Hetauda. Simara. Daman. Timure. Dhunse. Figure 8: Mean monthly temperature pattern of five climatology station within Study area. Mean monthly temperature ...
Trait andspecies species distribution along the altitudinal Traits and distribution along the altitudinal gradients of the Himalayas gradient of the Himalayas, Nepal
Trait and species distribution along the Trait and species distribution along the altitudinal altitudinal gradients of the Himalayas gradients of the Himalayas
Bishnu Prasad Dhakal June, 2018 Forest Ecology and Forest Management, 80436
Traits and species distribution along the altitudinal gradient of the Himalayas, Nepal
Trait and species distribution along the altitudinal Wageningen University gradients of the Himalayas M.Sc. thesis by Dhakal BP, MSc student Forest and Nature Conservation
June, 2018
FEM 80436
Supervisor: Prof. Dr. ir. L. Poorter, Forest Ecology and Forest Management Group, Wageningen University Co-supervisor: Surya Kumar Maharjan, PhD student, Forest Ecology and Management Group, Wageningen University
The MSc report may not be copied in whole or in parts without the written permission of the author and the chair group.
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Acknowledgements
This great opportunity to expand my knowledge and learn different cultures was provided by Wagenighen University. I would like to express my grateful to Prof. Dr. Ir. Lourens Poorter for his admirable guidance, supervision, encouragement, constructive feedback and suggestions from designing to completing of this study. I am equally gratitude to my cosupervisor Surya kumar Maharjan for his effective guidance, fruitful ideas, feedback and suggestions from field works to accomplish of this work. I also express my sincere thankful to Dr.Ir. Frank Sterck for his constructive ideas to design field works. My sincere thanks goes to all who helped during my course and field study in Nepal. It is my great pleasure to thank Department of Forest Research and Survey, providing me leave for this study. I am indebted to thanks to Wageningen University for providing me the opportunity to pursue M.Sc. Forest and Nature conservation and many thanks goes to Netherlands Fellowship Program (NFP) for providing me scholarship for this study. I would like to express sincere thanks to Marina Makri, Amit Karki, Amul Acharya, Basanta Sharma, Binod Singh and Narendra Prasad Guragain for their support, advice and encourage for me. I would like to thank all my family and friends who directly and indirectly encouraged and advised me during field work, data analysis and accomplish this study. At last but not the least, I would also like to greet my parents for their blessing to complete my study. I am very much thankful to my beloved wife who was brave enough to look after our daughter on my absence during this two years course and special thanks to my daughter Sambhavi Dhakal for her patience to my long absence.
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Table of Contents Acknowledgement ..................................................................................................................... iii List of table................................................................................................................................ vi List of figure ............................................................................................................................. vii Summary ................................................................................................................................. viii 1.
Introduction ..................................................................................................................... 1
1.1
Problem statement ....................................................................................................... 1
1.2
Theoretical framework ................................................................................................ 3
1.2.1
Plant functional traits ............................................................................................... 3
1.2.2
Plasticity of the traits ............................................................................................... 5
1.2.3
Functional trait strategies or trait syndromes ........................................................... 6
1.2.4
Assembly rules and trait-environment linkages ....................................................... 6
1.2.5
Ecological niche and species response curve........................................................... 9
1.2.6
Conceptual model .................................................................................................. 11
1.3 2.
Research objective, research question and hypotheses ............................................. 15 Materials and Methods .................................................................................................. 16
2.1
Study area .................................................................................................................. 16
2.1.1
Climate ................................................................................................................... 18
2.1.2
Geology .................................................................................................................. 21
2.1.3
Vegetation .............................................................................................................. 23
2.1.4
Plant species distribution in central Nepal ............................................................. 25
2.2
Data and Data Source ................................................................................................ 28
2.2.1
Species selection .................................................................................................... 28
2.2.2
Sampling design and data collection...................................................................... 28
2.2.3
Trait calculations .................................................................................................... 29
2.2.4
Data Analysis ......................................................................................................... 32
3.
Results ........................................................................................................................... 34
3.1
Correlation amongst altitudinal parameter ................................................................ 34
3.2
Plant functional traits versus altitude gradient .......................................................... 34
3.3
Plant species traits and their best prediction potentials ............................................. 35 iv
3.4
Traits association and plant strategies ....................................................................... 38
3.5
Plant functional traits response to environmental variables ...................................... 42
4.
Discussion ..................................................................................................................... 45
4.1
Trait associations and plant strategies ....................................................................... 45
4.1.1
Leaf economics versus toughness spectrum .......................................................... 45
4.1.2
Leaf display efficiency versus branch size ............................................................ 47
4.2
Plant species traits and their best prediction potentials ............................................. 48
4.3
Plant functional traits versus altitudinal gradient ...................................................... 49
4.4
Plant functional traits response to environmental variables ...................................... 50
5.
Conclusions and recommendations ............................................................................... 52
5.1
Conclusions ............................................................................................................... 52
5.2
Strengths and limitations of the study, and recommendations for further research .. 52
5.3
Management implications.......................................................................................... 53
Bibliography ............................................................................................................................. 55
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List of table Table 1: Definition and unit of functional traits and their response to associate environmental variables. .................................................................................................................... 13 Table 2: Bioclimatic profile of Nepal.Bio-climatic profile of Nepal ....................................... 18 Table 3: Vegetation types of Nepal classified by Tree Improvement and Silvicultural Component ................................................................................................................. 23 Table 4: Spearman’s correlation coefficient between altitudinal parameters ......................... 34 Table 5: Pearson’s correlation between plant functional traits and altitudinal parameters of species ........................................................................................................................ 34 Table 6: Regression coefficient (β) and coefficient of determination(R2) obtained from Linear Regression analysis. ................................................................................................... 35
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List of figure Figure 1:Illustration of traits ...................................................................................................... 4 Figure 2:Illustration of The general concept of assembly rule.. ................................................. 7 Figure 3: Illustration of assembly rule in broad sense. .............................................................. 8 Figure 4: Illustration niche breadth of species. ........................................................................ 10 Figure 5: Illustration of species response curve ...................................................................... 10 Figure 6: Conceptual model of the research............................................................................. 14 Figure 7: Map of Study area.. ................................................................................................... 17 Figure 8: Mean monthly temperature pattern of five climatology station within Study area. Mean monthly temperature of study area closely akin to the national pattern. ..... 20 Figure 9: Mean monthly rainfall pattern of five climatology station within Study area. Mean monthly rainfall patter of study area also closely resemble to the national pattern. 20 Figure 10: Schematic cross-section Study area, from South to North and the corresponding Vegetation types within study area. ........................................................................ 25 Figure 11: Map of Central Nepal altitudinal gradient.. ............................................................ 26 Figure 12: Forest types in central Midlands along the elevation gradients. ............................. 27 Figure 13: South facing forest of pure Quercus semicarpifolia at 2488m, Simbhanjyang, Makawanpur, Nepal ................................................................................................ 27 Figure 14: Sub-alpine Zone of North facing treeline in central Himalayas region of Nepal ... 27 Figure 15: Scatter plots showing the bivariate relationship between the best predictor traits, average altitude and altitudinal range of 32 Himalayan species . ......................... 37 Figure 16: Loading plots (A) Traits and Species scores (b) of 192 samples from 32 tree species based on 25 plant functional traits. ............................................................ 40 Figure 17: Relationship between key-traits of tree along the altitudinal gradients. ................. 42 Figure 18: Loading plots (a) Traits and Species scores (b) of a Redundancy Analysis. ......... 44
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Summary Justification: How environmental factors influence plant species distribution, diversity and composition is one of the central question in ecology and conservation biology. Many environmental factors, such as temperature co-vary along the altitudinal gradient, and it provides therefore, an excellent natural laboratory to evaluate potential effects of environmental factors and climate change on plant species distribution. In this study, I used the Himalayan altitudinal gradient, one the steepest and longest altitudinal gradients, to understand how environmental factors interact with plant functional traits and ultimately define the patterns of plant species distributions along the altitudinal gradient. Aim: In this study, I evaluated 1) which functional traits are the best predictors of plant species distributions along the altitudinal gradients, 2) what trait syndromes or plant strategies can be recognized among the plant species along the altitudinal gradient, and 3) which environmental factors are responsible for shaping plant species distributions along the altitudinal gradient. Study design: : In this study, 32 tree species were selected that varied in their distribution from sub-tropical forests to upper temperate forests of Nepal. For each species, 6 pole-sized trees were measured: 3 at the lower limits of their altitudinal ranges, and 3 trees at the upper limits. For each tree, 27 functional traits were quantified. The altitudinal niche parameters of the plant species (minimum, maximum, average altitude and altitudinal range) were quantified based on National Forest Inventory data from Department of Forest Research and Survey, Nepal. Global datasets such as climatic data from WorldClim, soil data from ISRIC and aridity and potential evapotranspiration data from CGIAR-CSI were used as sources of environmental data. Multiple linear regression analysis was used to evaluate the functional traits best predicting plant species distributions along the altitudinal gradient. Principal component analysis was used to evaluate traits syndromes or plant strategies among the plant species along the altitudinal gradient. Finally, a redundancy analysis was used to evaluate key environmental factors responsible for shaping plant species distributions along the altitudinal gradient. Result: The leaf thickness, bark dry matter content and branch dry matter content of species increasing with the average altitudinal niche of the species, probably because thicker leaves, tougher bark and tougher tissues allow species to cope with higher irradiance, higher frost and higher environmental fluctuations at the high altitudes. Lower altitude species were found to viii
have a resource acquisitive strategy while high altitude species were found to have a resource conservative strategy. The resource acquisitive strategy allows low altitude species to make best use of their favourable environmental conditions and grow fast while a resource conservative strategy allows high altitude species to withstand harsh environmental conditions at high altitudes. Mean annual temperature was found to be the key environmental factor shaping plant species distributions along the altitudinal gradient, probably because temperature changes most predictively variable along altitudinal gradient because it is physically associated with altitude, as for every 100 m increase in altitude, air temperature drops by 0.60C on an average. Conclusions: Leaf thickness, bark dry matter content and branch dry matter content were found to be the best predictors of plant species distribution along the altitudinal gradient. Lower altitude species were found to have a resource acquisitive strategy while high altitude species were found to have resource a conservative strategy. Mean annual temperature was found to be the key environmental factor shaping plant species distributions along the altitudinal gradient. Key words: Environmental gradient, plant functional traits, resource acquisitive versus conservative strategy, species niches, trait syndromes
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1.
Introduction
1.1
Problem statement
Understanding how environmental factors are influencing distribution pattern, abundance and co-existence of tree species along environmental gradient are vital issues in ecology, conservation and management of natural ecosystems. Plant species have their own specific requirements of climatic and environmental conditions to grow, persist and reproduce (Bhatta et al., 2015) On a large scale climatic variable especially rainfall (rainfall changes with altitude) can play a significant role in species distribution. In addition soil fertility, topography and irradiance can play vital role in species distribution in small scale (Amissah et al., 2014; Bongers et al., 1999; Engelbrecht et al., 2007; Toledo et al., 2012). Hence, any kind of change in environmental or climatic condition of the sites helps to change the individual species abundance or plant species composition in the ecology (Bhatta et al., 2015). One of the most pronounced and predictable environmental gradient in nature is the altitudinal gradient, with the Himalaya as a prime example where the altitudinal gradient and associated environmental conditions change over a short distance. Nepal has a broad parallel zone of vegetation ranging from tropical to alpine zone within the short horizontal distance (Jackson, 1994) which provides an ideal situation to explore the plant species richness and distribution along elevation gradient as response to certain environmental condition. Altitudinal gradient, such as found in the Himalayan region can to assess ecological aspect of plants and their relationship with climatic factors (Klimeš, 2003; Körner, 2000) and how species may respond to climatic change. Environmental variables such as temperature, precipitation, potential evapo-transpiration (PET), and ultraviolet radiation can play vital role to determine distribution of species along altitudinal gradient (Bhattarai et al., 2004; Funnell & Parish, 2001; Körner, 1999). Climatic factors can set the upper limits of plant species distribution whereas both climatic factors and biotic factors (e.g., competition, pollination) are essential for the lower distribution range of species (Bhattarai et al., 2004; MacArther, 1972). Furthermore, far less species are distributed in and above treeline ( Körner, 2007). The atmospheric variables such as pressure and gases (O2 and CO2), temperature, radiation under the cloudy sky and higher fraction of UV-B radiation change due to decreasing land area with elevation (Körner, 2007). In addition, the
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species distribution patterns along the elevation gradient also depends on various functional traits of plants interaction with these climatic factors. Climate change has the strongest and wider impact for natural system (IPCC, 2014) and Himalayan region are the most sensitive region to climate change after the polar region (K.C. & Ghimire, 2015). In general, global temperature is warming by 0.850C over the period of 1880 to 2012 (IPCC, 2014) and precipitation patterns are becoming substantially irregular (IPCC, 2014; Walther et al., 2002). However, temperature and annual precipitation have increased in Himalayan region at approximately 1.50C and 362 mm respectively in the last 60 years (1950-2010) (Salick et al., 2014). Climate change is the main impacting factor within the Himalayan ecosystem (Gaire et al., 2014) which affects in the change of environmental conditions and, availability and condition of resources (Nicotra et al., 2010). Furthermore, it impacts various life development stages of the tree species including germination, recruitment, growth and spatial distribution (Khanal et al., 2016).Thus, species composition may change due to this effect. In order to cope with such a global climate change pattern, plant species may be shift their distribution towards poleward in latitude and upward in elevation following the environmental conditions to which they are acclimatised (Nicotra et al., 2010; Walther et al., 2002). The tree species are moved to poleward and towards the higher elevation due to the effect of global warming in temperate elevation gradient, however, there are little evidence on shifting of the tree species in tropical and sub-tropical zone (Colwell et al., 2008). The past study showed that the altitudinal limits of tree species has already shifted upward (Gaire et al., 2014; K.C. & Ghimire, 2015) not only high altitudinal species but also recorded species from lower and middle mountains areas (Khanal et al., 2016) due to the effect of climate change. However, the study conducted by Bhatta et al., (2018) found that species assemblages in alpine vegetation of central Nepal are downward shifted rather than upward due to the warmer winter, increased precipitation, reduced grazing pressure. The ecological niche of plants are important to predict possible response to climate change (Borchert, 1998; Toledo et al., 2012). Studies of altitudinal gradient of plant species distribution in the Himalayas, Nepal are very limited or almost not done. To know which functional traits of plants are more responsible to the distribution of species along the elevation gradient it would be helpful to make future policies for conservation and management of species. Here, my aim is to explore the role of functional traits in shaping the pattern of species distribution along the altitudinal gradient of
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central Nepal. Such knowledge on role of plant functional traits in shaping plant species distribution along the altitudinal gradient will be crucial in: Providing information on how species distribution pattern changes with altitude; Predicting species distribution pattern of species along elevation gradient of other region of Nepal; Formulating management policies and species conservation that are distributed in altitudinal gradient; Providing information of plant strategies to adapt in climate change situation.
1.2
Theoretical framework
1.2.1 Plant functional traits Plant functional traits are currently widely used in plant ecology, although its meaning is varies among the plant ecologists (Violle et al., 2007). “Traits” are any morphological, physiological, or phenological structures that can be measured at individual level without interaction with environment or any other organizational level (Violle et al., 2007). Furthermore, “functional trait is defined as morphological, physiological and phenological (M-P-P) trait (Figure 1) which indirectly influences on fitness of the individuals by its effects on performance trait i.e., growth, reproduction and survival of the plants ( Reich et al., 2003; Violle et al., 2007). Functional trait-based research approach is widely used approach in ecological and evolutionary research to understand how species respond to-and how they affect their’ environments. Functional traits are characteristics of plants that helps to grow, persist and reproduce and increase plant fitness (Figure 1) (Poorter, 2007). Plant traits are considered to be major drivers shaping the assembly of communities and species diversity (Kunstler et al., 2016). Species responses to the environment is best evaluated by using a suite of functional traits (Eviner, 2004) because environmental factors filter out that traits which are not suitable in certain environment.
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Figure 1: Illustration of traits, in which different Morphological, Physiological and Phenological (M-P-P) traits interact/interrelated (dashed double arrows) with each other which influences the individual fitness by effecting the performance traits (survival, growth and reproduction) (Adapted from Violle et al., 2007)
“Soft” and “hard traits” are two types of functional traits (Garnier et al., 2001). Soft traits are plant functional traits which are easily and quickly measured traits, such as specific leaf area (SLA), seed shape, seed mass etc. Hard traits are difficult or complex to measure, such as fecundity, life span, dispersal distance etc. (Weiher et al., 1999). Soft traits can be used as surrogate of hard traits even though they are often closely related to hard traits. For example, the soft trait, specific leaf area (SLA) is used as an indicator of relative growth rate as a hard trait (S. Díaz & Cabido, 1997) and combination of seed mass and above ground vegetative biomass (soft traits) can predict fecundity (hard trait) of the plants (Shipley & Dion, 1992; Weiher et al., 1999). Some of the main traits are highly correlated to each other, such as above ground biomass and plant height (Weiher et al., 1999). The morpho-physiological, phenological traits allow plants to cope with adverse environmental conditions and play a significant role in shaping the distribution of species along the elevation gradient (Maharjan et al., 2011). Plant species can survive in any environment if its functional traits are appropriate even though the environmental conditions are changed or plant invades a new territory. The distribution of plant species are largely dependent on the ecological sorting processes because survival of species depends on functional traits that are suited to certain environment (Reich et al., 2003). Therefore, plant functional traits are currently used in plant ecosystem ecology and community assemblage (Violle et al., 2007). 4
1.2.2 Plasticity of the traits Plant traits vary within individuals species, which is known as “plasticity” (Nicotra et al., 2010). However, Gratani, (2014) defined “phenotypic plasticity is a change in the phenotype expressed by a single genotype in different environments”. The phenotypic plasticity is depends on the genotypic norms of reaction and both of these two are linear function of the environments in which grows (Lande, 2009; Van Kleunen & Fischer, 2005). Phenotypic plasticity is only seen when there is enough variability in genetic level by correlating genetic with other traits (Gratani, 2014). Nevertheless, phenotypic plasticity can play significant role in permitting to persevere in environments rather than genetic diversity in rapid changing global climate (Gratani, 2014; Vitasse et al., 2010). Phenotypic plasticity changes through generation in time and space (Phillips, 2006). Plasticity can successfully cope with different novel environmental conditions, even though it is still depends on the genotype (Lande, 2009; Nicotra et al., 2010). Plants are able to adapt in different physical and biotic environmental conditions and trade-off among the various functions within an organism with the reflection of functional traits. The developmental stage of plants is not only affected by genetic factors but also by environmental factors and adapt the novel conditions through phenotypic plasticity (Lande, 2009; Provine, 1997). Therefore, plasticity allows plants to cope with fast changes in climatic conditions (Chevin et al., 2010; Lande, 2009; Nicotra et al., 2010). Those species that successfully survive in novel environmental conditions have highly adaptive plasticity (Gratani, 2014). Plasticity is more important for lasting persistence of species whereas rapid phenotypic adaptation may be able to prevent current species extinction from climate change (Lande, 2009). In addition to phenotypic plasticity can express to show the changing pattern in species distribution, community composition and production capacity under changing global climate (Gratani, 2014; Lande, 2009). There are different kind of plasticity such as physiological, anatomical and morphological and it has different functions in plants growth and adaptations. Furthermore, physiological plasticity responsible to grow and reproduce and tends to increase colonization capacity of plants in gaps and open area. Physiological plasticity play primary role to acclimatize plants to adverse environments and other two plasticity have secondary role. Morphological plasticity is responsible to enhance capacity of plants to grow in shade environment. In general, light demanding and early-successional species are more plastic than shade tolerant and late-successional species. Thus, phenotypic
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plasticity rather than genetic diversity paly fundamental role to persist plants in their environments under rapid global warming condition (Gratani, 2014). 1.2.3 Functional trait strategies or trait syndromes Plant species can successfully survive in certain environments through the developmental strategy resulting from adaptive evolution process and/or plastic responses, which is known as trait syndromes. Plants have their own strategies that consists of trade-offs or covariations between various functional traits to optimally survive in a specific environment (Reich et al., 2003). For instance, shade tolerant species can grow, persist and reproduce in low light condition while light demanding species require high level of light in order to establish and reproduce (Poorter, 1999) and late successional/shade tolerance species can survive in low resource availability, have lower specific leaf area, low nitrogen content, low respiration and assimilation and low allocation of biomass in leaf. This is the strategy to protect from herbivore, mechanical damage and pathogens (Poorter & Bongers, 2006; Reich et al., 2003). Similarly, species which can grow in dry area have thick leaves with thick cuticles and small thick walled cells, low specific leaf area and long leaf life span (Reich et al., 2003). Thick leaves have thicker cuticles due to higher concentration of lipids. That’s why thicker leaves able to reduce water loss from leaves (Villar & Merino, 2001). Furthermore, it can success to conserve water and nutrients because of higher construction costs and also able to defence with pathogens and herbivores (Kitajima & Poorter, 2010). Species show a spectrum in trait values and hence, in plant strategies. In general, species that follow resource-capture strategy, have fast tissue turn over, rapid short term growth, tissues with high N content whereas species following a resource-conservation strategy, have low respiration, slow tissue turn over, low N content (Díaz et al., 2004; Grime et al., 2016; Reich et al., 2003; Reich et al., 1997). 1.2.4 Assembly rules and trait-environment linkages The assembly rule in ecology was first applied by Diamond, (1975) (Díaz et al., 1998) in animal ecology and later plant ecologists (Drake, 1990; Watkins & Wilson, 1992; Wilson et al., 1995) in plant ecology. Assembly rules are defined as generalized constraint to species coexistence in which species of the regional pool will be formed into the local community (Diamond, 1975; Díaz et al., 1998; Drake, 1990; Wilson & Gitay, 1995). Various species are present in species pool which interact with various plant traits (morphological, physiological or ecological) to form a local community after filtering out unsuitable traits by environmental 6
variables. Therefore, there are two sets of data in ecological communities: a species pool and a matrix of different functional traits (Figure 2). Assembly rules are also defined in boarder sense by Keddy (1992), local communities are formed after intervention on the regional species pool as filters. He further mentioned environmental variables include climatic conditions, disturbance regimes and biotic interactions which act as a filter and successively eliminate species from regional pool which are not suitable under the certain set of the conditions (Figure 3). Among all the filters, biotic interaction are the most important filters in assembly rule (Díaz et al., 1998). These filters activate functional traits and remove those functional traits which are not suitable to the certain environment and make communities by those species which survive in filters (Keddy, 1992). In tropical forest ecosystem, environmental filters are the most important and predictable factors to form communities, although, system are more complex (Lebrija-Trejos et al., 2010). Different studies state that individual plant
Figure 2: Illustration of The general concept of assembly rule. Total species pool interact with different traits of plants and environmental factors acts as a filter (deletion of unsuitable traits under certain environment). Consequently, form the community of those species which have suitable traits on certain environmental conditions. (Adapted from Keddy, 1992).
performance is determined by the functional traits, however, these relationship are not limiting factors for assemblage of communities (Lebrija-Trejos et al., 2010). The communities formation are akin to evolution through natural selection and those communities having a suitable trait are able to establish, grow and reproduce in the environment (Díaz et al., 1998; Keddy, 1992). The assembly and response rules of the community ecology (Keddy, 1992) is described as functional traits that are suitable in specific environmental conditions faced along
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altitudinal gradient which might be clue for distribution of plant species in climate change situation. Plant species distribution along the elevation gradient do not only depend on plant strategies but also on trait-environment linkages. Among all filters, climate, fire (disturbance) and site productivity (interaction) are more impacting filters (Woodward & Diament, 1991). Consequently, trait-environmental linkage can be seen due to the effect of these filters (Díaz et al., 1998; Keddy, 1992; Woodward & Diament, 1991). These early statement of traitenvironment linkage of Woodward & Diament (1991) and Keddy, (1992) are supported by different other studies such as Díaz et al., (1998), Thuiller et al., (2004), Holmgren & Poorter, (2007) and Poorter & Markesteijn, (2008). Trait-environment linkages are consistent and predictable association between plant characteristics and certain environmental conditions (Díaz et al., 1998).
Figure 3: Illustration of assembly rule in broad sense. Assembly rule in boarder sense are various kinds of filters forced to the regional species pool, traits or functional types and consequently form the community. (Adapted from Diaz et al., 1998).
In community formation process, species are also selected on the basis of assembly rules, which specify the subset of species in the total pool which are allowed to survive in specific habitat (Keddy, 1992). With increasing altitude plant traits are differed from the regional pool to local species communities in high mountain regions due to filtering effect (Díaz et al., 1998), as a result, the different species are distributed in the Himalayas.
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1.2.5 Ecological niche and species response curve Each species has its own climatic or environmental requirement in the nature which is known as “ecological amplitude or niche” of the species (Bhatta et al., 2015). The species niche can be described in term of the fundamental and the realized niche. The fundamental (or physiological) niche is the combination of environmental conditions and resources which support to maintain existence of species, use of resources and produce off-spring when limiting factors are absent in its habitat (McGill et al., 2006). The realized (or ecological) niche is full range of environmental conditions in which a species occur and reproduce when there are presence of limiting factors, i.e. competitors. Hence, the physiological niche is always greater than ecological niche. Niche differentiation (i.e. competing species use environment differently) is a vital mechanism to explain species co-existence in ecosystem (Born et al., 2014). Segregating of resources (i.e. species use different resources) tends to make higher opportunity to coexist species. In addition to partitioning of resources also tends to increase opportunity of more species coexist (Rasmann et al., 2014). Tropics have higher productivity of resources that tends to create strong division of the ecological resources, consequently, making greater number of niches and high rate of specialization with higher species diversity (MacArther, 1972; Rasmann et al., 2014). However, lower productivity in tropical forests create higher opportunity for species diversity and specialization (Huston & Wolverton, 2009; Rasmann et al., 2014). Stable condition may be favoured for the specialization of the plants. Therefore, species that grow in unstable and stressful environments (which is typical in high elevation habitats and high latitudes) have wider niches than greater stable area (tropical area) (MacArther, 1972; Rasmann et al., 2014; Vazquez & Stevens, 2004). Generally, the most productive species dominate under high resource condition and outcompete less productive species which therefore can grow and persist in low resource conditions (McGill et al., 2006; Sterck et al., 2011). Hence, niche breadth (Figure 4) of the species may be increased with increasing latitude (Rasmann et al., 2014; Vazquez & Stevens, 2004).
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Figure 4: Illustration niche breadth of species. (Maharjan unpublished).
Species response curves shows how species occurrence (presence/absence) or abundance varies along an environmental gradient. They have been used to describe and visualize the ecological niche of a species (Figure 5). The species response curves may be vary in shape, amplitude, width and optimum (Austin & Gaywood, 1994; Huisman et al., 1993; Jongman et al., 1995; Toledo et al., 2012). Symmetric bell-shaped response curves are the most used in ecology, response curves can show a wide variety of shape (Austin, 1976; Huisman et al., 1993) due to the interaction between extreme environmental stress and species which follow skewed or non-unimodal (Oksanen & Minchin, 2002). Response curves are unimodal, symmetric, skewed and non-unimodal. The response curves are not always unimodal.
Figure 5: Illustration of species response curve representative fundamental and realized niche of a species. (Adopted from Austin & Smith, 1989).
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1.2.6 Conceptual model The theoretical framework of this study is based on community assembly rules (Keddy, 1992). Environmental factors change with the increasing altitude which filter out the traits that are unsuitable to the specific conditions of the environment (Figure 6). The different environmental variables in mountain regions are changing with increasing altitude in different directions and find those species which are well adapted to the certain local condition along the elevation gradient (Körner, 2007). Altitude is an apparent, feasible and fundamental parameter in montane environment which influences the climate (K.C. & Ghimire, 2015). Generally, in montane region, for every 100 m altitude increase, the air temperature declined by 0.60C on an average (Barry, 1992; K.C. & Ghimire, 2015) and atmospheric pressure drop out about 1.1% (Körner, 2007). Solar radiation is generally increased with increasing altitude due to diminishing turbidity under clear sky and its availability depends on the presence of clouds and fogs, both of which increase with altitude (Körner, 2007). In the Himalayas precipitation increases with altitude up to the level of condensation of clouds, that is marked by the maximum precipitation and then decrease drastically above it. In montane environment, low temperature and change in air temperature limit the microbial activity and recycling of nutrients (Parish, 2002) and slow decomposition of leaf litter and organic matter that obstructs the soil formation process (Leuschner et al., 2007) and water retention capacity of the soil (Huston, 1995). Therefore, soils at higher altitude are very thin, less developed, stony infertile (low soil nitrogen and phosphorous content) and often more acidic than lower altitude (Parish, 2002). The different environmental conditions are prevailing along the altitudinal gradient which choose the suitable functional traits that are suitable in certain set of environment (Keddy, 1992). Some functional traits are recognized to have a certain characteristics depending on one or more environmental variables that changes with altitude, as shown in figure 6, table 1 and described below. Leaf Area (LA): Leaf area is the one-side or projected surface area of a single leaf or an average leaf or leaf lamina, expressed in mm (Cornelissen et al., 2003; PérezHarguindeguy et al., 2013). Leaf size decreases with decreasing temperatures because of low temperature stress, high radiation (Körner et al., 1986; Moser et al., 2007; PérezHarguindeguy et al., 2013), drought and low nutrients (Cordell et al., 1998; Pérez-
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Harguindeguy et al., 2013). The lower carbon gained by the canopy is one of the main reason to reduce leaf area with increasing altitude (Leuschner et al., 2007). Leaf Mass per unit leaf Area (LMA): Specific leaf area (SLA) is the oven dried weight of leaf divided by its one side area of a fresh leaf (Pérez-Harguindeguy et al., 2013) and LMA is the inverse of the SLA (1/SLA) (Enrique et al., 2016; Pérez-Harguindeguy et al., 2013) and expressed in g/cm2. LMA increases significantly with elevation (Van de Wega et al., 2009) and slow growing, woody evergreen and perennial species have higher LMA than other species (Poorter et al., 2009). The high LMA tends to increase the longevity of the leaf in order to optimise the scarce resources (nutrients) in higher elevation area (Enrique et al., 2016). Leaf Nitrogen Concentration (LNC): Leaf nitrogen concentration is the total amounts of N per unit of dry leaf mass, expressed in mg/g (Pérez-Harguindeguy et al., 2013). LNC varies with nature of the species (evergreen, deciduous, herbs, trees) and altitude (Shi et al., 2012). LNC initially increases within certain elevation above the sea level where mean annual temperature is 8.50C and subsequently constant or decreases with altitude because it may be linear relationship between temperature and altitude or competition between temperature-plant physiological and biogeochemical hypothesis (Shi et al., 2012). Leaf Habit (LH): Plants are characterized as a deciduous and evergreen based on their leaf characteristics. Drought avoiders plants have a deciduous leaf habitat (Poorter & Markesteijn, 2008).The evergreenness of trees are predominated at low and high altitude region while deciduous trees predominated at mid-altitudinal region (Kikuzawa, 1996). The evergreenness of leaf habitat conserve nutrient in the nutrient deficient high altitude area. Deciduousness is one of the plant traits to avoid drought-induced cavitation and leaf shedding is another strategy to reduce transpirational water loss in dry seasons (Poorter & Markesteijn, 2008). Wood density (WD): Wood density is the ratio between oven-dry weights of wood divided by its green volume (Chave et al., 2006; Zobel & Jett, 1995). The relationship between WD and altitude is not clearly defined. However, WD is negatively correlated with moisture availability, i.e. species grown in dry area have higher wood density than those from wetter areas. Denser wood have high capacity to cope with physical damage, pathogens, predators and drought-induced xylem cavitation- a major cause of plant mortality at drier sites (Chave et al., 2006; Hacke & Sperry, 2001) and are also resistant to breakage and uprooting from strong wind (Tyree & Sperry, 1989).
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Plant Height (H): Generally, plant height is the distance between the ground and tip of the canopy of plant. Plan height is defined as the shortest distance between the highest tip point of the canopy (main photosynthesis tissue) on a plant and ground level, expressed in meter (m) (Cornelissen et al., 2003; Pérez-Harguindeguy et al., 2013). Plants growing in higher altitude have generally lower/smaller height compared to lower elevation due to the poor/thinner and unfertile soil condition, high radiation, low temperature-induced stress, high wind and aridity, high UV light, and lower competition for light (Leuschner et al., 2007; Parish, 2002). The ground level temperature is high with increasing altitude because of low mean temperature, high precipitation, shorter growing period, and low atmospheric pressure resulting in the shorter plant height (Pellissier et al., 2010). Moreover, one of the main reason to decrease the plant height in higher altitude is reduction of carbon gain toward higher altitude (Leuschner et al., 2007; Moser et al., 2007). Lower height of plant can be adapted to cope with the strong wind (Parish, 2002). Table 1: Definition and unit of functional traits and their response to associate environmental variables. (Alt. =Altitude; Temp.=Temperature; Irrad=Irradiance; Preci.=Precipitation; Soil water=Soil water content; Soil N=Soil nitrogen content; Soil P= Phosphorous) with increasing altitude Functional Trait
Unit
Trait
Definitions
LA
One-side projected surface area of a single leaf or leaf lamina. Total amount of N per unit of dry leaf mass. Evergreen leaves
mm2
Response to environmental factors in altitudinal gradient Alt. Temp. Irrad. Preci. Soil Soil Soil water N P -
mg/g
-
-
?
?
?
-
?
-
+
+
+
+
+
+
+
Deciduous leaves
-
-
-
-
-
-
-
-
Ratio between oven-dry mass of a wood sample divided by its green volume The shortest distance between highest photosynthetic tissue in the canopy and i) the ground level (trees, shrubs and herbs) or ii) the basal point of attachment (epiphytes and hemi-parasites). Ratio between the root biomass and the shoot biomass.
g/cm2
+
+
?
+
+
?
?
m
-
-
-
-
-
-
-
g/g
+
?
?
?
-
-
-
LNC Leaf habitat Leaf habit WD
H
R/S
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Figure 6: Conceptual model of the research. The increase in altitude has an effect on the environmental variables (light, water, and soil fertility), which subsequently act as a filters influencing functional responses in plants, measured as functional traits (LA= leaf area; LMA=leaf mass to area ratio; LNC= leaf nitrogen content; SA=sapwood area; WD= wood density; and H= plant height)
Clearly, plants have ability to cope with various environmental conditions through different functional traits (Maharjan et al., 2011). Always a question arise, therefore, which and how these functional traits are associated and what plant strategies play the significant role in shaping of plant species distributing along the altitudinal gradient. Here we studied the tree species distribution pattern in relation to the altitudinal gradient of central region of Nepal and related these patterns to 27 traits are important for tree growth, survival and reproduction.
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1.3
Research objective, research question and hypotheses
The aim of this research is to understand how functional traits shape species distribution along the altitudinal gradient of the Himalayas, Nepal. I will address the following research questions and corresponding hypotheses: Which plant traits are the best predictors species distributions along the altitudinal gradient? I hypothesized that plant height, basal area, diameter, height-diameter ratio, leaf size, leaf dry matter content, leaf thickness, specific leaf area, leaf area ratio, leaf mass per unit area, leaf nitrogen concentration, leaf phosphorus concentration, leaf potassium concentration, branch area, specific branch length, bark dry matter content, wood dry matter content, xylem conduit diameter, wood density are the best predictors of species distribution along the altitudinal gradient and they will decrease with increasing elevation. What trait syndromes (i.e. plant strategies) can be recognized along the altitudinal gradient? I hypothesized that low temperature, precipitation and infertile soil at higher altitude favour resource conservation strategy of plants, which characterised by slow tissue turnover, slow growth, low N, P and K content and vice-versa. Which environmental factors shape species trait and species distribution? I hypothesized that temperature and rainfall will shape species distribution along the altitudinal gradient.
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2.
Materials and Methods
2.1
Study area
Nepal is a landlocked mountainous country situated in central Himalayas between India in the Southern border and China in the Northern border. It covers an area of 147,181(0.1% of the global area) square kilo meters and biogeographically, lies between two biogeographical realms: the Palaeotropic (Indo-Malayan) in the South and the Palaearctic (Holartic) in the North (Bhuju et.al. 2007; Paudel et al., 2012; GoN/MFSC, 2014). It has horizontal distance ca. 150-200 km from the South to the North and altitude ranges from ca. 67 m asl alluvial plaints to 8,848 m asl the highest peak in the World (GoN/MFSC, 2014). Physiologically, Nepal is divided mainly into three zone as Terai, Hills and Mountains (Lillesø et al., 2005) from South to North along the altitude and Land Resource and Mapping Project (LRMP) (1986) further divided these areas in five zone (Table 2) as Terai, Siwalik, Mid-Hills of Middle mountain, High mountain and High Himalaya respectively (Lillesø et al., 2005; GoN/MFSC, 2014). It offer one of the longest and steepest altitudinal gradients in the world and harbour the highest percentage of the global flora and fauna. These physiological zone are closely resemble to seven bio-climatic zone (Table 2) identified by Nayajy (2000) (see Lillesø et al., 2005 for reference) and adopted by Lillesø et al. (2005). It has a wide range of climatic conditions due to the extreme variation in the altitude which implies severe changes environmental conditions along the altitudinal gradients. The study area (Figure 6) covers two provinces of Nepal province number two which contains south central low land (Lower tropical zone: 100 m to 300 m, Bara) and province number three that contains upper-tropical inner valley (400 m, Makawanpur) to alpine scrubs (3,900 m, Lauribinayak, Rasuwa) with focus on tree species only. The study area from Bara to Lauribinayak, Rasuwa are resemble to physiological and bio-climatic zone as shown in table 1. For this study, we considered two transects, with first transect from lowland of tropical Terai hardwood mixed forest (90 m, Bara) to upper temperate mixed broadleaved forest (2,488m, Simbhanjyang, Makawanpur), and the second transect that lies on upper-tropical zone, Dhading to alpine scrub grass land (3,900m, Lauribinayak, Rasuwa). The study area lies between 260 51’ and 270 02’ N latitudes and 840 51’ and 850 16’ E longitudes (Kanel, 2009) to 270 02’ and 270 23’ N latitudes and 850 01’ and 850 45’ E longitudes (Manandhar, 1980).
16
Figure 7: Map of Study area. Study area covers two province of Nepal (Province no 2 and province no 3) Province no two covers low land are province no 3 covers other three bioclimatic zone.
17
Table 2: Bioclimatic profile of Nepal. Bio-climatic profile of Nepal. Mean annual temperature and mean annual precipitation both decreases with elevation giving rise to extensive bioclimatic gradients from tropical to alpine climate along the altitudinal gradient in Nepal (Adopted from Nayaju, 2000 in Lillesø et al., 2005, page no 17)
Bioclimatic zones
Physiographic zones
Altitude ranges (m)
Trans-Himalayan Alpine Sub-alpine Temperate
High Himalaya
Sub-tropical Upper tropical Lower tropical
High Mountain Middle Mountain (MidHills) Siwalik Terai
>3000 4000-5000 3000-4000 2000-3000
Mean temperature (0 C) 6.5 6.9 12.7
Mean precipitation (mm) 223 1132 1685
1000-2000 300-1000 6.5) and found in Shorea robusta
22
forest in Terai and also found on grassland rocks at higher Himalayas (Jackson, 1994; LRMP, 1986). Rhodustalfs are deep red soil and it is sub-order of Alfisols. They are found in river terraces of Siwalik Hills and Middle mountains and another sub-order, Haplustalfs are more common in Terai to the middle mountains (Jackson, 1994). Spodosols soil which are found between 3000-4000 m have dominant conifer trees (Jackson, 1994; LRMP, 1986). 2.1.3 Vegetation Nepal has diverse vegetation types. It has South to North longest and steepest altitudinal gradients. Vegetation changes with these altitudinal gradients ranging from Terai hard wood mixed forest to alpine scrub land as shown in fig 12 (Stainton, 1972). Dobremez, (1976) identified 198 different vegetation types in seven bioclimatic zone as shown in table 1 (HMG/MFSC-TISC, 2002)These 189 vegetation types are synthesised into 118 according to Biodiversity Profile Project-1995, furthermore, International Union for Nature Conservation (IUCN) further reduced these forest types in 59 in order to use for Tree Improvement and Silviculture Component (TISC) forest and vegetation types of Nepal. TISC reduced these 59 forest types to 36 as shown in table 2 in order to simplify ecological picture of Nepal (HMG/MFSC-TISC, 2002). However, Stainton (1972) classified into35 forest types. Table 3: Vegetation types of Nepal classified by Tree Improvement and Silvicultural Component, 2002 (Adapted form Shrestha et al., 2002 and representation of study area based on observation of field during collection time) Ecological Zone Lower Tropical < 300
Vegetation types Lower tropical Sal and Mixed Broadleaved Forest
Upper Tropical (300-1000) Sub-tropical (10002000)
Hill Sal Forest Chir Pine Forest
Chir Pine-Broad leaved Forest
Eugenia-Ostodes Forest Schima Castanopsis Forest
Cedar Forest
Representation of Study area Pathlaya, Nijgadha area (100 m) to Amlekhaganj (300 m) Bara Main species: Shorea robusta, Terminalia alata, Dalbergia latifolia, Syzigum cuminia, Lagerstromia parviflora, Haldinia cordifolia, Malloptus philippnsis, and Acacia catechu, D. sissoo and Bombax ceiba are found on riverine site Siwalik area of Bara to Southern belt of Lamidanda (1300 m), Makwanpur Kalikatar (800m), to Mahabhir (1800 m), Makwanput, But most common from Lamidanda (1300 m) Southern slope of upper part of Chunia (Shorea robusta mixed with Pinus roxburghi and Pinus roxburghi mixed with oak, Rhododendron arboreum and Schima wallichii upto Mahabhir (1800 m) area of Makawanpur Schima wallichii :Hetauda (465 m to Aghor Forest ( 2000 m) but most common from 700 m below Kalikatar, Makawanpur and Castanopsis forest: descended to Hetauda 480 m, most common from upper part of Bhaise Army barrack (660 m), abundantly on below chunia and distributed up to 2000 m in Aghor forest but Castanopis indica are not found above Mahabhir area -
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Remarks
Ecological Zone Temperate Forest (2000-3000)
Vegetation types Cypress Forest Deciduous Maple-MangnoliaSorbus Forest Deciduous Walnut-MapleAlder Forest Eastern Himalayan OakLaurel Forest Lithocarpus Forest Lower Temperate Oak Forest
Mixed Blue Pine-Oak Forest Mixed Rhododendron-Maple forest Mountain Oak-Rhododendron Forest
Olea Forest Rhododendron Forest Spruce Forest
Temperate Juniper Forest Temperate Mountain Oak Forest
Upper Temperate Blue Pine Forest
Sub-alpine (3000-4000)
West Himalayan FirHemlock-Oak Forest Birch-Rhododendron Forest
Fir Forest
Fir-Blue Pine Forest
Representation of Study area Quercus semicarpifolia, Q. lanata are observed in lower part of Symbhanjyang to Mahabhir of Makawanpur mixed with broadleaved species such as Rhododendron abroreum. We also observed Q. lanata are distributed lower to near chunia (1300m). Deurali (2600 m) to below Dhimsa (3000 m), Rasuwa, Quercus semicarpifolia, Rhododendron arboreum (upper limit), R. abroreum var cinnamomum, R. barbatum, R. abroreum var roseace, R. campanalatum (lower limit) From Deurali (2650m) to near to Chandanbari (app. 3200m) Picea smithiana are distributed with Acer, Rhododendron species and Quercus semicarpifolia. And we also found some tree of this species at 3473 m along the DhunseGosainkunda treeking route. Pure Oak forest are observed in Simbhanjyang at 2488 m altitude of Makawanpur and oak with other broad leaved forest are observed in Daman Area of Makwanpur district. Daman area Makawanpur, Deurali (2600m) to Dhimsa (3000 m) area of Rasuwa in southern aspects of up to middle Pinus wallichiana is distributed and also in sub-alpine forest of southern aspects it mixed with Juniper species, Rhododendron species and other broadleaved species. Birch tree often grow with Abies spectabilish forest (HMG/MFSC-TISC,2002) but we observed patch of the Betula utilish at 3550m between Chandari bari and cholang parti in north facing, moist ravines and also near treeline in Lauribinayak with fir and Betula alnoids are found on Deurali to cholang parti mixed with Picea smithina, Acer species and Rhododendron species. Dhimsa (3000m) to Lauribinayak (3900 m) up to treeline.Most dominantly common on northern aspects and along the ridges from Chandari bari with R. campanulatum( more in upper site), R. barbatum, R. arborem var roseum, R. arboreum var cinnamomum in lower canopy and Betula utilish are also found on 3350 m in moist draingae. This forest especially on narrow valley of Kali Gandaki (HMG/MFSC-TISC, 2002) but we observed from Dhimsa (3000 m) to Lauribinayak
24
Remarks
Ecological Zone
Vegetation types
Fir-Hemlock-Oak Forest Fir-Oak-Rhododendron Forest Larch Forest
Alpine (4000-5000)
Sub-alpine Mountain Oak Forest Dry Alpine Scrubs Moist Alpine Scrubs
Trans-Himalayan (< 3000)
Upper Alpine Meadows Trans-Himalayan High Alpine Vegetation Trans-Himalayan Lower Caragana Steeppe Trans-Himalayan Upper Caragana Steppe
Representation of Study area (3900 m), Rasuwa. Fir are mainly on the northern aspects and Blue pine are distributed on southern aspect lower belt of this site. Langtang valley area but did not encounter in our study area but planted some tree on Chandari bari. -
Remarks
Southern part above Lauribinayak to Gosaikunda (4200 m) Norther part above Lauribinayak to Goisaikunda (4200 m) -
Figure 10: Schematic cross-section Study area, from South to North and the corresponding Vegetation types within study area. (Adapted from Bhattari et al., (2004)
2.1.4 Plant species distribution in central Nepal Central Nepal covers an area between Arun-Koshi river watershed in the eastern part and Kali Gandaki River in the western part. Stainton (1972) classified twelve forest types in the central midland of Nepal (Figure 11). Eastern Himalayan forest are generally distributed on north and western Himalayan forest are on south facing slopes (Stainton, 1972) because northern side are more wetter than southern side and eastern species well grow in wet side and western species are able to grown in drier side. The vegetation changes from Shorea robust (Sal) in the lowlands to moist alpine scrub land in the high lands (Bhattarai et al., 2004; Stainton, 1972). In the tropical zone the Terai hardwood mixed forest is dominated by Asian 25
Dipterocarpaceae of Shorea robusta forest which in association with Terminalia alata, Syzigium cumini, Dalbergia latifolia, Largestromia parviafolia,Terminalia belerica, Adina cordifolia, Mallotus philippensis, Haldinia cordifolia, Emblica officinalis (Grau et al., 2007; HMG/MFSC-TISC, 2002; Stainton, 1972). However, Acacia catechu-Dalbergia sissoo forest are found along the river belt of plain area ranging up to subtropical zones. The tree species of the lauraceous (Schima-Castanopsis) are dominantly distributed in the sub-tropical zone (Grau et al., 2007; HMG/MFSC-TISC, 2002; Stainton, 1972). The temperate zone is dominated by evergreen oak forests and Abies spectabilis, Betula utilis, and Rhododendron campanulatum are distributed up to treeline (Stainton, 1972). Above the treeline shrubby type of vegetation such as Rhododendron anthropogony, Berberis angulosa (Adhikari et al., 2012) species are found.
Figure 11: Map of Central Nepal altitudinal gradient. (Adopted from Stainton, 1972).
The species distribution range is wider in the middle of the gradients and narrows at lower and higher elevation zone (Bhattarai & Vetaas, 2006). The species richness of the plants is the highest in the mid elevational zone and lower in comparison to lower and higher elevational zone (Bhattarai et al., 2004; Vetaas & Grytnes, 2002). The endemic and rare species are more confined in the higher altitudinal zone (Vetaas & Grytnes, 2002) than others.
26
Figure 12: Forest types in central Midlands along the elevation gradients, dark green colour denotes the lower distribution range of forest types and light green colour denotes the upper distribution range of forest types, (Adopted from Stainton, 1972).
Figure 14: Sub-alpine Zone of North facing treeline in central Himalayas region of Nepal,: Abies spectabilis, Betula utilis, Juniperus recuva, and upper shrub land of Rhododendron anthropogony, Berbris angulosa 3900 m, Lauribinayak, Rasuwa, Nepal (Dhakal BP, 11 December, 2017)
Figure 14: South facing forest of pure Quercus semicarpifolia at 2488m, Simbhanjyang, Makawanpur, Nepal (Dhakal BP, 22 November, 2017)
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2.2
Data and Data Source
2.2.1 Species selection Nepal harbours 6,973 angiosperm and 26 gymnosperm (GoN/MFSC, 2014). DFRS (2015) identified 443 tree species from 1,553 permanent plots during national forest inventory from 2010 to 2014. Species were selected on the basis of their occurrence as well as their economic, and ecological value from central development region. Data were not collected in the field for some species because they were already leaf less although they were on the list. All together we selected 39 tree species, out of them 32 have complete data (according to distribution range), and however 7 tree species have incomplete data (according to upper/lower distribution range). For this study we used only those samples which have got complete data set. Samples were collected from natural forest of Bara to Lauribinayak, Rasuwa. The study area was divided in 1,000 meter altitudinal ranges from 100 m a.s.l. to 4,500 m a.s.l.. Therefore, it includes five out of seven bioclimatic zones and ranged from lower tropical to sub-alpine zone as mentioned in table 1. The alpine and trans-Himalayan zone were excluded from the study area because of scarce and more sporadic vegetation (Dobremez, 1976; Grytnes et al, 2002) in addition these area were difficult to access. The species that were used during data collection and their natural distribution range are presented in Annex 1. The samples were collected from the lower and upper altitudinal distribution range of the species. Samples were collected from 6 (3 at lower and 3 at upper altitudinal distribution range) individuals (Pole size tree, DBH = 10 to 30 cm) for each species along the altitudinal gradient. 2.2.2 Sampling design and data collection In order to collect branch and leaf level traits data for this study, a branch approach (Zhang et al., 2016) was used. Tree level measurement was taken first and branches were selected for the sample. Trees were mainly selected which had best opportunity to get more light and resources. Trees were categorized in five classes (Tree_CPI: 1 to 5; 5 means 100% crown exposed to light and 1 means completely shaded by other vegetation) and the field data forms were recorded. The next sample tree was selected based on the distance between two trees (if more than 30 m) and height (different height than previously sampled tree) of those trees. Diameter at breast height (DBH, at 1.30 m from ground) of a tree was measured by diameter tape. Total height (m) and crown height of trees (m) were measured by Vertex IV and Transponder. The crown width of tree (m) was measured in two perpendicular directions by 28
general measuring tape. The geographical position (Latitude and Longitude) of trees were also recorded by 60 CSX GPS. Altitude (m) by GPS, aspect (0) and slope (0) by Silva Compass of the area were also measured and recorded during field work. After tree level measurement, branches were selected based on their exposure to light. The branches were categorized in to five classes as tree CPI. Similar to trees, the branch with CPI ≥3 were selected and a branch approximately equal to 70 cm were cut. From the sampled branch of 75 cm length, bottom 5 cm were cut and stored in 50% ethanol to prevent drying. Branch length (cm), width (cm) in two perpendicular directions by measuring tape and counted the number of apices. All leaves from 70 cm branch picked out and from these 8 were randomly (24 in case of conifers) selected, representative leaves were used for leaf area calculation. Photo was taken in field by Nikon D3400 camera for further analysis of leaf area. Thickness (mm), fresh weight (gm) and Chlorophyll concentration in leaf by SPAD-502 meter of leaves were measured in the field. The total weight (gm) of fresh leaves pulled out from branch was measured and recorded in data sheet. After that fresh total weight of branch (gm) was taken. The approximately10 cm long branch section from middle part of 70 cm branch was cut and used for quantification of volume and then was store in envelope for over drying for wood density and dry matter content estimation. For this, length of sampled branch (cm) in two perpendicular direction was measured by a calliper. Diameter of middle sections with bark and without bark from lower, middle and upper end in two perpendicular directions of the branch were measured. Fresh weight of middle section with bark and without bark was also measured from the sampled branch. 2.2.3 Trait calculations Basal area (BA, m2) of the tree was calculated as 0.25*π* DBH2, and this was used to determine a growth of tree. Branch cross sectional area (Branch A, cm2) was calculated by using Newton’s formula, (ds2+4dm2+dl2)/6. For calculation of Branch cross section Area, average the two readings in perpendicular directions of branch length and diameter of the branches were taken. BranchA is an indicator of the investment in biomechanical and hydraulic support of the branch (Poorter et al., 2018). To calculate branch leaf area, 8 leaves from the sampled branch were selected and photographs were taken by Nikon D3400 and leaf area was calculated by using ImageJ software (Java 1.8.0_161). The dry matter content of wood, branch, bark and wood density were calculated from middle 10 cm branch section. Branch wood oven dry mass (bark and wood) divided by branch fresh 29
volume in order to calculate wood density (WD, g cm-3). Bark density (BarkD, g cm-3) was calculated bark oven-dry mass, divided by fresh volume of bark, Branch density (BranchD, g cm-3) as branch oven-dry mass without bark over fresh volume of branch without bark. Specific branch length (SBL, cm g-1) was calculated as branch length of sample branch by dividing its dry mass that indicates the biomass productivity of branch (Poorter et al., 2018). Specific leaf area (SLA, cm2 g-1), is the ratio of leaf area to its dry mass. For SLA, 8 leaves were randomly selected from branch with the photograph taken by Nikon D3400 camera, the leaf area was calculated by using ImageJ (Java 1.8.0_161) software. SLA was calculated as fresh leaf area divided by its oven-dry mass. For the needle leaf (coniferous) species SLA was calculated based on 24 sampled pine needles. SLA is the indicator of the biomass productivity of the leaf display at leaf level (Poorter et al., 2018). Leaf Mass Fraction (LMF, g g-1) was calculated as total dry matter content of leaves divided by whole dry biomass of branches (i.e. the sum dry mass of branches and leaves). LMF indicates the amount of biomass of branch allocated for leaves to capture light. Leaf Area Ratio (LAR, cm2 g-1) was calculated by multiplying SLA and LMF. LAR is the indicator of the biomass productivity of branch level. Branch Volume was calculated by using Newton’s formula ((ds2+4dm2+dl2) * L/6). Total Leaf Dry Matter Content (g g-1) was calculated by multiplying Leaf Dry matter Content (LDMC, dry matter content of sample leaf over fresh weight of leaf) and fresh weight of leaf from branch. Chlorophyll content per unit of leaf area (Chl, µ cm-2). SPAD reading value of sample leaves were used to calculated Chlorophyll content per unit of leaf (Chl, µ cm-2) by using an equation for rainforest trees (Coste et al., 2010) : Chl = (117.1 * SPAD)/ (148.84SPAD) that indicates the how much light can be harvest by the leaf. Leaf Dry Matter Content (LDMC, g g-1) was dry weight of sample over its dry weight and Petiole Dry Matter Content (PDMC) was calculated as dividing its dry weight by fresh weight. Toughness and construction cost of leaves and petioles were indicate by LDMC and PDMC of leaves. The leaf dry mass per unit of volume is the leaf density (LD, g cm-3) that was calculated by total dry biomass of leaves (i.e. sum of total dry matter content of branch and leaves) over fresh volume of leaves. Higher density of leaf show that low foliage photosynthesis rate per unit dry mass and then slow assimilation rates (Niinemets, 2001; Poorter et al., 2018). Sampled leaves were dried in oven at 600C for 48 hours in an oven dried leaves were ground and sieved through 2 mm sieve. This sample was ready for further analysis.
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Leaf Nitrogen Concentration (LPC) Nitrogen was determined by semi-micro kjeldahl (Block Digestion) method. Weigh 0.2g tree leaves sample and it was digested in Sulfuric acid+Selenium digestion mixture at 300˚C more than three hours untill it became clear. The total volume of sample was made up to 40ml with distill water. The digested solution was mixed homogenously with vertex mixture. Keep the solution for overnight for complete sedimentation of aliquot. Again the sample was diluted ten times into test tube and mixed with the help of vortex mixture. After homogenous mixing of sample, 0.1 ml sample was taken into a test tube and 5ml A & 5ml B colouring reagents were added. Sample was mixed and kept for half an hour for full colour development. Direct observation was taken by spectrophotometer CECIL CE7200 at 655nm wave length with comparison of known concentration of nitrogen standard solution (1.0, 2.0, 3.0, 4.0, and 5.0%). Leaf Phosphorus Content (LPC) Tree leaves sample was digested in block digester. Weigh 0.2g sample and was digested in Sulfuric acid+Selenium digestion mixture at 300˚C more than three hours. The volume of sample was made up to 40ml with distill water and mixed well. The solution was kept overnight for complete sedimentation of aliquot. After sedimentation 10 ml digested samples were taken in a 50ml volumetric flask. 10 ml mixed coloring reagent was added and the volume sample was made up to 50ml with distill water. The solution was mixed gently and waited for half an hour for full color development. Measure the absorbance of phosphorus in a 10mm optical cell at 440nm. Phosphorus was determined by direct observation taken by spectrophotometer CECIL CE7200 at 440nm wave length with comparison of known phosphorus standard solution (0.2, 0.4, 0.6, 0.8 and 1.0%) and the amount of phosphorus was calculated. Leaf Potassium Concentration (LKC) Plant sample was also digested in sulfuric acid +selenium digestion mixture at 300˚C more than three hours. After digestion cool the test tube and the total volume of sample was made up to 40ml with distill water. The digested solution was mixed homogenously with vertex mixture. The solution was kept overnight for complete sedimentation of aliquot. Again the sample was diluted ten times into test tube and mixed with the help of vortex mixture. The direct reading was taken by the flame photometer (Systronic 128) with comparison of potassium standard (0.5, 1.0, 1.5, 2.0, and 2.5% K). 31
2.2.4 Data Analysis Which plant functional traits are the best predictors of species distribution along the altitudinal gradients? To find the best predictors of species distribution along the altitudinal gradient of the Himalayas, a linear multiple regression analysis was used. First, I correlated plant functional traits with each altitudinal parameter (Amin, Aavg and Amax) to know which of the altitudinal parameter were significantly correlated with more functional traits. The correlation analysis showed that 18 functional traits were correlated with Amin (17) and Amax (16). However, the absolute average correlation coefficient were more or less same with all altitudinal variables. Thus, I selected Aavg for further statistical analysis because average altitude of species represents wider ecological niche and represents both elevation of the species. All functional traits were average with species level and then regressed with average altitude and altitudinal species distribution range. What trait syndromes (i.e. plant strategies) can be recognized along the altitudinal gradient? Principal component analysis (PCA) was used to assess how plants traits are associated among each other and what traits syndromes or plant strategies are more responsible to shaping species distribution along the altitude among studied 32 plant (pole) species. PCA analysis used 28 traits of 32 species. Which environmental factors shape species trait and distribution? Twenty two environmental variables were extracted from freely available website such as Worldclim for climate data, ISRIC for soil data and CGIAR-CSI for potential evaporation and aridity. I selected seven environmental variables from twenty two environmental variables that described the main axis of environmental variation/gradient and independent to each other; such as mean annual temperature (MAT), mean diurnal range (MDR), temperature seasonality (TS), precipitation of wettest month (PWM), precipitation of driest month (PDM), precipitation of coldest quarter (PCQ) and soil pH (pH_30 cm). I selected these environmental variables based on cluster dendrogram made by including all environmental variables and then value of correlation coefficient. The selection of seven environmental variables was also based on fact that which affect the growth of plants by changing resources (light, rainfall and nutrients) and conditions (temperature) (Amissah et al., 2014) and expected to change with 32
increasing altitude. A Redundancy analysis (RDA) was used to assess which environmental variables are responsible in shaping species traits and species distribution. Traits value were calculated in Ms excel and other statistical analysis such as correlation and multiple regression were done by using IBM SPSS statistics 22. PCA and significance of environmental variable (RDA) were done by using Canoco 5.
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3.
Results
3.1
Correlation amongst altitudinal parameter
Species distribution along the altitudinal gradients was quantified using the minimum (Amin), average (Aavg) and maximum (Amax) altitude where species occurred. All three altitudinal parameters were strongly correlated among themselves (Spearman’s correlation, r>0.92 and P 0.92, P
