as 13.7 to 16.4 °C.

SPECIES RANGE AND CRITICAL LOAD

Balsam fir, Abies balsamea (L.) Mill., grows in the 

northeastern and northern midwestern United States, 

mountainous areas of the eastern United States, and 

much of eastern and central Canada. Optimum growth 

occurs in cool, moist areas with precipitation from 760 

to 1100 mm, average January temperatures from -18 

to -12 °C, and average July temperatures from 16 to 

18 °C. Balsam fir can be found on silty or stony loams 

and grows best on moist soil with an organic layer 

pH of 6.5 to 7.0. Growth is slowest on gravelly sands 

and in peat swamps. It can be found from sea level to 

approximately 1917 m elevation in the northern part of 

its range (Frank 1990).

We assigned a critical load of 9 to 21.1 kg N ha-1 yr-1

for balsam fir. Bedison and McNeil (2009) found 

increased basal area increment along a deposition 

gradient of <3.5 to >7 kg N ha-1 yr-1, and Thomas et al. 

(2010) found increased growth and increased survival 

as deposition increased from 3 to 9 kg N ha-1 yr-1; the 

critical load from these studies is >9 kg N ha-1 yr-1. 

As fertilization increased N inputs on Mt. Ascutney 

in Vermont from 5.4 (bulk deposition) to 21.1 

kg N ha-1 yr-1 (McNulty et al. 1996), however, balsam 

fir basal area growth decreased; the critical load for 

this study is less than 21.1 kg N ha-1 yr-1. Critical load 

values and citations are shown in Table 3.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for balsam fir, including the balsam 

fir section of the Forest Service silvics handbook (Frank 

1990), the PLANTS database (NRCS 2014), and high 

importance value data from the Climate Change Atlas 

(U.S. Forest Service, n.d.). In general, any factor that 

inhibits balsam fir growth would be expected to result 

in a decreased need for N and thus a lower critical 

load; optimal growth conditions would result in an 

increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 3.2.
16

Annual Precipitation

Reported annual precipitation ranges include 527 to 

1896 mm (U.S. Forest Service, n.d.), 330 to 1524 mm 

(NRCS 2014), and 390 to 1400 mm (Frank 1990). 

Optimal precipitation was reported as 760 to 1100 mm 

(Frank 1990). The high importance value range of 674 

to 1317 mm (U.S. Forest Service, n.d.) was associated 

with optimal growth. Values outside of this range were 

associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 405 to 586 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges include pH 4 to 6 (NRCS 

2014), as well as an ideal range of pH 6.5 to 7.0 in the 

upper organic layers (Frank 1990). We associated soil 

pH values of 4.7 to 7.0, the high importance value 

range from the Climate Change Atlas (U.S. Forest 

Service, n.d.), with optimal growth and values outside 

of this range with suboptimal growth.

Percent Clay and Percent Coarse Sand

Balsam fir commonly grows on Spodosols, Inceptisols, 

and Histosols (Frank 1990). High importance values 

from the Climate Change Atlas (U.S. Forest Service, 

n.d.) were used to set thresholds for percent clay and 

percent coarse sand. Optimal clay is 3.8 to 32.5 percent; 

optimal coarse sand is 61.9 to 91.7 percent.

Table 3.1—Effects of nitrogen deposition on balsam fir

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Adirondack State Park, NY < 3.5 to >7.0 (wet) Increased basal area increment Bedison and McNeil 2009

Northeastern, Midwestern U.S. 3 to 9 Increased growth and survival Thomas et al. 2010

Mt. Ascutney, VT 5.4 (bulk) to 21.1 Decreased basal area growth McNulty et al. 1996

Table 3.2—Abiotic modifying factors for balsam fir

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m < 900, > 1400 900 to 1400 4

Aspect southwestern northeastern 2

Slope gradient % > 6.4 0.0 to 6.4 3.7

January temperature °C < -16.3, > -7.7 -16.3 to -7.7 4.3

July temperature °C < 17.3, > 20.1 17.3 to 20.1 4.7

May-September temperature °C < 13.7, > 16.4 13.7 to 16.4 4.7

Annual precipitation mm < 674, > 1317 674 to 1317 4.3

May-September precipitation mm < 405, > 586 405 to 586 4.3

Soil pH < 4.7, > 7.0 4.7 to 7.0 4.3

Clay % < 3.8, > 32.5 3.8 to 32.5 3.7

Coarse sand % < 61.9, > 91.7 61.9 to 91.7 4

Permeability cm hr-1 < 0.8, > 11.1 0.8 to 11.1 3

Depth to bedrock m < 0.5 ≥ 0.5 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
17

EXTERNAL INFLUENCES

Insect Pests

The balsam wooly adelgid (Adelgis picea) poses the 

most serious insect threat to balsam fir (Dukes et al. 

2009, Frank 1990). Spruce budworm (Choristoneura 

fumiferana) also poses a significant threat (Dukes et al. 

2009) to balsam fir and is one of the most destructive 

insects in northern spruce-fir forests. We assumed that 

having an abundance of insect pests would correlate 

with suboptimal growth, while low levels or the absence 

of insect pests would correlate with optimal growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low levels or the absence of fungal pathogens 

would correlate with optimal growth.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.8 to 11.1 cm hr-1, the permeability rates for 

moderately slow to moderately rapidly draining clay 

loams to sandy loams (O’Geen 2012).

Depth to Bedrock

We associated depth to bedrock of <0.5 m with 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and depths ≥0.5 m with optimal growth. 
18

4 ACER RUBRUM (RED MAPLE)

(NRCS 2014), and high importance value data from 

the Climate Change Atlas (U.S. Forest Service, n.d.). In 

general, any factor that inhibits red maple growth would 

be expected to result in a decreased need for N and 

thus a lower critical load; optimal growth conditions 

would result in an increased demand for N and a 

higher critical load. General values were used for base 

saturation, Ca:Al, and biomass removal as described in 

Chapter 2 (pages 13-14). Graphs showing importance 

values for abiotic modifying factor ranges by species are 

in Appendix 1. Optimal and suboptimal growth ranges 

and weight of evidence values are shown in Table 4.2.

Elevation

Elevation effects depend on latitude. We set elevation 

ranges for the northeastern United States based on 

basal area dominance at various elevations in the Green 

Mountains of Vermont as reported by Beckage et al. 

(2008) as well as elevation ranges from the White 

Mountains of New Hampshire (Leak and Graber 

1974). Growth is expected to be optimal below 600 m 

in elevation and suboptimal at higher elevations.

Aspect

Shade tolerance and drought tolerance are both 

intermediate for red maple (NRCS 2014). Because it is 

not clear which aspect is most favorable for growth, we 

have not associated aspect with growth for this species.

Slope Gradient

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

thresholds for slope. We expect red maple growth to be 

optimal from 0.0 to 13.0 percent.

Average January, July, and May to 

September Temperatures

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

temperature thresholds. We associated January 

temperatures from -12.3 to 8.5 °C, July temperatures 

from 18.7 to 27.2 °C, and May to September 

temperatures from 15.2 to 25.3 °C with optimal 

growth. Values outside of these ranges were associated 

with suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

Red maple, Acer rubrum L., grows across southeastern 

and central Canada, as well as much of the eastern and 

central United States. Red maple grows and thrives 

across a wide variety of sites; growth is restricted by 

extreme cold in Canada and the dry climate in the 

prairie states. Although red maple can be found from 

mountain ridges to swamps, best development occurs 

on moderately well-drained, moist sites at low to 

intermediate elevations in the mountains of Kentucky 

and Tennessee. Aspect does not appear to play a strong 

role in growth (Walters and Yawney 1990).

We assigned a critical load of >11 to 26 kg ha-1 yr-1

for red maple. Several sources indicate that red 

maple growth and survival increases with increasing 

N deposition. Thomas et al. (2010) found increased 

growth and survival with increased deposition from 

3 to 11 kg N ha-1 yr-1, while Bedison and McNeil 

(2009) found increased basal area increment between 

<3.5 and >7.0 kg N ha-1 yr-1. In West Virginia, red 

maple basal area increased with deposition from 12 

to 14 kg N ha-1 yr-1 (Elias 2008). At a site on Mt. 

Ascutney in Vermont, maple sprouting increased on 

sites with high spruce/fir mortality as fertilization 

increased N input from 5.4 (bulk deposition) to 21.1 

kg ha-1 yr-1 (McNulty et al. 1996), most likely as a 

direct (fertilization) or indirect (decreased competition) 

result of N addition. This research indicates that the 

lower critical load for red maple is >11 kg N ha-1 yr-1, 

while the upper critical load is >21.1 kg N ha-1 yr-1. 

More research is needed to define the upper limit for 

red maple critical load. In the interim, we have assigned 

an upper value of 26 kg N ha-1 yr-1 for red maple. 

This is based on the high deposition value (>25 kg 

N ha-1 yr-1) in a European study; significant N leaching 

occurred at all forested plots above this deposition 

(Dise and Wright 1995). Critical load values and 

citations are shown in Table 4.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for red maple, including the red 

maple section of the Forest Service silvics handbook 

(Walters and Yawney 1990), the PLANTS database 
19

Annual Precipitation

Reported precipitation ranges for red maple include 

578 to 2289 mm (U.S. Forest Service, n.d.) and 635 

to 2030 mm (NRCS 2014). We associated the high 

importance value range of 786 to 1448 mm (U.S. Forest 

Service, n.d.) with optimal growth. Values outside of 

this range were associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 410 to 684 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges for red maple include 4.7 to 

7.3 (NRCS 2014) and 2.7 to 8.4 (U.S. Forest Service, 

n.d.). We associated soil pH values of 4.6 to 7.0, high 

importance values from the Climate Change Atlas 

(U.S. Forest Service, n.d.), with optimal growth. Values 

outside of this range were associated with suboptimal 

growth.

Table 4.1—Effect of nitrogen deposition on red maple

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Interacting factors Citation

Northeastern, Midwestern 

U.S.

3 to 11 Increased growth, no change in 

survival

Thomas et al. 2010

Adirondack State Park, NY <3.5 to >7.0 Increased basal area increment Bedison and McNeil 

2009

Vermont 5.4 (bulk) to 21.1 Increased seedling and sprouts 

on high mortality plots

McNulty et al. 1996

West Virginia 12 to 14 Increased basal area S deposition

20 to 26 kg ha-1 yr-1

Elias 2008

Table 4.2—Abiotic modifying factors for red maple

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 600 ≤ 600 3

Aspect NA NA

Slope gradient % > 13.0 0.0 to 13.0 4

January temperature °C < -12.3, > 8.5 -12.3 to 8.5 4.7

July temperature °C < 18.7, > 27.2 18.7 to 27.2 5

May-September temperature °C < 15.2, > 25.3 15.2 to 25.3 5

Annual precipitation mm < 786, > 1448 786 to 1448 4.7

May-September precipitation mm < 410, > 684 410 to 684 4.7

Soil pH < 4.6, > 7.0 4.6 to 7.0 4.3

Clay % < 3.7, > 40.5 3.7 to 40.5 3.3

Coarse sand % < 56.1, > 97.7 56.1 to 97.7 3.7

Permeability cm hr-1 < 0.7, > 12.0 0.7 to 12.0 3

Depth to bedrock m < 1.0 ≥ 1.0 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
20

Depth to Bedrock

We assigned depth to bedrock values of <1.0 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and depths ≥1.0 m for optimal growth.

EXTERNAL INFLUENCES

Insect Pests

We assumed that having an abundance of insect pests 

would correlate with suboptimal growth, while low 

levels or the absence of insect pests would correlate 

with optimal growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low levels or the absence of fungal pathogens 

would correlate with optimal growth.

Percent Clay and Percent Coarse Sand

Red maple grows on a wide variety of soils but grows 

best on moderately drained, moist soils. Dominant 

soil orders are Entisols, Inceptisols, Ultisols, Alfisols, 

Spodosols, and Histosols (Walters and Yawney 1990). 

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for percent clay and percent coarse sand. 

Optimal clay is 3.7 to 40.5 percent; optimal coarse sand 

is 56.1 to 97.7 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.7 to 12.0 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).
21

5 ACER SACCHARUM (SUGAR MAPLE)

of 9 to 15 kg ha-1 yr-1 wet N deposition (approximately 

11 to 17 kg ha-1 yr-1 wet + dry N) in North Carolina 

and Virginia. At a site on Mt. Ascutney in Vermont, 

maple sprouting increased on sites with high spruce/

fir mortality as fertilization increased N input from 5.4 

(bulk deposition) to 21.1 kg ha-1 yr-1 (McNulty et al. 

1996), most likely as a direct (fertilization) or indirect 

(decreased competition) result of N addition. At higher 

levels of fertilization, sugar maples were negatively 

impacted; sugar maple seedling survival decreased in 

Michigan as fertilization increased N inputs from 6.8 

to 11.8 kg ha-1 yr-1 to 36.8 to 41.8 kg ha-1 yr-1. Sugar 

maple dieback increased at a base-poor site in Quebec, 

Canada, when fertilization increased N inputs from 

8.5 to 34.5 kg ha-1 yr-1 (Moore and Houle 2009, 2013), 

although not significantly. This research indicates that 

the lower critical load for sugar maple is >8.5 and <9.5 

kg N ha-1 yr-1, while the upper critical load is >21.1 and 

<34.5 kg N ha-1 yr-1. The results from Duchesne et al. 

(2002) were not used to set the critical load because 

the response to N was not analyzed separately. More 

research is needed to define the upper limit for sugar 

maple critical load. In the interim, we have assigned 

a value of 26 kg N ha-1 yr-1. This is based on the high 

deposition value (>25 kg N ha-1 yr-1) in a European 

study; significant N leaching occurred at all forested 

plots above this deposition (Dise and Wright 1995). 

Critical load values and citations are shown in Table 

5.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for sugar maple, including the sugar 

maple section of the Forest Service silvics handbook 

(Godman et al. 1990), the PLANTS database (NRCS 

2014), and high importance value data from the 

Climate Change Atlas (U.S. Forest Service, n.d.). 

In general, any factor that inhibits sugar maple 

growth would be expected to result in a decreased 

need for N and thus a lower critical load; optimal 

growth conditions result in an increased demand for 

N and a higher critical load. Thus a declining sugar 

maple stand on an unglaciated summit, shoulder, or 

backslope in the Appalachian Plateau, as described 

by Horsley et al. (2000), would have a lower critical 

load compared to a sugar maple on a fertile site at a 

SPECIES RANGE AND CRITICAL LOAD

Sugar maple, Acer saccharum Marsh., grows throughout 

the central and northern Midwest and eastern United 

States, as well as southeastern Canada. Sugar maple 

grows best on fertile, moist, well-drained soils; it grows 

poorly on nutrient-poor, dry, and shallow soils and 

is rarely found in swamps or wet soils. It occurs at 

elevations up to 2,500 feet (762 m) in New England 

and New York, up to 1,600 feet (488 m) in the upper 

Midwest, and from 3,000 to 5,500 feet (914 to 1676 

m) in the Appalachians (Godman et al. 1990). Bailey 

et al. (2004) found that stands with soil Ca and Mg 

below threshold values (<2 percent Ca saturation and 

<0.5 percent Mg saturation in upper B horizon; <4 

percent Ca saturation and <0.6 percent Mg saturation 

in lower B horizon), when combined with two or 

more moderate or severe insect defoliations in the 

past 10-year period, had high sugar maple mortality. 

All declining stands with low soil Ca and Mg were 

located on unglaciated upper landscape positions, 

particularly unglaciated summits, shoulders, and upper 

backslopes, where weatherable minerals are below the 

rooting zone and water flow paths are less likely to 

bring ions released from bedrock into the root zone. 

In Canadian sugar maples, soil B horizon Ca >28.4 

percent was necessary to prevent foliar Ca deficiency; 

base saturation of 33.4 percent prevented P deficiency 

(Ouimet et al 2013). In Vermont, Schaberg et al. (2006) 

found that trees with low foliar Ca and high foliar Al 

had elevated branch dieback and decreased basal area 

relative to trees with high Ca and low Al. Foliar Ca and 

Al were significantly and positively correlated with soil 

Ca and Al.

We assigned a critical load of 8.5 to 26 kg N ha-1 yr-1

for sugar maple. Thomas et al. (2010) found increased 

growth and no change in survival with increased 

N deposition from 3 to 10.5 kg N ha-1 yr-1, while 

Duchesne et al. (2002) found that sugar maple basal 

area increment in Quebec was negatively correlated 

with N and S wet deposition and soil exchangeable 

acidity over a range of 3.2-9.5 kg N ha-1 yr-1 and 

6.5-15.5 kg S ha-1 yr-1 deposition. Stand decline rate 

was positively associated with N and S deposition and 

soil exchangeable acidity. Research from Boggs et al. 

(2005) supports a relatively high critical load; sugar 

maple basal area increased across a deposition gradient 
22

Elevation

Elevation effects depend on latitude. We set elevation 

ranges for the northeastern United States based on 

basal area dominance at various elevations in the Green 

Mountains of Vermont as reported by Beckage et al. 

(2008) as well as elevation ranges from the White 

lower elevation. General values were used for Ca:Al 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 5.2.

Table 5.1—Effect of nitrogen deposition on sugar maple

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Interacting factors Citation

Northeastern, 

Midwestern U.S.

3 to 10.5 Increased growth, no change in 

survival

Thomas et al. 2010

Quebec 3.2 to 9.5 (wet) Basal area increment negatively 

correlated with N and S deposition

S deposition 6.5 to 15.5 Duchesne et al. 2002

Vermont 5.4 (bulk) to 21.1 Increased seedling and sprouts on 

high mortality plots

McNulty et al. 1996

Michigan 6.8-11.8 to 36.8-41.8 Decreased seedling survival Talhelm et al. 2013

Quebec 8.5 to 34.5 Increased (not significant) dieback, 

decreased foliar Ca

Moore and Houle 

2009

North Carolina and 

Virginia

9 to 15 (wet) Increased basal area Boggs et al. 2005

West Virginia 12 to 14 Decreased basal area S deposition

20 to 26 kg ha-1 yr-1

Elias 2008

Pennsylvania and 

New York

Declining trees on unglaciated 

summits, shoulders, upper backslopes

Horsley et al. 2000

Table 5.2—Abiotic modifying factors for sugar maple

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 800 ≤ 800 4

Aspect southwestern northeastern 2

Slope gradient % < 0.1, > 11.0 0.1 to 11.0 4

January temperature °C < -12.9, > 0.5 -12.9 to 0.5 5

July temperature °C < 18.2, > 25.1 18.2 to 25.1 5

May-September temperature °C < 14.6, > 22.3 14.6 to 22.3 5

Annual precipitation mm < 773, > 1352 773 to 1352 5

May-September precipitation mm < 406, > 581 406 to 581 5

Soil pH < 4.8, > 7.1 4.8 to 7.1 4.3

Clay % < 4.8, > 37.1 4.8 to 37.1 3.7

Coarse sand % < 57.9, > 96.0 57.9 to 96.0 3.7

Permeability cm hr-1 < 0.6, > 10.2 0.6 to 10.2 3

Depth to bedrock m < 1.0 ≥ 1.0 3

B-horizon base saturation % < 20 ≥ 20 3

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
23

Mountains of New Hampshire (Leak and Graber 

1974). Growth is expected to be optimal below 

approximately 800 m in elevation, and suboptimal at 

higher elevations.

Aspect

Sugar maple is shade tolerant, with medium drought 

tolerance (NRCS 2014). Currently northeastern aspects 

are associated with optimal growth and southwestern 

aspects with suboptimal growth.

Slope Gradient

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

thresholds for slope. Growth for sugar maple is 

expected to be optimal from 0.1 to 11.0 percent.

Average January, July, and May to 

September Temperatures

Godman et al. (1990) reports an average January 

temperature range of -18 to -10 °C and an average July 

temperature range of 16 to 27 °C. High importance 

values from the Climate Change Atlas (U.S. Forest 

Service, n.d.) were used to set temperature thresholds. 

We associated January temperatures from -12.9 to 0.5 

°C, July temperatures from 18.2 to 25.1 °C, and May 

to September temperatures from 14.6 to 22.3 °C with 

optimal growth, and values outside of these ranges with 

suboptimal growth.

Annual Precipitation

Reported precipitation ranges include 576 to 2289 mm 

(U.S. Forest Service, n.d.), 559 to 2030 mm (NRCS 

2014), and 510 to 2030 mm, with an ideal value of 

1270 mm (Godman et al. 1990). The high importance 

value range of 773 to 1352 mm (U.S. Forest Service, 

n.d.) was associated with optimal growth. Values 

outside of this range were associated with suboptimal 

growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 406 to 581 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges include pH 3.7 to 7.9 

(NRCS 2014), 4.1 to 8.1 (U.S. Forest Service, n.d.), 

and 3.7 to 7.3, with an ideal range of pH 5.5 to 7.3. 

(Godman et al. 1990). We associated soil pH values of 

4.8 to 7.1, the high importance value range from the 

Climate Change Atlas (U.S. Forest Service, n.d.), with 

optimal growth and values outside of this range with 

suboptimal growth.

At Hubbard Brook Experimental Forest in New 

Hampshire, soil pH is 3.4-3.8 in the organic horizons. 

Sugar maple at high elevations experience high 

mortality and low foliar Ca. Calcium additions resulted 

in increased soil pH (Oie = 5.0), increased foliar and 

root Ca, and increased seedling survival ( Juice 2006).

Percent Clay and Percent Coarse Sand

Sugar maple grows on loamy and sandy soils. 

Dominant soil orders are Mollisols, Alfisols, and 

Spodosols (Godman et al. 1990). We used high 

importance values from the Climate Change Atlas 

(U.S. Forest Service, n.d.) to set thresholds for percent 

clay and percent coarse sand. Optimal clay is 4.8 

to 37.1 percent; optimal coarse sand is 57.9 to 96.0 

percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.6 to 10.2 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

We assigned depth to bedrock values of <1.0 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and depths ≥1.0 m for optimal growth.

Base Saturation

Research indicates that sugar maple on soils with 

higher base saturation, and thus higher fertility, have 

increased growth, and decreased mortality (Duchesne 

et al. 2002, Horsley et al. 2000). Sullivan et al. (2013) 

found that a B horizon base saturation of 12 percent 

was the cut-off below which there was a near absence 

of sugar maple seedling regeneration. Regeneration 
24

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by an abundance of fungal pathogens would have 

suboptimal growth. Low or absent levels of fungal 

pathogens would correlate with optimal growth.

was highest above 20 percent base saturation. Ouimet 

et al. (2013) found that B horizon Ca >28.4 percent 

was necessary to prevent foliar Ca deficiency, indicating 

that even higher levels of soil base saturation may be 

necessary for maximum forest health.

EXTERNAL INFLUENCES

Insect Pests

We assumed that abundant levels of insect pests would 

correlate with suboptimal growth, while low or absent 

levels of insect pests would correlate with optimal 

growth.
25

6 BETULA ALLEGHANIENSIS (YELLOW BIRCH)

would be expected to result in a decreased need for 

N and thus a lower critical load; optimal growth 

conditions result in an increased demand for N and a 

higher critical load. General values were used for base 

saturation, Ca:Al, and biomass removal as described in 

Chapter 2 (pages 13-14). Graphs showing importance 

values for abiotic modifying factor ranges by species 

are in Appendix 1. Optimal and suboptimal growth 

ranges and weight of evidence values are shown in 

Table 6.2.

Elevation

Elevation effects depend on latitude. We set elevation 

ranges for the northeastern United States based 

on basal area dominance of yellow birch at various 

elevations in the Green Mountains of Vermont as 

reported by Beckage et al. (2008), as well as elevation 

ranges reported for the White Mountains of New 

Hampshire (Leak and Graber 1974). Growth is 

expected to be optimal between approximately 550 and 

800 m in elevation.

Aspect

Yellow birch has medium drought and shade 

tolerance and prefers cool, moist sites. We associated 

northeastern aspects with optimal growth and 

southwestern aspects with suboptimal growth.

Slope Gradient

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

thresholds for slope. We expect growth for yellow birch 

to be optimal from 0.6 to 10.3 percent.

Average January, July, and May to 

September Temperatures

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

temperature thresholds. We associated January 

temperatures from -13.3 to -6.3 °C, July temperatures 

from 16.5 to 20.4 °C, and May to September 

temperatures from 13.0 to 16.8 °C with optimal 

growth, and values outside of these ranges with 

suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

Yellow birch, Betula alleghaniensis Britton, grows in 

southeastern Canada and in the northeastern and 

north central United States south to the Appalachian 

Mountains (Erdmann 1990). In New England, soil 

drainage is one of the most important factors affecting 

yellow birch (Post et al. 1969).Yellow birch can grow on 

a great variety of soils but grows best on well drained, 

fertile loams and moderately well drained sandy loams 

in cool, moist climates (Erdmann 1990). In the Green 

Mountains of Vermont, yellow birch grows well up 

to 792 m (Beckage et al. 2008); it can be found up to 

950 m in the White Mountains of New Hampshire 

(Leak and Graber 1974). It has better growth at lower 

elevations and on the northwest aspect. (Erdmann 

1990). Yellow birch decline has been observed across 

the growing region since the 1930s; this may be 

related to winter thaw-freeze events, which have been 

increasing over time (Bourque et al. 2005).

We assigned a critical load of 3 to 17 kg ha-1 yr-1 for 

yellow birch. Thomas et al. (2010) found no change in 

growth and decreased survival for yellow birch over a 

deposition range of 3 to 11 kg N ha-1 yr-1. Boggs et al. 

(2005) found decreased yellow birch basal area across 

a deposition gradient of 9 to 15 kg ha-1 yr-1 wet N 

deposition (approximately 11 to 17 kg ha-1 yr-1 wet + 

dry N) in North Carolina and Virginia. Peak natural 

log of yellow birch basal area occurred at 13 kg ha-1 yr-1

wet N (approximately 15 kg ha-1 yr-1 wet + dry N), for 

a study-based critical load range of 13-17 kg ha-1 yr-1. 

At a site on Mt. Ascutney in Vermont, birch basal area 

decreased but stump sprouting increased as fertilization 

increased N input from 5.4 (bulk deposition) to 21.1 

kg ha-1 yr-1 (McNulty et al. 1996). Critical load values 

and citations are shown in Table 6.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for yellow birch, including the yellow 

birch section of the Forest Service silvics handbook 

(Erdmann 1990), the PLANTS database (NRCS 

2014), and high importance value data from the 

Climate Change Atlas (U.S. Forest Service, n.d.). In 

general, any factor that inhibits yellow birch growth 
26

Annual Precipitation

Reported precipitation ranges include 587 to 2289 mm 

(U.S. Forest Service, n.d.), 635 to 2030 mm (NRCS 

2014), and 640 to 1270 mm (Erdmann 1990). We 

associated the high importance value range of 866 

to 1558 mm (U.S. Forest Service, n.d.) with optimal 

growth. Values outside of this range were associated 

with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 437 to 695 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges include 4 to 8 (NRCS 2014) 

and 4.5 to 7.6 (U.S. Forest Service, n.d.). We associated 

the pH range of 4.7 to 5.9, from the Climate Change 

Atlas (U.S. Forest Service, n.d.) high importance values, 

with optimal growth. Values outside of this range were 

associated with suboptimal growth.

Percent Clay and Percent Coarse Sand

Yellow birch grows best on well drained, fertile loams 

and moderately well drained sandy loams. Dominant 

soil orders are Alfisols, Inceptisols, and Spodosols 

(Erdmann 1990). High importance values from the 

Climate Change Atlas (U.S. Forest Service, n.d.) were 

used to set thresholds for percent clay and percent 

coarse sand. Optimal clay is 4.5 to 18.8 percent; 

optimal coarse sand is 61.4 to 87.0 percent.

Table 6.1—Effect of nitrogen deposition on yellow birch

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Northeastern, Midwestern U.S. 3 to 11 No change in growth; decreased survival Thomas et al. 2010

Mt. Ascutney, VT 5.4 (bulk) to 21.1 Decreased basal area; increased stump 

sprouting

McNulty et al. 1996

NC and VA 9 to 15 (wet) Decreased basal area (BA); peak BA at 

13 kg ha-1 yr-1 wet N

Boggs et al. 2005

Table 6.2—Abiotic modifying factors for yellow birch

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m < 550, > 800 550 to 800 4

Aspect southwestern northeastern 2

Slope gradient % < 0.6, > 10.3 0.6 to 10.3 3

January temperature °C < -13.3, > -6.3 -13.3 to -6.3 3.7

July temperature °C < 16.5, > 20.4 16.5 to 20.4 3.7

May-September temperature °C < 13.0, > 16.8 13.0 to 16.8 3.7

Annual precipitation mm < 866, > 1558 866 to 1558 3.3

May-September precipitation mm < 437, > 695 437 to 695 3.3

Soil pH < 4.7, > 5.9 4.7 to 5.9 3.3

Clay % < 4.5, > 18.8 4.5 to 18.8 2.7

Coarse sand % < 61.4, > 87.0 61.4 to 87.0 2.7

Permeability cm hr-1 < 0.8, > 10.6 0.8 to 10.6 2.3

Depth to bedrock m < 0.8 ≥ 0.8 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low High

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
27

EXTERNAL INFLUENCES

Insect Pests

We assumed that abundant insect pests correlate with 

suboptimal growth, while low or absent insect pests 

correlate with optimal growth. Birch borer (Agrilus 

anxius) is the primary pest of concern for yellow birch 

(Erdmann 1990).

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by an abundance of fungal pathogens would have 

suboptimal growth. Low or absent levels of fungal 

pathogens would correlate with optimal growth. Nectria 

galligeno is the primary pathogen of concern for yellow 

birch (Erdmann 1990).

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.8 to 10.6 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

We assigned depth to bedrock values of <0.8 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and ≥0.8 m for optimal growth.
28

7 BETULA PAPYRIFERA (PAPER BIRCH)

1400 m in elevation, and suboptimal when elevation is 

<670 or >1400 m.

Aspect

Paper birch is both drought intolerant and shade 

intolerant and prefers cool, moist sites. We associated 

northeastern aspects with optimal growth and 

southwestern aspects with suboptimal growth.

Slope Gradient

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

thresholds for slope. Growth for paper birch is expected 

to be optimal from 0.0 to 6.9 percent.

Average January, July, and May to 

September Temperatures

Paper birch grows in similar areas as quaking aspen, 

which has an average January temperature range of 

-30 to -3 °C (Perala 1990). Safford et al. (1990) reports 

an average July temperature range of 13 to 21 °C. For 

the purposes of this report, we set optimal growth 

ranges using high importance values from the Climate 

Change Atlas data (U.S. Forest Service, n.d.), which 

does not include the entire range for paper birch. 

Optimal growth may also occur at lower temperatures. 

We associated January temperatures from -16.2 to -8.5 

°C, July temperatures from 17.2 to 21.7 °C, and May 

to September temperatures from 13.6 to 18.0 °C with 

optimal growth and values outside of these ranges with 

suboptimal growth.

Annual Precipitation

Reported precipitation ranges for paper birch include 

436 to 1896 mm (U.S. Forest Service, n.d.), 305 to 

1524 mm (NRCS 2014), and 300 to 1520 (Safford et 

al. 1990). The high importance value range of 670 to 

1299 mm (U.S. Forest Service, n.d.) was associated 

with optimal growth. Values outside of this range were 

associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

SPECIES RANGE AND CRITICAL LOAD

Paper birch, Betula papyrifera Marsh., grows across 

a broad swath of Canada and much of the northern 

and mountainous United States (Safford et al. 1990). 

Paper birch can grow on a great variety of soils but 

grows best on well drained, sandy loam soils in cool, 

moist climates. It grows up to the tree line in southern 

locations and on cool northern aspects in the northern 

part of its range (Safford et al. 1990).

We assigned a critical load of 9.5 to 21.1 kg N ha-1 yr-1

for paper birch. Thomas et al. (2010) found no change 

in growth and increased survival for paper birch over a 

N deposition range of 3 to 9.5 kg ha-1 yr-1. At a site on 

Mt. Ascutney in Vermont, birch basal area decreased 

but stump sprouting increased as fertilization increased 

N input from 5.4 (bulk deposition) to 21.1 kg ha-1 yr-1

(McNulty et al. 1996). This research indicates that the 

critical load for paper birch is above 9.5 kg N ha-1 yr-1

and below 21.1 kg N ha-1 yr-1. Critical load values and 

citations are shown in Table 7.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for paper birch, including the paper 

birch section of the Forest Service silvics handbook 

(Safford et al. 1990), the PLANTS database (NRCS 

2014), and high importance value data from the 

Climate Change Atlas (U.S. Forest Service, n.d.). In 

general, any factor that inhibits paper birch growth 

would be expected to result in a decreased need for 

N and thus a lower critical load; optimal growth 

conditions result in an increased demand for N and a 

higher critical load. General values were used for base 

saturation, Ca:Al, and biomass removal as described in 

Chapter 2 (pages 13-14). Graphs showing importance 

values for abiotic modifying factor ranges by species are 

in Appendix 1. Optimal and suboptimal growth ranges 

and weight of evidence values are shown in Table 7.2.

Elevation

We set elevation ranges for the northeastern United 

States based on elevation ranges for paper birch in New 

Hampshire from Leak and Graber (1974). Growth is 

expected to be optimal between approximately 670 and 
29

from the Climate Change Atlas (U.S. Forest Service, 

n.d.) were used to set thresholds for percent clay and 

percent coarse sand. Optimal clay is 3.2 to 39.1 percent; 

optimal coarse sand is 61.3 to 95.8 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.9 to 11.9 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

We assigned depth to bedrock values of <0.6 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and values ≥0.6 for optimal growth.

thresholds for average May to September precipitation. 

We associated the precipitation range from 425 to 579 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Soil pH ranges reported for paper birch include 4.2 to 

7.4 (NRCS 2014) and 4.5 to 8.1 (U.S. Forest Service, 

n.d.). We associated the high importance range of pH 

4.7 to 7.0 from the Climate Change Atlas (U.S. Forest 

Service, n.d.) with optimal growth. Values outside of 

this range were associated with suboptimal growth.

Percent Clay and Percent Coarse Sand

Paper birch grows best on well drained, sandy loams. 

Dominant soil orders are Spodosols, Inceptisols, and 

Entisols (Safford et al. 1990). High importance values 

Table 7.1—Effect of nitrogen deposition on paper birch

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Northeastern, 

Midwestern U.S.

3 to 9.5 No change in growth; 

increased survival

Thomas et al. 2010

Mt. Ascutney, VT 5.4 (bulk) to 21.1 Decreased basal area; 

increased stump sprouting

McNulty et al. 1996

Table 7.2—Abiotic modifying factors for paper birch

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m < 670, > 1400 670 to 1400 3

Aspect southwestern northeastern 2

Slope gradient % > 6.9 0.0 to 6.9 3.3

January temperature °C < -16.2, > -8.5 -16.2 to -8.5 4

July temperature °C < 17.2, > 21.7 17.2 to 21.7 4.3

May-September temperature °C < 13.6, > 18.0 13.6 to 18.0 4.3

Annual precipitation mm < 670, > 1299 670 to 1299 3.3

May-September precipitation mm < 425, > 579 425 to 579 4

Soil pH < 4.7, > 7.0 4.7 to 7.0 3.7

Clay % < 3.2, > 39.1 3.2 to 39.1 2.7

Coarse sand % < 61.3, > 95.8 61.3 to 95.8 3.3

Permeability cm hr-1 < 0.9, > 11.9 0.9 to 11.9 2.7

Depth to bedrock m < 0.6 ≥ 0.6 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
30

EXTERNAL INFLUENCES

Insect Pests

We assumed that abundant insect pests correlate with 

suboptimal growth, while low or absent insect pests 

correlate with optimal growth. Birch borer (Agrilus 

anxius) is the primary pest of concern for paper birch 

(Dukes et al. 2009, Safford et al. 1990).

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent fungal pathogens would 

correlate with optimal growth.
31

8 CASTANEA DENTATA (AMERICAN CHESTNUT)

ABIOTIC MODIFYING FACTORS

Information from PLANTS database (NRCS 2014), 

the Climate Change Atlas (U.S. Forest Service, n.d.), 

and Wang et al. (2013) were used to set the range of 

modifying factors for American chestnut. In general, 

any factor that inhibits American chestnut growth 

would be expected to result in a decreased need for 

N and thus a lower critical load; optimal growth 

conditions result in an increased demand for N and a 

higher critical load. Due to the devastating effects of 

chestnut blight, the effect of the fungal pathogen may 

supersede abiotic site factors. In addition, American 

chestnut is not an important component of Forest 

Inventory and Analysis sites. For this reason, ranges 

for abiotic modifying factors were set using Climate 

Change Atlas data with an importance value threshold 

of 2 (U.S. Forest Service, n.d.). General values were 

used for base saturation, Ca:Al, and biomass removal as 

described in Chapter 2 (pages 13-14). Graphs showing 

importance values for abiotic modifying factor ranges 

by species are in Appendix 1. Optimal and suboptimal 

growth ranges and weight of evidence values are shown 

in Table 8.2.

Elevation

Elevation effects depend on latitude. We have provided 

an elevation range for American chestnut in the 

northeastern United States based on general elevation 

ranges of co-occurring tree species. Growth is generally 

expected to be optimal below approximately 550 m and 

suboptimal above 550 m in elevation.

Aspect

American chestnut appears to have intermediate 

shade tolerance ( Joesting et al. 2008) and medium 

drought tolerance (NRCS 2014). Because it is not clear 

which aspect is most favorable for growth, we did not 

associate aspect with growth for this species.

Slope Gradient

Remaining American chestnut trees in Canada are 

found on gentle slopes (Tindall et al. 2004). Optimal 

slope ranges above an importance value of 10 cannot 

be established with current data. However, the 

distribution over an importance value of 2 for Climate 

SPECIES RANGE AND CRITICAL LOAD

American chestnut, Castanea dentata, was at one time a 

dominant forest tree throughout the north and central 

eastern United States, from Maine to Georgia and west 

to the Ohio River Valley. By 1950, most large American 

chestnut trees had been destroyed by chestnut blight 

(Cryphonectria parasitica). American chestnuts still 

appear across their former range but are typically 

killed by chestnut blight before they mature. American 

chestnut hybrids are being developed in an attempt to 

restore chestnuts to North American forests (eFloras, 

n.d.). American chestnuts were historically found on 

well-drained, acidic soil and were rare over limestonederived soil. They could be found at elevations up to 

2000 m in the southern Appalachians on southern 

slopes, up to 900 m in the Catskills of New York, and 

up to 130 m in New Hampshire (Russell 1987). They 

grow best in well drained, acidic, medium textured 

soils from pH 4.5 to 6.5, with an optimum pH of 5.5 

(Youngsteadt 2014). They are found in regions with a 

minimum of 150 frost free days over a precipitation 

range of 889 to 1524 mm (NRCS 2014).

Because American chestnut does not occur as a mature 

tree in most forest stands, there is little research on 

its interaction with N deposition. We have assigned 

a critical load of 3 to 17 kg N ha-1 yr-1 for American 

chestnut. This is based on the critical load for oaks 

(Quercus spp.) and American beech (Fagus grandifolia), 

which, with American chestnut, are members of the 

family Fagaceae. American beech had no change in 

growth or survival over a deposition range of 3 to 11 

kg N ha-1 yr-1 (Thomas et al. 2010) but had decreased 

basal area over a deposition range of 9 to 15 wet (11 to 

17 wet + dry) N deposition (Boggs et al. 2005). Oaks 

had no change in growth or increased growth with 

increasing N deposition from 3 to 11 kg ha-1 yr-1, and 

no change in survival or decreased survival (Thomas 

et al. 2010). Chestnut oaks had a growth peak at 5 kg 

N ha-1 yr-1; scarlet oaks had a survival peak at 7 kg N 

ha-1 yr-1. For the Fagaceae family, the lower critical load 

appears to be >3 and <9.5, while the upper critical load 

is >11 and <17 kg N ha-1 yr-1; thus the critical load of 3 

to 17 for growth and survival. This critical load should 

be revised when data specific for American chestnut 

becomes available. Critical load values and citations are 

shown in Table 8.1.
32

Change Atlas data (U.S. Forest Service, n.d.) provides a 

slope range of 0.8 to 16.1 percent.

Average January, July, and May to 

September Temperatures

American chestnut is prone to frost damage (Gurney 

et al. 2011) and is not present in much of northern 

New England. Due to limited presence of mature trees, 

optimal temperature ranges above an importance value 

of 10 cannot be established. However, the distribution 

over an importance value of 2 for Climate Change 

Atlas data (U.S. Forest Service, n.d.) provides a January 

temperature range of -5.7 to 1.5 °C, a July temperature 

range of 19.5 to 23.6 °C, and a May to September 

temperature range of 16.6 to 20.9 °C.

Table 8.1—Effect of nitrogen deposition on American chestnut

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Species Citation

Northeastern, 

Midwestern U.S.

3 to 11 No change in growth or survival American beech Thomas et al. 2010

Northeastern, 

Midwestern U.S.

3 to 11 Increased growth, decreased 

survival

red oak Thomas et al. 2010

3 to 11 No change in growth or survival white oak, black oak

3 to 10.5 No change in growth, decreased 

survival; survival peak 5 kg ha-1 yr-1

chestnut oak

5 to 9.5 Increased growth, decreased 

survival; growth peak at 7 kg ha-1 yr-1

scarlet oak

WV Up to 12 to 14 Decreased basal area red oak Elias 2008

NC and VA 9 to 15 (wet) Decreased basal area American beech Boggs et al. 2005

Table 8.2—Abiotic modifying factors for American chestnut

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 550 ≤ 550 2

Aspect N/A N/A

Slope % < 0.8, > 16.1 0.8 to 16.1 2

January temperature °C < -5.7, > 1.5 -5.7 to 1.5 3

July temperature °C < 19.5, > 23.6 19.5 to 23.6 3

May-September temperature °C < 16.6, > 20.9 16.6 to 20.9 3

Annual precipitation mm < 956, > 1546 956 to 1546 3.3

May-September precipitation mm < 442, > 640 442 to 640 3.3

Soil pH < 4.5, > 5.6 4.5 to 5.6 2.3

Clay % < 13.8, > 40.0 13.8 to 40.0 2.3

Coarse sand % < 52.2, > 86.9 52.2 to 86.9 2

Permeability cm hr-1 < 1.1, > 7.5 1.1 to 7.5 1.7

Depth to bedrock m < 0.5 ≥ 0.5 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
33

Permeability

Distribution over an importance value of 2 for Climate 

Change Atlas data (U.S. Forest Service, n.d.) provides 

a range from 1.1 to 7.5 cm hr-1, the rate for moderately 

slow to moderately rapidly draining sandy loams 

(O’Geen 2012).

Depth to Bedrock

We associated depth to bedrock of <0.5 m with 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and depths ≥0.5 m with optimal growth.

EXTERNAL INFLUENCES

Insect Pests

In a study of mycorrhizal inoculation and fertilization 

of American chestnut and American chestnut/Chinese 

chestnut hybrids, gypsy moth (Lymantria dispar) 

performance was highest on fertilized hybrid trees and 

lowest on unfertilized American chestnut seedlings 

(Rieske et al. 2003). We assumed that abundant levels 

of insect pests correlate with suboptimal growth, while 

low levels or the absence of insect pests correlate with 

optimal growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. In the case of American chestnut, since 

chestnut blight is nearly universally present, growth 

conditions are almost always suboptimal. The presence 

of fungal pathogens would correlate with suboptimal 

growth, while the absence of fungal pathogens would 

correlate with optimal growth.

Annual Precipitation

Wang et al. (2013) report that American chestnut 

grows in areas from 813 to 2032 mm annual 

precipitation; the majority of trees are found in areas 

with 1016 to 1219 mm. The range of 835-2283 mm 

annual precipitation from the Climate Change Atlas 

(U.S. Forest Service, n.d.) is similar. The PLANTS 

database (NRCS 2014) provides a range from 889 to 

1524 mm. Due to limited presence of mature trees, 

optimal temperature ranges above an importance value 

of 10 cannot be established. However, the distribution 

over an importance value of 2 for Climate Change 

Atlas data (U.S. Forest Service, n.d.) provides an annual 

precipitation range of 956 to 1546 mm.

May to September Precipitation

Distribution over an importance value of 2 for Climate 

Change Atlas data (U.S. Forest Service, n.d.) provides 

a May to September precipitation range of 442 to 640 

mm. However, American chestnut growth is expected 

to be suboptimal at all precipitations.

Soil pH

The PLANTS database (NRCS 2014) reports that 

American chestnut is found on soils with pH 5.5 to 6.5. 

Surviving American chestnuts in Canada were found 

on soils of pH 4 to 6 (Tindall et al. 2004). The entire 

distribution range in the Climate Change Atlas is 4.1 

to 6.8, while distribution over an importance value of 

2 ranges from 4.5 to 5.6 (U.S. Forest Service, n.d.). 

We associated soil pH values from pH 4.5 to 5.6 with 

the optimal growth. Values outside of this range are 

associated with suboptimal growth.

Percent Clay and Percent Coarse Sand

American chestnut is most common on well-drained, 

sandy soils (Wang et al. 2013). Distribution over an 

importance value of 2 for Climate Change Atlas data 

(U.S. Forest Service, n.d.) provides a range of 13.8 to 

40.0 percent clay and 52.2 to 86.9 percent coarse sand. 

However, American chestnut growth is expected to be 

suboptimal at all soil textures.
34

9 FAGUS GRANDIFOLIA (AMERICAN BEECH)

increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 9.2.

Elevation

Elevation effects depend on latitude. We set elevation 

ranges for the northeastern United States based on 

basal area dominance at various elevations in the 

Green Mountains of Vermont and elevation ranges 

from the White Mountains of New Hampshire. Beech 

was present between 549 and 792 m in the northern 

hardwoods-boreal ecotone of the Green Mountains, 

with the greatest basal area at lower elevations 

(Beckage et al. 2008). In the White Mountains of New 

Hampshire, beech was found between approximately 

610 and 750 m (Leak and Graber 1974). Growth is 

expected to be suboptimal above 750 m and optimal 

below 750 m.

Aspect

According to PLANTS database (NRCS 2014), beech 

are both drought tolerant and shade tolerant. Tubbs and 

Houston (1990) report that more beech trees are found 

on cooler northern slopes in middle-range latitudes. 

For this reason, northeastern aspects are associated with 

optimal growth and southwestern aspects are associated 

with suboptimal growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for American beech is 

expected to be optimal from 0.6 to 12.1 percent.

Average January, July, and May to 

September Temperatures

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

temperature thresholds. We associated January 

temperatures from -11.6 to 0.7 °C; July temperatures 

from 17.7 to 24.8 °C; and May to September 

SPECIES RANGE AND CRITICAL LOAD

American beech, Fagus grandifolia Ehrh., grows across 

much of the eastern United States and southeastern 

Canada. American beech grows best in loamy, high 

humus, dry-mesic soils, although it can also grow on 

poorly drained soils. The largest trees are found on 

alluvial bottomlands of the Ohio and Mississippi River 

Valleys. It occurs at elevations up to 980 m in New 

England and up to 1830 m in the southern part of its 

range (Tubbs and Houston 1990). Across its range, 

American beech is affected in varying degrees by beech 

bark disease, a moderately lethal, relatively slow acting 

disease (Lovett et al. 2006).

We assigned a critical load for growth of 11 to 17 kg N 

ha-1 yr-1 for American beech based on the results from 

several studies. Thomas et al. (2010) found no change 

in growth or survival with increased deposition from 3 

to 11 kg N ha-1 yr-1 deposition; the minimum critical 

load, based on this study, is ≥11 kg N ha-1 yr-1. Boggs 

et al. (2005) found decreased American beech basal 

area growth at high depositions along a gradient from 

9 to 15 kg N ha-1 yr-1 wet deposition (approximately 

11 to 17 kg N ha-1 yr-1 wet + dry) in North Carolina 

and Virginia. American beech basal area growth was 

highest at 13 kg N ha-1 yr-1 wet (approximately 15 

kg N ha-1 yr-1 wet + dry). The maximum critical load, 

based on this study, is 17 kg N ha-1 yr-1. Elias (2008) 

found decreased basal area for American beech with N 

deposition of 12 to 14 kg ha-1 yr-1 and S deposition of 

20-26 kg ha-1 yr-1 in West Virginia, making the critical 

load range for this study <14 kg N ha-1 yr-1. A summary 

of studies used to set the critical load for American 

beech is provided in Table 9.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for American beech, including the 

American beech section of the Forest Service silvics 

handbook (Tubbs and Houston 1990), the PLANTS 

database (NRCS 2014), and the Climate Change Atlas 

(U.S. Forest Service, n.d). In general, any factor that 

inhibits American beech growth would be expected 

to decrease the need for N and thus lower the critical 

load; optimal growth conditions would result in an 
35

May to September Precipitation

High importance values from the Climate Change 

Atlas data (U.S. Forest Service, n.d.) were used to 

determine thresholds for average May to September 

precipitation. We associated the precipitation range 

from 429 to 621 mm with optimal growth and 

precipitation outside of this range with suboptimal 

growth.

Soil pH

Tubbs and Houston (1990) reported that American 

beech is typically found on soils with pH of 4.1 to 6 

and rarely on soils over pH 7.0. NRCS (2014) provides 

a pH range of 4.1 to 7.2. Climate Change Atlas data 

temperatures from 14.3 to 22.0 °C with optimal growth 

and temperatures outside of this range with suboptimal 

growth. General ranges were verified using reported 

habitat preferences for American beech (Tubbs and 

Houston 1990).

Annual Precipitation

Precipitation ranges in the literature include 720 to 

2283 mm (U.S. Forest Service, n.d.), 711 to 2032 

mm (NRCS 2014), and 760 to 1270 mm (Tubbs and 

Houston 1990). We associated the high importance 

value range of 883 to 1418 mm from Climate Change 

Atlas data (U.S. Forest Service, n.d.) with optimal 

growth. Values outside of this range were associated 

with suboptimal growth.

Table 9.1—Effect of nitrogen deposition on American beech

Location

N deposition range 

(kg ha-1 yr-1)

Increased N 

deposition effects Interacting factors Citation

Northeastern U.S., 

Midwestern U.S.

3 to 11 No change in growth or 

mortality

N/A Thomas et al. 2010

North Carolina and 

Virginia

9 to 15 (wet) Decreased basal area N/A Boggs et al. 2005

West Virginia 12 to 14 Decreased basal area S deposition

20 to 26 kg ha-1 yr-1

Elias 2008

Table 9.2—Abiotic modifying factors for American beech

Variable Units

Suboptimal 

growth range

Optimal growth 

range

Weight of 

evidencea

Elevation m > 750 ≤ 750 4

Aspect southwestern northeastern 2

Slope gradient % < 0.6, > 12.1 0.6 to 12.1 3.7

January temperature °C < -11.6, > 0.7 -11.6 to 0.7 4.7

July temperature °C < 17.7, > 24.8 17.7 to 24.8 4.7

May-September temperature °C < 14.3, > 22.0 14.3 to 22.0 4.7

Annual precipitation mm < 883, > 1418 883 to 1418 4.3

May-September precipitation mm < 429, > 621 429 to 621 4.3

Soil pH < 4.6, > 6.8 4.6 to 6.8 4

Clay % < 4.7, > 33.5 4.7 to 33.5 3

Coarse sand % < 54.4, > 93.2 54.4 to 93.2 3.3

Permeability cm hr-1 < 0.6, > 7.5 0.6 to 7.5 3

Depth to bedrock m < 0.8 ≥ 0.8 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
36

(U.S. Forest Service, n.d.) have a high importance value 

range of pH 4.6 to 6.8; we associated these values with 

optimal growth and values outside of this range with 

suboptimal growth.

Percent Clay and Percent Coarse Sand

American beech grows best on loamy soils and is most 

often found on Oxisols, Alfisols, and Spodosols (Tubbs 

and Houston 1990). We used high importance value 

ranges from Climate Change Atlas data (U.S. Forest 

Service, n.d.) to set thresholds for percent clay and 

percent coarse sand. Optimal clay is 4.7 to 33.5 percent; 

optimal sand is 54.4 to 93.2 percent.

Permeability

High importance values from Climate Change Atlas 

data (U.S. Forest Service, n.d.) indicate optimal soil 

permeability ranges from 0.6 to 7.5 cm hr-1, the range 

for a moderately slow draining clay loam to moderately 

rapidly draining sandy loam (O’Geen 2012).

Depth to Bedrock

We associated depth to bedrock of <0.8 m with 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and ≥0.8 m with optimal growth.

EXTERNAL INFLUENCES

Insect Pests

We assumed that abundant insect pests correlate with 

suboptimal growth, and low levels or the absence of 

insect pests correlate with optimal growth. Infestation 

of beech with beech scale (Cryptococcus fagisuga) leaves 

beech trees vulnerable to Neonectria fungi, which 

subsequently causes beech bark disease (Lovett et al. 

2006). In a Connecticut study, populations of beech 

scale were higher on trees with higher bark nitrogen 

content (Wargo 1988).

Fungal Pathogens

Larger trees have a higher mortality rate as a result of 

beech bark disease (and invasion by Neonectria spp.) 

than smaller trees (Morin et al. 2007). Researchers 

found that mature American beech trees with higher 

bark N content had more severe symptoms of beech 

bark disease than younger trees with lower bark N 

(Latty el al. 2003). As with insect pests, we assumed 

that trees weakened by abundant levels of fungal 

pathogens would exhibit suboptimal growth. Absent 

or low levels of fungal pathogens would correlate with 

optimal growth.
37

10 FRAXINUS AMERICANA (WHITE ASH)

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 10.2.

Elevation

We set elevation ranges for the northeastern United 

States based on basal area dominance of hardwood 

trees at various elevations in the Green Mountains 

of Vermont as reported by Beckage et al. (2008). We 

generally expect growth to be optimal below 610 m and 

suboptimal at higher elevations.

Aspect

White ash trees are shade intolerant and have low 

drought tolerance (NRCS 2014). We have currently 

associated northeastern exposures with optimal growth 

and southwestern exposures with suboptimal growth. 

However, it may be that aspect does not have a strong 

influence on white ash growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for white ash is expected 

to be optimal from 0.1 to 7.3 percent.

Average January, July, and May to 

September Temperatures

Schlesinger (1990) reports an average January 

temperature range of -14 to 12 °C and an average July 

temperature range of 18 to 27 °C. High importance 

values from the Climate Change Atlas (U.S. Forest 

Service, n.d.) were used to set temperature thresholds. 

We associated January temperatures from -8.7 to 0.3 

°C, July temperatures from 19.6 to 25.6 °C, and May 

to September temperatures from 16.4 to 22.6 °C with 

optimal growth, and values outside of these ranges with 

suboptimal growth.

Annual Precipitation

Reported precipitation ranges include 465 to 2289 mm 

(U.S. Forest Service, n.d.), 711 to 2030 mm (NRCS 

2014), and 760 to 1520 mm (Schlesinger 1990). We 

associated the high importance value range of 785 

SPECIES RANGE AND CRITICAL LOAD

White ash, Fraxinus americana L., grows in much of 

central and eastern United States and in southeastern 

Canada. It prefers fertile, well-drained sandy to clay 

loam soils of pH 5 to 7.5 with high nitrogen content 

and moderate to high calcium content (Schlesinger 

1990). In Pennsylvania, ash dieback was highest on 

upper slope positions with relatively low base cation 

status (Royo and Knight 2012). White ash can be 

found in areas with January temperatures from -14 

to 12 °C and July temperatures from 18 to 27 °C; 

precipitation generally ranges from 760 to 1520 mm 

yr-1. White ash grows up to 600 m in the Adirondacks 

and up to 1050 m in the Cumberland Mountains 

(Schlesinger 1990).

We assigned a critical load of >11 to 18 kg ha-1 yr-1

for white ash. Thomas et al. (2010) found a significant 

increase in growth and no change in survival 

with increased N deposition from 3 to 11 kg N 

ha-1 yr-1. Dietz and Moorcroft (2011) found increased 

mortality for northern mid successional hardwoods 

with increasing N deposition; visual inspection of 

supplementary material indicate that mortality was 

lowest from approximately 5 to 16 kg ha-1 yr-1 wet N 

deposition (7 to 18 kg ha-1 yr-1 wet + dry N). We used 

the low end of the range from Thomas et al. (2010) and 

the upper end of the range from Dietz and Moorcroft 

(2011) to set the critical load. Critical load values and 

citations are shown in Table 10.1.

ABIOTIC MODIFYING FACTORS

 Multiple sources were used to set the ranges of 

modifying factors for white ash, including the white 

ash section of the Forest Service silvics handbook 

(Schlesinger 1990), the PLANTS database (NRCS 

2014), and high importance value data from the 

Climate Change Atlas (U.S. Forest Service, n.d.). In 

general, any factor that inhibits white ash growth would 

be expected to result in a decreased need for N and thus 

a lower critical load; optimal growth conditions result 

in an increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 
38

to 1264 mm (U.S. Forest Service, n.d.) with optimal 

growth. Values outside of this range were associated 

with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 393 to 552 

mm with optimal growth, and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges include 4.7 to 7.5 (NRCS 

2014), 5.0 to 7.5 (Schlesinger 1990), and 2.7 to 8.4 

(U.S. Forest Service, n.d.). We associated soil pH 

values of 5.0 to 7.2, high importance values from the 

Climate Change Atlas (U.S. Forest Service, n.d.), with 

optimal growth and values outside of this range with 

suboptimal growth.

Percent Clay and Percent Coarse Sand

White ash can be found on sandy and clay loam 

soils. It is most common on fertile soils with a high 

nitrogen and calcium content. Dominant soil orders 

are Inceptisols, Alfisols, and Spodosols (Schlesinger 

1990). We used high importance values from the 

Climate Change Atlas (U.S. Forest Service, n.d.) to 

set thresholds for percent clay and percent coarse sand. 

Optimal clay is 6.9 to 41.8 percent; optimal coarse sand 

is 54.9 to 97.9 percent.

Table 10.1—Effect of nitrogen deposition on white ash

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Northeastern, 

Midwestern U.S.

3 to 11 Increased growth, no change 

in survival

Thomas et al. 2010

Eastern and Central U.S. 5 to 16 (wet NO3) Increased mortality probability Dietze and Moorcroft 2011

Table 10.2—Abiotic modifying factors for white ash

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 610 ≤ 610 2

Aspect southwestern northeastern 2

Slope % < 0.1, > 7.3 0.1 to 7.3 4

January temperature °C < -8.7, > 0.3 -8.7 to 0.3 4.7

July temperature °C < 19.6, > 25.6 19.6 to 25.6 4.7

May-September temperature °C < 16.4, > 22.6 16.4 to 22.6 4.7

Annual precipitation mm < 785, > 1264 785 to 1264 4.7

May-September precipitation mm < 393, > 552 393 to 552 5

Soil pH < 5.0, > 7.2 5.0 to 7.2 4

Clay % < 6.9, > 41.8 6.9 to 41.8 3.7

Coarse sand % < 54.9, > 97.9 54.9 to 97.9 3.7

Permeability cm hr-1 < 0.4, > 6.7 0.4 to 6.7 3.3

Depth to bedrock m < 1.0 ≥ 1.0 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
39

killed millions of trees across the eastern United States 

and Canada (U.S. Forest Service and Michigan State 

University, n.d.).

Fungal pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent levels of fungal pathogens would 

correlate with optimal growth.

Bacterial Pathogens

Ash yellows is a disease caused by Candidatus 

phytoplasma fraxini, wall-less microbes that block 

phloem tubes, causing slow growth and decline 

in affected ash trees. Ash yellows is thought to be 

transmitted by leaf hoppers and other insects and can 

be exacerbated by drought and competition. It can 

be difficult to diagnose because many symptoms are 

nonspecific (Pokorny and Sinclair 1994). We presume 

trees affected by ash yellows would have suboptimal 

growth.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.4 to 6.7 cm hr-1, the rates for moderately slow to 

moderately rapidly draining clay loams to sandy loams 

(O’Geen 2012).

Depth to Bedrock

We associated depth to bedrock of <1.0 m with 

suboptimal growth and ≥1.0 m for optimal growth. 

These are based on minimum rooting depth values for 

white ash (NRCS 2014).

EXTERNAL INFLUENCES

Insect Pests

We assumed that an abundance of insect pests correlate 

with suboptimal growth, while low or absent levels of 

insect pests correlate with optimal growth. Emerald 

ash borers (Agrilus planipennis) present the biggest 

threat to white ash trees (Dukes et al. 2009) and have 
40

11 FRAXINUS PENNSYLVANICA (GREEN ASH)

Elevation

Elevation effects depend on latitude. Green ash 

growth is expected to be optimal at low elevations 

in the northeastern United States. We have set an 

approximate elevation threshold of 330 m for optimal 

growth.

Aspect

According to NRCS (2014), green ash trees are shade 

tolerant and have moderate drought tolerance. We 

have currently associated northeastern exposures with 

optimal growth and southwestern exposures with 

suboptimal growth. However, it may be that aspect does 

not have a strong influence on green ash growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for green ash is expected 

to be optimal from 0.0 to 3.8 percent.

Average January, July, and May to 

September Temperatures

Kennedy (1990) reported an average January 

temperature range for green ash of -18 to 13 °C and 

an average July temperature range of 18 to 27 °C. 

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.), which are 

similar to these ranges, to set temperature thresholds. 

We associated January temperatures from -15.5 to 6.4 

°C, July temperatures from 20.8 to 27.7 °C, and May 

to September temperatures from 16.9 to 25.6 °C with 

optimal growth and values outside of these ranges with 

suboptimal growth.

Annual Precipitation

Reported precipitation ranges for green ash include 430 

to 2289 mm (U.S. Forest Service, n.d.), 381 to 1803 

mm (NRCS 2014), and 380 to 1520 mm (Kennedy 

1990). We associated the high importance value range 

of 469 to 1416 mm (U.S. Forest Service, n.d.) with 

optimal growth. Values outside of this range were 

associated with suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

Green ash, Fraxinus pennsylvanica Marsh., a riverside 

and bottomlands tree, is found in much of central and 

eastern United States and Canada. It tolerates a wide 

variety of soils, including land subject to flooding, 

but prefers fertile, moist, well-drained soil (Kennedy 

1990). Green ash can be found in areas with January 

temperatures from -18 to 13 °C and July temperatures 

from 18 to 27 °C; precipitation generally ranges from 

380 to 1520 mm yr-1 (Kennedy 1990).

We assigned a critical load of >11 to 18 kg N ha-1 yr-1

for green ash; this is based on the critical load for white 

ash. Thomas et al. (2010) found a significant increase 

in growth and no change in survival with increased 

N deposition from 3 to 11 kg N ha-1 yr-1. Dietz and 

Moorcroft (2011) found increased mortality for 

northern mid successional hardwoods with increasing 

N deposition; visual inspection of supplementary 

material indicate that mortality was lowest from 5 to 16 

kg ha-1 yr-1 wet N deposition (7 to 18 kg ha-1 yr-1 wet + 

dry N). We used the low end of the range from Thomas 

et al. (2010) and the upper end of the range from Dietz 

and Moorcroft (2011) to set the critical load. Critical 

load values and citations are shown in Table 11.1.

ABIOTIC MODIFYING FACTORS

 Multiple sources were used to set the ranges of 

modifying factors for green ash, including the green 

ash section of the Forest Service silvics handbook 

(Kennedy 1990), the PLANTS database (NRCS 2014), 

and high importance value data from the Climate 

Change Atlas (U.S. Forest Service, n.d.). In general, 

any factor that inhibits green ash growth would be 

expected to result in a decreased need for N and thus a 

lower critical load; optimal growth conditions result in 

an increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 11.2.
41

Percent Clay and Percent Coarse Sand

Green ash can tolerate a wide range of soils, but grows 

best on fertile, moist, well drained soils. Dominant soil 

orders are Inceptisols and Entisols (Kennedy 1990). We 

used high importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) to set thresholds for 

percent clay and percent coarse sand. Optimal clay is 

11.1 to 49.0 percent; optimal coarse sand is 79.5 to 

100.0 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.2 to 8.6 cm hr-1, the rates for slowly to 

moderately rapidly draining sandy clays to sandy loams 

(O’Geen 2012).

May to September precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 318 to 579 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges include pH 4.7 to 8.1 (NRCS 

2014) and 5.0 to 8.0 (Kennedy 1990). We associated 

the Climate Change Atlas (U.S. Forest Service, n.d.) 

high importance value range of 5.5 to 7.8 with optimal 

growth. Values outside of this range were associated 

with suboptimal growth.

Table 11.1—Effect of nitrogen deposition on green ash

Location

N deposition range 

(kg ha-1 yr-1)

Increased N deposition 

effects Interacting factors Citation

Northeastern, 

Midwestern U.S.

3 to 11 Increased growth, no 

change in survival

Thomas et al. 2010

Eastern and Central U.S. 5 to 16 (wet NO3) Decreased mortality 

probability

Dietze and Moorcroft 

2011

Table 11.2—Abiotic modifying factors for green ash

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 330 ≤ 330 2

Aspect southwestern northeastern 2

Slope gradient % > 3.8 0.0 to 3.8 4

January temperature °C < -15.5, > 6.4 -15.5 to 6.4 4.3

July temperature °C < 20.8, > 27.7 20.8 to 27.7 5

May-September temperature °C < 16.9, > 25.6 16.9 to 25.6 5

Annual precipitation mm < 469, > 1416 469 to 1416 4.3

May-September precipitation mm < 318, > 579 318 to 579 4.3

Soil pH < 5.5, > 7.8 5.5 to 7.8 4.3

Clay % < 11.1, > 49.0 11.1 to 49.0 3.7

Coarse sand % < 79.5, > 100.0 79.5 to 100.0 4

Permeability cm hr-1 < 0.2, > 8.6 0.2 to 8.6 3.3

Depth to bedrock m < 1.0 ≥ 1.0 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high 3

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

Elevation abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
42

Depth to Bedrock

We associated depth to bedrock of <1.0 m for 

suboptimal growth and values ≥1.0 m for optimal 

growth. These are based on minimum rooting depth 

values for green ash as reported in PLANTS database 

(NRCS 2014).

EXTERNAL INFLUENCES

Insect Pests

We assumed that abundant insect pests correlate with 

suboptimal growth, while low or absent insect pests 

correlate with optimal growth. Emerald ash borers 

(Agrilus planipennis) present a significant health threat 

to green ash trees (Dukes et al. 2009). The larvae of 

emerald ash borers feed on the inner bark of ash trees 

and disrupt the flow of nutrients and water. Millions 

of trees have died across the eastern United States and 

Canada as a result of this insect (U.S. Forest Service 

and Michigan State University, n.d.).

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by an abundance of fungal pathogens would have 

suboptimal growth. Low or absent levels of fungal 

pathogens would correlate with optimal growth.

Bacterial Pathogens

Ash yellows is a disease caused by Candidatus 

phytoplasma fraxini, microbes that lack rigid cell walls. 

The microbes block phloem tubes, resulting in slow 

growth and decline in affected ash trees. Ash yellows 

is thought to be transmitted by leaf hoppers and 

other insects, and can be exacerbated by drought and 

competition. It can be difficult to diagnose, as many 

symptoms are nonspecific (Pokorny and Sinclair 1994). 

Trees affected by ash yellows would be presumed to 

have a suboptimal growth.
43

12 JUGLANS CINEREA (BUTTERNUT)

biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 12.2.

Elevation

We set elevation ranges for the northeastern United 

States based on basal area dominance of hardwood 

trees at various elevations in the Green Mountains 

of Vermont as reported by Beckage et al. (2008). We 

generally expect growth to be optimal below 610 m and 

suboptimal at higher elevations.

Aspect

Butternut has low drought tolerance and is shade 

intolerant (NRCS 2014). Because it is not clear which 

aspect is most favorable for growth, we have not 

associated aspect with growth for this species.

Slope Gradient

We used an importance value threshold of 3 from the 

Climate Change Atlas (U.S. Forest Service, n.d.) to set 

optimal growth ranges for slope. Growth for butternut 

is expected to be optimal from 0.7 to 11.5 percent.

Average January, July, and May to 

September Temperatures

Because butternut was not an important component of 

sites included in the Climate Change Atlas (U.S. Forest 

Service, n.d.), optimal growth was established using a 

high importance value threshold of 3, which resulted 

in a January temperature range from -11.8 to -1.8 °C, a 

July temperature range from 20.0 to 25.1 °C, and a May 

to September temperature range from 16.9 to 22.1 °C.

Annual Precipitation

Annual precipitation for butternut was reported to 

range from 666 to 1525 mm (U.S. Forest Service, n.d.), 

635 to 2032 mm (NRCS 2014), and 630 to 2030 mm 

(Rink 1990). We associated the range of 731 to 1139 

mm, determined using an importance value threshold 

of 3 (U.S. Forest Service, n.d.), with optimal growth.

SPECIES RANGE AND CRITICAL LOAD

Butternut, Juglans cinerea (L.), grows in the 

northeastern and north central United States, as well 

as a small part of southern Canada, over a precipitation 

range of 630-2030 mm per year and an average 

temperature range of 4 to 16 °C. Butternut grows best 

on well-drained, stream-side soils and is rarely found 

on dry, compact, or infertile soils. It can be found at 

elevations up to 1500 m in the South (Rink 1990). 

However, in the Great Smoky Mountains of Tennessee 

and North Carolina, butternut was found most often 

from 400 to 700 m in elevation in floodplain forests. 

Butternut populations in the Great Smoky Mountains 

and elsewhere have declined dramatically as a result of 

butternut canker caused by the fungus Ophiognomonia 

clavigignenti-juglandacearum (Parks et al. 2013).

We assigned a critical load of 7 to 18 kg ha-1 yr-1 for 

butternut. This is based solely on the northern midsuccessional hardwood forest type in Dietze and 

Moorcroft (2011); visual inspection of supplementary 

material indicate that mortality was lowest from 5 to 

16 kg ha-1 yr-1 wet N deposition (approximately 7 to 18 

kg/ha/N wet + dry deposition). Critical load citations 

and values are shown in Table 12.1. The critical load 

should be revised when species specific information 

becomes available.

ABIOTIC MODIFYING FACTORS

 Multiple sources were used to set the ranges of 

modifying factors for butternut, including the butternut 

section of the Forest Service silvics handbook (Rink 

1990), the PLANTS database (NRCS 2014), and the 

Climate Change Atlas (U.S. Forest Service, n.d.). In 

general, any factor that inhibits butternut growth would 

be expected to result in a decreased need for N and 

thus a lower critical load; optimal growth conditions 

result in an increased demand for N and a higher 

critical load. Butternut is not an important component 

of Forest Inventory and Analysis sites. For this reason, 

ranges for abiotic modifying factors were set using 

Climate Change Atlas data with an importance value 

threshold of 3 (U.S. Forest Service, n.d.). General 

values were used for base saturation, Ca:Al, and 
44

May to September Precipitation

The Climate Change Atlas (U.S. Forest Service, n.d.) 

provides a range from 441 to 543 mm for average May 

to September precipitation using an importance value 

threshold of 3. We associated this range with optimal 

growth.

Soil pH

We associated soil pH values from 5.2 to 7.3, set 

using an importance value threshold of 3 in the 

Climate Change Atlas (U.S. Forest Service, n.d.), with 

optimal growth and values outside of this range with 

suboptimal growth. PLANTS database (NRCS 2014) 

reports a pH range of 6 to 7 for butternut.

Percent Clay and Percent Coarse Sand

Butternut grows best on well-drained riparian soils 

and is most often found on Alfisols and Entisols (Rink 

1990). We used a Climate Change Atlas (U.S. Forest 

Service, n.d.) importance value threshold of 3 to set 

optimal ranges for soil texture. Clay ranges from 6.6 

to 42 percent; coarse sand ranges from 71.3 to 98.3 

percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.), set using an importance 

value threshold of 3, ranges from 0.7 to 6.7 cm hr-1, 

the rates for moderately slow to moderately rapidly 

draining clay loams to fine sandy and silty loams 

(O’Geen 2012).

Table 12.1—Effect of nitrogen deposition on butternut

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Eastern and Central U.S. 5 to 16 (wet NO3) Decreased mortality probability Dietze and Moorcroft 2011

Table 12.2—Abiotic modifying factors for butternut

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation M > 610 ≤ 610 2

Aspect N/A N/A

Slope gradient % < 0.7, > 11.5 0.7 to 11.5 2

January temperature °C < -11.8, > -1.8 -11.8 to -1.8 3

July temperature °C < 20.0, > 25.1 20.0 to 25.1 3

May-September temperature °C < 16.9, > 22.1 16.9 to 22.1 3

Annual precipitation mm < 731, > 1139 731 to 1139 3

May-September precipitation mm < 441, > 543 441 to 543 3

Soil pH < 5.2, > 7.3 5.2 to 7.3 2

Clay % < 6.6, > 42.0 6.6 to 42.0 2

Coarse sand % < 71.3, > 98.3 71.3 to 98.3 2

Permeability cm hr-1 < 0.7, > 6.7 0.7 to 6.7 1.5

Depth to bedrock M < 1.0 ≥ 1.0 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
45

Fungal Pathogens

We assumed that trees weakened by abundant fungal 

pathogens would have suboptimal growth. Low levels 

or absent fungal pathogens would correlate with 

optimal growth. Butternut canker, caused by the fungus 

Ophiognomonia clavigignenti-juglandacearum, is the 

primary fungal threat to butternut (Parks et al. 2013; 

Rink 1990). Melanconium oblongum is a secondary 

fungal pathogen of butternut that colonizes stressed 

branches (Rink 1990). Because butternut canker is a 

persistent threat to butternut, growth conditions may 

always be suboptimal.

Depth to Bedrock

We have assigned depth of bedrock values of <1.0 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and ≥1.0 m for optimal growth.

EXTERNAL INFLUENCES

Insect Pests

We assumed that abundant insect pests correlate with 

suboptimal growth, while low levels or the absence of 

insect pests correlate with optimal growth.
46

13 PICEA MARIANA (BLACK SPRUCE)

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 13.2.

Elevation

We set elevation ranges for the northeastern United 

States based on elevation ranges reported in Viereck 

and Johnston (1990). Growth is expected to be optimal 

from 150 to 760 m, and suboptimal outside of this 

range.

Aspect

Black spruce is shade tolerant and has low drought 

tolerance (NRCS 2014). We associated northeastern 

aspects with optimal growth and southwestern aspects 

with suboptimal growth.

Slope Gradient

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

thresholds for slope. We expect growth for black spruce 

to be optimal from 0 to 3.1 percent.

Average January, July, and May to 

September Temperatures

Climate Change Atlas data (U.S. Forest Service, n.d.) 

do not cover much of the range of black spruce, which 

extends well into northern Canada. Optimal values 

reported here apply to the range of black spruce in 

the United States. Black spruce grows in regions with 

January temperatures from -30 to -6 °C (Viereck and 

Johnston 1990) and July temperatures from 10 to 

27 °C; most black spruce grow in regions with July 

temperatures between 16 and 24 °C (Viereck and 

Johnston 1990). In the United States, black spruce 

can have high importance on plots with January 

temperatures of -16.8 to -9.1, July temperatures of 17.9 

to 19.8, and May to September temperatures of 14.1 

to 16.0. We associated these temperatures with optimal 

growth; temperatures outside of these ranges were 

associated with suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

Black spruce, Picea mariana (Mill.) B.S.P., grows in 

northern New England and across much of Canada 

to the tree line. Black spruce grows in cold, humid to 

subhumid climates with average annual temperatures 

of -11 to 7 °C. Precipitation ranges from 150 to 1520 

mm yr-1, although most black spruce grow in areas with 

precipitation between 380 to 760 mm per year. Black 

spruce is usually found over moist organic soils, such as 

acidic peat swamps, but it can grow on a wide variety 

of soils, including clay, loam, sand, and shallow soils. It 

grows from sea level to 1830 m and is most common 

between 150 and 760 m (Viereck and Johnston 1990).

We assigned a critical load of 5.4 to 21.1 kg N ha-1 yr-1

for black spruce. This critical load is based on the 

critical load for red spruce. Thomas et al. (2010) found 

very slight decreases in growth (-0.1 percent growth 

decrease per kg ha-1 yr-1) and no change in survival with 

increased deposition from 3 to 10 kg N ha-1 yr-1 in a 

multi-state gradient study. At a site on Mt. Ascutney 

in Vermont, live red spruce basal area decreased 

as fertilization increased N input from 5.4 (bulk 

deposition) to 21.1 kg ha-1 yr-1 (McNulty et al. 1996, 

2005). These studies indicate the lower critical load for 

spruce is >5.4 and <10 kg N ha-1 yr-1, while the upper 

critical load for spruce is less than 21.1 kg ha-1 yr-1. 

The range may be narrowed with further research. A 

summary of critical load citations and values are shown 

in Table 13.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for black spruce, including the black 

spruce section of the Forest Service silvics handbook 

(Viereck and Johnston 1990), the PLANTS database 

(NRCS 2014), and high importance value data from 

the Climate Change Atlas (U.S. Forest Service, n.d.). 

We assumed that in most situations, factors that 

result in decreased black spruce growth or survival 

also result in decreased need for N and thus a lower 

critical load; optimal growth conditions result in an 

increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 
47

We associated the precipitation range from 405 to 495 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth. These values apply 

to black spruce in the United States.

Soil pH

We associated soil pH values of 4.7 to 7.3 from 

the Climate Change Atlas high importance values 

(U.S. Forest Service, n.d.) with optimal growth and 

values outside of this range with suboptimal growth. 

PLANTS database (NRCS 2014) reports a pH range 

of 4.7 to 6.5 for black spruce.

Percent Clay and Percent Coarse Sand

Black spruce grows best on dark colored peats in the 

Lake States and Canada and in central Canada on 

Annual Precipitation

Literature values for black spruce precipitation range 

were 499 to 1800 mm (U.S. Forest Service, n.d.), 127 to 

1524 mm (NRCS 2014), and 150 to 1520 mm (Viereck 

and Johnston 1990). Optimal precipitation ranges were 

380 to 760 mm (Viereck and Johnston 1990) and a 

high importance value range of 624 to 1035 mm (U.S. 

Forest Service, n.d.). We associated the high importance 

value range with optimal growth. Values outside of this 

range were associated with suboptimal growth. These 

values apply to black spruce in the United States.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

Table 13.1—Effect of nitrogen deposition on black spruce

Location N deposition range 

(kg ha-1 yr-1)

Increased N deposition effects Citation

Northeastern, 

Midwestern U.S. 3 to 10 Slightly decreased growth, no change in survival Thomas et al. 2010

Vermont 5.4 (bulk) to 21.1 Decreased red spruce basal area 

with fertilization McNulty et al. 1996, 2005

Table 13.2—Abiotic modifying factors for black spruce

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m < 150, > 760 150 to 760 3

Aspect southwestern northeastern 2

Slope gradient % > 3.1 0.0 to 3.1 3

January temperature °C < -16.8, > -9.1 -16.8 to -9.1 3.7

July temperature °C < 17.9, > 19.8 17.9 to 19.8 4

May-September temperature °C < 14.1, > 16.0 14.1 to 16.0 4

Annual precipitation mm < 624, > 1035 624 to 1035 3.7

May-September precipitation mm < 405, > 495 405 to 495 4

Soil pH < 4.7, > 7.3 4.7 to 7.3 3.3

Clay % < 4.0, > 52.4 4.0 to 52.4 2.3

Coarse sand % < 63.7, > 94.8 63.7 to 94.8 3

Permeability cm hr-1 < 0.8, > 11.2 0.8 to 11.2 1.7

Depth to bedrock m < 0.4 ≥ 0.4 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
48

the better drained glacial deposits, river terraces, and 

outwash plains (Viereck and Johnston 1990). Common 

soil orders are Histosols, Spodosols, Inceptisols, and 

Entisols. We used high importance values from the 

Climate Change Atlas (U.S. Forest Service, n.d.) to 

set thresholds for percent clay and percent coarse sand. 

Optimal clay is 4.0 to 52.4 percent; optimal coarse sand 

is 63.7 to 94.8 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.8 to 11.2 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

We have assigned depth to bedrock values of <0.4 m for 

suboptimal growth, based on NRCS (2014) minimum 

rooting depth, and ≥0.4 m for optimal growth.

EXTERNAL INFLUENCES

Insect pests

We assumed that abundant levels of insect pests 

correlate with suboptimal growth, while low levels 

or the absence of insect pests correlate with optimal 

growth.

Fungal Pathogens

We assumed that trees weakened by abundant fungal 

pathogens would have suboptimal growth. Low levels 

or the absence of fungal pathogens would correlate with 

optimal growth.
49

14 PICEA RUBENS (RED SPRUCE)

described in Chapter 2 (pages 13-14). Graphs showing 

importance values for abiotic modifying factor ranges 

by species are in Appendix 1. Optimal and suboptimal 

growth ranges and weight of evidence values are shown 

in Table 14.2.

Elevation

We set elevation ranges for the northeastern United 

States based on basal area dominance at various 

elevations in the Green Mountains of Vermont 

as reported by Beckage et al. (2008), as well as on 

elevation ranges reported for the White Mountains of 

New Hampshire (Leak and Graber 1974). Growth is 

expected to be optimal between elevations of about 610 

and 1200 m and suboptimal at elevations <610 m and 

>1200 m.

Aspect

Red spruce is shade tolerant and has medium drought 

tolerance (NRCS 2014). We associated northeastern 

aspects with optimal growth and southwestern aspects 

with suboptimal growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for red spruce is expected 

to be optimal from 0.6 to 8.9 percent.

Average January, July, and May to 

September Temperatures

Blum (1990) reports an average January temperature 

range of -18 to -1 °C. July temperatures can range from 

11 to 27 °C (Blum 1990). High importance values 

from the Climate Change Atlas (U.S. Forest Service, 

n.d.) were used to set temperature thresholds. We 

associated January temperatures from -13.1 to -7.3 

°C, July temperatures from 16.5 to 19.5 °C, and May 

to September temperatures from 13.1 to 15.9 °C with 

optimal growth and values outside of these ranges with 

suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

Red spruce, Picea rubens Sarg., grows in the 

northeastern United States, along the Appalachian 

Mountains, and in southeastern Canada. Red spruce 

grows in cool, moist climates; it is typically found 

on acidic (pH 4.0 to 5.5) and shallow till soils in the 

northeastern states. It grows from sea level to 4,500 

feet (1372 m) in the northern part of its range and 

from 3,200 feet to 6,200 feet (975 to 1890 m) in the 

southern part of its range (Blum 1990).

We assigned a critical load of 5.4 to 21.1 kg N 

ha-1 yr-1 for red spruce. Bedison and McNeil (2009) 

found increased basal area increment of red spruce 

along a wet N deposition gradient of <3 to >7 kg N 

ha-1 yr-1 (approximately 5 to 9 kg ha-1 yr-1 wet + dry N 

deposition) in Adirondack State Park, New York, while 

Thomas et al. (2010) found a very slight decrease in 

growth (-0.1 % growth decrease per kg ha-1 yr-1) and 

no change in survival with increased deposition from 3 

to 10 kg N ha-1 yr-1 in a multi-state gradient study. At a 

site on Mt. Ascutney in Vermont, live red spruce basal 

area decreased as fertilization increased N input from 

5.4 (bulk deposition) to 21.1 kg ha-1 yr-1 (McNulty 

et al. 1996, 2005). These studies indicate the lower 

critical load for red spruce is >5 and <10 kg N ha-1 yr-1, 

while the upper critical load for red spruce is <21.1 

kg ha-1 yr-1. The range may be narrowed with further 

research. Critical load citations and values are shown in 

Table 14.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for red spruce, including the red 

spruce section of the Forest Service silvics handbook 

(Blum 1990), the PLANTS database (NRCS 2014), 

and high importance value data from the Climate 

Change Atlas (U.S. Forest Service, n.d.). We assumed 

that in most situations, factors that result in decreased 

red spruce growth or survival also result in decreased 

need for N and thus a lower critical load; optimal 

growth conditions result in an increased demand for 

N and a higher critical load. General values were used 

for base saturation, Ca:Al, and biomass removal as 
50

We associated the precipitation range from 438 to 671 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges for red spruce include 4.0 to 

5.5 (Blum 1990), 4.0 to 5.8 (NRCS 2014), and 4.5 to 

7.1 (U.S. Forest Service, n.d.). We associated soil pH 

values of 4.7 to 5.9, the high importance value range 

from the Climate Change Atlas (U.S. Forest Service, 

n.d.), with optimal growth and values outside of this 

range with suboptimal growth.

Annual Precipitation

Reported precipitation ranges include 834 to 2062 mm 

(U.S. Forest Service, n.d.), 711 to 1321 mm (NRCS 

2014), and 910 to 1320 mm (Blum 1990). The high 

importance value range of 923 to 1470 mm (U.S. Forest 

Service, n.d.) was associated with optimal growth. We 

associated values outside of this range with suboptimal 

growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

Table 14.1—Effect of nitrogen deposition on red spruce

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Northeastern and 

Midwestern U.S. 3-10 Slightly decreased growth, no change in survival Thomas et al. 2010

Adirondacks, NY <3 to >7 Increased basal area increment 

along deposition gradient Bedison and McNeil 2009

Vermont 5.4 (bulk) to 21.1 Decreased red spruce basal area 

with fertilization McNulty et al. 1996, 2005

Table 14.2—Abiotic modifying factors for red spruce

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m < 610, > 1200 610 to 1200 4

Aspect southwestern northeastern 2

Slope gradient % < 0.6, > 8.9 0.6 to 8.9 2.7

January temperature °C < -13.1, > -7.3 -13.1 to -7.3 3.3

July temperature °C < 16.5, > 19.5 16.5 to 19.5 4

May-September temperature °C < 13.1, > 15.9 13.1 to 15.9 4

Annual precipitation mm < 923, > 1470 923 to 1470 3.3

May-September precipitation mm < 438, > 671 438 to 671 3.3

Soil pH < 4.7, > 5.9 4.7 to 5.9 3.3

Clay % < 4.1, > 18.9 4.1 to 18.9 2.7

Coarse sand % < 61.8, > 77.8 61.8 to 77.8 3

Permeability cm hr-1 < 0.6, > 7.1 0.6 to 7.1 2

Depth to bedrock m < 0.3 ≥ 0.3 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
51

Percent Clay and Percent Coarse Sand

Red spruce grows on a variety of forest soils. Dominant 

soil orders are Spodosols, Inceptisols, and Histosols 

(Blum 1990). High importance values from the 

Climate Change Atlas (U.S. Forest Service, n.d.) were 

used to set thresholds for percent clay and percent 

coarse sand. Optimal clay is 4.1 to 18.9 percent; 

optimal coarse sand is 61.8 to 77.8 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.6 to 7.1 cm hr-1, the rates for moderately slow to 

moderately rapidly draining clay loams to sandy loams 

(O’Geen 2012).

Depth to Bedrock

We assigned depth to bedrock values of <0.3 m for 

suboptimal growth, based on NRCS (2014) minimum 

rooting depth, and ≥0.3 m for optimal growth.

EXTERNAL INFLUENCES

Insect Pests

We assumed that abundant insect pests correlate with 

suboptimal growth, while low levels or the absence 

of insect pests correlate with optimal growth. Spruce 

budworm (Choristoneura fumiferana) is one of the most 

detrimental pests to red spruce (Blum 1990, Dukes et 

al. 2009).

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent fungal pathogens would 

correlate with optimal growth.
52

15 PINUS RESINOSA (RED PINE)

Aspect

Red pine is intolerant of shade and moderately drought 

tolerant (NRCS 2014). We associated southwestern 

aspects with optimal growth and northeastern aspects 

with suboptimal growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for red pine is expected to 

be optimal from 0 to 3.7 percent.

Average January, July, and May to 

September Temperatures

Rudolph (1990) reports an average January temperature 

range of -18 to -4 °C and an average July temperature 

range of 16 to 21 °C. High importance values from 

the Climate Change Atlas (U.S. Forest Service, 

n.d.) were used to set temperature thresholds. We 

associated January temperatures from -12.7 to -5.3 

°C, July temperatures from 19.2 to 22.6 °C, and May 

to September temperatures from 15.5 to 19.4 °C with 

optimal growth and values outside of these ranges with 

suboptimal growth.

Annual Precipitation

Reported precipitation ranges include 542 to 1476 mm 

(U.S. Forest Service, n.d.), 381 to 1524 mm (NRCS 

2014), and 510 to 1520 mm, with a main range of 510 

to 1010 mm (Rudolf 1990). The high importance value 

range of 724 to 942 mm (U.S. Forest Service, n.d.) was 

associated with optimal growth. Values outside of this 

range were associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 388 to 508 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

Red pine, Pinus resinosa Ait., grows in southeastern 

Canada, and in the northeastern and north central 

United States (Rudolf 1990). Red pine usually grows 

on dry soils low in fertility. Although it does not grow 

on alkaline surface soils, it will grow on acid soils 

overlying limestone or calcareous soils. It prefers cool to 

warm summers with average temperatures between 16 

to 21°C, and cold winters with temperatures between 

-18 and -4 °C. Precipitation is low to moderate and 

ranges between 510 to 1010 mm yr-1 over much most 

of its range. Red pine typically occurs between 210 and 

400 m in New England and between 945 and 1290 m 

in the south (Rudolf 1990).

We assigned a critical load of 3 to 10 kg ha-1 yr-1 for red 

pine. Thomas et al. (2010) found significantly decreased 

growth and no change in survival for red pine over a 

deposition range of 3 to 10 kg N ha-1 yr-1. Critical load 

citations and values are shown in Table 15.1.

ABIOTIC MODIFYING FACTORS

 Multiple sources were used to set the ranges of 

modifying factors for red pine, including the red pine 

section of the Forest Service silvics handbook (Rudolf 

1990), the PLANTS database (NRCS 2014), and 

high importance value data from the Climate Change 

Atlas (U.S. Forest Service, n.d.). In general, any factor 

that inhibits red pine growth would be expected to 

result in a decreased need for N and thus a lower 

critical load; optimal growth conditions result in an 

increased demand for N and a higher critical load 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 15.2.

Elevation

Elevation effects depend on latitude. We set elevation 

ranges for the northeastern United States based 

on elevations reported in the Forest Service silvics 

handbook (Rudolf 1990). Red pine growth is expected 

to be optimal between elevations of 210 and 820 m.
53

were used to set thresholds for percent clay and percent 

coarse sand. Optimal clay is 3.3 to 32.5 percent; 

optimal coarse sand is 62.3 to 96.9 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 1.0 to 13.0 cm hr-1, the rates for moderately slow 

to rapidly draining clay loams to loamy sands (O’Geen 

2012).

Depth to Bedrock

We have assigned depth to bedrock values of <1.0 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and ≥1.0 m for optimal growth.

Soil pH

Reported soil pH ranges include 4.5 to 6.0 (NRCS 

2014, Rudolf 1990). Pot experiments indicate that ideal 

growth occurs at pH 5.5, with growth inhibitions at pH 

7.5 (Liu et al. 2009). We associated soil pH values of 

5.4 to 7.3, from the high importance value range in the 

Climate Change Atlas (U.S. Forest Service, n.d.), with 

optimal growth and values outside of this range with 

suboptimal growth. Further research may be needed to 

verify the upper end of the range.

Percent Clay and Percent Coarse Sand

Red pine is commonly found on dry soils with low 

fertility. Dominant soil orders are Entisols, Spodosols, 

Alfisols, and Inceptisols. High importance values from 

the Climate Change Atlas (U.S. Forest Service, n.d.) 

Table 15.1—Effect of nitrogen deposition on red pine

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Northeastern and 

Midwestern U.S. 3 to 10 Significantly decreased growth; no change in survival Thomas et al. 2010

Table 15.2—Abiotic modifying factors for red pine

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m < 210, > 820 210 to 820 3

Aspect northeastern southwestern 2

Slope gradient % > 3.7 0.0 to 3.7 3

January temperature °C < -12.7, > -5.3 -12.7 to -5.3 3.7

July temperature °C < 19.2, > 22.6 19.2 to 22.6 3.7

May-September temperature °C < 15.5, > 19.4 15.5 to 19.4 3.7

Annual precipitation mm < 724, > 942 724 to 942 4

May-September precipitation mm < 388, > 508 388 to 508 4

Soil pH < 5.4, > 7.3 5.4 to 7.3 3.3

Clay % < 3.3, > 32.5 3.3 to 32.5 2.3

Coarse sand % < 62.3, > 96.9 62.3 to 96.9 3

Permeability cm hr-1 < 1.0, > 13.0 1.0 to 13.0 1.7

Depth to bedrock m < 1.0 ≥ 1.0 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
54

EXTERNAL INFLUENCES

Insect pests

We assumed that an abundance of insect pests 

correlates with suboptimal growth, while low or absent 

levels of insect pests correlate with optimal growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent levels of fungal pathogens would 

correlate with optimal growth.
55

16 PINUS RIGIDA (PITCH PINE)

elevations reported in Little and Garrett (1990). Pitch 

pine growth is expected to be optimal below 610 m in 

elevation.

Aspect

Pitch pine occurs on southern and western exposures in 

hilly sections (Little and Garrett 1990). We associated 

southwestern aspects with optimal growth and 

northeastern aspects with suboptimal growth.

Slope Gradient

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

thresholds for slope. We expect growth for pitch pine to 

be optimal from 0.0 to 5.6 percent.

Average January, July, and May to 

September Temperatures

High importance values from the Climate Change Atlas 

(U.S. Forest Service, n.d.) were used to set temperature 

thresholds. We associated January temperatures from 

-2.5 to -0.3 °C, July temperatures from 21.6 to 24.4°C, 

and May to September temperatures from 18.3 to 21.2 

°C with optimal growth and values outside of these 

ranges with suboptimal growth.

Annual Precipitation

Reported precipitation ranges for pitch pine include 

815 to 2289 mm (U.S. Forest Service, n.d.) and 940 

to 1420 mm (Little and Garett 1990). We set the 

high importance value range of 1045 to 1203 mm 

(U.S. Forest Service, n.d.) with optimal growth. Values 

outside of this range were associated with suboptimal 

growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 435 to 525 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

Pitch pine, Pinus rigida Mill., grows in southeastern 

New England and south along the Appalachian 

Mountain range. Pitch pine usually grows on shallow 

sandy or gravelly soils low in fertility with a pH range 

of 3.5 to 5.1 (Little and Garrett 1990). It prefers a 

humid climate with warm summers and cool winters. 

Precipitation is moderate and ranges between 940 

to 1420 mm yr-1 over much of its range. Pitch pine 

typically occurs below 610 m in New York and between 

430 and 1370 m in the south. In hilly sections, pitch 

pine occurs on southern and western exposures (Little 

and Garrett 1990).

We assigned a critical load of 3 to 10 kg ha-1 yr-1 for 

pitch pine, based on red pine response to N deposition 

in Thomas et al (2010). Thomas et al. (2010) found 

significantly decreased growth and no change in 

survival for red pine over a deposition range of 3 to 

10 kg N ha-1 yr-1. Critical load citations and values are 

shown in Table 16.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for pitch pine, including the pitch 

pine section of the Forest Service silvics handbook 

(Little and Garrett 1990) and high importance value 

data from the Climate Change Atlas (U.S. Forest 

Service, n.d.). In general, any factor that inhibits pitch 

pine growth would be expected to result in a decreased 

need for N and thus a lower critical load; optimal 

growth conditions result in an increased demand for 

N and a higher critical load. General values were used 

for base saturation, Ca:Al, and biomass removal as 

described in Chapter 2 (pages 13-14). Graphs showing 

importance values for abiotic modifying factor ranges 

by species are in Appendix 1. Optimal and suboptimal 

growth ranges and weight of evidence values are shown 

in Table 16.2.

Elevation

Elevation effects depend on latitude. We set elevation 

ranges for the northeastern United States based on 
56

Soil pH

Little and Garrett (1990) report that pitch pine is 

typically found on soils with pH of 3.5 to 5.1. We 

associated soil pH values of 4.5 to 4.9, the high 

importance value range from the Climate Change Atlas 

(U.S. Forest Service, n.d.), with optimal growth and 

values outside of this range with suboptimal growth.

Percent Clay and Percent Coarse Sand

Pitch pine growth is typically restricted to shallow, 

coarse, sandy soils. Dominant soil orders are Spodosols, 

Alfisols, Entisols, and Ultisols (Little and Garett 

1990). High importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) were used to 

set thresholds for percent clay and percent coarse sand. 

Optimal clay is 2.4 to 16.9 percent; optimal coarse sand 

is 60.0 to 87.9 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values 

range from 5.8 to 15.9 cm hr-1, rates for moderately to 

rapidly draining very fine sandy loams to loamy sand 

(O’Geen 2012).

Depth to Bedrock

Because pitch pine does not have an extensive entry 

in the PLANTS database (NRCS 2014), we assigned 

depth to bedrock values based on red pine. Depth 

to bedrock of <1.0 m is associated with suboptimal 

growth, while values ≥ 1.0 m are associated with 

optimal growth.

Table 16.1—Effect of nitrogen deposition on pitch pine

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Northeastern, 

Midwestern U.S.

3 to 10 (for red pine) Significantly decreased growth; 

no change in survival

Thomas et al. 2010

Table 16.2—Abiotic modifying factors for pitch pine

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 610 ≤ 610 3

Aspect northeastern southwestern 2

Slope gradient % > 5.6 0.0 to 5.6 2.3

January temperature °C < -2.5, > -0.3 -2.5 to -0.3 3.7

July temperature °C < 21.6, > 24.4 21.6 to 24.4 3.7

May-September temperature °C < 18.3, > 21.2 18.3 to 21.2 3.7

Annual precipitation mm < 1045, > 1203 1045 to 1203 3.7

May-September precipitation mm < 435, > 525 435 to 525 3.7

Soil pH < 4.5, > 4.9 4.5 to 4.9 3

Clay % < 2.4, > 16.9 2.4 to 16.9 2

Coarse sand % < 60.0, > 87.9 60.0 to 87.9 2.7

Permeability cm hr-1 < 5.8, > 15.9 5.8 to 15.9 1.3

Depth to bedrock m < 1.0 ≥ 1.0 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
57

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent levels of fungal pathogens would 

correlate with optimal growth.

EXTERNAL INFLUENCES

Insect Pests

We assumed that having an abundance of insect pests 

correlates with suboptimal growth, while low or absent 

levels of insect pests correlate with optimal growth.
58

17 PINUS STROBUS (EASTERN WHITE PINE)

handbook (Wendel and Smith 1990). Eastern white 

pine growth is expected to be optimal below 460 m and 

suboptimal at higher elevations.

Aspect

Eastern white pine has intermediate shade tolerance 

and no drought tolerance (NRCS 2014). In Pennsylvania 

and the southern Appalachians, white pine is found 

on northerly aspects (Wendel and Smith 1990). We 

associated northeastern aspects with optimal growth 

and southwestern aspects with suboptimal growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for eastern white pine is 

expected to be optimal from 0.2 to 10.4 percent.

Average January, July, and May to 

September Temperatures

According to Wendel and Smith (1990), eastern 

white pine prefers cool, humid climates. Average July 

temperatures range from 18 to 23 °C. High importance 

values from the Climate Change Atlas (U.S. Forest 

Service, n.d.) were used to set temperature thresholds. 

We associated January temperatures from -9.6 to 1.9 

°C, July temperatures from 19.3 to 23.8 °C, and May 

to September temperatures from 15.8 to 21.4 °C with 

optimal growth, and values outside of these ranges with 

suboptimal growth.

Annual Precipitation

Precipitation ranges for eastern white pine include 556 

to 2289 mm (U.S. Forest Service, n.d.), 508 to 2030 

mm (NRCS 2014), and 510 to 2030 mm (Wendel 

and Smith 1990). We associated the Climate Change 

Atlas high importance value range of 814 to 1364 mm 

with optimal growth. Values outside of this range were 

associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

SPECIES RANGE AND CRITICAL LOAD

Eastern white pine, Pinus strobus L., grows in 

southeastern Canada and in the northeastern and 

north central United States south to the Appalachian 

Mountains (Wendel and Smith 1990). Eastern white 

pine can grow on a great variety of soils, but competes 

best on well drained, sandy soils with low to medium 

quality. It grows poorly on heavy clay soils and 

poorly drained bottomlands. July temperatures in the 

white pine growing region range from 18 to 23 °C; 

precipitation ranges from 510 to 2030 mm. White pine 

grows from sea level to 460 m or sometimes higher in 

the north, and from 370 to 1070 m in the Appalachians 

(Wendel and Smith 1990).

We assigned a critical load of 5 to 9.5 kg N ha-1 yr-1

for eastern white pine, based on Thomas et al. (2010), 

which found slightly increased growth and slightly 

decreased survival for eastern white pine over a 

deposition range of 5 to 9.5 kg N ha-1 yr-1. Critical load 

citations and values are shown in Table 17.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for eastern white pine, including the 

eastern white pine section of the Forest Service silvics 

handbook (Wendel and Smith 1990), the PLANTS 

database (NRCS 2014), and high importance value data 

from the Climate Change Atlas (U.S. Forest Service, 

n.d.). In general, any factor that inhibits eastern white 

pine growth would be expected to result in a decreased 

need for N and thus a lower critical load; optimal 

growth conditions result in an increased demand for 

N and a higher critical load. General values were used 

for base saturation, Ca:Al, and biomass removal as 

described in Chapter 2 (pages 13-14). Graphs showing 

importance values for abiotic modifying factor ranges 

by species are in Appendix 1. Optimal and suboptimal 

growth ranges and weight of evidence values are shown 

in Table 17.2.

Elevation

Elevation effects depend on latitude. We set elevation 

ranges for the northeastern United States based 

on elevations reported in the Forest Service silvics 
59

We associated the precipitation range from 417 to 590 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges for eastern white pine include 

4 to 6.5 (NRCS 2014) and 4.1 to 7.8 (U.S. Forest 

Service, n.d.). We associated soil pH values of 4.9 

to 7.1, from Climate Change Atlas high importance 

values (U.S. Forest Service, n.d.), with optimal growth. 

Values outside of this range were associated with 

suboptimal growth.

Percent Clay and Percent Coarse Sand

Eastern white pine competes best on well drained 

sandy soils with low to medium site quality. The species 

can be found on Inceptisols, Ultisols, Spodosols, 

Entisols, and Alfisols (Wendel and Smith 1990). We 

used high importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) to set thresholds for 

percent clay and percent coarse sand. Optimal clay is 

2.6 to 32.8 percent; optimal coarse sand is 57.1 to 95.3 

percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.8 to 13.2 cm hr-1, the rates for moderately slow 

to rapidly draining clay loams to loamy sands (O’Geen 

2012).

Depth to Bedrock

We assigned depth to bedrock values of <1.0 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and values ≥1.0 m for optimal growth. 

Rooting restrictions at <50 cm in Maine resulted in 

Table 17.1—Effect of nitrogen deposition on eastern white pine

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Northeastern, 

Midwestern U.S.

5 to 9.5 Slightly increased growth; 

slightly decreased survival

Thomas et al. 2010

Table 17.2—Abiotic modifying factors for eastern white pine

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 460 ≤ 460 3

Aspect southwestern northeastern 2

Slope gradient % < 0.2, > 10.4 0.2 to 10.4 3.7

January temperature °C < -9.6, > 1.9 -9.6 to 1.9 4

July temperature °C < 19.3, > 23.8 19.3 to 23.8 4.3

May-September temperature °C < 15.8, > 21.4 15.8 to 21.4 4.3

Annual precipitation mm < 814, > 1364 814 to 1364 3.7

May-September precipitation mm < 417, > 590 417 to 590 3.7

Soil pH < 4.9, > 7.1 4.9 to 7.1 3

Clay % < 2.6, > 32.8 2.6 to 32.8 2.7

Coarse sand % < 57.1, > 95.3 57.1 to 95.3 3

Permeability cm hr-1 < 0.8, > 13.2 0.8 to 13.2 2

Depth to bedrock m < 1.0 ≥ 1.0 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
60

reductions in sapwood area and diameter breast height 

for white pine (Granger 2004) and may predispose 

white pine to decline. This indicates that at least 50 

cm of unrestricted rooting should be available to white 

pine, and depths of 1.0 m may be deeper than necessary.

EXTERNAL INFLUENCES

Insect Pests

We assumed that an abundance of insect pests correlate 

with suboptimal growth, while low or absent levels of 

insect pests correlate with optimal growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent levels of fungal pathogens would 

correlate with optimal growth. White pine blister rust 

(Cronartium ribicola) is the most damaging fungal 

disease of white pine (Wendel and Smith 1990). Wet 

weather, especially in the spring, may exacerbate fungal 

diseases (University of New Hampshire 2012).
61

18 POPULUS GRANDIDENTATA (BIGTOOTH ASPEN)

optimal growth and elevations outside of this range 

with suboptimal growth.

Aspect

Because aspen is both drought intolerant and shade 

intolerant, it is not clear which aspect is most favorable 

to growth. For this reason we have not associated aspect 

with growth for this species.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for bigtooth aspen is 

expected to be optimal from 0 to 4.3 percent.

Average January, July, and May to 

September Temperatures

Reported temperature ranges include a January 

temperature range of -18 to 2 °C and a July 

temperature range of 16 to 26 °C (Laidly 1990). 

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) are within this range, 

and we used them to set optimal values. Optimal 

January temperature is set as -13.0 to -4.9 °C, the July 

temperature range is 19.3 to 22.3 °C, and the May 

to September temperature range is 15.6 to 19.2 °C. 

Temperatures outside of these ranges are associated 

with suboptimal growth.

Annual Precipitation

The Climate Change Atlas (U.S. Forest Service, 

n.d.) provides a range from 475 to 1579 mm for 

annual precipitation for bigtooth aspen, with a high 

importance value range of 725 to 1005 mm. This range 

is associated with optimal growth and is similar to 

the optimal range of 799 to 1069 reported in Laidly 

(1990). We associated 725 to 1005 mm of precipitation 

with optimal growth. Values outside of this range were 

associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

SPECIES RANGE AND CRITICAL LOAD

Bigtooth aspen, Populus grandidentata Michx., grows 

in southeastern Canada and northeastern and north 

central United States (Laidly 1990). Although aspen 

can grow on a variety of soils, it grows best in moist, 

well drained, loamy and sandy soils that are high in 

organic matter and nutrients (Laidly 1990). Good 

soil aeration is important to bigtooth aspen growth. It 

grows in regions with a January average temperature 

range of -18 to 2 °C and a July temperature range of 16 

to 26 °C, with 510 to 1270 mm annual precipitation. 

It is most abundant on floodplains and lower slopes 

between 150 and 610 m (Laidly 1990).

We assigned a critical load of 3 to 11 kg N ha-1 yr-1 for 

bigtooth aspen. Thomas et al. (2010) found no change 

in growth but steadily decreasing survival for bigtooth 

aspen between 3 and 11 kg N ha-1 yr-1. Critical load 

values and citations for bigtooth aspen are shown in 

table 18.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for bigtooth aspen, including the 

bigtooth aspen section of the Forest Service silvics 

handbook (Laidly 1990), the PLANTS database 

(NRCS 2014), and high importance value data from 

the Climate Change Atlas (U.S. Forest Service, n.d.). In 

general, any factor that inhibits aspen growth would be 

expected to result in a decreased need for N and thus a 

lower critical load; optimal growth conditions result in 

an increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 18.2.

Elevation

We set elevation ranges for the northeastern United 

States based on elevation limits described in the Forest 

Service silvics handbook (Laidly 1990). For this species, 

we associated elevations from 150 to 610 m with 
62

We associated the precipitation range from 386 to 499 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Neither NRCS (2014) nor Laidly (1990) report a 

pH range associated with bigtooth aspen. Quaking 

aspen, a close relative, has a reported pH range of 4.3 

to 9.0 (NRCS 2014). We associated soil pH values 

of 5.4 to 7.3, the high importance values from the 

Climate Change Atlas (U.S. Forest Service, n.d.), with 

optimal growth and values outside of this range with 

suboptimal growth.

Percent Clay and Percent Coarse Sand

Bigtooth aspen grows best on moist, fertile sands and 

sandy loams and is most often found on Spodosols, 

Alfisols, and Inceptisols (Laidly 1990). We used high 

importance values from the Climate Change Atlas 

(U.S. Forest Service, n.d.) to set thresholds for percent 

clay and percent coarse sand. Optimal clay is 3.1 

to 31.8 percent; optimal coarse sand is 71.7 to 95.1 

percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 1.0 to 13.0 cm hr-1, the rates for moderately slow 

to rapidly draining clay loams to loamy sands (O’Geen 

2012).

Depth to Bedrock

We assigned depth to bedrock values of <0.8 m for 

suboptimal growth, based on minimum rooting depth 

for quaking aspen (NRCS 2014), and ≥0.8 m for 

optimal growth.

Table 18.1—Effect of nitrogen deposition on bigtooth aspen

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Species Citation

Northeastern, 

Midwestern U.S.

3 to 11 No change in growth; 

decreased survival

Bigtooth aspen Thomas et al. 2010

Table 18.2—Abiotic modifying factors for bigtooth aspen

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m < 150, > 610 150 to 610 3

Aspect N/A N/A

Slope gradient % > 4.3 0.0 to 4.3 3

January temperature °C < -13.0, > -4.9 -13.0 to -4.9 3.7

July temperature °C < 19.3, > 22.3 19.3 to 22.3 3.7

May-September temperature °C < 15.6, > 19.2 15.6 to 19.2 3.7

Annual precipitation mm < 725, > 1005 725 to 1005 3.7

May-September precipitation mm < 386, > 499 386 to 499 4

Soil pH < 5.4, > 7.3 5.4 to 7.3 3

Clay % < 3.1, > 31.8 3.1 to 31.8 2.3

Coarse sand % < 71.7, > 95.1 71.7 to 95.1 2.7

Permeability cm hr-1 < 1.0, > 13.0 1.0 to 13.0 2

Depth to bedrock m < 0.8 ≥ 0.8 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
63

EXTERNAL INFLUENCES

Insect Pests

We assumed that an abundance of insect pests 

correlates with suboptimal growth, while low levels 

or the absence of insect pests correlate with optimal 

growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low levels or the absence of fungal pathogens 

would correlate with optimal growth. Hypoxylon 

canker is the one of the most lethal fungal diseases of 

aspen in North America; moisture stress, clone variety, 

and stand density are among the factors influencing 

canker incidence and mortality. Fertilization with N, P, 

K did not have a significant effect on canker incidence 

(Ostry 2013).
64

19 POPULUS TREMULOIDES (QUAKING ASPEN)

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for aspen, including the aspen section 

of the Forest Service silvics handbook (Perala 1990), the 

PLANTS database (NRCS 2014), and high importance 

value data from the Climate Change Atlas (U.S. Forest 

Service, n.d.). In general, any factor that inhibits aspen 

growth would be expected to result in a decreased need 

for N and thus a lower critical load; optimal growth 

conditions result in an increased demand for N and a 

higher critical load. General values were used for base 

saturation, soil Ca:Al, and biomass removal as described 

in Chapter 2 (pages 13-14). Graphs showing importance 

values for abiotic modifying factor ranges by species are 

in Appendix 1. Optimal and suboptimal growth ranges 

and weight of evidence values are shown in Table 19.2.

Elevation

We set elevation ranges for the northeastern United 

States based on elevation limits described in the Forest 

Service silvics handbook (Perala 1990). Growth is 

expected to be optimal below 910 m in the northeastern 

United States and suboptimal at higher elevations.

Aspect

Because aspen is both drought intolerant and shade 

intolerant, it is not clear which aspect is most favorable 

to growth. For this reason, we did not associate aspect 

with growth for this species.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for quaking aspen is 

expected to be optimal from 0.0 to 3.5 percent.

Average January, July, and May to 

September Temperatures

Quaking aspen grows in areas with an average January 

temperature range of -30 to -3 °C and an average July 

temperature range of 16 to 23 °C (Perala 1990). The 

Climate Change Atlas data (U.S. Forest Service, n.d.) 

does not include the entire range for quaking aspen. 

The optimal temperature thresholds in this report, set 

SPECIES RANGE AND CRITICAL LOAD

Quaking (or trembling) aspen, Populus tremuloides

Michx., grows across a broad swath of Canada and 

the northern United States and can be found in 

mountainous regions through most of the United 

States, extending down into the mountains of Mexico. 

Although aspen can grow on a variety of soils, it grows 

best in moist, well drained, loamy soils that are high 

in organic matter and nutrients (Perala 1990). In a 

Canadian study across multiple sites, moist, nutrient 

rich soil resulted in the highest aspen site index (Chen 

et al. 2002). Growth on sandy soils lacking moisture 

and nutrients is usually poor; heavy, clayey soils also 

limit aspen growth. It occurs at elevations up to 910 

m in the north and as high as 3050 m in Arizona. 

Growth is best on aspects where moisture conditions 

are most favorable; this varies across the county. In 

the southwestern United States, northern aspects are 

more favorable, while in Alaska and western Canada, 

southwestern and western aspects are more favorable 

(Perala 1990).

Recent research indicates that quaking aspen dieback 

in multiple regions in North America is the result of 

drought stress and various secondary factors, including 

insect and fungal infestations (Frey et al. 2004, Worrall 

2013). Increasing temperatures and decreasing growing 

season precipitation resulted in decreased suitable 

habitat, especially in marginal areas such as shallow 

soils over bedrock. In the western United States, 

factors that can exacerbate moisture stress include low 

elevations, upper slope positions, southwestern aspects, 

poor climatic suitability, stand stocking, age, and poor 

soils (Worrall et al. 2013). Increased ozone (O3) has 

also been found to depress growth of some aspen clones 

(Moran and Kubiske 2013, Wang et al. 1986). It may be 

hard to differentiate the effects of multiple atmospheric 

pollutants on aspen growth and mortality.

We assigned a critical load of 5 to 9 kg N ha-1 yr-1 for 

aspen. Thomas et al. (2010) found decreased survival 

and increased growth for quaking aspen over a 

deposition range of 3 to 11 N kg ha-1 yr-1; peak growth 

occurred around 7 kg N ha-1 yr-1. The critical load for 

growth, when accounting for variability, is 5 to 9 kg N 

ha-1 yr-1. The critical load source is shown in Table 19.1.
65

using high importance values from the Climate Change 

Atlas, apply to quaking aspen in the eastern United 

States. We associated January temperatures from -16.8 

to -6.8 °C, July temperatures from 18.6 to 22.0 °C, and 

May to September temperatures from 14.8 to 18.4 °C 

with optimal growth and values outside of these ranges 

with suboptimal growth.

Annual Precipitation

Reported precipitation ranges for quaking aspen 

include 427 to 1800 mm (U.S. Forest Service, n.d.), 

178 to 1524 mm (NRCS 2014), and 410 to 1020 mm 

(Perala 1990). The high importance value range of 544 

to 895 mm (U.S. Forest Service, n.d.) was associated 

with optimal growth. Values outside of this range were 

associated with suboptimal growth.

Stand factors can influence the effect of precipitation 

on aspen mortality. For example, Bell et al. (2014) 

found that stands with high aspen density and low 

stand age were more susceptible to mortality as a result 

of decreasing winter precipitation.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 373 to 514 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Table 19.1—Effect of nitrogen deposition on quaking aspen

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Northeastern, 

Midwestern U.S.

3 to 11 Increased growth, with peak at 7 

kg N ha-1 yr-1; decreased survival

Thomas et al. 2010

Table 19.2—Abiotic modifying factors for quaking aspen

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 910 ≤ 910 3

Aspect N/A N/A

Slope gradient % > 3.5 0.0 to 3.5 4

January temperature °C < -16.8, > -6.8 -16.8 to -6.8 4

July temperature °C < 18.6, > 22.0 18.6 to 22.0 4.3

May-September temperature °C < 14.8, > 18.4 14.8 to 18.4 5

Annual precipitation mm < 544, > 895 544 to 895 5

May-September precipitation mm < 373, > 514 373 to 514 4.3

Soil pH < 4.9, > 7.7 4.9 to 7.7 4.3

Clay % < 3.6, > 38.8 3.6 to 38.8 4.3

Coarse sand % < 65.4, > 95.8 65.4 to 95.8 3.7

Permeability cm hr-1 < 0.8, > 12.2 0.8 to 12.2 3.7

Depth to bedrock m < 0.8 ≥ 0.8 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
66

Soil pH

Reported soil pH values for quaking aspen include 4.3 

to 9.0 (NRCS 2014) and 4.5 to 8.1 (U.S Forest Service, 

n.d.). We associated soil pH values of 4.9 to 7.7, the 

high importance value range from the Climate Change 

Atlas (U.S. Forest Service, n.d.), with optimal growth 

and values outside of this range with suboptimal 

growth.

Percent Clay and Percent Coarse Sand

Quaking aspen growth is best on soils that are well 

drained, loamy, and high in organic matter, calcium, 

magnesium, potassium, and nitrogen. Dominant soil 

orders are Alfisols, Spodosols, and Inceptisols (Perala 

1990). We used high importance values from the 

Climate Change Atlas (U.S. Forest Service, n.d.) to 

set thresholds for percent clay and percent coarse sand. 

Optimal clay is 3.6 to 38.8 percent; optimal coarse sand 

is 65.4 to 95.8 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.8 to 12.2 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

We assigned depth to bedrock values of <0.8 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and ≥0.8 m for optimal growth.

EXTERNAL INFLUENCES

Insect pests

We assumed an abundance of insect pests correlates 

with suboptimal growth, while low levels or the absence 

of insect pests correlate with optimal growth. Increased 

nitrogen content in leaves may increase insect pest 

populations. In the great lakes, forest tent caterpillar 

(Malacosoma disstria Hübner) pupal weight was 

positively correlated with higher nitrogen content in 

quaking aspen leaves (Hemming and Lindroth 1995).

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by an abundance of fungal pathogens would have 

suboptimal growth. Low or absent levels of fungal 

pathogens would correlate with optimal growth. 

Hypoxylon canker is one of the most lethal fungal 

diseases of aspen in North America; moisture stress, 

clone variety, and stand density are among the 

factors influencing canker incidence and mortality. 

Fertilization with N, P, K did not have a significant 

effect on canker incidence (Ostry 2013).
67

20 QUERCUS ALBA (WHITE OAK)

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 20.2.

Elevation

We based optimal elevation for white oak on 

information from Rogers (1990). Growth is expected to 

be optimal below 150 m in elevation and suboptimal at 

higher elevations.

Aspect

According to Rogers (1990), white oak trees grow 

best on northern and eastern exposures. They have 

intermediate shade tolerance and medium drought 

tolerance (NRCS 2014). We associated northeastern 

exposures with optimal growth and southwestern 

exposures with suboptimal growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for white oak is expected 

to be optimal from 0.1 to 9.4 percent.

Average January, July, and May to 

September Temperatures

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

temperature thresholds. We associated January 

temperatures from -9.7 to 3.8 °C, July temperatures 

from 21.5 to 26.5 °C, and May to September 

temperatures from 18.0 to 23.7 °C with optimal 

growth, and values outside of these ranges with 

suboptimal growth.

Annual precipitation

Reported precipitation ranges include 465 to 2289 mm 

(U.S. Forest Service, n.d.), 762 to 2030 mm (NRCS 

2014), and 760 to 2030 mm, with an ideal value of 

1020 mm (Rogers 1990). We associated the high 

importance value range of 808 to 1450 mm (U.S. Forest 

Service, n.d.) with optimal growth. Values outside of 

this range were associated with suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

White oak, Quercus alba L., is found in most of the 

eastern United States, as well as in extremely southern 

parts of eastern Canada. It grows on sandy plains, 

gravelly ridges, fertile uplands, and well drained loams, 

growing well on all but the driest and shallowest soils. 

Average temperatures range from 7 to 21 °C, while 

precipitation ranges from 760 to 2030 mm yr-1 (Rogers 

1990). White oak can be found below 150 m in the 

north, and as a scrub tree at 1350 m in the southern 

Appalachians, with best growth in lower slopes and 

coves with northerly or easterly aspects. White oak is 

more abundant, but smaller, on west and south facing 

slopes. Oak wilt and golden oak scale are the most 

serious threat to white oak health (Rogers 1990).

We assigned a critical load of 11 to 18 kg ha-1 yr-1 for 

white oak. Thomas et al. (2010) found no change in 

growth or survival for white oak between 3 to 11 kg 

N ha-1 yr-1. Elias (2008) found decreased basal area for 

oak with N deposition between 12 and 14 kg ha-1 yr-1, 

while Dietz and Moorcroft (2011) found increased 

mortality for northern mid successional hardwoods 

with increasing N deposition; visual inspection of 

supplementary material indicate that mortality was 

lowest from 5 to 16 kg ha-1 yr-1 wet N deposition (7 to 

18 kg ha-1 yr-1 wet + dry N).We used the upper end of 

the range from Thomas et al. (2010) and the upper end 

of the range from Dietz and Moorcroft (2011) to set 

the critical load. Critical load values and citations are 

shown in Table 20.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for white oak, including the white 

oak section of the Forest Service silvics handbook 

(Rogers 1990), the PLANTS database (NRCS 2014), 

and high importance value data from the Climate 

Change Atlas (U.S. Forest Service, n.d.). In general, 

any factor that inhibits white oak growth would be 

expected to result in a decreased need for N and thus a 

lower critical load; optimal growth conditions result in 

an increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 
68

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 433 to 578 

mm with optimal growth, and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges include pH 4.5 to 6.8 (NRCS 

2014) and 2.7 to 8 (U.S. Forest Service, n.d.). We 

associated the high importance value range of pH 4.7 to 

6.9 (U.S. Forest Service, n.d.) with optimal growth, and 

values outside of this range with suboptimal growth.

Percent Clay and Percent Coarse Sand

White oak can be found growing on a variety of soils, 

including sandy plains, gravelly ridges, and welldrained loamy soils. Dominant soil orders are Alfisols 

and Ultisols (Rogers 1990). We used high importance 

values from the Climate Change Atlas (U.S. Forest 

Service, n.d.) to set thresholds for percent clay and 

percent coarse sand. Optimal pecent clay is 11.4 to 

42.7 percent; optimal coarse sand is 50.0 to 99.5 

percent.

Table 20.1—Effect of nitrogen deposition on white oak

Location

N deposition range 

(kg ha-1 yr-1)

Increased N 

deposition effects Interacting factors Citation

Northeastern and 

Midwestern U.S.

3 to 11 No change in growth 

or survival

Thomas et al. 2010

West Virginia 12 to 14 Decreased basal area S deposition

20-26 kg ha-1 yr-1

Elias 2008

Eastern and 

Central U.S.

5 to 18 (wet NO3)

(5 to 16 lowest mortality)

Increased mortality 

probability

Dietze and 

Moorcroft 2011

Table 20.2—Abiotic modifying factors for white oak

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 150 ≤ 150 3

Aspect southwestern northeastern 2

Slope gradient % < 0.1, > 9.4 0.1 to 9.4 3.7

January temperature °C < -9.7, > 3.8 -9.7 to 3.8 5

July temperature °C < 21.5, > 26.5 21.5 to 26.5 5

May-September temperature °C < 18.0, > 23.7 18.0 to 23.7 5

Annual precipitation mm < 808, > 1450 808 to 1450 4.7

May-September precipitation mm < 433, > 578 433 to 578 4.7

Soil pH < 4.7, > 6.9 4.7 to 6.9 4.3

Clay % < 11.4, > 42.7 11.4 to 42.7 3.7

Coarse sand % < 50.0, > 99.5 50.0 to 99.5 3.3

Permeability cm hr-1 < 0.5, > 7.5 0.5 to 7.5 3.3

Depth to bedrock m < 1.2 ≥ 1.2 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
69

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.5 to 7.5 cm hr-1, rates for moderately slow to 

moderately rapidly draining clay loams to sandy loams 

(O’Geen 2012).

Depth to Bedrock

We associated depth to bedrock of <1.2 m with 

suboptimal growth, and ≥1.2 m for optimal growth, 

based on minimum rooting depth from PLANTS 

database (NRCS 2014).

EXTERNAL INFLUENCES

Insect Pests

We assumed that an abundance of insect pests 

correlates with suboptimal growth, while low or absent 

levels of insect pests correlate with optimal growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent levels of fungal pathogens would 

correlate with optimal growth.
70

21 QUERCUS PRINUS (CHESTNUT OAK)

the northeastern United States. We set an approximate 

elevation threshold of 330 m for optimal growth.

Aspect

According to McQuilkin (1990), chestnut oak are most 

common on southern and western exposures. In a study 

in West Virginia, chestnut oak growth increased on 

southwestern aspects and decreased on northeastern 

aspects (Fekedulegn et al. 2003). We associated 

southwestern exposures with optimal growth and 

northeastern exposures with suboptimal growth.

Slope Gradient

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

thresholds for slope. We expect growth of chestnut oak 

to be optimal from 1.3 to 15.9 percent.

Average January, July, and May to 

September Temperatures

High importance values from the Climate Change Atlas 

(U.S. Forest Service, n.d.) were used to set temperature 

thresholds. We associated January temperatures from 

-4.6 to 3.3 °C, July temperatures from 20.2 to 25.1 °C, 

and May to September temperatures from 17.6 to 22.7 

°C with optimal growth, and values outside of these 

ranges with suboptimal growth.

Annual Precipitation

Annual precipitation is reported to range from 758 to 

2289 mm (U.S. Forest Service, n.d.) and 810 to 2030 

mm (McQuilkin 1990), with an ideal range of 1020 to 

1220 mm (McQuilkin 1990). We associated the high 

importance value range of 920 to 1591 mm (U.S. Forest 

Service, n.d.), which includes the reported ideal range, 

with optimal growth. Values outside of this range were 

associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 429 to 643 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

Chestnut oak, Quercus prinus L., is frequently found 

growing on dry, infertile ridges in the Appalachian 

region of the eastern United States, although it 

grows best on fertile, well drained soils along streams. 

Chestnut oak grows from sea level in the north to 

1400 m in the southern Appalachians and is most 

common on south and west facing slopes. Precipitation 

is typically between 1020 to 1220 mm yr-1, although 

it can range from 810 to 2030 mm yr-1 (McQuilkin 

1990).

We assigned a critical load of 3 to10.5 N kg ha-1 yr-1

for chestnut oak. Thomas et al. (2010) found no change 

in growth and decreased survival with increased N 

deposition from 3 to 10.5 kg N ha-1 yr-1. Survival was 

highest around 5 kg N ha-1 yr-1; the critical load based 

on survival alone would be approximately 3 to 7 kg 

ha-1 yr-1. The upper end of the range is supported by 

results from Elias (2008), who found decreased basal 

area for oak with N deposition between 12 and 14 kg 

ha-1 yr-1. Critical load values and citations are shown in 

Table 21.1.

ABIOTIC MODIFYING FACTORS

 Multiple sources were used to set the ranges of 

modifying factors for chestnut oak, including the 

chestnut oak section of the Forest Service silvics 

handbook (McQuilkin 1990) and high importance 

value data from the Climate Change Atlas (U.S. 

Forest Service, n.d.). In general, any factor that 

inhibits chestnut oak growth would be expected to 

result in a decreased need for N and thus a lower 

critical load; optimal growth conditions result in an 

increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 21.2.

Elevation

Elevation effects depend on latitude. Chestnut oak 

growth is expected to be optimal at low elevations in 
71

Soil pH

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to determine 

pH ranges for chestnut oak. We associated soil pH 

values of 4.5 to 5.9 with optimal growth and values 

outside of this range with suboptimal growth.

Percent Clay and Percent Coarse Sand

Chestnut oak is typically found growing on dry upland 

sites; dominant soil orders are Ultisols and Inceptisols 

(McQuilkin 1990). We used high importance values 

from the Climate Change Atlas (U.S. Forest Service, 

n.d.) to set thresholds for percent clay and percent 

coarse sand. Optimal clay is 14.2 to 44.3 percent; 

optimal coarse sand is 53.1 to 90.1 percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 1.0 to 7.5 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

We assigned depth to bedrock values of <0.8 m for 

suboptimal growth and ≥0.8 for optimal growth. This 

is an approximate threshold because depth to bedrock 

was not given in the PLANTS database (NRCS 2014).

Table 21.1—Effect of nitrogen deposition on chestnut oak

Location

N deposition range 

(kg ha-1 yr-1)

Increased N 

deposition effects Interacting factors Citation

Northeastern, 

Midwestern U.S.

3 to 10.5 No change in growth, 

increased mortality

Thomas et al. 2010

West Virginia 12 to 14 Decreased basal area S deposition

20-26 kg ha-1 yr-1

Elias 2008

Table 21.2—Abiotic modifying factors for chestnut oak

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 330 ≤ 330 2

Aspect northeastern southwestern 2

Slope gradient % < 1.3, > 15.9 1.3 to 15.9 3

January temperature °C < -4.6, > 3.3 -4.6 to 3.3 4

July temperature °C < 20.2, > 25.1 20.2 to 25.1 4.3

May-September temperature °C < 17.6, > 22.7 17.6 to 22.7 4

Annual precipitation mm < 920, > 1591 920 to 1591 3.7

May-September precipitation mm < 429, > 643 429 to 643 4

Soil pH < 4.5, > 5.9 4.5 to 5.9 3.7

Clay % < 14.2, > 44.3 14.2 to 44.3 3

Coarse sand % < 53.1, > 90.1 53.1 to 90.1 3

Permeability cm hr-1 < 1.0, > 7.5 1.0 to 7.5 2.7

Depth to bedrock m < 0.8 ≥ 0.8 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
72

EXTERNAL INFLUENCES

Insect Pests

We assumed that an abundance of insect pests 

correlates with suboptimal growth. Low or absent levels 

of insect pests correlate with optimal growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent levels of fungal pathogens would 

correlate with optimal growth.
73

22 QUERCUS RUBRA (NORTHERN RED OAK)

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 22.2.

Elevation

We set elevation ranges for the northeastern United 

States based on basal area dominance of hardwood 

trees at various elevations in the Green Mountains 

of Vermont as reported by Beckage et al. (2008). We 

generally expect growth to be optimal below 610 m and 

suboptimal at higher elevations.

Aspect

According to NRCS (2014), red oaks are shade 

intolerant and have little drought tolerance. They are 

most common on northern and eastern exposures 

(Sander 1990). However, Fekedulegn et al. (2003) 

did not find a difference in red oak growth between 

northeastern and southwestern aspect. We have 

currently associated northeastern exposures with 

optimal growth and southwestern exposures with 

suboptimal growth. However, it may be that aspect does 

not have a strong influence on red oak growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for red oak is expected to 

be optimal from 0.0 to 9.0 percent.

Average January, July, and May to 

September Temperatures

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

temperature thresholds. We associated January 

temperatures from -13.2 to 0.5 °C, July temperatures 

from 19.7 to 25.4 °C, and May to September 

temperatures from 16.2 to 22.4 °C with optimal growth 

and values outside of these ranges with suboptimal 

growth.

Annual Precipitation

Reported precipitation ranges include 565 to 2289 

mm (U.S. Forest Service, n.d.), 762 to 2030 mm 

(NRCS 2014), and 760 to 2030 mm (Sander 1990). 

SPECIES RANGE AND CRITICAL LOAD

Northern red oak, Quercus rubra L., is found in much 

of central and eastern United States, as well southern 

parts of eastern Canada. Although it can be found on 

clay to loamy sand soils, red oak grows best on deep, 

well drained loam to silty clay loam soils (Sander 1990). 

Average temperatures range from 4 to 16 °C, while 

precipitation ranges from 760 to 2030 mm yr-1. Red 

oak can be found up to 1070 m in West Virginia and 

up to 1680 m in the southern Appalachians, with best 

growth in lower or middle slopes with northerly or 

easterly aspects. Oak wilt and gypsy moth infestations 

can have a major impact on red oak health and 

mortality (Sander 1990).

We assigned a critical load of 3 to 14 kg N ha-1 yr-1

for red oak. Thomas et al. (2010) found a significant 

increase in growth and a small decrease in survival with 

increased N deposition from 3 to 11 kg N ha-1 yr-1. 

Seedling growth decreased as ambient N deposition 

increased from approximately 2.4 kg ha-1 yr-1 wet 

N deposition to approximately 10 kg ha-1 yr-1 wet 

N at a site in Illinois (BassiriRad et al. 2015). Elias 

(2008) found decreased basal area for red oak with N 

deposition between 12 and 14 kg N ha-1 yr-1.We used 

the low end of the range from Thomas et al. (2010) and 

the upper end of the range from Elias (2008) to set 

the critical load. Critical load values and citations are 

shown in Table 22.1.

ABIOTIC MODIFYING FACTORS

We used multiple sources to set the ranges of 

modifying factors for red oak, including the red oak 

section of the Forest Service silvics handbook (Sander 

1990), the PLANTS database (NRCS 2014), and 

high importance value data from the Climate Change 

Atlas (U.S. Forest Service, n.d.). In general, any factor 

that inhibits red oak growth would be expected to 

result in a decreased need for N and thus a lower 

critical load; optimal growth conditions result in an 

increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 
74

We associated the high importance value range of 699 

to 1264 mm (U.S. Forest Service, n.d.) with optimal 

growth. Values outside of this range were associated 

with suboptimal growth.

Red oak appears to be moderately responsive to 

changes in precipitation. Drought years resulted in 

small decreases in growth for red oak in West Virginia 

(Fekedulegn et al. 2003). In Pennsylvania, red oaks on 

sites with higher soil pH and base saturation had better 

basal area growth recovery following drought than red 

oaks on sites with lower soil base saturation and pH 

(Demchik and Sharpe 2000).

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 398 to 565 

mm with optimal growth, and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH ranges for red oak include 4.3 to 7.3 

(NRCS 2014) and 2.7 to 7.9 (U.S. Forest Service, n.d.). 

We used high importance values from the Climate 

Table 22.1—Effect of nitrogen deposition on red oak

Location

N deposition range 

(kg ha-1 yr-1)

Increased N 

deposition effects Interacting factors Citation

Illinois, Indiana 2.4 to 10.1 (wet) Decreased seedling 

growth

BassiriRad et al. 2015

Northeastern, 

Midwestern U.S.

3 to 11 Increased growth, 

small decrease in 

survival

Thomas et al. 2010

West Virginia 12 to 14 Decreased basal area S deposition

20 to 26 kg ha-1 yr-1

Elias 2008

Table 22.2—Abiotic modifying factors for red oak

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 610 ≤ 610 2

Aspect southwestern northeastern 2

Slope gradient % > 9.0 0.0 to 9.0 3.7

January temperature °C < -13.2, > 0.5 -13.2 to 0.5 4

July temperature °C < 19.7, > 25.4 19.7 to 25.4 4.3

May-September temperature °C < 16.2, > 22.4 16.2 to 22.4 4.3

Annual precipitation mm < 699, > 1264 699 to 1264 4

May-September precipitation mm < 398, > 565 398 to 565 4.3

Soil pH < 4.8, > 7.2 4.8 to 7.2 4

Clay % < 4.0, > 35.9 4.0 to 35.9 3

Coarse sand % < 58.6, > 99.0 58.6 to 99.0 3.3

Permeability cm hr-1 < 0.6, > 12.4 0.6 to 12.4 2.7

Depth to bedrock m < 0.9 ≥ 0.9 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
75

Change Atlas (U.S. Forest Service, n.d.) to associate 

soil pH values of 4.8 to 7.2 with optimal growth. Values 

outside of this range were associated with suboptimal 

growth.

Percent Clay and Percent Coarse Sand

Red oak grows best on deep, well drained loam and silty 

clay loam. Dominant soil orders are Spodosols, Alfisols, 

Inceptisols, Mollisols, Ultisols, and Entisols. We used 

high importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) to set thresholds for 

percent clay and percent coarse sand. Optimal clay is 

4.0 to 35.9 percent; optimal coarse sand is 58.6 to 99.0 

percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.6 to 12.4 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

Based on PLANTS database (NRCS 2014) minimum 

rooting depths, we associated depth to bedrock of <0.9 

m with suboptimal growth and ≥0.9 m for optimal 

growth.

EXTERNAL INFLUENCES

Insect Pests

We assumed that abundant levels of insect pests 

correlate with suboptimal growth, while low or absent 

levels of insect pests correlate with optimal growth. 

Gypsy moths (Lymantria dispar (L.)) are the most 

serious pest of red oak trees (Lovett et al. 2006, Sander 

1990).

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth, and low or absent levels of fungal pathogens 

would correlate with optimal growth.
76

23 THUJA OCCIDENTALIS (NORTHERN WHITE-CEDAR)

Elevation

We set elevation ranges for the northeastern United 

States based on elevations for northern white-cedar 

reported in Johnston (1990). Northern white-cedar is 

expected to have optimal growth between 150 and 600 

m in elevation.

Aspect

Northern white-cedar is generally drought intolerant 

(NRCS 2014), although this varies by site; drought 

resistance is low to moderate on imperfectly drained 

sites and high on calcareous sites (Boulfroy et al. 2012). 

Shade tolerance is intermediate (NRCS 2014). We 

have associated southwestern aspects with suboptimal 

growth and northeastern aspects with optimal growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for northern white-cedar 

is expected to be optimal from 0.0 to 3.4 percent.

Average January, July, and May to 

September Temperatures

Johnston (1990) reports an average January temperature 

range of -12 to -4 °C and an average July temperature 

range of 16 to 22 °C. High importance values from 

the Climate Change Atlas (U.S. Forest Service, 

n.d.) were used to set temperature thresholds. We 

associated January temperatures from -16.3 to -7.5 

°C, July temperatures from 17.9 to 21.1 °C, and May 

to September temperatures from 14.1 to 17.4 °C with 

optimal growth, and values outside of these ranges 

with suboptimal growth. The low optimal average 

temperature from the Climate Change Atlas is cooler 

that the low average January temperature reported in 

the Forest Service silvics handbook.

Annual Precipitation

Precipitation ranges for northern white-cedar include 

499 to 1800 mm (U.S. Forest Service, n.d.), 889 to 

1397 mm (NRCS 2014), and 510 to 1400 ( Johnston 

SPECIES RANGE AND CRITICAL LOAD

Northern white-cedar, Thuja occidentalis L., grows in 

southeastern Canada, the northeastern United States, 

and the Lake States. Growth is best in humid climates 

with adequate rainfall over organic or calcareous 

mineral soils; northern white-cedar does not develop 

well on extremely wet or dry sites ( Johnston 1990). 

Growth and health are positively correlated with Ca 

and Mg and negatively correlated with acidity and 

Al (Boulfroy et. al. 2012, Kell 2009). Precipitation 

generally ranges from 710 to 1170 mm yr-1, average 

January temperatures range from -12 to -4 °C, and 

average July temperatures range from 16 to 22 °C. 

Northern white-cedar grows from sea level to 600 m, 

though it is most common between 150 and 600 m 

( Johnston 1990).

We assigned a critical load of 3 to 8.5 kg N ha-1 yr-1

for northern white-cedar. Thomas et al. (2010) found 

slightly decreased growth and no change in survival 

over a deposition range of 3 to 8.5 kg N ha-1 yr-1; 

growth appeared to peak at 5.5 kg N ha-1 yr-1. Critical 

load values and citations are shown in Table 23.1.

ABIOTIC MODIFYING FACTORS

We used multiple sources to set the ranges of 

modifying factors for northern white-cedar, including 

the northern white-cedar section of the Forest Service 

silvics handbook ( Johnston 1990), the PLANTS 

database (NRCS 2014), and high importance value 

data from the Climate Change Atlas (U.S. Forest 

Service, n.d.). In general, any factor that inhibits 

northern white-cedar growth would be expected to 

result in a decreased need for N and thus a lower 

critical load; optimal growth conditions result in an 

increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 23.2.
77

1990). We associated the high importance value range 

of 658 to 1124 mm (U.S. Forest Service, n.d.) with 

optimal growth. Values outside of this range were 

associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 389 to 499 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Reported soil pH values for northern white-cedar 

include 5.2 to 7.0 (NRCS 2014) and 5.5 to 7.2 

( Johnston 1990). We associated soil pH values of 5.2 to 

7.3, the high importance value range from the Climate 

Change Atlas (U.S. Forest Service, n.d.), with optimal 

growth. We associated values outside of this range with 

suboptimal growth.

Percent Clay and Percent Coarse Sand

Northern white-cedar is most often found on cool, 

moist, nutrient rich sites with high organic matter 

or calcareous mineral soils. Dominant soil orders 

are Histosols, Inceptisols, and Entisols ( Johnston 

1990). We used high importance values from the 

Climate Change Atlas (U.S. Forest Service, n.d.) to 

set thresholds for percent clay and percent coarse sand. 

Optimal clay is 4.2 to 25.8 percent; optimal coarse sand 

is 65.1 to 91.5 percent.

Table 23.1—Effect of nitrogen deposition on northern white-cedar

Location

N deposition range 

(kg ha-1 yr-1)

Increased N 

deposition effects Citation

Northeastern, 

Midwestern U.S.

3 to 8.5 Decreased growth and 

no change in survival

Thomas et al. 2010

Table 23.2—Abiotic modifying factors for northern white-cedar

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m < 150, > 600 150 to 600 3

Aspect southwestern northeastern 2

Slope gradient % > 3.4 0.0 to 3.4 3.7

January temperature °C < -16.3, > -7.5 -16.3 to -7.5 4

July temperature °C < 17.9, > 21.1 17.9 to 21.1 4.3

May-September temperature °C < 14.1, > 17.4 14.1 to 17.4 4.3

Annual precipitation mm < 658, > 1124 658 to 1124 4.3

May-September precipitation mm < 389, > 499 389 to 499 4.3

Soil pH < 5.2, > 7.3 5.2 to 7.3 3.7

Clay % < 4.2, > 25.8 4.2 to 25.8 3

Coarse sand % < 65.1, > 91.5 65.1 to 91.5 3.7

Permeability cm hr-1 < 0.6, > 12.5 0.6 to 12.5 2

Depth to bedrock m < 0.7 ≥ 0.7 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
78

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.6 to 12.5 cm hr-1, the rate for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

We assigned depth to bedrock values of <0.7 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and ≥0.7 m for optimal growth.

EXTERNAL INFLUENCES

Insect Pests

Northern white-cedar does not have significant insect 

pests; for this reason, we assumed that insect pests do 

not significantly affect the growth of this species.

Fungal Pathogens

Northern white-cedar does not have significant fungal 

pathogens; for this reason, we assumed that fungal 

pathogens do not significantly affect the growth of this 

species.
79

24 TSUGA CANADENSIS (EASTERN HEMLOCK)

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 24.2.

Elevation

We set elevation ranges for the northeastern United 

States based on elevation ranges in Godman and 

Lancaster (1990) for New England. Eastern hemlock 

growth is expected to be optimal below 730 m, and 

suboptimal at higher elevations.

Aspect

Because eastern hemlock is drought intolerant and 

shade tolerant, we associated southwestern aspects 

with suboptimal growth, and northeastern aspects with 

optimal growth.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for eastern hemlock is 

expected to be optimal from 0.5 to 8.2 percent.

Average January Temperature

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

average January temperature ranges. We associated the 

temperature range from -10.3 to -3.2 °C with optimal 

growth and temperatures outside of this range with 

suboptimal growth. This is within the range from the 

eastern hemlock section in Godman and Lancaster 

(1990), which provides an average January temperature 

range of -12 to 6 °C. Although hemlock growth has 

been positively associated with warmer winters (Tardif 

et al. 2001), warmer winters also bring an increased risk 

of hemlock wooly adelgid. Cold winter temperatures 

are associated with decreased hemlock wooly adelgid 

populations and decreased hemlock mortality 

(Eschtruth 2013, Paradis et al. 2008). All hemlock 

wooly adelgid are likely to die with an average mean 

winter temperature of -5 °C (Paradis et al. 2008).

Average July Temperature

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

SPECIES RANGE AND CRITICAL LOAD

Eastern hemlock, Tsuga candensis (L.) Carrière, grows 

in the northeastern and central-eastern United States, 

as well as southeastern Canada. Eastern hemlock grows 

best in moist sandy loams, loamy sands, and silty loams 

with good drainage. It occurs at elevations up to 730 

m in New England, 300 to 910 m in New York and 

Pennsylvania, and from 610 to 1520 m in the southern 

Appalachians (Godman and Lancaster 1990). Eastern 

hemlock populations in the southern part of the range 

are often subject to infestation of hemlock wooly 

adelgid (Adelgis tsugae), which can result in tree death 

in 4-5 years (Lovett et al. 2006), although tree decline 

may occur much more gradually (Eschtruth et al. 2006, 

2013).

We assigned a critical load of >11 to 23 kg ha-1 yr-1 for 

eastern hemlock. Thomas et al. (2010) found no change 

in growth or survival with increased N deposition from 

3 to 11 kg N ha-1 yr-1. For late successional conifers, 

Dietze and Moorcroft (2011) found that decreased 

probability of mortality was correlated with increased N 

deposition between 6 to 23 kg ha-1 yr-1 NO3 wet; visual 

inspection of supplementary material indicate that 

mortality was lowest from approximately 10 to 21 kg 

ha-1 yr-1 NO3 wet (12 to 23 kg ha-1 yr-1 wet + dry N). 

Critical load citations and values are shown in Table 

24.1.

ABIOTIC MODIFYING FACTORS

Multiple sources were used to set the ranges of 

modifying factors for eastern hemlock, including 

the eastern hemlock section of the Forest Service 

silvics handbook (Godman and Lancaster 1990), 

the PLANTS database (NRCS 2014), and high 

importance value data from the Climate Change Atlas 

(U.S. Forest Service, n.d.). In general, any factor that 

inhibits eastern hemlock growth would be expected 

to result in a decreased need for N and thus a lower 

critical load; optimal growth conditions result in an 

increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 
80

the range for average July temperature. We associated 

the July temperature range from 18.9 to 22.2 °C with 

optimal growth and temperatures outside of this range 

with suboptimal growth. Average July temperature is 

used to delimit preferred growth climate for eastern 

hemlock, but does not necessarily describe optimum 

growth temperatures. Optimum July temperature 

for photosynthesis, in a simulated field environment 

in Wisconsin, was found to be 17 °C. Optimum 

temperatures for photosynthesis were lower in May 

(Adams and Loucks 1971). High summer temperatures 

(21 °C) in August in Massachusetts resulted in carbon 

storage near zero (Hadley and Schedlbauer 2002); 

storage was highest in the cooler months of April and 

May.

Average May to September Temperature

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September temperature. 

We associated the temperature range from 15.4 to 19.0 

°C with optimal growth and temperatures outside of 

this range with suboptimal growth.

Annual Precipitation

Annual precipitation ranges for eastern hemlock 

include 720 to 2289 mm (U.S. Forest Service, n.d.), 

813 to 1397 mm (NRCS 2014), and 740 to 1520 

mm (Godman and Lancaster 1990). We associated 

the Climate Change Atlas (U.S. Forest Service, n.d.) 

Table 24.1—Effect of nitrogen deposition on eastern hemlock

Location

N deposition range 

(kg ha-1 yr-1)

Increased N 

deposition effects Citation

Northeastern, 

Midwestern U.S.

3 to 11 No change in growth 

or survival

Thomas et al. 2010

Eastern and 

Central U.S.

6 to 23 (wet NO3)

(12 to 23 wet+ dry N 

lowest mortality)

Decreased mortality 

probability

Dietze and Moorcroft

Table 24.2—Abiotic modifying factors for eastern hemlock

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 730 ≤ 730 3

Aspect southwestern northeastern 2

Slope gradient % < 0.5, > 8.2 0.5 to 8.2 3

January temperature °C < -10.3, > -3.2 -10.3 to -3.2 4

July temperature °C < 18.9, > 22.2 18.9 to 22.2 4.3

May-September temperature °C < 15.4, > 19.0 15.4 to 19.0 4.3

Annual precipitation mm < 866, > 1314 866 to 1314 4

May-September precipitation mm < 417, > 597 417 to 597 4

Soil pH < 4.9, > 6.7 4.9 to 6.7 3.7

Clay % < 3.4, > 26.2 3.4 to 26.2 3

Coarse sand % < 55.6, > 94.0 55.6 to 94.0 3

Permeability cm hr-1 < 0.7, > 12.2 0.7 to 12.2 2.3

Depth to bedrock m < 0.7 ≥ 0.7 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.
81

high importance value range of 866 to 1314 mm with 

optimal growth. Values outside of this range were 

associated with suboptimal growth.

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 417 to 597 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

Soil pH ranges reported for eastern hemlock include 

4.1 to 7.6 (U.S. Forest Service, n.d.) and 4.2 to 5.7 

(NRCS 2014). We associated soil pH values of 4.9 

to 6.7, the high importance value range from the 

Climate Change Atlas (U.S. Forest Service, n.d.), with 

optimal growth and values outside of this range with 

suboptimal growth.

Percent Clay and Percent Coarse Sand

Eastern hemlock is found on moist to very moist 

sandy loams, loamy sands, and silt loams with good 

drainage. Spodosols are the dominant soil order for 

this species (Godman and Lancaster 1990). We used 

high importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) to set thresholds for 

percent clay and percent coarse sand. Optimal clay is 

3.4 to 26.2 percent; optimal coarse sand is 55.6 to 94.0 

percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.7 to 12.2 cm hr-1, the rates for moderately slow 

to moderately rapidly draining clay loams to sandy 

loams (O’Geen 2012).

Depth to Bedrock

We assigned depth to bedrock values of <0.7 m for 

suboptimal growth, based on minimum rooting depth 

(NRCS 2014), and values ≥0.7 for optimal growth.

EXTERNAL INFLUENCES

Insect Pests

Research indicates that increased N may increase 

the palatability of certain tree species to insect pests. 

McClure (1991) found that trees fertilized with 

nitrogen had higher rates of hemlock wooly adelgid 

infestation and reduced health compared to unfertilized 

trees. Reduced vigor and growth could result in lower 

nutrient demands. The range of hemlock wooly adelgid 

will likely increase with warmer temperatures (Dukes 

et al. 2009, Paradis et al 2008). Drought increases the 

risk of mortality for trees infested with hemlock wooly 

adelgid, while cold winter temperatures reduce insect 

populations and tree mortality (Eschtruth et al. 2013).

We assumed that an abundance of insect pests would 

correlate with suboptimal growth, while low or absent 

levels of pests would correlate with optimal growth.

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low or absent levels of fungal pathogens would 

correlate with optimal growth.
82

25 ULMUS AMERICANA (AMERICAN ELM)

Elevation

We set elevation ranges for the northeastern United 

States based on elevation in the Forest Service silvics 

handbook (Bey 1990). Growth is generally expected to 

be optimal below approximately 550 m and suboptimal 

above 550 m in elevation.

Aspect

American elm has intermediate shade and drought 

tolerance (Bey 1990). Because it is not clear which 

aspect is most favorable for growth, we have not 

associated aspect with growth for this species.

Slope Gradient

We used high importance values from the Climate 

Change Atlas (U.S. Forest Service, n.d.) to set 

thresholds for slope. Growth for American elm is 

expected to be optimal from 0.0 to 5.4 percent.

Average January, July, and May to 

September Temperatures

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to set 

temperature thresholds. We associated January 

temperatures from -13.1 to -0.1 °C, July temperatures 

from 20.9 to 27.0 °C, and May to September 

temperatures from 17.2 to 23.4 °C with optimal growth 

and values outside of these ranges with suboptimal 

growth. These are within temperature ranges reported 

in Bey (1990).

Annual Precipitation

The distribution range from the Climate Change Atlas 

(U.S. Forest Service, n.d.) provides a range of 436 to 

1774 mm annual precipitation. The range for high 

importance value stands is 600 to 1139 mm. According 

to Bey (1990), the range for American elm is 380 to 

1520 mm annually, with 760 to 1270 mm over the 

central part of the range. We have associated the high 

importance value range, from 600 to 1139 mm, with 

optimal growth. Values outside of this range were 

associated with suboptimal growth.

SPECIES RANGE AND CRITICAL LOAD

American elm, Ulmus americana L., grows in the eastern 

and central United States, as well as in south central 

and southeastern Canada, over a precipitation range of 

380 to 1520 mm yr-1 (Bey 1990). Temperature spans 

a wide range; July average temperatures range from 16 

to 27 °C, while January average temperatures range 

from -16 to 16 °C. American elm grows best on rich, 

well-drained soil and grows poorly on dry soils, soils 

high in organic matter, and soils with a high water table 

(Bey 1990). It is most commonly found on flats and 

bottomlands, but can be found up to 760 m in West 

Virginia. American elm is highly susceptible to Dutch 

elm disease (Ophiostoma ulmi/novi-ulmi) (Dukes et al. 

2009), an introduced fungal pathogen that is spread 

through feeding by European and native elm bark 

beetles (Scolytus multistriatus and Hylurgopinus rufipes, 

respectively) (Bey 1990).

We assigned a critical load of 7 to 18 kg N ha-1 yr-1

for American elm. This range is based solely on the 

northern mid successional hardwood forest type in 

Dietze and Moorcroft (2011); visual inspection of 

supplementary material indicates that mortality was 

lowest from 5 to 16 kg ha-1 yr-1 wet N deposition 

(approximately 7 to 18 kg/ha/N wet + dry deposition). 

This critical load should be revised when American elm 

specific data becomes available. Critical load citations 

and values are shown in table 25.1.

ABIOTIC MODIFYING FACTORS

We used multiple sources to set the ranges of modifying 

factors for American elm, including the American elm 

section of the Forest Service silvics handbook (Bey 

1990) and high importance value data from the Climate 

Change Atlas (U.S. Forest Service, n.d.). In general, 

any factor that inhibits American elm growth would be 

expected to result in a decreased need for N and thus a 

lower critical load; optimal growth conditions result in 

an increased demand for N and a higher critical load. 

General values were used for base saturation, Ca:Al, 

and biomass removal as described in Chapter 2 (pages 

13-14). Graphs showing importance values for abiotic 

modifying factor ranges by species are in Appendix 1. 

Optimal and suboptimal growth ranges and weight of 

evidence values are shown in Table 25.2.
83

May to September Precipitation

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.) were used to determine 

thresholds for average May to September precipitation. 

We associated the precipitation range from 386 to 576 

mm with optimal growth and precipitation outside of 

this range with suboptimal growth.

Soil pH

High importance values from the Climate Change 

Atlas (U.S. Forest Service, n.d.), supported by 

information from Bey (1990), which provides an 

American elm pH range of 5.5 to 8.0, were used to 

set pH values. We associated soil pH values of 5.4 

to 7.6, from the high importance value range, with 

optimal growth and values outside of this range with 

suboptimal growth.

Percent Clay and Percent Coarse Sand

American elm grows best on rich, well-drained soils, 

and is most often found on Alfisols, Inceptisols, 

Mollisols, and Ultisols (Bey 1990). We used high 

importance values from the Climate Change Atlas 

(U.S. Forest Service, n.d.) to set thresholds for percent 

clay and percent coarse sand. Optimal clay is 13.0 

to 42.1 percent; optimal coarse sand is 75.4 to 100 

percent.

Permeability

Optimal soil permeability from Climate Change Atlas 

(U.S. Forest Service, n.d.) high importance values range 

from 0.3 to 6.2 cm hr-1, the rate for moderately slow 

to moderately draining clay loams to fine sandy loams 

(O’Geen 2012). Permeability outside of this range is 

associated with suboptimal growth.

Table 25.2—Abiotic modifying factors for American elm

Variable Units

Suboptimal 

growth range

Optimal 

growth range

Weight of 

evidencea

Elevation m > 550 ≤ 550 3

Aspect N/A N/A

Slope gradient % > 5.4 0.0 to 5.4 4

January temperature °C < -13.1, > -0.1 -13.1 to -0.1 4.3

July temperature °C < 20.9, > 27.0 20.9 to 27.0 4.7

May-September temperature °C < 17.2, > 23.4 17.2 to 23.4 4.7

Annual precipitation Mm < 600, > 1139 600 to 1139 4.3

May-September precipitation Mm < 386, > 576 386 to 576 4.3

Soil pH < 5.4, > 7.6 5.4 to 7.6 4.3

Clay % < 13.0, > 42.1 13.0 to 42.1 3.7

Coarse sand % < 75.4, > 100.0 75.4 to 100.0 4

Permeability cm hr-1 < 0.3, > 6.2 0.3 to 6.2 3.3

Depth to bedrock m < 1.0 ≥ 1.0 3

B-horizon base saturation % < 15 ≥ 15 2

Soil Ca:Al mol:mol < 2 ≥ 2 2

Biomass removal low high

Insect pests abundant low or absent

Fungal pathogens abundant low or absent

a Refer to Tables 2.2 and 2.3 for description of values.

Table 25.1—Effect of nitrogen deposition on American elm

Location

N deposition range 

(kg ha-1 yr-1) Increased N deposition effects Citation

Eastern and 

Central U.S. 5 to 16 (wet NO3) Decreased mortality probability Dietze and Moorcroft
84

Depth to Bedrock

We associated bedrock depth of <1.0 m with 

suboptimal growth and ≥1.0 m with optimal growth. 

Rooting depth varies with soil conditions. In heavy, wet, 

soils the root system is typically within approximately 

1 m of the surface. On medium textured soils, roots can 

penetrate up to 3 m, while on dry soils the roots may 

extend up to approximately 6 m (Bey 1990).

EXTERNAL INFLUENCES

Insect Pests

We assumed that an abundance of insect pests 

correlates with suboptimal growth, while low levels 

or the absence of insect pests correlate with optimal 

growth. Winter moth (Ophorophtera brumata) is one of 

many pests of concern (Dukes et al. 2009).

Fungal Pathogens

As with insect pests, we assumed that trees weakened 

by abundant fungal pathogens would have suboptimal 

growth. Low levels or the absence of fungal pathogens 

would correlate with optimal growth. Because Dutch 

elm disease is a persistent threat to American elm, 

growth may always be suboptimal.
85

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APPENDIX 1

Importance Values Versus Climate and Soil 

Characteristics for Each Species

Data from the Climate Change Atlas (U.S. Forest Service, n.d.)
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APPENDIX 2

Ranges for Optimal and Suboptimal Growth for Site, Climate, 

and Soil Variables

Climate and soil data in these figures were provided by the Landscape Change Research 

Group (U.S. Forest Service, n.d.). Current climate parameter data are from Hayhoe et al. 

(2007) and are based on United States Historical Climatology network data from 1961-

1990. Soil and topographic parameter data are from STATSGO data (Soil Conservation 

Service 1991). For further information, see the Climate Change Atlas website: https://

www.fs.fed.us/nrs/atlas/. Optimal elevation ranges were derived from information in 

“Silvics of North America” (Burns and Honkala 1990), Leak and Graber (1974), Beckage 

et al. (2008), and other sources. Depth to bedrock (minimum rooting depth) values come 

from the PLANTS database (NRCS 2014). Because optimal and suboptimal ranges are 

not known for minimum rooting depth, arrows indicate the beginning of the range.
144

0 200 400 600 800 1000 1200 1400 1600

Quercus alba

Fraxinus pennsylvanica

Quercus prinus

Pinus strobus

Castanea dentata

Ulmus americana

Acer rubrum

Fraxinus americana

Juglans cinerea

Pinus rigida

Quercus rubra

Tsuga canadensis

Fagus grandifolia

Acer saccharum

Populus tremuloides

Thuja occidentalis

Populus grandidentata

Picea mariana

Pinus resinosa

Betula alleghaniensis

Picea rubens

Betula payrifera

Abies balsamea

Elevation (m): range for optimal growth

0 5 10 15 20 25

Picea mariana

Thuja occidentalis

Populus tremuloides

Pinus resinosa

Fraxinus pennsylvanica

Populus grandidentata

Ulmus americana

Pinus rigida

Abies balsamea

Betula payrifera

Quercus rubra

Acer rubrum

Fraxinus americana

Quercus alba

Acer saccharum

Pinus strobus

Tsuga canadensis

Fagus grandifolia

Picea rubens

Betula alleghaniensis

Juglans cinerea

Castanea dentata

Quercus prinus

Slope gradient (%): ranges for optimal and suboptimal growth

Suboptimal growth

Optimal growth
145

-25 -20 -15 -10 -5 0 5 10 15 20

Picea mariana

Populus tremuloides

Thuja occidentalis

Abies balsamea

Betula papyrifera

Fraxinus pennsylvanica

Betula alleghaniensis

Quercus rubra

Picea rubens

Ulmus americana

Populus grandidentata

Acer saccharum

Pinus resinosa

Acer rubrum

Juglans cinerea

fagus grandifolia

Tsuga canadensis

Quercus alba

Pinus strobus

Fraxinus americana

Castanea dentata

Quercus prinus

Pinus rigida

Average January temperature (°C): range for optimal and suboptimal growth

Suboptimal growth

Optimal growth

12 14 16 18 20 22 24 26 28 30

Picea rubens

Betula alleghaniensis

Betula payrifera

Abies balsamea

Fagus grandifolia

Picea mariana

Thuja occidentalis

Acer saccharum

Populus tremuloides

Acer rubrum

Tsuga canadensis

Pinus resinosa

Populus grandidentata

Pinus strobus

Castanea dentata

Fraxinus americana

Quercus rubra

Juglans cinerea

Quercus prinus

Fraxinus pennsylvanica

Ulmus americana

Quercus alba

Pinus rigida

Average July temperature (°C): ranges for optimal and suboptimal growth

Suboptimal growth

Optimal growth
146

10 12 14 16 18 20 22 24 26 28

Betula alleghaniensis

Picea rubens

Betula payrifera

Abies balsamea

Picea mariana

Thuja occidentalis

Fagus grandifolia

Acer saccharum

Populus tremuloides

Acer rubrum

Tsuga canadensis

Pinus resinosa

Populus grandidentata

Pinus strobus

Quercus rubra

Fraxinus americana

Castanea dentata

Juglans cinerea

Fraxinus pennsylvanica

Ulmus americana

Quercus prinus

Quercus alba

Pinus rigida

Average May-September temperature (°C): ranges for optimal and suboptimal 

growth

Suboptimal growth

Optimal growth

0 500 1000 1500 2000 2500

Fraxinus pennsylvanica

Populus tremuloides

Ulmus americana

Picea mariana

Thuja occidentalis

Betula papyrifera

Abies balsamea

Quercus rubra

Pinus resinosa

Populus grandidentata

Juglans cinerea

Acer saccharum

Fraxinus americana

Acer rubrum

Quercus alba

Pinus strobus

Tsuga canadensis

Betula alleghaniensis

Fagus grandifolia

Quercus prinus

Picea rubens

Castanea dentata

Pinus rigida

Precipitation (mm): range for optimal and suboptimal growth

Suboptimal growth

Optimal growth
147

0 200 400 600 800 1000 1200

Fraxinus pennsylvanica

Populus tremuloides

Ulmus americana

Populus grandidentata

Pinus resinosa

Thuja occidentalis

Fraxinus americana

Quercus rubra

Abies balsamea

Picea mariana

Acer saccharum

Acer rubrum

Pinus strobus

Tsuga canadensis

Betula payrifera

Fagus grandifolia

Quercus prinus

Quercus alba

Pinus rigida

Betula alleghaniensis

Picea rubens

Juglans cinerea

Castanea dentata

May to September precipitation (mm): ranges for optimal and suboptimal growth

Suboptimal growth

Optimal growth

2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5

Pinus rigida

Castanea dentata

Quercus prinus

Fagus grandifolia

Acer rubrum

Picea rubens

Betula alleghaniensis

Quercus alba

Abies balsamea

Betula papyrifera

Picea mariana

Acer saccharum

Quercus rubra

Tsuga canadensis

Pinus strobus

Populus tremuloides

Fraxinus americana

Thuja occidentalis

Juglans cinerea

Populus grandidentata

Pinus resinosa

Ulmus americana

Fraxinus pennsylvanica

Soil pH: ranges for optimal and suboptimal growth

Suboptimal growth

Optimal growth
148

0 10 20 30 40 50 60 70 80

Pinus rigida

Pinus strobus

Populus grandidentata

Betula payrifera

Pinus resinosa

Tsuga canadensis

Populus tremuloides

Acer rubrum

Abies balsamea

Picea mariana

Quercus rubra

Picea rubens

Thuja occidentalis

Betula alleghaniensis

Fagus grandifolia

Acer saccharum

Juglans cinerea

Fraxinus americana

fraxinus pennsylvanica

Quercus alba

Ulmus americana

Castanea dentata

Quercus prinus

Soil clay (%): ranges for optimal and suboptimal growth

Suboptimal growth

Optimal growth

0 20 40 60 80 100 120 140 160

Quercus alba

Castanea dentata

Quercus prinus

Fagus grandifolia

Fraxinus americana

Tsuga canadensis

Acer rubrum

Pinus strobus

Acer saccharum

Quercus rubra

Pinus rigida

Betula payrifera

Betula alleghaniensis

Picea rubens

Abies balsamea

Pinus resinosa

Picea mariana

Thuja occidentalis

Populus tremuloides

Juglans cinerea

Populus grandidentata

Ulmus americana

Fraxinus pennsylvanica

Soil coarse sand (%): ranges for optimal and suboptimal growth

Suboptimal growth

Optimal growth
149

0 2 4 6 8 10 12 14 16 18 20

Fraxinus pennsylvanica

Ulmus americana

Fraxinus americana

Quercus alba

Picea rubens

Fagus grandifolia

Acer saccharum

Quercus rubra

Thuja occidentalis

Juglans cinerea

Acer rubrum

Tsuga canadensis

Betula alleghaniensis

Abies balsamea

Picea mariana

Populus tremuloides

Pinus strobus

Betula papyrifera

Populus grandidentata

Quercus prinus

Pinus resinosa

Castanea dentata

Pinus rigida

Soil permeability (cm/hr): ranges for optimal and suboptimal growth

Suboptimal growth

Optimal growth

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Picea rubens

Picea mariana

Abies balsamea

Castanea dentata

Betula papyrifera

Thuja occidentalis

Tsuga canadensis

Betula alleghaniensis

Fagus grandifolia

Populus grandidentata

Populus tremuloides

Quercus prinus

Quercus rubra

Acer rubrum

Acer saccharum

Fraxinus americana

Fraxinus pennsylvanica

Juglans cinerea

Pinus resinosa

Pinus rigida

Pinus strobus

Ulmus americana

Quercus alba

Depth to bedrock (minimum rooting depth) (m): range for optimal and 

suboptimal growth

Suboptimal growth

Optimal growth
Robin-Abbott, Molly J.; Pardo, Linda H. 2017. How climatic conditions, site, and soil 

characteristics affect tree growth and critical loads of nitrogen for northeastern

tree species. Gen. Tech. Rep. NRS-172. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 149 p.

Forest health is affected by multiple factors, including topography, climate, and soil 

characteristics, as well as pests, pathogens, competitive interactions, and anthropogenic 

deposition. Species within a stand may respond differently to site factors depending on 

their physiological requirements for growth, survival, and regeneration. We determined 

optimal ranges of topographic (elevation, aspect, slope gradient), climatic (average 

temperature for January, July, and May to September; annual and May to September 

precipitation), and soil (pH, percent clay, percent coarse sand, permeability, depth to 

bedrock) parameters for 23 tree species of the northeastern United States. We primarily 

used importance values (a measure of how dominant a species is in a given forest area 

under existing site conditions) from a published analysis of more than 100,000 U.S. Forest 

Service Forest Inventory and Analysis plots to set optimal ranges for the abiotic factors. 

The region included in this assessment is defined by level 2 ecoregions: mixed wood 

plains in the Eastern Temperate Forest Ecoregion; Atlantic highlands and mixed wood 

shield in the Northern Forest Ecoregion. In addition to summarizing ranges for abiotic 

modifying factors, we also determined the critical load of nitrogen—the deposition below 

which no harmful ecological effects occur—for each species. The information can be used 

in forest health assessments to determine whether species growth at a site is expected 

to be optimal or suboptimal, and can also be used to modify critical load ranges for each 

species based on site conditions.

KEY WORDS: nitrogen deposition, topography, precipitation, temperature

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