Introduction

In natural and human-dominated environments, the interplay between biotic and abiotic forces creates spatial heterogeneity. This heterogeneity generally takes the form of a mosaic composed of patches, each defined by specific relationships between vegetation and environmental conditions (Jenny 1958; Whittaker 1967; White 1979; Urban et al. 1987; Legendre and Fortin 1989; Wu and Loucks 1995; Grossman et al. 1999; Wagner and Fortin 2005). The patches, as represented on a forest map delimiting numerous stands, can be analyzed to define the ecological forces (e.g., latitudinal gradient) structuring the heterogeneity of an area (beta diversity) (Hills 1960; Damman 1979; Zonneveld 1989; Gerardin and Ducruc 1990). Patches can be positioned on an ordination diagram along ecological gradients (continuum concept) and then grouped to form homogeneous vegetation landscapes (community concept) (Whittaker 1967; Ahti et al. 1968; Rowe and Sheard 1981; Zonneveld 1989; Borcard and Legendre 1994; McGarrigal and Cushman 2005). A homogeneous landscape is a portion of land with specific vegetation, physical environment, climate, and disturbance characteristics (both natural and human) (Rowe 1962; Daubenmire 1968; Dufrêne and Legendre 1991). From the macro to the micro scale, a hierarchy of increasingly homogeneous landscapes can be defined (Bailey 1987; Urban et al. 1987; Allen and Hoekstra 1992; Klijn and de Haes 1994; White and Jentsch 2001). At the macro scale, climate is the main driver explaining vegetation distribution (Damman 1979; Bailey 1983; Pojar et al. 1987; Allen and Hoekstra 1992; Payette 1992; Bailey 2009). Bioclimatic zones are at this level (Halliday 1937; Hare 1950; Saucier et al. 2009). At the meso scale, climate, physical features, and disturbances are considered. Finally, at the micro scale, the scale of a field observer, microclimate, physical environment, and disturbances are important variables (Bailey 1987; Lertzman and Fall 1998).

Understanding the causes of spatial heterogeneity and how they vary with scale is a central theme in landscape ecology (Turner 1989; Turner et al. 1993; White et al. 1999). Statistical methods have been developed to test hypotheses about beta diversity (Borcard et al. 1992; Legendre et al. 2005; Peres-Neto et al. 2006; Tuomisto and Ruokolainen 2006). Ecological land classification (ELC), a field related to landscape and numerical ecology, aims to divide large territories into relatively homogeneous landscapes at different levels of observation, and to characterize the contribution of various sets of explanatory variables to vegetation variation. Knowledge of these patterns and processes, and the ecological gradients creating them, is important for ecosystem-based management and biodiversity conservation (Bailey 1983; Gauthier et al. 2001; Legendre et al. 2005; Bailey 2009). Historically, ELC has adopted two main approaches. The first approach is based on vegetation–climate relationships and the second on relationships between vegetation, climate, and the physical environment (Bailey et al. 1985).

The first approach was mainly used from the beginning of the twentieth century to the 1970s. The aim was to identify vegetation changes on mesic soils along ecological gradients (latitudinal, longitudinal, altitudinal) and to associate these changes to macroclimatic variations (temperature and precipitation). Changes in regional (or zonal) vegetation and climate along gradients justified the delineation of bioclimatic units on a subcontinental scale (Halliday 1937; Hustich 1949; Hare 1950; Dansereau 1957; Rey 1960; Küchler 1964; Grandtner 1966; Ahti et al. 1968; Rowe 1972).

The second approach evolved with the influence of geomorphology and a desire to understand ecosystem relationships at a finer scale to help guide resource management decision making. Using ground plots, aerial photographs and satellite images, the physical environment (edaphic-topographic criteria) and its relationship with vegetation and climate were analyzed to delineate biogeoclimatic units. These studies generally covered smaller areas (meso scale) than the vegetation-climate approach (macro scale). From this perspective, within homogeneous ecological units, similar site conditions are expected to support the same type of plant community, including the assemblage of tree species. Together, Betula papyrifera, Populus tremuloides, Abies balsamea, and Picea glauca constitute an example of an assemblage existing on sites with specific combinations of physical features, microclimate, and disturbances. The early-successional stage is dominated by the light-demanding species (e.g., B. papyrifera) of this assemblage, which are progressively replaced in late-successional stages by shade-tolerant species (e.g., A. balsamea), as time since the last disturbance increases. Landscapes with high fire frequencies (e.g., <150 years) are characterized by a large proportion of area occupied by early-successional species (Bergeron et al. 2001; Gauthier et al. 2001; Couillard et al. 2012). This description of forest dynamics corresponds to the notions of potential vegetation (Küchler 1964; Powell 2000; Saucier et al. 2009), habitat type (Daubenmire 1968), site type (Hills 1960; Pojar et al. 1987), and série de végétation (vegetation series) (Rey 1960; Grandtner 1966).

Landscapes can be viewed as a continuum of potential vegetation assemblages along a site gradient (toposequence). Climate–site–vegetation relationships, climatic and edaphic climaxes, site potential and toposequence are key words within this second approach. (Hills 1960; Rowe 1962; Whittaker 1967; Jurdant et al. 1977; Bailey 1983, 2009). The concept of permanence comes into play in this approach, which doesn’t explicitly deal with disturbances.

Although they had received some attention since the beginning of the twentieth century (Clements 1910), natural disturbances have been studied more intensively since 1970 (Heinselman 1973; Payette 1992; Turner et al. 1993; Bergeron et al. 2001). Emphasis has been placed on characterizing the spatial and temporal variability of disturbance regimes (e.g., fire frequency). These studies have demonstrated that forest landscapes are more complex and diversified than originally estimated. Dynamic equilibrium, landscape heterogeneity, and hierarchical patch dynamics are the main concepts structuring these analyses (White 1979; McCune and Allen 1985; Wu and Loucks 1995; Cleland et al. 1997; White et al. 1999; Powell 2000). ELCs were adapted to acknowledge this new approach, which assumes that vegetation is controlled mainly by climate and natural disturbances (Omi et al. 1979; Cissel et al. 1999). From this perspective emerged a landscape classification based on natural disturbance characteristics (fire) in connection with more or less stable vegetation (equilibrium). This approach was mostly applied to larger vegetation landscapes, but could also be considered at a finer scale to the dynamics of a site (Turner et al. 1993; Lertzman and Fall 1998; White et al. 1999).

More recently, human-caused disturbances have been identified as an important driver of landscape dynamics. Anthropogenic activities have altered landscape age class distribution by provoking the loss of old forests (Boucher et al. 2009; Cyr et al. 2009), and have homogenized forest composition by substantially increasing the frequency of early-successional stands on various sites along the toposequence. The relationship between vegetation and the physical environment then becomes much more diffuse than in natural environments (Carleton and MacLellan 1994; Östlund et al. 1997; Lorimer 2001; Schulte et al. 2007; Laquerre et al. 2009). However, ELCs have not yet incorporated human disturbances and are therefore missing an important driver of landscape change.

The first goal of this research is to classify the heterogeneity of the study area within a hierarchy of relatively homogeneous landscapes, on the basis of vegetation and four sets of explanatory variables (climate, physical environment, natural and human disturbances). The second goal is to quantify the proportion of vegetation variation explained by each set and their combinations for some levels of observation in the hierarchical classification.

Methods

Study area

The study region (175,000 km2) belongs to the circumboreal zone, and forms an important part of two bioclimatic domains: the A. balsamea–B. papyrifera in the south, and further north, the Picea mariana-feathermoss domain (Rowe 1972; Saucier et al. 2009) (Fig. 1). Six of the boreal zone’s most common tree species are well represented. Three are shade-tolerant species: Picea mariana (Mill.) BSP., A. balsamea (L.) Mill., and Picea glauca (Moench) Voss. Three are light-demanding species: Pinus banksiana Lamb., B. papyrifera Marsh., and P. tremuloides Michx. The proportions of these species change along ecological gradients describing the climate, physical environment, and natural and human disturbances. These gradients are used to define and describe homogeneous landscapes. Human disturbances are included in the explanatory variables because some portions of the study area have been affected by anthropogenic activities for almost 100 years.

Fig. 1
figure 1

Location of the study area (outlined in red) according to the ecological land classification of the Ministère des ressources naturelles du Québec (Saucier et al. 2009)

Full size image

Sources of information

This study draws on two main contemporary sources of information: forest inventory plots and geospatial databases derived from forest maps. These databases have been developed from Spatial Information on Forest composition based on Tessera (SIFORT). Forest maps from 1970 to 1980 make up the SIFORT-1 database, and forest maps from 1980 to 1990, the SIFORT-2 database. These information sources have been used to characterize 606 ecological districts. The average area of the districts is 200 km2. Each ecological district corresponds to a uniform area described by a specific pattern of surficial deposits, topography, and regional vegetation (Robitaille and Saucier 1998), and was characterized by vegetation (Y-matrix) and explanatory variables (X-matrix).

The Y-matrix was constructed using three complementary vegetation themes, each corresponding to a different aspect or organization level of the vegetation: tree species (e.g., the abundance of Pinus banksiana as a species in an ecological district), forest types (e.g., the abundance of forest stands dominated by Pinus banksiana), and the combination of potential vegetation types and successional stages (e.g., Re2-S2: Picea mariana potential vegetation-Re2 in the early stage of succession-S2) (Appendix 1 in Supplementary Material). The description of tree species and potential vegetation-successional stages is based on forest inventory plots (1970–2000). The SIFORT-2 database was used to describe the forest types.

The X-matrix contains the description of the 606 ecological districts in relation to four sets of explanatory variables (Appendix 2 in Supplementary Material). Climatic variables were estimated using data recorded at meteorological stations and extrapolated to each ecological district by the BioSIM simulator (Régnière 1996). The physical environment was characterized according to the Ministère des Ressources naturelles (MRN) database describing each ecological district, including the relative proportion of area for each surficial deposit type and physiographic variable (Robitaille and Saucier 1998). Natural disturbances were described in terms of the recent history of fires, insect outbreaks, and windthrows over the last 100–150 years. Forest maps (SIFORT-2) were used to evaluate the areas affected by light and severe epidemics, windthrows, and fires. Forest inventory plots (n = 53,635) were used to characterize fire history relative to periods of origin (e.g., 1901–1930). In this article, each of these periods is named according to a year close to its central year (e.g., the 1901–1930 period is referred to as the period centered on 1921). The number of years of infestation by spruce budworm and the frequency of natural fires per 100 km2 from 1938 to 1998 were derived from MRN archives. Human disturbances were described by the relative proportions of agriculture, fallow land, logging, and human-induced fires (from 1938 to 1998). These variables were obtained from forest maps (SIFORT-1 and 2).

Data analysis (Fig. 2)

Unconstrained analysis

An unconstrained analysis involving vegetation alone was run to illustrate ELCs produced by pioneers in this field (Halliday 1937; Dansereau 1957; Küchler 1964; Rowe 1972). This analysis was performed using the R statistical language (R Development Core Team 2010, version 2.11.1). A K-means clustering of the 606 districts was computed for the three vegetation themes (Legendre and Legendre 2012). Prior to clustering, the variables were subjected to a Hellinger transformation to give less weight to abundant tree species and preserve an ecologically meaningful distance among sites (Legendre and Gallagher 2001).

Fig. 2
figure 2

Method used a to define the ecological land classification of homogeneous vegetation landscapes (ELCH) and b to quantify the proportion of vegetation variation explained by each set of explanatory variables and by their combinations

Full size image

Constrained analysis, delineation of homogeneous landscapes, and variation partitioning of the vegetation

The ecological land classification of homogeneous vegetation landscapes (ELCH) was developed using a redundancy analysis (RDA) involving a Y-matrix formed by all vegetation themes (tree species, forest types, potential vegetation-successional stages) and constrained by an X-matrix composed of four sets of explanatory variables (climate, natural disturbances, physical environment, and human disturbances). All canonical axes resulting from the RDA were submitted to K-means clustering in order to form groups of ecological districts. K-means clustering allows the formation of two or more groups of ecological districts. While K-means results can be completely hierarchical (smaller groups nested in larger ones), the method does not guarantee this outcome (Legendre and Legendre 2012). This gradual stratification was used to develop a hierarchy of homogeneous landscapes formed of three levels of observation. The landscapes were positioned on an ordination diagram according to the centroid of each landscape, as calculated using the canonical scores of each ecological district. Ordination axis 1 was positioned vertically and axis 2 horizontally, because this configuration represents north–south and east–west gradients in their usual orientation in the study area. The homogeneous landscapes were also characterized using histograms showing the relative proportions of period of origin and disturbance type (fires or insect outbreaks) noted in each forest inventory plot. Variation partitioning of the vegetation was used to quantify the contribution of each set of explanatory variables to vegetation changes along the levels of observation considered in this study (Borcard et al. 1992; Legendre et al. 2005; Peres-Neto et al. 2006; Tuomisto and Ruokolainen 2006). Variation partitioning was performed following the steps proposed by Borcard et al. (2011).

Results

An overview of the vegetation of the study area is used as an introduction to the ELCH. Other descriptions in line with approaches presented in the introduction are described in Appendix 3 in Supplementary Material.

The vegetation of the study area

Nine classes of vegetation, combining dominant tree species, forest types, and potential vegetation-successional stages, describe the vegetation of the area. The following description is restricted to tree species because this theme provides a good overview of the vegetation heterogeneity (Fig. 3). Eastern units V-1, V-2, and V-3 are characterized by an abundance of A. balsamea, B. papyrifera, and Picea mariana. Towards the north (V-2, V-3), Picea mariana becomes more abundant than A. balsamea and B. papyrifera. In the central-eastern unit (V-3), early-successional species (P. tremuloides and Pinus banksiana) are abundant. In the north-east, V-4 is dominated by Picea mariana and A. balsamea, the latter mainly confined to hills. Pinus banksiana is scattered and concentrated on sandy deposits. In the central unit (V-5), Picea mariana and Pinus banksiana are abundant. A composition similar to V-3 is found in the southern and central-western units (V-6 and V-7), but A. balsamea is less abundant than in the east (V-1, V-2, V-3). In the north-west, V-8 shows similarities to V-4 in terms of abundance of Picea mariana. Finally, the most northwestern unit V-9 consists mainly of non-forested peatlands and Picea mariana on organic deposits.

Fig. 3
figure 3

Description of the study area according to vegetation (V). The homogeneous landscapes defined in Fig. 7a are outlined in black

Full size image

Ecological land classification of homogeneous vegetation landscapes (ELCH)

RDA and mapping of the scores of the first four canonical axes

The ELCH is based on a RDA, and mainly on scores of the first 4 canonical axes. These canonical axes are closely related to ecological gradients describing the heterogeneity of the study area (Fig. 2). These first 4 canonical axes of the RDA explain 37 % of the vegetation’s variability. The first canonical axis (Fig. 4a) reflects the changes in vegetation and explanatory variables occurring from south to north in the study area. For example, changes along the latitudinal gradient are primarily related to the decrease in the annual number of growing degree-days and forest stands affected by the last spruce budworm outbreak (Sbom) (Fig. 5a). The second canonical axis (Fig. 4b) is described by three longitudinal bands mostly reflecting natural disturbances (Fig. 5b). The central band is dominated by relatively young stands (1921f). It differs, on one hand, from the southern portion, which is well populated with A. balsamea (AbbaS) whose dynamics are related to Sbom and, on the other hand, from the northern part, where old stands (PimaF, Pima-AbbaF) from fires of the period centered on 1851 (before 1870) are well represented. In the central band, the abundance of sandy deposits favors the presence of young forests often dominated by Pinus banksiana.

Fig. 4
figure 4

Maps of the scores of the first 4 canonical axes of the ecological districts of the RDA (Fig. 2). The darker the gray, the higher the positive scores. The variation explained by each canonical axis is indicated in brackets. The homogeneous landscapes defined in Fig. 7a are outlined in black

Full size image
Fig. 5
figure 5

Vegetation and explanatory variables related to the first 4 canonical axes of the redundancy analysis (RDA, Fig. 2). A Variables with the highest positive scores on the canonical axis. B Variables with the highest negative scores on the canonical axis. Codes and description of variables are presented in Table 2. On the maps, the darker the gray, the greater the proportion of the variable

Full size image

The third canonical axis (Fig. 4c) characterizes changes that occur from south-east to north-west. This latitudinal-oblique gradient is strongly linked to changes in the physical environment (Ele variable, Fig. 5c), particularly the transition from a hilly (south-east) to relatively flat topography (north-west). These changes are accompanied by an increase in wetlands (D_7). The fourth canonical axis (Fig. 4d) primarily defines the impact of human activities (Fig. 5d). From approximately 1880 to 1940, land clearing for agricultural settlement had a widespread impact on both the southeastern and southwestern sectors. Beginning in 1905, coal-fired steam locomotives were used in the southern part of the territory to link the agroforestry regions of Abitibi and Lac Saint-Jean (Fig. 1). These activities contributed to numerous human-induced fires (Hf1, Fig. 5, Hardy and Seguin 1984) and promoted changes in both age structure and forest composition. These changes consist mostly in the expansion of P. tremuloides (PotrF) and the presence of many stands originating from the period centered on 1951 (after 1930). During the second half of the twentieth century, mechanized logging spread throughout the A. balsamea–B. papyrifera domain, and gradually towards the north into the Picea mariana–feathermoss domain.

K-means cluster analysis and grouping of ecological districts

A K-means clustering applied to the scores of the canonical axes relative to the ecological districts (Fig. 2) allows the formation of three groupings of ecological districts (Fig. 6). In the first grouping (Fig. 6a), ecological districts strongly associated with the A. balsamea–B. papyrifera domain are distinguished from those belonging to the Picea mariana-feathermoss domain. In the second grouping (Fig. 6b), the Picea mariana-feathermoss domain is split to highlight the wide central band described by the RDA (previous section). In the third grouping (Fig. 6c), the northern part of this last domain (pink) is characterized according to the proportion of wetlands and related attributes (group 5 vs. group 6). The central band (green, Fig. 6b) is divided into an eastern (group 3, Fig. 6c) and a western subsection (group 4). A. balsamea is more abundant and relief is well defined (hilly) in the eastern subsection (Fig. 5). The southern portion (blue) is described relative to the effects of Sboms (group 1) and human disturbances (group 2).

Fig. 6
figure 6

Gradual segmentation of the study area based on a K-means clustering (Fig. 2). Codes and description of forest types are presented in Table 2. The homogeneous landscapes defined in Fig. 7a are outlined in black

Full size image

Ecological land classification of homogeneous vegetation landscapes (ELCH)

The development of the ELCH is based on ecological gradients described in the two previous sections. The sequence of analyses highlights four results of particular importance in the delineation and description of the ELCH: the third grouping of ecological districts (Fig. 6c), the delineation of the homogeneous landscapes (Fig. 7a), the ordination diagram showing the position of homogeneous landscapes along ecological gradients (Fig. 7c), and the period-disturbance histograms (Fig. 7c). The ELCH is hierarchical with three levels of observation (Fig. 7). The first level (n = 2, Fig. 7b, c) is the most general and highlights the major territory subdivisions corresponding to bioclimatic domains. The second level (n = 4) distinguishes homogeneous landscapes characterizing the southern and the northern portions of the two bioclimatic domains. The third level is composed of nine elements of landscape classification and 14 geographically distinct homogeneous landscapes. Some of the 14 landscapes are dominated by young forests originating from fires dating back to the period centered on 1921 (222, 221, 24, 131). In others, located in the northern part, forest landscapes are older (period centered on 1851: 132, 231, 232, 25). In the southern part, landscapes are under the influence of insect outbreaks and fires (121, 123). In the two southern extremities, landscapes are strongly affected by human activities (21-pe, 14-pe, 11-pe, 122-pe) and P. tremuloides expansion (Fig. 7; Table 1, Appendices 4 to 7 in Supplementary Material). Homogeneous landscape 122-pe was not classified as a managed landscape by the numerical analysis (Figs. 7a, 6c, dark blue-color). Considering the importance of the human activities, we decided to classify this landscape with those affected by human activities (Table 1; Fig. 5d).

Fig. 7
figure 7

Ecological land classification of homogeneous vegetation landscapes (ELCH). a Map of homogeneous vegetation landscapes. b Description of homogeneous landscapes at three levels of observation. c Homogeneous landscapes positioned on an ordination diagram (ecological gradients) and described on disturbance histograms. The ordination diagram is related to the RDA (redundancy analysis) of two matrices: Y-vegetation and X-explanatory variables (Fig. 2)

Full size image
Table 1 Synthetic description of homogeneous landscapes
Full size table
Table 2 Codes and description of variables used to describe the homogeneous landscapes
Full size table

The transition from 9 to 14 landscapes of level III is mainly justified by disjunctions in geographic distribution (Fig. 7a, b). For example, landscape 12 is divided into three landscapes 121, 122, and 123-pe (pe: Populus expansion), each occupying a specific portion of the study area. Some landscapes are also distinguished in reference to the geographical units delineated on Fig. 6a and b. For example, landscapes 131 and 132 (Fig. 7a), which forms a large unit in Fig. 6c (pale green), is separated into two landscapes in Fig. 6a and b. Landscape 131 is placed high along axis 1 of the ordination diagram (Fig. 7c), revealing its affinities with the Picea mariana-feathermoss domain. Considering the hilly topography, the relative abundance of A. balsamea and its grouping with homogeneous landscape 132 in some analyses (Fig. 3), we classified homogeneous landscape 131 within the northern portion of the A. balsamea–B. papyrifera domain.

Proportion of variation explained by sets of variables along spatial levels of observation (variation partitioning of vegetation)

After presenting the ELCH, we are now interested in quantifying the relative importance of the four sets of explanatory variables in explaining the vegetation heterogeneity. To achieve this goal, we used the partitioning of vegetation throughout the study area as well as in three portions of it (Fig. 8). The analyses reveal that the explained vegetation variation is always greater than the unexplained portion. This suggests that the heterogeneity of the study area, its beta diversity, is structured along ecological gradients. This structure indicates that portions of the territory are different from others, allowing the delineation of homogeneous landscapes.

Fig. 8
figure 8

Relative proportion of vegetation variation (%) explained by each of the four sets of explanatory variables (climate [C], natural disturbances [ND], physical environment [PE], and human disturbances [HD]) in relation to the entire area and three of its portions. The variation explained by the four sets in each portion is indicated in brackets. The double common variation by a set is the sum of the double fractions containing this set (e.g., double common fraction of the set C = PE∩C + C∩ND + C∩HD). The triple common variation by a set is the sum of the triple fractions containing this set (e.g., triple common fraction of the set C = PE∩C∩ND + PE∩HD∩C + HD∩ND∩C) (Fig. 2)

Full size image

The total proportion of variation attributed to natural disturbances is relatively high, regardless of geographical entity (Fig. 8, NDt). The unique fractions of variation (e.g., NDu) explained by each set of explanatory variables are generally small, except for natural disturbances. This indicates that some changes in natural disturbances are relatively independent of changes in other sets, especially physical environment. The three large latitudinal bands presented previously (southern, central, northern) and characterizing the natural disturbances are the main examples (Figs. 4b, 6b).

The common fractions of variation explained by the sets of explanatory variables are generally high, especially for natural disturbances. The common variation of natural disturbances is mainly attributed to triple and quadruple combinations. This result confirms that changes in vegetation are closely related to changes in natural disturbances, in combination with other sets. The variation explained by climate (unique and double combinations) is higher for the entire area than the two bioclimatic domains. The impact of human disturbances is generally low, except in the western portion (mainly the Abitibi region), where a high proportion of variation is explained by this set of variables, in combination with the three others (quadruple combination). This indicates that changes in vegetation are closely integrated or dependent on changes in all of the sets, from southern to northern Abitibi (Fig. 3, Appendix 7 in Supplementary Material).

Discussion

The ELCH supplements ELCs based solely on vegetation

The ELCH is strongly influenced by the pioneers and others authors interested by landscape ecology (e.g., Rowe 1972). The similarity between the vegetation map of the study area (Fig. 3) and that of the homogeneous landscapes (Fig. 7) reveals that vegetation alone is a faithful indicator, a phytometer, of explanatory variables at the meso scale (Halliday 1937; Hills 1960; Damman 1964; Barnes et al. 1982). However, the ELCH better describes landscapes patterns than ELCs based on vegetation and climate (e.g., Halliday 1937) or using vegetation, climate, and the physical environment (e.g., Hills 1960) (Fig. 9).

Fig. 9
figure 9

Conceptual model comparing: 1- the traditional ELC (ecological land classification) approach based on climate (C) and physical environment (PE). Natural and human disturbances are considered a posteriori, 2- the proposed ELCH (ELC of homogeneous vegetation landscapes). In this last approach, all the sets of variables are considered a priori, with special emphasis on natural (ND) and human disturbances (HD)

Full size image

The ELCH considers natural disturbances and other sets of natural explanatory variables

The usefulness of the ELCH lies in its a priori inclusion of natural disturbances and other natural sets of explanatory variables (climate, physical environment). We have shown that natural disturbances are the predominant set explaining vegetation variation (the sum of unique and common variations). The unique variation explained by natural disturbances is considered to be part of the total variation independent of the other sets of explanatory variables. This unique variation might also be defined as the expression of the dominance of natural disturbances over the other sets. This concurs with authors who consider natural disturbances as overlaying other environmental gradients (Heinselman 1973; White 1987; Payette 1992). In addition, this study has shown that natural disturbances vary regionally (White 1987; Mansuy et al. 2010). Some homogeneous landscapes are dominated by young forests, others are older, some are under the influence of insect outbreaks and fires, while the rest are strongly affected by human activities. These landscape types (younger vs. older), based on natural disturbances criteria, are described by specific age class distributions and are in line with those proposed by Turner et al. (1993) and discussed by Lertzman and Fall (1998) and White et al. (1999). Using the vocabulary of these authors, some landscapes of the study area are more stable than others, or are closer to their equilibrium stage.

Although natural disturbances are the main factor, the variation in vegetation explained by this set in combination with others is larger than the unique variation. Consequently, the control of the vegetation by environmental variables is mainly related to the integration of several sets. This paradigm of multiple factors controlling landscape heterogeneity follows numerous authors (Jenny 1958; White 1979, 1987), including those interested by vegetation variation partitioning (Borcard et al. 1992 and following). The integration of multiple factors is maintained for the entire territory and parts of it, all considered at the meso scale (Damman 1979; Bailey 1987; Lertzman and Fall 1998). At this scale, climate is never the dominant set of variables (Ohmann and Spies 1998). We attribute this result to small changes in temperature and precipitation along the latitudinal gradient of the study area. Two bioclimatic domains are present but the same six main species are still present in the landscapes. Climate rather becomes the main driver of landscape heterogeneity in larger territories (macro scale), such as bioclimatic zones (Damman 1979; Bailey 1983; Allen and Hoekstra 1992; Payette 1992; Wu and Loucks 1995; Grondin et al. 2007).

The ELCH considers human disturbances

The ELCH is also novel by its a priori inclusion of human disturbances. We have demonstrated that human activities do not play a major role in explaining the vegetation variation when the entire territory is considered. However, in the southern two ends which are closest to human settlement, four homogeneous landscapes show a strong anthropogenic influence. These four landscapes are well integrated into the sequence of canonical axes formed by the RDA (Fig. 2) and take their place after the sets of natural variables (Figs. 4d, 6c). These results concur with authors who consider human disturbances as an important factor in natural landscapes transformation, and one of the major issues in the context of ecosystem-based management implementation (Urban et al. 1987; White and Mladenoff 1994; Schulte et al. 2007; Boucher et al. 2009). In Abitibi (western portion of the study area), where human activities have had the greatest impact, two homogeneous landscapes (21-pe and 221) are superimposed on a uniform area with respect to the physical environment (mesic clay deposit), climate, and natural disturbances (abundance of fires centered on the period 1921). This indicates that anthropogenic disturbances can generate specific homogeneous landscapes. On mesic clay deposits, the expansion of P. tremuloides and A. balsamea could eventually continue northward, under the influence of logging, to the northern natural limit of P. tremuloides. The abundance of these two species and the consistent decrease of Picea mariana in mixed stands of the Picea mariana-feathermoss domain, favoured by intensive management practices and, possibly, climatic change, could contribute to the northward expansion of the A. balsamea–B. papyrifera domain into the northern boreal forest currently dominated by Picea mariana (Grondin and Cimon 2003; Laquerre et al. 2009; Arbour and Bergeron 2011).

Conclusion

This study builds on the research of authors who have described ecological gradients and used them to define an ELC. Our approach, the ELCH, is original in including, a priori, landscape disturbance patterns (natural and human) as sets of variables. A sequence of numerical analyses (RDA, K-means clustering, variation partitioning) has been used to describe the ecological gradients and define an ELC. We have shown that it is possible to elaborate on an ELC by integrating the main factors structuring landscape heterogeneity, even in areas with great variability of natural disturbances and a strong local influence of human disturbances. Landscape spatial heterogeneity of the study area, considered at the meso scale, is mainly explained by natural disturbances in synchronicity and overlapping with changes in the physical environment, climate, and human activities. Our integrative and quantitative approach of ecological gradients enhance and perhaps slightly modifies our perception and understanding of factors causing landscape heterogeneity in the circumboreal forest zone. This study could not have been carried out without the large databases available at the MRN. More detailed data, especially with respect to natural disturbances (fire origin maps), could lead to slightly different and more accurate results. Other numerical analyses could also be tried (e.g., fuzzy clustering). This first ELCH should be considered as a point of reference to define and compare natural and managed landscapes, to initiate more detailed studies on forest dynamics (e.g., natural variability of homogeneous landscapes), and to estimate the effects of climate changes on vegetation.