Diversity Patterns of Understory Woody Species in the Forests of Belasitsa Mountains, Bulgaria

Project

State and prospects of the Castanea sativa population in Belasitsa mountain:

climate change adaptation; maintenance of biodiversity and sustainable

ecosystem management.

Funded by: EEA and Norway Grants.

Beneficiary: Forest Research Institute at the Bulgarian Academy of Sciences

Report

Diversity patterns of understory woody species in the forests of Belasitsa mountain,

Bulgaria

2011

University of Twente

Satya Prakash Negi

DIVERSITY PATTERNS OF UNDERSTORY WOODY SPECIES IN THE FORESTS OF BELASITSA MOUNTAINS, BULGARIA

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ABSTRACT

Biodiversity is distributed heterogeneously across this planet. At regional and landscape level too, this striking variability of diversity patterns in plants has been well recognised. The dominant role of climate and its interactions with other environmental factors that create ‘microclimate’ in the area, and its effect on spatial distribution and composition of species has been acknowledged. Detailed knowledge of species’ ecological and geographic distributions is crucial to understand the two most important questions that are keys to biodiversity conservation and forecasting viz. ‘where the species occur’ and ‘why the species occur there’? Understory vegetation signifies the major component of temperate forest diversity. This study attempts to comprehend the diversity patterns of understory woody species, and to identify roles of chosen predictor variables in characterising this diversity in the forests of Belasitsa Mountain, Bulgaria. Remote sensing data helps in rapid assessment and monitoring of biodiversity. The ground based measure of understory woody species diversity has been computed using Shannon diversity index. This diversity index has been related with the remote sensing data. Normalised difference vegetation index (NDVI) and the land cover map of the study area were analysed to predict the habitats of higher diversity. Although ‘NDVI-productivity’ relationship appears to be consistent over most ecosystems, but there is no relationship between NDVI and understory woody species diversity in the present study. Rather, it is the forest type’s map that helps to identify the areas of higher diversity as tree species compositions are considered as biodiversity indicators. Higher diversity is found at mid-altitudinal zone which is a mixed forest zone dominated by chestnut and oak. The linear regression analyses to identify the roles of chosen predictor variables in different forests types show that altitude and overstory diversity have the significant effect on the diversity of understory woody species when whole forest of the study area is considered as one group. But in beech forest, it is the overstory diversity and live-crown ratio; while in mixed forest overstory diversity is the only factor that has significant effect on distribution of understory woody species diversity. Overall, it is the overstory diversity that is the common influencing factor in determining understory woody species diversity in the study area. Key words: Diversity pattern, microclimate, NDVI, overstory diversity, Shannon diversity index, understory diversity, understory woody species, woody species.


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1. INTRODUCTION

1.1. Background Biological diversity or biodiversity - the diversity of genes, species and ecosystems, is distributed heterogeneously across this planet. Some areas abound with high variation of life while others are low, and most falls somewhere in between (Gaston, 2000). Unravelling the mystery of these differences in diversity across space and time has long been a central focus for ecologists and bio-geographers (Whittaker, et al., 2005). It has long been recognised that the most perceptible spatial pattern of diversity is a latitudinal gradient of decreasing diversity with increasing latitude, i.e. from the equator to the poles (Gaston, 2000; Willig, 2003); which is the most universal bio-geographic pattern and is consistent for a wide spectrum of taxa in aquatic as well as terrestrial ecosystems (Barthlott, et al., 2007; Kier, 2005; Mutke & Barthlott, 2005; Rahbek, 1995). The importance of climate and its interactions with other environmental factors (Hogarth, et al., 1926), and the dominant role of climatic factors to explain plant distribution at global level was well recognised in the early 19th

century by von Humboldt and Bonpland (Huston, 1994).

At regional and landscape level too, the striking variability of plant diversity patterns has been well recognised. Ecologists have identified the factors controlling the distribution, abundance, and diversity of species in ecological communities; but the associated factors differ among localities resulting in variation in vegetation (Ohmann & Spies, 1998). The effect of altitude (elevation) is very well recognised (Lomolino, 2001), as there is a correspondence between the physical conditions and natural communities in increasing latitudinal gradient and increasing altitudinal gradient (Huston, 1994). According to Huston (1994) ‘a general rule of thumb for air temperature is that an increase in elevation of 1000 meters results in a decrease in temperature (6°C) equivalent to that associated with an increase in latitude corresponding to a linear distance of 500 to 750 Kilometres’. The altitudinal decline in diversity has often been considered as a miniature version of the latitudinal trend of declining diversity towards the colder climates (Adams, 2009; Brown & Lomolino, 1998; Lomolino, 2001). Generally, air temperature and atmospheric pressure decrease; while precipitation, radiation and wind velocity increase with altitude (Tranquillini, 1964). Rahbek (1995) after reviewing 97 research papers (with 163 examples) observed that species richness declines with elevation but decline is not necessarily monotic; the most observed pattern of diversity is a ‘mid-altitudinal bulge’. The comparatively low diversity at lower altitudinal level is due to higher anthropogenic disturbance; whereas low diversity at higher elevation is prominently due to the reduction of temperature with elevation and corresponding reduction in productivity (Rahbek, 1995). The atmospheric changes along the altitude create a ‘microclimate’ in the area that affects the composition and distribution of species is the principal reason why the vegetation in the mountains are often segmented into altitudinal levels or zones (Tranquillini, 1964). Other factors of vegetation community structure are interactions among species, historical processes such as dispersal and speciation, migration and extinction; but all these factors differ among localities (Ohmann & Spies, 1998). Understory vegetation represents the largest component of temperate forest diversity and provides important indications of site quality, overstory regeneration patterns and conservation status (Hutchinson, 1999). More importantly, understory vegetation is the critical habitat for wildlife species that too constitute a significant component of biological diversity (MacArthur & MacArthur, 1961). In forested ecosystems, composition and diversity are related to the environmental variables, which include availability of moisture, nutrients, and light; which are in turn related to topography, bedrock geology, soil characteristics, overstory structure, and land use history (Hutchinson, 1999). These environmental variables have been critical throughout human history, providing meaningful indicators for habitat


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selection (Chen, et al., 1999). Shirley (1929, 1945) in his seminal studies stressed microclimate as a determinant of ecological patterns in both plant and animal communities; and Geiger (2003) conceptualised ‘microclimate’ as the suite of climatic conditions measured in localised areas near the earth’s surface. Each component of the microclimatic environment differs uniquely across space and time in forested ecosystem (Chen, et al., 1999); and the importance of understory microclimate for production in the overstory canopy, for the distribution of understory species, and for the maintenance of belowground process is well documented (Geiger, et al., 2003). In forested landscape, the microclimate within each patch is distinctive (Chen, et al., 1999) that determines habitat heterogeneity resulting in distribution of species within patches. Habitat heterogeneity in a forested ecosystem resulting from topographic differences e.g. altitude, slope aspect and slope gradient are important factors in determining the non-uniform distribution of understory species (Huebner, et al., 1995). Altitudinal gradients are correlated with understory species composition (Small & McCarthy, 2003; Sternberg & Shoshany, 2001) and diversity (Hutchinson, 1999; Pausas & Carreras, 1995). Other topographic factors like slope aspect and slope gradient across a landscape contributes to habitat heterogeneity by manipulating microclimate resulting in non-uniform and non-random distribution of understory species (Huebner, et al., 1995). The angle of slope determines soil-forming processes as instability of steep slopes prevents the formation and accumulation of organic top-soils (Holten, 1998). Previous studies have shown that aspect, through its impact on microclimate, influences the spatial distribution of vascular plants (Badano, et al., 2005; Bale, et al., 1998; Bennie, et al., 2006; Cantlon, 1953; Olivero & Hix, 1998; Sternberg & Shoshany, 2001). In general, it has been observed that north facing slopes differ from adjoining south facing slopes in soil and air temperature, soil and atmospheric moisture, light intensity and wind velocity (Cantlon, 1953). In the northern hemisphere, north-facing slopes are moister and colder because they receive less solar radiation than similar south facing slopes (Holland & Steyn, 1975). Composition and diversity of understory depends on characteristics of the overstory vegetation (Bergstedt & Milberg, 2001; 2001; McKenzie & Halpern, 1999), as there are interactions between overstory and understory vegetation (Bergstedt & Milberg, 2001; Carleton & Maycock, 1980; Légaré, et al., 2001; Légaré, et al., 2002). Trees as ‘structural’ species create a physical structure of the environment and produce variability in microclimatic conditions; and in general create the habitat used by many others; generally smaller ‘interstitial’ species e.g. understory vegetation (Huston, 1994). Understory vegetation is influenced by overstory composition and structure through modifications of resource availability like light, moisture and soil nutrients and other effects, such as physical characteristics of the litter layer (Barbier, et al., 2008). Temperature and light are among the most important climatic variables that are controlled by the tree stratum (Frelich, et al., 2003; Nygaard & Ødegaard, 1999). Air temperature in the understory is also dependent on canopy structure, mainly canopy density (Sharpe, 1996). Since temperature and humidity regime variations can be expected to be correlated with light regimes, understory light can be used as a single synthetic factor grouping less apparent microclimatic variations (Barbier, et al., 2008). Brosofske, et al.(2001) found after analysing the relationship between understory vegetation and various site factors that canopy cover was the dominant site variable influencing diversity. Therefore, changes in the overstory structure are likely to change the understory vegetation, because the understory is more limited by light availability (Barbier, et al., 2008; Hart & Chen, 2006) when overstory canopy is closed leading to reduced understory diversity (Hutchinson, 1999). Brunet (1996) found that total species richness of the understory vegetation increased after canopy thinning in beech and oak forests. This seems to be a general pattern in temperate hardwood forests (Brunet, et al., 1996) as species numbers rose rapidly after group felling compared to the undisturbed woodland (Kirby, 1990). More light on the forest floor is favourable to more light demanding species (Oijen, et al., 2005). However, understory species vary in their optimal light


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requirement, or heliophilia (Barbier, et al., 2008) and plants are heliophilous, semi-heliophilous, and shade-tolerant according to their photosynthetic requirement of light. Variations in overstory structure and canopy density have been observed among tree species (Porté, et al., 2004), and diversity of understory depends on the composition of the overstory tree species as well (Barbier, et al., 2008; Beatty, 1984; Oijen, et al., 2005). Forest types influence understory composition by controlling ecosystem processes such as light transmittance and nutrient cycling (Légaré, et al., 2001). Therefore, tree species composition and diversity are considered as biodiversity indicator (MCPFE, 2003). Barbier et al. (2008) found after reviewing 200 articles that understory diversity is most influenced by dominant tree species (which occupied more than 70–80% of total cover). Their review paper further revealed that diversity is higher under deciduous trees than under coniferous trees; and Quercus species harbour more understory diversity than Fagus species. Therefore, topographic factors like altitude, slope aspect, and slope gradient ; and overstory characteristics like overstory composition and diversity, and overstory density play major role in creating ‘microclimate’ at local and landscape level, thereby influencing the composition and diversity of understory vegetation. Understanding the distribution of species is a fundamental need for ecology, conservation biology, and resource management at all scales (Stoms & Estes, 1993). Detailed knowledge of species’ ecological and geographical distributions is the fundamental ingredient for conservation and forecasting (Elith, et al., 2006). Stoms and Estes (1993) further say ‘before we can understand why species occur where they do and how their distributions are affected by global change, we must first know where they are’. Biodiversity data are commonly gathered in the field, and ground-based data have more accuracy (Aynekulu, et al., 2008); but they are not complete enough and difficult to carry out over a wider area on a multi-temporal basis (Aynekulu, et al., 2008; Duro, et al., 2007). Considering the accelerating rate of biodiversity loss (IUCN, 2010), rapid assessment and monitoring of biodiversity is critical for conservation planning (Bawa, et al., 2002; Duro, et al., 2007). In recent decades, potential application of remote sensing imagery in rapid assessment and monitoring of biodiversity has been recognized (Bawa, et al., 2002; Nagendra, 2001; Stoms & Estes, 1993) as remote sensing imagery can cover large areas in a timely, systematic and repeatable manner (Duro, et al., 2007; Kerr & Ostrovsky, 2003; Nagendra, 2001; W. Turner, et al., 2003). Despite its immense potential application, remote sensing data has not been used much in the studies on plant diversity (Trisurat, et al., 2000). This is less so in case of understory vegetation, despite the fact that the importance of the understory in forest ecosystems and potential adequacy of remote sensing tools for its characterization are recognised, there are not many studies on this topic (Caetano, et al., 1998). Although remote sensing generally cannot provide information at the species level, its use in biodiversity studies at regional and landscape level is based on the premise that relationships exist between the structure of the landscape and diversity of the species present within them (Fairbanks & McGwire, 2004). According to the productivity hypothesis, areas of higher net primary productivity (NPP) will tend to have higher species diversity (Hawkins, et al., 2003; Waide, et al., 1999). Possible information on plant diversity from remotely sensed data has been extracted in variety of ways. Normalised difference vegetation index (NDVI), which is the measure of greenness, has been related to NPP at broad scales (Box, et al., 1989; Prince, 1991). A direct correlation between productivity and species richness is expected as areas of high NPP have more resources to partition among competing species, thereby supporting a higher number of species and larger populations than areas with low NPP (Walker, et al., 1992). The sequence of relationships from NDVI to NPP and NPP to species richness has lead researchers to investigate the relationship between NDVI and ground-based measures of overall species richness (Bonn, et al., 2004; Jørgensen & Nøhr, 1996; Oindo & Skidmore, 2002; Running, et al., 2004; Skidmore, et al., 2003) and found


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positive correlation between NDVI and ground-based measurement of species diversity in most of studies. Mapping of the species richness and distribution is an important aspect of conservation and planning (Miller, 1994), as they help us to identify the areas of biodiversity importance where conservation resources should be focussed (Cardillo, et al., 1999). However, generally, producing such maps by conventional field methods, especially for large areas, is logistically impossible. Remotely sensed data holds incredible opportunity for mapping species habitats as species are closely linked to the distribution of highly diverse habitats(Miller, 1994); thereby providing us an indicator of species diversity (Oindo, 2001). Therefore, it is important to be able to predict species diversity and distribution based on mappable surrogate variables (Cardillo, et al., 1999). Since dominant trees species (forest types) influences understory diversity (Barbier, et al., 2008), and tree species composition are considered as biodiversity indicator (MCPFE, 2003); therefore, mapping of forest types helps us in predicting species diversity and distribution in them. Thus, the identification of forest types in turn helps in protecting the habitats of higher diversity. Management of natural resources requires measurement of species so that quantitative values can be ascribed to them and these values can be compared (Groombridge, 1992) across space and time. Diversity is a complex variable difficult to measure and express in a simple manner (Duro, et al., 2007). Probably due to the fact that the living world is most widely considered in terms of species, biodiversity is very commonly used as a synonym of species diversity (Groombridge, 1992) and species diversity is the most prominent and readily recognizable form of biodiversity (Myers, et al., 2000). Therefore, the basic unit of measurement for the vast majority of studies is conducted at the species level (Duro, et al., 2007). Plants, as primary producers are essential for virtually all forms of life and hence floral diversity is the basis for overall biodiversity. Therefore, in this study also, the focus is plant species, rather than populations or other taxa. Diversity is often described and generally understood mainly in two components viz. ‘species richness’ and ‘species evenness’ (Krebs, 1989; Magurran, 1988). Species richness is the number of species in a sample and species evenness is how equally abundant the species are (Huston, 1994; Krebs, 1989; Magurran, 1988). In other words, ‘evenness’ is the relative abundance of species in a sample. High evenness, which occurs when species are equal or virtually equal in abundance, is conventionally equated with high diversity (Huston, 1994; Krebs, 1989; Magurran, 1988). Studies on species diversity are often limited to species richness, but there is much to the interest of the ecologists in the relative abundance of species as well; because no community consists of species of equal abundance (Magurran, 1988). When information on commonness and rarity are included by calculating one or more indices that combine the measures of number of species in a sample together with the relative abundance of those species, then they are termed as ‘heterogeneity indices’ (Peet, 1974). For many ecologists the term ‘heterogeneity’ is synonymous with ‘diversity’ (Hurlbert, 1971). This type of diversity measure has enjoyed a great deal of popularity in recent decades (Magurran, 1988).

1.2. Problem statement Loss of biodiversity is one of the world’s most pressing crises and the main drivers of this loss are conversion of natural areas into farming and urban development, introducing invasive alien species, polluting or over-exploiting resources including water and soils, and harvesting wild plants and animals at unsustainable levels (IUCN, 2010). IUCN (2010) further estimates that the species extinction rates are 1000 to 10,000 times higher than the ‘background’ or natural extinction rates. Bulgaria is no exception, as the country is facing an array of threats of biodiversity loss, affecting all ecosystems from high mountain


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forests and lakes to the open waters and the benthic communities of the black sea (Bojinov, 1998). The importance of diversity in maintaining ecosystem functioning is well recognised as biological richness insures against system failure (Haddad, et al., 2011; Körner, 2002; Till-Bottraud & Gaudel, 2002) Need for comprehensive information of diversity pattern at landscape level Since the pattern of distribution varies among localities (Ohmann & Spies, 1998), comprehensive information about the diversity pattern at local and landscape level is important. As it is not always possible to extend protection to all areas, the ‘hotspots’ (degree of diversity) are often used to prioritize conservation sites (Myers, et al., 2000). Bulgarian forests, which cover about 33 % of the country’s land area, play an important role in biodiversity conservation, as they are home to populations of 43 of world’s endangered species (Lovera, 2008). The old growths of natural and semi-natural forests in Europe are the most valuable forest types in terms of storing biodiversity, and the largest area of old growth forests in the European Union are found in Romania and Bulgaria (EEA, 2010a). Fet & Popov (2007) quote Griffiths et al. (2004) in the introduction of their book Biogeography and Ecology of Bulgaria, ‘ The Balkans offer great potential at European scale for conserving the last untouched wilderness on the continent----’, and ‘---- we are far from achieving the goal of understanding pattern and process in Balkan biodiversity’. The study area, Belasitsa Mountain is a mountain landscape and the dominant forest types along the altitudinal gradient are oak (Quercus petraea Matt.) forest, the sweet chestnut (Castanea sativa Mill.) forest, and the beech (Fagus sylvatica L.) forest (Raev & Dimitrov, 2005). Moreover, Belasitsa mountain harbours large areas of natural sweet chestnut forests in Bulgaria (Dimitrova, et al., 2007) which is the only native species of the genus in Europe (Bratanova-Dancheva, et al., 2004) . Also eight Bulgarian endemic vascular plants occur in the area (Peev & Delcheva, 2007). Habitats of sweet chestnut forests are in the list of habitats of special European interest and find a place in the list of Natura 2000 as per European Union’s ‘The Habitats Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and fauna’(Bratanova-Dancheva, et al., 2004). However, floristic diversity, composition and structure of chestnut forest vegetation have not been well studied (Dimitrova, et al., 2007), and need further investigation. Therefore, comprehensive information of diversity patterns of understory woody plant species in forests of Belasitsa Mountain can be helpful in implementing adequate conservation measures of the habitats of higher diversity, allowing for the efficient use of limited time and financial resources. Combining the influences of topographic & overstory factors Environment can be a limiting factor in many aspects of ecosystem functions, especially in alpine and mountain ecosystems where a ‘slight change in topography, meteorology, or vegetation cover can shift the controlling balance of environmental variables; and interactions of these variables along altitudinal gradients generate a highly heterogeneous environment’ (Jennifer, 1998). The relationships between different factors and understory composition have been examined individually in temperate forests (Légaré, et al., 2002; Turkington, et al., 2002), but few efforts have been made to investigate into the distribution pattern of understory woody plant species considering both along an altitudinal gradient and overstory structural factors (McKenzie & Halpern, 1999). This study combines the influence of overstory composition and diversity, and topographic factors in investigating the diversity pattern of understory woody species. Studying both species ‘richness’ and ‘evenness’


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As most of the studies on diversity are restricted to species richness (Magurran, 1988), species evenness is equally important to quantify relatively uncommon species (Krebs, 1989; Magurran, 1988; Peet, 1974). This study has dealt with both the richness and evenness so that both the aspects of commonness and rarity are taken into account. Therefore, Shannon diversity index (Shannon, 1948) that considers both richness and evenness, and which is one of the most commonly used and acceptable indicators of biodiversity (Kent & Coker, 1994; Magurran, 1988; Mouillot & Lepretre, 1999) has been used in analyses of species diversity in this study. Application of RS and GIS tools in predicting understory woody species diversity pattern Application of remote sensing data has not been used much in the studies on plant diversity (Trisurat, et al., 2000), and less so in the study of understory vegetation (Caetano, et al., 1998). As per productivity hypothesis, areas of higher NPP will be likely to have higher diversity of species within them (Hawkins, et al., 2003; Waide, et al., 1999). Since NDVI has been related to NPP at broad scale (Prince, 1991), a direct correlation between productivity and species richness is predictable as areas of high NPP have more resources to partition among competing species, thereby supporting a higher number of species (Walker, et al., 1992). Therefore, this study has attempted to investigate the relationships between satellite-based measures of vegetation productivity i.e. NDVI and ground-based measure of understory species diversity. Another potential application of remotely sensed (RS) data and geographic information systems (GIS) is mapping the habitats (forest types) of biodiversity rich areas. Since identifying habitats of higher diversity are important for the protection of species diversity (Debinski, et al., 1999); landscape level habitat analysis using RS and GIS generated map has the potential to aid in explaining species diversity patterns (M. Turner, 1989). It is, therefore, important to carry out this type of studies in Belasitsa Mountain, as these mountain areas are at the heart of Europe’s remaining wilderness areas (EEA, 2010b; Veen, et al., 2010).

1.3. Objectives of the study

1.3.1. General objective

The overall objective of study is to investigate the diversity patterns of understory woody plant species in relation to topographic and overstory factors along an altitude gradient; and to analyse how remote sensing and geographic information systems (RS & GIS) tools be used for predicting these diversity patterns of understory woody plant species in the forests of Belasitsa Mountain.

1.3.2. Specific objectives

1. To analyse the spatial diversity patterns of understory woody species in the forests of Belasitsa Mountain,

2. To investigate how topographic and overstory factors influence diversity patterns of understory woody species, and

3. To analyse the relationship between NDVI and diversity patterns of understory woody species.


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1.4. Research questions

1. What are the spatial diversity patterns of understory woody species in the forests of Belasitsa Mountain?

2. How do topographic & overstory factors influence the diversity patterns of understory woody species? and

3. What is the relationship between NDVI and diversity patterns of understory woody species?

1.5. Research hypotheses

Hypothesis – 1: H1

-> There is a unimodal (hump-shaped) diversity pattern of understory woody species in the forests of Belasitsa Mountain,

Hypothesis – 2: H1

-> The influences of each chosen topographic and overstory variable on understory woody species will differ in importance, with overstory diversity expected to be the most important variable in predicting understory woody species diversity,

Hypothesis – 3: H1 -> There is a positive relationship between NDVI and understory woody species diversity.


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2. MATERIALS AND METHODS

2.1. Study Area

2.1.1. Location The study area Belasitsa Mountain is situated in the south-western part of Bulgaria (Figure 2:1) between the valley of the river Strumeshnitsa and the Syarsko field. The state boundary between the Republic of Bulgaria and Republic of Greece runs along the central ridge up to Kongura peak (1951.3 m asl) then goes down in north-eastern direction to an altitude of 1662.6 m asl along one of the smaller ridges. The Radomir peak (2029 m asl) is the highest peak of Belasitsa mountain (Raev, et al., 2005)

Figure 2:1-Map of study area

2.1.2. Geomorphology & Soil The Belasitsa Mountain is formed as a result of orographic processes that have emerged during the Palaeozoic age. The oldest and most common rocks in the region are gneisses, which occupy about 88.5 % of the territory. At certain isolated locations one may come across spots of granite, serpentine, and amphibolites. The soil types demonstrate clear belts depending on the altitude: Chromic Luvisols are common in the mountain skirts and the low parts of mountain; and Dystric-Eutric Cambisols - in the higher parts (Raev, et al., 2005).

2.1.3. Climate The climate in the region is continental-Mediterranean and is characterised by mild even warm winters, however with frequent and often ample rainfalls and hot and dry summers. The mean annual air temperature is within the range of 12.5 - 14.0°C. The mean January temperature varies from 1.0 °C in the


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low parts to – 6.0 °C in the high mountain areas. The mean maximum July temperature is respectively 21.0°C and the minimum is 11.0°C. The average annual precipitation total is 676 mm, varying depending on the altitude from 600 – 700 mm in the low parts of mountain to about 900 mm in the higher parts (Raev, et al., 2005).

2.1.4. Major Forest Types According to the geobotanical subdivision of Bulgaria, the Belasitsa Mountain belongs to the European broadleaved district, the Macedonian-Thracian province, the Belasitsa district (Raev, et al., 2005). The vegetation types found in Belasitsa Mountain are the nemoral type (broadleaved deciduous forests of Central European types) and the Mediterranean type (Raev & Dimitrov, 2005). The most typical representatives of nemoral vegetation types are: Fagus sylvatica L. (European beech), Castanea sativa Mill. (Sweet chestnut), and Quercus petraea (Matt) Liebel. (Durmast/Sessile oak). Beech is the most widespread formation in the Belasitsa Mountains and occupies the higher altitudinal zone. Ranking the second in terms of distribution is the chestnut formation and occupies the altitudinal zone below beech formation. The Belasitsa Mountains harbour large area of natural sweet chestnut forests in Bulgaria. Oak occupies the lower altitudinal zone below chestnut formation.

2.2. Methodology The overall methodological approach that has been followed in the research has been presented as a schematic flowchart in the following diagram (Figure 2:2).

Figure 2:2-Methodological flow chart


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2.2.1. Field data collection The sampling design adopted is stratified purposive sampling with strata based on altitudinal intervals of 200 m. (Auerbach, 1993; Gracia, 2007; Kitayama, 1992; Odland & Birks, 1999). Stratified random sampling with strata based on altitudinal intervals would have been the best approach; but considering the mountain terrain compounded by inaccessibility due to lack of roads and walking trails through the mountain forests at different altitudes, and limited time of field work as well, this approach was not adopted. Moreover, the study is intended to capture only the understory woody species composition in different forest types along the whole altitudinal range. Therefore, to capture the maximum variation of the understory woody species at different altitudinal zones, stratified purposive sampling approach with sample plots in all the forest types in proportion to their spatial extent along an altitudinal gradient in the study area, seemed to the best feasible alternative for this study.

Figure 2:3-Stratum wise sample plots distribution Figure 2:4-Stratum wise number of sample plots

The study area was divided into altitudinal intervals of 200 m. These altitudinal intervals of 200 m formed the strata. A total of seven strata covering only the forested mountain were formed (Figure 2:3), thus not including the area above the tree line. Sample plots were collected from all available types of overstory composition involving the full range of altitude (452m a to 1703m asl), slope aspect and slope gradient. Altogether, data from 60 sample plots were collected. A histogram of number of sample plots per altitudinal zone is shown in the figure 4 (Figure 2:4) Circular nested quadrats were applied to each selected sample plot for recording data on understory woody species (Figure 2:5). In practice, first of all, the centre point of each sample plot was selected purposively, applying common sense by looking around the surrounding overstory and understory composition, slope aspect, and slope gradient so that the sample plot became representative of the surrounding area. Altitude of the centre point of the sample plot was recorded with the help of iPAQ. From the centre of the sample plot, 10 meters lengths were laid out in four directions viz. North, East, South and West, using measuring tape. At each 10 meter distance that now became the sub-plot centres of respective four sub-plots viz. North sub-plot, East sub-plot, South sub-plot and West sub-plot, two circular nested quadrats of 1 meter and 3 meter radii respectively were laid out in each sub-plot. In nut shell, each sample plot has four sub-plots viz. North sub-plot, East sub-plot, South sub-plot and West sub-plot; and each sub-plot in turn has two nested quadrats of 1 meter and 3 meter radii.


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Data on understory woody species was recorded from these nested quadrats. From the nested quadrats of 1 meter radius, data on understory woody species below 1.37 meter height was recorded, and from the nested quadrats of 3 meter radius, data on understory woody species above 1.37 meter height to diameter at breast height (dbh) less than 12 cm was recorded. All the species in the quadrats were identified with the help of scientists from Forest Research Institute, Sofia (Bulgaria) and species wise number of individuals recorded. Nomenclature is based on Flora of Bulgaria, volume 1& 2 (Stojanov, et al., 1966, 1967).

Figure 2:5 Figure 2:6 Figure 2:5-Circular nested quadrats in a sample plot for understory data Figure 2:6-Nearest neighbour method in a sample plot for overstory data Data on overstory composition was recorded by a nearest neighbour method. In this method, sample of four nearest trees viz. 1, 2, 3, and 4 from each sub-plot centre were selected, their distances measured and data of selected trees recorded (Figure 2:6). The process was repeated for remaining three sub-plots of a sample plot; thus each sample plot has data on total sample of sixteen tree species. The distances of these trees from the respective sub-plot centres were measured and recorded. All the tree species were identified and species wise number of individuals recorded. Diameter at breast height and total height & live crown length of each tree were recorded. Nomenclature of understory species and tree species is based on Flora of Bulgaria, volume 1& 2 (Stojanov, et al., 1966, 1967). Diameter at breast height was recorded with the help of calliper and a Haga altimeter was used for the measurement total height and live crown length of trees. Topographic measurements that included slope gradient and slope aspect of each plot were recorded with the help of Silva compass. Topographic maps and Belasitsa management block maps (available with Forest Research Institute, Sofia), compass, iPAQ, and Garmin12 GPS were used to navigate to each sample plot in the field.

2.2.2. Data analysis Variable wise analyses of data were done for each plot independently. Out of the total 60 field sample plots, only 58 field sample plots were used in the final data analyses. Since only natural forests were considered in the final data analyses, the two plots are plantations of pine (Pinus nigra) hence not included in the final analyses. The two sample plots of pines were used as reference data for accuracy assessment in land cover classification.


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Understory Diversity There are several measures of diversity known as ‘diversity indices’ to describe complexity of a biological community; each of these indices seeks to characterize the diversity of a sample or community by a single, quantitative value (Magurran, 1988; Sager & Hasler, 1969). Shannon diversity index (Shannon, 1948) has been used in this research as indicator of the woody species diversity as this diversity index is the most commonly used and acceptable indicator of biodiversity (Kent & Coker, 1994; Magurran, 1988; Mouillot & Lepretre, 1999). Moreover, the vogue for using measures incorporating species abundance has also lead to widespread use of the Shannon index (Magurran, 1988) and hence this study is focussing on species abundance as well. First of all, in each sub-plot viz. North sub-plot, East sub-plot, South sub-plot and West sub plot, species wise density in each nested quadrat (1meter and 3 meter) was calculated separately using the following formula:

= ni

where, / πr²

- ni:

- πr²: area of circle, where ‘r’ is radius of circles i.e. 1m & 3m respectively number of individuals of i th species in a quadrat

Similar calculations were done for all the species in all the quadrats of all the sub-plots viz. North, East, South and West. Then species-wise density of nested quadrats (1m and 3m) in all the sub-plots was finally summed up. Finally, species-wise average density of each sample plot was calculated (say) =

Species-wise total density of all the sub-plots/4 Finally, Shannon diversity index was calculated for each plot using the formula:

Shannon Diversity Index (H') = - ∑pi * ln pi

Here, ‘pi’ is the proportion of species i.e. average density of species made up of the i th species in a plot.

Overstory diversity Similarly, sample plot wise overstory diversity was calculated using the same Shannon diversity index.

Shannon Diversity Index (H') = - ∑pi * ln pi Here, ‘pi’ is the proportion of the total number of species made up of the i th species in the plot. Live-crown ratio Live-crown ratio is the ratio between the length of green crown and total height of tree. Live-crown ratio is related to stand density; as stand density increases, the inter-tree shading causes death in the lower branches of the crown, and hence reduction in the total live-crown ratio (Dean, 1999). When stand density decreases there is a gap in the canopy and no inter-tree shading; and hence live-crown ratio increases. In


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other words, when stand density is high, live-crown ratio of the trees will be low; and when stand density is low live-crown ratio will be more. Live crown-ratio of each tree was calculated using the formula =

Live crown ratio = Length of a live crown (m)/ Total height of a tree (m)

Finally, sample plot-wise average live-crown ratio was calculated and converted into sample plot-wise percentage live-crown ratio. Stand density Number of trees per hectare was calculated using a regression model generated from simulated data in ArcGIS with the help of Hawths tool. In the simulation, nearest neighbour method of density calculation was used. Accordingly, only four nearest trees from each sub-plot centre as per the data collected in the field were used in simulation to develop the regression model.

Figure 2:7-Computer simulated density data

Regression model for the stand density calculation: Ln (density) = Intercept + ln (average distance) Ln (density) = 8.6965-1.8785*ln (average distance), R2 =

Density = e 0.9705

8.6965-1.8785*ln (average distance)


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In simulated model, first of all fifty square plots were generated on the computer screen by using ArcGIS. The plot size is 100m × 100 m. In each row 10, 100, 1,000, 10,000 and 100,000 random points were generated in each successive plot. Again in each plot, 5 (five) random points were generated (Figure 2:7). The whole process was repeated ten times; and then with the help of Hawths tools in ArcGIS, the nearest distance of four points were extracted. Finally, from the extracted data average of four distances were calculated. Log natural (ln) density was used as independent variables and ‘ln’ of average distance was used as explanatory variable to develop the regression model. Simple linear regression and multiple linear regressions were tried to get the best fitted model. Finally, model generated from simple linear regression was selected as it gave the highest coefficient of determination (R² = 0.9705) compared to the model generated from multiple linear regression. Statistical analyses Statistical analyses were done using SPSS software and MS Excel. Linear regressions, both simple and multiple are the major statistical methods used in the analyses of the data in this study (Quinn & Keough, 2002). SPSS software was used mainly for regression analyses and its further tests like ANOVA and Post-hoc tests; while MS Excel was used for graphical analyses. In regression analyses, multiple linear regressions (MLR) were carried out in three different groups of forests viz. all forest, beech forest, and mixed forest (non-beech forest). In ‘all forest’, whole forest of the study area is considered as one group. ‘Beech forest’ and ‘mixed forest’ resulted after further categorisation of ‘all forest’ depending upon the dominance of overstory species in the study area computed on the basis of percentage composition of the overstory species in the sample plots. The topographic factors (altitude and slope angle) and overstory factors (overstory diversity, live-crown ratio and overstory density) are the independent variables; while understory woody species diversity is the dependent variable. Slope aspect was not included in the analyses, as it was found that Belasitsa Mountain faces the northern aspect. In MLR, the overstory and topographic factors were regressed with understory woody species in above mentioned three groups of forests separately to see how these independent variables differ in their influences in predicting understory woody species diversity in these three groups of forests. In analysing the relationship between understory woody species diversity and NDVI, simple linear regression (SLR) analysis was carried out. Here, NDVI is independent variable which was regressed with understory woody species diversity, the dependent variable. Here also, the SLR analyses were carried out in all the three groups of forests separately. Overstory composition & forest types During data collection in field it was observed that there are three main dominant natural tree species viz. beech, chestnut and oak in Belasitsa Mountain forest. It was also observed in the field study that beech occurs at higher altitude mostly in a pure form. Although few pure patches of chestnut and oak were also observed but these two species occur generally in a mixed form at lower altitude with few other broadleaved species. Therefore, sample plot wise percentage species composition was determined using the criteria as below :

1. Pure species: If particular species makes up 75 % or more of the total number of trees in a sample plot.

2. Mixed species:


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a) Mixed species (major trees eg. Beech, Chestnut, and Oak only): If the major tree species makes up between 50-75% of the total number of trees in a sample plot, then mixture of two species contributing highest percentage composition ‘dominant tree species1’ and ‘dominant tree species2’ eg. Fagus-Castanea mixed (here Fagus is dominant tree species1 and Castanea is dominant tree species2) or Castanea-Fagus mixed (here Castanea is dominant tree species1 and Fagus is dominant tree species2)

b) Miscellaneous mixed species (major tree species and other tree species): If no tree species makes up above 50 % of the total number of trees.

After the sample plot wise percentage composition of the species as per criteria mentioned above, the overstory species were grouped in different broad categories according to the dominance of the species. Further, one way analysis (ANOVA) was carried out to see how understory_H' significantly differ in different groups of overstory species composition (Quinn & Keough, 2002; Sokal & Rohlf, 1994). Post-hoc test using Hochberg’s GT2 (Hochberg, 1974) was carried out to check which groups of overstory compositions have significantly different levels of understory_H'. On the basis of ANOVA and post-hoc test, the broad categories of overstory compositions were further re-grouped into major forest types. The detail results of these analyses are shown and explained under paragraph ‘classification of all forest into broad categories’ in ‘results’ section. Land cover mapping Tree species compositions are considered as biodiversity indicator (MCPFE, 2003) as dominant tree species (forest types) influence understory diversity (Barbier, et al., 2008). Therefore, mapping of forest types (overstory composition) helps in identifying areas of higher biodiversity importance where conservation resources should be focussed (Cardillo, et al., 1999). Supervised image classification (ITC, 2009) for mapping the land cover of Belasitsa Mountain forest was done using the ASTER14 satellite image of the October, 2008. A colour composite image was created with the band combination of 3, 2, and 1 (red: near infra-red band 3, green: red visible band 2, and blue: green visible band1) at ratio of 1:1:1 was the best that allowed the identification of different habitat types in the study area. ASTER 14 doesn’t have blue visible band. Erdas Imagine and ArcGIS software were used for this purpose. In all, total 70 (seventy) training areas were used for this classification. The classes of interests i.e. beech, chestnut, oak, pine, and miscellaneous species etc. were difficult to identify and segregate in the ASTER14 satellite image because of spectral similarity of these species. Therefore, Google earth image of the study area was also used for visual interpretation and various interpretation elements like tone, texture, patterns, shape, size, and location etc. were used in identifying the features (classes of interests). A contour map generated from digital elevation model (DEM) was also used to know the location of dominant species; as forests in Belasitsa Mountain are segmented in to different altitudinal belts. Since pine plantations occurred in smaller areas that also were distributed in scattered patches here and there, the spectral features of the pine plantations could not be identified in both the images viz. ASTER14 image as well as Google earth image. So, in case of pine, shape files of the reference data of the pine were overlaid on the ASTER14 image and used for spectral delineation of the feature. Therefore, the same data set has been used for training as well as reference in case of pine. This will surely influence the


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accuracy as the overall accuracy will always be high. Moreover, there was no alternative left to identify and delineate the pine patches. Maximum likelihood classification (ML) was used as classification algorithm as it considers not only the cluster centres but also the shape, size and orientation of the clusters (ITC, 2010). Validation of the result (ITC, 2010) was done to assess the quality by comparing it to reference data (ground truth), and error matrix was generated. The overall methodological approach adopted in land cover mapping of Belasitsa Mountain forest is shown as a flow chart in figure 2:8. Since there were mixture of spectral classes (mixed pixels), the classified raster map was converted into vector map using ArcGIS and these mixed pixels were re-classified manually assigning to the classes to the interests pixel by pixel using ArcGIS. Of course, this post classification removal of noise further affects the overall classification accuracy greatly.

2:8-Flowchart of land cover mapping of Belasitsa Mountain forests

Normalised Difference Vegetation Index analysis Vegetation indices is defined as dimensionless, radiometric measures that function as indicators of relative abundance and activity of green vegetation, often including leaf-area-index(LAI), percentage green cover, chlorophyll content, green biomass, and absorbed photosynthetically active radiation(APAR) (Jensen, 2007). There are many vegetation indices in use, and Rouse et.al. (1974) developed normalised difference vegetation index (NDVI). The NDVI index was largely applied to the original Landsat MSS digital remote sensor data (Jensen, 2007). The NDVI is calculated from the visible and the near-infra red light reflected by vegetation. It has been established that NDVI based on the ratio of vegetation reflectance between the


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visible and near-infrared light, gives a measure of the photosynthetical active biomass (Jørgensen & Nøhr, 1996). Therefore, NDVI is defined as:

NDVI = (anir - avis)/ (anir + avis

Where a)

nir and avis

represent surface reflectances averaged over ranges of wavelengths in the visible (λ ~ 0.6 µm, “red”) and near infrared, IR(λ ~ 0.8 µm) regions of the spectrum, respectively (Carlson & Ripley, 1997).

Healthy vegetation absorbs most of the visible light that hits it, and reflects a large portion of the near-infra red light (Jensen, 2007). The above coefficient constitutes a good measurement of the physiological activity of the plants, and can simply be written as NDVI = (NIR–R)/ (NIR+R), where NIR is Near Infrared band, and R is Red band. Normalisation reduces the differences due to overall brightness of sunlight or of surfaces that can strongly influence the image (Running, et al., 1994). Previous studies have found positive correlation between NDVI and ground-based measure of species diversity (Bonn, et al., 2004; Jørgensen & Nøhr, 1996; Running, et al., 2004). This correlation was based on the premise of the sequence of relationships from NDVI to NPP (Box, et al., 1989) and then the ‘productivity hypothesis’ that further relates NPP to species richness (Waide, et al., 1999). Therefore, in this study, NDVI was computed using ASTER14 images of October, 2008 in ERDAS Imagine software, and then statistical analyses were carried out to see the relationship between NDVI and ground-based measure of understory woody species diversity.


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3. RESULTS

3.1. Spatial pattern of understory woody species diversity in Belasitsa Mountain In total, thirty one understory woody species were found in all the sample plots; of which twenty seven species are of tree species in habit and the remaining four species are shrubs (Appendix-1). This indicates that shrub richness is very low. A scatter plot of the spatial distribution of understory woody species diversity (understory_H') along the altitude gradient in Belasitsa Mountain forests (Figure 3:1) shows that diversity increases with altitude, peaks at the altitude between 600 m and 900 m., and then decrease gradually and finally dips down sharply above the altitude of 1000 m and then remains constant, though slightly increases at the altitude between 1400 - 1500 m but marginally, and it is about 0.40 only.

Figure 3:1 Figure 3:2

Figure 3:1-Scatterplot showing diversity pattern of understory woody species diversity in Belasitsa Mountain in all altitudinal range (300m to 1700m) Figure 3:2-Scatterplot showing diversity pattern of understory woody species in Belasitsa Mountain in the altitudinal range below the altitude of 1000 m (300 m to 1000 m)

Further, polynomial equation was tested through the observation below the altitude of 1000 m to see whether this gives significant coefficient for the second order relationship (Figure 3:2). The scatter plot of the spatial distribution of understory_H' in Belasitsa Mountain below 1000 m (Figure 3:2) also shows that diversity increases with altitude, peaks at the mid-altitudinal zone and gradually decreases as the altitude increases. Polynomial regression between understory_H' and two different altitudinal ranges viz.: all altitudinal range (300 to 1700 m) & altitude below 1000 m (300 to 1000m) were analysed; and the results are shown in the table 3:1.

Table 3:1-Polynomial regression between understory_H' and two altitudinal ranges in Belasitsa Mountain. (Understory_H' considered as the dependent variable and altitude as the independent variable).

Group¹ Adjusted R² Std. error of F p the estimate 1. All altitudinal range (n= 58) 0.525 0.440 22.024 0.000* 2. Altitude below 1000m (n= 41) 0.088 0.489 2.919 0.066

¹The ‘group’ column specifies which altitudinal ranges were used in formulating the model.

* = p ≤0.05 level of significance


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In case of all altitudinal range, the relationship between the variables is significant and the coefficient of determination i.e. the adjusted R2

is 0.525. This shows that 53% of the variation in spatial distribution of understory_H' in Belasitsa Mountain can be explained by altitudinal gradient at 95% confidence level. The relationship between altitude and understory_H' is significant but not very strong, because 47% of the variability in spatial distribution can be explained by factors other than what is accounted for by the regression model that uses only altitude.

The relationship between the variables below 1000 m altitude is not significant and the coefficient of determination is 0.088. This shows that 9% of the variation in spatial distribution of understory_H' below 1000 m altitude can be explained by altitudinal gradient at 95% confidence level. The relationship is very poor because 91 % of the variability in spatial distribution can be explained by factors other than the altitudinal gradient.

3.2. Influences of topographic & overstory factors on understory woody species diversity The effects of topographic factors (altitude and slope angle) and overstory factors (overstory diversity, live-crown ratio and overstory density) on understory_H' in three groups of forests viz.: all forest, beech forest, and mixed forest were analysed separately. In ‘all forest’, whole forest of the study area is considered as one group. This group is further broadly classified into major groups according to the percentage composition of the species which has been explained under paragraph ‘classification of all forest into broad categories’. The main purpose of this separate analsyses was to see how the chosen predictor variables differ in different groups of forests in explaining understory woody species diversity in Belasitsa Mountain forests. All forest: A correlation matrix between the variables in this forest group is shown in the table 3:2. This correlation matrix shows the relationship between different variables. It shows that understory_H' positively correlates with slope angle, overstory_H' and live-crown ratio; while negatively correlates with altitude and overstory density. This reflects that understory_H' increases with increase in slope angle, overstory_H' and live-crown ratio; while it decreases with increase in altitude and overstory density. Table 3:2-Correlation matrix between variables (n=58)

Understory_H' Altitude Slope angle Overstory_H' Live-crown ratio Overstory density

Understory_H' 1.000 Altitude - 0.666 1.000 Slope Angle 0.172 - 0.048 1.000 Overstory_ H' 0.762 - 0.532 0.279 1.000 Live-crown ratio 0.157 - 0.150 0.316 0.169 1.000 Overstory density - 0.358 0.345 - 0.106 - 0.361 - 0.056 1.000

Further, multiple linear regression (MLR) analysis relating understory_H' to all predictor variables viz. topographic factors (altitude and slope angle) and overstory factors (overstory diversity, live-crown ratio and overstory density) was performed. This analysis was done for all the sample plots of the study area (hence, all forest) irrespective of categorisation of overstory species composition and /or forest types. The summarised output of the MLR analysis is shown in the table 3:3.


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Table 3:3-All forest: MLR relating understory_H' and topographic and overstory factors in Belasitsa Mountain. (Understory_H' is considered as the dependent variable and topographic & overstory factors as independent variables)

Coefficient Estimate Standard Standardized Tolerance t p ( B ) error coefficient (ß)

Intercept 0.972 0.344 2.825 0.007 Altitude - 0.001 0.000 - 0.355 0.669 - 3.675 0.001* Slope angle - 0.001 0.007 - 0.008 0.827 - 0.093 0.926 Overstory_H' 0.745 0.133 0.561 0.625 5.618 0.000* Live-crown ratio 0.001 0.006 0.010 0.881 0.115 0.909 Overstory density -6.818E-5 0.000 - 0. 034 0.836 - 0. 389 0.699

* = p ≤0.05 level of significance, n=58 Overall coefficient of determination (R²) is 0.676 with adjusted R² of 0.645 and the model is overall significant as F value = 21.683 and p < 0.05. The MLR analyses show that only altitude and overstory diversity (overstory_H') have significant effects on diversity pattern of understory woody species in the forests of Belasitsa Mountain. There is no collinearity amongst the independent variables. Further, stepwise regressions (both forward and backward selection) were performed to develop a model with only variables that have significant effects on diversity of understory woody species. A linear model was developed using all the above independent variables with understory_H' as dependent variable. Both the forward and backward regressions yielded the same model and the final model depicting the influences of altitude and overstory_H' on understory_H' is shown as in the table (Table 3:4). Table 3:4-Model depicting the effects of altitude and overstory_H' on predicting understory_H' in all forests of Belasitsa Mountain.

Model R² Adjusted R² Std. error of F p the estimate Understory_H'=0.960 – 0.001(Altitude) + 0.754(Overstory_H') 0.675 0.663 0.3709748 57.067 0.000*

* = p ≤0.05 level of significance, n=58 The above table depicts that the model is overall significant as F value = 57.067 and p < 0.05, and overall coefficient of determination (R²) is 0.675 with adjusted R² of 0.663. This shows that 66% of the variation in diversity pattern of understory_H' in Belasitsa Mountain can be explained by altitude and overstory_H' 95% confidence level. The relationship is moderate, because 34% of the variability in diversity pattern can be explained by factors other than these two independent variables. As mentioned under ‘overstory composition and forest types’ paragraph, the overstory species in Belasitsa Mountain are segmented into different altitudinal zones. Since the above MLR analyses relates understory_H' and chosen predictor variables in ‘all forest’ of Belasitsa Mountain; it is prudent to see how the chosen predictor variables differ in explaining understory_H' in different forest types as well. Therefore, it is imperative to broadly classify the forests of Belasitsa Mountain into major categories. Classification of ‘all forest’ into broad categories The sample plot wise percentage species composition (as per the criteria as explained under ‘overstory composition and forest types’ paragraph) was determined to broadly classify ‘all forest’ of Belasitsa


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Mountain into major categories. The detailed sample plot wise computation of percentage composition of species is attached in appendices (Appendix-2). The major reason of this broad classification is to see how the chosen predictor variables differ in these broad categories of forest types in explaining understory_H' in the study area. The list of total overstory species observed in all the sample plots in the study area is attached in appendices (Appendix-3). In all, there are eleven categories of overstory species composition in different combinations of percentage composition of different overstory species in the sample plots. The eleven broad categories of different overstory species are:

1. Fagus sylvatica 2. Castanea sativa 3. Quercus petraea 4. Pinus nigra 5. Fagus - Castanea mixed 6. Fagus - Quercus mixed 7. Castanea - Fagus mixed 8. Castanea - Quercus mixed 9. Quercus - Castanea mixed 10. Castanea - Miscellaneous mixed and 11. Miscellaneous mixed.

The graph in figure 3:3 depicts the number of sample plots falling in the respective combinations of major overstory compositions. Scatter plot of the species composition is shown in figure 3:4. It is evident from the figures 3:3 and 3:4 that Fagus sylvatica (beech) occurs mostly as a pure forest while other species mostly occur in mixed form. The graph in the figure 3:4 further reveals that pure beech occurs at the higher altitude (above 1000 m) whereas all other species occur in mixed form at middle and lower altitude.


Figure 3:3-Number of sample plots falling in different categories of overstory species composition Figure 3:4-Scatterplot showing composition of overstory species in Belasitsa Mountain

A box plot is showing the understory_H' in different combinations of overstory species composition (Figure 3:5). It shows that understory_H' in beech forest is quite low as compared to other overstory species composition. To see how understory_H' significantly differs in different groups of overstory species composition, one way analysis of variance ( ANOVA) was carried out, and the result is shown in the table 3:5. As per the classification criteria, there are eleven categories of overstory composition, but only eight categories of overstory composition have been used in ANOVA. Since two categories of overstory composition viz.: ‘Fagus-Quercus mixed’ and ‘Castanea-Quercus mixed’ falls in one sample plot


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each, and two sample plots are of Pinus nigra plantation; therefore these three categories of overstory composition were not included in the ANOVA.

Figure 3:5-Box plot showing understory_H' in different overstory species composition in sample plots

(Different symbols indicate significant differences at ≤ 0.05)

Table 3:5-ANOVA: Understory_H' and different categories of overstory composition.

Sum of Squares df Mean Square F Sig. Between Groups 17.254 7 2.465 21.356 0.000* Within Groups 5 .540 48 0.115 Total 22.793 55

* = p ≤0.05 level of significance The table 3:5 shows that there are significantly different levels of understory_H' between different groups of overstory composition (F₇,₄₈ = 21.356, p = <0.05). Further, Post-hoc test using Hochberg’s GT2 was carried out to check which groups of overstory compositions have significantly different levels of understory_H'. In Post-hoc test, Hochberg’s GT2 was used instead of generaly used Tukey’s HSD test and Student-Neunman-Keuls(SNK) test, because here the variances of the samples are not homogeneous. Homogeneity of variances of the samples was tested using Levene’s test (Carroll & Schneider, 1985), and the table 3:6 shows that variances are not homogeneous. Levene’s test works by testing the null hypothesis that the variances of the samples are the same. Since Levene’s test is not significant, variances are not the same and therefore, Hochberg’s GT2 was used. The output of the comparison of means of different overstory composition using Hochberg’s GT2 is attached in the appendices (Appendix - 4). Table 3:6-Test of homogeneity of variances across samples.

Levene Statistic df1 df2 Sig.

0.705 7 48 0.668

p ≤0.05 level of significance The post-hoc test shows that there are significantly different levels of understory_H' between beech and other categories of overstory composition. Moreover, beech and beech dominated mixed (Fagus-Castanea mixed) sample plots occurs at higher altitude (Figure 3:4); therefore these two categories of overstory composition can be grouped into one category as ‘beech forest’. Sample plots of pure chestnut and pure oak are very few (Figure 3:3), and these two species generally occur in mixed form in association with other overstory species at lower altitude (Figure 3:4). Therefore, categories of overstory composition belonging


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to pure chestnut ( Castanea sativa), pure oak ( Quercus petraea), and chestnut and oak dominated mixed forest can be grouped into one category as ‘mixed forest’. In summary, all the eleven categories of overstory composition are further regrouped into two broad categories viz. ‘beech forest’ and non-beech forest i.e ‘mixed forest’. As explained above it is worth re-iterating for the sake of clarity that this regrouping of elven categories of forest into two broad categories has been done based on the following criteria: firstly, there is distinct zonations of the distribution of beech and mixed forest (non-beech forest) (Figure 3:4). Secondly, pure beech and beech dominated mixed sample plots cover almost half of the total sample plots while remaining half of the sample plots are mixed forests of chestnut, oak and other miscellaneous species (Figure 3:3). Thirdly, there are significantly different levels of understory_H' between beech and other categories of overstory composition (Figure 3:5). Therefore, there are two broad categories of forest now viz. ‘beech forest’ and ‘mixed forest’. Since the main purpose of classification of forests of the study area into broad categories was to see how the chosen predictor variables differ in explaining understory_H' in different forest types; therefore MLR analyses were carried out separately in these two broad categories of forest types (beech forest and mixed forest). MLR performed between all the predictor variables and understory_H' in ‘beech forest’ is shown in the Table:3:7. Table 3:7-Beech forest: MLR relating understory_H' and topographic and overstory factors in Belasitsa Mountain. (Understory_H' as the dependent variable and topographic & overstory factors as independent variables).

Coefficient Estimate Standard Standardized Tolerance t p ( B ) error coefficient (ß) Intercept - 0.878 0.307 - 2.860 0.009 Altitude - 4.821E-5 0.000 - 0.029 0.285 - 0.203 0.841 Slope angle - 0.001 0.005 - 0.017 0.741 - 0.192 0.849 Overstory_H' 0.818 0.207 0.654 0.212 3.945 0.001* Live-crown ratio 0.022 0.005 0.384 0.651 4.060 0.001* Overstory density - 6.699E-5 0.000 - 0. 048 0.786 - 0.555 0.585

* = p ≤0.05 level of significance, n=27 Overall coefficient of determination (R²) is 0.878 with adjusted R² of 0.849 and the model is overall significant as F value = 30.140 and p < 0.05. The MLR analyses show that only overstory_H' and live-crown ratio have significant effects on diversity pattern of understory woody species in beech forests. There is no collinearity amongst the independent variables. Further, stepwise regressions (both forward and backward selection) were performed to develop a model with only variables that have significant effects on diversity of understory woody species in beech forest. A linear model was developed using all the above independent variables with understory_H' as dependent variable. Both the forward and backward regression yielded the same model and the final model depicting the influences of overstory_H’ and live-crown ratio on understory_H' is shown in the table 3:8. Table 3:8-Model depicting the effects of overstory_H' and live-crown ratio on understory_H' in beech forest.

Model Adjusted R² Std. error of F p the estimate

Understory_H' = - 0.959 +0.874 (Overstory_H') + 0.021 ( Live-crown ratio) 0.864 0.1887498 83.797 0.000*

* = p ≤0.05 level of significance, n=27


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The above table depicts that the model is overall significant as F value = 83.797 and p < 0.05, and adjusted R² is 0.864. This shows that 86% of the variation in diversity pattern of understory_H' in beech forest can be explained by overstory_H' and live-crown ratio at 95% confidence level. The relationship is quite strong because only 14% of the variability in diversity pattern in beech forest can be explained by factors other than these two independent variables i.e. overstory_H' and live-crown ratio. Similarly, MLR performed between all the predictor variables and understory_H' in ‘Mixed forest’ is shown in the table 3:9. Table 3:9-Mixed forest: MLR relating understory_H' and topographic and overstory factors in Belasitsa Mountain. (Understory_H' as the dependent variable and topographic & overstory factors as independent variables)

Coefficient Estimate Standard Standardized Tolerance t p ( B ) error coefficient (ß) Intercept 1.303 0.599 2.175 0.039 Altitude 0.000 0.001 0.107 0.832 0.660 0.515 Slope angle 0.004 0.011 0.056 0.774 0.334 0.741 Overstory_H' 0.468 0.167 0.472 0.764 2.797 0.010* Live crown - 0.011 0.007 - 0.253 0.834 -1.566 0.130 Overstory density 0.000 0.000 - 0.223 0.900 -1.439 0.163

* = p ≤0.05 level of significance, n=31

Overall coefficient of determination (R²) is 0.457 with adjusted R² of 0.349 and the model is overall not significant as F value = 4.216 and p > 0.05. The MLR analyses show that only overstory_H' has significant effects on diversity pattern of understory woody species in mixed forests. There is no collinearity amongst the independent variables. Further, stepwise regressions (both forward and backward selection) using all the above independent variables with understory_H' as dependent variable. Both the forward and backward regressions yielded the same model and the final model depicting the influence of only overstory_H' on understory_H' in is shown in the table 3:10. Table 3:10-Model depicting the effect of overstory_H' on predicting understory_H' in mixed forest.

Model Adjusted R² Std. error of F p the estimate

Understory_H' = 0.755 + 0.584 (Overstory_H') 0.324 0.3835472 15.361 0.000*

* = p ≤0.05 level of significance, n=31 The above table depicts that the model is overall significant as F value = 15.361 and p < 0.05, and adjusted R² is 0.324. This shows that only 32% of the variation in diversity pattern of understory_H' in mixed forest can be explained by overstory_H' at 95% confidence level. The relationship is quite poor because 68% of the variability in diversity pattern in mixed forest can be explained by factors other than overstory_H'. Finally, comparative overview of the models with significant effects of the chosen predictor variables (topographic and overstory variables) on respective group of forests are shown in the table 3:11.


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Table 3:11-Summary table of the models comparing the significant effects of topographic and overstory factors in predicting understory_H' in different groups of forests.

Group1

1. All forest (n= 58) Understory_H'=0.960 – 0.001(Altitude) + 0.754 (Overtsory_H')

Model

2. Beech forest (n= 27) Understory_H' = - 0.959 +0.874 (Overstory_H') + 0.021 (Live-crown ratio) 3. Mixed forest (n = 31) Understory_H' = 0.755 + 0.584 (Overstory_H')

1

3.3. Relationship between NDVI and understory woody species diversity

The ‘group’ column specifies which forest types were used in formulating the model

NDVI map of the study area with sample points overlaid on the map is shown in the figure (Figure 3:6). The values of NDVI ranges from -0.27 to 0.62 in this NDVI map. The histogram of the NDVI values (Figure 3:7) shows that the mean NDVI value is 0.40 with standard deviation of 0.069. High positive values of NDVI correspond to dense vegetation cover, whereas low and negative NDVI values are usually associated with low density of vegetation cover, bare soil, clouds or non-vegetated areas.

Figure 3:6-NDVI map with sample plots distribution

In the NDVI map of the study area, values at the higher altitude (i.e. southern side) are showing low and negative, but in reality there is a dense vegetation of beech forest. This low NDVI values at higher altitude is probably due to the effect of clouds at higher altitude. Simple linear regression (SLR) was performed between NDVI and understory_H' in three groups of forests viz. all forest, beech forest, and mixed forest separately to see the relationship between NDVI and understory_H'. First of all, SLR was performed between NDVI and understory_H' in ‘all forest’ group and the scatter plot and summary output of this relationship is shown in the figure 3:8 and the table 3:12 respectively.


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Figure 3:7-Histogram of NDVI values Figure 3:8-Relationship between NDVI and understory_H' Table 3:12-All forest: Summary output of the relationship between NDVI and understory_H' in all forest of Belasitsa Mountain.

Coefficient Estimate Standard Standardized t p (B) error coefficient (ß) Intercept 0.984 0.479 2.055 0.044 NDVI -0.501 1.179 - 0.056 - 0.425 0.672

Level of significance = 0.05, n=58 Overall coefficient of determination (R2

) is 0.003 and the model is overall not significant as F value = 0.181 and p>0.05. This shows that only 0.3% of the variation in diversity pattern in all forest of Belasitsa Mountain can be explained NDVI at 95% confidence level. The relationship is very poor and hence NDVI is a very poor predictor of understory_H' because 99.7 % of the variability in diversity pattern of understory woody species in all forest of Belasitsa Mountain can be explained by factors other than NDVI.

Similarly, SLR were performed between NDVI and understory_H' in ‘beech forest’ and ‘mixed forest’ as well to see the relationship of NDVI and understory_H' in these two different groups of forests. The scatter plot and summary output of the relationship between NDVI and understory_H' in beech forest is shown in the figure 3:9 and the table 3:13 respectively.


Figure 3:9-NDVI and understory woody species diversity in beech forest. Figure 3:10-NDVI and understory woody species diversity in mixed forest.


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Table 3:13-Beech forest: Summary output of the relationship between NDVI and understory_H' in beech forests of Belasitsa Mountain..

Coefficient Estimate Standard Standardized t p (B) error coefficient (ß) Intercept 0.823 0.603 1.365 0.184 NDVI -1.240 1.482 -0.165 -0.837 0.411 Level of significance = 0.05, n= 27 Overall coefficient of determination (R²) is 0.027 and the model is overall not significant as F value =0.700 and p>0.05. This shows that only 2.7% of the variation in diversity pattern in beech forest of can be explained NDVI at 95% confidence level. The relationship is very poor and hence NDVI is a very poor predictor of understory_H' in beech forests as well, because 97.3 % of the variability in diversity pattern of understory woody species in beech forest of Belasitsa Mountain can be explained by factors other than NDVI. SLR was performed between NDVI and understory_H' in ‘mixed forest’ as well. The scatter plot and summary output of the relationship between NDVI and understory_H' is shown in the figure 3:10 and the table 3:14 respectively. Table 3:14-Mixed forest: Summary output of the relationship between NDVI and understory_H' in mixed forest of Belasitsa Mountain.

Coefficient Estimate Standard Standardized t p (B) error coefficient (ß) Intercept 1.088 0.498 2.185 0.037 NDVI 0.165 1.214 0.025 0.136 0.893

Level of significance = 0.05, n = 31 Overall coefficient of determination (R²) is 0.001 and the overall model is not significant as F value = 0.019 and p>0.05. This shows that only 0.1% of the variation in diversity pattern in mixed forest of Belasitsa Mountain can be explained NDVI at 95% confidence level. The relationship is very poor and hence NDVI is a very poor predictor of understory_H' because 99.9 % of the variability in diversity pattern of understory woody species in mixed forest of Belasitsa Mountain can be explained by factors other than NDVI. Finally, the comparative overview of models depicting the relationship between NDVI and understory_H' in different groups of forests is shown in the table 3:15. Table 3:15-Summary table of the models comparing the relationship between NDVI and understory_H' in different groups of forests.

Group¹ Model R2

the estimate Std. error of F p

1. All forest (n= 58) Understory_H' = 0.984 – 0.501(NDVI) 0.003 1.179 0.181 0.672 2. Beech forest(n= 27) Understory_H' = -0.823 -1.240(NDVI) 0.027 1.482 0.700 0.411 3. Mixed forest (n = 31) Understory_H' = 1.088 + 0.165 (NDVI) 0.001 1.214 0.019 0.893

¹The ‘group’ column specifies which forest types were used in formulating the model.

* = p ≤0.05 level of significance.


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3.4. Land cover mapping of Belasitsa Mountain Maps are potent tools for communicating information. The maps help us to identify the areas of biodiversity importance where conservation resources should be focussed (Cardillo, et al., 1999). As explained earlier, understory vegetation is closely associated with dominant tree species (forest types); therefore, tree species compositions (forest types) are considered as biodiversity indicator (MCPFE, 2003). Further, mapping of forest types helps in predicting species diversity and distribution in them. Thus, the identification of forest types in turn helps in protecting the habitats of higher diversity. Mapping of the species richness and distribution is an important aspect of conservation and planning (Miller, 1994).

Figure 3:11- Land cover map of Belasitsa Mountain, Bulgaria

The land cover map of Belasitsa Mountain forest is shown in the figure 3:11. The table 3:16 shows the ‘error matrix’ resulting from classifying training set pixels and testing the accuracy. The overall classification accuracy is 71.43% and overall Kappa statistics is 0.6186. The overall accuracy is the number of correctly classified pixels divided by the total number of pixels checked. This overall accuracy is the accuracy of the classification result as a whole. The producer’s accuracy is the probability that a sampled point on the map is that particular class. It is the number correctly classified divided by the reference totals. Conversely, user accuracy is the probability that a certain reference class has also been labelled that class. It is the number correctly classified divided by the classified totals. Kappa statistics or k' statistic take into account the fact that even assigning labels at random results in a certain degree of accuracy. It is generally thought to be a more robust measure than simple per cent agreement calculation since k' takes into account the agreement occurring by chance (ITC, 2010).


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Table 3:16-Error matrix showing producers, users and overall accuracy.

Class Reference Classified Number Producers Users Name Totals Totals Correct Accuracy (%) Accuracy (%)

Beech 28 31 24 85.71 77.42 Chestnut 16 12 8 50.00 66.67 Oak 8 10 5 62.50 50.00 Pine 2 2 2 00.00 100.00 Miscellaneous mixed 7 7 3 42.86 42.86 Built up 4 4 4 100.00 100.00 Agricultural field 5 4 4 80.00 100.00

Totals 70 70


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4. DISCUSSION

4.1. Spatial pattern of understory woody species diversity in Belasitsa Mountain Although understory woody species diversity is highest at the mid-altitudinal zone (600 - 900 m) in this study area (Figure 3:1), but increase and decrease of understory woody species diversity along the altitude is not gradual. This is due to the fact that beech occurs in pure form at a higher altitude ( Figure 3:4 & Figure 3:11), and beech forest harbour less understory diversity (Figure 3:5) (Barbier, et al., 2008). Since the altitude below 1000 m consists of mixed forest (Figure 3:6), the diversity pattern in this case show a typical hump-shaped pattern (Figure 3:2) with gradual increase and then gradual decrease; but the polynomial regression is not significant (Table 3:1). To analyse the general variation of species diversity with elevation, it should include data spanning the entire altitudinal gradient (Rahbek, 1995). Therefore, the separate analysis of diversity pattern below 1000 m could be misleading. Whether the altitudinal gradient is segmented or not, it is evident that the highest understory woody species diversity is at an altitudinal zone of 600 m to 900 m (Figures 3:1 & 3:2), which is almost the mid-altitudinal zone of the forested mountain of Belasitsa. Many studies have examined the diversity pattern of understory plant species along an altitude gradient (Rahbek, 1995); and the role of altitude in distribution of plant species in mountainous forests is well documented (Grytnes, 2003; Körner, 2007; Pausas & Austin, 2001). Species diversity increases gradually along an altitudinal gradient, peaks at mid-altitudinal zone, and then declines gradually. Most of the studies on diversity of species along an altitude gradient have shown this hump-shaped pattern of diversity (Grytnes & Beaman, 2006; Odland & Birks, 1999; Rahbek, 1995).

4.2. Influences of topographic & overstory factors on understory woody species diversity Distribution of understory woody species is well known to be related to local topographic and overstory factors (Whitney & Foster, 1988), and influences of these factors differ in importance(Beatty, 1984; Brosofske, et al., 2001; Huebner, et al., 1995; Pausas & Austin, 2001). In this study also this holds true as only altitude and overstory diversity have significant influences when the whole forest is considered together as one group (Table 3:4); while overstory diversity and live-crown ratio have significant influences in case of beech forest (Table 3:8). Overstory diversity is the only significantly influencing factor in mixed forest (Table 3:10). In all forest, these two variables are able to explain up to 66% of the variation in understory woody species diversity; while other factors viz. slope angle, live-crown ratio and stand density do not significantly influence the understory vegetation. This suggests a higher-level control of altitude and overstory diversity on understory plants that may outweigh the controlling influences of other topographic and overstory factors in Belasitsa Mountain forests. Similarly, in case of beech forest, overstory diversity and live-crown ratio are able to explain up to 86% of the variation in understory woody species diversity; while overstory diversity, the only significantly influencing factor in case of mixed forest is able to explain up to 32% of the understory woody species diversity. It is interesting to find that overall it is the overstory diversity that has significant influence on understory woody species diversity in all the three group of forests in our study. Therefore, it is clear that dominance of the overstory species is the most common influencing factor in understory woody species diversity in Belasitsa Mountain. Remarkably, altitude is no longer an influencing factor in beech forest and mixed forest in determining understory woody species diversity. But, as evident from the figure 3:4, there is a


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variation of overstory species distribution along an altitudinal gradient in Belasitsa Mountain. Therefore, beech forest & mixed forest can be considered as surrogate (substitute) for altitude; and hence altitude is intrinsically influencing the distribution of understory woody species diversity through overstory species composition (forest types) along the altitudinal gradient in beech forest and mixed forest as well. Further, the overstory species composition affects understory vegetation (Barbier, et al., 2008; Oijen, et al., 2005) through its influences on the physical and chemical properties of litter and soil on the forest floor (Whitney & Foster, 1988). Leaf litter, stem flow, and through fall can alter the nutrient levels and pH of the forest floor, creating unique microhabitat for ground flora (Crozier & Boerner, 1984; Sydes & Grime, 1981). In this study area, beech dominates above an altitude of about 1000 m, and the altitude below 1000 m is a mixed forest mainly dominated by sweet chestnut and sessile oak and their associates (Figure 3:4). Several studies have found that beech forests generally harbour less understory diversity than other deciduous forests (Barbier, et al., 2008; Chiarucci, et al., 2001). Crozier & Boerner (1984) found that maintenance of spatial heterogeneity in the forest floor is dependent upon the presence of different species of canopy trees as different tree species represent different microhabitats for understory species. Willner et al.(2004) after analysing 5815 relevés from Central European beech forests found that main gradients determining the floristic composition of beech forests are altitude and soil reaction; and other gradients proved less important in beech forests. Chiarucci et al. (2001) after studying floristic diversity in forest ecosystems of Tuscany in Italy found that beech forests had the lowest species richness; and chestnut had an intermediate, while oak had the highest species richness. In this study, diversity is highest at the mid-altitudinal zone which is dominated by chestnut species. Chiarucci et al. (2001) further observed that low species richness in beech forest is due to shading by beech canopy and low soil pH. Stand density and live-crown ratio, both surrogate variables for understory light intensity are not significantly influencing understory woody species diversity in ‘all forest’ and ‘mixed forest’. This result also is an agreement with few similar studies on diversity of understory woody species where stand density is not significant (Pausas & Austin, 2001). Live-crown ratio which is related to stand density too doesn’t have a significant effect. Basically, live-crown ratio is dependent on stand density. As stand density increases, intertree shading causes death in the lower branches of the crown and a reduction in the live-crown ratio (Dean, 1999). This interaction results in a negative relationship between average live-crown ratio and its stand density (Long, 1985); as average live-crown ratio decreases linearly with increase in relative stand density and vice versa. In other words, when stand density is high live-crown ratio decreases, and when stand density is low live-crown ratio increases. Remarkably, in beech forest, live-crown ratio has significant effect on understory diversity. This is entirely in agreement with similar other studies that low species richness in beech is due to shading (Chiarucci, et al., 2001) as beech often give a complete coverage. Therefore, understory light plays an important role in beech forest because understory comes up only when canopy gaps are created in beech forest allowing more light to penetrate (Degen, 2005) ). This phenomenon was conspicuously observed by the author during the field data collection where profuse regeneration of beech seedlings have come up wherever large canopy openings were created due to wind throw of over mature beech trees. As discussed earlier, live-crown ratio is negatively related to stand density; therefore, positive influence of live-crown ratio indicates low stand density and hence, more openings/gaps in the canopy consequently more understory species. Lastly, it is pertinent to ponder over the fact that most of the understory woody species in this study are the tree species recruits (seedlings and saplings etc.) and only four species are shrubs (Appendix-2). Since there is diversity of overstory species at mid and low altitudinal zone, and no diversity of overstory species at higher altitude; therefore, understory woody species diversity in the study area could be a reflection of


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regeneration by overstory species. However, it is apparent in this scenario also that overstory diversity is still influencing factor in determining the diversity of the recruits because higher diversity of overstory species resulting in diversity of the regeneration as well. To conclude, it is pertinent to re-iterate that overall it is the overstory diversity that is the common significantly influencing factor on understory woody species diversity in all the three group of forests in our study. Brunet et al. (1996) while studying the herb layer vegetation of beech and oak forests observed that species richness of the typical forest flora was unaffected by management. This means that irrespective of the management practices in particular forest type, generally beech forest harbour less diversity than other broadleaved forests.

4.3. Relationship between NDVI and understory woody species diversity It is obvious from the statistical output of the regression analysis between NDVI and understory woody species diversity (Table 3:12) in ‘all forest’ that NDVI is a poor predictor of understory woody species diversity. The same holds true even if the relationship between NDVI and understory woody species diversity is analysed separately in ‘beech forest’ (Table 3:13) and ‘mixed forest’ (Table 3:14). The results of these analyses are also in agreement with similar other studies where it has been found that remotely sensed index is not a better predictor of species diversity (Stoms & Estes, 1993; Walker, et al., 1992). Although ‘NDVI-productivity’ relationship appears to be consistent over most ecosystems worldwide (Oindo, 2001); NDVI is least reliable in complex terrain (due to the frequency of mixed pixels) and arid zones or snow packs (Box, et al., 1989). In such environments, vegetation biomass is low, so measures of NPP are poorly estimated using NDVI (Oindo, 2001). Skidmore et al.(2003) found that remotely sensed index was not a better predictor of species richness than integrated climatic indices. Moreover, estimating understory diversity is compounded by the fact that there is difficulty in quantifying part of the forest reflectance that is due to the overstory and part due to the background including understory vegetation (Caetano, et al., 1998; Spanner, et al., 1994; Tuanmu, et al., 2010). Because of this reason, many efforts have been made to overcome the challenge using simple ‘radiative transfer model’ (Caetano, et al., 1998) where the integrated forest reflectance is expressed as a linear sum of two components: a forest canopy component (free of background influences) and a background dependent component.; and advanced classification algorithms like artificial neural network ((Linderman, et al., 2004) etc.

4.4. Land cover mapping of Belasitsa Mountain Since it is evident from the above discussions that NDVI is a poor predictor of understory woody species diversity; therefore, land cover map assumes greater significance in predicting understory woody species diversity in this study area. Moreover, it is evident that the overstory diversity has the most common influencing factor in determining understory woody species diversity in the study area. Therefore, land cover map depicting the distribution of dominant overstory species (forest types) helps us to infer about the understory woody species diversity in them. For example, land cover map (Figure 3:11) shows different dominant vegetation types covering the study area, and it is clear from the study that altitudinal range of about 600 m to 900 m covers the highest zone of understory woody species diversity (Figures 3:1 & 3:2). This altitudinal zone is a mixed forest zone mostly dominated by sweet chestnut and sessile oak (Figure 3:4). Therefore, understory woody species diversity can be correlated with composition of overstory species (forest type); and it is obvious that mixed forests i.e. sweet chestnut and sessile oak dominated forests harbour higher diversity than beech forest (Appendix-5). In nut shell, it is clear that beech forest harbour less understory diversity than chestnut and oak forest; therefore, the map showing the


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distribution of these forest types (overstory species) helps us to know about the diversity of understory woody species in these forests as well. It is worthwhile to examine here about the problem associated with extracting accurate land cover information from remote sensing data based on ‘pixel-based image classification’. This problem is mainly due to the gap between theoretically available information in remote sensing images and limited classification ability based on spectral analysis (Benz, et al., 2004; Qian, et al., 2005; Walter, 2004). This gap produces ‘noisy’ results that contain many wrongly classified pixels (Qian, et al., 2005). This problem is compounded when pixels of the same land/ vegetal cover class may not have similar spectral property, and /or different land/ vegetal covers give almost similar spectral property (Qian, et al., 2005). This is exactly the same problem in classification of Belasitsa Mountain forest, where different species of broadleaved forests produce a large amount of mixed pixels - ‘salt and pepper’ noise in the classified image. In recent years, two categories of classification approaches have been developed to overcome this problem (Blaschke, et al., 2000) viz.: ‘per-field classification’ and ‘object-based classification’. These new approaches too have their own limitations (Benz, et al., 2004; De Kok, et al., 1999; Walter, 2004). Per-field classification requires priori information about boundaries of objects in the image, which limits application to many areas (Walter, 2004). Object-based classification typically starts with preliminary step of grouping neighbouring pixels into meaningful areas/objects through advanced image segmentation techniques (Benz, et al., 2004; De Kok, et al., 1999). Then further classification is performed on the generated objects instead of pixels; therefore, results of object-based classification rely highly on the correctness of the object generation step (Qian, et al., 2005). Recently, to avoid drawbacks from aforementioned approaches, Qian et al. (2005) claimed to have developed a novel technique known as ‘spatial contextual noise removal for post-classification’ that removes noise pixels produced by per-pixel-based classification. If any of above approaches were applied in the classification of Belasitsa Mountain forest in the present study, classified map would have been probably better with higher overall accuracy. These new approaches were not used in classification of land cover in the present study because of the limitation of time. Therefore, traditional per-pixel-based classification has been done with the possibility of incorrect classification especially in chestnut, oak, and miscellaneous mixed forests as these species are occurring mostly in mixed form.


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5. CONCLUSION AND RECOMMENDATION

5.1. Conclusion The study reveals that definitely there is distinctive spatial variation in diversity patterns of understory woody species in Belasitsa Mountain forests. This spatial variation in diversity is owing to the differing influences of topographic and overstory factors that create unique ‘microhabitats’ due to interactions of climate and various environmental factors across the geographic space in Belasitsa Mountain. The answers of each research questions need appraisal at this stage. Question 1: What is the spatial diversity pattern of understory woody species in the forests of Belasitsa Mountain? The understory woody species diversity calculated using Shannon diversity index was plotted against different altitudes of Belasitsa Mountain forests clearly indicates that diversity pattern of understory woody species vary across geographic space along the altitudinal gradient. Although there is no typical hump-shaped phenomenon because understory woody species diversity dips sharply at higher altitude; but it is highest near the mid-altitudinal zone of 600 m to 900 m elevation. Therefore, it is concluded that diversity of understory woody species varies along the altitudinal gradient. Question 2: How do topographic & overstory factors influence the diversity pattern of understory woody species? The results suggest that the influences of topographic and overstory factors vary in importance in different groups of forest. When whole forest of Belasitsa Mountain is considered as one group (All forest), the most influencing factors are altitude and overstory diversity. There is a distinct zonation of ‘beech’ and ‘mixed’ (non-beech) forests in Belasitsa Mountain. When the influences of topographic and overstory factors in these two types of forests are analysed separately, the factors vary in importance. In the case of ‘beech forest’, it is the overstory diversity and live-crown ratio that have dominant influence; while in the case of ‘mixed forest’, overstory diversity only has the significant influence in predicting understory woody species diversity in Belasitsa Mountain forest. It is established that variation of diversity is most prominent with overstory diversity (diversity of tree species), as overstory diversity is the common influencing factor in all the ‘three groups’ of forests having significant influence. Conservation managers aiming to restore and sustain the high diversity characteristics of these old-growth forests will need to consider the respective roles of various biophysical factors in these unique ecosystems. Question 3: What is the relationship between NDVI and diversity pattern of understory woody species? The results of the study suggest that NDVI is a poor predictor of understory woody species diversity. This holds true even when the relationship between NDVI and understory woody species diversity is analysed separately in two groups of forests viz. beech forest and mixed forest. Though ‘NDVI-productivity’ relationship appears to be consistent over most ecosystems worldwide, finding the relationship between understory diversity and NDVI is compounded by the fact that there is


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difficulty in quantifying part of the forest reflectance that is due to the overstory and part due to the background including understory vegetation. Moreover, beech often gives a complete coverage, so all the reflectance in case of beech forest will be from the canopy of beech only. Since NDVI is a poor predictor of understory woody species diversity, it is worth re-iterating that land cover classification of overstory species (forest types) can be used to infer understory woody species diversity through its correlation with overstory species composition (forest type).

5.2. Recommendations Other environmental factors especially edaphic factors and its associated characteristics e.g. soil type, soil nutrients, soil temperature, soil moisture and soil pH also needs to be considered in understanding the microclimate of the area. Incorporating these variables will help in better understanding of the influencing roles of different environmental factors in spatial distribution of understory woody species diversity in Belasitsa Mountain. In the present study only the natural forests were considered, but it was observed during the field work that large areas are covered with plantation of fast growing species especially Robinia pseudoacacia in the lower altitudinal range of Belasitsa Mountain. There are scattered patches of Pinus rubra plantations as well in the lower and mid-altitudinal ranges. To understand the exact diversity pattern of understory woody species diversity pattern in Belasitsa Mountain, these plantation especially areas covered Robinia pseudoacacia need to be covered in the study. Moreover, further comparative study of understory woody species diversity between natural forests and plantation forests in Belasitsa Mountain will be an interesting study. Belasitsa Mountain harbours the largest area of Castanea sativa forest in Bulgaria. Unfortunately, this valuable species is on the verge of extinction due to progressive deterioration of the species stands, despite declaring the species as protected species and its habitat as protected habitats. Periodic assessment of understory woody species diversity in successional stages of replacement of Castanea sativa by other co-dominant species is essential; so as to understand the dynamics of understory woody species vis-à-vis changing overstory composition for better management of higher conservation areas. Finally, similar study can be carried out focussing diversity of herbaceous species also to see whether it gives similar or differing diversity patterns in the forests of Belasitsa Mountain.

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43

APPENDICES Appendix - 1

List of understory woody species of Belasitsa Mountain forests, Bulgaria.

Sr. No.

Species name English name Family Habit

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31.

Acer campestre L. Acer platanoides L. Acer pseudoplatanus L. Acer tataricum L. Castanea sativa Mill. Carpinus betulus L. Carpinus orientalis Mill. Cystisus hirsutus L. Cornus mas L. Cornus sanguinea L. Corylus avellana L. Cerasus avium (L.) Moench Crataegus monogyna Jacq. Euonymus europaeus L. Fagus sylvatica L. Fraxinus ornus L. Juglans regia L. Ostrya carpinifolia Scop. Malus sylvestris Mill. Quercus petraea (Matt.) Liebl. Pinus nigra Arn. Platanus orientalis L. Pseudotsuga menziesii (Mirb.) Franco Robinia pseudacacia L. Ruscus aculeatus L. Salix caprea L. Sambucus nigra L. Sorbus acuparia L. Sorbus torminalis (L.) Crantz Tilia tomentosa Moench Vaccinium myrtillus L.

Field maple Norway maple Sycamore maple Tatar maple Sweet chestnut Hornbeam Oriental hornbeam Hairy broom Cornelian cherry Common dogwood Hazel/European common hazel Wild Cherry/ Sweet cherry Hawthorn (May-tree) Spindle/European spindle/Common spindle Common beech Manna ash or South European flowering ash Common walnut European hop-hornbeam Wild Crab (Crab apple)/Wild apple Sessile /Durmast oak European black pine/Austrian pine/Crimean pine Oriental plane-tree Douglas-fir False acacia/Black locust Butcher's broom Goat willow Elder/Common elder Mountain ash Wild service tree/ Chequer(s) tree / Checker(s) tree Silver lime/Silver linden Bilberry

ACERACEAE ACERACEAE ACERACEAE ACERACEAE FAGACEAE CORYLACEAE CORYLACEAE LEGUMINOSAE CORNACEAE CORNACEAE CORYLACEAE ROSACEAE ROSACEAE CELASTRACEAE FAGACEAE OLEACEAE JUGLANDACEAE CORYLACEAE ROSACEAE FAGACEAE PINACEAE PLATANACEAE PINACEAE LEGUMINOSAE RUSCACEAE SALICACEAE CAPRIFOLIACEAE ROSACEAE ROSACEAE TILIACEAE ERICACEAE

Tree Tree Tree Tree Tree Tree Tree Shrub Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Shrub Tree Shrub Tree Tree Tree Shrub

44

Appendix - 2 Sample plot wise percentage compsoition of overstory species

Sr.N

o

Sam

ple

Plo

t X

Y

Alti

tude

(m)

Tot

al n

o. o

f sp

ecie

s

Acer

cam

pest

re (%

)

Acer

pse

udop

lata

nus

(%)

B

etul

a p

endu

la (%

)

Car

pinu

s be

tulu

s (%

)

Car

pinu

s or

ient

alis

(%)

Cas

tane

a s

ativ

a (%

)

Cer

asus

avi

um (%

)

Fagu

s sy

lvat

ica

(%)

Frax

inus

orn

us (%

)

Jugl

ans

regi

a (%

)

Ost

rya

car

pini

folia

(%)

Pinu

s ni

gra

(%)

Plat

anus

orie

ntal

is (%

)

Que

rcus

pet

raea

(%)

Tili

a to

men

tosa

(%)

Tot

al p

erce

ntag

e

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Plot-1 Plot-2 Plot-3 Plot-4 Plot-5 Plot-6 Plot-7 Plot-8 Plot-9 Plot-10 Plot-11 Plot-12 Plot-13 Plot-14 Plot-15 Plot-16 Plot-17 Plot-18 Plot-19 Plot-20 Plot-21 Plot-22 Plot-23 Plot-24 Plot-25 Plot-26 Plot-27 Plot-28 Plot-29 Plot-30 Plot-31 Plot-32 Plot-33 Plot-34 Plot-35 Plot-36 PSP-1 PSP-16 PSP-20 PSP-21 PSP-22 PSP-26 PSP-28 PSP-30 PSP-34 PSP-35 PSP-36 PSP-38 PSP-40 PSP-42 PSP-43 PSP-44 PSP-48 PSP-50 PSP-52 PSP-53 PSP-60 PSP-64 PSP-65 PSP-66

180392 180286 180362 180493 182638 182973 183591 184049 184584 184417 185920 186944 180621 180849 182161 181929 178438 177815 179925 179237 182001 181617 181123 181527 181227 181167 179164 179298 179480 179777 181509 181560 181747 182694 182743 182897 181014 180253 182489 182244 182495 176504 177997 184007 184007 183740 184021 179752 178000 179503 179506 179247 178007 183494 185754 185505 183497 184008 183996 184253

4583040 4582632 4581739 4583424 4582095 4582275 4582870 4582330 4584295 4585384 4588044 4588100 4585866 4586090 4588058 4588310 4587024 4586690 4585062 4586224 4586572 4586326 4585892 4585123 4584692 4584217 4586499 4587090 4587030 4586999 4584912 4584046 4583669 4587239 4586898 4586584 4587056 4586553 4585041 4584804 4584785 4586307 4586510 4587047 4587799 4588053 4588039 4586040 4586041 4587048 4586800 4586806 4586278 4585801 4587537 4587546 4585569 4585538 4585799 4586042

1625 1632 1703 1571 1441 1401 1305 1314 1191 1076 554 542 755 810 487 358 490 474 1147 760 778 860 910 1110 1220 1290 610 550 550 610 1090 1140 1130 440 460 440 640 637 740 897 785 452 493 756 516 548 549 916 634 582 599 580 514 662 720 686 764 903 847 915

16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12.50 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 50.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12.50 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 12.50 0.00 12.50 0.00 0.00 0.00 50.00 0.00 75.00 93.75 31.25 25.00 0.00 0.00 0.00 0.00 0.00 31.25 75.00 0.00 0.00 0.00 68.75 93.75 56.25 43.75 25.00 50.00 0.00 75.00 100.00 43.75 43.75 62.50 75.00 100.00 0.00 68.75 81.25 18.75 0.00 18.75 25.00 68.75 50.00 18.75 6.25 12.50 43.75

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

100.00 100.00 100.00 100.00 100.00 93.75 100.00 100.00 100.00 93.75 0.00 0.00 0.00 87.50 0.00 0.00 0.00 0.00

100.00 0.00 0.00 68.75 75.00 100.00 100.00 100.00 25..00 0.00 6.25 0.00

100.00 100.00 100.00 0.00 0.00 0.00 12.50 0.00 31.25 18.75 6.25 0.00 0.00 0.00 0.00 0.00 0.00 93.75 0.00 6.25 0.00 0.00 56.25 62.50 18.75 43.75 62.50 81.25 56.25 56.25

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 37.50 0.00 0.00 0.00 12.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 31.25 0.00 31.25 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 37.50 6.25 62.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 0.00 6.25 6.25 0.00 0.00 6.25 0.00 6.25 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

100.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 37.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 18.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

100.00 87.50 37.50 0.00 0.00 0.00

100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 18.75 100.00 12.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 25.00 18.75 12.50 0.00 6.25 0.00 50.00 56.25 31.25 25.00 0.00 0.00 12.50 0.00 81.25 100.00 0.00 0.00 12.50 0.00 0.00 12.50 18.75 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12.50 0.00 0.00 0.00 0.00 0.00 0.00 25.00 0.00 0.00 0.00 0.00 0.00 0.00 6.25 0.00 18.75 18.75 0.00 0.00 0.00 25.00 6.25 0.00 18.75 12.50 0.00 6.25 0.00 0.00 0.00 0.00 6.25 0.00 0.00 0.00 18.75 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12.50 0.00 6.25 0.00

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

45

Appendix - 3 List of overstory species of Belasitsa Mountain forests, Bulgaria

Sr. No.

Species name English name Family Habit

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Acer campestre L. Acer pseudoplatanus L. Betula pendula Roth Carpinus betulus L. Carpinus orientalis Mill Castanea sativa Mill. Cerasus avium (L.) Moench Fagus sylvatica L. Fraxinus ornus L. Juglans regia L. Ostrya carpinifolia Scop. Pinus nigra Arn. Platanus orientalis L. Quercus petraea (Matt.) Liebl. Tilia tomentosa Moench

Field maple Sycamore maple Silver birch/European white birch Hornbeam Oriental hornbeam Sweet chestnut Hawthorn (May-tree) Common beech Manna ash or South European flowering ash Common walnut European hop-hornbeam European black pine/Austrian pine/Crimean pine Oriental plane-tree Sessile/Durmast oak Silver lime/Silver linden

ACERACEAE ACERACEAE BETULACEAE CORYLACEAE CORYLACEAE FAGACEAE ROSACEAE FAGACEAE OLEACEAE JUGLANDACEAE CORYLACEAE PINACEAE PLATANACEAE FAGACEAE TILIACEAE

Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree Tree

46

Appendix - 4

Post-hoc Tests (Hochberg’s GT2) Multiple Comparisons - Understory Woody Species Diversity

(I) Overstory Composition (J) Overstory Composition

Mean Difference

(I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

Fagus Castanea -1.1018644 .1353509 * .000 -1.546601 -.657128

Quercus -.3782776 .1572635 .399 -.895014 .138459

Fagus-Castanea -.8253931 .1690532 * .000 -1.380868 -.269918

Castanea-Fagus -1.4080610 .2096847 * .000 -2.097043 -.719079

Quercus-Castanea -1.0087350 .2514031 * .006 -1.834796 -.182674

Castanea-Misc. -1.4194255 .2096847 * .000 -2.108408 -.730443

Misc.Mixed -1.2120009 .1482695 * .000 -1.699185 -.724817

Castanea Fagus 1.1018644 .1353509 * .000 .657128 1.546601

Quercus .7235868 .1790524 * .005 .135256 1.311917

Fagus-Castanea .2764713 .1894912 .979 -.346159 .899102

Castanea-Fagus -.3061966 .2264853 .991 -1.050382 .437989

Quercus-Castanea .0931294 .2655776 1.000 -.779506 .965765

Castanea-Misc. -.3175611 .2264853 .987 -1.061747 .426625

Misc.Mixed -.1101365 .1712068 1.000 -.672688 .452415

Quercus Fagus .3782776 .1572635 .399 -.138459 .895014

Castanea -.7235868 .1790524 * .005 -1.311917 -.135256

Fagus-Castanea -.4471154 .2057155 .581 -1.123056 .228825

Castanea-Fagus -1.0297834 .2402240 * .002 -1.819112 -.240455

Quercus-Castanea -.6304574 .2773868 .500 -1.541895 .280980

Castanea-Misc. -1.0411478 .2402240 * .002 -1.830476 -.251819

Misc.Mixed -.8337233 .1890072 * .002 -1.454763 -.212683

Fagus-Castanea Fagus .8253931 .1690532 * .000 .269918 1.380868

Castanea -.2764713 .1894912 .979 -.899102 .346159

Quercus .4471154 .2057155 .581 -.228825 1.123056

Castanea-Fagus -.5826679 .2481023 .441 -1.397883 .232547

Quercus-Castanea -.1833419 .2842368 1.000 -1.117288 .750604

Castanea-Misc. -.5940324 .2481023 .407 -1.409247 .221182

Misc.Mixed -.3866078 .1989244 .763 -1.040234 .267018

Castanea-Fagus Fagus 1.4080610 .2096847 * .000 .719079 2.097043

Castanea .3061966 .2264853 .991 -.437989 1.050382

Quercus 1.0297834 .2402240 * .002 .240455 1.819112

Fagus-Castanea .5826679 .2481023 .441 -.232547 1.397883

Quercus-Castanea .3993260 .3101278 .995 -.619693 1.418344

Castanea-Misc. -.0113645 .2773868 1.000 -.922802 .900073

Misc.Mixed .1960601 .2344346 1.000 -.574245 .966366

47

Appendix - 4 (Contd.---)

(I) Overstory Composition

(J) Overstory

Composition

Mean Difference

(I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

Quercus-Castanea Fagus 1.0087350 .2514031 * .006 .182674 1.834796

Castanea -.0931294 .2655776 1.000 -.965765 .779506

Quercus .6304574 .2773868 .500 -.280980 1.541895

Fagus-Castanea .1833419 .2842368 1.000 -.750604 1.117288

Castanea-Fagus -.3993260 .3101278 .995 -1.418344 .619693

Castanea-Misc. -.4106905 .3101278 .993 -1.429709 .608328

Misc.Mixed -.2032659 .2723884 1.000 -1.098280 .691748

Castanea-Misc. Fagus 1.4194255 .2096847 * .000 .730443 2.108408

Castanea .3175611 .2264853 .987 -.426625 1.061747

Quercus 1.0411478 .2402240 * .002 .251819 1.830476

Fagus-Castanea .5940324 .2481023 .407 -.221182 1.409247

Castanea-Fagus .0113645 .2773868 1.000 -.900073 .922802

Quercus-Castanea .4106905 .3101278 .993 -.608328 1.429709

Misc.Mixed .2074246 .2344346 1.000 -.562881 .977730

Misc.Mixed Fagus 1.2120009 .1482695 * .000 .724817 1.699185

Castanea .1101365 .1712068 1.000 -.452415 .672688

Quercus .8337233 .1890072 * .002 .212683 1.454763

Fagus-Castanea .3866078 .1989244 .763 -.267018 1.040234

Castanea-Fagus -.1960601 .2344346 1.000 -.966366 .574245

Quercus-Castanea .2032659 .2723884 1.000 -.691748 1.098280

Castanea-Misc. -.2074246 .2344346 1.000 -.977730 .562881

*. The mean difference is significant at the 0.05 level.

Documents

Diversity Patterns of Understory Woody Species in the Forests of Belasitsa Mountains, Bulgaria