An ecological land classification approach to ...

An ecological land classification approach to modeling the production of forest biomass by Bharat Pokharel1,2 and Jeffery P. Dech1

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ABSTRACT

Forest site classification is a prerequisite to successful integrated forest resources planning and management. Traditionally, site classification has emphasized a phytocentric approach, with tools such as the site index having a rich and long history in forest site evaluation. The concept of site index was primarily devised to assess site productivity of an even-aged, single-species stand. Site index has been the primary method of forest site evaluation in support of management for traditional forest products. However, this method of site classification has been criticized as the needs, perspectives and social values of the public regarding forest management have changed the emphasis from timber production to multiplevalue forestry practices. There are alternative approaches to forest site classification that have the potential to meet the growing demands placed on forest information for inventory and modeling purposes. Ecological Land Classification (ELC), is a phytogeocentric approach that stratifies the landscape into ecologically meaningful units (ecosites) based on substrate characteristics, moisture regime and canopy composition. This approach offers a more holistic view of site productivity evaluation; however, until recently it has been difficult to acquire data to support widespread mapping of ecosites. Remote sensing technology along with predictive modeling and interpretive mapping techniques make the application of an ecosite-based approach at the forest landscape level possible. As forest management moves towards the consideration of a broader set of resources (e.g., woody biomass), there is an opportunity to develop new tools for linking forest productivity to the sustainable production of forest bioproducts with forest ecosites as a solid foundation for segmenting the landscape. Key words: forest site classification, site index, site productivity, Ecological Land Classification (ELC), ecosites, forest biomass, bioproducts RÉSUMÉ

La classification des sites forestiers est un pré-requis au succès de la planification et de l’aménagement des ressources forestières. Habituellement, la classification des sites a mise en évidence une approche phytocentrique, comptant sur des outils comme l’indice de qualité de la station qui affiche un historique riche et durable de l’évaluation des sites forestiers. Le concept d’indice de la qualité de station a été établi principalement pour évaluer la productivité de la station dans le cas d’un peuplement équienne composé d’une seule espèce. L’indice de la qualité de station a été la principale méthode d’évaluation d’un site forestier utilisée dans le cas de l’aménagement pour les produits forestiers traditionnels. Cependant, cette méthode de classification du site a fait l’objet de critiques compte tenu que les besoins, les perspectives et les valeurs sociales du public relativement à l’aménagement forestier ont réorienté l’accent mis sur la production de bois vers des pratiques forestières considérant les multiples valeurs du milieu. Il existe des approches alternatives à la classification selon la qualité de la station qui pourraient répondre aux demandes croissantes d’informations forestières destinées à l’inventaire et à la modélisation. La classification écologique du territoire est une approche phyto-géocentrique qui stratifie le territoire en unités écologiques significatives (écosites) en fonction des caractéristiques du substrat, du régime hydrique et de la composition du couvert. Cette approche offre une vision plus holistique de l’évaluation de la productivité de la station; cependant, il a été difficile jusqu’à récemment d’acquérir les données permettant la cartographie généralisée des écosites. Les technologies de télédétection associées à la modélisation prédictive et aux techniques d’interprétation des cartes font en sorte que l’utilisation d’une approche basée sur les écosites soit possible au niveau du territoire forestier. Maintenant que l’aménagement forestier prend en considération un plus grand ensemble de ressources (par ex., la biomasse ligneuse), il devient possible de développer de nouveaux outils permettant de relier la productivité forestière à la production durable de bioproduits forestiers et aux écosites forestiers à partir d’une base fiable de segmentation du territoire. Mots clés : classification des sites forestiers, indice de qualité, productivité de la station, classification écologique du territoire, écosites, biomasse forestière, bioproduits

1Department

of Biology and Chemistry, Nipissing University, Box 5002, 100 College Drive, North Bay, Ontario P1B 8L7. E-mail: [email protected] 2Corresponding author.

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Bharat Pokharel

Jeffery P. Dech

Introduction The forest ecosystem management approach places a significant emphasis on the role of scientific research and effectiveness monitoring in support of decision-making processes (Grumbine 1994). Forest resource inventories and productivity models are particularly important, as they form the basis of forest management planning (Clutter et al. 1983, Avery and Burkhart 2002). Significant opportunities are emerging as the forest sector shifts towards a broader multi-resource paradigm, where forest bioproducts (commodities derived from forest biomass) are central (Wetzel et al. 2006). However, a significant barrier to the sustainable development of new forest bioproducts is the lack of an inventory system designed to accommodate these non-traditional resources (Wetzel et al. 2006). Forest resources inventories have undergone a recent period of significant change, adapting to a wider array of applications and users, and making use of new technological approaches. Many recent studies have focused on the expansion or enhancement of forest resource inventories through techniques such as individual tree classification (Gobakken and Næsset 2004, Hyyppä et al. 2004), analysis of airborne LiDAR (Light Detection And Ranging) data (Lefsky et al. 1999, Woods et al. 2009) and imputation of missing data through model-based approaches (Van Deusen 1997, Eskelson et al. 2009). In addition, better baseline data and new analysis techniques are facilitating the inclusion of ecological site classification data in forest inventories (MacMillan et al. 2007). This creates the opportunity to analyze and manage forests through stratification into ecologically relevant entities (e.g., ecosites) rather than coarse-scale polygons that may incorporate multiple site conditions (Grumbine 1994). An enhanced forest resource inventory that includes information on ecological site types facilitates a variety of complex management decisions including efficient land use allocation (Montigny and MacLean 2005), selection of ecologically suitable silvicultural approaches (Taylor et al. 2000, Kabrick et al. 2008) and restoration of degraded habitat as well as integrated landscape-level forest planning (Racey et al. 1996, Taylor et al. 2000). Site productivity is a key variable in many empirical models – e.g., Forest Vegetation Simulator (FVS) (Teck et al. 1996), Prognosis (Stage 1973), TWIGS (The Woodman’s Ideal Growth Projection System) (Miner et al. 1988) and Mixedwood Growth Model (MGM) (University of Alberta 2010). Furthermore, process or mechanistic models are built on the idea that variation in plant productivity can be explained based on the physiological response of organisms 24

to the environmental complex (Billings 1952, Landsberg 1986, Landsberg and Gower 1997, Landsberg and Waring 1997). Various aspects of environmental complexity can be captured through classification systems that define ecosites. Therefore, Ecological Land Classification (ELC) schemes provide the logical fundamental units upon which forest management activities should be examined. Relatively few studies have explored the use of ecological site classes as strata in forest growth models for Ontario; however, this approach has been demonstrated to explain a significant amount of variation in growth estimates (Groot and Saucier 2008, Pokharel and Froese 2009). The forest bioeconomy represents a significant opportunity for sustainable development, with over 500 different potential products having an estimated potential annual market value of $100 billion in Canada (Wetzel et al. 2006). Improved knowledge of landscape-level variation in forest productivity would be very relevant in the context of shifting from timber production to multiple use forestry. For example, the functional zoning approach, emphasizes stratifying the forest landscape to meet wood supply demands by matching management activities to site potential, while simultaneously allocating areas to the conservation of ecosystem services, biodiversity and recreational opportunities (e.g., Messier 2007, Messier et al. 2009). The foundation for such a system should include an accurate, landscape-level representation of forest productivity that is stratified into ecologically meaningful units (e.g., ecosites), which would provide the opportunity to allocate the area for each purpose so as to sustain and optimize each value. The current coincidence of rapid advancements in the forest resource inventory and land-cover analysis coupled with the emerging potential for the development of new forest bioproducts provides a critical opportunity to develop ecosite-based growth and yield models that will provide the decision support necessary to sustainably manage a broad range of forest values (Proe et al. 1997). The objectives of this review are; to compare and contrast the development and performance of tree (phytocentric) and site-based (geocentric) approaches used to evaluate forest productivity, and to examine the benefits, obstacles and limitations of implementing a new approach (phytogeocentric) to forest productivity modeling that is based on ecologically relevant base units (ecosite polygons) and applicable to the management of new forest bioproducts (e.g., woody biomass) in Ontario.

Defining Forest Site Productivity When discussing forest site productivity, the terms “site”, “site quality”, and “site productivity” have been used interchangeably. The Society of American Foresters (1971) defined site as “an area considered in terms of its environment that determines the type and quality of vegetation the area can carry”. Site is collectively characterized as an interaction of the environmental factors that exist in a given area, whereas site quality refers to the productivity potential of a given site. The latter term, “site productivity”, is the quantitative measure of site quality.

General Approaches to Forest Site Classification The methods used to assess forest site productivity can be broadly categorized into three approaches: phytocentric (Leary 1985), geocentric (Leary 1985), and phytogeocentric j an Vier /f éVr ier 2011, Vol . 87, n o 1 — Th e f or esTr y Ch r on iCl e


(Table 1). The phytocentric approach emphasizes the characteristics of the vegetation at a given site, whereas the geocentric approach focuses on the physical characteristics of climate, topography and soil (Hägglund 1981, Leary 1985, Vanclay 1994, Skovsgaard and Vanclay 2008). These approaches are also known as effects and causes respectively (Pokharel and Froese 2009). Soil and climate are the major causes of forest site productivity, and topography regulates their spatial and temporal distribution at the micro level. The site effect is expressed through the growth of the extant vegetation. The third approach, phytogeocentric, uses a complex interaction between biotic and abiotic factors that exist on a given forest site. Below, the development of these approaches is compared, analyzed and discussed briefly with reference to the Province of Ontario wherever possible. As the forest sector broadens to include new bioproducts (e.g., woody biomass) we suggest that a new ecosite-based approach to forest planning and management is needed.

The Phytocentric Approach The phytocentric approach uses a phytometer as a relative indicator of the productivity of a forest ecosystem. There are numerous factors that comprise the environmental complex that ultimately affects plant growth and development; therefore, integrating their combined effects by measuring attributes of the plants themselves is a very practical way to assess the site effect (Hills 1952, Davis et al. 2005). To date, the phytocentric approach has been widely used in quantifying forest productivity; despite the fact that it is restricted in use to an area with vegetation cover present. Site index (Frothingham 1921, Jones 1969, Carmean 1975, Hägglund 1981), site form (Vanclay and Henry 1988), site productivity index (Huang and Titus 1993), growth intercept (Bull 1931) and indicator species (Cajander 1926, Carmean 1975, Daniel et al. 1979) are methods that use characteristics of the existing vegetation as an indicator of site productivity.

Here we will focus our examination on site index, which is the most widely used phytocentric approach to site productivity assessment in the forest ecosystems of North America (Jones 1969, Carmean 1975, Hägglund 1981, Kayahara et al. 1998, Stearns-Smith 2001, Pokharel and Froese 2009). Site index is the mean height of dominant or co-dominant trees at a reference age. The main reasons for the advocacy of height as a measure of site productivity were due to its simplicity, ease of application, wide applicability, freedom from the effect of density and high correlation to volume yields (Mader 1963). Bruce (1926) and Reineke (1927) promoted site index while developing yield tables and now it is widely used for quantifying site productivity in forestry in North America (Carmean 1975, Spurr and Barnes 1980, Monserud 1984a, Kayahara et al. 1998, Stearns-Smith 2001) and Europe (Hägglund 1981). In Ontario, Plonski (1956, 1960) developed and used site index classes in order to develop yield tables for various boreal tree species. For the last few decades, a number of efforts including Heger (1968), Payandeh (1974a, b), Plonski (1974), Newnham (1988), Carmean and Lenthall (1989) Carmean and Li (1998), Carmean (1996), Goelz and Burk (1992), Payandeh and Wang (1994a, b; 1995), Woods and Miller (1996) and Carmean et al. (1998; 2001; 2006a, b) were made to develop site index curves for major tree species in Ontario. However, to date no complete site index equations have been developed for the major tree species in Ontario; therefore many of the species share site index equations developed elsewhere (Carmean et al. 1989, Alemdag 1991, Carmean 1996). Site index was developed for single-species, even-aged stands (Jones 1969, Monserud 1988) and has been used successfully to characterize productivity in these particular forest conditions (Carmean 1975, Tesch 1981), because height growth is very sensitive to site productivity (Hägglund 1981, Vanclay 1994). However, the use of site index has received

Table 1. A comparison of three different approaches to evaluating forest site productivity


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criticism as it is applied in mixed-species, uneven-aged stands (Monserud 1984b, Monserud 1988, Pokharel and Froese 2009). The concept of using the site tree is the central strength of site index, but the ambiguity associated with its selection and measurement has been widely criticized in the published literature (Monserud 1984b, Monserud 1988, The Technical Advisory Committee 1998, Sharma et al. 2002, Mailly et al. 2004, Pokharel and Froese 2009). Several ambiguities introduce bias into determining the site index of a particular stand. Sources of bias that can be introduced include substantial variation in the number and selection criteria of site trees used to estimate top height between organizations and regions (Sharma et al. 2002), sampling error variation due to differences in the plot size and site tree location (e.g., site trees in a plot vs. site trees selected adjacent to the plot; Hägglund 1981), and polymorphism in height growth curves that introduces errors in extrapolating the height at a reference age (Jones 1969). In addition to these sources of bias, there are also numerous limitations of site index. Site index is not applicable to uneven-aged or mixed-species forest stands or bare land. Site index is species-specific and cannot be used for other species even on the same site. Species-specific conversions have been developed, but these conversions further compounded the bias from site index estimates (Nigh 2002). For deliquescent trees, total height measurement is difficult because the tree top is mostly obscured in a closed canopy forest. Site index for a given site may not be constant over the long term; it could alter due to management activities and variations in climatic and environmental factors (Landsberg and Hingston 1996, Avery and Burkhart 2002). Furthermore, stand density is assumed to have no significant effect on height growth; however, height and diameter growth pattern may vary under different stand densities (Gevorkiantz and Scholz 1944, Hägglund 1981). As a result, despite common and wide use of site index, it is by no means a completely satisfactory measure of site productivity of forest ecosystems.

The Geocentric Approach The geocentric approach uses climatic and soil factors as predictors that primarily affect plant growth and development. As these factors can be spatially linked, potential site productivity for a given tree species can be predicted and mapped at the landscape level (Gustafson et al. 2003, Monserud and Huang 2003). Such flexibility makes the geocentric approach an attractive alternative over the phytocentric approach from the perspective of large-scale integrated planning and management of forest resources. Depending on the scale of management being considered, and the availability of different data layers, various spatial factors can be related to forest productivity on the landscape. Climate characteristics such as rainfall, temperature, radiation and wind are essential site factors (Hägglund 1981, Avery and Burkhart 2002). For instance, Paterson’s CVP (Climate, Vegetation and Productivity) index used climatic variables such as evapotranspiration, annual temperature range, mean annual precipitation, length of growing season and mean monthly temperature of the warmest month to predict the maximum growth potential of a site in terms of volume production (Johnston et al. 1967, Hägglund 1981). However,

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Paterson’s CVP index is a coarse level prediction of regional site productivity and does not provide sufficient information at the management scale (Johnston et al. 1967). In Ontario, McKenney and Pedlar (2003) successfully used soil and climatic factors to estimate site productivity for jack pine and black spruce. Monserud et al. (2006a) used climatic variables including growing degree days greater than 5°C to predict site productivity for lodgepole pine in Alberta, Canada. Similarly, Pokharel and Froese (2009) and Crookston et al. (2007) used climatic variables in basal area increment models for individual trees in Ontario and the western parts of the United States, respectively. Climate explains the regional variation in tree growth, but the climate effect is diluted at the stand level (Vanclay 1994). Soil characteristics such as moisture, texture, depth, nutrient availability and soil temperature have a significant influence on tree growth; however, this effect depends on the species and soil type (Husch et al. 1982). Due to this relationship between soil properties and tree growth, soil characteristics are considered to be important variables when evaluating site productivity at the stand level (Grigal 2009). Using soil characteristics in evaluating site has a number of advantages. The soil is relatively stable and changes slowly over time (Husch et al. 1982); therefore it is independent of the shortterm changes to forest stand characteristics (Avery and Burkhart 2002). Some soil characteristics, such as depth, colour, moisture, and texture, are easy to determine and quantify in the field, but others, such as nutrient status, are very difficult to quantify and vary over time (Avery and Burkhart 2002). It is expensive and time-consuming to directly quantify all of these factors for every polygon in a forest management unit; however, a classification system that incorporates these factors provides a useful framework for characterizing sites based on these attributes. Climatic variables including radiation, precipitation and temperature influence species composition and productivity (Stage and Salas 2007); however, their spatial and temporal effects are governed by the physical properties of the soils and topographic variation. Furthermore, soil water availability and solar radiation vary within a micro scale. For instance, topographic features are closely associated with soil depth, soil profile development, available soil moisture and nutrients, and microclimate. Therefore, topographic variables such as slope, aspect and elevation can be used as surrogate variables for solar radiation, precipitation and temperature (Beers et al. 1966, Stage 1976, Stage and Salas 2007). With improving computer technology this approach is more effective, as these topographic variables are easy to estimate and manipulate from the Digital Elevation Model (DEM). As a result, it is possible to predict site productivity potential at the landscape level.

Phytogeocentric Approach The phytogeocentric approach uses both causes and effects variables in a holistic manner in order to classify forest site productivity. Such an approach utilizes the totality of site that is governed by its biotic, climatic and soil conditions as related to its capacity to produce vegetation (Hills 1960, Spurr and Barnes 1980). As a result, both environmental and biotic factors, which account for the dynamic processes within a forest ecosystem, and change with environmental conditions and

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disturbance regimes over time, are included in the classification scheme. Habitat types in the United States (Burger and Kotar 2003), the biogeoclimatic approach used in British Columbia (Krajina 1969, Klinka et al. 1980, Pojar et al. 1987) and Ecological Land Classification (ELC; Racey et al. 1996, Chambers et al. 1997, Lee et al. 1998, Taylor et al. 2000, Ecological Land Classification Working Group 2009) used in Ontario are examples where the phytogeocentric approach could be used in site productivity evaluation. Forest site classification was initially influenced by the European approach of using vegetation composition as an indicator of site productivity. Cajander (1926) developed the first practical system for site classification of forest ecosystems, which identified qualitative classes of site into site types based on defined plant species assemblages for the boreal forest of Finland. In Ontario, the first comprehensive site classification work was lead by G.A. Hills (Hills 1952, 1953, 1960; Hills and Pierpoint 1960). Hills’ approach emphasized the interaction of physiography, macroclimate and vegetation to describe variation in forest ecosystems at the landscape level (Rowe 1962). Mapping entities based on observations from aerial photography was central to the method, and a hierarchy of ecological units was established from large site regions derived from broad macroclimatic and physiographic trends to more specific forest types based on physical and biotic site factors (Jones 1984, Sims and Uhlig 1992). During the 1970s, the availability of computing resources and the expansion of multivariate statistical techniques favoured initiating a more quantitative approach to the description and classification of vegetation patterns (Carleton and Maycock 1978, 1981). These new vegetation description and analysis techniques were ultimately used to develop standard forest ecosystem classification (FEC) systems in Ontario (Sims and Uhlig 1992). In Ontario, the FEC systems were developed on a regional basis (northwest, northeast, central and southern) with the objective of providing a standard quantitative method for classifying forest ecosystems to support silvicultural decisions in an operational setting (Jones 1984). Thus, the FEC systems were designed to make use of features that could be easily identified in the field to make decisions in a dichotomous key regarding ecosite classification at the stand level (Sims and Uhlig 1992). The FEC systems were largely a field tool that made use of indicator species to inform silviculture; however, large-scale mapping of ecosites was made challenging by subtle topographic differences in regions such as the clay belt, and a general lack of soil maps at a scale appropriate for delineating ecosites (Jones 1984). While the FEC systems provided information in support of a variety of management tasks (e.g., prescribed burning, vegetation management, harvest operations, productivity assessment, habitat description); there remained a need to integrate the regional FEC systems into one tool to support modeling and assessment activities (Sims and Uhlig 1992). From 2005 to 2009, a new Ecological Land Classification (ELC) system was developed for the Province of Ontario (Ecological Land Classification Working Group 2009). This new system integrates the regional FEC systems into a single entity, is compatible with national ELC systems, and has expanded to include the entire landbase rather than just forested ecosystems (Ecological Land Classification Working


Group 2009). Ecosites in the ELC system remain a polygonlevel attribute representing combinations of substrate and vegetation types; however, the structure of the system is based on substrate characteristics and canopy composition. This makes the ELC system more suitable for interpretive mapping, and inclusion with the Forest Resource Inventory (Ecological Land Classification Working Group 2009). The large number of potential ecosites for a given region of the province (e.g., 224 in the Boreal Forest Region) provides a precise tool for stratifying forest ecosystems into units that are based on substrate features that directly influence productivity. Furthermore, the ELC system is compatible with interpretive or predictive mapping approaches, which makes this tool a very attractive option for linking inventory and modeling tasks to ecologically relevant units.

Ecosite Mapping and the Link to Landscape-Level Productivity Analysis Geographic Information Systems (GIS), high-resolution remote sensing data and the development of a wide variety of statistical techniques have rapidly expanded the use of predictive ecological mapping in biodiversity and conservation applications worldwide (Guisan and Zimmermann 2000). In forestry applications, these techniques have been developed and applied with success in British Columbia, where ecological site types in the Biogeoclimatic Ecosystem Classification (BEC) have been predicted using knowledge-based routines for automated polygon extraction and classification (MacMillan et al. 2007). These approaches have been reported to provide accurate and cost-effective tools for stratifying forest landscapes (MacMillan et al. 2007). Furthermore, these approaches have been transferred to Ontario, where knowledge-based methods were used to develop a predictive ecosystem map for the Romeo Malette Forest unit near Timmins, Ontario (Silvatech Group 2006). Predictive ecological mapping techniques may provide one practical option for stratifying the forest landscape into useful units for growth and yield modeling. At the forest inventory level, the Province of Ontario has recently revised the specifications of the Forest Resource Inventory (FRI), and included in these revisions is a change to polygons interpreted at the ecosite level (OMNR 2009). Thus, forest units with inventories developed under these specifications will have a population of polygons interpreted to represent a specific, identified ecosite. This will provide a common foundation for many forest management applications, including modeling forest productivity with respect to woody biomass.

Operational Use of Site Productivity Evaluation Approaches Ecological Land Classification systems provide the fundamental units upon which the forested landscape can be stratified, and are critical to support management and planning activities (Kimmins 1991), particularly with respect to new forest resources such as biomass and bioenergy (Proe et al. 1997). Building up inventory attributes and models tied to ecological units that are derived from knowledge of substratevegetation interactions offers several advantages that should be considered as we move towards multi-resource forest management and consider sustainable approaches to the harvest of new resources such as forest biomass.

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Resolution, repeatability and generality play key roles in the operational use of forest site productivity models. Site index has been perceived as a simple approach localized at the stand level for a specific tree species, which has less generality as the concept is devised for single-species, even-aged forest stands. Furthermore, ambiguity in selecting site trees and polymorphism in height growth limit the repeatability of site index across regions. In contrast, ELC ecosites are developed based on a complex interaction among ecological drivers, such as substrate, moisture regime, and existing vegetation structure and composition. The ecosite approach can be generalized across the landscape that consists of similar climatic and biophysical conditions, and is part of a hierarchical system that provides a structure for doing so (i.e., scaling up to the ecoregion). Ecosites are standard units with accepted definitions and classification schemes, which will be the basis of the future forest resource inventory. Therefore, ecosite-based models have a spatial scope of potentially many thousands of hectares. Accurate estimates of forest productivity also depend on the inclusion of driving variables in the site classification approach. For instance, in a forested ecosystem some species such as black spruce, which has wide ecological amplitude, could have a similar site index value for the upland and lowland sites, even though they are two distinct ecosystem types due to their different stand development, history and density. Under such circumstances, the ecosite-based productivity mapping approach accounts for ecological variation, and hence could be a useful tool for integrated forest management planning. Forest ecosystems are dynamic and can be frequently changed by stand- or landscape-level disturbances (e.g., wind throw, fire). Ecosite identifications are designed to be stable for 20 to 40 years (Ecological Land Classification Working Group 2009) and provide an opportunity to include recently disturbed sites or young stands in the same framework as mature forests by including sites that lack vegetation, or are developing a seedling/sapling layer. The potential to include more of the landbase in a given planning or management exercise by using ecosites as a basis for productivity modeling is promising. The forest resource inventory supports a variety of resource management and land-use applications, and the demands for this information to support new and unforeseen needs are increasing. These different demands require a common framework to facilitate a more holistic approach to forest resource management. For example, decisions about silviculture, allocation of forest area for harvests, design of conservation areas and parks, preservation of critical habitats and populations, establishment of recreational areas and identification of new bioproducts opportunities could all be related to ecosites. Thus, we suggest that productivity models, especially with respect to forest biomass and other new resources, should be built into this common framework. Ultimately, the usefulness of a model as a decision support tool is largely dependent on its end users. A number of factors, including the cost of acquiring data play a key role in making the model a useful operational tool. Users always conduct the cost–benefit analysis before using a model or tool to meet their specific objectives. Very useful models can remain in the research stream, because operational users lack

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the input data to run the model. Since, Ontario’s next cycle of the Forest Resource Inventory (FRI) has specified stands (polygons) will be delineated based on ecosite (OMNR 2009), inclusion of ecosites as a stratum in productivity models ensures that results could be applied at the landscape level through the FRI, and use of new models should be feasible with minimal cost and planning. Such provision makes the ecosite-based productivity and, subsequently, resources assessment approach operationally feasible in the Province of Ontario.

An Ecological Land Classification Approach to Modeling Forest Biomass in Ontario Forest biomass has been perceived as a viable feedstock for the large-scale production of biofuels, which has an advantageous carbon balance compared to agricultural sources (Righelato and Spracklen 2007). Given the potential importance of forest biomass as part of a renewable energy feedstock that could soon be under rapidly increasing demand, it is critical that consideration be given to the sustainability of this resource, and potential impacts on productivity and biodiversity (Tilman et al. 2009). Ecological site classification provides the most important planning tool (Kimmins 1991) in forest ecosystem management, and in order to identify a benchmark of sustainable biomass production for forests in Ontario. It seems logical that productivity models should be constructed from ecosites as base units. Biomass initiatives in the United Kingdom also have taken this approach (Proe et al. 1997). Driving variables for ecosite, such as moisture regime and substrate type, are often used to predict productivity at the stand level (Merchant et al. 1989, Buse and LeBlanc 1990). Coupling the site productivity prediction approach with predictive ecosystem mapping (PEM) allows us to generate a productivity layer at the landscape level. Either using the productivity index or integrating ecosite as a simulation unit in forest growth and yield simulation models such as the Forest Vegetation Simulator (FVS), would allow us to generate growth and yield projections over many thousands of hectares. For instance, inclusion of ecosite in a basal area increment model in Ontario explains over 2% to 14% of unexplained variability on individual tree growth (Pokharel and Froese 2009). Furthermore, Groot and Saucier (2008) determined that significant differences existed among ecosites in gross volume increment and volume increment efficiency of black spruce in northern Ontario. Given the potential productivity differences across a range of ecosites, simulating growth and yield at the tree and polygon levels over the period of a planning horizon would inform a resource manager making complex decisions such as choosing a harvesting cycle for biomass from a given ecosite. Ecosite mapping as a tool for site productivity evaluation provides an ecological basis for forest management, and allows us to optimize benefits from the forest without compromising its other values (e.g., biodiversity, wildlife habitat). FRI-based interpretation or predictive ecological mapping approaches would support a stratification of the landscape by ecosite, which could be supplemented by continuous data layers in a GIS database (e.g., slope, aspect, canopy height models). This initial layer could then be used as the basis for productivity modeling following either a regression tree (De’ath



and Fabricius 2000) or model simulation-based (Pokharel and Froese 2009) approach, and one could predict standing crop or growth rate expressed as biomass at the tree level and scale these values up to the polygon. Each of these approaches would provide advantages for forest management and research applications. Regression tree analysis is a technique that has been recently used for species habitat modeling in ecology (De’ath and Fabricius 2000, Guisan and Zimmermann 2000), and site productivity mapping in forestry (McKenney and Pedlar 2003). Generally, a regression tree is developed on a binary partitioning algorithm that successively splits a dataset into mutually exclusive homogeneous groups based on values of a response variable (De’ath 2002, Lepš and Šmilauer 2003, Faraway 2006). For each split, the algorithm considers each explanatory variable and selects the one that leads to the greatest reduction in the residual sum of squares for the response variable (McKenney and Pedlar 2003). The explanatory variables entered into the model could be both categorical and/or continuous; however, the categorical variable with K levels has 2K-1 – 1 possible splits. The tree is built minimizing the sum of squares within the newly created groups and the groups are identified by a mean value of the continuous response variable (De’ath and Fabricius 2000). Such a process can result in a large tree, which needs to be pruned in order to select only those nodes with explanatory variables that result in the greatest reduction in residual sum of the squares. Regression tree analysis offers many advantages for modeling continuous response variables at the landscape level, including the ability to handle a broad range of data conditions (e.g., categorical, continuous, non-linear, missing values), simple graphical representation of trees depicting hierarchical relationship between the response and covariates, and ease of transfer from tree-based decision rules to GIS platforms (De’ath and Fabricius 2000). We suggest that a regression tree approach would be highly applicable to the modeling of forest biomass (standing crop or growth rates) where an ecosite-based inventory and supplementary landscape data (e.g., soil texture, moisture regime, basal area, crown cover, quadratic mean diameter, stand dominant height and management history) are available. This approach would utilize ecosite and stand attributes as explanatory variables in a decision tree represented graphically while partitioning the forest unit and estimating biomass at the tree and polygon level. New forest inventories in Ontario will delineate the forest landscape based on ecosite, and supplementary attributes can be derived from the inventory, LiDAR data (Lefsky et al. 1999, Woods et al. 2009) or other sources of land cover information. Decision support tools developed based on a regression tree approach may facilitate the expansion of the capabilities of the FRI to predict new products such as forest biomass across the landscape. Such a decision support tool could be easily linked to other data sets in a GIS platform, which suggests operational users could adopt it with little need for new software or training. In addition to regression trees, the model simulationbased approach envisions a modeling framework that utilizes ecosite site as a dummy variable in its growth models. For instance, at the individual tree level, ecosite accounts for variation in tree growth due to site productivity (Pokharel and Froese 2009). We are interested in estimating biomass and its


change over time at the polygon level. As discussed in Monserud et al. (2006b) biomass can be predicted by tree component as a function of individual tree dimensions such as diameter and height. To predict the change in biomass, it is necessary to update individual tree attributes such as diameter and height periodically, i.e., every five or 10 years. Each individual tree should be updated periodically based on its competition status, growing stage and site productivity of the forest stand. Competition status and growing stage can be estimated from any inventory data, whereas FRI-based ecosite can be utilized to account for the productivity potential of a given forest stand. The use of ecosite as a dummy variable offers a capability of projecting change in individual tree dimensions and subsequently forecasting the biomass at a given polygon over time. Using an ecosite-based forest resource inventory as a starting point, one can map biomass (standing crop) and its change (growth) spatially across the forest landscape.

Future Research Initiatives Pokharel and Froese (2009) used ecosite as one of the variables in their basal area increment modeling for selected tree species in Ontario. This approach needs to be replicated for the major tree species of Ontario, and updated using the new ELC system that is under development for the entire province. As polygons are generated and identified with ecosite attributes through predictive or interpretive techniques, a strong link is formed between the applicability of predicted outcomes of the models and the initial conditions they are representing, which ultimately allows for the creation of a productivity map and subsequently resource availability maps across the landscape. Growth and yield simulation models (e.g., FVS) are able to project change in resource availability over time, and the results of these simulations could also be presented at an ecosite (polygon) level. Whereas techniques are developing for the prediction or interpretation of ecosite distributions over the forest landscape, these approaches use a variety of environmental variables, expert knowledge and modeling philosophies. As different methods are applied and evaluated, there must be vigorous testing of the accuracy of ecosite map products. The variation in approaches could compound uncertainties in predictive ecosite mapping, and it will be necessary to develop a standard (e.g., FRI polygons) for use with particular models. The predictive accuracy of PEM or interpreted ecosites must be evaluated, so that management decisions made based on ecosite mapping are unbiased.

Conclusions Forest biomass holds great potential as an alternative, renewable energy source in Canada (Wetzel et al. 2006) and around the world (IPCC 2007). Given the significant interest in developing forest bioenergy resources, concerns regarding the long-term sustainable production of feedstocks, including forest biomass, have emerged. Ecological land classification systems provide a common basis for examining multiple forest values (including woody biomass production), and can be mapped at the landscape level through predictive or interpretation techniques. In the near future, all forest units in Ontario will be segmented by ecosite as the next cycle of the Forest Resource Inventory is completed (OMNR 2009).

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Developing productivity models that stratify the forest according to ecosites would make them directly transferable to the landscape (forest unit) level, and the data required to run such models could be derived from the spatial layers of the Forest Resource Inventory. Furthermore, the use of ecosites as building blocks for productivity analysis would provide a link to other key forest attributes (e.g., critical wildlife habitat, non-timber forest products), and support an ecosystem management approach considering multiple forest values. Refining models to the ecosite level could also expand the function of forest growth simulation models (e.g., FVS), in which ecosite-based polygons could become the units within which projections of biomass standing crop and accumulation are predicted in order to meet provincial or national level renewable energy goals as well as support the existing forest resource industries. There is a significant opportunity as the forest resource paradigms and their associated management tools enter a period of considerable change. However, there is great need for future research to develop ecositebased productivity and simulation models, and assess their model prediction uncertainties and limitations. Ecosites represent a promising tool for stratifying forest landscapes in support of ecosystem management decisions, which will be particularly important as the resources and values society expects to be managed sustainably are growing to include novel bioproducts such as woody biomass from forests.

Acknowledgements This work was supported by a Post-Doctoral Fellowship (BP) from the Ontario Ministry of Research and Innovation and a Forest Bioproducts Research Chair (JPD) from a partnership of the Northern Ontario Heritage Fund Corporation (NOHFC), FedNor, Tembec, the Forestry Research Partnership and Nipissing University.

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