a biogeochemical examination of ontario’s boreal … biogeochemical examination of ontario’s...
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A biogeochemical examination of Ontario’s
boreal forest ecosite classification system
Aaron Tamminga (613) 539-8985
A thesis submitted to the School of
Environmental Studies
in partial fulfillment of the requirements
for a Bachelor of Science (Honours)
Queen’s University
Kingston, Ontario, Canada
April 2011
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ABSTRACT
The Canadian boreal forest is an important biome that is at risk from many
anthropogenic stressors. In order to inform managers, policymakers, and scientists about
ecological conditions in the boreal forest, Ontario has recently revised their ecological land
classification system with a focus on the standardization of units called ecosites, which are
homogeneous polygons (typically 10-100 hectares) of common vegetation and soil
combinations. While ecosites can provide useful information for applications such as forest
inventory and environmental planning, an understanding of whether ecosystem function is
captured by ecosite classes is necessary to evaluate potential extensions of the ecosite
framework’s applicability to more predictive uses. This study compared 14 biogeochemical
properties in replicate boreal forest sites representing 8 mineral soil ecosite classes and 3
organic soil ecosite classes in the Hearst Forest, Ontario. It was determined that there were
no statistically significant differences in any whole-profile averages of properties between
any mineral soil ecosite classes, and only one property (extractable ammonium) that
differed between organic soil classes. A grouping of similar ecosite classes into clusters
based on dominant vegetation and soil texture also revealed no significant differences,
although some properties (soil organic carbon, total nitrogen, and carbon:nitrogen ratio)
were approaching significance in the 0-10 cm depth increment of mineral soil. There were
also large differences between organic and mineral soil ecosite classes, and an analysis of
total aboveground and soil carbon revealed much higher carbon contained in organic
ecosite classes. The results of this study indicate that individual ecosite classes do not
display characteristic ecological function patterns, which, although further testing is
recommended, suggests a limited applicability of the ecosite classification system to
predictive uses.
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ACKNOWLEDGMENTS
I would like to thank my supervisor Neal Scott for his constant support through
summer field sampling, lab work during the school year, data analysis, and writing. I have
thoroughly enjoyed working together and have learned so much through the process.
Thanks also to my lab-mates Alison Beamish, Amanda Graham, Erin Jaggard, and Daniel
McParland for their help with sample processing and for their companionship, and to Karin
van Ewik and Erin Doxsey-Witfield for their lab help.
I would also like to thank Paul Treitz for his thoughts on the project and for being my
second examiner.
Finally, thanks to the ENSC 501 course coordinator Brian Cumming for running the course
and for allowing me to write this thesis under the guidelines outlined for ENSC 502 due to
the extensive data analysis that resulted from this project.
Financial support for this project was provided by the Natural Sciences and Engineering
Research Council through an Undergraduate Student Research Award and through grant
funding to Neal Scott.
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Table of Contents
Abstract ............................................................................................................................. ii
Acknowledgments ............................................................................................................ iii
List of tables .......................................................................................................................1
List of common abbreviations.............................................................................................1
Introduction ........................................................................................................................2
Literature review ................................................................................................................5
Introduction ........................................................................................................................5
Ontario’s boreal forest ..................................................................................................5
Acid deposition and climate change ............................................................................12
Sustainable forest management and ELC.....................................................................14
Methods............................................................................................................................21
Site description ...........................................................................................................21
Field sampling and measurements...............................................................................22
Soil sample preparation...............................................................................................24
Chemical and biological analyses................................................................................25
Results..............................................................................................................................28
Statistical analyses: .....................................................................................................29
Mineral soil ecosite averages.......................................................................................30
Organic ecosite averages.............................................................................................30
Ecosite clusters ...........................................................................................................31
Ecosite cluster carbon inventory..................................................................................32
Discussion ........................................................................................................................38
Conclusion........................................................................................................................48
References ........................................................................................................................50
Summary ..........................................................................................................................54
Appendix 1. Mineral soil ecosite class properties..............................................................55
Appendix 2. Organic soil ecosite properties ......................................................................58
Appendix 3. Mineral soil ecosite cluster properties ...........................................................61
Entire profile averages: ...............................................................................................61
Ecosite cluster O horizon properties: ...........................................................................64
Ecosite cluster 0-10 properties: ...................................................................................66
Ecosite cluster 10-20 properties: .................................................................................69
Ecosite cluster 20-40 properties...................................................................................71
Appendix 4. Carbon inventory data...................................................................................73
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LIST OF TABLES
Table 1. National ecological framework of Canada...........................................................17
Table 2. Ecosite classes and descriptions ..........................................................................21
Table 3. Ecosite cluster information..................................................................................29
Table 4. Soil properties by ecosite class for mineral soil sites............................................34
Table 5. Soil properties by ecosite class for organic soil sites............................................35
Table 6. Soil properties by ecosite cluster for mineral soil sites.........................................36
Table 7. Mineral soil ecosite cluster soil properties by depth.............................................37
Table 8. Ecosite cluster C inventory comparisons. ............................................................38
Table 9. Summary of classified ecosite totals. ...................................................................43
Table 10. Power analyses for mineral soil ecosite class averages.......................................45
LIST OF COMMON ABBREVIATIONS
ELC – Ecological land classification
OMNR – Ontario Ministry of Natural Resources
HFMI – Hearst Forest Management Inc.
LOI – Loss on ignition
WHC – Water holding capacity
% OM – Percent organic matter
SOC – Soil organic carbon
Net Nmin – Net nitrogen mineralization
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INTRODUCTION
The ecosystem as a group of living organisms within an area interacting with each
other and their physical environment (Tansley, 1935) is a fundamental concept in ecology.
However, specific definitions, interpretations, and delineation strategies change often and
are usually developed with a specific intended application. The process of ecological land
classification (ELC) is a hierarchical method attempting to define and stratify ecosystems
for the purpose of environmental management (Sims et al., 1996). Although many
permutations of ELC exist, they consistently utilize a hierarchical classification of nested
land “classes” defined at various spatial scales. This hierarchical model allows for a
standardized way to map, describe, and interpret both the biotic and abiotic components of
ecosystems at scales appropriate to a wide array of applications (Klijn & de Haes, 1994).
Because of its ability to provide detailed descriptions of ecological components of an area,
ELC is a useful tool for resource managers and can play an important role in biodiversity
conservation, land use planning, and sustainable development (Sims et al., 1996)
In Ontario, ELC has been used to inform management decisions for decades, and
the province is currently in the process of modifying its previous ELC system. One goal of
this process is to develop a consistent method for the classification of ecosystems at a fine
spatial scale (i.e. 1:8,000 to 1:50,000), termed “ecosites.” In the new system, ecosites are
defined as “landscape areas consisting of typical, recurring associations of vegetation type
and substrate type combinations” (Banton et al., 2009). The ecosite level in the hierarchy is
intended to capture the inherent variability within larger-scale classes in order to offer a
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consistent framework for forest inventories, ecological modeling, and other planning
applications (Banton et al., 2009).
As with any type of classification, however, ELC involves generalization and
human interpretation of natural patterns (Rowe, 1996). Ontario’s ecosite classification is no
different; the applicability of the system is currently largely limited by what can be
identified through field-based evaluation of vegetation and soils. Although vegetation
identification is relatively straightforward, soil classification is based entirely on field-
identifiable soil properties such as soil depth, texture, and moisture content (Banton et al.,
2009). As these basic characteristics are often key determinants of other soil properties such
as soil nutrient levels, the soil properties of different ecosites are based on these three
properties with the assumption that unmeasured soil properties are likely to be related to
these properties. This assumption has important implications for wider applications of the
ecosite classification system, yet it has not been thoroughly tested. In order to evaluate the
applicability of the ecosite system to uses such as forest stand productivity modeling,
susceptibility assessment (e.g. to acid deposition or climate change), and scaling-up of
biogeochemical properties, a thorough study of the relationship between many
biogeochemical properties and ecosite classes is needed.
This study focuses on ecosite classification in Ontario’s boreal forest, which is an
important Canadian natural resource that is under considerable stress due to a suite of
natural and anthropogenic disturbances (Brydges, 1998). I will use pre-classified ecosites in
the Hearst Forest near Hearst, Ontario to examine variation in soil properties such as
available nutrients among different ecosite types in the Hearst Forest. I will also seek to
determine whether consistent characteristics are present within replicate ecosite plots. The
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results of this study will serve to inform resource managers using the boreal ecosite
classification system about potential limitations or extensions of its uses while also
providing a thorough examination of aboveground-belowground relationships for a wide
range of soil-vegetation combinations in the boreal forest.
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LITERATURE REVIEW
Introduction
Canada’s boreal forest is an important biome that is subject to a wide range of
stressors. Forest harvesting activities, acid deposition, and climate change can all have
significant impacts on boreal forest ecosystems, and the need exists for a solid
understanding of the ecological processes that shape the boreal forest in order to ensure its
protection. This section reviews major threats to the boreal forest and illustrates the central
importance of understanding ecosystem function in boreal forest management. It then
outlines how ELC can be applied to environmental management through a review of
Canada’s ELC framework and describes how Ontario’s ecosite classification system can
potentially contribute a spatial perspective on ecosystem function that can inform many
aspects of sustainable boreal forest management.
Ontario’s boreal forest
The Canadian boreal forest forms a continuous belt that stretches from
Newfoundland and Labrador to the Yukon Territory and makes up nearly 80% of the
country’s forested area (Hosie, 1979). In Ontario, the boreal forest is defined by two
distinct physiographic regions: the igneous Precambrian rocks of the Boreal Shield ecozone
and the Paleozoic lacustrine carbonate bedrock of the Hudson Bay Lowland ecozone
(Marshall et al., 1999). In general, Ontario’s boreal region comprises expansive tracts of
forest, exposed bedrock, low-lying wetlands and freshwater systems, and many important
natural resources (Wiken, 1986). Although the area is largely undeveloped, activities such
as logging, hydropower generation, and mineral extraction contribute to increased human
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development (Wiken, 1986), and broad-scale problems such as climate change, acid
deposition, and changing fire patterns pose widespread threats (Brydges, 1998). With the
suite of potential impacts of resource use, global change phenomena, human development,
and conflicting stakeholder interests, careful management and long-term planning are
crucial to the continued protection of the Canadian boreal forest.
The need to account for such a variety of interests and threats makes Canadian
forest management a complicated endeavor. 94% of Canadian forests are owned by the
public, and forest industries operate under a system of leases similar to that of a landlord-
tenant system (Thompson & Pitt, 2003). This is different than many other countries, yet it
has given rise to a substantial forestry industry that is key to Canada’s national economy.
As of 1999, Canada controlled 15-30% of the worldwide share in lumber, pulp, and paper
commodities, ranked first globally in net value of exported forest products (44.2 billion
dollars), and employed 870,000 people directly or indirectly through the forestry sector
(Thompson & Pitt, 2003). However, despite a recent move toward more sustainable and
ecologically-informed forestry practices (Sharma & Henriques, 2005), this industry still
represents a stressor to the boreal forest, and careful consideration of the environmental
effects of logging operations is necessary to balance negative impacts with continued
production and the maintenance of healthy forests and the sector that relies on them.
Forest ecology and biogeochemistry
One of the most important factors to consider when designing forest management
systems is the role of plant nutrients and the maintenance of site fertility. Although forest
productivity is influenced by other factors such as temperature, water availability, and
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incoming radiation, nutrients are essential to forest growth and directly influenced by
forestry operations. The most important nutrients in forests are the macronutrients:
nitrogen, phosphorous, sulfur, potassium, calcium, and magnesium, each of which is
needed directly for plant growth. Micronutrients such as manganese, iron, chloride, copper,
zinc, boron, molybdenum, and cobalt are also required by plants, but are usually abundant
in soils and rarely limit plant growth (Binkley, 1986). Within a forest stand, nutrients exist
in many forms and distinct pools and are cycled between soils and plants. Plants take up
nutrients from the soil solution and incorporate them into biomass, which is then returned
to the soil through litterfall, root mortality, and tree mortality. This organic matter is then
decomposed by soil organisms such as bacteria and fungi that excrete enzymes to digest
organic molecules into smaller units, liberating nutrients and making them available to
plants again (Chapin et al., 2002). This cycle regulates fluxes between individual nutrient
pools, which vary in size and turnover rates.
To meet their physiological requirements, plants require a balanced supply of both
macro and micronutrients. If one nutrient is in low abundance relative to the amount
required by plants, it can limit plant growth. In terrestrial systems, nitrogen is often limiting
because producers require it in large amounts and it is costly (energy-wise) to obtain
(Vitousek & Howarth, 1991). However, much of the knowledge of nitrogen limitation is the
result of agricultural fertilizer addition studies in single crop systems that have large
nutrient requirements and experience substantial nutrient losses due to harvest (Vitousek &
Howarth, 1991). In systems with diverse communities such as natural forests, ecosystem-
level feedbacks complicate concepts of nutrient limitation, and nutrient-rich and nutrient-
poor sites respond differently to nutrient additions (Chapin et al., 1986). Despite this, many
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studies have found the boreal forest to be primarily nitrogen limited (Agren, 1983; Bonan,
1990; Van Cleve et al., 1983). This is of particular importance to managed boreal forests, as
it indicates that nitrogen must be a key consideration when planning nutrient management.
The nitrogen cycle follows the same basic nutrient cycle described earlier but with
several distinct features, and can be described by six main processes (adapted from
Binkley, 1986):
1. Nitrogen fixation: the use of energy by plants or microbes to reduce atmospheric
nitrogen into ammonium
2. Ammonium assimilation: the incorporation of ammonium into organic molecules
by organisms
3. Mineralization: the release of ammonium from decomposing organic matter
4. Nitrification: the microbial oxidation of ammonium to nitrite and then nitrate
5. Nitrate reduction: reduction of nitrate to ammonium for plant and microbe use
6. Denitrification: the reduction of nitrate in the absence of oxygen to nitrogen gas
(or nitrous oxide)
The interactions between these six processes explain how nitrogen is cycled through plants,
organic matter, microbes, and soil solution. If the cycle starts with the decomposition of
organic matter, the ammonium produced can be used several ways. It can be immobilized
in microbial biomass, used by plants, converted to nitrate, adsorbed onto soil particles, or
leached from the soil. Both nitrate and ammonium can be used by plants; use depends on
how much of each form is present, specific plant requirements, the mobility of each form
(negatively-charged nitrate is more mobile in soil solution than ammonium), and the
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abundance and activity of bacteria that perform nitrification and denitrification (Gardiner &
Miller, 2007).
The forms of nitrogen at each stage of this cycle are referred to in terms of distinct
pools. For example, total soil nitrogen includes nitrogen in all forms, much of which is
unavailable for plant uptake. The mineralization of organic molecules liberates ammonium
from unavailable pools, some of which can be nitrified to nitrate. These inorganic forms
exist in soil solution and make up the smaller yet more active available nitrogen pool
(Chapin et al., 2002). Due to the microbial and plant uptake of these forms of nitrogen, this
pool experiences much more rapid turnover than nitrogen held in undecomposed organic
matter. However, despite the importance of inorganic nitrogen for plant use and its
emphasis in most studies, there is evidence that organic nitrogen can also be utilized
directly by some plant species in nitrogen-limited boreal forest ecosystems. For example,
Näsholm et al. (1998) discovered that some boreal plants can bypass mineralization and use
organic nitrogen in the form of glycine. This finding illustrates the complexity of nitrogen
dynamics that must be considered, particularly in boreal forest studies.
The cycling of nitrogen and other nutrients is regulated by a variety of factors such
as microclimate, soil parent material and chemistry, chemical quality of organic matter, and
activity of soil organisms (Binkley, 1986). Each of these controls can be influenced by a
suite of species-specific factors (Chapin et al., 1997), making an understanding of species-
ecosystem interactions crucial to forest nutrition management. For example, organic matter
decomposition rates and nitrogen mineralization are dependent on litter quality (i.e.
carbon:nitrogen ratio and lignin content), which, in turn, is dependent on litter chemistry of
different tree species (Melillo et al., 1989; Scott & Binkley, 1997). Côté et al. (2000)
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studied this concept in the boreal forest, comparing nitrogen mineralization rates between
deciduous and coniferous stand types, and demonstrated greater mineralization rates
throughout the soil profile for deciduous sites. Differences in decomposition rates can also
lead to feedbacks that regulate overall site fertility; nutrient-poor sites often have slow-
growing plants that use nutrients efficiently and produce poor quality litter that is slowly
decomposed, while nutrient-rich sites often have quickly-growing plants that produce litter
that decomposes easily and reinforces fertility (Hobbie, 1992).
Plant species composition can also influence microclimate and soil organisms.
Microclimate affects nutrient cycling primarily through temperature and moisture, and
stands of different tree species create different canopy structures that regulate incoming
radiation and precipitation. This, in turn, impacts chemical reactions, microbial activity,
and, ultimately, nutrient cycling (Couteaux et al., 1995). Soil organisms responsible for the
breakdown of organic matter also have many direct and indirect linkages to aboveground
vegetation and to other organisms (Wardle et al., 2004). For example, plant grazing by
vertebrate herbivores can enhance rates of carbon exudation from plants to the soil, which
is used as a substrate for microbial decomposers and results in increased decomposition,
nutrient availability, and plant growth (De Deyn & Van der Putten, 2005).
Aside from vegetation species effects, soil physical characteristics are another
important control of nutrient cycling and availability. The weathering of parent material
into soil releases nutrients and impacts soil texture and bulk density, which are closely
linked to many aspects of soil fertility. Organic matter is also a key determinant of many
processes beyond its role as a substrate for decomposition. Soils with high percentages of
organic matter and small clay-sized particles with high surface-to-volume ratios have many
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negatively charged sites that can hold nutrient cations and release them to plants (Chapin et
al., 2002). Thus, texture and organic matter are often related to nutrient patterns, as
demonstrated by Hook & Burke's (2000) study of biogeochemistry in a shortgrass steppe
landscape. Texture, bulk density, and organic matter also impact soil water dynamics by
controlling how quickly water infiltrates and moves through soil and how strongly water is
held in soil pores (Gupta & Larson, 1979).
Silvicultural operations have many direct and indirect impacts on forest ecosystem
dynamics. The aboveground ecological effects of logging vary depending on forest type,
harvest method, and post-harvest strategies. For example, in a study of southern
Appalachian forests, Duffy & Meier (1992) found that herbaceous understories exhibited
reduced cover and richness even 87 years after clear cutting. In contrast, Halpern & Spies
(1995) demonstrated that understory diversity recovered nearly completely in less than 20
years following clear cutting and slash burning in the Pacific Northwest of the United
States. In the Canadian boreal forest, Peltzer et al. (2000) examined different harvesting and
regeneration treatments and found that the most intensive methods (e.g. disk-trenching,
drum-chopping and blading) resulted in reduced abundance of aspen, increased growth of
spruce, and higher proportions of grasses and forbs. In addition to these vegetation
composition and diversity effects, logging can have landscape-scale impacts such as habitat
fragmentation and associated reductions in animal habitat quality (Bayne & Hobson, 1998).
Belowground and fertility-related consequences of logging are also diverse and
contingent on methods and site conditions. Because considerable amounts of nutrients are
contained in tree biomass (Binkley, 1986), harvesting of trees disrupts nutrient cycles and
results in the removal of nutrients from forests. The relative impact of this, however,
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depends on factors such as harvest method (e.g. whole-tree versus stem-only), tree species,
and initial soil nutrient levels (Montagnini, 2000). Compaction, erosion, root damage, and
altered microclimate can also affect soils and have feedbacks related to vegetation
composition changes (Reich et al., 2001). For example, the harvest of northern hardwood
stands and conversion to red pine plantations resulted in increased soil moisture and
temperature levels (Liechty et al., 1992), which can impact rates of decomposition and
nutrient release. In the absence of trees following harvest, excess nutrients that would
normally be taken up by plants can also be lost from the system due to nitrate leaching and
denitrification (Binkley, 1986). Overall, a detailed understanding of soil nutrient dynamics
is critical to most aspects of forest management, including harvest timing, forest stand
productivity modeling, and regeneration planning.
Acid deposition and climate change
The boreal forest is also subject to more diffuse, less easily managed threats such as
climate change and acid deposition. Acid deposition is a significant problem, particularly
downwind of electricity plants, manufacturing operations, areas with high vehicle exhaust,
and agricultural areas, which contribute sulfur and nitrogen oxides and ammonia to the
atmosphere. These compounds can be carried long distances, be deposited, and have many
negative effects on terrestrial and aquatic ecosystems. Sulfur and nitrogen oxides react with
water in the atmosphere to form acids and have immediate acidifying effects on
ecosystems. Ammonia has more delayed effects related to conversion to ammonium and
microbial and plant interactions, but has potentially greater impacts on a longer timescale
(Galloway, 1995).
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When nitrogen is added to an ecosystem through nitrogen deposition, it can either
go into the soil or be used by plants which causes increases in plant growth (Aber et al,
1989). However, continued additions can outstrip an ecosystem’s ability to maintain and
use nitrogen, resulting in nitrogen saturation that impacts soil chemistry, forest composition
and productivity, and greenhouse gas dynamics (Aber, 1992). Excess nitrogen results in
increased nitrification rates and nitrate leaching, which causes the removal of plant-required
base cations such as calcium and magnesium in order to maintain charge balance (Aber et
al., 1998). If nitrate loss exceeds cation loss, however, soil acidification and a reduced
buffering ability of cation exchange can result, mobilizing toxic aluminum that can enter
aquatic systems (Dijk & Roelofs, 1988). Nitrogen saturation and changes in nitrogen
cycling can also have effects on trace gas emissions; Papen & Butterbach-Bahl (1999)
found significant positive correlations between nitrogen deposition and nitrous oxide
emissions from nitrogen-saturated spruce and beech forests in Germany. In the Canadian
boreal forest (particularly the south-central region) nitrogen deposition has resulted in
reduced tree growth, and there is evidence that certain watersheds are approaching nitrogen
saturation (Brydges, 1998). Given the low acid-neutralization capacity of boreal shield
bedrock and the general nitrogen limitation of the region, a more detailed understanding of
the effects of acid deposition and nitrogen additions on the boreal forest is essential to its
protection.
Increased atmospheric greenhouse gases and climate change are another threat to
the boreal forest that is inextricably linked through many feedbacks with acid deposition,
forest nutrition management, species compositions, and nutrient cycling. Since the
recognition of anthropogenic climate change and the subsequent spate of studies on its
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causes and effects, many researchers have focused on the role of the boreal forest. As such
a large, cool biome, the boreal forest is the largest reservoir of global terrestrial carbon
(Soja et al., 2007), making it crucial to global carbon dynamics. Early simulations based on
climate projections stressed the importance of water availability and outlined potential
feedbacks such as changes in vegetation that alter nitrogen availability which in turn
amplify vegetation changes (Pastor & Post, 1988). Studies also emphasized feedbacks
associated with albedo changes (e.g. though logging or treeline migration) that contributed
to further warming (Bonan et al., 1992). Whole-ecosystem manipulations have also
contributed; Lukewille & Wright (1997) found that catchment-scale increases in soil
temperature resulted in increased nitrogen runoff, which they attributed to higher nitrogen
mineralization rates. More recent investigations have suggested that predicted climate-
related changes such as treeline migration, decline of growth and health of certain tree
species, increased insect infestation and fire occurrence, and changes in species
composition are already occurring (Soja et al., 2007). With the broad scope of climate
change and its potential to influence and be influenced by other ecosystem changes, it is
something that must be considered in any long-term boreal forest management planning.
Sustainable forest management and ELC
Sustainable ecosystem management is a complex endeavor that requires
interdisciplinary collaboration and iterative, scientifically informed planning that balances
multiple environmental, social, and economic factors. In order to protect the boreal forest
from its wide range of stressors (e.g. logging operations, development, altered nutrient
cycles, acid deposition, and climate change), it is important that forest managers,
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government officials, and scientists have access to as much information as possible about
boreal forest ecosystem function. The need exists for spatial perspectives on these
problems, and one tool that can contribute to this need and inform sustainable boreal forest
management is ELC.
ELC systems are “hierarchical frameworks that subdivide regional landscapes of
common climate, geology, and vegetation into increasingly smaller homogenous
hierarchies based on the interrelationships between features such as physiography, soils,
and vegetation” (Zenner et al., 2010). ELC has been used in many forms since the 1970s,
including versions in the Netherlands (Van der Maarel, 1976), the United States (Bailey et
al., 1985), and Canada (Rowe & Sheard, 1981). Other classification systems existed earlier,
but were primarily focused on vegetation units without the recognition of the importance of
abiotic components in an ecosystem (Klijn & de Haes, 1994). While each method of ELC is
directed to a different region and set of purposes, they all share the common goal of
providing a way to interpret the biosphere as subsidiary ecosystems consisting of
interacting soils, biota, landforms, and climate. The applied focus of these methods is
usually on providing tools that are adaptable and appropriate to the variety of scales needed
by environmental planners and resource managers (Zenner et al., 2010).
The process of ELC involves recognizing and delineating ecological patterns. While
in some cases boundaries are clear (e.g. a land-water interface), there is usually the need to
create boundaries on continuous surfaces or gradations. For this reason, ELC should be
viewed as an expression of ecological theory in the creation of hypothesized homogenous
units (Rowe & Sheard, 1981). This human interpretation of natural patterns lends itself to
the fundamental hierarchical arrangement of ELC: different levels of homogeneity exist
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and can be distinguished depending on the scale of examination (Klijn & de Haes, 1994).
This results in a hierarchy where broad factors such as climate and geology are important at
coarser spatial and temporal scales while vegetation and fauna show more influence at finer
scales. It also leads to a structure where interdependencies between scales (such as the
interactions and feedbacks between climate, soil, vegetation, and fauna) are important.
In practice, ELC is usually based on regional to site-specific information about
geomorphology, plant communities, disturbance regimes, and soils. Delineation of unit
classes depends on site sampling, traditional land surveys, expert knowledge, and,
increasingly, remote sensing techniques (Zenner et al., 2010). Aside from the classification
process, the second important aspect of ELC is the creation of maps based on the
determined classification. Mapping processes gives ELC its distinctly spatial grounding,
has many educational and practical purposes, and lends itself to the intuitive interpretation
and detailed evaluation of patterns. To create the most accurate and useful ELC system
possible, this evaluation should combine iteratively with refinement of the classification
(Rowe, 1996).
In Canada, a national ELC framework exists that classifies the country into a
hierarchy of units, from the sub-continental to the sub-provincial (Table 1).
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Table 1. National ecological framework of Canada. Adapted from Marshall et al. (1999)
Level Number
of units
General
scale
Definition Intended
application
Ecozone 15 1:3,000,000 An area of the earth’s surface
representative of large and
very generalized ecological
units characterized by
interactive and adjusting
abiotic and biotic factors
National and
provincial
planning and
policy
Ecoprovince 53 1:1,000,000 A subdivision of an ecozone
characterized by major
assemblages of structural or
surface forms, faunal realms,
and vegetation, hydrology,
soil, and macroclimate.
Provincial
planning and
policy
Ecoregion 194 1:500,000 A subdivision of an
ecoprovince characterized by
distinctive regional ecological
factors, including climate,
physiography, vegetation,
soil, water, and fauna
Regional
strategic planning
and policy
Ecodistrict 1021 1:250,000-
1:500,000
A subdivision of an ecoregion
characterized by a distinctive
assemblage of relief,
landforms, geology, soil,
vegetation, water bodies, and
fauna.
Sub-regional
planning and
policy, watershed
studies
While this framework is generally used at the national level, provincial variations and
extensions exist. In Ontario, the national framework is extended to finer spatial scales
(1:8,000 – 1:50,000) through ecosites, which are defined as “landscape areas consisting of
typical, recurring associations of vegetation and substrate types” (Banton et al., 2009). The
standardization of a unit at this scale is part of recent revisions to the provincial ELC
system in an attempt to attain a more consistent ecological perspective. With their site-level
focus, the primary target application for ecosites is the creation of a consistent framework
for inventory, forest and natural heritage planning, and increased general ecological
knowledge.
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Ecosites are primarily based on physical landscape features such as substrate depth,
texture, and landform, which are then related to vegetation patterns to create distinct
polygons. The process of ecosite classification involves first determining the approximate
boundary of a relatively homogenous polygon (generally several hectares in size) based on
site reconnaissance and air photos. The site is then surveyed and sampled to determine
vegetation cover, relief, substrate texture and moisture at several locations. These features
are then applied to a series of keys that are structured according to a hierarchy of influence,
whereby site factors that relate most directly to vegetation and productivity (e.g. moisture
and substrate depth) are front-loaded to have more influence and ensure that the ecosites are
based primarily on relatively static features (Banton et al., 2009). If within-ecosite
differences are discovered, the most common conditions are used for the classification
process.
Once an ecosite has been classified, it can be related to a general factsheet for that
ecosite class. Ecosites are intended to be common within their main geographic region,
meaning a site classified as a certain ecosite type within the boreal region should have the
same general features as another site of that ecosite type also in the boreal region (Banton et
al., 2009). The ecosite factsheets contain generalizations of each ecosite’s characteristics,
including vegetation and substrate descriptions. A description of ecology (including
generalized nutrient regimes) related to the determined vegetation and substrate factors is
also given.
The detailed descriptions of each ecosite hold a wealth of information that can
inform sustainable forest management through fine-scale mapping of ecological traits. The
vegetation classifications and substrate descriptions are undoubtedly useful, but as
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previously described, many aspects of forest management are intimately related to nutrient
dynamics that cannot easily be determined through field sampling. Soil texture, soil
moisture and vegetation regimes that are sampled for ecosite classification are important
controls on forest biogeochemistry, but the relationship between these traits and nutrient
dynamics is implied, not based on explicit sampling (Banton et al., 2009). This study aims
to evaluate the extent to which the ecosite framework can be applied to uses requiring a
detailed biogeochemical understanding through a rigorous testing of this relationship.
Some examples of studies relating ecosite type to forest nutritional status exist, yet
provincial differences and inconsistent methods have often obscured results. Kopra & Fyles
(2005) conducted a literature survey of nutrient regimes for three Canadian boreal forest
types (jack pine, black spruce, and mixedwood), and found that nutritional differences
between classes did exist. However, to account for classification differences between
provinces, they had to group ecosites and lost much of the detail of provincial systems.
Ontario’s recently revised ecosite classification system warrants directed research that can
be an important step in the evaluation process needed for the creation of any robust ELC.
As described by Sims et al. (1996), there is a need to integrate full ecosystem studies,
especially the dynamics of carbon and nutrient budgets, into ELC in order to provide viable
long-term solutions to ecosystem management, and Ontario’s system could contribute to
this. If ecosites are a good predictor of site biogeochemistry, the system could potentially
be extended to a much wider range of applications without costly soil nutrient sampling.
For example, the fine scale information it provides would be directly useful to forestry
operations planning by providing a spatial understanding of which sites are the most fertile,
most susceptible to nutrient loss, or most suitable to particular species. The more explicit
20
incorporation of soil nutrient dynamics to the ecosite system could also contribute to more
nuanced predictive ecological models to inform harvest planning.
Ecosite classification could also inform regional planners and officials about acid
deposition effects and susceptibility assessment. Ecosites could be used for fine scale
interpretation of relationships between bedrock composition, soil chemistry and ecological
impacts of acid deposition to determine whether some site types have already experienced
negative effects and if others are susceptible (Klijn & de Haes, 1994). Given the spatial
nature of the system and the goal of creating electronic complete coverage ecosite maps
(Banton et al., 2009), site-specific results could then be easily amalgamated for regional
applications. A similar process could inform regional or national carbon stock assessments.
While ELC has not been used explicitly for this purpose, soil carbon stocks are often
estimated by deriving relationships between carbon and factors such as soil type, climate,
and land use and then applying these relationships to unsampled areas (e.g. Scott et al.,
2002). If carbon (both soil organic carbon and carbon in aboveground biomass) levels have
consistent trends related to ecosite class, ELC could provide a framework that includes the
necessary ecological factors and spatial coverage to contribute to improved soil carbon
inventories and predictive modeling.
Overall, Ontario’s ecosite classification system has the potential to inform managers
and planners in a wide variety of applications. In addition to the basic soil and vegetation
relationships on which the system is based, there is the possibility to extend its use to
subtler soil chemical factors. In line with Rowe's (1996) stress of the need for iterative
testing and revision of any ELC system, this study aims to evaluate the relationship
21
between ecosite type and site biogeochemistry, which will be valuable for many aspects of
sustainable forestry practices and environmental management in general.
METHODS
Site description
This study was conducted at the Hearst Forest, which consists of approximately
1,525,000 ha surrounding the town of Hearst, Ontario (49˚40’N, 83˚40’W). The climate of
the area is classified as “modified continental” with strong continental temperature
extremes combined with the modifying influence and added moisture of the Great Lakes
and the Hudson Bay (Ekstrom, 2007). The area exhibits very little topographic relief due to
the combination of several glaciations and lacustrine deposits from glacial lake Barlow-
Ojibway. In some areas, however, exposed pre-Cambrian bedrock contributes to a
topography that varies from gently rolling to very hilly. The soils of the forest fall generally
into two main types: the Great Clay Belt in the north and central portions (glacial-lacustrine
sediments and soils consisting of clays through silt clays to clay loams), and more varied
clays, loams, and sands in the southern, southwest, and northeast portions of the forest
(Ekstrom, 2007). There are also areas of poorly-drained, low-lying organic soils
interspersed throughout the forest, particularly in the Great Clay Belt region.
The vegetation is typical of the northeastern boreal forest, with black spruce (Picea
mariana) as the predominant species. In lowland areas with organic soils, black spruce is
often found in association with cedar (Thuja occidentalis) and tamarack (Larix laricina).
Black spruce is also found in better-drained upland sites, where it often grows in
22
association with white spruce (Picea glauca), jack pine (Pinus banksiana), balsam fir
(Abies balsamea), and trembling aspen (Populus tremuloidies). Other important tree species
include balsam poplar (Populus balsamifera) and white birch (Betula papyrifera), which
typically grow on upland mineral soils (Ekstrom, 2007). Many of these species are valued
as merchantable timber; the forest is managed under a 20-year Sustainable Forest License,
administered by the Ontario Ministry of Natural Resources (OMNR) with Hearst Forest
Management Inc. (HFMI) as the license holder. Several independent logging firms operate
under HFMI guidance, providing both conifer and hardwood timber to local mills and
manufacturing operations.
Field sampling and measurements
To evaluate variation in the biogeochemical characteristics between ecosite classes
throughout the forest, I used pre-existing forest inventory plots that had been established
earlier in summer 2010 as part of an enhanced forest inventory project undertaken by
researchers with the OMNR, HFMI, Queen’s University, and other groups. As part of this
project, the forest was stratified into nine Forest Unit classes based on vegetation type, and
representative sites were established and sampled for vegetation composition and field-
based soil physical parameters. Each site was also classified for ecosite type according to
the OMNR ecosite classification system (AFRIT-GEOIDE, 2010). Working with the
available established sites, I selected three replicate sites (when possible) for 11 ecosite
types commonly found throughout the forest that represented a range of soil and vegetation
conditions (Table 2).
23
Table 2. Ecosite classes and descriptions
Ecosite type Description Number of sites
B052 Dry to fresh, coarse, spruce-fir conifer 3
B055 Dry to fresh, coarse, aspen-birch hardwood 3
B067 Moist, coarse, spruce-fir conifer 3
B098 Fresh, silty to fine loamy, black spruce jack pine dominated 3
B101 Fresh, silty to fine loamy, spruce-fir conifer 3
B104 Fresh, silty to fine loamy, aspen-birch hardwood 3
B116 Moist, fine, spruce-fir conifer 3
B119 Moist, fine, aspen-birch hardwood 3
B127 Organic poor conifer swamp 2
B128 Organic intermediate conifer swamp 2
B129 Organic rich conifer swamp 2
The ecosite types selected included mineral soil sites (ecosites B052, B055, B067,
B098, B101, B104, B116, and B119) and saturated organic soil sites (ecosites B127, B128,
and B129). All soil samples were collected in August 2010. Soil sampling procedures
differed slightly between the two site types. At each mineral soil site, I established an 11 m
transect running north-south through the plot center. At each endpoint, I first sampled the O
horizon material within a 20 cm diameter ring, noting depth to mineral soil. The two O
horizon samples were combined in a sealable plastic bag as a single composite sample.
With the O horizon material removed, I sampled the mineral soil in the same spots using a
5.6 cm diameter soil corer (Giddings Machine Corporation, Fort Collins, CO) to 40 cm
depth. The cores were partitioned into depth increments of 0-10 cm, 10-20 cm, and 20-40
cm, and increments from both cores were bulked as composite samples. Soil samples were
kept on ice in coolers during travel and then refrigerated at 5˚C until analysis.
At organic sites, an 11 m north-south transect was also used, with composite
samples made up of a sample from each end. However, because there was no mineral soil
present, the soil cores were partitioned into “surface” and “subsurface” increments based on
visible differences in the organic composition. Cores were taken to 40 cm depth (once
24
overlying sphagnum moss was removed), and increment depth values were based on hole
depth to account for compaction of the organic soil in the core.
To measure in situ available nutrient pools in the soil solution, I inserted resin strips
at each site. Resin strips are electrostatically-charged membranes designed to monitor
nutrient uptake and availability for plants by absorbing cations and anions from the soil
solution (Lathja et al., 1999). I cut large ion-exchange resin membrane sheets typically
used in the wastewater industry (GE Water, Trevose, PA) into 10x2 cm strips and inserted
them vertically into slits cut into the top 10 cm of mineral soil (or the surface organic soil
for organic plots). At each site, I inserted two sets of anion and cation resin strips
approximately 3 m from the plot center each direction along the transect. The resin strips
were left in the soil over a period of two months (Aug 22-25 through Oct 24-25). At the end
of this period they were removed from the soil, rinsed with de-ionized water to remove
large soil particles, and kept refrigerated until laboratory extraction. A set of two anion
resin strips and two cation resin strips were rinsed in the same fashion and treated as
controls.
Soil sample preparation
In the lab, I sieved the mineral soil samples through a 2 mm sieve to separate the
rocks and the roots from the rest of the soil. O horizon samples from mineral sites and soils
from organic sites were passed through a 5.6 mm sieve to homogenize the sample and
remove coarse woody debris (which was designated as wood pieces larger than 1 cm in
diameter). Roots and rocks were then rinsed, dried at 65˚C for 24 hours and weighed. I also
dried a small subsample (8-10 g) from each soil sample at 105˚C for 24 hours to determine
25
the percent moisture; this was used to calculate the total soil dry weight and fine earth bulk
density based on the volume of the corer minus the volume of the roots and rocks. All
subsequent analyses were performed on <2mm soil.
Chemical and biological analyses
I determined soil pH by combining 10 g of <2mm soil with 10 ml of de-ionized
water and placing the mixture on an automatic shaker table for 5 minutes and then
measuring the pH of the mixture with a benchtop Oakton pH meter (Oakton Instruments,
Vernon Hills, IL).
To determine water-holding capacity (WHC) of the soils (0-10 cm samples and
subsurface organic samples only), I placed 25 g of soil (10 g for organic samples) and 25
ml (10 ml for organic samples) of water into stoppered funnels with a small piece of
aquarium fiber in the bottom. I covered the funnels with cling-wrap to prevent evaporation,
and then let them sit overnight. The next day, I removed the stoppers and allowed the water
to drain for at least 6 hours. I then weighed the samples, dried them at 105˚C overnight, and
reweighed them to determine a moisture content associated with 100% WHC for each soil.
I determined organic matter content using the loss-on-ignition (LOI) method. I first
measured the empty mass of ceramic crucibles than had been pre-ashed at 400˚C. I then
added 3 g of field-moist soil to the beakers and placed the samples into an oven at 105˚ C
for 24 hours. After 24 hours, I allowed the samples to cool down in a dessicator and
reweighed them. I then ignited the samples in a muffle furnace at 400˚C for 16 hours. The
samples were then cooled in the dessicator and reweighed on the analytical balance to an
26
accuracy 0.1 mg. To calculate the LOI %, which is assumed to equal the percent OM, I
used the equation:
LOI% = 100[(weight105 – weight400)/weight105]
where weight105 = the weight after oven drying at 105˚C and weight400 = the weight after
ignition at 400˚C.
Following ignition at 400˚C, the soils were returned to the muffle furnace at 950˚ C
for 2 hours to evolve all carbon dioxide from any carbonate materials. The mass loss during
this stage can be used to determine percent carbonate as described by Rayment & Lyons
(2010). After ignition, the samples were cooled in a dessicator and reweighed. Percent
carbonate was calculated with the equation:
% carbonate = 100[(weight400 – weight950)/weight150]
where weight950 = the weight after ignition at 950˚C, weight105 = the weight after oven
drying at 105˚C and weight400 = the weight after ignition at 400˚C. Percent carbonate is
assumed to approximate the LOI at 950˚C multiplied by 1.36, where the factor 1.36
accounts for CO2 = 44g/mol and CO32-
= 60 g/mol in the original sample.
To extract ammonium and nitrate from the soil samples, I first weighed out 10 g of
soil (3 g for organic samples), placed it into plastic specimen cups and added 75.0 ml 2N
potassium chloride (KCl). I then put the cups on an automatic shaker table for 45 minutes
and the filtered samples into plastic scintillation vials using plastic funnels and Fisherbrand
Q5 filter paper. The filtrates were kept frozen until being analyzed colorimetrically on an
Astoria2 Analyzer (Astoria Pacific International, Clackamas, Oregon) by cadmium
reduction (for nitrate) and a phenolate method (for ammonium) to determine the amount of
nitrogen as nitrate (NO3-N) and nitrogen as ammonium (NH4-N) in the filtrate. Values were
27
then corrected by subtracting blank KCl extraction values and expressed on a soil dry
weight basis.
The resin strips were extracted with 0.5N hydrochloric acid (HCl) to cause the soil
solution ions that had bound to the strips while they were in the field to be displaced by H+
and Cl- ions and released into the HCl solution so that concentrations could be analyzed.
First, each strip was rinsed with de-ionized water to remove any remaining soil particles.
The strips were then placed in 100 mL of 0.5N HCl and shaken on an automatic shaker
table for 30 minutes. Both cations strips and both anion strips from each site were extracted
together, yielding one composite anion sample and one composite cation sample for each
site. After shaking, the strips were removed from the acid, rinsed again with de-ionized
water, air dried, and weighed. The ion-containing HCl for the anion samples was then
neutralized to between pH 5 and pH 9 with 5N sodium hydroxide (NaOH), while the cation
extracts were left acidified. Both extract types were then analyzed colorimetrically in the
same method as the KCl extractions to determine the amount of NO3-N and NH4-N in the
extracts. The control anion and cation strips were also treated in the same manner. I then
corrected the resin strip values for the control extractions and expressed NO3-N and NH4-N
values per dry weight of resin strip.
To determine the total organic carbon and total nitrogen of the soils, samples were
air-dried and then ground using a ball mill. A 0.3g sample was then weighed into aluminum
foil, wrapped, and analyzed by thermal combustion using a LECO Truspec CN (LECO
Corporation, St. Joseph, MI). Values were corrected for residual water content using the
percent moisture of air-dried soil determined by the 105˚C oven drying stage in the LOI
procedure. As some samples were from areas with soils classified as calcareous, the amount
28
of inorganic carbon contained in carbonate (determined by multiplying % carbonate
determined through LOI by 0.20014 to account for C = 12g/mol and CO32-
= 60 g/mol) was
subtracted off of the total carbon, yielding total soil organic carbon (SOC).
To determine microbial respiration rates and nitrogen dynamics, 20 g of soil (5 g for
organic soils) was brought up to 60% WHC to provide a standardized moisture level
optimal for microbial activity. I then incubated the soils for 7 days at 21˚C in tightly-sealed
mason jars along with 3.5 mL of 0.5N NaOH in a small vial. This NaOH acted as a trap to
absorb CO2 produced by microbial respiration throughout the incubation period. At the end
of the incubation, I titrated the NaOH with 0.5N HCl to see how much CO2 had been
produced and absorbed. I also performed initial and final KCl extractions as previously
described to determine how levels of NO3-N and NH4-N changed throughout the incubation
period and to calculate net nitrogen mineralization (net Nmin).
RESULTS
To evaluate whether the measured properties showed consistent trends within each
ecosite class, individual sites were first compared as averages by ecosite class. Due to the
inconsistent sampling methods between the mineral soil and organic sites and the lack of
similar depth increments, mineral soil ecosites were considered separately from organic
sites. Following examination, it was determined that the depth increments at four mineral
soil sites (sites SP1M3, PO1M1, PO1P12, and MWCM6) had been delineated incorrectly
during field sampling, and that deep O horizons had resulted in organic material being
included in deeper depth increments (0-10 and 10-20). As a result, the mineral soil
increments at these sites had excessive amounts of organic material (>30% OM), which
29
changed most soil properties and rendered quantitative comparisons with correctly handled
sites meaningless. For this reason, these four sites were excluded from statistical analyses
Following individual ecosite type analysis, mineral soil ecosite clusters made up of
several ecosite types were created based on broad vegetation and soil texture
characteristics. Amalgamating ecosite types allowed for easier examination of vegetation-
soil relationships at a more general level of the ecosite classification framework, and
provided groups with better replication for robust statistical comparisons. Clusters were
based on two factors: soil texture (coarse or fine) and vegetation type (deciduous or
coniferous), yielding four combinations (Table 3).
Table 3. Ecosite cluster information
Ecosite Cluster Code
Ecosite classes
included Number of sites
Coniferous Coarse CC B052, B067 5
Deciduous Coarse DC B055 3
Coniferous Fine CF B098, B101, B116 8
Deciduous Fine DF B104, B119 4
Statistical analyses:
For statistical testing, soil properties were first tested for normality using the
Shapiro-Wilk’s Test and for homogeneity of variance using Levene’s Test. If a variable
was not normally distributed, four potential transformations were applied in this order:
square root, natural log, log, and inverse. If values less than one were present prior to
transformation, a constant value was added to ensure consistent transformation effects.
Normally distributed variables were tested for significant differences (p<0.05) between
ecosite types using analysis of variance (ANOVA) if assumptions of homogeneity of
30
variance were met. If variance was heterogeneous, the Brown-Forsythe test was used
instead (p<0.05). If significant differences were found, between-ecosite differences were
determined post-hoc using Tukey’s test. Variables that did not meet the assumptions (even
with transformations) for parametric testing were analyzed using the Kruskal-Wallis test
(p<0.05) based on median ranks.
Mineral soil ecosite averages
For an overall evaluation of mineral soil ecosite trends, soil properties for each site
were averaged across all depths (or added for areal totals such as SOC, total N, NH4-N, and
NO3-N). Ecosite class averages were then determined for replicate sites (Table 4).
Statistical testing revealed substantial within-ecosite class variability for most properties,
and ANOVA (or Kruskal-Wallis) showed no significant differences. The soil property that
showed the strongest differences (despite not being statistically significant) was total N
(p=0.116), which was particularly high in ecosite class B119 compared to other classes.
Percent moisture (p=0.187) and percent OM (p=0.185) also showed some differences;
ecosite class B116 had the highest average percent moisture (46.6%) as well as the highest
average percent OM (13.37%). Most other properties were either highly variable within
ecosite class or relatively similar between all classes. See Appendix 1 for charts of
individual properties by mineral soil ecosite class.
Organic ecosite averages
Organic ecosite averages (Table 5) were calculated in a similar fashion as mineral
ecosite averages, averaging across both depths (surface and subsurface increments) or
31
adding all depths for areal totals. As with mineral soil ecosites, most soil properties were
highly variable, but NH4-N was found to be statistically different (p=0.047) between B127
and B129, and between B128 and B129. Ecosite classes B127 and B128 were quite similar
for most properties, but B129 seemed to differ from the other two in some other chemical
as it had higher SOC and total N. General differences between organic ecosites (Table 5)
and mineral ecosites (Table 4) were also evident: organic ecosites had much higher percent
moistures, percent OM, and C:N ratios, but much lower bulk densities. See Appendix 2 for
charts of individual properties by organic soil ecosite class.
Ecosite clusters
Mineral soil ecosite cluster properties were evaluated as overall averages (Table 6)
in the same method as mineral ecosite class averages. Properties were also compared on a
depth-by-depth basis to display trends throughout the soil profile and to determine whether
ecosite clusters exhibit unique depth effects (Table 7). Although the process of grouping
ecosite classes into clusters yielded larger sample sizes for comparisons, no significant
differences were found between ecosite clusters at any depth or for average values.
However, depth-by-depth examination of soil properties showed expected trends such as
decreasing percent moisture, percent OM, and C:N ratio and increasing bulk density with
depth. The 0-10 cm increment also showed stronger relationships by ecosite cluster than
overall averages, with SOC (p=0.086) and total N (p=0.055) nearing statistical significance.
SOC and total N also showed generally consistent patterns across all ecosite clusters; SOC
and total N were generally lowest in the O horizon and decreased with depth throughout the
mineral soil layers (0-10, 10-20, and 20-40). However, since the 20-40 layer is a larger
32
overall increment (20 cm depth of soil instead of 10 cm), and areal values of SOC and total
N are dependent on bulk density, depth increment length, and C or N concentration, values
of SOC and total N are higher in the 20-40 layer than they would be if presented as a
percentage value. For this reason, SOC and total N values in the 20-40 layer were generally
similar to or higher than values in the 10-20 layer. NH4-N and NO3-N values were more
variable, but generally decreased with depth in the mineral soil increments. See Appendix 3
for ecosite cluster property charts.
Ecosite cluster carbon inventory
To evaluate the applicability of the ecosite classification system for carbon
inventory uses, SOC values were combined with aboveground C values for each site and
then compared by ecosite cluster. Aboveground C data were provided by the OMNR, who
performed a detailed inventory of tree species and diameter at breast height for each site
throughout the summer of 2010. Aboveground biomass (Mg/ha) was calculated for each
site using allometric relationships summarized in Ter-Mikaelian & Korzukhin (1997), and a
factor of 0.50 was used to convert biomass values to C. Because SOC values were
calculated on an areal basis (Mg/ha) to 40 cm depth for both organic and mineral soil sites,
comparisons in this case between both types of sites were valid and organic classes B127,
B128, and B129 were grouped together as a cluster (Org). Ecosite cluster C stocks (Table
8) were compared in terms of SOC, aboveground C, and total C (SOC and aboveground C
added). While aboveground C did not vary significantly between clusters, SOC and Total C
did (p=0.000 and p=0.000, respectively). SOC in the organic cluster was significantly
higher than all four mineral soil clusters. For total C, all four mineral soil clusters had
33
significantly lower values than the organic cluster. See Appendix 4 for carbon inventory
charts.
34
Table 4. Soil properties by ecosite class for mineral soil sites. Values are average (standard error) of ecosite replicates, averaged across
all depths for % moisture, bulk density, pH, and % OM. SOC and total N are combined totals to 40 cm depth (including O horiz.) and
were used to calculate C:N ratio. NH4-N and NO3-N are combined totals to 20cm depth (including O horiz.), and respiration, resin
NH4-N, resin NO3-N, net Nmin, and WHC are averages for 0-10 cm only. Missing values represent resin strips that were disturbed or
unusable, or standard errors that could not be calculated (n=1). Values with different superscript letters are significantly different
(p<0.05).
Ecosite
Class
%
Moist.
Bulk
Dens.
(g/
cm3) pH
%
OM
SOC
(Mg/
ha)
Total N
(kg/ha)
NH4-N
(g/ha)
NO3-N
(g/ha)
Resp.
(ugC/
gsoil/
min)
Resin
NH4-N
(ug N/g
resin
strip)
Resin
NO3-N
(ug N/g
resin
strip)
Net NMin
(mg N/kg
soil/day)
C:N
ratio
WHC (%
moisture
at 100%
WHC)
B052 32.3a 1.13
a 6.14
a 8.10
a 64.9
a 4267.3
a 3.33
a 0.46
a 46.61
a 9.95
a 0.04
a 4.41
a 15.20
a 68.3
a
(9.8) (0.05) (0.13) (3.34) (8.1) (540.5) (0.88) (0.44) (26.94) (5.74) (0.04) (1.36) (0.02) (7.8)
B055 23.1a 1.21
a 6.05
a 4.62
a 76.6
a 5764.0
a 6.10
a 0.08
a 64.57
a 4.29
a 0.09
a 0.73
a 13.37
a 81.2
a
(2.5) (0.06) (0.30) (0.13) (3.4) (419.5) (2.74) (0.04) (3.40) (1.93) (0.05) (0.58) (0.75) (4.3)
B067 24.8a 1.19
a 6.04
a 6.43
a 58.4
a 3622.2
a 5.25
a 0.21
a 55.65
a 0.00
a 0.83
a 3.82
a 16.31
a 62.2
a
(3.4) (0.07) (0.60) (1.63) (8.0) (632.2) (2.63) (0.21) (9.58) (0.70) (1.35) (0.64) (8.6)
B098 24.8a 1.19
a 6.61
a 5.80
a 72.9
a 5527.1
a 7.37
a 2.02
a 55.31
a 0.28
a 0.04
a 4.08
a 13.08
a 74.7
a
(0.6) (0.04) (0.25) (0.92) (12.8) (824.5) (1.07) (1.98) (11.68) (0.02) (2.03) (0.33) (3.6)
B101 25.7a 1.17
a 6.84
a 7.31
a 81.5
a 5614.1
a 3.89
a 0.24
a 73.36
a 7.27
a 0.17
a 3.06
a 14.49
a 95.2
a
(3.9) (0.04) (0.17) (0.77) (11.9) (115.1) (2.15) (0.24) (3.94) (5.20) (0.00) (1.89) (1.82) (8.0)
B104 23.0a 1.20
a 6.03
a 5.58
a 89.0
a 5810.5
a 1.72
a 0.00
a 49.96
a 0.32
a 4.12
a 15.14
a 74.9
a
(0.8) (0.00) (0.98) (1.38) (26.3) (1466.9) (0.04) (0.00) (5.29) (0.32) (0.65) (0.70) (5.0)
B116 46.6a 1.14
a 6.54
a 13.37
a 83.7
a 5235.4
a 10.10
a 2.29
a 65.13
a 6.24
a 0.52
a 5.22
a 15.95
a 86.5
a
(3.2) (0.11) (0.29) (1.97) (9.9) (526.9) (6.53) (1.06) (35.13) (2.93) (0.41) (2.48) (0.84) 30.8
B119 31.3a 1.34
a 6.93
a 7.66
a 104.5
a 7194.7
a 4.54
a 8.38
a 57.32
a 9.46
a 0.23
a 3.39
a 14.48
a 120.2
a
(2.9) (0.03) (0.09) (0.40) (14.7) (778.4) (0.04) (5.99) (2.56) (9.03) (0.04) (2.26) (0.47) (3.5)
35
Table 5. Soil properties by ecosite class for organic soil sites. Values are average (standard error) of ecosite replicates, averaged across
all depths for % moisture, bulk density, pH, % OM, NH4-N and NO3-N. SOC and total N are combined totals to 40 cm depth and were
used to calculate C:N ratio. Respiration, net NMin, and WHC values are for subsurface increment, and resin NH4-N and NO3-N values
are for surface increment. Missing values represent standard errors that could not be calculated (n=1). Values with different
superscript letters are significantly different (p<0.05).
Ecosite
Class
%
Moist.
Bulk
density
(g/
cm3) pH % OM
SOC
(Mg/
ha)
Total N
(kg/ha)
NH4-
N
(g/ha)
NO3-
N
(g/ha)
Resp.
(ugC/
gsoil/
min)
Resin
NH4-N
(ug N/g
resin
strip)
Resin
NO3-N
(ug N/g
resin
strip)
Net NMin
(mg N/kg
soil/day)
C:N
Ratio
WHC (%
moisture
at 100%
WHC)
B127 289.9a 0.095
a 5.07
a 86.66
a 170.7
a 4848.8
a 1.93
a 7.26
a 76.35
a 29.92
a 76.83
a 20.75
a 34.62
a 628.9
a
(92.4) (0.011) (0.69) (3.06) (46.1) (880.8) (0.90) (7.26) (34.52) (13.09) (76.74) (3.23) (3.23) (217.4)
B128 576.7a 0.082
a 6.59
a 86.77
a 175.0
a 4929.0
a 1.97
a 0.76
a 104.66
a 0.75
a 2.35
a 35.21
a 35.21
a 651.0
a
(147.0) (0.010) (0.64) (2.08) (33.7) (653.2) (0.29) (0.65) (30.62) (0.07) (1.85) (2.16) (2.16) (110.9)
B129 473.2a 0.090
a 6.68
a 86.43
a 237.1
a 6397.3
a 8.58
b 0.05
a 127.36
a 1.74
a 2.17
a 37.53
a 37.53
a 580.4
a
(0.4) (0.008) (0.29) (2.18) (19.7) (1032.3) (1.16) (0.05) (62.88) (1.76) (2.97) (2.97) (66.3)
36
Table 6. Soil properties by ecosite cluster for mineral soil sites. Values are average (standard error) of ecosite cluster replicates,
averaged across all depths for % moisture, bulk density, pH, and % OM. SOC and Total N are combined totals to 40 cm depth
(including O horiz.), and were used to calculate C:N ratio. NH4-N and NO3-N are combined totals to 20cm depth (including O horiz.),
and respiration, resin NH4-N, resin NO3-N, net Nmin, and WHC are averages for 0-10 cm only. Values with different superscript letters
are significantly different (p<0.05).
Ecosite
Cluster
%
Moist.
Bulk
Density
(g/cm3) pH
%
OM
SOC
(Mg/
ha)
Total
N
(kg/ha)
NH4-
N
(g/ha)
NO3-N
(g/ha)
Resp.
(ugC/
gsoil/
min)
Resin
NH4-N
(ug N/g
resin
strip)
Resin
NO3-N
(ug
N/g
resin
strip)
Net Nmin
(mg
N/kg
soil/day)
C:N
Ratio
WHC
(%
moisture
at 100%
WHC)
CC 27.79a 1.16
a 6.08
a 7.10
a 61.0
a 3880.3
a 4.48
a 0.31
a 52.04
a 6.63
a 0.44
a 4.11
a 15.87
a 64.6
a
(4.06) (0.04) (0.33) (1.44) (8.3) (417.2) (1.54) (0.19) (10.25) (4.69) (0.37) (0.80) (0.45) (5.5)
DC 23.08a 1.21
a 6.05
a 4.63
a 76.6
a 5764.0
a 6.10
a 0.04
a 64.57
a 4.29
a 0.09
a 0.73
a 13.37
a 81.2
a
(2.49) (0.06) (0.30) (0.08) (3.4) (419.5) (2.74) (0.08) (3.40) (1.93) (0.05) (0.58) (0.75) (4.3)
CF 33.19a 1.19
a 6.64
a 9.02
a 79.1
a 5439.5
a 7.53
a 1.68
a 63.51
a 5.59
a 0.25
a 4.25
a 14.51
a 84.2
a
(4.13) (0.04) (0.13) (1.48) (6.0) (326.8) (2.38) (0.80) (12.43) (2.17) (0.16) (1.15) (0.65) (10.7)
DF 27.14a 1.27
a 6.48
a 6.62
a 96.8
a 6502.6
a 3.13
a 4.19
a 53.64
a 9.46
a 0.27
a 3.76
a 14.81
a 97.6
a
(2.67) (0.04) (0.48) (0.84) (13.1) (786.9) (0.81) (3.44) (3.20) (9.03) (0.13) (0.98) (0.39) (13.3)
37
Table 7. Mineral soil ecosite cluster soil properties by depth. Values are average (standard error) of ecosite cluster replicates.
Ecosite
Cluster Depth
%
Moist
Bulk
Dens.
(g/
cm3) pH % OM
SOC
(Mg/
ha)
Total N
(kg/ha)
NH4-N
(g/ha)
NO3-N
(g/ha)
Resp.
(ugC/
gsoil/
min)
Resin
NH4-N
(ug
N/g
resin
strip)
Resin
NO3-N
(ug
N/g
resin
strip)
Net Nmin
(mg N/kg
soil/day)
C:N
Ratio
WHC (%
moisture
at 100%
WHC)
CC O horiz 116.5 0.21 6.01 72.53 13.0 442.7 0.77 0.035 55.25
(28.1) (0.06) (0.38) (8.22) (5.3) (196.8) (0.42) (0.034) (23.56)
0-10 26.7 0.86 5.99 6.00 25.3 1359.4 2.54 0.201 52.04 6.63 0.44 4.11 18.29 64.6
(0.8) (0.03) (0.28) (0.78) (4.9) (226.8) (1.20) (0.124) (10.25) (4.69) (0.37) (0.80) (0.95) (5.5)
10-20 19.3 1.23 5.63 3.01 11.0 869.8 1.17 0.073 13.97
(1.0) (0.03) (0.35) (0.35) (2.8) (214.7) (0.14) (0.056) (2.78)
20-40 18.4 1.40 6.36 2.27 11.8 1208.4 10.12
(1.6) (0.07) (0.52) (0.29) (2.5) (230.8) (1.96)
DC O horiz 71.2 0.70 6.79 53.17 3.0 66.8 0.18 0.003 45.14
(12.2) (0.21) (0.15) (6.28) (0.6) (16.9) (0.11) (0.003) (2.27)
0-10 31.4 0.89 6.40 9.01 41.4 2234.9 3.96 0.027 64.57 4.29 0.09 0.73 18.74 81.2
(4.6) (0.05) (0.48) (0.78) (1.7) (213.0) (2.91) (0.025) (3.40) (1.93) (0.05) (0.58) (1.23) (4.3)
10-20 22.2 1.11 5.26 4.64 19.3 1416.9 1.95 0.050 13.89
(3.0) (0.05) (0.21) (0.44) (1.3) (186.1) (1.06) (0.040) (1.06)
20-40 18.9 1.42 6.27 2.02 12.9 2045.5 6.46
(1.3) (0.12) (0.32) (0.78) (0.9) (165.8) (0.89)
CF O horiz 156.7 0.19 6.23 62.78 13.5 452.2 1.68 0.051 35.83
(33.7) (0.03) (0.22) (6.64) (5.4) (205.6) (1.44) (0.031) (3.51)
0-10 37.1 0.77 6.09 11.06 37.8 1811.1 2.38 1.245 63.51 5.59 0.25 4.25 21.30 84.2
(6.9) (0.09) (0.19) (2.14) (4.2) (219.5) (0.88) (0.716) (12.43) (2.17) (0.16) (1.15) (0.95) (10.7)
10-20 20.4 1.26 6.17 3.97 15.0 1194.0 3.46 0.380 11.92
(1.4) (0.09) (0.15) (0.51) (3.0) (152.9) (0.89) (0.196) (0.95)
20-40 20.4 1.50 7.25 2.88 12.9 1982.1 6.42
(1.0) (0.05) (0.16) (0.26) (2.0) (187.9) (0.70)
DF O horiz 141.6 0.24 6.13 70.88 4.8 144.8 0.14 0.000 41.67
(36.0) (0.04) (0.32) (5.74) (1.3) (63.0) (0.03) (0.000) (7.35)
0-10 41.9 0.81 5.94 12.36 49.9 2729.1 0.95 3.220 53.64 9.46 0.27 3.76 18.19 97.6
(8.9) (0.05) (0.45) (2.88) (10.7) (563.2) (0.14) (2.982) (3.20) (9.03) (0.13) (0.98) (0.21) (13.3)
10-20 23.7 1.27 6.06 5.24 24.7 1671.4 2.04 0.968 14.47
(1.0) (0.11) (0.33) (0.95) (5.6) (212.7) (0.70) (0.570) (1.75)
20-40 18.5 1.52 6.98 2.81 17.4 1957.3 8.51
(1.1) (0.06) (0.65) (0.32) (4.9) (276.0) (1.33)
38
Table 8. Ecosite cluster C inventory comparisons. Values are average (standard error) for ecosite
cluster replicates. Values with different superscript letters are significantly different (p<0.05)
Ecosite
Cluster
SOC
(Mg/ha)
Aboveground C
(Mg/ha)
Total C
(Mg/ha)
CC 61.0a 48.4
a 109.4
a
(8.3) (8.1) (7.3)
DC 76.6a 55.8
a 132.4
a
(3.4) (11.2) (14.3)
CF 79.1a 46.9
a 126.0
a
(6.0) (3.7) (3.9)
DF 96.8a 46.7
a 143.5
a
(13.1) (10.4) (20.6)
Org 194.2b 58.9
a 253.1
b
(20.7) (5.3) (23.0)
DISCUSSION
The main purpose of this study was to determine whether key soil properties indicative of
various ecosystem functional characteristics varied among different forest ecosite types at Hearst
Forest. A comparison of the 14 soil properties (Table 4) measured shows that most of the
properties are either remarkably similar across all ecosite classes (e.g. bulk density, C:N ratio,
pH) or quite variable within each ecosite class (e.g. NH4-N, NO3-N, resin NH4-N, resin NO3-N).
Although some properties appeared to be approaching statistical significance (e.g. total N), given
the overall lack of significant results at this level of analysis, it can be concluded that for this
dataset, ecosite class cannot be reliably used as a predictor of the measured soil properties in
mineral soil sites.
However, comparing stands aggregated by general site “types” yields some differences
between these classes, particularly between mineral (Table 4) and organic soil (Table 5) classes.
Organic sites had higher levels of organic matter, higher percent moisture, lower bulk density
and higher C:N ratios. These traits are expected of these organic soil sites, which were lowland,
39
water-saturated sites with coniferous (typically eastern white cedar or black spruce) vegetation.
These conditions lead to organic matter accumulation due to slow anaerobic decomposition
(Gardiner & Miller, 2007) and poor quality, slowly degraded coniferous litter. While the vastly
different soil compositions between organic and mineral soil sites were evident, there were few
differences within the three organic ecosite classes. The only statistically significant difference
was NH4-N, which was higher in B129 than B128 and B127. This finding supports the ecosite
classification system, which differentiates the three organic classes as nutrient poor (B127),
nutrient intermediate (B128) and nutrient rich (B129). However, other measures of soil nutrients
(NO3-N, resin NH4-N, and resin NO3-N) did not follow this trend. Overall, despite general
differences between site types with completely difference substrate compositions, the main
finding of this study is that sites within an ecosite class do not display consistent, predictable soil
characteristics.
In terms of ecosystem science, this conclusion may seem surprising: there are well-
established causal links and interactions between many of the measured soil properties and e.g.
tree species, soil texture, and soil moisture regimes that are used for ecosite classification. For
example, fine-textured soils tend to hold more moisture and have higher cation exchange
capacity (Silver et al., 2000), and N mineralization rates are generally positively related to soil
moisture (Powers, 1990) so it could be hypothesized that a fine textured, moist ecosite should
have higher N mineralization rates and higher NH4-N and NO3-N levels. Similarly, deciduous
species generally produce litter with higher N levels (Berg & Meentemeyer, 2002), and higher
quality litter results in greater net N mineralization (Scott & Binkley, 1997) and microbial
activity (Witkamp, 1966), so it could be expected that ecosites with deciduous vegetation would
have higher NH4-N, NO3-N and microbial respiration levels. However, in ecosystem-scale field
40
studies such as this one and in the context of the ecosite classification procedures, there are
confounding factors that may have caused substantial within-ecosite class variability.
One important factor that must be considered when sampling soils from specific ecosite
types is the highly variable nature of most soil properties. Properties vary temporally and
spatially (both vertically in the soil profile and horizontally across an area), so most ecological
studies rely on averages or generalizations to represent this variability. ELC itself is a method of
attempting to account for soil (and vegetation) spatial variability at many scales (Zenner et al.,
2010), but because of practical limitations related to sampling strategies it is impossible to
accurately capture ecological variability at all scales. While ecosites represent the finest scale
mapped in ELC, soil and vegetation variability are present even at much finer scales. For
example, Simmons et al., (2009) found in a study of bottomland forests that microtopography at
scales of decimeters or less affected hydrologic conditions, nutrient concentrations, and species
compositions. Interspecific differences in plant characteristics can also have fine-scale influences
on soil properties; Vinton & Burke (1995) found differences in C and N mineralization rates
between soils under different shortgrass steppe species as well as between soils directly under
plants compared to unvegetated areas.
Microbial communities, another important control on many soil properties, also vary
substantially at fine scales. For example, Snajdr et al., (2008) assessed the spatial variability of
enzyme activities and microbial biomass in upland hardwood forest soils and found significant
differences between samples taken only 1 cm apart. While the ecosite scale unit in Ontario’s
ELC is intended to account for heterogeneous ecological conditions within ecosite polygons and
capture “recurring associations of vegetation type and substrate type combinations” (Banton et
al., 2009), and ecosystem ecology generally considers ecosystem function as the aggregation of
41
fine-scale processes, the combination of spatial variability and limited sampling time and
resources in both ecosite classification and this study may have contributed to the lack of
statistically different patterns by ecosite class. The soils aspect of ecosite classification is based
on several soil cores intended to be representative of the modal conditions of the entire site, and I
used two cores along a transect aggregated as a composite sample. It is conceivable that neither
of these sampling methods adequately accounted for within-ecosite variability, leading to the
highly variable results of this study.
Temporal variability in forest properties during succession may also affect comparisons
of soil properties between ecosite classes. While the ecosite classification system is structured to
account for vegetation growth stages (for example, each ecosite class has modifiers for general
vegetation height), I did not have enough plots to select from to control for this within my ecosite
class replicates. It is possible that past harvest activities at the sites I sampled, and/or
successional status, affected the relationship between vegetation and soil properties. For
example, a site that was classified when it was in a transitional stage in succession following
harvest would have had less time to develop firm vegetation-soil relationships based on a
dominant vegetation type. While soil physical properties (and hence ecosite class) would not be
greatly affected, the more variable functional properties I examined such as nutrient levels may
be quite different. Temporal variability over shorter time scales may also be important, as
differences in moisture related to precipitation timing could influence the initial ecosite
classification procedure as well as the determined moisture characteristics when I sampled.
Although ecosites are generally not classified directly following precipitation events, both my
measured percent moisture and WHC (a more stable, integrative measure of potential site
moisture levels) values showed no differences by ecosite class. This indicates the ecosite
42
classification system is not even capturing observed variation in soil moisture (which is
explicitly identified for each ecosite class), suggesting the possible confounding influence of
spatial or temporal variability in moisture levels.
Temporal variability could also affect attempts to assign fertility status to ecosite classes.
While the revised ecosite classification system includes options for an edatope grid placing each
ecosite class on a chart of moisture and fertility, this has not yet been developed, so a direct
comparison between my results and expected fertility-moisture relationships is not possible.
However, temporal variability and the particular factors used to define fertility are important
considerations when comparing ecosites. Measures such as extractable and resin-available NH4-
N and NO3-N are useful indicators of nutrient status, but my one-time samples might not account
for temporal nutrient variability and so may not be representative of general function. For
example, a site with high nutrient fluxes and efficient cycles could be very fertile and productive
if plant nutrient use is high, leading to low levels of available nutrients. While resin strips may
account for this variability better (as they were left in the soil for two months), it would also be
useful to have a longer record of available nutrients including more of the growing season.
Fertility measures in the classification system are based on the fact that appropriate availability
of nutrients and water on a site determines what vegetation will be found (i.e. vegetation types
are implicitly associated with nutrient requirements), which may be a more stable and integrative
way to interpret general site fertility. However, based on this assumption, it would follow that
properties such as net N mineralization and microbial respiration would reflect fertility
differences related to vegetation-soil combinations. As I did not find trends in these more holistic
measures of ecosystem function, it appears that relationships between fertility and ecosite class
do not exist for the ecosites that I sampled.
43
Without knowledge of the exact fertility properties implied for each ecosite class by the
classification system (since the edatope grid is still in development), however, it is possible that
the lack of functional differences between classes is simply a reflection of overall ecological
consistencies between the chosen ecosites within the Hearst Forest. While I did capture a range
of ecosite classes that represented a substantial portion of total sites classified for the enhanced
forest inventory project, many more site types exist in the forest (Table 9). A more detailed and
extensive sampling within the Hearst Forest and the inclusion of ecosites in different regions
combined with a comparison of nutrient statuses expected through the classification system
would be necessary to determine whether or not the classes I examined are actually different
enough to expect functional differences. Although differences would be expected based on
vegetation-soil relationships, it is possible that in the context of the classification framework the
sites I examined are essentially similar (aside from the mineral-organic substrate split) in terms
of most of the properties I measured.
44
Table 9. Summary of classified ecosite totals. Ecosite classes I examined are noted in bold.
Ecosite
class
Number of
sites % of total
B034 12 2.7
B035 10 2.2
B037 2 0.4
B040 1 0.2
B049 23 5.1
B050 13 2.9
B052 13 2.9
B053 1 0.2
B055 21 4.7
B065 3 0.7
B067 3 0.7
B070 6 1.3
B082 8 1.8
B083 9 2.0
B085 4 0.9
B086 2 0.4
B088 22 4.9
B098 15 3.4
B099 6 1.3
B101 16 3.6
B102 1 0.2
B104 21 4.7
B114 39 8.7
B115 1 0.2
B116 15 3.4
B117 2 0.4
B119 39 8.7
B127 62 13.9
B128 47 10.5
B129 21 4.7
B164 6 1.3
B222 1 0.2
B223 2 0.4
Total: 447 100
While soil temporal and spatial variability, lack of adequate within site replication, and
the potential lack of distinct differences in terms of the classification framework are all factors
that could explain the overall negative results of this study, another major factor that weakened
45
overall conclusions was inadequate replication within each ecosite class (2 or 3 replicates for
each ecosite class). This resulted from a combination of several factors, including a limited range
of pre-classified sites to choose from at the time of sampling, limited time in the field, and
mishandling of several sites that could not be included in statistical comparisons. To determine
whether some of the observed trends would have been found to be statistically significant with
larger sample sizes, I performed power analyses on a selection of mineral ecosite class entire-
profile average properties using the program G*Power. Bulk density, % moisture, total N and
NH4-N were chosen because they were representative of different soil physical and chemical
conditions and also met assumptions of homogeneity of variance and normality. Necessary total
sample sizes at the 0.05 significance level (Table 10) were calculated based on effect sizes
determined from ecosite class sample sizes, means and overall standard deviations.
Table 10. Power analyses for mineral soil ecosite class averages
Property Effect size Critical F
Total sample
size
Sample size
per class (8
classes)
Actual
power
Bulk density 0.5605 2.1396 80 10 0.9591
% moisture 0.6474 2.1781 64 8 0.9656
Total N 0.7271 2.2074 56 7 0.9774
NH4-N 0.5775 2.1396 80 10 0.9695
It was determined that a sample size ranging from 56-80 would be appropriate, depending
on factor of interest. Bulk density (which had low variability but was similar between all classes)
and NH4-N (which was highly variable) had smaller effect sizes and consequently required larger
sample sizes, while % moisture and total N (which showed stronger initial trends) required
smaller sample sizes. This is a feasible number of samples for a large-scale study, supporting
further inquiry into the properties and suggesting that sample size may have limited conclusions
46
with my dataset. However, this power analysis only determined sample size required to find one
significant difference between the two most different ecosite classes; an examination of ecosite
class averages indicates that many properties are still either very similar or variable and that
distinct class-by-class differences may not appear with a realistic sample size.
Another way I attempted to account for sample size issues was through the aggregation of
ecosites classes into ecosite clusters (Table 6, Table 7). However, soil properties were still quite
variable and no significant differences were found in either overall averages or depth-by-depth
comparisons. This could be due to the fact that sample sizes were still too small or the fact that
although the ecosite clusters were made up of classes of similar vegetation and soil texture
compositions, combining different ecosite classes with supposedly different properties into a
single class may have introduced more variability to each cluster. It did however appear that
some properties such as SOC (p=0.132), total N (p=0.130), and C:N ratio (p=0.154) were
approaching statistical significance in ways that made sense in terms of vegetation type and
texture differences. For example, both fine textured clusters had higher SOC levels than coarse
clusters, and fine textured soils generally hold more C (Schimel et al., 1994). C:N ratio was also
lowest in the deciduous coarse cluster, which could be explained by the generally high quality of
deciduous litter compared to coniferous litter. Differences between clusters also appeared to be
manifested most strongly in the upper 10 cm of mineral soil, where properties such as SOC
(p=0.086), total N (p=0.055) and C:N ratio (p=0.067) were very close to a 0.05 significance
level. For SOC and total N, the largest difference was between the coniferous coarse and
deciduous coarse clusters, with higher SOC and total N in the deciduous coarse cluster. As litter
fall represents a major pathway for vegetation effects on soils, it is understandable that
differences were most pronounced in the upper mineral soil layers. The accumulation of SOC
47
and N in the deciduous cluster also makes sense in terms of vegetation effects. While controls on
OM accumulation in soils are complex and vary by ecosystem, Berg et al. (2001) compared OM
between deciduous (Douglas fir) and coniferous (red alder) boreal forest stands on similar soils
and found higher OM levels in deciduous stands. While deciduous litter is typically better quality
and may decompose more quickly (Raich & Tufekciogul, 2000) and Berg et al. (2001) did find
much higher N concentrations in deciduous litter than in coniferous, the greater overall amounts
of litter inputs in deciduous stands outweighed decomposition differences and led to OM
accumulation. The same may occur at deciduous ecosites in the Hearst Forest, which generally
appeared to have lower C:N ratios yet higher levels of SOC and total N than coniferous ecosites.
The carbon inventory analysis by ecosite cluster (Table 8) was one area where the ecosite
classification system showed promise, at least in a broad sense. While no differences in SOC,
aboveground C, or total C were found within the mineral soil clusters, both SOC and total C
were significantly higher in the organic cluster than in all four mineral clusters. Since
aboveground C did not vary between any clusters, the higher total C in organic clusters can be
attributed to the high levels of SOC in organic sites. This goes along with other studies of boreal
forest SOC stocks such as Rapalee et al. (1998), who found that the largest SOC stocks in the
boreal forest of northern Manitoba were in poorly drained peat areas. In terms of relative pool
sizes, SOC accounted for an average of 61% of total C in mineral ecosites and 76% in organic
ecosites. These findings support the use of the ecosite classification system as a way of
estimating regional carbon stocks, but only at a coarse division between mineral and organic
sites. It is possible that a more detailed sampling of a larger number of ecosites would reveal
ecosite class trends that could inform carbon inventories, but for this dataset it is limited to
broader generalizations.
48
CONCLUSION
In the context of the applicability of the ecosite classification system, my conclusion that
ecosite classes do not display characteristic biogeochemical properties can be interpreted in
several ways. In terms of the intended applications of the ecosite unit (providing a consistent
framework for operational planning of forests, inventory, natural heritage planning, and
increased understanding of ecological patterns), my results do not necessarily weaken the
strength of the system. By providing a spatial understanding of dominant vegetation and general
soil conditions, ecosites are a valuable tool for many aspects of sustainable boreal forest
management. For example, they can provide the ecological basis for rare species conservation
efforts through a delineation of important habitat areas, or facilitate conscientious, informed
environmental planning efforts. However, the fact that they do not represent a classification of
ecosystem function weakens their potential as a basis for predictive modeling, which usually
requires an understanding of what processes occur within an ecosystem and what the controls on
those processes are. For this reason, the use of the ecosite unit should be generally restricted to
its intended purposes and attempts to apply ecosites to more predictive uses such as modeling
potential responses to global change phenomena should be considerate of the limitations of the
classification system. The potential exists for the use of the system at a more generalized
interpretation of ecosite classes (e.g. comparisons between mineral and organic soil ecosites), but
even these relationships require further testing.
Given the limited scope of this study, however, there are further steps that could be taken
to expand on my findings and draw more definite conclusions. A similar study designed to better
account for within site variability (more soil cores at each site) and within class variability (more
replicates for each ecosite class) based on the power analyses I performed would provide a much
49
clearer understanding of whether ecosite classes have characteristic biogeochemical properties or
if the lack of definite trends in this study was due primarily to a lack of strong ecological
gradients between the chosen ecosite classes. This would also be supported by comparisons with
ecosites in different regions and by analysis of temporal trends (e.g. monitoring fertility and
productivity over a time span of several years). More detailed statistical analyses such as
multivariate comparisons of important ecological characteristics should also be used to
determine whether ecosite classes can be characterized by combinations of factors.
Overall, this study represents one part of the necessary iterative testing of ELC systems
advocated by Rowe (1996). ELC units represent hypothesized ecological distinctions; this study
contributed a test of hypothesized ecosystem functional differences in a small sample of ecosite
classes in one particular region. Through continued testing of ecological relationships and
revision of the ecosite classification system if necessary, Ontario’s ELC system will continue to
improve and be an important contribution to sustainable forest management on many levels.
50
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54
SUMMARY
• Based on whole-profile averages for replicates of each ecosite class, it was determined
that mineral soil ecosite classes showed no statistically significant differences for any
properties.
• Most properties were either extremely variable (e.g. nutrient levels, net nitrogen
mineralization) or quite consistent (e.g. bulk density, pH) between mineral soil ecosite
classes.
• Organic soil ecosites showed similar trends as mineral sites, except for the finding that
NH4-N was higher in B129 than B128 and B127.
• There were large differences between organic and mineral soil sites (organic sites had
lower bulk densities, higher % moisture, higher SOC), but these were not tested
statistically.
• Grouping mineral soil ecosite classes into ecosite clusters to achieve better replication
reduced variability, but no significant trends emerged.
• An analysis of properties on a depth-by-depth basis for mineral ecosite clusters revealed
that the strongest trends were in the upper 10 cm of mineral soil, with some properties
nearing statistical significance (SOC p = 0.086, total N p=0.055).
• An analysis of total C (aboveground and SOC) showed that organic sites contained much
more total C than mineral sites, but that aboveground C was similar for all sites.
• The results indicate that individual ecosite classes do not show characteristic properties in
terms of ecological function
• The lack of trends could be explained by soil variability within sites, low levels of
replication, or a lack of strong functional gradients between the chosen ecosite classes
• These results indicate that the ecosite classification system does not accurately capture
ecosystem function, but further testing of this relationship is recommended.
55
APPENDIX 1. MINERAL SOIL ECOSITE CLASS PROPERTIES
Figure 1. Entire profile average % moisture
for mineral soil ecosites. Error bars = 1 SE
Figure 2. Entire profile average bulk density
for mineral soil ecosites. Error bars = 1 SE
Figure 3. Entire profile average pH for
mineral soil ecosites. Error bars = 1 SE
Figure 4. Entire profile average % OM for
mineral soil ecosites. Error bars = 1 SE
Figure 5. Entire profile total SOC for
mineral soil ecosites. Error bars = 1 SE
Figure 6. Entire profile total N for mineral
soil ecosites. Error bars = 1 SE
56
Figure 7. Total NH4-N (to 20cm depth) for
mineral soil ecosites. Error bars = 1 SE
Figure 8. Total NO3-N (to 20 cm depth) for
mineral soil ecosites. Error bars = 1 SE
Figure 9. Average respiration for mineral
soil ecosites (0-10 cm increment). Error bars
= 1 SE
Figure 10. Resin NH4-N for mineral soil
ecosites (0-10 cm increment). Error bars = 1
SE
Figure 11. Resin NO3-N for mineral soil
ecosites (0-10 cm increment). Error bars = 1
SE
Figure 12. Net N mineralization for mineral
soil ecosites (0-10 cm increment). Error bars
= 1 SE
57
Figure 13. Entire profile average C:N ratio
for mineral soil ecosites. Error bars = 1 SE
Figure 14. WHC for mineral soil ecosites (0-
10 cm increment). Error bars = 1 SE
58
APPENDIX 2. ORGANIC SOIL ECOSITE PROPERTIES
Figure 15. Entire profile average % moisture
for organic soil ecosites. Error bars = 1 SE
Figure 16. Entire profile average bulk
density for organic soil ecosites. Error bars
= 1 SE
Figure 17. Entire profile average pH for
organic soil ecosites. Error bars = 1 SE
Figure 18. Entire profile average %OM
organic soil ecosites. Error bars = 1 SE
Figure 19. Entire profile total SOC for
organic soil ecosites. Error bars = 1 SE
Figure 20. Entire profile total N for organic
soil ecosites. Error bars = 1 SE
59
Figure 21. Entire profile total NH4-N for
organic soil ecosites. Error bars = 1 SE
Figure 22. Entire profile total NO3-N for
organic soil ecosites. Error bars = 1 SE
Figure 23. Respiration for organic soil
ecosites (subsurface inrement). Error bars =
1 SE
Figure 24. Resin NH4-N for organic soil
ecosites (surface increment). Error bars = 1
SE
Figure 25. Resin NO3-N for organic soil
ecosites (surface increment). Error bars = 1
SE
Figure 26. Net N mineralization for organic
soil ecosites (subsurface increment). Error
bars = 1 SE
60
Figure 27. Entire profile average C:N ratio
for organic soil ecosites. Error bars = 1 SE
Figure 28. WHC for organic soil ecosites
(subsurface increment). Error bars = 1 SE
61
APPENDIX 3. MINERAL SOIL ECOSITE CLUSTER PROPERTIES
Entire profile averages:
Figure 29. Entire profile average % moisture
for mineral soil ecosite clusters. Error bars =
1 SE
Figure 30 Entire profile average bulk density
for mineral soil ecosite clusters. Error bars =
1 SE
Figure 31. Entire profile average pH for
mineral soil ecosite clusters. Error bars = 1
SE
Figure 32. Entire profile average %OM for
mineral soil ecosite clusters. Error bars = 1
SE
62
Figure 33. Entire profile total SOC for
mineral soil ecosite clusters. Error bars = 1
SE
Figure 34. Entire profile total N for mineral
soil ecosite clusters. Error bars = 1 SE
Figure 35. NH4-N (to 20 cm depth) for
mineral soil ecosite clusters. Error bars = 1
SE
Figure 36. NO3-N (to 20 cm depth) for
mineral soil ecosite clusters. Error bars = 1
SE
Figure 37. Respiration for mineral soil
ecosite clusters (0-10 cm increment). Error
bars = 1 SE
Figure 38. Resin NH4-N for mineral soil
ecosite clusters (0-10 cm increment). Error
bars = 1 SE
63
Figure 39. Resin NO3-N for mineral soil
ecosite clusters (0-10 cm increment). Error
bars = 1 SE
Figure 40. Net N mineralization for mineral
soil ecosite clusters (0-10 cm increment).
Error bars = 1 SE
Figure 41. Entire profile average C:N ratio
for mineral soil ecosite clusters. Error bars =
1 SE
Figure 42. WHC for mineral soil ecosite
clusters (0-10 cm increment). Error bars = 1
SE
64
Ecosite cluster O horizon properties:
Figure 43. Ecosite cluster O horizon %
moisture. Error bars = 1 SE
Figure 44. Ecosite cluster O horizon bulk
density. Error bars = 1SE
Figure 45. Ecosite cluster O horizon pH.
Error bars = 1 SE
Figure 46. Ecosite cluster O horizon % OM.
Error bars = 1 SE
Figure 47. Ecosite cluster O horizon SOC.
Error bars = 1SE
65
Figure 48. Ecosite cluster O horizon total N.
Error bars = 1SE
Figure 49. Ecosite cluster O horizon NH4-N.
Error bars = 1SE
Figure 50. Ecosite cluster O horizon NO3-N.
Error bars = 1SE
66
Ecosite cluster 0-10 properties:
Figure 51. Ecosite cluster 0-10 % moisture.
Error bars = 1SE
Figure 52. Ecosite cluster 0-10 bulk density.
Error bars = 1SE
Figure 53. Ecosite cluster 0-10 pH. Error
bars = 1SE
Figure 54. Ecosite cluster 0-10 % OM. Error
bars = 1SE
Figure 55. Ecosite cluster 0-10 SOC. Error
bars = 1SE
Figure 56. Ecosite cluster 0-10 total N. Error
bars = 1SE
67
Figure 57. Ecosite cluster 0-10 NH4-N. Error
bars = 1SE
Figure 58. Ecosite cluster 0-10 NO3-N. Error
bars = 1SE
Figure 59. Ecosite cluster 0-10 respiration.
Error bars = 1SE
Figure 60. Ecosite cluster 0-10 resin NH4-N.
Error bars = 1SE
Figure 61. Ecosite cluster 0-10 resin NO3-N.
Error bars = 1SE
Figure 62. Ecosite cluster 0-10 net N
mineralization. Error bars = 1SE
68
Figure 63. Ecosite cluster 0-10 C:N ratio.
Error bars = 1SE
Figure 64. Ecosite cluster 0-10 WHC. Error
bars = 1SE
69
Ecosite cluster 10-20 properties:
Figure 65. Ecosite cluster 10-20 % moisture.
Error bars = 1SE
Figure 66. Ecosite cluster 10-20 bulk
density. Error bars = 1SE
Figure 67. Ecosite cluster 10-20 pH. Error
bars = 1SE
Figure 68. Ecosite cluster 10-20 %OM.
Error bars = 1SE
Figure 69. Ecosite cluster 10-20 SOC. Error
bars = 1SE
Figure 70. Ecosite cluster 10-20 total N.
Error bars = 1SE
70
Figure 71. Ecosite cluster 10-20 NH4-N.
Error bars = 1SE
Figure 72. Ecosite cluster 10-20 NO3-N.
Error bars = 1SE
Figure 73. Ecosite cluster 10-20 C:N ratio.
Error bars = 1SE
71
Ecosite cluster 20-40 properties
Figure 74. Ecosite cluster 20-40 % moisture.
Error bars = 1SE
Figure 75. Ecosite cluster 20-40 bulk
density. Error bars = 1SE
Figure 76. Ecosite cluster 20-40 pH. Error
bars = 1SE
Figure 77. Ecosite cluster 20-40 %OM.
Error bars = 1SE
Figure 78. Ecosite cluster 20-40 SOC. Error
bars = 1SE
Figure 79. Ecosite cluster 20-40 total N.
Error bars = 1SE
72
Figure 80. Ecosite cluster 20-40 C:N ratio.
Error bars = 1SE
73
APPENDIX 4. CARBON INVENTORY DATA
Figure 81. Whole-profile total SOC by
ecosite cluster. Error bars = 1SE
Figure 82. Aboveground carbon by ecosite
cluster. Error bars = 1SE
Figure 83. Total C (aboveground and SOC)
by ecosite cluster. Error bars = 1SE